U.S. patent application number 17/534170 was filed with the patent office on 2022-06-02 for differentiation of lyme disease and southern tick-associated rash illness.
This patent application is currently assigned to Colorado State University Research Foundation. The applicant listed for this patent is Colorado State University Research Foundation, The United States of America, as represented by the secretary, Department of Health and Human Servic, The United States of America, as represented by the secretary, Department of Health and Human Servic, Gary P. Wormser. Invention is credited to John T. Belisle, Claudia R. Molins, Gary P. Wormser.
Application Number | 20220170066 17/534170 |
Document ID | / |
Family ID | |
Filed Date | 2022-06-02 |
United States Patent
Application |
20220170066 |
Kind Code |
A1 |
Belisle; John T. ; et
al. |
June 2, 2022 |
DIFFERENTIATION OF LYME DISEASE AND SOUTHERN TICK-ASSOCIATED RASH
ILLNESS
Abstract
The present disclosure provides a biosignature that
distinguishes Lyme disease, including early Lyme disease, from
STARI. The present disclosure also provides methods for detecting
Lyme disease and STARI, as well as methods for treating subjects
diagnosed with Lyme disease or STARI.
Inventors: |
Belisle; John T.; (Fort
Collins, CO) ; Molins; Claudia R.; (Bethesda, MD)
; Wormser; Gary P.; (New York, NY) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Wormser; Gary P.
Colorado State University Research Foundation
The United States of America, as represented by the secretary,
Department of Health and Human Servic |
Forth Collins
Bethesda |
CO
MD |
US
US
US |
|
|
Assignee: |
Colorado State University Research
Foundation
Fort Collins
CO
The United States of America, as represented by the secretary,
Department of Health and Human Servic
Bethesda
MD
|
Appl. No.: |
17/534170 |
Filed: |
November 23, 2021 |
Related U.S. Patent Documents
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
|
|
16703401 |
Dec 4, 2019 |
11230728 |
|
|
17534170 |
|
|
|
|
PCT/US2018/036688 |
Jun 8, 2018 |
|
|
|
16703401 |
|
|
|
|
62516824 |
Jun 8, 2017 |
|
|
|
International
Class: |
C12Q 1/04 20060101
C12Q001/04; G01N 33/68 20060101 G01N033/68 |
Goverment Interests
GOVERNMENTAL RIGHTS
[0002] This invention was made with government support under
AI100228 and AI099094, each awarded by National Institutes of
Health. The government has certain rights in the invention.
Claims
1. A method for treating a subject in need thereof, wherein the
subject is suspected as having Lyme disease or STARI, the method
comprising: (a) obtaining a disease score from a mass spectrometry
based test; (b) diagnosing the subject with Lyme disease or STARI
based on the disease score; and (c) if the subject is diagnosed
with Lyme disease, administering a treatment to the subject
diagnosed with Lyme disease based on the disease score, wherein the
treatment is a pharmacological treatment for Lyme disease selected
from an antibiotic, an antibacterial agent, an immune modulator, an
anti-inflammatory agent, or a combination thereof; (d) if the
subject is diagnosed with STARI, optionally administering a
treatment to the subject diagnosed with STARI based on the disease
score, wherein the treatment is a pharmacological treatment is
selected from an antibiotic, an antibacterial agent, an
anti-inflammatory agent, or a combination thereof, wherein the mass
spectrometry-based test comprises: (i) deproteinizing a blood
sample from a subject to produce a metabolite extract, wherein the
subject has at least one symptom that is associated with Lyme
disease or STARI; (ii) performing liquid chromatography coupled to
mass spectrometry on a sample of the metabolite extract; (iii)
providing abundance values for each molecular feature in Table A,
Table C, or Table D: TABLE-US-00013 TABLE A Predicted Retention
Chemical m/z Time Compound Structure (based Metabolite MF (positive
(see Predicted on accurate Class or # Name ion) Mass examples)
Formula mass) Pathway 1 CSU/ 166.0852 165.078 1.86
C.sub.9H.sub.11NO.sub.2 Phenylalanine Phenylalanine CDC-001
metabolism 2 CSU/ 270.3156 269.3076 18.02 C.sub.18H.sub.39N -- --
CDC-012 3 CSU/ 284.3314 283.3236 18.13 C.sub.19H.sub.41N -- --
CDC-013 4 CSU/ 300.6407 599.268 18.27
C.sub.33H.sub.37N.sub.5O.sub.6 Asp Phe Arg Tyr Peptide CDC-014 (SEQ
ID NO: 1) 5 CSU/ 300.2892 299.2821 19.66 C.sub.18H.sub.37NO.sub.2
Palmitoyl N-acyl CDC-019 ethanolamide ethanolamine metabolism 6
CSU/ 734.5079 1449.9753 17.81 -- -- -- CDC-039 7 CSU/ 370.1837
369.1757 19.7 C.sub.19H.sub.23N.sub.5O.sub.3 -- -- CDC-062 8 CSU/
811.1942 810.1869 12.07 C.sub.42H.sub.30N.sub.6O.sub.12 -- --
CDC-066 9 CSU/ 947.7976 946.7936 14.55 C.sub.62H.sub.106O.sub.6 TAG
(59:7) Triacylglycerol CDC-067 metabolism 10 CSU/ 410.2033 409.196
17.18 -- -- -- CDC-072 11 CSU/ 1487.0005 1485.9987 18.17 -- -- --
CDC-075 12 CSU/ 137.0463 136.0378 1.37 C.sub.4H.sub.8O.sub.5
Threonate Sugar CDC-086 metabolite 13 CSU/ 811.7965 810.7882 12.07
-- -- -- CDC-107 14 CSU/ 616.1776 615.1699 15.43 -- -- -- CDC-132
15 CSU/ 713.4492 712.4391 19.35 C.sub.38H.sub.65O.sub.10P PG (32:5)
Glycero- CDC-152 phospholipid metabolism 16 CSU/ 502.3376 484.3039
19.87 C.sub.27H.sub.40N.sub.4O.sub.4 Gln Leu Pro Lys Peptide
CDC-155 (SEQ ID NO: 2) 17 CSU/ 415.3045 414.2978 20.19 -- -- --
CDC-158 18 CSU/ 366.3729 365.3655 22.79 -- -- -- CDC-164 19 CSU/
333.1446 332.1373 12.89 C.sub.12H.sub.20N.sub.4O.sub.7 Glu Gln Gly
Peptide CDC-166 20 CSU/ 241.1069 240.0996 14.7
C.sub.12H.sub.16O.sub.5 3-Carboxy-4- Fatty acid CDC-205
methyl-5-propyl-2- metabolism furanpropanoic acid (CMPF) 21 CSU/
464.1916 463.1849 13.05 C.sub.16H.sub.29N.sub.7O.sub.7S Arg Asp Cys
Ala Peptide CDC-211 (SEQ ID NO: 3) 22 CSU/ 1249.2045 1248.1993
15.31 -- -- -- CDC-212 23 CSU/ 1248.9178 1247.9141 15.3 -- -- --
CDC-213 24 CSU/ 158.1539 157.1466 15.36 -- -- -- CDC-219 25 CSU/
529.3381 528.3296 16.89 C.sub.24H.sub.44N.sub.6O.sub.7 Gln Val Leu
Leu Peptide CDC-227 Gly (SEQ ID NO: 4) 26 CSU/ 282.2776 264.2456
20.56 C.sub.18H.sub.32O -- -- CDC-229 27 CSU/ 190.1260 189.1187
14.12 C.sub.9H.sub.19NOS 8- 2- CDC-235 Methylthiooctanal
oxocarboxylic doxime acid metabolism 28 CSU/ 382.3675 381.3603
20.23 C.sub.24H.sub.47NO.sub.2 Erucicoyl N-acyl CDC-238
ethanolamide ethanolamine metabolism 29 CSU/ 477.2968 476.2898
22.79 C.sub.31H.sub.40O.sub.4 Lys Lys Thr Thr Peptide CDC-244 (SEQ
ID NO: 5) 30 CSU/ 459.3968 458.3904 19.08 -- -- -- CDC-248 31 CSU/
342.2635 341.2565 15.62 C.sub.19H.sub.35NO.sub.4 -- -- CDC-253 32
CSU/ 529.3827 1022.6938 17.86 -- -- -- CDC-254 33 CSU/ 459.2502
458.2429 19.02 C.sub.23H.sub.39O.sub.7P Lyso PA (20:4) Glycero-
CDC-258 hospholipid metabolism 34 CSU/ 239.0919 238.0844 11.66
C.sub.12H.sub.14O.sub.5 Trans-2, 3, 4- Phenylpropanoid CDC-002
trimethoxy- and cinnamate polyketide metabolism 35 CSU/ 389.2174
388.2094 15.47 C.sub.19H.sub.32O.sub.8 Methyl Fatty acid CDC-028
10,12,13,15- metabolism bisepidioxy-16- hydroperoxy-8E-
octadecenoate 36 CSU/ 285.2065 284.1991 16.02
C.sub.16H.sub.28O.sub.4 -- -- CDC-182 37 CSU/ 279.1693 278.1629
11.05 C.sub.15H.sub.22N.sub.2O.sub.3 Phe Leu Dipeptide CDC-204 38
CSU/ 714.6967 1427.3824 11.76 -- -- -- CDC-247
TABLE-US-00014 TABLE C Predicted Retention Chemical m/z Time
Compound Structure Metabolite MF (positive (see Predicted (based on
Class or # Name ion) Mass examples) Formula accurate mass) Pathway
1 CSU/ 166.0852 165.078 1.86 C.sub.9H.sub.11NO.sub.2 Phenylalanine
Phenylalanine CDC-001 metabolism 2 CSU/ 270.3156 269.3076 18.02
C.sub.18H.sub.39N -- -- CDC-012 3 CSU/ 284.3314 283.3236 18.13
C.sub.19H.sub.41N -- -- CDC-013 4 CSU/ 300.6407 599.268 18.27
C.sub.33H.sub.37N.sub.5O.sub.6 Asp Phe Arg Tyr Peptide CDC-014 (SEQ
ID NO: 1) 5 CSU/ 300.2892 299.2821 19.66 C.sub.18H.sub.37NO.sub.2
Palmitoyl N-acyl CDC-019 ethanolamide ethanolamine metabolism 6
CSU/ 734.5079 1449.9753 17.81 -- -- -- CDC-039 7 CSU/ 370.1837
369.1757 19.7 C.sub.19H.sub.23N.sub.5O.sub.3 -- -- CDC-062 8 CSU/
811.1942 810.1869 12.07 C.sub.42H.sub.30N.sub.6O.sub.12 -- --
CDC-066 9 CSU/ 947.7976 946.7936 14.55 C.sub.62H.sub.106O.sub.6 TAG
(59:7) Triacylglycerol CDC-067 metabolism 10 CSU/ 410.2033 409.196
17.18 -- -- -- CDC-072 11 CSU/ 1487.0005 1485.9987 18.17 -- -- --
CDC-075 12 CSU/ 137.0463 136.0378 1.37 C.sub.4H.sub.8O.sub.5
Threonate Sugar CDC-086 metabolite 13 CSU/ 811.7965 810.7882 12.07
-- -- -- CDC-107 14 CSU/ 616.1776 615.1699 15.43 -- -- -- CDC-132
15 CSU/ 713.4492 712.4391 19.35 C.sub.38H.sub.65O.sub.10P PG (32:5)
Glycero- CDC-152 phospholipid metabolism 16 CSU/ 502.3376 484.3039
19.87 C.sub.27H.sub.40N.sub.4O.sub.4 Gln Leu Pro Lys Peptide
CDC-155 (SEQ ID NO: 2) 17 CSU/ 415.3045 414.2978 20.19 -- -- --
CDC-158 18 CSU/ 366.3729 365.3655 22.79 -- -- -- CDC-164 19 CSU/
333.1446 332.1373 12.89 C.sub.12H.sub.20N.sub.4O.sub.7 Glu Gln Gly
Peptide CDC-166 20 CSU/ 241.1069 240.0996 14.7
C.sub.12H.sub.16O.sub.5 3-Carboxy-4- Fatty acid CDC-205
methy1-5-propyl- metabolism 2-furanpropanoic acid (CMPF) 21 CSU/
464.1916 463.1849 13.05 C.sub.16H.sub.29N.sub.7O.sub.7S Arg Asp Cys
Ala Peptide CDC-211 (SEQ ID NO: 3) 22 CSU/ 1249.2045 1248.1993
15.31 -- -- -- CDC-212 23 CSU/ 1248.9178 1247.9141 15.3 -- -- --
CDC-213 24 CSU/ 158.1539 157.1466 15.36 -- -- -- CDC-219 25 CSU/
529.3381 528.3296 16.89 C.sub.24H.sub.44N.sub.6O.sub.7 Gln Val Leu
Leu Peptide CDC-227 Gly (SEQ ID NO: 4) 26 CSU/ 282.2776 264.2456
20.56 C.sub.18H.sub.32O -- -- CDC-229 27 CSU/ 190.1260 189.1187
14.12 C.sub.9H.sub.19NOS 8- 2- CDC-235 Methylthiooctanal
oxocarboxylic doxime acid metabolism 28 CSU/ 382.3675 381.3603
20.23 C.sub.24H.sub.47NO.sub.2 Erucicoyl N-acyl CDC-238
ethanolamide ethanolamine metabolism 29 CSU/ 477.2968 476.2898
22.79 C.sub.31H.sub.40O.sub.4 Lys Lys Thr Thr Peptide CDC-244 (SEQ
ID NO: 5) 30 CSU/ 459.3968 458.3904 19.08 -- -- -- CDC-248 31 CSU/
342.2635 341.2565 15.62 C.sub.19H.sub.35NO.sub.4 -- -- CDC-253 32
CSU/ 529.3827 1022.6938 17.86 -- -- -- CDC-254 33 CSU/ 459.2502
458.2429 19.02 C.sub.23H.sub.39O.sub.7P Lyso PA (20:4) Glycero-
CDC-258 hospholipid metabolism 34 CSU/ 886.4296 1770.8438 12.18 --
-- -- CDC-003 35 CSU/ 181.0859 180.0788 14.7
C.sub.10H.sub.12O.sub.3 5'-(3'-Methoxy-4'- Endogenous CDC-004
hydroxphenyl)- metabolite gamma- associated valerolactone with
microbiome 36 CSU/ 286.1444 285.1371 16.08 C.sub.17H.sub.19NO.sub.3
Piperine Alkaloid CDC-006 metabolism 37 CSU/ 463.2339 462.2248
16.36 C.sub.25H.sub.34O.sub.8 Ala Lys Met Asn Peptide CDC-008 (SEQ
ID NO: 6) 38 CSU/ 242.2844 241.2772 17.1 C.sub.16H.sub.35N -- --
CDC-009 39 CSU/ 590.4237 589.4194 19.24 -- -- -- CDC-017 40 CSU/
553.3904 552.3819 23.38 C.sub.35H.sub.52O.sub.5 Furohyperforin
Endogenous CDC-026 metabolite- derived from food 41 CSU/ 399.2364
398.2313 16.23 -- -- -- CDC-030 42 CSU/ 580.4144 1158.8173 18.26 --
-- -- CDC-042 43 CSU/ 704.4985 1372.925 18.7 -- -- -- CDC-052 44
CSU/ 623.4521 1210.8362 19.55 -- -- -- CDC-061 45 CSU/ 389.2178
388.2099 15.52 C.sub.19H.sub.32O.sub.8 -- -- CDC-070 CSU/ 1111.6690
1110.6656 17.89 -- -- -- 46 CDC-074 47 CSU/ 482.4040 481.3976 19.99
-- -- -- CDC-083 48 CSU/ 533.1929 532.1854 20.84
C.sub.23H.sub.28N.sub.6O.sub.9 Asp His Phe Asp Peptide CDC-084 (SEQ
ID NO: 7) 49 CSU/ 466.3152 465.3085 14.73 C.sub.26H.sub.43NO.sub.6
Glycocholic acid Bile acid CDC-087 metabolism 50 CSU/ 683.4728
1347.9062 17.56 -- -- -- CDC-091 51 CSU/ 227.0897 204.1002 9.68
C.sub.9H.sub.16O.sub.5 -- -- CDC-095 52 CSU/ 183.1016 182.0943
10.89 C.sub.10H.sub.14O.sub.3 -- -- CDC-098 53 CSU/ 476.3055
475.2993 11.09 C.sub.26H.sub.41N.sub.3O.sub.5 -- -- CDC-099 54 CSU/
215.1283 214.1209 12.32 C.sub.11H.sub.18O.sub.4 alpha-Carboxy-
Endogenous CDC-112 delta- metabolite- decalactone derived from food
55 CSU/ 519.1881 518.1813 12.33 C.sub.20H.sub.30N.sub.4O.sub.12
Poly-g-D- Poly D- CDC-115 glutamate glutamate metabolism 56 CSU/
1086.1800 2170.3435 15.38 -- -- -- CDC-128 57 CSU/ 285.2061
284.1993 15.99 C.sub.16H.sub.28O.sub.4 -- -- CDC-133 58 CSU/
357.1363 356.1284 15.98 C.sub.20H.sub.20O.sub.6 Xanthoxylol
Endogenous CDC-134 metabolite- derived from food 59 CSU/ 299.1853
298.1781 16.24 C.sub.16H.sub.26O.sub.5 Tetranor-PGE1 Prostaglandin
CDC-136 metabolism 60 CSU/ 334.2580 333.2514 16.36 -- -- -- CDC-137
61 CSU/ 317.2317 316.2254 16.63 -- -- -- CDC-138 62 CSU/ 331.2471
330.2403 17.26 C.sub.18H.sub.34O.sub.5 11,12,13- Fatty acid CDC-141
trihydroxy-9- metabolism octadecenoic acid 63 CSU/ 583.3480
582.3379 18.04 C.sub.27H.sub.46N.sub.6O.sub.8 Leu Lys Glu Pro
Peptide CDC-144 Pro (SEQ ID NO: 8) 64 CSU/ 648.4672 647.4609 19.98
C.sub.34H.sub.66NO.sub.8P PE (29:1) Glycero- CDC-157 phospholipid
metabolism 65 CSU/ 445.2880 854.5087 12.48 C.sub.45H.sub.74O.sub.15
(3b,21b)-12- Endogenous CDC-165 Oleanene- metabolite- 3,21,28-triol
28- derived from [arabinosyl-(1- food >3)-arabinosyl- (1->3)-
arabinoside] 66 CSU/ 1486.7386 2971.4668 14.97 -- -- -- CDC-181 67
CSU/ 668.4686 1317.8969 18.04 C.sub.16H.sub.28O.sub.4 Omphalotin A
Endogenous CDC-183 metabolite- derived from food 68 CSU/ 454.2924
436.2587 18.1 C.sub.21H.sub.41O.sub.7P Lyso-PA (18:1) Glycero-
CDC-184 phospholipid metabolism 69 CSU/ 607.9324 606.9246 19.01 --
-- -- CDC-186 70 CSU/ 521.4202 503.3858 21.06 -- -- -- CDC-188 71
CSU/ 176.0746 175.0667 2.31 -- -- -- CDC-193 72 CSU/ 596.9082
1191.8033 19.1 -- -- -- CDC-194 73 CSU/ 532.5606 531.5555 18.38 --
-- -- CDC-203 74 CSU/ 337.1667 336.1599 20.67
C.sub.12H.sub.24N.sub.4O.sub.7 -- -- CDC-206 75 CSU/ 415.1634
207.0784 12.2 C.sub.8H.sub.9N.sub.5O.sub.2 6-Amino-9H- Endogenous
CDC-210 purine-9- metabolite- propanoic acid derived from food 76
CSU/ 364.3407 346.3068 20.72 -- -- -- CDC-218 77 CSU/ 989.5004
1976.9858 12.03 -- -- -- CDC-222 78 CSU/ 819.6064 1635.8239 12.06
-- -- -- CDC-224 79 CSU/ 286.2737 285.2666 19.08
C.sub.17H.sub.35NO.sub.2 Pentadecanoyl N-acyl CDC-237 ethanolamide
ethanolamine metabolism 80 CSU/ 614.4833 613.4772 19.78 -- -- --
CDC-245 81 CSU/ 298.2740 297.2668 16.44 C.sub.18H.sub.35NO.sub.2
3-Ketospingosine Sphingolipid CDC-250 metabolism 82 CSU/ 1003.7020
1002.696 18.46 -- -- -- CDC-252
TABLE-US-00015 TABLE D Predicted Retention Chemical m/z Time
Compound Structure Metabolite MF (positive (see Predicted (based on
Class or # Name ion) Mass examples) Formula accurate mass) Pathway
1 CSU/ 166.0852 165.07 8 1.86 C.sub.9H.sub.11NO.sub.2 Phenylalanine
Phenylalanine CDC-001 metabolism 2 CSU/ 270.3156 269.3076 18.02
C.sub.18H.sub.39N -- -- CDC-012 3 CSU/ 284.3314 283.3236 18.13
C.sub.19H.sub.41N -- -- CDC-013 4 CSU/ 300.6407 599.268 18.27
C.sub.33H.sub.37N.sub.5O.sub.6 Asp Phe Arg Tyr Peptide CDC-014 (SEQ
ID NO: 1) 5 CSU/ 300.2892 299.2821 19.66 C.sub.18H.sub.37NO.sub.2
Palmitoyl N-acyl CDC-019 ethanolamide ethanolamine metabolism 6
CSU/ 734.5079 1449.9753 17.81 -- -- -- CDC-039 7 CSU/ 370.1837
369.1757 19.7 C.sub.19H.sub.23N.sub.5O.sub.3 -- -- CDC-062 8 CSU/
811.1942 810.1869 12.07 C.sub.42H.sub.30N.sub.6O.sub.12 -- --
CDC-066 9 CSU/ 947.7976 946.7936 14.55 C.sub.62H.sub.106O.sub.6 TAG
(59:7) Triacylglycerol CDC-067 metabolism 10 CSU/ 410.2033 409.196
17.18 -- -- -- CDC-072 11 CSU/ 1487.0005 1485.9987 18.17 -- -- --
CDC-075 12 CSU/ 137.0463 136.0378 1.37 C.sub.4H.sub.8O.sub.5
Threonate Sugar CDC-086 metabolite 13 CSU/ 811.7965 810.7882 12.07
-- -- -- CDC-107 14 CSU/ 616.1776 615.1699 15.43 -- -- -- CDC-132
15 CSU/ 713.4492 712.4391 19.35 C.sub.38H.sub.65O.sub.10P PG (32:5)
Glycero- CDC-152 phospholipid metabolism 16 CSU/ 502.3376 484.3039
19.87 C.sub.27H.sub.40N.sub.4O.sub.4 Gln Leu Pro Lys Peptide
CDC-155 (SEQ ID NO: 2) 17 CSU/ 415.3045 414.2978 20.19 -- -- --
CDC-158 18 CSU/ 366.3729 365.3655 22.79 -- -- -- CDC-164 19 CSU/
333.1446 332.1373 12.89 C.sub.12H.sub.20N.sub.4O.sub.7 Glu Gln Gly
Peptide CDC-166 20 CSU/ 241.1069 240.0996 14.7
C.sub.12H.sub.16O.sub.5 3-Carboxy-4- Fatty acid CDC-205
methy1-5-propyl- metabolism 2-furanpropanoic acid (CMPF) 21 CSU/
464.1916 463.1849 13.05 C.sub.16H.sub.29N.sub.7O.sub.7S Arg Asp Cys
Ala Peptide CDC-211 (SEQ ID NO: 3) 22 CSU/ 1249.2045 1248.1993
15.31 -- -- -- CDC-212 23 CSU/ 1248.9178 1247.9141 15.3 -- -- --
CDC-213 24 CSU/ 158.1539 157.1466 15.36 -- -- -- CDC-219 25 CSU/
529.3381 528.3296 16.89 C.sub.24H.sub.44N.sub.6O.sub.7 Gln Val Leu
Leu Gly Peptide CDC-227 (SEQ ID NO: 4) 26 CSU/ 282.2776 264.2456
20.56 C.sub.18H.sub.32O -- -- CDC-229 CSU/ 190.1260 189.1187 14.12
C.sub.9H.sub.19NOS 8- 2- CDC-235 Methylthiooctanal oxocarboxylic 27
doxime acid metabolism 28 CSU/ 382.3675 381.3603 20.23
C.sub.24H.sub.47NO.sub.2 Erucicoyl N-acyl CDC-238 ethanolamide
ethanolamine metabolism 29 CSU/ 477.2968 476.2898 22.79
C.sub.31H.sub.40O.sub.4 Lys Lys Thr Thr Peptide CDC-244 (SEQ ID NO:
5) 30 CSU/ 459.3968 458.3904 19.08 -- -- -- CDC-248 31 CSU/
342.2635 341.2565 15.62 C.sub.19H.sub.35NO.sub.4 -- -- CDC-253 32
CSU/ 529.3827 1022.6938 17.86 -- -- -- CDC-254 33 CSU/ 459.2502
458.2429 19.02 C.sub.23H.sub.39O.sub.7P Lyso PA (20:4) Glycero-
CDC-258 hospholipid metabolism 34 CSU/ 239.0919 238.0844 11.66
C.sub.12H.sub.14O.sub.5 Trans-2, 3, 4- Phenyl- CDC-002
trimethoxycinnamate propanoid and polyketide metabolism CSU/
389.2174 388.2094 15.47 C.sub.19H.sub.32O.sub.8 Methyl Fatty acid
CDC-028 10,12,13,15- metabolism 35 bisepidioxy-16- hydroperoxy-8E-
octadecenoate 36 CSU/ 285.2065 284.1991 16.02
C.sub.16H.sub.28O.sub.4 -- -- CDC-182 37 CSU/ 279.1693 278.1629
11.05 C.sub.15H.sub.22N.sub.2O.sub.3 Phe Leu Dipeptide CDC-204 38
CSU/ 714.6967 1427.3824 11.76 -- -- -- CDC-247 39 CSU/ 886.4296
1770.8438 12.18 -- -- -- CDC-003 40 CSU/ 181.0859 180.0788 14.7
C.sub.10H.sub.12O.sub.3 5'-(3'-Methoxy-4'- Endogenous CDC-004
hydroxyphenyl)- metabolite gamma- associated valerolactone with
microbiome 41 CSU/ 286.1444 285.1371 16.08 C.sub.17H.sub.19NO.sub.3
Piperine Alkaloid CDC-006 metabolism 42 CSU/ 463.2339 462.2248
16.36 C.sub.25H.sub.34O.sub.8 Ala Lys Met Asn Peptide CDC-008 (SEQ
ID NO: 6) 43 CSU/ 242.2844 241.2772 17.1 C.sub.16H.sub.35N -- --
CDC-009 44 CSU/ 590.4237 589.4194 19.24 -- -- -- CDC-017 45 CSU/
553.3904 552.3819 23.38 C.sub.35H.sub.52O.sub.5 Furohyperforin
Endogenous CDC-026 metabolite- derived from food 46 CSU/ 399.2364
398.2313 16.23 -- -- -- CDC-030 47 CSU/ 580.4144 1158.8173 18.26 --
-- -- CDC-042 48 CSU/ 704.4985 1372.925 18.7 -- -- -- CDC-052 49
CSU/ 623.4521 1210.8362 19.55 CDC-061 -- -- -- 50 CSU/ 389.2178
388.2099 15.52 C.sub.19H.sub.32O.sub.8 -- -- CDC-070 51 CSU/
1111.6690 1110.6656 17.89 -- -- -- CDC-074 52 CSU/ 482.4040
481.3976 19.99 -- -- -- CDC-083 53 CSU/ 533.1929 532.1854 20.84
C.sub.23H.sub.28N.sub.6O.sub.9 Asp His Phe Asp Peptide CDC-084 (SEQ
ID NO: 7) 54 CSU/ 466.3152 465.3085 14.73 C.sub.26H.sub.43NO.sub.6
Glycocholic acid Bile acid CDC-087 metabolism 55 CSU/ 683.4728
1347.9062 17.56 -- -- -- CDC-091 56 CSU/ 227.0897 204.1002 9.68
C.sub.9H.sub.16O.sub.5 -- -- CDC-095 57 CSU/ 183.1016 182.0943
10.89 C.sub.10H.sub.14O.sub.3 -- -- CDC-098 58 CSU/ 476.3055
475.2993 11.09 C.sub.26H.sub.41N.sub.3O.sub.5 -- -- CDC-099 59 CSU/
215.1283 214.1209 12.32 C.sub.11H.sub.18O.sub.4 alpha-Carboxy-
Endogenous CDC-112 delta- metabolite- decalactone derived from food
60 CSU/ 519.1881 518.1813 12.33 C.sub.20H.sub.30N.sub.4O.sub.12
Poly-g-D- Poly D- CDC-115 glutamate glutamate metabolism 61 CSU/
1086.1800 2170.3435 15.38 -- -- -- CDC-128 62 CSU/ 285.2061
284.1993 15.99 C.sub.16H.sub.28O.sub.4 -- -- CDC-133 63 CSU/
357.1363 356.1284 15.98 C.sub.20H.sub.20O.sub.6 Xanthoxylol
Endogenous CDC-134 metabolite- derived from food 64 CSU/ 299.1853
298.1781 16.24 C.sub.16H.sub.26O.sub.5 Tetranor-PGE1 Prostaglandin
CDC-136 metabolism 65 CSU/ 334.2580 333.2514 16.36 -- -- -- CDC-137
66 CSU/ 317.2317 316.2254 16.63 -- -- -- CDC-138 67 CSU/ 331.2471
330.2403 17.26 C.sub.18H.sub.34O.sub.5 11,12,13- Fatty acid CDC-141
trihydroxy-9- metabolism octadecenoic acid 68 CSU/ 583.3480
582.3379 18.04 C.sub.27H.sub.46N.sub.6O.sub.8 Leu Lys Glu Pro Pro
Peptide CDC-144 (SEQ ID NO: 8) 69 CSU/ 648.4672 647.4609 19.98
C.sub.34H.sub.66NO.sub.8P PE (29:1) Glycero- CDC-157 phospholipid
metabolism 70 CSU/ 445.2880 854.5087 12.48 C.sub.45H.sub.74O.sub.15
(3b,21b)-12- Endogenous CDC-165 Oleanene- metabolite- 3,21,28-triol
28- derived from [arabinosyl-(1- food >3)-arabinosyl- (1->3)-
arabinoside] 71 CSU/ 1486.7386 2971.4668 14.97 -- -- -- CDC-181 72
CSU/ 668.4686 1317.8969 18.04 C.sub.16H.sub.28O.sub.4 Omphalotin A
Endogenous CDC-183 metabolite- derived from food 73 CSU/ 454.2924
436.2587 18.1 C.sub.21H.sub.41O.sub.7P Lyso-PA (18:1) Glycero-
CDC-184 phospholipid metabolism 74 CSU/ 607.9324 606.9246 19.01 --
-- -- CDC-186 75 CSU/ 521.4202 503.3858 21.06 -- -- -- CDC-188 76
CSU/ 176.0746 175.0667 2.31 -- -- -- CDC-193 77 CSU/ 596.9082
1191.8033 19.1 -- -- -- CDC-194 78 CSU/ 532.5606 531.5555 18.38 --
-- -- CDC-203 79 CSU/ 337.1667 336.1599 20.67
C.sub.12H.sub.24N.sub.4O.sub.7 -- -- CDC-206 80 CSU/ 415.1634
207.0784 12.2 C.sub.8H.sub.9N.sub.5O.sub.2 6-Amino-9H- Endogenous
CDC-210 purine-9- metabolite- propanoic acid derived from food 81
CSU/ 364.3407 346.3068 20.72 -- -- -- CDC-218 82 CSU/ 989.5004
1976.9858 12.03 -- -- -- CDC-222 83 CSU/ 819.6064 1635.8239 12.06
-- -- -- CDC-224 84 CSU/ 286.2737 285.2666 19.08
C.sub.17H.sub.35NO.sub.2 Pentadecanoyl N-acyl CDC-237 ethanolamide
ethanolamine metabolism 85 CSU/ 614.4833 613.4772 19.78 -- -- --
CDC-245 86 CSU/ 298.2740 297.2668 16.44 C.sub.18H.sub.35NO.sub.2
3-Ketospingosine Sphingolipid CDC-250 metabolism
87 CSU/ 1003.7020 1002.696 18.46 -- -- -- CDC-252 88 CSU/ 223.0968
222.0895 14.69 C.sub.12H.sub.14O.sub.4 -- -- CDC-005 89 CSU/
286.1437 285.1364 16.06 C.sub.17H.sub.19NO.sub.3 -- -- CDC-007 90
CSU/ 1112.6727 1111.6663 17.86 -- -- -- CDC-010 91 CSU/ 454.2923
453.2867 18.08 C.sub.21H.sub.44NO.sub.7P Glycerophospho- N-acyl
CDC-011 N-Palmitoyl ethanolamine Ethanolamine metabolism 92 CSU/
522.3580 521.3483 18.5 C.sub.26H.sub.52NO.sub.7P PC (18:1) Glycero-
CDC-015 phospholipid metabolism 93 CSU/ 363.2192 362.2132 18.58
C.sub.21H.sub.30O.sub.5 4,50- Sterol CDC-016 dihydrocortisone
metabolism 94 CSU/ 388.3939 387.3868 19.53 -- -- -- CDC-018 95 CSU/
256.2632 255.2561 20.08 C.sub.16H.sub.33NO Palmitic amide Primary
Fatty CDC-020 Acid Amide Metabolism 96 CSU/ 394.3515 376.3171 20.09
-- -- -- CDC-021 97 CSU/ 228.1955 227.1885 20.99 -- -- -- CDC-022
98 CSU/ 284.2943 283.2872 21.15 C.sub.18H.sub.37NO Stearamide
Primary Fatty CDC-023 Acid Amide Metabolism 99 CSU/ 338.3430
337.3344 22.14 C.sub.22H.sub.43NO 13Z- Primary Fatty CDC-024
Docosenamide Acid Amide (Erucamide) Metabolism 100 CSU/ 689.5604
688.5504 22.52 C.sub.38H.sub.77N.sub.2O.sub.6P SM(d18:1-15:0)/
Sphingolipid CDC-025 SM(d18:1/14:1-OH) metabolism 101 CSU/ 432.2803
431.2727 10.8 C.sub.25H.sub.37NO.sub.5 Ala Ile Lys Thr Peptide
CDC-027 (SEQ ID NO: 9) 102 CSU/ 385.2211 384.2147 15.84
C.sub.16H.sub.28N.sub.6O.sub.5 Lys His Thr Peptides CDC-029 103
CSU/ 449.3261 879.6122 17.07 C.sub.46H.sub.89NO.sub.12S C22-OH
Sphingolipid CDC-031 Sulfatide metabolism 104 CSU/ 467.3821
444.2717 17.1 C.sub.24H.sub.40O.sub.8 2-glyceryl-6-keto-
Prostaglandin CDC-032 PGF1.alpha. metabolism 105 CSU/ 836.5936
835.5845 17.15 C.sub.44H.sub.85NO.sub.11S C20 Sulfatide
Sphingolipid CDC-033 metabolism 106 CSU/ 792.5646 791.5581 17.17
C.sub.42H.sub.82NO.sub.10P PS (36:0) Glycero- CDC-034 phospholipid
metabolism 107 CSU/ 356.2802 355.2722 17.35 -- -- -- CDC-035 108
CSU/ 806.5798 805.5746 17.71 C.sub.43H.sub.84NO.sub.10P PS (37:0)
Glycero- CDC-036 phospholipid metabolism 109 CSU/ 762.5582 761.5482
17.79 C.sub.41H.sub.80NO.sub.9P PS-O (35:1) Glycero- CDC-037
phospholipid metabolism 110 CSU/ 718.5308 700.4946 17.88
C.sub.39H.sub.73O.sub.8P PA (36:2) Glycero- CDC-038 phospholipid
metabolism 111 CSU/ 690.4825 1361.924 17.95 -- -- -- CDC-040 112
CSU/ 426.1798 425.1725 18.03 -- -- -- CDC-041 113 CSU/ 741.5154
1481.0142 18.24 C.sub.83H.sub.150O.sub.17P.sub.2 CL (74:6) Glycero-
CDC-043 phospholipid metabolism 114 CSU/ 864.6245 863.6166 18.17
C.sub.46H.sub.89NO.sub.11S C22 Sulfatide Sphingolipid CDC-044
metabolism 115 CSU/ 558.4017 1080.7347 18.28 -- -- -- CDC-045 116
CSU/ 719.5012 1402.9377 18.26 -- -- -- CDC-046 117 CSU/ 536.3897
1053.7382 18.36 -- -- -- CDC-047 118 CSU/ 538.8674 1058.696 18.4 --
-- -- CDC-048 119 CSU/ 653.4619 1270.8593 18.43 -- -- -- CDC-049
120 CSU/ 732.5450 714.5092 18.47 C.sub.40H.sub.75O.sub.8P PA (37:2)
Glycero- CDC-050 phospholipid metabolism 121 CSU/ 748.5232
1478.0059 18.58 -- -- -- CDC-051 122 CSU/ 682.4841 1328.9008 18.77
-- -- -- CDC-053 123 CSU/ 360.3615 359.3555 18.89 -- -- -- CDC-054
124 CSU/ 441.2412 440.2325 19.09 C.sub.20H.sub.132N.sub.4O.sub.7
Pro Asp Pro Leu Peptide CDC-055 (SEQ ID NO: 10) 125 CSU/ 638.4554
1240.847 18.92 -- -- -- CDC-056 126 CSU/ 755.5311 1474.9941 18.94
C.sub.83H.sub.144O.sub.17P.sub.2 CL (74:9) Glycero- CDC-057
phospholipid metabolism 127 CSU/ 711.5023 1386.9417 19.09 -- -- --
CDC-058 128 CSU/ 784.5530 1567.0908 19.27 -- -- -- CDC-059 129 CSU/
645.4660 1271.8896 19.36 -- -- -- CDC-060 130 CSU/ 300.2886
282.2569 19.84 C.sub.18H.sub.34O.sub.2 13Z- Fatty acid CDC-063
octadecenoic metabolism acid 131 CSU/ 309.0981 308.0913 2.06
C.sub.15H.sub.16O.sub.7 -- -- CDC-064 132 CSU/ 561.2965 1120.5778
11.7 C.sub.54H.sub.88O.sub.24 Camellioside D Endogenous CDC-065
metabolite- derived from food 133 CSU/ 1106.2625 2209.5193 14.53 --
-- CDC-068 134 CSU/ 371.2070 370.1997 15.52
C.sub.15H.sub.26N.sub.6O.sub.7 His Ser Lys Peptide CDC-069 135 CSU/
443.2649 442.256 15.52 C.sub.19H.sub.34N.sub.6O.sub.6 Pro Gln Ala
Lys Peptide CDC-071 (SEQ ID NO: 11) 136 CSU/ 850.6093 849.6009
17.63 C.sub.48H.sub.84NO.sub.9P PS-O (42:6) Glycero- CDC-073
phospholipid metabolism 137 CSU/ 697.4896 1358.909 18.32 -- -- --
CDC-076 138 CSU/ 439.8234 877.6325 18.71 -- -- -- CDC-077 139 CSU/
567.8897 566.8818 18.73 -- -- -- CDC-078 140 CSU/ 435.2506 434.243
19 C.sub.21H.sub.39O.sub.7P Lyso-PA (18:2) Glycero- CDC-079
phospholipid metabolism 141 CSU/ 834.6136 833.6057 18.83
C.sub.45H.sub.88NO.sub.10P PS (39:0) Glycero- CDC-080 phospholipid
metabolism 142 CSU/ 534.8834 533.8771 18.82 -- -- -- CDC-081 143
CSU/ 468.8441 467.8373 19.13 -- -- -- CDC-082 144 CSU/ 312.3259
311.319 22.05 -- -- -- CDC-085 145 CSU/ 228.1955 227.1884 15.22 --
-- -- CDC-088 146 CSU/ 385.2211 384.2143 15.83
C.sub.20H.sub.32O.sub.7 Lys His Thr Peptide CDC-089 147 CSU/
403.2338 402.2253 15.84 C.sub.16H.sub.30N.sub.6O.sub.6 Lys Gln Gln
Peptide CDC-090 148 CSU/ 675.4753 1348.9377 18.37 -- -- -- CDC-092
149 CSU/ 682.4841 1345.9257 18.76 -- -- -- CDC-093 150 CSU/
762.5401 1506.0367 19.36 -- -- -- CDC-094 151 CSU/ 189.1122
188.1049 12.27 C.sub.9H.sub.14O.sub.4 Nonanedioic Acid Fatty acid
CDC-177 metabolism 152 CSU/ 169.0860 168.0786 9.94
C.sub.9H.sub.12O.sub.3 2,6-Dimethoxy-4- Endogenous CDC-097
methylphenol metabolite- derived from food 153 CSU/ 276.1263
275.1196 11.16 C.sub.15H.sub.17NO.sub.4 -- -- CDC-100 154 CSU/
314.0672 313.06 11.56 C.sub.10H.sub.12N.sub.5O.sub.5P -- -- CDC-101
155 CSU/ 201.1122 200.1047 11.56 C.sub.10H.sub.16O.sub.4
Decenedioic acid Fatty acid CDC-102 metabolism 156 CSU/ 115.0391
114.0318 11.57 C.sub.5H.sub.6O.sub.3 2-Hydroxy-2,4- Phenylalanine
CDC-103 pentadienoate metabolism 157 CSU/ 491.1569 490.1504 11.56
C.sub.24H.sub.26O.sub.11 -- -- CDC-104 158 CSU/ 241.1054 218.1157
11.57 C.sub.10H.sub.18O.sub.5 3-Hydroxy- Fatty acid CDC-105 sebacic
acid metabolism 159 CSU/ 105.0914 104.0841 11.57 -- -- -- CDC-106
160 CSU/ 311.1472 328.1391 12.22 C.sub.18H.sub.20N.sub.2O.sub.4 Phe
Tyr Peptide CDC-108 161 CSU/ 271.1543 270.1464 12.24 -- -- --
CDC-109 162 CSU/ 169.0860 168.0787 12.24 C.sub.9H.sub.12O.sub.3
2,6-Dimethoxy-4- Endogenous CDC-110 methylphenol metabolite-
derived from food 163 CSU/ 187.0967 186.0889 12.24
C.sub.9H.sub.14O.sub.4 -- -- CDC-111 164 CSU/ 475.1635 474.1547
12.25 C.sub.25H.sub.22N.sub.4O.sub.6 His Cys Asp Thr Peptide
CDC-113 (SEQ ID NO: 12) 165 CSU/ 129.0547 128.0474 12.33
C.sub.6H.sub.8O.sub.3 (4E)-2- Fatty acid CDC-114 Oxohexenoic
metabolism acid 166 CSU/ 125.0599 124.0527 13.12
C.sub.7H.sub.8O.sub.2 4-Methylcatechol Catechol CDC-116 metabolism
167 CSU/ 247.1550 246.1469 13.13 C.sub.12H.sub.22O.sub.5 3-Hydroxy-
Fatty acid CDC-117 dodecanedioic metabolism acid 168 CSU/ 517.2614
516.2544 13.13 C.sub.21H.sub.36N.sub.6O.sub.9 Gln Glu Gln Ile
Peptide CDC-118 (SEQ ID NO: 13) 169 CSU/ 301.0739 300.0658 13.14
C.sub.16H.sub.12O.sub.6 Chrysoeriol Endogenous CDC-119 metabolite-
derived from food 170 CSU/ 327.1773 304.1885 14.17
C.sub.16H.sub.24N.sub.4O.sub.2 -- -- CDC-120 171 CSU/ 387.2023
386.1935 14.51 C.sub.19H.sub.30O.sub.8 Citroside A Endogenous
CDC-121 metabolite- derived from food 172 CSU/ 875.8451 1749.684
14.55 -- -- -- CDC-122 173 CSU/ 737.5118 736.5056 14.52
C.sub.42H.sub.73O.sub.8P PA (39:5) Glycero- CDC-123 phospholipid
metabolism 174 CSU/ 1274.3497 1273.3481 14.96 -- -- -- CDC-124 175
CSU/ 1274.2092 1273.2 14.96 -- -- -- CDC-125 176 CSU/ 1486.5728
2971.1328 14.95 -- -- -- CDC-126 177 CSU/ 965.3818 964.3727 15.37
-- -- --
CDC-127 129 CSU/ 1086.0562 2170.0908 15.38
C.sub.97H.sub.167N.sub.5O.sub.48 NeuAcalpha2- Sphingolipid CDC-178
3Galbeta1- metabolism 3GalNAcbeta1-4 (9-0Ac- NeuAcalpha2-
8NeuAcalpha2- 3)Galbeta1- 4Glcbeta- Cer(d18:1/18:0) 179 CSU/
1086.4344 2169.8474 15.39 -- -- -- CDC-130 180 CSU/ 1240.7800
1239.7712 15.38 -- -- -- CDC-131 181 CSU/ 317.1956 316.1885 16.24
C.sub.12H.sub.24N.sub.6O.sub.4 Arg Ala Ala Peptide CDC-135 182 CSU/
299.2219 298.2148 16.64 C.sub.17H.sub.30O.sub.4 8E- Fatty acid
CDC-139 Heptadecenedioic metabolism acid 183 CSU/ 748.5408 747.5317
17.23 C.sub.40H.sub.78NO.sub.9P PS-O (34:1) Glycero- CDC-140
phospholipid metabolism 184 CSU/ 712.4935 1422.9749 17.82
C.sub.79H.sub.140P.sub.17P.sub.2 CL (70:7) Glycero- CDC-142
phospholipid metabolism 185 CSU/ 674.5013 673.4957 17.99
C.sub.37H.sub.72NP.sub.7P PE-P (32:1) Glycero- CDC-143 phospholipid
metabolism 186 CSU/ 677.9537 676.9478 18.36 -- -- -- CDC-145 187
CSU/ 531.3522 530.3457 18.4 C.sub.35H.sub.46O.sub.4 -- -- CDC-146
188 CSU/ 585.2733 584.2649 18.39 C.sub.33H.sub.36N.sub.4O.sub.6
15,16- Bilirubin CDC-147 Dihydrobiliverdin breakdown products-
Porphyrin metabolism 189 CSU/ 513.3431 512.3352 18.4 -- -- --
CDC-148 190 CSU/ 611.9156 610.9073 18.59 -- -- -- CDC-149 191 CSU/
549.0538 531.0181 18.38 -- -- -- CDC-150 192 CSU/ 755.5311
1509.0457 18.93 -- -- -- CDC-151 193 CSU/ 599.4146 598.4079 19.59
C.sub.40H.sub.54O.sub.4 Isomytiloxanthin Isoflavinoid CDC-153 194
CSU/ 762.5029 761.4919 19.66 C.sub.43H.sub.72NO.sub.8P PE (38:7)
Glycero- CDC-154 phospholipid metabolism 195 CSU/ 741.4805 740.4698
19.96 C.sub.40H.sub.69O.sub.10P PG (34:5) Glycero- CDC-156
phospholipid metabolism 196 CSU/ 516.3532 498.3199 20.27
C.sub.23H.sub.42N.sub.6O.sub.6 Ala Leu Ala Pro Lys Peptide CDC-159
(SEQ ID NO: 14) 197 CSU/ 769.5099 768.5018 20.53
C.sub.42H.sub.73O.sub.10P PG (36:5) Glycero- CDC-160 phospholipid
metabolism 198 CSU/ 862.5881 861.5818 20.86 -- -- -- CDC-161 199
CSU/ 837.5358 836.5274 21.11 C.sub.53H.sub.72O.sub.8 Amitenone
Endogenous CDC-162 metabolite- derived from food 200 CSU/ 558.3995
540.367 21.44 C.sub.26H.sub.48N.sub.6O.sub.6 Leu Ala Pro Lys Ile
Peptide CDC-163 (SEQ ID NO: 15) 201 CSU/ 1105.9305 2209.8462 14.53
-- -- -- CDC-167 202 CSU/ 329.1049 328.0976 14.61
C.sub.18H.sub.16O.sub.6 2-Oxo-3- Phenylalanine CDC-168
phenylpropanoic metabolism acid 203 CSU/ 1241.2053 1240.2 15.38 --
-- -- CDC-169 204 CSU/ 1088.6731 1087.6676 17.85 -- -- -- CDC-170
205 CSU/ 667.4391 666.4323 20.35 C.sub.37H.sub.63O.sub.8P PA (24:5)
Glycero- CDC-171 phospholipid metabolism 206 CSU/ 133.0497 132.0423
11.57 C.sub.5H.sub.8O.sub.4 2-Acetolactic Pantothenate CDC-172 acid
and CoA Biosynthesis Pathway 207 CSU/ 259.1540 258.1469 11.75 -- --
-- CDC-173 208 CSU/ 311.1472 288.1574 12.23
C.sub.10H.sub.20N.sub.6O.sub.4 Asn Arg Dipeptide CDC-174 209 CSU/
147.0652 146.0579 12.33 C.sub.6H.sub.10O.sub.4 .alpha.-Ketopantoic
Pantothenate CDC-175 acid and CoA Biosynthesis Pathway 210 CSU/
169.0860 168.0788 12.29 C.sub.9H.sub.12O3 Epoxyoxophorone
Endogenous CDC-176 metabolite- derived from food 211 CSU/ 187.0965
186.0894 9.93 C.sub.9H.sub.14O.sub.4 5- Endogenous CDC-096
Butyltetrahydro- metabolite- 2-oxo-3- derived from furancarboxylic
food acid 212 CSU/ 139.1116 138.1044 12.95 C.sub.9H.sub.14O.sub.4
3,6-Nonadienal Endogenous CDC-178 metabolite- derived from food 213
CSU/ 515.2811 514.2745 13.14 C.sub.26H.sub.42O.sub.10 Cofaryloside
Endogenous CDC-179 metabolite- derived from food 214 CSU/ 283.1522
282.1444 13.93 C.sub.25H.sub.42N.sub.2O.sub.7S Epidihydrophaseic
Endogenous CDC-180 acid metabolite - derived from food 215 CSU/
706.9750 705.9684 18.7 -- -- -- CDC-185 216 CSU/ 834.5575 833.5502
20.32 -- -- -- CDC-187 217 CSU/ 683.4727 1364.9294 17.54 -- -- --
CDC-189 218 CSU/ 728.9890 1455.9633 18.63 -- -- -- CDC-190 219 CSU/
726.5104 1451.0035 18.64 C.sub.18H.sub.144O.sub.17P.sub.2 CL (72:7)
Glycero- CDC-191 phospholipid metabolism 220 CSU/ 633.9280 632.9206
18.47 -- -- -- CDC-192 221 CSU/ 209.0784 208.0713 9.92
C.sub.17H.sub.24O.sub.3 Benzylsuccinate Phenylpropanoic CDC-195
acid metabolism 222 CSU/ 792.5483 1566.055 18.46 -- -- -- CDC-196
223 CSU/ 618.9221 1218.8083 19.02 -- -- -- CDC-197 224 CSU/
549.0543 531.0189 18.37 -- -- -- CDC-198 225 CSU/ 553.7262 552.7188
18.74 -- -- -- CDC-199 226 CSU/ 756.0320 755.0266 18.95 -- -- --
CDC-200 227 CSU/ 639.6307 638.6205 19.58 -- -- -- CDC-201 228 CSU/
753.4414 730.4513 19.37 C.sub.42H.sub.67O.sub.8P PA (39:8) Glycero-
CDC-202 phospholipid metabolism 229 CSU/ 328.3204 327.3148 20.72
C.sub.20H.sub.41NO.sub.2 Stearoyl N-acyl CDC-207 ethanolamide
ethanolamine metabolism 230 CSU/ 514.3718 1009.7122 18.42
C.sub.56H.sub.99NO.sub.14 3-O-acetyl- Sphingolipid CDC-208
sphingosine- metabolism 2,3,4,6-tetra-O- acetyl- GalCer(d18:1/h2
2:0) 231 CSU/ 630.4594 1241.8737 19.95 -- -- -- CDC-209 232 CSU/
244.2270 243.22 17.17 C.sub.14H.sub.29NO.sub.2 Lauroyl N-acyl
CDC-214 ethanolamide ethanolamine metabolism 233 CSU/ 463.3426
924.6699 18.08 -- -- -- CDC-215 234 CSU/ 468.3892 450.3553 19.17
C.sub.31H.sub.46O.sub.2 -- -- CDC-216 235 CSU/ 438.3787 420.3453
19.93 -- -- -- CDC-217 236 CSU/ 792.0006 790.995 12.04 -- -- --
CDC-220 237 CSU/ 792.2025 791.1947 12.04 -- -- -- CDC-221 238 CSU/
791.6016 790.594 12.04 -- -- -- CDC-223 239 CSU/ 1115.5593
2228.1028 14.95 -- -- -- CDC-225 240 CSU/ 1486.9176 2970.7976 14.96
-- -- -- CDC-226 241 CSU/ 430.3161 412.2845 20.23
C.sub.23H.sub.40O.sub.6 -- -- CDC-228 242 CSU/ 297.2793 296.2734
20.66 C.sub.19H.sub.36O.sub.2 Methyl oleate Oleic acid CDC-230
ester 243 CSU/ 714.3655 1426.718 11.73 -- -- -- CDC-231 244 CSU/
714.5306 1427.0479 11.76 -- -- -- CDC-232 245 CSU/ 989.7499
1977.4865 12.03 -- -- -- CDC-233 246 CSU/ 221.0744 220.0672 13.7
C.sub.7H.sub.12N.sub.2O.sub.6 L-beta-aspartyl- Peptide CDC-234
L-serine 247 CSU/ 313.2734 312.2663 18.91 C.sub.19H.sub.36O.sub.3
2-oxo- Fatty acid CDC-236 nonadecanoic metabolism acid 248 CSU/
337.2712 314.282 20.66 C.sub.19H.sub.38O.sub.3 2-Hydroxy- Fatty
acid CDC-239 nonadecanoic metabolism acid 249 CSU/ 441.3687
440.3614 21.26 C.sub.30H.sub.48O.sub.2 4,4-Dimethyl- Sterol CDC-240
14a-formyl-5a- metabolism cholesta-8,24- dien-3b-ol 250 CSU/
425.3735 424.3666 21.5 C.sub.30H.sub.48O Butyrospermone Sterol
CDC-241 metabolism 251 CSU/ 356.3517 355.3448 21.67
C.sub.22H.sub.45NO.sub.2 Eicosanoyl N-acyl CDC-242 ethanolamide
ethanolamine metabolism 252 CSU/ 393.2970 370.3082 22.46
C.sub.22H.sub.42O.sub.4 -- -- CDC-243 253 CSU/ 167.9935 166.9861
13.2 C.sub.7H.sub.5NS.sub.2 -- -- CDC-246 254 CSU/ 677.6170
676.6095 20.71 C.sub.47H.sub.80O.sub.2 Cholesterol ester Sterol
CDC-249 (20:2) metabolism 255 CSU/ 460.2695 459.2627 16.87
C.sub.26H.sub.37NO.sub.6 -- -- CDC-251 CSU/ 630.4765 612.4417 18.11
-- -- -- 256 CDC-255 257 CSU/ 514.3734 1026.7281 18.41 -- -- --
CDC-256 258 CSU/ 667.4754 1315.916 19.28 -- -- -- CDC-257 259 CSU/
516.8549 1031.6945 18.43 -- -- -- CDC-259 260 CSU/ 740.5242
1479.0334 19.4 C.sub.83H.sub.148O.sub.17P.sub.2 CL (74:7) Glycero-
CDC-260 hospholipid
metabolism 261 CSU/ 1104.0614 2206.1096 15.2 -- -- -- CDC-261
wherein each molecular feature is identified by its mass to charge
ratio; and (iv) inputting the abundance values from step (iii) into
a classification model trained with samples of metabolite extracts
derived from suitable controls, wherein if the molecular features
of Table A or Table C are provided, the classification model is
Least Absolute Shrinkage and Selection Operator (LASSO) and if the
molecular features of Table D are provided, the classification
model is Random Forest (RF), and wherein the classification model
produces a disease score and the disease score distinguishes
subjects with Lyme disease and STARI.
2. The method of claim 1, wherein an area under the curve (AUC)
value for an ROC curve of the classification model is about 0.8 or
greater.
3. The method of claim 1, wherein the classification model has a
sensitivity from about 0.8 to about 1 and/or a specificity from
about 0.8 to about 1, and optionally an area under the curve (AUC)
value for an ROC curve that is about 0.8 or greater.
4. The method of claim 1, wherein the classification model has a
sensitivity from about 0.85 to about 1 and/or a specificity from
about 0.85 to about 1, and optionally an area under the curve (AUC)
value for an ROC curve that is about 0.8 or greater.
5. The method of claim 1, wherein the classification model has a
sensitivity from about 0.9 to about 1 and/or a specificity from
about 0.9 to about 1, and optionally an area under the curve (AUC)
value for an ROC curve that is about 0.8 or greater.
6. The method of claim 5, wherein the classification model has an
accuracy of at least 97% for detecting a sample from a subject with
Lyme disease and an accuracy of at least 87% for detecting a sample
from a subject with STARI.
7. The method of claim 1, wherein: abundance values are provided
for each molecular feature in Table A or Table D; and the suitable
controls comprise a blood sample from a subject known to be
positive for Lyme disease, and a blood sample from a subject known
to be positive for STARI.
8. The method of claim 7, wherein the classification model has an
accuracy of at least 95% for detecting a sample from a subject with
Lyme disease and an accuracy of at least 85% for detecting a sample
from a subject with STARI.
9. The method of claim 7, wherein the classification model has an
accuracy of at least 97% for detecting a sample from a subject with
Lyme disease and an accuracy of at least 87% for detecting a sample
from a subject with STARI.
10. The method of claim 1, wherein: abundance values are provided
for each molecular feature in Table C or Table D; the suitable
controls include a blood sample from a subject known to be positive
for Lyme disease, a blood sample from a subject known to be
positive for STARI, and a blood sample from a healthy subject.
11. The method of claim 10, wherein the classification model has an
accuracy of at least 85% for detecting a sample from a subject with
Lyme disease, an accuracy of at least 85% for detecting a sample
from a subject with STARI, and an accuracy of at least 85% for
detecting a sample from a healthy subject.
12. The method of claim 10, wherein the classification model has an
accuracy of at least 85% for detecting a sample from a subject with
Lyme disease, an accuracy of at least 90% for detecting a sample
from a subject with STARI, and an accuracy of at least 90% for
detecting a sample from a healthy subject.
13. The method of claim 1, wherein the blood sample is a serum
sample.
14. The method of claim 1, wherein the subject has at least one
symptom that is associated with both Lyme disease and STARI.
15. The method of claim 1, wherein the subject has an erythema
migrans rash.
16. The method of claim 1, wherein the subject's serum is negative
for antibodies to Lyme disease-causing Borrelia species.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation application of U.S.
application Ser. No. 16/703,401, filed Dec. 4, 2019, which
application is a continuation of International Application No.
PCT/US2018/036688, with an international filing date of Jun. 8,
2018, which PCT application claims the benefit of U.S. provisional
application No. 62/516,824, filed Jun. 8, 2017. The contents of the
above-mentioned applications are hereby incorporated by reference
in their entirety.
REFERENCE TO SEQUENCE LISTING
[0003] The instant application contains a Sequence Listing which
has been submitted electronically in ASCII format and is hereby
incorporated by reference in its entirety. Said ASCII copy, created
on Apr. 27, 2021, is named "CSURF_065620-642266_ST25.txt", and is
2.58 KB in size.
FIELD OF THE INVENTION
[0004] The present disclosure relates to human disease detection
tools and methods, and in particular pertains to tools and methods
for detecting Lyme disease and southern tick-associated rash
illness (STARI), and for distinguishing Lyme disease from
STARI.
BACKGROUND OF THE INVENTION
[0005] Lyme disease is a multisystem bacterial infection that in
the United States is primarily caused by infection with Borrelia
burgdorferi sensu stricto. Over 300,000 cases of Lyme disease are
estimated to occur annually in the United States, with over 3.4
million laboratory diagnostic tests performed each year. Symptoms
associated with this infection include fever, chills, headache,
fatigue, muscle and joint aches, and swollen lymph nodes; however,
the most prominent clinical manifestation in the early stage is the
presence of one or more erythema migrans (EM) skin lesions. This
annular, expanding erythematous skin lesion occurs at the site of
the tick bite in 70 to 80% of infected individuals and is typically
5 cm or more in diameter. Although an EM lesion is a hallmark for
Lyme disease, other types of skin lesions can be confused with EM,
including the rash of southern tick-associated rash illness
(STARI).
[0006] A strict geographic segregation of Lyme disease and STARI
does not exist, as there are regions where STARI and Lyme disease
are co-prevalent. Clinically, the skin lesions of STARI and early
Lyme disease are indistinguishable, and no laboratory tool or
method exists for the diagnosis of STARI or differentiation of
STARI from Lyme disease. The only biomarkers evaluated for
differential diagnosis of early Lyme disease and STARI have been
serum antibodies to B. burgdorferi. However, these tests have poor
sensitivity for early stages of Lyme disease, and thus a lack of B.
burgdorferi antibodies cannot be used as a reliable differential
marker for STARI. Thus, there is a need for diagnostic methods to
differentiate between Lyme disease and STARI, and that facilitate
proper treatment, patient management and disease surveillance.
SUMMARY OF THE INVENTION
[0007] In one aspect, the present disclosure encompasses a method
for analyzing a blood sample from a subject, the method comprising:
(a) deproteinizing the blood sample to produce a metabolite
extract; (b) performing liquid chromatography coupled to mass
spectrometry on a sample of the metabolite extract; and (c)
providing abundance values for each molecular feature in Table A,
Table B, Table C, or Table D.
[0008] In another aspect, the present disclosure encompasses a
method for classifying a subject as having Lyme disease or STARI,
the method comprising: (a) deproteinizing a blood sample from a
subject to produce a metabolite extract, wherein the subject has at
least one symptom that is associated with Lyme disease or STARI;
(b) performing liquid chromatography coupled to mass spectrometry
on a sample of the metabolite extract; (c) providing abundance
values for each molecular feature in Table A, Table B, Table C, or
Table D; and (d) inputting the abundance values from step (c) into
a classification model trained with samples of metabolite extracts
derived from suitable controls, wherein the classification model
produces a disease score and the disease score distinguishes
subjects with Lyme disease or STARI.
[0009] In another aspect, the present disclosure encompasses a
method for treating a subject with Lyme disease, the method
comprising: (a) obtaining a disease score from a test; (b)
diagnosing the subject with Lyme disease based on the disease
score; and (c) administering a treatment to the subject with Lyme
disease, wherein the test comprises measuring the amount of each
molecular feature in Table A, Table B, Table C, or Table D;
providing abundance values for each molecular feature measured; and
inputting the abundance values into a classification model trained
with samples derived from suitable controls, wherein the
classification model produces a disease score and the disease score
distinguishes subjects with Lyme disease from subjects with STARI,
and optionally from healthy subjects. In certain examples, the test
comprises (i) deproteinizing a blood sample from a subject to
produce a metabolite extract; (ii) performing liquid chromatography
coupled to mass spectrometry on a sample of the metabolite extract;
(iii) providing abundance values for each molecular feature in
Table A, Table B, Table C, or Table D; and (iv) inputting the
abundance values from step (iii) into a classification model
trained with samples of metabolite extracts derived from suitable
controls, wherein the classification model produces a disease score
and the disease score distinguishes subjects with Lyme disease. In
further examples, the subject has at least one symptom of Lyme
disease. In still further examples, the Lyme disease is early Lyme
disease and optionally the symptom is an EM rash.
[0010] In another aspect, the present disclosure encompasses a
method for treating a subject with STARI, the method comprising:
(a) obtaining a disease score from a test; (b) diagnosing the
subject with STARI based on the disease score; and (c)
administering a treatment to the subject with STARI, wherein the
test comprises measuring the amount of each molecular feature in
Table A, Table B, Table C, or Table D; providing abundance values
for each molecular feature measured; and inputting the abundance
values into a classification model trained with samples derived
from suitable controls, wherein the classification model produces a
disease score and the disease score distinguishes subjects with
STARI. In certain examples, the test comprises (i) deproteinizing a
blood sample from a subject to produce a metabolite extract; (ii)
performing liquid chromatography coupled to mass spectrometry on a
sample of the metabolite extract; (iii) providing abundance values
for each molecular feature in Table A, Table B, Table C, or Table
D; and (iv) inputting the abundance values from step (iii) into a
classification model trained with samples of metabolite extracts
derived from suitable controls, wherein the classification model
produces a disease score and the disease score distinguishes
subjects with STARI from subjects with Lyme disease, including
early Lyme disease, and optionally from healthy subjects. In
further examples, the subject has at least one symptom of STARI. In
still further examples, the symptom is an EM or an EM-like
rash.
[0011] Other aspects and iterations of the invention are described
below.
BRIEF DESCRIPTION OF THE FIGURES
[0012] The disclosure contains at least one photograph executed in
color. Copies of this patent application publication with color
photographs will be provided by the Office upon request and payment
of the necessary fee.
[0013] FIG. 1 is a block diagram depicting a metabolic profiling
pocess for the identification and application of differentiating
molecular features (MFs). LC-MS data from an initial Discovery-Set
of early Lyme disease (EL) and STARI samples was used to identify a
list of MFs that were targeted in a second LC-MS run. The data from
both LC-MS runs was combined to form the Targeted-Discovery-Set.
The MFs were then screened for consistency and robustness and this
resulted in a final early Lyme disease-STARI biosignature of 261
MFs. This biosignature was used for downstream pathway analysis and
for classification modeling. Two training-data sets along with the
261 MF biosignature list were used to train multiple classification
models, random forest (RF) and least absolute shrinkage and
selection operator (LASSO). Data from samples of two Test-Sets (not
included for the Discovery/Training-Set data) were blindly tested
against the two-way (EL vs STARI) and three-way [EL vs STARI vs
healthy controls (HC)] classification models. The regression
coeficients used for each MF in the LASSO two-way and three-way
classification models are provided in Table 5 and Table 7,
resepectively.
[0014] FIG. 2 is a graphical depiction of pathways differentially
regulated in patients with early Lyme disease and STARI. The 122
presumptively identified MFs were analyzed using MetaboAnalyst to
identify perturbed pathways between early Lyme disease and STARI.
The color and size of each circle is based on P values and pathway
impact values. Pathways with a >0.1 impact were considered to be
perturbed and differentially regulated between patients with early
Lyme disease and STARI. There were a total of four pathways
affected: 1) glycerophospholipid metabolism; 2) sphingolipid
metabolism; 3) valine, leucine and isoleucine biosynthesis; and 4)
phenylalanine metabolism.
[0015] FIGS. 3A-E show metabolite identification and the
association with NAE and PFAM metabolism. Structural identification
of palmitoyl ethanolamide (FIG. 3A and FIG. 3B) and other NAEs in
the 261 MF biosignature indicated alteration of NAE metabolism
(FIG. 3C), a pathway that can influence the production of PFAMs.
Further MF identification revealed that palmitamide (FIG. 3D and
FIG. 3E) and other PFAMs also differed in abundance between STARI
and early Lyme disease patients. Structural identification was
achieved by retention-time alignment (FIG. 3A and FIG. 3D) of
authentic standard (top panel), authentic standard spiked in pooled
patient sera (middle panel), and the targeted metabolite in pooled
patient sera (bottom panel), and by comparison of MS/MS spectra
(FIG. 3B and FIG. 3E) of the authentic standards (top) and the
targeted metabolites in patient sera (bottom). Retention-time
alignments for palmitoyl ethanolamide (FIG. 3A) and palmitamide
(FIG. 3D) were generated with extracted ion chromatograms for m/z
300.2892 and m/z 256.2632, respectively. The relationship of PFAM
formation to NAE metabolism is highlighted in pink in FIG. 3C. The
* and ** represent steps for the formation of palmitoyl
ethanolamide and palmitamide, respectively. PLA, phospholipase A;
PLC, phospholipase C; PLD, phospholipase D; ADH, alcohol
dehydrogenase; PAM, peptidylglycine .alpha.-amidating
monooxygenase.
[0016] FIGS. 4A-C graphically depict comparisons of MF abundances
from the Lyme disease-STARI biosignature against healthy controls.
FIG. 4A: Fourteen of the metabolites with level 1 or level 2
structural identifications were evaluated for abundance differences
between early Lyme disease (green squares) and STARI (blue
triangles) normalized to the metabolite abundance in healthy
controls. Included are metabolites identified for NAE and PFAM
metabolism. GP-NAE: glycerophospho-N-palmitoyl ethanolamine; Lyso
PA (20:4): arachidonoyl lysophosphatidic acid; CMPF:
3-carboxy-4-methyl-5-propyl-2-furanpropanoic acid. The relative
mean abundance and 95% confidence intervals are shown for each
metabolite.
[0017] FIG. 4B: Abundance fold change ranges (x-axis) plotted
against the percent of MFs from the 261 MF early Lyme disease-STARI
biosignature that have increased (light blue) or decreased (dark
blue) abundances in STARI relative to healthy controls, and
increased (light green) or decreased (dark green) abundances in
early Lyme disease relative to healthy controls. FIG. 4C: The
percent overlap of MFs between STARI and early Lyme disease that
increase (dark purple) or decrease (light purple) relative to
healthy controls within each abundance fold change range. An
overlap of 30%, 16%, 5%, 0%, 0% and 4% was found for MFs with
increased abundance relative to healthy controls, and 12%, 13%, 0%,
7%, 0%, and 8% for MFs with decreased abundance relative to healthy
controls for the MFs falling within the 1.0-1.4, 1.5-1.9, 2.0-2.4,
2.5-2.9, 3.0-3.4, and .gtoreq.3.5 abundance fold ranges,
respectively.
[0018] FIGS. 5A-C graphically depict evaluations of the performance
of classification models described in the Example 1. FIG. 5A: LASSO
scores (X.beta.; i.e. the linear portion of the regression model)
were calculated for Test-Set data of early Lyme disease and STARI
serum samples by multiplying the transformed abundances of the 38
MFs identified in the two-way LASSO model by the LASSO coefficients
of the model and summing for each sample. Scores are plotted along
the y-axis; serum samples are plotted randomly along the x-axis for
easier viewing. FIG. 5B: An ROC curve demonstrates the level of
discrimination that is achieved between early Lyme disease and
STARI using the 38 MFs of the two-way LASSO classification model by
depicting a true positive rate (sensitivity; early Lyme disease)
versus a false positive rate (specificity; STARI) for the Test-Set
samples (Table 7). The AUC was calculated to be 0.986. The diagonal
line represents an AUC value of 0.5. The performance of two-tiered
testing (red dot) on the same sample set (Test-Set 1) was included
as a reference for the sensitivity and specificity of the current
clinical laboratory test for Lyme disease. FIG. 5C: LASSO scores
(X.beta.i) were calculated for the Test-Set data of early Lyme
disease (green spheres), STARI (blue spheres), and healthy control
(black spheres) serum samples by multiplying the transformed
abundances of the 82 MFs identified in the three-way LASSO model by
each of three LASSO coefficients used in the model. Each axis
represents the sample score in the direction of one of the three
sample groups. Scores are used in calculation of probabilities of
class membership, with highest probability determining the
predicted class. Although there is overlap, the three groups
predominantly occupy distinct areas of the plot.
[0019] FIGS. 6A-B graphically depict evaluations of intra-and
inter-group variability in healthy controls (FIG. 6A) and STARI
subjects (FIG. 6B). Linear discriminant analysis was performed
using the 82 MFs picked by LASSO in the three-way classification
model to assess the intra-group variability based on the
geographical region or laboratory from which healthy control
(CO-black, solid; FL-light gray, dotted; and NY-dark gray, dashed)
and STARI (MO-dark blue, solid; NC-light blue, dotted; and
Other-black, dashed) sera were obtained. Only slight intra-group
variation was observed. This analysis also compared and showed
clear differentiation of all healthy control from STARI samples
regardless of geographical region or laboratory origin. Healthy
controls from FL were included in this analysis to demonstrate that
healthy controls from an area with low incidence of Lyme disease
and where STARI cases occur do not differ from the healthy controls
obtained from other regions and used in the classification
modeling.
[0020] FIGS. 7A-B show data from level 1 identification of stearoyl
ethanolamide. Confirmation of the structural identity of stearoyl
ethanolamide was achieved by retention-time alignment (FIG. 7A) of
authentic standard (top panel), authentic standard spiked in pooled
patient sera (middle panel), and the targeted metabolite in pooled
patient sera; and by comparison of MS/MS spectra (FIG. 7B) of the
authentic standard (top) and the targeted metabolite in pooled
patient sera (bottom). Retention-time alignments for stearoyl
ethanolamide (FIG. 7A) were generated with extracted ion
chromatograms for m/z 328.3204. MS/MS spectra for stearoyl
ethanolamide were obtained with a collision energy of 20 eV.
[0021] FIGS. 8A-B show data from level 1 identification of
pentadecanoyl ethanolamide. Confirmation of the structural identity
of pentadecanoyl ethanolamide was achieved by retention-time
alignment (FIG. 8A) of authentic standard (top panel), authentic
standard spiked in pooled patient sera (middle panel), and the
targeted metabolite in pooled patient sera; and by comparison of
MS/MS spectra (FIG. 8B) of the authentic standard (top) and the
targeted metabolite in pooled patient sera (bottom). Retention-time
alignments for pentadecanoyl ethanolamide (FIG. 8A) were generated
with extracted ion chromatograms for m/z 286.2737. MS/MS spectra
for pentadecanoyl ethanolamide were obtained with a collision
energy of 20 eV.
[0022] FIGS. 9A-B show data from level 1 identification of
eicosanoyl ethanolamide. Confirmation of the structural identity of
eicosanoyl ethanolamide was achieved by retention-time alignment
(FIG. 9A) of authentic standard (top panel), authentic standard
spiked in pooled patient sera (middle panel), and the targeted
metabolite in pooled patient sera; and by comparison of MS/MS
spectra (FIG. 9B) of the authentic standard (top) and the targeted
metabolite in pooled patient sera (bottom). Retention-time
alignments for eicosanoyl ethanolamide (FIG. 9A) were generated
with extracted ion chromatograms for m/z 356.3517. MS/MS spectra
for eicosanoyl ethanolamide were obtained with a collision energy
of 20 eV.
[0023] FIGS. 10A-B show data from level 1 identification of
glycerophospho-N-palmitoyl ethanolamine. Confirmation of the
structural identity of glycerophospho-N-palmitoyl ethanolamine was
achieved by retention-time alignment (FIG. 10A) of authentic
standard (top panel), authentic standard spiked in pooled patient
sera (middle panel), and the targeted metabolite in pooled patient
sera; and by comparison of MS/MS spectra (FIG. 10B) of the
authentic standard (top) and the targeted metabolite in pooled
patient sera (bottom). Retention-time alignments for
glycerophospho-N-palmitoyl ethanolamine (FIG. 10A) were generated
with extracted ion chromatograms for m/z 454.2923. MS/MS spectra
for glycerophospho-N-palmitoyl ethanolamine were obtained with a
collision energy of 20 eV.
[0024] FIGS. 11A-B show data from level 1 identification of
stearamide. Confirmation of the structural identity of stearamide
was achieved by retention-time alignment (FIG. 11A) of authentic
standard (top panel), authentic standard spiked in pooled patient
sera (middle panel), and the targeted metabolite in pooled patient
sera; and by comparison of MS/MS spectra (FIG. 11B) of the
authentic standard (top) and the targeted metabolite in pooled
patient sera (bottom). Retention-time alignments for stearamide
(FIG. 11A) were generated with extracted ion chromatograms for m/z
284.2943. MS/MS spectra for stearamide were obtained with a
collision energy of 20 eV.
[0025] FIGS. 12A-B show data from level 1 identification of
erucamide. Confirmation of the structural identity of erucamide was
achieved by retention-time alignment (FIG. 12A) of authentic
standard (top panel), authentic standard spiked in pooled patient
sera (middle panel), and the targeted metabolite in pooled patient
sera; and by comparison of MS/MS spectra (FIG. 12B) of the
authentic standard (top) and the targeted metabolite in pooled
patient sera (bottom). Retention-time alignments for erucamide
(FIG. 12A) were generated with extracted ion chromatograms for m/z
338.3430. MS/MS spectra for erucamide were obtained with a
collision energy of 20 eV.
[0026] FIGS. 13A-B show data from level 1 identification of
L-phenylalanine. Confirmation of the structural identity of
L-phenylalanine was achieved by retention-time alignment (FIG. 13A)
of authentic standard (top panel), authentic standard spiked in
pooled patient sera (middle panel), and the targeted metabolite in
pooled patient sera; and by comparison of MS/MS spectra (FIG. 13B)
of the authentic standard (top) and the targeted metabolite in
pooled patient sera (bottom). Retention-time alignments for
L-phenylalanine (FIG. 13A) were generated with extracted ion
chromatograms for m/z 166.0852. MS/MS spectra for L-phenylalanine
were obtained with a collision energy of 20 eV.
[0027] FIGS. 14A-B show data from level 1 identification of
nonanedioic acid. Confirmation of the structural identity of
nonanedioic acid was achieved by retention-time alignment (FIG.
14A) of authentic standard (top panel), authentic standard spiked
in pooled patient sera (middle panel), and the targeted metabolite
in pooled patient sera; and by comparison of MS/MS spectra (FIG.
14B) of the authentic standard (top) and the targeted metabolite in
pooled patient sera (bottom). Retention-time alignments for
nonanedioic acid (FIG. 14A) were generated with extracted ion
chromatograms for m/z 189.1122. MS/MS spectra for nonanedioic acid
were obtained with a collision energy of 10 eV.
[0028] FIGS. 15A-B show data from level 1 identification of
glycocholic acid. Confirmation of the structural identity of
glycocholic acid was achieved by retention-time alignment (FIG.
15A) of authentic standard (top panel), authentic standard spiked
in pooled patient sera (middle panel), and the targeted metabolite
in pooled patient sera; and by comparison of MS/MS spectra (FIG.
15B) of the authentic standard (top) and the targeted metabolite in
pooled patient sera (bottom). Retention-time alignments for
glycocholic acid (FIG. 15A) were generated with extracted ion
chromatograms for m/z 466.3152. MS/MS spectra for glycocholic acid
were obtained with a collision energy of 20 eV.
[0029] FIGS. 16A-B show data from level 1 identification of
3-carboxy-4-methyl-5-propyl-2-furanpropanoic acid (CMPF).
Confirmation of the structural identity of CMPF was achieved by
retention-time alignment (FIG. 16A) of authentic standard (top
panel), authentic standard spiked in pooled patient sera (middle
panel), and the targeted metabolite in pooled patient sera; and by
comparison of MS/MS spectra (FIG. 16B) of the authentic standard
(top) and the targeted metabolite in pooled patient sera (bottom).
Retention-time alignments for CMPF (FIG. 16A) were generated with
extracted ion chromatograms for m/z 241.1069. MS/MS spectra for
CMPF were obtained with a collision energy of 20 eV.
[0030] FIG. 17 shows data from level 2 identification of Lyso PA
(20:4) by MS/MS spectral matching. The MS/MS fragmentation pattern
for m/z 459.2502 (**) in pooled sera at RT 19.02 is shown. A match
to the fragmentation of arachidonoyl lysophosphatidic acid (Lyso PA
(20:4)) in the Metlin database is indicated by (*). MS/MS spectra
for m/z 459.2502 were obtained with a collision energy of 20
eV.
[0031] FIG. 18 shows data from level 2 identification of
3-ketosphingosine by MS/MS spectral matching. The MS/MS
fragmentation pattern for m/z 298.2740 (**) in pooled sera at RT
16.44 is shown. A match to the fragmentation of 3-ketosphingosine
in the Metlin database is indicated by (*). MS/MS spectra for m/z
298.2740 were obtained with a collision energy of 20 eV.
DETAILED DESCRIPTION
[0032] Lyme disease is an illness caused by a Borrelia species
(e.g., Borrelia burgdorferi, Borrelia garinii, Borellia afzelii,
etc.) and is transmitted to humans through the bite of infected
blacklegged ticks (Ixodes species). Lyme disease can go through
several stages and may cause different symptoms, depending on how
long a subject has been infected and where in the body the
infection has spread. The stages of Lyme disease include Stage 1,
Stage 2, and Stage 3. Stage 1 Lyme disease may also be referred to
as "early localized Lyme disease" or "early Lyme disease" and
usually develops about 1 day to about 4 weeks after infection.
Non-limiting examples of symptoms of Stage 1 Lyme disease include
erythema migrans and flu-like symptoms, such as lack of energy,
headache, stiff neck, fever, chills, muscle pain, joint pain, and
swollen lymph nodes. Stage 1 Lyme disease may result in one or more
than one symptom. In some cases, Stage 1 Lyme disease does not
result in any symptoms. Stage 2 Lyme disease may also be referred
to as "early disseminated infection" and usually develops about 1
month to about 4 months after infection. Non-limiting examples of
symptoms of Stage 2 Lyme disease include an erythema migrans (or
additional erythema migrans rash sites), pain, weakness, numbness
in the arms and/or legs, Bell's palsy (facial drooping), headaches,
fainting, poor memory, reduced ability to concentrate,
conjunctivitis, episodes of pain, redness and swelling in one or
more large joints, rapid heartbeats (palpitations), and serious
heart problems. Stage 3 Lyme disease may also be referred to as
"late persistent Lyme disease" and usually develops months to years
after infection. Non-limiting examples of symptoms of Stage 3 Lyme
disease include arthritis, numbness and tingling in the hands,
numbness and tingling in the feet, numbness and tingling in the
back, tiredness, Bell's palsy (facial drooping), problems with
memory, mood, sleep speaking, and heart problems (pericarditis). A
subject diagnosed with Lyme disease, or suspected of having Lyme
disease, may be identified on the basis of one or more symptoms,
geographic location, and possibility of tick bite. Currently,
several routine diagnostic tests are known for diagnosing Lyme
disease. Typically these tests detect and/or quantify antibodies to
one or more Borellia antigens, and are performed using common
immunoassay methods such as enzyme-linked immunoassays (EIA or
ELISA), immunofluorescence assays, or Western immunoblots.
Generally, these tests are most reliable only a few weeks after an
infection. Positive PCR and/or positive culture may also be used.
(See, e.g., Moore et al., "Current Guidelines, Common Clinical
Pitfalls, and Future Directions for Laboratory Diagnosis of Lyme
Disease, United States," Emerg Infect Dis. 2016, Vol. 22, No. 7).
In one example, diagnostic testing may comprise a
commercially-available C6 EIA. The C6 Lyme EIA measures antibody
reactivity to a synthetic peptide corresponding to the sixth
invariable region of VlsE, a highly conserved surface protein of
the causative Borrelia burgdorferi bacterium. Alternatively, or in
addition, diagnostic testing may comprise using IgM and/or IgG
immunoblots following a positive or equivocal first-tier assay. As
used herein, a subject that is negative for antibodies to Lyme
disease causing Borrelia species need only be negative by one
method of testing.
[0033] Southern tick-associated rash illness (STARI) is an illness
associated with a bite from the lone star tick, Amblyomma
americanum. The causative agent of STARI is unknown. The rash of
STARI is a red, expanding "bull's-eye" lesion that develops around
the site of a lone star tick bite. The rash of STARI may be
referred to as an EM rash or an EM-like rash. The rash usually
appears within 7 days of tick bite and expands to a diameter of 8
centimeters (3 inches) or more. Non-limiting examples of additional
symptoms associated with STARI include discomfort and/or itching at
the bite site, muscle pain, joint pain, fatigue, fever, chills, and
headache. A subject diagnosed with STARI, or suspected of having
STARI, may be identified on the basis of one or more symptom,
geographic location, and possibility of tick bite.
[0034] Complicating the clinical differentiation between Lyme
disease, and in particular early Lyme disease, and STARI are shared
symptoms (for example, an EM or EM-like rash), co-prevalence of
STARI and Lyme disease in certain geographic regions, and poor
sensitivity of common diagnostic methods for early stages Lyme
disease. The present disclosure provides a biosignature that
identifies Lyme disease and southern tick-associated rash illness
(STARI), and distinguishes one from the other. Various aspects of
the biosignature and its use are described in detail below.
I. Definitions
[0035] So that the present disclosure may be more readily
understood, certain terms are first defined. Unless defined
otherwise, all technical and scientific terms used herein have the
same meaning as commonly understood by one of ordinary skill in the
art to which examples of the disclosure pertain. Many methods and
materials similar, modified, or equivalent to those described
herein can be used in the practice of the examples of the present
disclosure without undue experimentation, the preferred materials
and methods are described herein. In describing and claiming the
examples of the present disclosure, the following terminology will
be used in accordance with the definitions set out below.
[0036] The term "about," as used herein, refers to variation of in
the numerical quantity that can occur, for example, through typical
measuring techniques and equipment, with respect to any
quantifiable variable, including, but not limited to, mass, volume,
time, distance, wave length, frequency, voltage, current, and
electromagnetic field. Further, given solid and liquid handling
procedures used in the real world, there is certain inadvertent
error and variation that is likely through differences in the
manufacture, source, or purity of the ingredients used to make the
compositions or carry out the methods and the like. The term
"about" also encompasses these variations, which can be up to
.+-.5-10%, but can also be .+-.9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, 1%,
etc. Whether or not modified by the term "about," the claims
include equivalents to the quantities.
[0037] As used herein, the term "accuracy" refers to the ability of
a test (e.g., a diagnostic test, a classification model, etc.) to
correctly differentiate one type of subject (e.g., a subject with
Lyme disease) from one or more different types of subjects (e.g., a
subject with STARI, a healthy subject, etc.). Accuracy is equal to
(true positive result)+(true negative result)/(true positive
result)+(true negative result)+(false positive result)+(false
negative result).
[0038] The term "biosignature" refers to a plurality of molecular
features forming a distinctive pattern which is indicative of a
disease or condition of an animal, preferably a human.
[0039] The term "molecular feature" refers to a small molecule
metabolite in a blood sample that has a mass less than 3000 Da. The
term "abundance value" refers to an amount of a molecular feature
in a blood sample. The abundance value for a molecular feature may
be identified via any suitable method known in the art. Molecular
features are defined herein by a positive ion m/z charge ratio
.+-.a suitable tolerance to account for instrument variability
(e.g., .+-.15 ppm) and optionally one or more additional
characteristic such as retention time or a chemical structure based
on accurate mass; and abundance values for each molecular feature
are obtained from a measurement of the area under the peak for the
monoisotopic mass of each molecular feature determined by mass
spectrometry. Given that the present disclosure identifies the
molecular features (i.e., small molecule metabolites) forming each
biosignature, alternative methods for measuring the amount of the
metabolites may be used without departing from the scope of the
invention.
[0040] As used herein, the term "ROC" means "receiver operating
characteristic". A ROC analysis may be used to evaluate the
diagnostic performance, or predictive ability, of a test or a
method of analysis. A ROC graph is a plot of sensitivity and
specificity of a test at various thresholds or cut-off values. Each
point on a ROC curve represents the sensitivity and its respective
specificity. A threshold value can be selected based on an ROC
curve, wherein the threshold value is a point where sensitivity and
specificity both have acceptable values. The threshold value can be
used in applying the test for diagnostic purposes. It will be
understood that if only specificity is optimized, then the test
will be less likely to generate a false positive (diagnosis of the
disease in more subjects who do not have the disease) at the cost
of an increased likelihood that some cases of disease will not be
identified (e.g., false negatives). If only sensitivity is
optimized, the test will be more likely to identify most or all of
the subjects with the disease, but will also diagnose the disease
in more subjects who do not have the disease (e.g., false
positives). A user is able to modify the parameters, and therefore
select an ROC threshold value suitable for a given clinical
situation, in ways that will be readily understood by those skilled
in the art.
[0041] Another useful feature of the ROC curve is an area under the
curve (AUC) value, which quantifies the overall ability of the test
to discriminate between different sample properties, for example,
to discriminate between subjects with Lyme disease and those STARI;
to discriminate between subjects with STARI and healthy subjects;
subjects or to discriminate between subjects with Lyme disease,
STARI, and healthy subjects. A test that is no better at
identifying true positives than random chance will generate a ROC
curve with an AUC of 0.5. A test having perfect specificity and
sensitivity (i.e., generating no false positives and no false
negatives) will have an AUC of 1.00. In reality, most tests will
have an AUC somewhere between these two values.
[0042] As used herein, the term "sensitivity" refers to the
percentage of truly positive observations which is classified as
such by a test, and indicates the proportion of subjects correctly
identified as having a given condition. In other words, sensitivity
is equal to (true positive result)/[(true positive result)+(false
negative result)].
[0043] As used herein, the term "specificity" refers to the
percentage of truly negative observations which is classified as
such by a test, and indicates the proportion of subjects correctly
identified as not having a given condition. Specificity is equal to
(true negative result)/[(true negative result)+(false positive
result).
[0044] As used herein, the term "subject" refers to a mammal,
preferably a human. The mammals include, but are not limited to,
humans, primates, livestock, rodents, and pets. A subject may be
waiting for medical care or treatment, may be under medical care or
treatment, or may have received medical care or treatment.
[0045] As used interchangeably herein, the terms "control group,"
"normal group," "control subject," or "healthy subject" refer to a
subject, or a group of subjects, not previously diagnosed with the
disease in question and/or treated for the disease in question for
atherapeutically effective amount of time (e.g., 12 months or
more).
[0046] As used herein, the term "blood sample" refers to a
biological sample derived from blood, preferably peripheral (or
circulating) blood. The blood sample can be whole blood, plasma or
serum.
[0047] The terms "treat," "treating," or "treatment" as used
herein, refer to the provision of medical care by a trained and
licensed health professional to a subject in need thereof. The
medical care may be a diagnostic test, a therapeutic treatment,
and/or a prophylactic or preventative measure. The object of
therapeutic and prophylactic treatment is to prevent or slow down
(lessen) an undesired physiological change or disease/disorder.
Beneficial or desired clinical results of therapeutic or
prophylactic treatments include, but are not limited to,
alleviation of symptoms, diminishment of extent of disease,
stabilized (i.e., not worsening) state of disease, a delay or
slowing of disease progression, amelioration or palliation of the
disease state, and remission (whether partial or total), whether
detectable or undetectable. "Treatment" can also mean prolonging
survival as compared to expected survival if not receiving
treatment. Alternatively, the medical care may be a recommendation
for no intervention. For example, no medical intervention may be
needed for diseases that are self-limiting. Those in need of
treatment include those already with the disease, condition, or
disorder as well as those prone to have the disease, condition or
disorder or those in which the disease, condition or disorder is to
be prevented.
II. Biosignatures
[0048] In an aspect, the present disclosure provides a biosignature
that provides an accuracy of detecting Lyme disease equal to or
greater than about 80%. In another aspect, the present disclosure
provides a biosignature that provides an accuracy of detecting
STARI disease equal to or greater than about 80%.
[0049] A method for identifying a Lyme disease biosignature and/or
a STARI biosignature is detailed in the examples. Generally
speaking, the method comprises: a) obtaining test blood samples and
control blood samples; b) analyzing the test blood samples and
control blood samples by mass spectrometry to obtain abundance
values for a plurality of molecular features in the test blood
samples and the control blood samples; and c) applying a
statistical modeling technique to select for a plurality of
molecular features that distinguish test blood samples from control
blood samples with an accuracy equal to or greater than about 80%.
Test blood samples are from subjects with Lyme disease and/or
STARI, either of which is confirmed using known diagnostic methods
as described above; and control bloods samples are from subjects
confirmed to be free of Lyme, STARI, or both using known diagnostic
methods for each. The molecular features that distinguish test
blood samples from control blood samples comprise the biosignature
for that disease.
[0050] A blood sample may be a whole blood sample, a plasma sample,
or a serum sample. Any of a variety of methods generally known in
the art for collecting a blood sample may be utilized. Generally
speaking, the sample collection method preferably maintains the
integrity of the sample such that abundance values for each
molecular feature can be accurately measured. A blood sample may be
used "as is", or a blood sample may be processed to remove
undesirable constituents. In preferred examples, a blood sample is
processed using standard techniques to remove high-molecular weight
species, and thereby obtain an extract comprising small molecule
metabolites. This is referred to herein as "deproteinization" or a
"deproteinization step." For example, a solvent or solvent mixture
(e.g., methanol or the like) may be added to a blood sample to
precipitate these high-molecular weight species followed by a
centrifugation step to separate the precipitate and the
metabolite-containing supernatant. In another example, proteases
may be the added to a blood sample. In another example, size
exclusion chromatography may be used.
[0051] Analysis using mass spectrometry, preferably high resolution
mass spectrometry, yields abundance measures for a plurality of
molecular features. The abundance value for each molecular feature
may be obtained from a measurement of the area under the peak for
the monoisotopic mass of each molecular feature. Identification and
extraction of molecular features involves finding and quantifying
all the known and unknown compounds/metabolites down to the lowest
abundance, and extracting all relevant spectral and chromatographic
information. Algorithms are available to identify and extract
molecular features. Such algorithms include for example the
Molecular Feature Extractor (MFE) by Agilent. MFE locates ions that
are covariant (rise and fall together in abundance) but the
analysis is not exclusively based on chromatographic peak
information. The algorithm uses the accuracy of the mass
measurements to group related ions-related by charge-state
envelope, isotopic distribution, and/or the presence of adducts and
dimers. It assigns multiple species (ions) that are related to the
same neutral molecule (for example, ions representing multiple
charge states or adducts of the same neutral molecule) to a single
compound that is referred to as a feature. Using this approach, the
MFE algorithm can locate multiple compounds within a single
chromatographic peak. Specific parameters for MFE may include a
minimum ion count of 600, an absolute height of 2,000 ion counts,
ion species H+ and Na+, charge state maximum 1, and compound ion
count threshold of 2 or more ions. Once the molecular feature has
been identified and extracted, the area under the peak for the
monoisotopic mass is used to determine the abundance value for the
molecular feature. The monoisotopic mass is the sum of the masses
of the atoms in a molecule using the unbound, ground-state, rest
mass of the principal (most abundant) isotope for each element
instead of the isotopic average mass. Monoisotopic mass is
typically expressed in unified atomic mass units (u), also called
daltons (Da).
[0052] A molecular feature is identified as a potential molecular
feature for utilization in a biosignature of the present disclosure
if it is present in at least 50% of either the test blood samples
or the control blood samples. For example, the molecular feature
may be present in at least 50%, at least 55%, at least 60%, at
least 65%, at least 70%, at least 75%, at least 80%, at least 85%,
at least 90%, at least 95% or 100% of either the test blood samples
or the control blood samples. Additionally, a molecular feature is
identified as a potential molecular feature for utilization in a
biosignature of the present disclosure if it is significantly
different in abundance between the test blood samples and the
control blood samples. Specifically, a molecular feature is
identified as being significantly different if the difference in
abundance value of the molecular feature in the test blood samples
versus the abundance value of the molecular feature in the blood
biological samples has a p-value is less than 0.1, preferably less
than 0.05, less than 0.01, less than 0.005, or less than 0.001.
[0053] To increase the stringency of the biosignature, replicates
of the test blood samples and control blood samples may be
analyzed. For example, the test blood samples and control blood
samples may be analyzed in duplicate. Alternatively, the test
biological samples and control biological samples may be analyzed
in triplicate. Additionally, the test blood samples and control
blood samples may be analyzed four, five or six times. The
replicate analysis is used to down-select the plurality of
molecular features. The down-selection results in a biosignature
with increased stringency.
[0054] Once a plurality of potential molecular features has been
generated, a statistical modeling technique may be applied to
select for the molecular features that provide an accuracy of
disease detection that is clinically meaningful. Several
statistical models are available to select the molecular features
that comprise a biosignature of the present disclosure.
Non-limiting examples of statistical modeling techniques include
LDA, classification tree (CT) analysis, random forests, and LASSO
(least absolute shrinkage and selection operator) logistic
regression analysis. Various methods are known in the art for
determining an optimal cut-off that maximizes sensitivity and/or
specificity to serve as a threshold for discriminating samples
obtained from subjects with Lyme disease or STARI. For the
biosignatures in Table A, Table C, and Table D, the cut-off is
determined by a data point of the highest specificity at the
highest sensitivity on the ROC curve. However, the cut-off can be
set as required by situational circumstances. For example, in
certain clinical situations it may be desirable to minimize
false-positive rates. These clinical situations may include, but
are not limited to, the use of an experimental treatment (e.g., in
a clinical trial) or the use of a treatment associated with serious
adverse events and/or a higher than average number of side effects.
Alternatively, it may be desirable to minimize false-negative rates
in other clinical situations. Non-limiting examples may include
treatment with a non-pharmacological intervention, the use of a
treatment with a good risk-benefit profile, or treatment with an
additional diagnostic agent. For the biosignatures in Table A,
molecular feature stability across many samples and different LC-MS
analyses was used as the cut-off.
[0055] In one example, the present disclosure provides a
biosignature comprising each molecular feature in Table A, wherein
the molecular features in the table are defined at least by their
m/z ratio .+-.15 ppm, in some examples .+-.10 ppm, in some examples
.+-.5 ppm (depending upon instrument variability). A biosignature
comprising each molecular feature in Table A provides a 9800
probability of accurately detecting a sample from a subject with
Lyme disease, including early Lyme disease, and an 89% probability
of accurately detecting a sample from a subject with STARI, when
discriminating between a classification of Lyme disease and STARI.
A skilled artisan will appreciate that in certain examples one or
more molecular feature may be eliminated from the model without a
clinically meaningful, negative impact on the model.
TABLE-US-00001 TABLE A Predicted Chemical Retention Structure m/z
Time Compound (based on Metabolite MF (positive (see Predicted
accurate Class or # Name ion) Mass examples) Formula mass) Pathway
1 CSU/CDC-001 166.0852 165.078 1.86 C.sub.9H.sub.11NO.sub.2
Phenylalanine Phenylalanine metabolism 2 CSU/CDC-012 270.3156
269.3076 18.02 C.sub.18H.sub.39N -- -- 3 CSU/CDC-013 284.3314
283.3236 18.13 C.sub.19H.sub.41N -- -- 4 CSU/CDC-014 300.6407
599.268 18.27 C.sub.33H.sub.37N.sub.5O.sub.6 Asp Phe Arg Tyr
Peptide (SEQ ID NO: 1) 5 CSU/CDC-019 300.2892 299.2821 19.66
C.sub.18H.sub.37NO.sub.2 Palmitoyl N-acyl ethanolamide ethanolamine
metabolism 6 CSU/CDC-039 734.5079 1449.9753 17.81 -- -- -- 7
CSU/CDC-062 370.1837 369.1757 19.7 C.sub.19H.sub.23N.sub.5O.sub.3
-- -- 8 CSU/CDC-066 811.1942 810.1869 12.07
C.sub.42H.sub.30N.sub.6O.sub.12 -- -- 9 CSU/CDC-067 947.7976
946.7936 14.55 C.sub.62H.sub.106O.sub.6 TAG(59:7) Triacylglycerol
metabolism 10 CSU/CDC-072 410.2033 409.196 17.18 -- -- -- 11
CSU/CDC-075 1487.0005 1485.9987 18.17 -- -- -- 12 CSU/CDC-086
137.0463 136.0378 1.37 C.sub.4H.sub.8O.sub.5 Threonate Sugar
metabolite 13 CSU/CDC- 107 811.7965 810.7882 12.07 -- -- -- 14
CSU/CDC-132 616.1776 615.1699 15.43 -- -- -- 15 CSU/CDC-152
713.4492 712.4391 19.35 C.sub.38H.sub.65O.sub.10P PG(32:5)
Glycerophosph olipid metabolism 16 CSU/CDC-155 502.3376 484.3039
19.87 C.sub.27H.sub.40N.sub.4O.sub.3 Gln Leu Pro Lys Peptide (SEQ
ID NO: 2) 17 CSU/CDC-158 415.3045 414.2978 20.19 -- -- -- 18
CSU/CDC-164 366.3729 365.3655 22.79 -- -- -- 19 CSU/CDC-166
333.1446 332.1373 12.89 C.sub.12H.sub.20N.sub.4O.sub.7 Glu Gln Gly
Peptide 20 CSU/CDC-205 241.1069 240.0996 14.7
C.sub.12H.sub.16O.sub.5 3-Carboxy-4- Fatty acid methyl-5-propyl-2-
metabolism furanpropanoic acid (CMPF) 21 CSU/CDC-211 464.1916
463.1849 13.05 C.sub.16H.sub.29N.sub.7O.sub.7S Arg Asp Cys Ala
Peptide (SEQ ID NO: 3) 22 CSU/CDC-212 1249.2045 1248.1993 15.31 --
-- -- 23 CSU/CDC-213 1248.9178 1247.9141 15.3 -- -- -- 24
CSU/CDC-219 158.1539 157.1466 15.36 -- -- -- 25 CSU/CDC-227
529.3381 528.3296 16.89 C.sub.24H.sub.44N.sub.6O.sub.7 Gln Val Leu
Peptide Leu Gly (SEQ ID NO: 4) 26 CSU/CDC-229 282.2776 264.2456
20.56 C.sub.18H.sub.32O -- -- 27 CSU/CDC-235 190.1260 189.1187
14.12 C.sub.9H.sub.19NOS 8- 2- Methylthiooctanal oxocarboxylic
doxime acid metabolism 28 CSU/CDC-238 382.3675 381.3603 20.23
C.sub.2H.sub.47NO.sub.2 Erucicoyl N-acyl ethanolamide ethanolamine
metabolism 29 CSU/CDC-244 477.2968 476.2898 22.79
C.sub.31H.sub.40O.sub.4 Lys Lys Thr Thr Peptide (SEQ ID NO: 5) 30
CSU/CDC-248 459.3968 458.3904 19.08 -- -- -- 31 CSU/CDC-253
342.2635 341.2565 15.62 C.sub.19H.sub.35NO.sub.4 -- -- 32
CSU/CDC-254 529.3827 1022.6938 17.86 -- -- -- 33 CSU/CDC-258
459.2502 458.2429 19.02 C.sub.23H.sub.39O.sub.7P Lyso PA(20:4)
Glycerohospho lipid metabolism 34 CSU/CDC-002 239.0919 238.0844
11.66 C.sub.12H.sub.14O.sub.5 Trans-2, 3, 4- Phenylpropano
trimethoxy- id and cinnamate polyketide metabolism 35 CSU/CDC-028
389.2174 388.2094 15.47 C.sub.19H.sub.32O.sub.8 Methyl Fatty acid
10,12,13,15- metabolism bisepidioxy-16- hydroperoxy-8E-
octadecenoate 36 CSU/CDC-182 285.2065 284.1991 16.02
C.sub.16H.sub.28O.sub.4 -- -- 37 CSU/CDC-204 279.1693 278.1629
11.05 C.sub.15H.sub.22N.sub.2O.sub.3 Phe Leu Dipeptide 38
CSU/CDC-247 714.6967 1427.3824 11.76 -- -- --
[0056] In one example, the present disclosure provides a
biosignature comprising each molecular feature in Table B, wherein
the molecular features in the table are defined at least by their
m/z ratio .+-.15 ppm, in some examples .+-.10 ppm, in some examples
.+-.5 ppm (depending upon instrument variability). The biosignature
comprises each molecular feature in Table B that maintains an
absolute abundance fold change of 2 or greater between Lyme disease
and STARI, and maintains an abundance coefficient of variation of
0.2 or less between STARI blood samples, and maintains an abundance
coefficient of variation of 0.2 or less between Lyme disease blood
samples. A skilled artisan will appreciate that in certain examples
one or more molecular features may be eliminated from the model
without a clinically meaningful, negative impact on the model.
TABLE-US-00002 TABLE B Predicted Chemical Retention Structure m/z
Time Compound (based on Metabolite MF (positive (see Predicted
accurate Class or # Name ion) Mass examples) Formula mass) Pathway
1 CSU/CDC-006 286.1444 285.1371 16.08 C.sub.17H.sub.19NO.sub.3
Piperine Alkaloid metabolism 2 CSU/CDC-021 394.3515 376.3171 20.09
-- -- -- 3 CSU/CDC-023 284.2943 283.2872 21.15 C.sub.18H.sub.37NO
Stearamide Primary Fatty Acid Amide Metabolism 4 CSU/CDC-083
482.404 481.3976 19.99 -- -- -- 5 CSU/CDC-086 137.0463 136.0378
1.37 C.sub.4H.sub.8O.sub.5 Threonate Sugar metabolite 6 CSU/CDC-217
438.3787 420.3453 19.93 -- -- -- 7 CSU/CDC-219 158.1539 157.1466
15.36 -- -- -- 8 CSU/CDC-211 464.1916 463.1849 13.05
C.sub.16H.sub.29N.sub.7O.sub.7S Arg Asp Cys Ala Peptide (SEQ ID NO:
3) 9 CSU/CDC-240 441.3687 440.3614 21.26 C.sub.30H.sub.48O.sub.2
4,4-Dimethyl- Sterol 14a-formyl-5a- metabolism cholesta-8,24-
dien-3b-ol 10 CSU/CDC-248 459.3968 458.3904 19.08 -- -- -- 11
CSU/CDC-258 459.2502 458.2429 19.02 C.sub.23H.sub.39O.sub.7P Lyso
PA(20:4) Glycerophospho lipid metabolism
[0057] In one example, the present disclosure provides a
biosignature comprising each molecular feature in Table C, wherein
the molecular features in the table are defined at least by their
m/z ratio .+-.15 ppm, in some examples .+-.10 ppm, in some examples
.+-.5 ppm (depending upon instrument variability). The biosignature
comprising each molecular feature in Table C provides an 85%
probability of accurately detecting a sample from a subject with
Lyme disease, including early Lyme disease, an 92% probability of
accurately detecting a sample from a subject with STARI, and a 93%
probability of accurately detecting a sample from a healthy
subject, when discriminating between a status (classification) of
Lyme disease, STARI, and healthy. A skilled artisan will appreciate
that in certain examples one or more molecular features may be
eliminated from the model without a clinically meaningful, negative
impact on the model.
TABLE-US-00003 TABLE C Predicted Chemical Retention Structure m/z
Time Compound (based on Metabolite MF (positive (see Predicted
accurate Class or # Name ion) Mass examples) Formula mass) Pathway
1 CSU/CDC-001 166.0852 165.078 1.86 C.sub.9H.sub.11NO.sub.2
Phenylalanine Phenylalanine metabolism 2 CSU/CDC-012 270.3156
269.3076 18.02 C.sub.18H.sub.39N -- -- 3 CSU/CDC-013 284.3314
283.3236 18.13 C.sub.19H.sub.41N -- -- 4 CSU/CDC-014 300.6407
599.268 18.27 C.sub.33H.sub.37N.sub.5O.sub.6 Asp Phe Arg Tyr
Peptide (SEQ ID NO: 1) 5 CSU/CDC-019 300.2892 299.2821 19.66
C.sub.18H.sub.37NO.sub.2 Palmitoyl N-acyl ethanolamide ethanolamine
metabolism 6 CSU/CDC-039 734.5079 1449.9753 17.81 -- -- -- 7
CSU/CDC-062 370.1837 369.1757 19.7 C.sub.19H.sub.23N.sub.5O.sub.3
-- -- 8 CSU/CDC-066 811.1942 810.1869 12.07
C.sub.42H.sub.30N.sub.6O.sub.12 -- -- 9 CSU/CDC-067 947.7976
946.7936 14.55 C.sub.62H.sub.106O.sub.6 TAG(59:7) Triacylglycerol
metabolism 10 CSU/CDC-072 410.2033 409.196 17.18 -- -- -- 11
CSU/CDC-075 1487.0005 1485.9987 18.17 -- -- -- 12 CSU/CDC-086
137.0463 136.0378 1.37 C.sub.4H.sub.8O.sub.5 Threonate Sugar
metabolite 13 CSU/CDC-107 811.7965 810.7882 12.07 -- -- -- 14
CSU/CDC-132 616.1776 615.1699 15.43 -- -- -- 15 CSU/CDC-152
713.4492 712.4391 19.35 C.sub.38H.sub.65O.sub.10P PG(32:5)
Glycerophosph olipid metabolism 16 CSU/CDC-155 502.3376 484.3039
19.87 C.sub.27H.sub.40N.sub.4O.sub.4 Gln Leu Pro Lys Peptide (SEQ
ID NO: 2) 17 CSU/CDC-158 415.3045 414.2978 20.19 -- -- -- 18
CSU/CDC-164 366.3729 365.3655 22.79 -- -- -- 19 CSU/CDC-166
333.1446 332.1373 12.89 C.sub.12H.sub.20N.sub.4O.sub.7 Glu Gln Gly
Peptide 20 CSU/CDC-205 241.1069 240.0996 14.7
C.sub.12H.sub.16O.sub.5 3-Carboxy-4- Fatty acid methyl-5-propyl-
metabolism 2-furanpropanoic acid (CMPF) 21 CSU/CDC-211 464.1916
463.1849 13.05 C.sub.16H.sub.29N.sub.7O.sub.7S Arg Asp Cys Ala
Peptide (SEQ ID NO: 3) 22 CSU/CDC-212 1249.2045 1248.1993 15.31 --
-- -- 23 CSU/CDC-213 1248.9178 1247.9141 15.3 -- -- -- 24
CSU/CDC-219 158.1539 157.1466 15.36 -- -- -- 25 CSU/CDC-227
529.3381 528.3296 16.89 C.sub.24H.sub.44N.sub.6O.sub.7 Gln Val Leu
Leu Peptide Gly (SEQ ID NO: 4) 26 CSU/CDC-229 282.2776 264.2456
20.56 C.sub.18H.sub.32O -- -- 27 CSU/CDC-235 190.1260 189.1187
14.12 C.sub.9H.sub.19NOS 8- 2- Methylthiooctanal oxocarboxylic
doxime acid metabolism 28 CSU/CDC-238 382.3675 381.3603 20.23
C.sub.24H.sub.47NO.sub.2 Erucicoyl N-acyl ethanolamide ethanolamine
metabolism 29 CSU/CDC-244 477.2968 476.2898 22.79
C.sub.31H.sub.40O.sub.4 Lys Lys Thr Thr Peptide (SEQ ID NO: 5) 30
CSU/CDC-248 459.3968 458.3904 19.08 -- -- -- 31 CSU/CDC-253
342.2635 341.2565 15.62 C.sub.19H.sub.35NO.sub.4 -- -- 32
CSU/CDC-254 529.3827 1022.6938 17.86 -- -- -- 33 CSU/CDC-258
459.2502 458.2429 19.02 C.sub.23H.sub.39O.sub.7P Lyso PA(20:4)
Glycerohospho lipid metabolism 34 CSU/CDC-003 886.4296 1770.8438
12.18 -- -- -- 35 CSU/CDC-004 181.0859 180.0788 14.7
C.sub.10H.sub.12O.sub.3 5'-(3'-Methoxy-4'- Endogenous
hydroxyphenyl)- metabolite gamma- associated valerolactone with
microbiome 36 CSU/CDC-006 286.1444 285.1371 16.08
C.sub.17H.sub.19NO.sub.3 Piperine Alkaloid metabolism 37
CSU/CDC-008 463.2339 462.2248 16.36 C.sub.25H.sub.34O.sub.8 Ala Lys
Met Asn Peptide (SEQ ID NO: 6) 38 CSU/CDC-009 242.2844 241.2772
17.1 C.sub.16H.sub.35N -- -- 39 CSU/CDC-017 590.4237 589.4194 19.24
-- -- -- 40 CSU/CDC-026 553.3904 552.3819 23.38
C.sub.35H.sub.52O.sub.5 Furohyperforin Endogenous metabolite-
derived from food 41 CSU/CDC-030 399.2364 398.2313 16.23 -- -- --
42 CSU/CDC-042 580.4144 1158.8173 18.26 -- -- -- 43 CSU/CDC-052
704.4985 1372.925 18.7 -- -- -- 44 CSU/CDC-061 623.4521 1210.8362
19.55 -- -- -- 45 CSU/CDC-070 389.2178 388.2099 15.52
C.sub.19H.sub.32O.sub.8 -- -- 46 CSU/CDC-074 1111.6690 1110.6656
17.89 -- -- -- 47 CSU/CDC-083 482.4040 481.3976 19.99 -- -- -- 48
CSU/CDC-084 533.1929 532.1854 20.84 C.sub.23H.sub.28N.sub.6O.sub.9
Asp His Phe Asp Peptide (SEQ ID NO: 7) 49 CSU/CDC-087 466.3152
465.3085 14.73 C.sub.26H.sub.43NO.sub.6 Glycocholic acid Bile acid
metabolism 50 CSU/CDC-091 683.4728 1347.9062 17.56 -- -- -- 51
CSU/CDC-095 227.0897 204.1002 9.68 C.sub.9H.sub.16O.sub.5 -- -- 52
CSU/CDC-098 183.1016 182.0943 10.89 C.sub.10H.sub.14O.sub.3 -- --
53 CSU/CDC-099 476.3055 475.2993 11.09
C.sub.26H.sub.41N.sub.3O.sub.5 -- -- 54 CSU/CDC-112 215.1283
214.1209 12.32 C.sub.11H.sub.18O.sub.4 alpha-Carboxy- Endogenous
delta- metabolite- decalactone derived from food 55 CSU/CDC-115
519.1881 518.1813 12.33 C.sub.20H.sub.30N.sub.4O.sub.12 Poly-g-D-
Poly D- glutamate glutamate metabolism 56 CSU/CDC-128 1086.1800
2170.3435 15.38 -- -- -- 57 CSU/CDC-133 285.2061 284.1993 15.99
C.sub.16H.sub.28O.sub.4 -- -- 58 CSU/CDC-134 357.1363 356.1284
15.98 C.sub.20H.sub.20O.sub.6 Xanthoxylol Endogenous metabolite-
derived from food 59 CSU/CDC-136 299.1853 298.1781 16.24
C.sub.16H.sub.26O.sub.5 Tetranor-PGE1 Prostaglandin metabolism 60
CSU/CDC-137 334.2580 333.2514 16.36 -- -- -- 61 CSU/CDC-138
317.2317 316.2254 16.63 -- -- -- 62 CSU/CDC-141 331.2471 330.2403
17.26 C.sub.18H.sub.34O.sub.5 11,12,13- Fatty trihydroxy-9- acid
octadecenoic metabolism acid 63 CSU/CDC-144 583.3480 582.3379 18.04
C.sub.27H.sub.46N.sub.6O.sub.8 Leu Lys Glu Pro Peptide Pro (SEQ ID
NO: 8) 64 CSU/CDC-157 648.4672 647.4609 19.98
C.sub.34H.sub.66NO.sub.8P PE(29:1) Glycerophosph olipid metabolism
65 CSU/CDC-165 445.2880 854.5087 12.48 C.sub.45H.sub.74O.sub.15
(3b,21b)-12- Endogenous Oleanene- metabolite- 3,21,28-triol 28-
derived from [arabinosyl-(1- food >3)-arabinosyl- (1->3)-
arabinoside 66 CSU/CDC-181 1486.7386 2971.4668 14.97 -- -- -- 67
CSU/CDC-183 668.4686 1317.8969 18.04 C.sub.16H.sub.28O.sub.4
Omphalotin A Endogenous metabolite- derived from food 68
CSU/CDC-184 454.2924 436.2587 18.1 C.sub.21H.sub.41O.sub.7P
Lyso-PA(18:1) Glycerophosph olipid metabolism 69 CSU/CDC-186
607.9324 606.9246 19.01 -- -- -- 70 CSU/CDC-188 521.4202 503.3858
21.06 -- -- -- 71 CSU/CDC-193 176.0746 175.0667 2.31 -- -- -- 72
CSU/CDC-194 596.9082 1191.8033 19.1 -- -- -- 73 CSU/CDC-203
532.5606 531.5555 18.38 -- -- -- 74 CSU/CDC-206 337.1667 336.1599
20.67 C.sub.12H.sub.24N.sub.4O.sub.7 -- -- 75 CSU/CDC-210 415.1634
207.0784 12.2 C.sub.8H.sub.9N.sub.5O.sub.2 6-Amino-9H- Endogenous
purine-9- metabolite- propanoic acid derived from food 76
CSU/CDC-218 364.3407 346.3068 20.72 -- -- -- 77 CSU/CDC-222
989.5004 1976.9858 12.03 -- -- -- 78 CSU/CDC-224 819.6064 1635.8239
12.06 -- -- -- 79 CSU/CDC-237 286.2737 285.2666 19.08
C.sub.17H.sub.35NO.sub.2 Pentadecanoyl N-acyl ethanolamide
ethanolamine metabolism 80 CSU/CDC-245 614.4833 613.4772 19.78 --
-- -- 81 CSU/CDC-250 298.2740 297.2668 16.44
C.sub.18H.sub.35NO.sub.2 3-Ketospingosine Sphingolipid metabolism
82 CSU/CDC-252 1003.7020 1002.696 18.46 -- -- --
[0058] In one example, the present disclosure provides a
biosignature comprising each molecular feature in Table D, wherein
the molecular features in the table are defined at least by their
m/z ratio .+-.15 ppm, in some examples .+-.10 ppm, in some examples
.+-.5 ppm (depending upon instrument variability). The biosignature
comprising each molecular feature in Table D provides an 97%
probability of accurately detecting a sample from a subject with
Lyme disease, including early Lyme disease, and an 89% probability
of accurately detecting a sample from a subject with STARI, when
discriminating between a classification of Lyme disease and STARI.
The biosignature comprising each molecular feature in Table D
provides an 85% probability of accurately detecting a sample from a
subject with Lyme disease, including early Lyme disease, a 92%
probability of accurately detecting a sample from a subject with
STARI, and a 93% probability of accurately a sample from a healthy
subject, when discriminating between a classification of Lyme
disease, STARI, and healthy. A skilled artisan will appreciate that
one or more molecular features may be eliminated from the model
without a clinically meaningful, negative impact on the model.
TABLE-US-00004 TABLE D Predicted Chemical Retention Structure m/z
Time Compound (based on Metabolite MF (positive (see Predicted
accurate Class or # Name ion) Mass examples) Formula mass) Pathway
1 CSU/CDC-001 166.0852 165.078 1.86 C.sub.9H.sub.11NO.sub.2
Phenylalanine Phenylalanine metabolism 2 CSU/CDC-012 270.3156
269.3076 18.02 C.sub.18H.sub.39N -- -- 3 CSU/CDC-013 284.3314
283.3236 18.13 C.sub.19H.sub.41N -- -- 4 CSU/CDC-014 300.6407
599.268 18.27 C.sub.33H.sub.37N.sub.5O.sub.6 Asp Phe Arg Tyr
Peptide (SEQ ID NO: 1) 5 CSU/CDC-019 300.2892 299.2821 19.66
C.sub.18H.sub.37NO.sub.2 Palmitoyl N-acyl ethanolamide ethanolamine
metabolism 6 CSU/CDC-039 734.5079 1449.9753 17.81 -- -- -- 7
CSU/CDC-062 370.1837 369.1757 19.7 C.sub.19H.sub.23N.sub.5O.sub.3
-- -- 8 CSU/CDC-066 811.1942 810.1869 12.07
C.sub.42H.sub.30N.sub.6O.sub.12 -- -- 9 CSU/CDC-067 947.7976
946.7936 14.55 C.sub.62H.sub.106O.sub.6 TAG(59:7) Triacylglycerol
metabolism 10 CSU/CDC-072 410.2033 409.196 17.18 -- -- -- 11
CSU/CDC-075 1487.0005 1485.9987 18.17 -- -- -- 12 CSU/CDC-086
137.0463 136.0378 1.37 C.sub.4H.sub.8O.sub.5 Threonate Sugar
metabolite 13 CSU/CDC-107 811.7965 810.7882 12.07 -- -- -- 14
CSU/CDC-132 616.1776 615.1699 15.43 -- -- -- 15 CSU/CDC-152
713.4492 712.4391 19.35 C.sub.38H.sub.65O.sub.10P PG(32:5)
Glycerophosph olipid metabolism 16 CSU/CDC-155 502.3376 484.3039
19.87 C.sub.27H.sub.40N.sub.4O.sub.4 Gln Leu Pro Lys Peptide (SEQ
ID NO: 2) 17 CSU/CDC-158 415.3045 414.2978 20.19 -- -- -- 18
CSU/CDC-164 366.3729 365.3655 22.79 -- -- -- 19 CSU/CDC-166
333.1446 332.1373 12.89 C.sub.12H.sub.20N.sub.4O.sub.7 Glu Gln Gly
Peptide 20 CSU/CDC-205 241.1069 240.0996 14.7
C.sub.12H.sub.16O.sub.5 3-Carboxy-4- Fatty acid methyl-5-propyl-
metabolism 2-furanpropanoic acid (CMPF) 21 CSU/CDC-211 464.1916
463.1849 13.05 C.sub.16H.sub.29N.sub.7O.sub.7S Arg Asp Cys Ala
Peptide (SEQ ID NO: 3) 22 CSU/CDC-212 1249.2045 1248.1993 15.31 --
-- -- 23 CSU/CDC-213 1248.9178 1247.9141 15.3 -- -- -- 24
CSU/CDC-219 158.1539 157.1466 15.36 -- -- -- 25 CSU/CDC-227
529.3381 528.3296 16.89 C.sub.24H.sub.44N.sub.6O.sub.7 Gln Val Leu
Leu Peptide Gly (SEQ ID NO: 4) 26 CSU/CDC-229 282.2776 264.2456
20.56 C.sub.18H.sub.32O -- -- 27 CSU/CDC-235 190.1260 189.1187
14.12 C.sub.9H.sub.19NOS 8- 2- Methylthiooctanal oxocarboxylic
doxime acid metabolism 28 CSU/CDC-238 382.3675 381.3603 20.23
C.sub.24H.sub.47NO.sub.2 Erucicoyl N-acyl ethanolamide ethanolamine
metabolism 29 CSU/CDC-244 477.2968 476.2898 22.79
C.sub.31H.sub.40O.sub.4 Lys Lys Thr Thr Peptide (SEQ ID NO: 5) 30
CSU/CDC-248 459.3968 458.3904 19.08 -- -- -- 31 CSU/CDC-253
342.2635 341.2565 15.62 C.sub.19H.sub.35NO.sub.4 -- -- 32
CSU/CDC-254 529.3827 1022.6938 17.86 -- -- -- 33 CSU/CDC-258
459.2502 458.2429 19.02 C.sub.23H.sub.39O.sub.7P Lyso PA(20:4)
Glycerophosph olipid metabolism 34 CSU/CDC-002 239.0919 238.0844
11.66 C.sub.12H.sub.14O.sub.5 Trans-2, 3, 4- Phenyl-
trimethoxycinna propanoid and mate polyketide metabolism 35
CSU/CDC-028 389.2174 388.2094 15.47 C.sub.19H.sub.32O.sub.8 Methyl
Fatty acid 10,12,13,15- metabolism bisepidioxy-16- hydroperoxy-8E-
octadecenoate 36 CSU/CDC-182 285.2065 284.1991 16.02
C.sub.16H.sub.28O.sub.4 -- -- 37 CSU/CDC-204 279.1693 278.1629
11.05 C.sub.15H.sub.22N.sub.2O.sub.3 Phe Leu Dipeptide 38
CSU/CDC-247 714.6967 1427.3824 11.76 -- -- -- 39 CSU/CDC-003
886.4296 1770.8438 12.18 -- -- -- 40 CSU/CDC-004 181.0859 180.0788
14.7 C.sub.10H.sub.12O.sub.3 5'-(3'-Methoxy-4'- Endogenous
hydroxyphenyl)- metabolite gamma- associated valerolactone with
microbiome 41 CSU/CDC-006 286.1444 285.1371 16.08
C.sub.17H.sub.19NO.sub.3 Piperine Alkaloid metabolism 42
CSU/CDC-008 463.2339 462.2248 16.36 C.sub.25H.sub.34O.sub.8 Ala Lys
Met Asn Peptide (SEQ ID NO: 6) 43 CSU/CDC-009 242.2844 241.2772
17.1 C.sub.16H.sub.35N -- -- 44 CSU/CDC-017 590.4237 589.4194 19.24
-- -- -- 45 CSU/CDC-026 553.3904 552.3819 23.38
C.sub.35H.sub.52O.sub.5 Furohyperforin Endogenous metabolite-
derived from food 46 CSU/CDC-030 399.2364 398.2313 16.23 -- -- --
47 CSU/CDC-042 580.4144 1158.8173 18.26 -- -- -- 48 CSU/CDC-052
704.4985 1372.925 18.7 -- -- -- 49 CSU/CDC-061 623.4521 1210.8362
19.55 -- -- -- 50 CSU/CDC-070 389.2178 388.2099 15.52
C.sub.19H.sub.32O.sub.8 -- -- 51 CSU/CDC-074 1111.6690 1110.6656
17.89 -- -- -- 52 CSU/CDC-083 482.4040 481.3976 19.99 -- -- -- 53
CSU/CDC-084 533.1929 532.1854 20.84 C.sub.23H.sub.28N.sub.6O.sub.9
Asp His Phe Asp Peptide (SEQ ID NO: 7) 54 CSU/CDC-087 466.3152
465.3085 14.73 C.sub.26H.sub.43NO.sub.6 Glycocholic acid Bile acid
metabolism 55 CSU/CDC-091 683.4728 1347.9062 17.56 -- -- -- 56
CSU/CDC-095 227.0897 204.1002 9.68 C.sub.9H.sub.16O.sub.5 -- -- 57
CSU/CDC-098 183.1016 182.0943 10.89 C.sub.10H.sub.14O.sub.3 -- --
58 CSU/CDC-099 476.3055 475.2993 11.09
C.sub.26H.sub.41N.sub.3O.sub.5 -- -- 59 CSU/CDC-112 215.1283
214.1209 12.32 C.sub.11H.sub.18O.sub.4 alpha-Carboxy- Endogenous
delta- metabolite- decalactone derived from food 60 CSU/CDC-115
519.1881 518.1813 12.33 C.sub.20H.sub.30N.sub.4O.sub.12 Poly-g-D-
Poly D- glutamate glutamate metabolism 61 CSU/CDC-128 1086.1800
2170.3435 15.38 -- -- -- 62 CSU/CDC-133 285.2061 284.1993 15.99
C.sub.16H.sub.28O.sub.4 -- -- 63 CSU/CDC-134 357.1363 356.1284
15.98 C.sub.20H.sub.20O.sub.6 Xanthoxylol Endogenous metabolite-
derived from food 64 CSU/CDC-136 299.1853 298.1781 16.24
C.sub.16H.sub.26O.sub.5 Tetranor-PGE1 Prostaglandin metabolism 65
CSU/CDC-137 334.2580 333.2514 16.36 -- -- -- 66 CSU/CDC-138
317.2317 316.2254 16.63 -- -- -- 67 CSU/CDC-141 331.2471 330.2403
17.26 C.sub.18H.sub.34O.sub.5 11,12,13- Fatty acid trihydroxy-9-
metabolism octadecenoic acid 68 CSU/CDC-144 583.3480 582.3379 18.04
C.sub.27H.sub.46N.sub.6O.sub.8 Leu Lys Glu Pro Peptide Pro (SEQ ID
NO: 8) 69 CSU/CDC-157 648.4672 647.4609 19.98
C.sub.34H.sub.66NO.sub.8P PE(29:1) Glycerophosph olipid metabolism
70 CSU/CDC-165 445.2880 854.5087 12.48 C.sub.45H.sub.74O.sub.15
(3b,21b)-12- Endogenous Oleanene- metabolite- 3,21,28-triol 28-
derived [arabinosyl-(1- from >3)-arabinosyl- food (1->3)-
arabinoside] 71 CSU/CDC-181 1486.7386 2971.4668 14.97 -- -- -- 72
CSU/CDC-183 668.4686 1317.8969 18.04 C.sub.16H.sub.28O.sub.4
Omphalotin A Endogenous derived from food 73 CSU/CDC-184 454.2924
436.2587 18.1 C.sub.21H.sub.41O.sub.7P Lyso-PA(18:1) Glycerophosph
olipid metabolism 74 CSU/CDC-186 607.9324 606.9246 19.01 -- -- --
75 CSU/CDC-188 521.4202 503.3858 21.06 -- -- -- 76 CSU/CDC-193
176.0746 175.0667 2.31 -- -- -- 77 CSU/CDC-194 596.9082 1191.8033
19.1 -- -- -- 78 CSU/CDC-203 532.5606 531.5555 18.38 -- -- -- 79
CSU/CDC-206 337.1667 336.1599 20.67 C.sub.12H.sub.24N.sub.4O.sub.7
-- -- 80 CSU/CDC-210 415.1634 207.0784 12.2
C.sub.8H.sub.9N.sub.5O.sub.2 6-Amino-9H- Endogenous purine-9-
metabolite- propanoic derived acid from food 81 CSU/CDC-218
364.3407 346.3068 20.72 -- -- -- 82 CSU/CDC-222 989.5004 1976.9858
12.03 -- -- -- 83 CSU/CDC-224 819.6064 1635.8239 12.06 -- -- -- 84
CSU/CDC-237 286.2737 285.2666 19.08 C.sub.17H.sub.35NO.sub.2
Pentadecanoyl N-acyl ethanolamide ethanolamine metabolism 85
CSU/CDC-245 614.4833 613.4772 19.78 -- -- -- 86 CSU/CDC-250
298.2740 297.2668 16.44 C.sub.18H.sub.35NO.sub.2 3-Ketospingosine
Sphingolipid metabolism 87 CSU/CDC-252 1003.7020 1002.696 18.46 --
-- -- 88 CSU/CDC-005 223.0968 222.0895 14.69
C.sub.12H.sub.14O.sub.4 -- -- 89 CSU/CDC-007 286.1437 285.1364
16.06 C.sub.17H.sub.19NO.sub.3 -- -- 90 CSU/CDC-010 1112.6727
1111.6663 17.86 -- -- -- 91 CSU/CDC-011 454.2923 453.2867 18.08
C.sub.21H.sub.44NO.sub.7P Glycerophospho- N-acyl N-Palmitoyl
ethanolamine Ethanolamine metabolism 92 CSU/CDC-015 522.3580
521.3483 18.5 C.sub.26H.sub.52NO.sub.7P PC(18:1) Glycerophosph
olipid metabolism 93 CSU/CDC-016 363.2192 362.2132 18.58
C.sub.21H.sub.30O.sub.5 4,5.alpha.- Sterol dihydrocortisone
metabolism 94 CSU/CDC-018 388.3939 387.3868 19.53 -- -- -- 95
CSU/CDC-020 256.2632 255.2561 20.08 C.sub.16H.sub.33NO Palmitic
amide Primary Fatty Acid Amide Metabolism 96 CSU/CDC-021 394.3515
376.3171 20.09 -- -- -- 97 CSU/CDC-022 228.1955 227.1885 20.99 --
-- -- 98 CSU/CDC-023 284.2943 283.2872 21.15 C.sub.18H.sub.37NO
Stearamide Primary Fatty Acid Amide Metabolism 99 CSU/CDC-024
338.3430 337.3344 22.14 C.sub.22H.sub.43NO 13Z- Primary Fatty
Docosenamide Acid Amide (Erucamide) Metabolism 100 CSU/CDC-025
689.5604 688.5504 22.52 C.sub.38H.sub.77N.sub.2O.sub.6P
SM(d18:1-15:0) / Sphingolipid SM(d18:1/14:1- metabolism OH) 101
CSU/CDC-027 432.2803 431.2727 10.8 C.sub.25H.sub.37NO.sub.5 Ala Ile
Lys Thr Peptide (SEQ ID NO: 9)
102 CSU/CDC-029 385.2211 384.2147 15.84
C.sub.16H.sub.28N.sub.6O.sub.5 Lys His Thr Peptides 103 CSU/CDC-
449.3261 879.6122 17.07 C.sub.46H.sub.89NO.sub.12S C22-OH
Sphingolipid Sulfatide metabolism 104 CSU/CDC-032 467.3821 444.2717
17.1 C.sub.24H.sub.40O.sub.8 2-glyceryl-6- Prostaglandin
keto-PGF1.alpha. metabolism 105 CSU/CDC-033 836.5936 835.5845 17.15
C.sub.44H.sub.85NO.sub.11S C20 Sulfatide Sphingolipid metabolism
106 CSU/CDC-034 792.5646 791.5581 17.17 C.sub.42H.sub.82NO.sub.10P
PS(36:0) Glycerophosph olipid metabolism 107 CSU/CDC-035 356.2802
355.2722 17.35 -- -- -- 108 CSU/CDC-036 806.5798 805.5746 17.71
C.sub.43H.sub.84NO.sub.10P PS(37:0) Glycerophosph olipid metabolism
109 CSU/CDC-037 762.5582 761.5482 17.79 C.sub.41H.sub.80NO.sub.9P
PS-O(35:1) Glycerophosph olipid metabolism 110 CSU/CDC-038 718.5308
700.4946 17.88 C.sub.39H.sub.73O.sub.8P PA(36:2) Glycerophosph
olipid metabolism 111 CSU/CDC-040 690.4825 1361.924 17.95 -- -- --
112 CSU/CDC-041 426.1798 425.1725 18.03 -- -- -- 113 CSU/CDC-043
741.5154 1481.0142 18.24 C.sub.83H.sub.150O.sub.17P CL(74:6)
Glycerophosph olipid metabolism 114 CSU/CDC-044 864.6245 863.6166
18.17 C.sub.46H.sub.89NO.sub.11S C22 Sulfatide Sphingolipid
metabolism 115 CSU/CDC-045 558.4017 1080.7347 18.28 -- -- -- 116
CSU/CDC-046 719.5012 1402.9377 18.26 -- -- -- 117 CSU/CDC-047
536.3897 1053.7382 18.36 -- -- -- 118 CSU/CDC-048 538.8674 1058.696
18.4 -- -- -- 119 CSU/CDC-049 653.4619 1270.8593 18.43 -- -- -- 120
CSU/CDC-050 732.5450 714.5092 18.47 C.sub.40H.sub.75O.sub.8P
PA(37:2) Glycerophosph olipid metabolism 121 CSU/CDC-051 748.5232
1478.0059 18.58 -- -- -- 122 CSU/CDC-053 682.4841 1328.9008 18.77
-- -- -- 123 CSU/CDC-054 360.3615 359.3555 18.89 -- -- -- 124
CSU/CDC-055 441.2412 440.2325 19.09 C.sub.20H.sub.32N.sub.4O.sub.7
Pro Asp Pro Leu -- (SEQ ID NO: 10) 125 CSU/CDC-056 638.4554
1240.847 18.92 -- -- -- 126 CSU/CDC-057 755.5311 1474.9941 18.94
C.sub.83H.sub.144O.sub.17P.sub.2 CL(74:9) Glycerophosph olipid
metabolism 127 CSU/CDC-058 711.5023 1386.9417 19.09 -- -- -- 128
CSU/CDC-059 784.5530 1567.0908 19.27 -- -- -- 129 CSU/CDC-060
645.4660 1271.8896 19.36 -- -- -- 130 CSU/CDC-063 300.2886 282.2569
19.84 C.sub.18H.sub.34O.sub.2 13Z- Fatty acid octadecenoic
metabolism acid 131 CSU/CDC-064 309.0981 308.0913 2.06
C.sub.15H.sub.16O.sub.7 -- -- 132 CSU/CDC-065 561.2965 1120.5778
11.7 C.sub.54H.sub.88O.sub.24 Camellioside D Endogenous metabolite-
derived from food 133 CSU/CDC-068 1106.2625 2209.5193 14.53 -- --
-- 134 CSU/CDC-069 371.2070 370.1997 15.52
C.sub.15H.sub.26N.sub.6O.sub.7 His Ser Lys Peptide 135 CSU/CDC-071
443.2649 442.256 15.52 C.sub.19H.sub.34N.sub.6O.sub.6 Pro Gln Ala
Lys Peptide (SEQ ID NO: 11) 136 CSU/CDC-073 850.6093 849.6009 17.63
C.sub.48H.sub.84NO.sub.9P PS-O(42:6) Glycerophosph olipid
metabolism 137 CSU/CDC-076 697.4896 1358.909 18.32 -- -- -- 138
CSU/CDC-077 439.8234 877.6325 18.71 -- -- -- 139 CSU/CDC-078
567.8897 566.8818 18.73 -- -- -- 140 CSU/CDC-079 435.2506 434.243
19 C.sub.21H.sub.39O.sub.7P Lyso-PA(18:2) Glycerophosph olipid
metabolism 141 CSU/CDC-080 834.6136 833.6057 18.83
C.sub.45H.sub.88NO.sub.10P PS(39:0) Glycerophosph olipid metabolism
142 CSU/CDC-081 534.8834 533.8771 18.82 -- -- -- 143 CSU/CDC-082
468.8441 467.8373 19.13 -- -- -- 144 CSU/CDC-085 312.3259 311.319
22.05 -- -- -- 145 CSU/CDC-088 228.1955 227.1884 15.22 -- -- -- 146
CSU/CDC-089 385.2211 384.2143 15.83 C.sub.20H.sub.32O.sub.7 Lys His
Thr Peptide 147 CSU/CDC-090 403.2338 402.2253 15.84 C16H30N6O6 Lys
Gln Gln Peptide 148 CSU/CDC-092 675.4753 1348.9377 18.37 -- -- --
149 CSU/CDC-093 682.4841 1345.9257 18.76 -- -- -- 150 CSU/CDC-094
762.5401 1506.0367 19.36 -- -- -- 151 CSU/CDC-177 189.1122 188.1049
12.27 C.sub.9H.sub.14O.sub.4 Nonanedioic Acid Fatty acid metabolism
152 CSU/CDC-097 169.0860 168.0786 9.94 C.sub.9H.sub.12O.sub.3
2,6-Dimethoxy-4- Endogenous methylphenol metabolite- derived from
food 153 CSU/CDC-100 276.1263 275.1196 11.16
C.sub.15H.sub.17NO.sub.4 -- -- 154 CSU/CDC-101 314.0672 313.06
11.56 C.sub.10H.sub.12N.sub.5O.sub.5P -- -- 155 CSU/CDC-102
201.1122 200.1047 11.56 C.sub.10H.sub.16O.sub.4 Decenedioic acid
Fatty acid metabolism 156 CSU/CDC-103 115.0391 114.0318 11.57
C.sub.5H.sub.6O.sub.3 2-Hydroxy-2,4- Phenylalanine pentadienoate
metabolism 157 CSU/CDC-104 491.1569 490.1504 11.56
C.sub.24H.sub.26O.sub.11 -- -- 158 CSU/CDC-105 241.1054 218.1157
11.57 C.sub.10H.sub.18O.sub.5 3-Hydroxy- Fatty acid sebacic acid
metabolism 159 CSU/CDC-106 105.0914 104.0841 11.57 -- -- -- 160
CSU/CDC-108 311.1472 328.1391 12.22 C.sub.18H.sub.20N.sub.2O.sub.4
Phe Tyr Peptide 161 CSU/CDC-109 271.1543 270.1464 12.24 -- -- --
162 CSU/CDC-110 169.0860 168.0787 12.24 C.sub.9H.sub.12O.sub.3
2,6-Dimethoxy-4- Endogenous methylphenol metabolite- derived from
food 163 CSU/CDC-111 187.0967 186.0889 12.24 C.sub.9H.sub.14O.sub.4
-- -- 164 CSU/CDC-113 475.1635 474.1547 12.25
C.sub.25H.sub.22N.sub.4O.sub.6 His Cys Asp Thr Peptide (SEQ ID NO:
12) 165 CSU/CDC-114 129.0547 128.0474 12.33 C.sub.6H.sub.8O.sub.3
(4E)-2- Fatty acid Oxohexenoic metabolism acid 166 CSU/CDC-116
125.0599 124.0527 13.12 C.sub.7H.sub.8O.sub.2 4-Methylcatechol
Catechol metabolism 167 CSU/CDC-117 247.1550 246.1469 13.13
C.sub.12H.sub.22O.sub.5 3-Hydroxy- Fatty acid dodecanedioic
metabolism acid 168 CSU/CDC-118 517.2614 516.2544 13.13
C.sub.21H.sub.36N.sub.6O.sub.9 Gln Glu Gln Ile Peptide (SEQ ID NO:
13) 169 CSU/CDC-119 301.0739 300.0658 13.14 C.sub.16H.sub.12O.sub.6
Chrysoeriol Endogenous metabolite- derived from food 170
CSU/CDC-120 327.1773 304.1885 14.17 C.sub.16H.sub.24N.sub.4O.sub.2
-- -- 171 CSU/CDC-121 387.2023 386.1935 14.51
C.sub.19H.sub.30O.sub.8 Citroside A Endogenous metabolite- derived
from food 172 CSU/CDC-122 875.8451 1749.684 14.55 -- -- -- 173
CSU/CDC-123 737.5118 736.5056 14.52 C.sub.42H.sub.73O.sub.8P
PA(39:5) Glycerophosph olipid metabolism 174 CSU/CDC-124 1274.3497
1273.3481 14.96 -- -- -- 175 CSU/CDC-125 1274.2092 1273.2 14.96 --
-- -- 176 CSU/CDC-126 1486.5728 2971.1328 14.95 -- -- -- 177
CSU/CDC-127 965.3818 964.3727 15.37 -- -- -- 178 CSU/CDC-129
1086.0562 2170.0908 15.38 C.sub.97H.sub.167N.sub.5O.sub.48
NeuAcalpha2- Sphingolipid 3Galbeta1- metabolism
3GalNAcbeta1-4(9-OAc- NeuAcalpha2- 8NeuAcalpha2-3) Galbeta1-
4Glcbeta- Cer(d18:1/18:0) 179 CSU/CDC-130 1086.4344 2169.8474 15.39
-- -- -- 180 CSU/CDC-131 1240.7800 1239.7712 15.38 -- -- -- 181
CSU/CDC-135 317.1956 316.1885 16.24 C.sub.12H.sub.24N.sub.6O.sub.4
Arg Ala Ala Peptide 182 CSU/CDC-139 299.2219 298.2148 16.64
C.sub.17H.sub.30O.sub.4 8E- Fatty acid Heptadecenedioic metabolism
acid 183 CSU/CDC-140 748.5408 747.5317 17.23
C.sub.40H.sub.78NO.sub.9P PS-O(34:1) Glycerophosph olipid
metabolism 184 CSU/CDC-142 712.4935 1422.9749 17.82
C.sub.79H.sub.140O.sub.17P.sub.2 CL(70:7) Glycerophosph olipid
metabolism 185 CSU/CDC-143 674.5013 673.4957 17.99
C.sub.37H.sub.72NO.sub.7P PE-P(32:1) Glycerophosph olipid
metabolism 186 CSU/CDC-145 677.9537 676.9478 18.36 -- -- -- 187
CSU/CDC-146 531.3522 530.3457 18.4 C.sub.35H.sub.46O.sub.4 -- --
188 CSU/CDC-147 585.2733 584.2649 18.39
C.sub.33H.sub.36N.sub.4O.sub.6 15,16- Bilirubin Dihydrobiliverdin
breakdown products- Porphyrin metabolism 189 CSU/CDC-148 513.3431
512.3352 18.4 -- -- -- 190 CSU/CDC-149 611.9156 610.9073 18.59 --
-- -- 191 CSU/CDC-150 549.0538 531.0181 18.38 -- -- -- 192
CSU/CDC-151 755.5311 1509.0457 18.93 -- -- -- 193 CSU/CDC-153
599.4146 598.4079 19.59 C.sub.40H.sub.54O.sub.4 Isomytiloxanthin
Isoflavinoid 194 CSU/CDC-154 762.5029 761.4919 19.66
C.sub.43H.sub.72NO.sub.8P PE(38:7) Glycerophosph olipid metabolism
195 CSU/CDC-156 741.4805 740.4698 19.96 C.sub.40H.sub.69O.sub.10P
PG(34:5) Glycerophosph olipid metabolism 196 CSU/CDC-159 516.3532
498.3199 20.27 C.sub.23H.sub.42N.sub.6O.sub.6 Ala Leu Ala Pro
Peptide Lys (SEQ ID NO: 14) 197 CSU/CDC-160 769.5099 768.5018 20.53
C.sub.42H.sub.73O.sub.10P PG(36:5) Glycerophosph olipid metabolism
198 CSU/CDC-161 862.5881 861.5818 20.86 -- -- -- 199 CSU/CDC-162
837.5358 836.5274 21.11 C.sub.53H.sub.72O.sub.8 Amitenone
Endogenous metabolite- derived from food 200 CSU/CDC-163 558.3995
540.367 21.44 C.sub.26H.sub.48N.sub.6O.sub.6 Leu Ala Pro Lys Ile
Peptide (SEQ ID NO: 15) 201 CSU/CDC-167 1105.9305 2209.8462 14.53
-- -- -- 202 CSU/CDC-168 329.1049 328.0976 14.61
C.sub.18H.sub.16O.sub.6 2-Oxo-3- Phenylalanine phenylpropanoic
metabolism acid 203 CSU/CDC-169 1241.2053 1240.2 15.38 -- -- -- 204
CSU/CDC-170 1088.6731 1087.6676 17.85 -- -- -- 205 CSU/CDC-171
667.4391 666.4323 20.35 C.sub.37H.sub.63O.sub.8P PA(24:5)
Glycerophosph olipid metabolism 206 CSU/CDC-172 133.0497 132.0423
11.57 C.sub.5H.sub.8O.sub.4 2-Acetolactic Pantothenate
acid and CoA Biosynthesis Pathway 207 CSU/CDC-173 259.1540 258.1469
11.75 -- -- -- 208 CSU/CDC-174 311.1472 288.1574 12.23
C.sub.10H.sub.20N.sub.6O.sub.4 Asn Arg Dipeptide 209 CSU/CDC-175
147.0652 146.0579 12.33 C.sub.6H.sub.10O.sub.4 .alpha.-Ketopantoic
Pantothenate acid and CoA Biosynthesis Pathway 210 CSU/CDC-176
169.0860 168.0788 12.29 C.sub.9H.sub.12O.sub.3 Epoxyoxophorone
Endogenous metabolite- derived from food 211 CSU/CDC-096 187.0965
186.0894 9.93 C.sub.9H.sub.14O.sub.4 5- Endogenous Butyltetrahydro-
metabolite- 2-oxo-3- derived furancarboxylic from food acid 212
CSU/CDC-178 139.1116 138.1044 12.95 C.sub.9H.sub.14O.sub.4
3,6-Nonadienal Endogenous metabolite- derived from food 213
CSU/CDC-179 515.2811 514.2745 13.14 C.sub.26H.sub.42O.sub.10
Cofaryloside Endogenous metabolite- derived from food 214
CSU/CDC-180 283.1522 282.1444 13.93 C.sub.25H.sub.42N.sub.2O.sub.7S
Epidihydrophaseic Endogenous acid metabolite- derived from food 215
CSU/CDC-185 706.9750 705.9684 18.7 -- -- -- 216 CSU/CDC-187
834.5575 833.5502 20.32 -- -- -- 217 CSU/CDC-189 683.4727 1364.9294
17.54 -- -- -- 218 CSU/CDC-190 728.9890 1455.9633 18.63 -- -- --
219 CSU/CDC-191 726.5104 1451.0035 18.64
C.sub.81H.sub.144O.sub.17P.sub.2 CL(72:7) Glycerophosph olipid
metabolism 220 CSU/CDC-192 633.9280 632.9206 18.47 -- -- -- 221
CSU/CDC-195 209.0784 208.0713 9.92 C.sub.17H.sub.24O.sub.3
Benzylsuccinateic Phenylpropanoic acid metabolism 222 CSU/CDC-196
792.5483 1566.055 18.46 -- -- -- 223 CSU/CDC-197 618.9221 1218.8083
19.02 -- -- -- 224 CSU/CDC-198 549.0543 531.0189 18.37 -- -- -- 225
CSU/CDC-199 553.7262 552.7188 18.74 -- -- -- 226 CSU/CDC-200
756.0320 755.0266 18.95 -- -- -- 227 CSU/CDC-201 639.6307 638.6205
19.58 -- -- -- 228 CSU/CDC-202 753.4414 730.4513 19.37
C.sub.42H.sub.67O.sub.8P PA(39:8) Glycerophosph olipid metabolism
229 CSU/CDC-207 328.3204 327.3148 20.72 C.sub.20H.sub.41NO.sub.2
Stearoyl N-acyl ethanolamide ethanolamine metabolism 230
CSU/CDC-208 514.3718 1009.7122 18.42 C.sub.56H.sub.99NO.sub.14
3-O-acetyl- Sphingolipid sphingosine- metabolism 2,3,4,6-tetra-O-
acetyl- GalCer(d18:1/h2 2:0) 231 CSU/CDC-209 630.4594 1241.8737
19.95 -- -- -- 232 CSU/CDC-214 244.2270 243.22 17.17
C.sub.14H.sub.29NO.sub.2 Lauroyl N-acyl ethanolamide ethanolamine
metabolism 233 CSU/CDC-215 463.3426 924.6699 18.08 -- -- -- 234
CSU/CDC-216 468.3892 450.3553 19.17 C.sub.31H.sub.46O.sub.2 -- --
235 CSU/CDC-217 438.3787 420.3453 19.93 -- -- -- 236 CSU/CDC-220
792.0006 790.995 12.04 -- -- -- 237 CSU/CDC-221 792.2025 791.1947
12.04 -- -- -- 238 CSU/CDC-223 791.6016 790.594 12.04 -- -- -- 239
CSU/CDC-225 1115.5593 2228.1028 14.95 -- -- -- 240 CSU/CDC-226
1486.9176 2970.7976 14.96 -- -- -- 241 CSU/CDC-228 430.3161
412.2845 20.23 C.sub.23H.sub.40O.sub.6 -- -- 242 CSU/CDC-230
297.2793 296.2734 20.66 C.sub.19H.sub.36O.sub.2 Methyl oleate Oleic
acid ester 243 CSU/CDC-231 714.3655 1426.718 11.73 -- -- -- 244
CSU/CDC-232 714.5306 1427.0479 11.76 -- -- -- 245 CSU/CDC-233
989.7499 1977.4865 12.03 -- -- -- 246 CSU/CDC-234 221.0744 220.0672
13.7 C.sub.7H.sub.12N.sub.2O.sub.6 L-beta-aspartyl- Peptide
L-serine 247 CSU/CDC-236 313.2734 312.2663 18.91
C.sub.19H.sub.36O.sub.3 2-oxo- Fatty acid nonadecanoic metabolism
acid 248 CSU/CDC-239 337.2712 314.282 20.66 C.sub.19H.sub.38O.sub.3
2-Hydroxy- Fatty acid nonadecanoic metabolism acid 249 CSU/CDC-240
441.3687 440.3614 21.26 C.sub.30H.sub.48O.sub.2 4,4-Dimethyl-
Sterol 14a-formyl-5a- metabolism cholesta-8,24- dien-3b-ol 250
CSU/CDC-241 425.3735 424.3666 21.5 C.sub.30H.sub.48O Butyrospermone
Sterol metabolism 251 CSU/CDC-242 356.3517 355.3448 21.67
C.sub.22H.sub.45NO.sub.2 Eicosanoyl N-acyl ethanolamide
ethanolamine metabolism 252 CSU/CDC-243 393.2970 370.3082 22.46
C.sub.22H.sub.42O.sub.4 -- -- 253 CSU/CDC-246 167.9935 166.9861
13.2 C.sub.7H.sub.5NS.sub.2 -- -- 254 CSU/CDC-249 677.6170 676.6095
20.71 C.sub.47H.sub.80O.sub.2 Cholesterol ester Sterol (20:2)
metabolism 255 CSU/CDC-251 460.2695 459.2627 16.87
C.sub.26H.sub.37NO.sub.6 -- -- 256 CSU/CDC-255 630.4765 612.4417
18.11 -- -- -- 257 CSU/CDC-256 514.3734 1026.7281 18.41 -- -- --
258 CSU/CDC-257 667.4754 1315.916 19.28 -- -- -- 259 CSU/CDC-259
516.8549 1031.6945 18.43 -- -- -- 260 CSU/CDC-260 740.5242
1479.0334 19.4 C.sub.83H.sub.148O.sub.17P.sub.2 CL(74:7)
Glycerophosph olipid metabolism 261 CSU/CDC-261 1104.0614 2206.1096
15.2 -- -- --
III. Methods for Analyzing a Blood Sample from a Subject
[0059] In another aspect, the present disclosure provides a method
for analyzing a blood sample from a subject. The method comprises
performing liquid chromatography coupled to mass spectrometry on a
blood sample, and providing abundance values for each molecular
feature in Table A, Table B, Table C, or Table D. Preferably, the
method further comprises deproteinizing a blood sample from a
subject to produce a metabolite extract and then performing liquid
chromatography coupled to mass spectrometry on a sample of the
metabolite extract. The method may comprise providing abundance
values for each molecular feature in Table A or Table C. The method
may comprise providing abundance values for each molecular feature
in Table B or Table D. The method may comprise providing abundance
values for each molecular feature in Table A, Table B, or Table D.
The method may comprise providing abundance values for each
molecular feature in Table C or Table D.
[0060] A subject may be a human or a non-human mammal including,
but not limited to, a livestock animal, a companion animal, a lab
animal, or a zoological animal. A subject may be a rodent, e.g., a
mouse, a rat, a guinea pig, etc. A subject may also be a livestock
animal. Non-limiting examples of suitable livestock animals may
include pigs, cows, horses, goats, sheep, llamas and alpacas.
Alternatively, a subject may be a companion animal. Non-limiting
examples of companion animals may include pets such as dogs, cats,
rabbits, and birds. A subject may be a zoological animal. As used
herein, a "zoological animal" refers to an animal that may be found
in a zoo. Such animals may include non-human primates, large cats,
wolves, and bears. In preferred examples, a subject is human.
[0061] Methods of the present disclosure for analyzing a blood
sample may be used to monitor the progression or resolution of Lyme
disease or STARI. A skilled artisan will also appreciate that
infection with Borrelia species that cause Lyme disease, or with
the causative agent(s) of STARI, likely commences prior to
diagnosis or the onset of symptoms associated with the disease. For
at least these reasons, a suitable blood sample may be from a
subject that may or may not have a symptom associated with Lyme
disease or STARI. Non-limiting examples of symptoms associated with
Lyme disease and STARI are described above. A subject may have at
least one symptom associated with Lyme disease, at least one
symptom associated with STARI, or at least one symptom associated
with Lyme disease and STARI. As a non-limiting example, a subject
can have an erythema migrans (EM) rash or an EM-like rash.
Alternatively, a subject may not have a symptom of Lyme disease or
STARI but may be at risk of having Lyme disease or STARI.
Non-limiting examples of risk factors for Lyme disease or STARI
include living in or visiting a region endemic for Lyme disease or
STARI, spending time in wooded or grassy areas, camping, fishing,
gardening, hiking, hunting and/or picnicking in a region endemic
for Lyme disease or STARI, and not removing tick(s) promptly or
properly. In each of the above examples, suitable subjects, whether
or not they have a symptom associated with Lyme disease or STARI at
the time a blood sample is obtained, may or may not have received
(or be receiving) treatment for Lyme disease, STARI, or another
disease with symptoms similar to Lyme disease or STARI.
[0062] A blood sample may be a whole blood sample, a plasma sample,
or a serum sample. Any of a variety of methods generally known in
the art for collecting a blood sample may be utilized. Generally
speaking, the sample collection method preferably maintains the
integrity of the sample such that abundance values for each
molecular feature in Table A, Table B, Table C, or Table D can be
accurately measured according to the disclosure. A blood sample may
be used "as is", or a blood sample may be processed to remove
undesirable constituents. In preferred examples, a blood sample is
processed using standard techniques to remove high-molecular weight
species, and thereby obtain an extract comprising small molecule
metabolites. This is referred to herein as "deproteinization" or a
"deproteinization step." For example, a solvent or solvent mixture
(e.g., methanol or the like) may be added to a blood sample to
precipitate these high-molecular weight species followed by a
centrifugation step to separate the precipitate and the
metabolite-containing supernatant. In another example, proteases
may be the added to a blood sample. In another example, size
exclusion chromatography may be used.
[0063] A single blood sample may be obtained from a subject.
Alternatively, the molecular features may be detected in blood
samples obtained over time from a subject. As such, more than one
blood sample may be collected from a subject over time. For
instance, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 or
more blood samples may be collected from a subject over time. For
example, 2, 3, 4, 5, or 6 blood samples are collected from a
subject over time. Alternatively, 6, 7, 8, 9, or 10 blood samples
are collected from a subject over time. Further, 10, 11, 12, 13, or
14 blood samples are collected from a subject over time. Still
further, 14, 15, 16 or more blood samples are collected from a
subject over time. The blood samples collected from the subject
over time may be used to monitor Lyme disease or STARI in a
subject. Alternatively, the blood samples collected from the
subject over time may be used to monitor response to treatment in a
subject.
[0064] When more than one sample is collected from a subject over
time, blood samples may be collected 0.5, 1, 2, 3, 4, 5, 6, 7, 8,
9, 10, 11, 12 or more days apart. For example, blood samples may be
collected 0.5, 1, 2, 3, or 4 days apart. Alternatively, blood
samples may be collected 4, 5, 6, or 7 days apart. Further, blood
samples may be collected 7, 8, 9, or 10 days apart. Still further,
blood samples may be collected 10, 11, 12 or more days apart.
[0065] Once a sample is obtained, it is processed in vitro to
measure abundance values for each molecular feature in Table A,
Table B, Table C, or Table D. All suitable methods for measuring
the abundance value for each of the molecular features known to one
of skill in the art are contemplated within the scope of the
invention. For example, mass spectrometry may be used to measure
abundance values for each molecular feature in Table A, Table B,
Table C, or Table D. The abundance values may be determined through
direct infusion into the mass spectrometer. Alternatively,
techniques coupling a chromatographic step with a mass spectrometry
step may be used. The chromatographic step may be liquid
chromatography. In certain examples, the abundance value for each
of the molecular features may be determined utilizing liquid
chromatography followed by mass spectrometry (LC-MS). In some
examples, the liquid chromatography is high performance liquid
chromatography (HPLC). Non-limiting examples of HPLC include
partition chromatography, normal phase chromatography, displacement
chromatography, reversed phase chromatography, size exclusion
chromatography, ion exchange chromatography, bioaffinity
chromatography, aqueous normal phase chromatography or ultrafast
liquid chromatography. As used herein "mass spectrometry" describes
methods of ionization coupled with mass selectors. Non-limiting
examples of methods of ionization include matrix-assisted laser
desorption/ionization (MALDI), electrospray ionization (ESI), and
atmospheric pressure chemical ionization (ACPI). Non-limiting
examples of mass selectors include quadropole, time of flight
(TOF), and ion trap. Further, the mass selectors may be used in
combination such as quadropole-TOF or triple quadropole.
[0066] In one example, an aliquot of a serum metabolite extract may
be applied to a Poroshell 120, EC-C8, 2.1.times.100 mm, 2.7 .mu.m
LC Column (Agilent Technologies, Palo Alto, Calif.), and
metabolites may be eluted with a nonlinear gradient of acetonitrile
in formic acid (e.g., 0.1%) at a flow rate of 250 .mu.l/min with an
Agilent 1200 series LC system. The eluent may be introduced
directly into an Agilent 6520 quadrapole time of flight mass
(Q-TOF) spectrometer and MS may be performed as previously
described (27, 50). LC-MS and LC-MS/MS data may be collected under
the following parameters: gas temperature, 310.degree. C.; drying
gas at 10 liters per min; nebulizer at 45 lb per in.sup.2;
capillary voltage, 4,000 V; fragmentation energy, 120 V; skimmer,
65 V; and octapole RF setting, 750 V. The positive-ion MS data for
the mass range of 75 to 1,700 Da may be acquired at a rate of 2
scans per sec in both centroid and profile modes in 4-GHz
high-resolution mode. Positive-ion reference masses may be used to
ensure mass accuracy. To monitor instrument performance, quality
control samples having a metabolite extract of healthy control
serum may be analyzed in duplicate at the beginning of each
analysis day and every 20 samples during the analysis day. In view
of the specifics disclosed in this example, a skilled artisan will
be able to optimize conditions as needed when using alternative
equipment or approaches.
IV. Methods for Classifying a Subject as Having Lyme Disease or
STARI
[0067] In another aspect, the present disclosure provides a method
for classifying a subject as having Lyme disease or STARI. The
method comprises analyzing a blood sample from a subject as
described in Section III to provide abundance values for each
molecular feature in Table A, Table B, Table C, or Table D; and
comparing the abundance values to a reference set of abundance
values. The statistical significance of any difference between the
abundance values measured in the subject's blood sample as compared
to the abundance values from the reference set is then determined.
If the difference is statistically significant then a subject may
be classified as having Lyme disease or STARI; if the difference is
not statistically significant then a subject may be classified as
not having Lyme disease or STARI. For instance, when using
p-values, the abundance value of a molecular feature in a test
blood sample is identified as being significantly different from
the abundance value of the molecular feature in the reference set
when the p-value is less than 0.1, preferably less than 0.05, less
than 0.01, less than 0.005, or less than 0.001. Abundance values
for the molecular features from the reference set may be determined
before, after, or at the same time, as the abundance values for the
molecular features from the subject's blood sample. Alternatively,
abundance values for the molecular features from a reference set
stored in a database may be used.
[0068] Any suitable reference set known in the art may be used;
alternatively a new reference set may be generated. A suitable
reference set comprises the abundance values for each of the
molecular feature in Table A, Table B, Table C, or Table D in blood
sample(s) obtained from control subjects known to be positive for
Lyme disease, known to be positive for STARI, known to be negative
for Lyme disease, known to be negative for STARI, known to be
negative for Lyme disease and STARI, healthy subjects, or any
combination thereof. Further, control subjects known to be negative
for Lyme disease and/or STARI may also be known to be suffering
from a disease with overlapping symptoms, may exhibit serologic
cross-reactivity with Lyme disease, and/or may be suffering for
another spirochetal infection. A subject suffering from a disease
with overlapping symptoms may have one or more of the symptoms of
Lyme disease described above. Non-limiting examples of diseases
with overlapping symptoms include tick-bite hypersensitivity
reactions, certain cutaneous fungal infections and bacterial
cellulitis with non-Lyme EM-like lesions, syphilis, fibromyalgia,
lupus, mixed connective tissue disorders (MCTD), chronic fatigue
syndrome (CFS), rheumatoid arthritis, depression, mononucleosis,
multiple sclerosis, sarcoidosis, endocarditis, colitis, Crohn's
disease, early ALS, early Alzheimers disease, encephalitis, Fifth's
disease, gastroesophageal reflux disease, infectious arthritis,
interstitial cystis, irritable bowel syndrome, juvenile arthritis,
Menieres syndrome, osteoarthritis, prostatitis, psoriatic
arthritis, psychiatric disorders (bipolar, depression, etc.),
Raynaud's syndrome, reactive arthritis, scleroderma, Sjogren's
syndrome, sleep disorders, and thyroid disease. Specifically, a
disease with overlapping symptoms is selected from the group
consisting of syphilis and fibromyalgia. Further, the disclosure
provides a method of correctly distinguishing a subject with early
Lyme disease from a subject exhibiting serologic cross-reactivity
with Lyme disease. A 2-tier serology-based assay is frequently used
to diagnose Lyme disease. However, such an assay suffers from poor
sensitivity in subjects with early Lyme disease. Non-limiting
examples of diseases that exhibit serologic cross-reactivity with
Lyme disease include infectious mononucleosis, syphilis,
periodontal disease caused by Treponema denticola, granulocytic
anaplasmosis, Epstein-Barr virus infection, malaria, Helicobacter
pylori infections, bacterial endocarditis, rheumatoid arthritis,
multiple sclerosis, infections caused by other spirochetes, and
lupus. Specifically, a disease with serologic cross-reactivity is
selected from the group consisting of infectious mononucleosis and
syphilis. Non-limiting examples of other spirochetal infections
include syphilis, severe periodontitis, leptospirosis, relapsing
fever, rate-bite fever, bejel, yaws, pinta, and intestinal
spirochaetosis. Specifically, another spirochetal infection is
selected from the group consisting of syphilis and severe
periodontitis.
[0069] In one example, a method for classifying a subject as having
Lyme disease comprises: (a) deproteinizing a blood sample from a
subject to produce a metabolite extract; (b) performing liquid
chromatography coupled to mass spectrometry on a sample of the
metabolite extract; (c) providing abundance values for each
molecular feature in Table A, Table B, Table C, or Table D; and (d)
inputting the abundance values from step (c) into a classification
model trained with samples of metabolite extracts derived from
suitable controls, wherein the classification model produces a
disease score and the disease score distinguishes subjects with
Lyme disease. In one example, the subject has at least one symptom
associated with Lyme disease. In a specific example, the subject
has an erythema migrans rash or an EM-like rash. In another
example, the subject does not have a symptom of Lyme disease but is
at risk of having Lyme disease. In each of the above examples, the
subject may or may not have received (or be receiving) treatment
for Lyme disease, STARI, or another disease with symptoms similar
to Lyme disease or STARI.
[0070] In another example, a method for classifying a subject as
having STARI comprises: (a) deproteinizing a blood sample from a
subject to produce a metabolite extract; (b) performing liquid
chromatography coupled to mass spectrometry on a sample of the
metabolite extract; (c) providing abundance values for each
molecular feature in Table A, Table B, Table C, or Table D; and (d)
inputting the abundance values from step (c) into a classification
model trained with samples of metabolite extracts derived from
suitable controls, wherein the classification model produces a
disease score and the disease score distinguishes subjects with
STARI. In one example, the subject has at least one symptom
associated with STARI. In a specific example, the subject has an
erythema migrans rash or an EM-like rash. In another example, the
subject does not have a symptom of STARI but is at risk of having
STARI. In each of the above examples, the subject may or may not
have received (or be receiving) treatment for Lyme disease, STARI,
or another disease with symptoms similar to Lyme disease or
STARI.
[0071] In another example, a method for classifying a subject as
having Lyme disease or STARI comprises: (a) deproteinizing a blood
sample from a subject to produce a metabolite extract; (b)
performing liquid chromatography coupled to mass spectrometry on a
sample of the metabolite extract; (c) providing abundance values
for each molecular feature in Table A, Table B, Table C, or Table
D; and (d) inputting the abundance values from step (c) into a
classification model trained with samples of metabolite extracts
derived from suitable controls, wherein the classification model
produces a disease score and the disease score distinguishes
subjects with Lyme disease from subjects STARI, and optionally
further distinguishes healthy subjects. In one example, the subject
has at least one symptom associated with Lyme disease and/or at
least one symptom associated with STARI. In a specific example, the
subject has an erythema migrans rash or an EM-like rash. In another
example, the subject does not have a symptom of Lyme disease or
STARI but is at risk of having Lyme disease or STARI. In each of
the above examples, the subject may or may not have received (or be
receiving) treatment for Lyme disease, STARI, or another disease
with symptoms similar to Lyme disease or STARI.
[0072] In each of the above examples, the classification model has
been trained with samples derived from suitable controls. Any
suitable classification system known in the art may be used,
provided the model produced therefrom has an accuracy of at least
80% for detecting a sample from a subject with Lyme disease,
including early Lyme disease, and/or an accuracy of at least 80%
for detecting a sample from a subject with STARI. For example, a
classification model may have an accuracy of about 80%, about 85%,
about 90%, about 95%, or greater for detecting a sample from a
subject with Lyme disease, including early Lyme disease, and/or an
accuracy of about 80%, about 85%, about 90%, about 95%, or greater
for detecting a sample from a subject with STARI. Non-limiting
examples of suitable classification models include LASSO, RF, ridge
regression, elastic net, linear discriminant analysis, logistic
regression, support vector machines, CT, and kernel estimation. In
various examples, the model has a sensitivity from about 0.8 to
about 1, and/or a specificity from about 0.8 to about 1. In certain
examples, area under the ROC curve may be used to evaluate the
suitability of a model, and an AUC ROC value of about 0.8 or
greater indicates the model has a suitable accuracy.
[0073] The classification model produces a disease score and the
disease score distinguishes: (i) samples from subjects with Lyme
disease from samples from subjects with STARI, or (ii)
distinguishes samples from subjects with Lyme disease from samples
from control subjects, or (iii) distinguishes samples from subjects
with STARI from samples from control subjects, or (iv)
distinguishes samples from subjects with Lyme disease, samples from
subjects with STARI and samples from control subjects from one
another. As a non-limiting example, LASSO scores for a subject's
sample may be calculated by multiplying the respective regression
coefficients resulting from LASSO analysis by the transformed
abundance of each MF in the biosignature and summing for each
sample. In a further example, the sample score may be transformed
into probabilities for each sample being classified to each sample
group. As another non-limiting example, the transformed abundances
of all MFs are used to classify the sample into one of the sample
groups in each classification tree developed in an RF model, where
the levels of chosen MFs are used sequentially to classify the
samples, and the final classification is determined by majority
votes among all such classification trees in the RF model. Scores
from alternative classification models may be calculated as is
known in the art.
[0074] In one example, abundance values are provided for each
molecular feature in Table A, Table B, or Table D; the suitable
controls comprise a blood sample known to be positive for Lyme
disease and a blood sample known to be positive for STARI; and the
classification model has an accuracy of at least 80%, at least 85%,
at least 90%, or at least 95% for detecting a sample from a subject
with Lyme disease and an accuracy of at least 80% or at least 85%
for detecting a sample from a subject with STARI. Alternatively,
abundance values are provided for each molecular feature in Table
A, Table B, or Table D; the suitable controls include a blood
sample known to be positive for Lyme disease, a blood sample known
to be positive for STARI, and a blood sample known to be negative
for both Lyme disease and STARI; and the classification model has
an accuracy of at least 80%, at least 85%, or at least 90%, even
more preferably at least 95% for detecting a sample from a subject
with Lyme disease and an accuracy of at least 80% or at least 85%
for detecting a sample from a subject with STARI. In still another
alternative, abundance values are provided for each molecular
feature in Table C or Table D; the suitable controls include a
blood sample known to be positive for Lyme disease, a blood sample
known to be positive for STARI, and a blood sample known to be
negative for both Lyme disease and STARI; and the classification
model has an accuracy of at least 80%, preferably at least 85% for
detecting a sample from a subject with Lyme disease; an accuracy of
at least 80%, at least 85%, or at least 90% for detecting a sample
from a subject with STARI; and an accuracy of at least 80%, at
least 85%, at least 90%, or at least 95% for detecting a sample
from a healthy subject.
V. Methods for Treating a Subject as Having Lyme Disease or
STARI
[0075] Another aspect of the disclosure is a method for treating a
subject based on the subject's classification as having Lyme
disease or STARI as described in Section IV. Treatment may be with
a non-pharmacological treatment, a pharmacological treatment, or an
additional diagnostic test.
[0076] In one example, the method comprises (a) obtaining a disease
score from a test; (b) diagnosing the subject with Lyme disease
based on the disease score; and (c) administering a treatment to
the subject with Lyme disease, wherein the test comprises measuring
the amount of each molecular feature in Table A, Table B, Table C,
or Table D; providing abundance values for each molecular feature
measured; and inputting the abundance values into a classification
model trained with samples derived from suitable controls, wherein
the classification model produces a disease score and the disease
score distinguishes subjects with Lyme disease from subjects with
STARI, and optionally from healthy subjects. In some examples, the
test is a method of Section IV. In further examples, the test
comprises (i) deproteinizing a blood sample from a subject to
produce a metabolite extract; (ii) performing liquid chromatography
coupled to mass spectrometry on a sample of the metabolite extract;
(iii) providing abundance values for each molecular feature in
Table A, Table B, Table C or Table D; and (iv) inputting the
abundance values from step (iii) into a classification model
trained with samples of metabolite extracts derived from suitable
controls, wherein the classification model produces a disease score
and the disease score distinguishes subjects with Lyme disease.
Suitable controls are described above. In one example, abundance
values are provided for each molecular feature in Table A, Table B,
or Table D; the suitable controls comprise a blood sample known to
be positive for Lyme disease and a blood sample known to be
positive for STARI; and the classification model has an accuracy of
at least 80%, at least 85%, at least 90%, or at least 95% for
detecting a sample from a subject with Lyme disease and an accuracy
of at least 80% or at least 85% for detecting a sample from a
subject with STARI. Alternatively, abundance values are provided
for each molecular feature in Table A, Table B, or Table D; the
suitable controls include a blood sample known to be positive for
Lyme disease, a blood sample known to be positive for STARI, and a
blood sample known to be negative for both Lyme disease and STARI;
and the classification model has an accuracy of at least 80%, at
least 85%, or at least 90%, even more preferably at least 95% for
detecting a sample from a subject with Lyme disease and an accuracy
of at least 80% or at least 85% for detecting a sample from a
subject with STARI. In still another alternative, abundance values
are provided for each molecular feature in Table C or Table D; the
suitable controls include a blood sample known to be positive for
Lyme disease, a blood sample known to be positive for STARI, and a
blood sample known to be negative for both Lyme disease and STARI;
and the classification model has an accuracy of at least 80%,
preferably at least 85% for detecting a sample from a subject with
Lyme disease; an accuracy of at least 80%, at least 85%, or at
least 90% for detecting a sample from a subject with STARI; and an
accuracy of at least 80%, at least 85%, at least 90%, or at least
95% for detecting a sample from a healthy subject.
[0077] Treatment may comprise one or more standard treatments for
Lyme disease. Non-limiting examples of standard pharmacological
treatments for Lyme disease include an antibiotic, an antibacterial
agent, a vaccine, an immune modulator, an anti-inflammatory agent,
or a combination thereof. Suitable antibiotics include, but are not
limited to, amoxicillin, doxycycline, cefuroxime axetil,
amoxicillin-clavulanic acid, macrolides, ceftriaxone, cefotaxmine,
and penicillin G. Antibiotics may be administered orally or
parenterally. Alternatively, treatment may comprise one or more
experimental pharmacological treatment (e.g., treatment in a
clinical trial). In each of the above examples, treatment may be
for the acute or disseminated stage of the disease, or may be a
prophylactic treatment. For example, following successful
resolution of a primary Borrrelia infection, the subject may be
treated with a vaccine to prevent future infections. In still other
examples, treatment may comprise further diagnostic testing. For
example, if a subject has early Lyme disease but was negative for
Lyme disease by current diagnostic testing (e.g., first-tier
testing performed using the C6 EIA and second tier testing using
IgM and/or IgG immunoblots following a positive or equivocal
first-tier assay), additional testing may be ordered after an
amount of time has elapsed (e.g., 3, 5, 7, 10, 14 days or more) to
confirm the initial diagnosis.
[0078] In another example, the method comprises (a) obtaining a
disease score from a test; (b) diagnosing the subject with STARI
based on the disease score; and (c) administering a treatment to
the subject with STARI, wherein the test comprises measuring the
amount of each molecular feature in Table A, Table B, Table C, or
Table D; providing abundance values for each molecular feature
measured; and inputting the abundance values into a classification
model trained with samples derived from suitable controls, wherein
the classification model produces a disease score and the disease
score distinguishes subjects with STARI from subjects with Lyme
disease, including early Lyme disease, and optionally from healthy
subjects. In some examples, the test is a method of Section IV. In
further examples, the test comprises (i) deproteinizing a blood
sample from a subject to produce a metabolite extract; (ii)
performing liquid chromatography coupled to mass spectrometry on a
sample of the metabolite extract; (iii) providing abundance values
for each molecular feature in Table A, Table B, Table C or Table D;
and (iv) inputting the abundance values from step (iii) into a
classification model trained with samples of metabolite extracts
derived from suitable controls, wherein the classification model
produces a disease score and the disease score distinguishes
subjects with STARI. Suitable controls are described above. In one
example, abundance values are provided for each molecular feature
in Table A, Table B, or Table D; the suitable controls comprise a
blood sample known to be positive for Lyme disease and a blood
sample known to be positive for STARI; and the classification model
has an accuracy of at least 80%, at least 85%, at least 90%, or at
least 95% for detecting a sample from a subject with Lyme disease
and an accuracy of at least 80% or at least 85% for detecting a
sample from a subject with STARI. Alternatively, abundance values
are provided for each molecular feature in Table A, Table B, or
Table D; the suitable controls include a blood sample known to be
positive for Lyme disease, a blood sample known to be positive for
STARI, and a blood sample known to be negative for both Lyme
disease and STARI; and the classification model has an accuracy of
at least 80%, at least 85%, or at least 90%, even more preferably
at least 95% for detecting a sample from a subject with Lyme
disease and an accuracy of at least 80% or at least 85% for
detecting a sample from a subject with STARI. In still another
alternative, abundance values are provided for each molecular
feature in Table C or Table D; the suitable controls include a
blood sample known to be positive for Lyme disease, a blood sample
known to be positive for STARI, and a blood sample known to be
negative for both Lyme disease and STARI; and the classification
model has an accuracy of at least 80%, preferably at least 85% for
detecting a sample from a subject with Lyme disease; an accuracy of
at least 80%, at least 85%, or at least 90% for detecting a sample
from a subject with STARI; and an accuracy of at least 80%, at
least 85%, at least 90%, or at least 95% for detecting a sample
from a healthy subject.
[0079] Treatment may comprise one or more standard treatments for
STARI. There are no therapeutic agents specifically approved for
STARI, in part because the causative agent is not known.
Nonetheless, non-limiting examples of standard pharmacological
treatments for STARI include an antibiotic, an antibacterial agent,
a vaccine, an immune modulator, an anti-inflammatory agent, or a
combination thereof. Suitable antibiotics include, but are not
limited to, amoxicillin, doxycycline, cefuroxime axetil,
amoxicillin-clavulanic acid, macrolides, ceftriaxone, cefotaxmine,
and penicillin G. Antibiotics may be administered orally or
parenterally. Alternatively, treatment may comprise one or more
experimental pharmacological treatment (e.g., treatment in a
clinical trial). In each of the above examples, treatment may be
for acute disease, or may be a prophylactic treatment. For example,
following successful treatment of STARI (as defined the by the
current clinical standard of the time), the subject may be treated
with a vaccine to prevent future infections. In still other another
example, treatment may comprise further diagnostic testing. For
example, if a subject is diagnosed with STARI, additional testing
may be ordered after an amount of time has elapsed (e.g., 3, 5, 7,
10, 14 days or more) to confirm the initial diagnosis. In yet
another example, treatment may consist of supportive care only,
e.g., non-pharmacological treatments or over-the-counter
pharmaceutical agents to alleviate symptoms, such as fever, aches,
etc.
[0080] In certain examples, obtaining a result from a test of
Section IV comprises analyzing a blood sample obtained from the
subject as described in Section III and/or classifying the subject
as described in Section IV. In certain examples, obtaining a result
from a test of Section IV comprises requesting (e.g., placing a
medical order or prescription) from a third party a test that
analyzes a blood sample obtained from the subject as described in
Section III and classifies the subject as described in Section IV,
or requesting from a third party a test that analyzes a blood
sample obtained from the subject as described in Section III, and
then performing the classification as described in Section IV.
[0081] In each of the above examples, the method may further
comprise obtaining a second result (for a sample obtained from the
subject after treatment has begun) from the same test of Section IV
as before treatment and adjusting treatment based on the test
result.
[0082] Accordingly, yet another aspect of the disclosure is a
method for monitoring the effectiveness of a therapeutic agent
intended to treat a subject with Lyme disease or STARI. The method
comprises obtaining a result from a test of Section IV,
administering a therapeutic agent to the subject, obtaining a
result from the same test of Section IV as before treatment,
wherein the treatment is effective if the disease score classifies
the subject as more healthy than before. A first sample obtained
before treatment began may be used as a baseline. Alternatively,
the first sample may be obtained after treatment has begun. Samples
may be collected from a subject over time, including 0.5, 1, 2, 3,
4, 5, 6, 7, 8, 9, 10, 11, 12 or more days apart. For example, blood
samples may be collected 0.5, 1, 2, 3, or 4 days apart.
Alternatively, blood samples may be collected 4, 5, 6, or 7 days
apart. Further, blood samples may be collected 7, 8, 9, or 10 days
apart. Still further, blood samples may be collected 10, 11, 12 or
more days apart.
[0083] The following examples are included to demonstrate preferred
examples of the invention. It should be appreciated by those of
skill in the art that the techniques disclosed in the examples that
follow represent techniques discovered by the inventors to function
well in the practice of the invention. Those of skill in the art
should, however, in light of the present disclosure, appreciate
that changes may be made in the specific examples that are
disclosed and still obtain a like or similar result without
departing from the spirit and scope of the invention. Therefore,
all matter set forth or shown in the examples and accompanying
drawings is to be interpreted as illustrative and not in a limiting
sense.
EXAMPLES
[0084] The following examples illustrate various iterations of the
invention.
Example 1
[0085] Lyme disease is a multisystem bacterial infection that in
the United States is primarily caused by infection with Borrelia
burgdorferi sensu stricto. Over 300,000 cases of Lyme disease are
estimated to occur annually in the United States, with over 3.4
million laboratory diagnostic tests performed each year (1, 2).
Symptoms associated with this infection include fever, chills,
headache, fatigue, muscle and joint aches, and swollen lymph nodes;
however, the most prominent clinical manifestation in the early
stage is the presence of one or more erythema migrans (EM) skin
lesions (3). This annular, expanding erythematous skin lesion
occurs at the site of the tick bite in 70 to 80% of infected
individuals and is typically 5 cm or more in diameter (4, 5).
Although an EM lesion is a hallmark for Lyme disease, other types
of skin lesions can be confused with EM (3, 5, 6). These include
rashes caused by tick-bite hypersensitivity reactions, certain
cutaneous fungal infections, bacterial cellulitis and the rash of
southern tick-associated rash illness (STARI) (7, 8).
[0086] STARI is associated with a bite from the lone star tick
(Amblyomma americanum) and, in addition to the development of an
EM-like skin lesion, individuals with STARI can present with mild
systemic symptoms (including muscle and joint pains, fatigue,
fever, chills, and headache) that are similar to those occurring in
patients with Lyme disease (7, 9, 10). These characteristics of
STARI have led some to postulate that the etiology of this illness
is a Borrelia species, including B. burgdorferi (10, 11) or B.
lonestari (12-15); however, multiple studies have refuted that
STARI is caused by B. burgdorferi (7, 16-19) and additional cases
associating B. lonestari with STARI have not emerged (20, 21).
Additionally, STARI patients have been screened serologically for
reactivity to rickettsial agents, but no evidence was obtained to
demonstrate that rickettsia causes this illness (10, 22). Thus, at
present no infectious etiology is known for STARI.
[0087] STARI cases occur over the geographic region where the lone
star tick is present. This includes a region that currently expands
from central Texas and Oklahoma upward into the Midwestern states
and eastward, including the southern states and along the Atlantic
coast into Maine (23). Unlike STARI, Lyme disease is transmitted to
humans through the bite of the blacklegged tick (Ixodes scapularis)
that is present in the northeastern, mid-Atlantic, and
north-central United States, and the western blacklegged tick (I.
pacificus), which is present on the Pacific Coast (24). The
geographic distribution of human Lyme disease and the vectors for
this disease is expanding (24-26), and there is a similar expansion
of areas inhabited by the lone star tick (23). Importantly, a
strict geographic segregation of Lyme disease and STARI does not
exist, as there are regions where STARI and Lyme disease are
co-prevalent (25). Thus, there is a growing need for diagnostic
methods to differentiate between Lyme disease and STARI, and that
facilitate proper treatment, patient management and disease
surveillance.
[0088] Clinically, the skin lesions of STARI and early Lyme disease
are indistinguishable, and no laboratory tool or method exists for
the diagnosis of STARI or differentiation of STARI from Lyme
disease. The only biomarkers evaluated for differential diagnosis
of early Lyme disease and STARI have been serum antibodies to B.
burgdorferi (10, 16). However, these tests have poor sensitivity
for early stages of Lyme disease, and thus a lack of B. burgdorferi
antibodies cannot be used as a reliable differential marker for
STARI.
[0089] The experiments herein describe the development of a
metabolomics-driven approach to identify biomarkers that
discriminate early Lyme disease from STARI, and provide evidence
that these two diseases are biochemically distinct. A retrospective
cohort of well-characterized sera from patients with early Lyme
disease and STARI was evaluated to identify a differentiating
metabolic biosignature. Using statistical modeling, this metabolic
biosignature accurately classified test samples that included
healthy controls. Additionally, the metabolic biosignature revealed
that N-acyl ethanolamine (NAE) and primary fatty acid amide (PFAM)
metabolism differed significantly between these two diseases.
[0090] Clinical samples: A total of 220 well-characterized
retrospective serum samples from three different repositories were
used to develop and test a metabolic biosignature that accurately
classifies early Lyme disease and STARI (Table 2). All samples from
Lyme disease patients were culture confirmed and/or PCR positive
for B. burgdorferi. The median age for early Lyme disease patients
was 45 years and 74% were males. STARI patients had an overall
median age of 45 years and 55% were males.
[0091] To establish a Lyme disease diagnostic baseline, the
recommended two-tiered serology testing for Lyme disease was
performed on all samples. First-tier testing was performed using
the C6 EIA and was positive for 66% of Lyme disease samples. When
STARI and healthy controls were tested by the C6 EIA, two STARI
samples (2%) and five healthy controls (9%) tested positive or
equivocal. Two-tiered testing using IgM and IgG immunoblots as the
second-tier test following a positive or equivocal first-tier assay
resulted in a sensitivity of 44% for early Lyme disease samples
(duration of illness was not considered for IgM immunoblot
testing). The sensitivity of two-tiered testing for early Lyme
disease samples included in the Discovery/Training-Sets and the
Test-Sets was 38% and 53%, respectively. All STARI and healthy
control samples were negative by two-tiered testing (Table 2).
[0092] Development of a metabolic biosignature for early Lyme
disease and STARI differentiation: Metabolic profiling by liquid
chromatography-mass spectrometry (LC-MS) of a retrospective cohort
of well-characterized sera from patients with early Lyme disease
(n=40) and STARI (n=36) (Table 2 and FIG. 1) comprising the
Discovery-Set (i.e. Test-Set samples that were not used in
molecular feature selection) resulted in a biosignature of 792
molecular features (MFs) that differed significantly
(adjusted-p<0.05) with a .gtoreq.2 fold change in relative
abundance between early Lyme disease and STARI. Down-selection of
MFs based on their robustness in replicate analyses of the same
sera produced a refined biosignature of 261 MFs (FIG. 1 and Table
3). Of these 261 MFs, 60 and 201 displayed an increased and
decreased abundance, respectively, in early Lyme disease as
compared to STARI. The large number of MFs that differed
significantly between early Lyme disease and STARI patients
indicated that these two patient groups had distinguishing
biochemical profiles. These variances were applied to define
alterations of specific metabolic pathways (FIG. 1) and used to
develop diagnostic classification models (FIG. 1).
[0093] In silico analysis of metabolic pathways: Presumptive
chemical identification was applied to the 261 MFs. This yielded
predicted chemical formulae for 149 MFs, and 122 MFs were assigned
a putative chemical structure based on interrogation of each MF's
monoisotopic mass (+ or -15 ppm) against the Metlin database and
the Human Metabolome Database (HMDB) (Table 3). An in silico
interrogation of potentially altered metabolic pathways was
performed using the presumptive identifications for the 122 MFs and
MetaboAnalyst (28). Four differentiating pathways were predicted to
have the greatest impact, with the most significant being
glycerophospholipid metabolism and sphingolipid metabolism (FIG. 2
and Table 4). Specifically, the MetaboAnalyst analysis indicated
that differences in phosphatidic acid, phosphatidylethanolamine,
phosphatidylcholine and lysophosphotidylcholine were the major
contributors to altered glycerophospholipid metabolism between
STARI and early Lyme disease (Table 4). Altered sphingolipid
metabolism between these two groups was attributable to changes in
the relative abundances of sphingosine, dehydrosphinganine and
sulfatide (Table 4). Manual interrogation of the predicted
structural identifications revealed that 26 and 7 of the 122 MFs
assigned a putative structural identification were associated with
glycerophospholipid and sphingolipid metabolism, respectively
(Table 3).
[0094] Elucidation of altered NAE metabolism: The prediction of
altered metabolic pathways was based on the presumptive structural
identification of the early Lyme disease versus STARI
differentiating MFs. Thus, to further define the metabolic
differences between these two patient groups, structural
confirmation of selected MFs was undertaken. Two MFs that displayed
relatively large abundance differences (m/z 300.2892, RT 19.66; and
m/z 328.3204, RT 20.72) were putatively identified as
sphingosine-C18 or 3-ketosphinganine, and sphingosine-C20 or
N,N-dimethyl sphingosine, respectively. However, both of these MFs
had alternative predicted structures of palmitoyl ethanolamide and
stearoyl ethanolamide, respectively. The interrogation of authentic
standards against these two serum MFs revealed RTs and MS/MS
spectra that identified the m/z 300.2892 and m/z 328.3204 products
as palmitoyl ethanolamide (FIGS. 3A and 3B) and stearoyl
ethanolamide (FIG. 7), respectively. These two products, as well as
other NAEs, are derived from phosphatidylethanolamine and
phosphatidylcholine, and represent a class of structures termed
endocannabinoids and endocannabinoid-like (29) (FIG. 3C). Further
analysis of the 122 MFs identified five additional MFs with a
predicted structure that mapped to the NAE pathway. Specifically,
MF m/z 286.2737, RT 19.08 was putatively identified as a
sphingosine-C17 or pentadecanoyl ethanolamide, and was confirmed to
be the latter (FIG. 8). MF m/z 356.3517, RT 21.67 was putatively
identified and confirmed to be eicosanoyl ethanolamide (FIG. 9),
and MF m/z 454.2923, RT 18.08 was confirmed to be
glycerophospho-N-palmitoyl ethanolamine (FIG. 10), which is an
intermediate in the formation of palmitoyl ethanolamide. A second
group of lipids, the PFAMs that act as signaling molecules and that
are potentially associated with the metabolism of NAEs were also
identified as having significant relative abundance differences
between the early Lyme disease and STARI patient samples.
Specifically, MFs m/z 256.2632, RT 20.08; m/z 284.2943, RT 21.15;
and m/z 338.3430, RT 22.14 were confirmed to be palmitamide (FIG.
3D and FIG. 3E), stearamide (FIG. 11) and erucamide (FIG. 12),
respectively.
[0095] The large number of differentiating MFs associated with NAE
metabolism suggested that this is a major biological difference
between STARI and early Lyme disease (FIG. 3C and Table 3). Four
additional MFs of the 261 MF biosignature, and that fit known host
biochemical pathways, were also structurally confirmed. These
included L-phenylalanine (FIG. 13), nonanedioic acid (FIG. 14),
glycocholic acid (FIG. 15) and
3-carboxy-4-methyl-5-propyl-2-furanpropanoic acid (CMPF) (FIG. 16).
Additionally, two MFs that provided strong matches to MS/MS spectra
in the Metlin databases were putatively identified as arachidonoyl
lysophosphatidic acid [Lyso PA (20:4)] (FIG. 17) and
3-ketosphingosine (FIG. 18).
[0096] The 261 MF biosignature list revealed metabolic
dissimilarity between Lyme disease and STARI: To test whether early
Lyme disease and STARI represent distinct metabolic states that
would be reflected in the comparison of MF abundances in these two
disease states to those of healthy controls, the abundance
fold-change for each structurally confirmed MF in early Lyme
disease and STARI sera as compared to healthy controls was
determined. This revealed that the majority of these MFs maintained
fold change differences with respect to healthy controls that
allowed for segregation of early Lyme disease and STARI patient
samples (FIG. 4A). For three MFs (3-ketosphingosine, CMPF, and Lyso
PA 20:4), the levels in early Lyme disease were increased as
compared to the healthy controls while the levels in STARI were
decreased. Additionally, all of the NAEs and PFAMs had abundances
in early Lyme disease patients that were closer to those of healthy
controls, whereas the abundances in STARI were greatly increased.
This analysis was expanded to all 261 MFs of the early Lyme
disease-STARI biosignature (FIG. 4B). The percent of MFs with
increased and decreased abundances relative to healthy controls
were similar across the abundance fold changes for both early Lyme
disease and STARI. However, when the MFs with increased or
decreased abundances were compared between early Lyme disease and
STARI for each range of abundance fold change, the concordance was
low (0 to 30%) (FIG. 4C). This indicated that the metabolic changes
in early Lyme disease and STARI as compared to healthy controls
differed.
[0097] Diagnostic classification of early Lyme disease vs STARI:
Classification models were used to determine whether the 261 MF
biosignature could be applied to discriminate early Lyme disease
from STARI (Table 1 and FIG. 1). Specifically, two classification
models, least absolute shrinkage and selection operator (LASSO) and
random forest (RF) were trained with the 261 MF biosignature using
abundance data from the Training-Set samples only (FIG. 1).
Test-Set samples were not used for molecular feature selection or
to train the classification models. The LASSO model selected 38
MFs, and RF by default does not perform feature selection and thus
used all 261 MFs for classification of the STARI and early Lyme
disease patient populations (Table 3 and Table 5). When Test-Set
samples (FIG. 1) (i.e. those not included in the
Discovery/Training-Set) were tested in duplicate, early Lyme
disease samples were classified by RF and LASSO with an accuracy of
97% and 98%, respectively. The STARI samples had a classification
accuracy of 89% with both models (Table 1 and Table 6). A depiction
of the LASSO scores for the Test-Set data showed segregation of the
early Lyme disease and STARI patient samples, and demonstrated the
discriminating power of the 38 MFs selected by the LASSO model
(FIG. 5A). A receiver operating characteristic (ROC) curve was
plotted to demonstrate the performance of the LASSO model for
differentiating early Lyme disease from STARI patients. The area
under the curve (AUC) was calculated to be 0.986 (FIG. 5B). The 38
MFs of the LASSO model encompassed four of the 14 structurally
confirmed metabolites: CMPF, L-phenylalanine, palmitoyl
ethanolamide, and arachidonoyl lysophosphatidic acid (Table 3).
[0098] Diagnostic classification of early Lyme disease vs STARI vs
healthy controls: Separate three-way classification models using
LASSO and RF were developed by including LC-MS data collected for
healthy controls in the Training-Set samples (FIG. 1). For model
training LASSO selected 82 MFs (Table 3). The regression
coefficients for the 82 MFs selected by LASSO are provided in Table
7. Evaluation of the RF and LASSO three-way classification models
with Test-Set samples (those not used in the
Discovery/Training-Sets) revealed classification accuracies of 85%
and 92% for early Lyme disease and STARI, respectively.
Surprisingly, healthy controls were classified with accuracies of
95% and 93% with the RF and LASSO models, respectively (Table 1 and
Table 8). Plotting of LASSO scores calculated for Test-Set data
revealed three groupings that corresponded with early Lyme disease,
STARI and healthy controls (FIG. 5C). Of the early Lyme disease
samples that were misclassified with the RF model (n=9), all were
predicted to be healthy controls; and those misclassified by the
LASSO model (n=9), three were classified as STARI and six as
healthy controls. Of the STARI samples that were misclassified by
the RF and LASSO models (n=3 for both models), all samples were
misclassified as early Lyme disease. When healthy controls were
misclassified using the RF model (n=2) and LASSO model (n=3), all
were misclassified as early Lyme disease.
[0099] Of the 38 MFs selected by LASSO for the two-way
classification model, 33 were included in the 82 MFs of the LASSO
three-way classification model (Table 3). The 82 MFs of the LASSO
three-way classification included seven of the 14 structurally
confirmed metabolites: 3-ketosphingosine, glycocholic acid and
pentadecanoyl ethanolamide, as well as the four included in the
LASSO two-way classification model (Table 3).
[0100] Biosignature was not influenced by geographic variability:
Since retrospective samples collected by multiple laboratories were
used in these studies, analyses were performed to assess whether a
geographic bias was introduced. To address this, three healthy
control groups and three STARI groups (all early Lyme disease
samples came from one geographic region) were evaluated by linear
discriminant analysis using the 82 MFs of the LASSO three-way
classification model (FIG. 6). For healthy controls, those samples
used in the modeling (collected in New York and Colorado) were
evaluated. Additionally, healthy controls from Florida, a region
with low prevalence for Lyme disease and reported to have STARI
cases, were included to evaluate whether samples collected in the
southern United States would differ from those collected in New
York or Colorado. For STARI, three patient samples groups collected
in Missouri, NC and other states (included VA, GA, KY, TN, AL, IA
and NE) were compared. The linear discriminant analysis
demonstrated that although slight variation exists between the
three healthy control groups (NY, CO and FL), there is greater
variability between all healthy controls and all STARI samples than
within healthy controls or STARI samples based on geographic
location of collection (FIG. 6).
[0101] Discussion: The inability to detect B. burgdorferi by PCR or
culture and the lack of a serological response to B. burgdorferi
antigens in STARI patients is widely accepted as evidence that the
etiologies of STARI and Lyme disease differ (7, 16). This is
further supported by the different tick species associated with
these two diseases (8, 25). Nevertheless, the strong overlap in
clinical symptoms, including the development of an EM-like skin
lesion, creates confusion and controversy for the clinical
differentiation of STARI and Lyme disease (30). The data reported
here demonstrated marked differences between the metabolic profiles
of early Lyme disease and STARI patients, and thus provide
compelling positive data to support the concept that these two
illnesses are distinct entities. Interestingly, metabolic pathway
analyses and the structural identification of several MFs with
significant abundance differences between early Lyme disease and
STARI identified multiple NAEs. These endogenous lipid mediators
are derived from phosphatidylcholine and phospahtidylethanolamine
via the endocannabinoid system (FIG. 3C) (29).
Arachidonoylethanolamide (AEA) is the most widely studied
endocannabinoid, as it is an endogenous agonist of the cannabinoid
receptors; however, it is a minor component of animal tissues. In
contrast, congeners of AEA, such as the NAEs identified in the
early Lyme disease-STARI biosignature, are significant products of
animal tissues, including the skin (29, 31). The serum levels of
NAEs possessing long-chain saturated fatty acids were significantly
increased in the serum of STARI patients. These NAEs are produced
in response to inflammation, and act in an anti-inflammatory manner
as agonists of PPAR-.alpha. or by enhancing AEA activity (32, 33).
The NAEs are generally degraded via fatty acid amide hydrolase;
however, it was recently demonstrated that NAEs can be converted to
N-acylglycine structures via an alcohol dehydrogenase, and further
degraded to PFAMs (34). Interestingly, the data generated from
these studies not only demonstrated a STARI-associated increase in
NAEs with saturated fatty acids, but also an increase in the
corresponding PFAMs. Although the mechanism for the increased
abundance of NAEs and PFAMs in STARI patients is unknown, decrease
in fatty acid amide hydrolase activity which releases free fatty
acids from both NAEs and PFAMs would result in the observed
increase in abundance of these metabolites (35). The
anti-inflammatory activity of the NAEs also raises the possibility
that these metabolites are partially responsible for the milder
symptoms associated with STARI (9). As the enzymes involved with
the genesis and degradation of NAEs and PFAMs are known (29, 36),
studies can be constructed to further elucidate the mechanism(s) by
which NAEs and PFAMs accumulate in the sera of STARI patients.
[0102] This current work expands demonstrates the ability to
distinguish early Lyme disease from an illness with nearly
identical symptoms or what would be considered a Lyme disease-like
illness (37). The existing diagnostic algorithm for Lyme disease is
a two-tiered serologic approach that utilizes an EIA or IFA as a
first-tier test followed by IgM and IgG immunoblotting as the
second-tier test (38). For early Lyme disease, the sensitivity of
this diagnostic is 29-40% and the specificity is 95-100% (39). The
current antibody-based approaches do not distinguish between active
and previous infections, an important limitation. In the current
study all of the STARI samples were negative by two-tiered testing,
and only 2% were positive by the first-tier EIA. Early Lyme disease
samples were 44% positive (38% positivity for the early Lyme
disease samples used in the Discovery and Training Sets and 53%
positivity for early Lyme disease samples used in the Test Sets) by
two-tiered testing. In contrast, when classification modeling was
applied to the 261 MFs of the early Lyme disease-STARI
biosignature, diagnostic accuracy for early Lyme disease was
dramatically increased (85 to 98% accuracy depending on the model)
as compared to serology. Classification by RF or LASSO was overall
highly accurate for early Lyme disease and STARI, in particular
when using the two-way classification models. Interestingly, when
healthy controls were introduced and used to develop a three-way
classification model there was a slight increase in the accuracy
for STARI and decrease in the accuracy for early Lyme disease, but
healthy controls were classified with a 93-95% accuracy. This was
surprising as healthy controls were not used to create the initial
261 MF biosignature, and furthers supported that STARI and early
Lyme disease are metabolically distinct from healthy controls, but
in different ways.
[0103] To date the development of a diagnostic tool for STARI or
for differentiation of early Lyme disease and STARI has received
little attention. As the geographic distribution of Lyme disease
continues to expand (25, 26), so will the geographic range where
there is overlap of Lyme disease and STARI. Thus, a diagnostic tool
that accurately differentiates these two diseases could have a
major impact on patient management. Lyme disease is treated with
antibiotics, and although there is no defined infectious etiology
for STARI, this illness is also commonly treated in a similar
manner (7, 20, 40). Establishment of a robust diagnostic tool would
not only facilitate antibiotic stewardship, it would also allow for
proper studies to assess the true impact of therapies for STARI.
Lyme disease is also a reportable disease and in order to maintain
accurate disease surveillance in low incidence areas, it is
essential that diseases such as STARI be excluded (30).
Additionally, vaccines are currently being developed for Lyme
disease (41-44) and as these are tested, it will be important to
identify STARI patients in order to properly assess vaccine
efficacy.
[0104] To apply the discoveries of this work towards the
development of an assay that can be used for the clinical
differentiation of early Lyme disease and STARI, it should first be
determined whether an emphasis should be placed on the diagnosis of
Lyme disease or STARI. As there is no defined etiology of STARI,
and Lyme disease is not necessarily self-limiting without
antibiotics and can have subsequent complications if untreated, we
envision that the final assay would focus on being highly sensitive
for early Lyme disease and be primarily applied in regions where
Lyme disease and STARI overlap. Although existing laboratory tests
for Lyme disease emphasize specificity, this strategy needs to be
reconsidered for a differential diagnostic test of STARI and early
Lyme disease, since any illness presenting with an EM in a region
with a known incidence of Lyme disease would likely be treated with
antibiotics (7, 20, 40). As with all diagnostic tests, use of a
metabolic biosignature for differentiation of early Lyme disease
and STARI would need to be performed in conjunction with clinical
evaluation of the patient, and consideration of their medical
history and epidemiologic risk for these two diseases.
[0105] The approach outlined in this study applies
semi-quantitative mass spectrometry and the use of biochemical
signatures for the classification of patients. Clinical application
of such an approach would likely occur in a specialized clinical
diagnostic laboratory. However, it should be noted that the
second-tier immunoblot assays for the serological diagnosis of Lyme
disease are already performed in specialized laboratories (1, 45,
46). Mass spectrometry assays are currently used in clinical
laboratories for the analyses of small molecule metabolites. The
majority of these tests are under Clinical Laboratory Improvement
Amendments (CLIA) guidelines, but an FDA cleared mass
spectrometry-based test for inborn metabolic errors is in use (47).
The most accurate quantification of metabolites by mass
spectrometry is achieved by Multiple Reaction Monitoring (MRM)
assays (48). Such assays are developed with the knowledge of a MF's
chemical structure. To this end, the chemical structure of 14 MFs
have been identified. The chemical structure of the remainder of
the MFs can be identified by the methods described herein. It
should be noted that the NAEs and PFAMs that were revealed via our
pathway analyses are amenable to MRM assays (49). These metabolites
are now being investigated for their ability to accurately classify
STARI and early Lyme disease.
[0106] The data reported here were generated from the analysis of
retrospectively collected serum samples from various repositories
that have been archived for different lengths of time. To reduce
the impact of the potential variability associated with these
samples, stringent criteria were applied to the data analysis. In
addition to the requirement of a significant fold change, those MFs
selected for the final early Lyme disease-STARI biosignature were
required to be present in at least 80% of samples within a sample
group and maintain the median fold-change difference in at least
50% of samples within a group. While the STARI and healthy control
sera were collected by multiple laboratories and from multiple
geographic locations, the early Lyme disease sera were obtained
from a single laboratory. This is a potential limitation of the
study. However, linear discriminate analysis was applied to assess
the variability within the healthy control and STARI samples
collected by different laboratories. This analysis demonstrated
little to no variability among the STARI or healthy control samples
indicating that the criteria used for MF selection effectively
reduced non-biological variability. As noted, data were collected
by non-absolute semi-quantitative mass spectrometry. Nevertheless,
this is a common practice applied in the development of
differentiating biosignatures for infectious diseases (27, 50-53),
and the workflow ensured that the most robust MFs were selected and
used for classification modeling.
[0107] Without knowledge of a known etiologic agent, it is
recognized that STARI simply encompasses a clinical syndrome. The
STARI samples used in this current work included those collected in
studies used to define this illness (9), as well as samples
collected outside those original studies. Additional samples
collected prospectively will be useful to assess the applicability
of our current metabolic biosignature in a real world scenario.
Future sample collection will also target patient populations with
non-Lyme EM-like lesions, including tick-bite hypersensitivity
reactions, certain cutaneous fungal infections and bacterial
cellulitis. Additionally, other factors such as confections with
other vector-borne pathogens will need to be addressed with
prospective studies. In the Southeastern United States, there is
evidence for enzootic transmission of B. burgdorferi; however, it
is debatable whether Lyme disease occurs in this region (11, 30,
54, 55). The current study was not designed to provide evidence for
or against the presence of Lyme disease in the southern United
States. Nevertheless, metabolic profiling offers a novel approach
that is orthogonal to the methods currently employed to address
this issue.
Example 2
[0108] STARI is an illness that has received little attention over
the years, but is a confounding factor in diagnosing early Lyme
disease in areas where both illnesses overlap and contributes to
the debate surrounding the presence of Lyme disease in the southern
United States. No diagnostic tool exists for STARI or for
differentiating early Lyme disease from STARI. Based on documented
differences between early Lyme disease and STARI (9, 16, 56), we
metabolically profiled serum to develop a biochemical biosignature
that when applied could accurately classify early Lyme disease and
STARI patients (See Example 1). This example describes the design
of the study described in Example 1.
[0109] An unbiased-metabolomics study was designed to directly
compare the metabolic host responses between these two illnesses,
and subsequently evaluate how this metabolic biosignature
distinguishes these two illnesses. The use of unbiased metabolomics
for biosignature discovery does not lend itself to power
calculations to determine sample size. Thus, sample sizes were
selected based on our previous studies (27, 50, 51). To obtain a
sufficient number of well-characterized STARI sera, retrospectively
collected samples from two separate studies were used.
Specifically, the first set of STARI serum samples (n=33) was
obtained from the CDC repository. These samples were collected
through a prospective study performed between 2007 and 2009 (57).
Patients were enrolled through CDC outreach efforts (n=17) or by
contract with the University of North Carolina at Chapel Hill
(n=16). The states where patients were recruited included NC, 18;
VA, 4; TN, 3; KY, 2; GA, 2; IA, 2; AL, 1; and NE, 1. All samples
were collected pre-treatment with the exception of one patient who
was treated with doxycycline 1-2 days before the serum sample was
obtained. The second set of STARI samples (n=22) was obtained from
the New York Medical College serum repository (20). These samples
were collected between 2001 and 2004 from patients living in
Missouri.
[0110] Sufficient numbers of well-characterized early Lyme disease
serum samples were acquired from New York, an area of high
incidence for Lyme disease and low incidence of STARI (9).
Specifically, all early Lyme disease samples (n=70) were culture
and/or PCR positive for B. burgdorferi and were collected
pre-treatment. To ensure appropriate representation of both
non-disseminated and disseminated forms of early EM Lyme disease,
samples from patients with a single EM that were skin culture
and/or PCR positive for B. burgdorferi and blood culture negative
(n=35), and patients with multiple EMs or a single EM that were
blood culture positive (n=35) were used. Early Lyme disease samples
were collected between 1992 and 2007, and 1 to 33 days post-onset
of symptoms. To understand the relationship of our findings to a
healthy control population serum samples from healthy donors were
also included in the study. These were procured from repositories
at New York Medical College, the CDC and the University of Central
Florida. A detailed description of inclusion and exclusion criteria
for each patient and donor population is provided in Table 2. All
participating institutions obtained institutional review board
(IRB) approval for this study. IRB review and approval for this
study ensured that the retrospective samples used had been
collected under informed consent.
[0111] All samples were analyzed in duplicate and were randomized
prior to processing for LC-MS analyses. Healthy control sera were
used as quality control samples for each LC-MS experiment. The
serum samples and respective LC-MS data files of each patient group
and healthy controls were randomly separated into a
Discovery-Set/Training-Sets 1 and 2, and Test-Sets 1 and 2.
Specifically, 40 of the 70 early Lyme disease and 36 of the 55
STARI samples were randomly selected as the Discovery-Set samples.
This sample set was used for molecular feature selection. To train
the classification models, two training-sets were used. The first,
Training-Set 1, was identical to the Discovery-Set (i.e. contained
the same early Lyme disease and STARI samples) and the second,
Training-Set 2, had the same samples as Training-Set 1 with the
addition of 38 of the 58 healthy control samples. Lastly, Test-Sets
1 and 2 were created. Test-Set 1 was comprised of 30 early Lyme
disease and 19 STARI samples that were not included in the
Discovery/Training samples sets. Test-Set 2 had the same samples as
those used in Test-Set 1 with the addition of 20 healthy control
samples that were not included in the Training-Set 2 samples.
Test-Sets 1 and 2 were exclusively used for blinded testing of the
classification models.
[0112] Randomization into Discovery/Training-Sets or Test-Sets was
done in a manner that ensured bias was not introduced based on the
repository from which STARI samples were obtained or on whether the
early Lyme disease samples were from a non-disseminated or
disseminated case. Biosignature development was performed by
screening MFs based on stringent criteria outlined in FIG. 1 and
detailed in the Biosignature development section (below).
Example 3
[0113] This example describes methods used for Lyme disease
serologic testing of all serum samples used in the examples above.
Standard two-tiered testing was performed on all samples (38). The
C6 B. burgdorferi (Lyme) ELISA (Immunetics, Boston, Mass.) was used
as a first-tier test, and any positive or equivocal samples were
reflexed to Marblot IgM and IgG immunoblots (MarDx Diagnostics,
Inc., Carlsbad, Calif.) as the second-tier test. Serologic assays
were performed according to the manufacturer's instructions, and
the data were interpreted according to established CDC guidelines
(38). Duration of illness, however, was not considered for test
interpretation.
Example 4
[0114] This example describes liquid chromatography-mass
spectrometry (LC-MS) methods used in the examples above. Serum
samples were randomized prior to extraction of small molecule
metabolites and LC-MS analyses. Small molecule metabolites were
extracted from sera as previously reported (27). An aliquot (10
.mu.l) of the serum metabolite extract was applied to a Poroshell
120, EC-C8, 2.1.times.100 mm, 2.7 .mu.m LC Column (Agilent
Technologies, Palo Alto, Calif.). The metabolites were eluted with
a 2-98% nonlinear gradient of acetonitrile in 0.1% formic acid at a
flow rate of 250 .mu.l/min with an Agilent 1200 series LC system.
The eluent was introduced directly into an Agilent 6520 quadrapole
time of flight mass (Q-TOF) spectrometer and MS was performed as
previously described (27, 50). LC-MS and LC-MS/MS data were
collected under the following parameters: gas temperature,
310.degree. C.; drying gas at 10 liters per min; nebulizer at 45 lb
per in.sup.2; capillary voltage, 4,000 V; fragmentation energy, 120
V; skimmer, 65 V; and octapole RF setting, 750 V. The positive-ion
MS data for the mass range of 75 to 1,700 Da were acquired at a
rate of 2 scans per sec. Data were collected in both centroid and
profile modes in 4-GHz high-resolution mode. Positive-ion reference
masses of 121.050873 m/z and 922.009798 m/z were introduced to
ensure mass accuracy. To monitor instrument performance, quality
control samples having a metabolite extract of healthy control
serum (BioreclamationIVT, Westbury, N.Y.) was analyzed in duplicate
at the beginning of each analysis day and every 20 samples during
the analysis day.
Example 5
[0115] This example describes the methods used for biosignature
development as described in the examples above. LC-MS data from an
initial Discovery-Set of samples comprised of randomly selected
early Lyme disease (n=40) and randomly selected STARI patients
(n=36) that were exclusively used for molecular feature selection
and classification model training were processed with the Molecular
Feature Extractor algorithm tool of the Agilent MassHunter
Qualitative Analysis software version B.05.00 (Agilent
Technologies, Santa Clara, Calif.). The MFs were aligned between
data files with a 0.25 min retention time window and 15 ppm mass
tolerance. Comparative analyses of differentiating MFs between
patient groups were performed using the workflow presented in FIG.
1A. Specifically, the Discovery-Set data was analyzed using Mass
Profiler Pro (MPP) software version B.12.05 (Agilent Technologies).
Using MPP a univariate, unpaired t-test was performed on each
metabolite to test for a difference in mean (standardized)
abundance between early Lyme disease and STARI groups. Multiple
testing was accounted for by computing false-discovery rate
(FDR)-adjusted p-values (Benjamin and Hochberg, 1995). To prevent
selection of MFs biased by uncontrolled variables (diet, other
undisclosed illnesses, etc.), only MFs present in 50% or more of
samples in at least one group and that differed between the groups
with a significance of adjusted-p<0.05 were selected.
Quantitative Analysis software version B.05.01 (Agilent
Technologies) was used to extract area abundance values for all
differentially selected MFs from the MS data files. Duplicate MFs
were removed by assessing adduct ions, as well as mass, retention
time and abundance similarities; this resulted in the Discovery MF
List. A duplicate LC-MS analysis of the Discovery-Set samples was
performed and the area abundance for MFs of the discovery MF List
were extracted using the Quantitative Analysis software. These data
with those from the first LC-MS analysis formed the
Targeted-Discovery-Set.
[0116] Abundance data from the Targeted-Discovery-Set data files
were normalized using a two-step method. First, abundances (area
under the peak for the monoisotopic mass) of each Discovery MF were
normalized by the median intensity of the stable MFs detected in
each individual sample (58). Stable MFs were those identified in
the original extraction of LC-MS data files with the Agilent
MassHunter Qualitative Analysis software and present in at least
50% of all sample data files. Secondly, median fold changes of
stable MFs between the initial quality control sample (applied at
the beginning of the LC-MS analysis) and each of the subsequent
quality control samples (applied every 20 clinical samples
throughout the LC-MS analysis) were calculated. The median fold
change calculated for the quality control sample that directly
followed each series of 20 clinical samples was multiplied against
the normalized Discovery-MF abundances in the clinical samples of
that series. This second normalization step was performed to
correct for instrument variability. To apply stringency to the
development of a final early Lyme disease-STARI biosignature, MFs
were filtered based on consistency in the duplicate LC-MS data sets
by requiring the same directional abundance change between the
patient groups. Specifically, MFs with at least a .gtoreq.2-fold
abundance difference and a 1.5-fold abundance difference between
the medians of the two groups (early Lyme disease and STARI) for
LC-MS analysis-1 and LC-MS analysis-2, respectively, were selected.
Further criteria applied to ensure that the most robust MFs were
being selected included: removing MFs with >20% missing values
in both groups, and selecting only MFs where at least 50% of the
samples within a patient group produced a fold change of .gtoreq.2
in comparison to the mean of the other patient group. This
selection process resulted in the MFs included in the early Lyme
disease-STARI biosignature.
Example 6
[0117] This example describes the methods used for prediction and
verification of MF chemical structure. Confirmation of the chemical
structures of selected MFs was performed by LC-MS-MS to provide
level-1 or level-2 identifications (59). Commercial standards
palmitoyl ethanolamide, stearoyl ethanolamide, eicosanoyl
ethanolamide, glycerophospho-N-palmitoyl ethanolamine,
pentadecanoyl ethanolamide, and erucamide were obtained from Cayman
Chemical (Ann Arbor, Mich., USA). Commercial standards piperine and
nonanedioic acid were obtained from Sigma Aldrich (Saint Louis,
Mo., USA). Commercial standards methyl oleate, stearamide,
palmitamide, CMPF, and glycocholic acid were obtained from Santa
Cruz Biotechnology, Inc. (Santa Cruz, Calif., USA). The LC
conditions used were the same as those used for the LC-MS analyses
of serum metabolites. MS/MS spectra of the targeted MFs and
commercial standards were obtained with an Agilent 6520 Q-TOF mass
spectrometer. Electrospray ionization was performed in the positive
ion mode as described for MS analyses, except the mass spectrometer
was operated in the 2 GHz extended dynamic range mode. The positive
ion MS/MS data (50 to 1,700 Da) were acquired at a rate of 1 scan
per sec. Precursor ions were selected by the quadrupole and
fragmented via collision-induced dissociation (CID) with nitrogen
at collision energies of 10, 20, or 40 eV. To provide a level-1
identification, the MS/MS spectra of the targeted metabolites were
compared to spectra of commercial standards. Additionally, LC
retention time comparisons between the targeted MF and the
respective standard were made. A retention time window of .+-.5 sec
was applied as a cutoff for identification. The MS/MS spectra of
selected serum metabolites were compared to spectra in the Metlin
database for a level-2 identification.
Example 7
[0118] Metabolic pathway analysis in the examples above was
performed by MetaboAnalyst. The experimentally obtained
monoisotopic masses corresponding to the MFs of the 261
biosignature list were searched against HMDB using a 15 ppm window.
The resulting list of potential metabolite structures were applied
to the MetaboAnalyst pathway analysis tool (28) Settings for
pathway analysis included applying Homo sapiens pathway library;
the Hypergeometric Test for the over-representation analysis and
Relative-betweenness centrality to estimate node importance in the
pathway topology.
Example 8
[0119] Methods for statistical analyses and classification modeling
are described in this example. Methods to filter the list of MFs
and to normalize abundances are described in the section on
biosignature development. Prior to analysis, the normalized
abundances were log 2 transformed and each MF was scaled to have a
mean of zero and standard deviation of 1. Statistical analyses were
performed using R software (60).
[0120] For classification modeling, Training- and Test-Set samples
were used as previously described (27, 50) and as shown in FIG. 1.
Separate classification analyses were performed for comparison of
two groups (early Lyme disease and STARI) and three groups (early
Lyme disease, STARI and healthy controls). For each scenario, two
classification approaches were applied: random forest (RF) using
the RandomForest package (61), with 16 features randomly selected
for each clade and a total of 500 trees; and LASSO logistic
(two-way) and multinomial (three-way) regression analysis using the
glmnet package (62), with the tuning parameter chosen for minimum
misclassification error over a 10-fold cross-validation. The ROC
curve and AUC were generated for predicted responses on the
Test-Set samples only using the pROC package (63). For the purpose
of visualization, LASSO scores for individual patient samples were
calculated by multiplying the respective regression coefficients
(Table 5 and Table 7) resulting from LASSO analysis by the
transformed abundance of each MF in the biosignature (38 MFs in the
case of two-way classification and 82 MFs in the case of three-way
classification) and summing for each sample. The rgl package was
used to generate the 3-dimensional scatterplot of LASSO scores
(64).
[0121] A linear discriminant analysis was performed with the 82 MFs
selected by the three-way LASSO model using linear discriminant
analysis function in R. MF abundance data included in the linear
discriminant analysis were from healthy controls from Colorado,
Florida, and New York, and from STARI patients from North Carolina,
Missouri, and other states. Before linear discriminant analysis
data were transformed by taking the log 2 value and standardizing
to the mean 0 and variance 1 within each MF. Samples were
differentiated by healthy and STARI.
REFERENCES
[0122] 1. A. F. Hinckley, N. P. Connally, J. I. Meek, B. J.
Johnson, M. M. Kemperman, K. A. Feldman, J. L. White, P. S. Mead,
Lyme disease testing by large commercial laboratories in the United
States. Clin. Infect. Dis. 59, 676-681 (2014). [0123] 2. C. A.
Nelson, S. Saha, K. J. Kugeler, M. J. Delorey, M. B. Shankar, A. F.
Hinckley, P. S. Mead, Incidence of Clinician-Diagnosed Lyme
Disease, United States, 2005-2010. Emerg. Infect. Dis. 21,
1625-1631 (2015). [0124] 3. G. Stanek, G. P. Wormser, J. Gray, F.
Strle, Lyme borreliosis. Lancet 379, 461-473 (2012). [0125] 4. A.
C. Steere, S. E. Malawista, J. A. Hardin, S. Ruddy, W. Askenase, W.
A. Andiman, Erythema chronicum migrans and Lyme arthritis. The
enlarging clinical spectrum. Ann. Intern. Med. 86, 685-698 (1977).
[0126] 5. G. P. Wormser, Clinical practice. Early Lyme disease. N.
Engl. J. Med. 354, 2794-2801 (2006). [0127] 6. R. B. Nadelman,
Erythema migrans. Infect. Dis. Clin. North Am. 29, 211-239 (2015).
[0128] 7. E. J. Masters, H. D. Donnell, Epidemiologic and
diagnostic studies of patients with suspected early Lyme disease
Missouri, 1990-1993. J. Infect. Dis. 173, 1527-1528 (1996). [0129]
8. E. Masters, S. Granter, P. Duray, P. Cordes, Physician-diagnosed
erythema migrans and erythema migrans-like rashes following Lone
Star tick bites. Arch. Dermatol. 134, 955-960 (1998). [0130] 9. G.
P. Wormser, E. Masters, J. Nowakowski, D. McKenna, D. Holmgren, K.
Ma, L. Ihde, L. F. Cavaliere, R. B. Nadelman, Prospective clinical
evaluation of patients from Missouri and New York with erythema
migrans-like skin lesions. Clin. Infect. Dis. 41, 958-965 (2005).
[0131] 10. G. L. Campbell, W. S. Paul, M. E. Schriefer, R. B.
Craven, K. E. Robbins, D. T. Dennis, Epidemiologic and diagnostic
studies of patients with suspected early Lyme disease, Missouri,
1990-1993. J. Infect. Dis. 172, 470-480 (1995). [0132] 11. K. L.
Clark, B. Leydet, S. Hartman, Lyme borreliosis in human patients in
Florida and Georgia, USA. Int. J. Med. Sci. 10, 915-931 (2013).
[0133] 12. R. M. Bacon, R. D. Gilmore, Jr., M. Quintana, J.
Piesman, B. J. Johnson, DNA evidence of Borrelia lonestari in
Amblyomma americanum (Acari: Ixodidae) in southeast Missouri. J.
Med. Entomol. 40, 590-592 (2003). [0134] 13. A. S. Varela, M. P.
Luttrell, E. W. Howerth, V. A. Moore, W. R. Davidson, D. E.
Stallknecht, S. E. Little, First culture isolation of Borrelia
lonestari, putative agent of southern tick-associated rash illness.
J. Clin. Microbiol. 42, 1163-1169 (2004). [0135] 14. A. M. James,
D. Liveris, G. P. Wormser, I. Schwartz, M. A. Montecalvo, B. J.
Johnson, Borrelia lonestari infection after a bite by an Amblyomma
americanum tick. J. Infect. Dis. 183, 1810-1814 (2001). [0136] 15.
C. N. Grigery, P. Moyer, S. E. Little, E. J. Masters, Bacteriocidal
activity of lizard and mouse serum for Borrelia lonestari, putative
agent of a Lyme-like illness (AKA STARI or Masters disease) in
Missouri. Mo. Med. 102, 442-446 (2005). [0137] 16. M. T. Philipp,
E. Masters, G. P. Wormser, W. Hogrefe, D. Martin, Serologic
evaluation of patients from Missouri with erythema migrans-like
skin lesions with the C6 Lyme test. Clin. Vaccine Immunol. 13,
1170-1171 (2006). [0138] 17. M. W. Felz, F. W. Chandler, Jr., J. H.
Oliver, Jr., D. W. Rahn, M. E. Schriefer, Solitary erythema migrans
in Georgia and South Carolina. Arch. Dermatol. 135, 1317-1326
(1999). [0139] 18. K. B. Kirkland, T. B. Klimko, R. A. Meriwether,
M. Schriefer, M. Levin, J. Levine, W. R. Mac Kenzie, D. T. Dennis,
Erythema migrans-like rash illness at a camp in North Carolina: a
new tick-borne disease? Arch. Intern. Med. 157, 2635-2641 (1997).
[0140] 19. S. M. Rich, P. M. Armstrong, R. D. Smith, S. R. Telford,
3rd, Lone star tick-infecting borreliae are most closely related to
the agent of bovine borreliosis. J. Clin. Microbiol. 39, 494-497
(2001). [0141] 20. G. P. Wormser, E. Masters, D. Liveris, J.
Nowakowski, R. B. Nadelman, D. Holmgren, S. Bittker, D. Cooper, G.
Wang, I. Schwartz, Microbiologic evaluation of patients from
Missouri with erythema migrans. Clin. Infect. Dis. 40, 423-428
(2005). [0142] 21. E. J. Masters, H. D. Donnell, Lyme and/or
Lyme-like disease in Missouri. Mo. Med. 92, 346-353 (1995). [0143]
22. W. L. Nicholson, E. Masters, G. P. Wormser, Preliminary
serologic investigation of `Rickettsia amblyommii` in the aetiology
of Southern tick associated rash illness (STARI). Clin. Microbiol.
Infect. 15 Suppl 2, 235-236 (2009). [0144] 23. Y. P. Springer, C.
S. Jarnevich, D. T. Barnett, A. J. Monaghan, R. J. Eisen, Modeling
the Present and Future Geographic Distribution of the Lone Star
Tick, Amblyomma americanum (Ixodida: Ixodidae), in the Continental
United States. Am. J. Trop. Med. Hyg. 93, 875-890 (2015). [0145]
24. M. B. Hahn, C. S. Jarnevich, A. J. Monaghan, R. J. Eisen,
Modeling the Geographic Distribution of Ixodes scapularis and
Ixodes pacificus (Acari: Ixodidae) in the Contiguous United States.
J. Med. Entomol. Epub ahead of print. (2016). [0146] 25. K. J.
Kugeler, G. M. Farley, J. D. Forrester, P. S. Mead, Geographic
Distribution and Expansion of Human Lyme Disease, United States.
Emerg. Infect. Dis. 21, 1455-1457 (2015). [0147] 26. P. M. Lantos,
L. E. Nigrovic, P. G. Auwaerter, V. G. Fowler, Jr., F. Ruffin, R.
J. Brinkerhoff, J. Reber, C. Williams, J. Broyhill, W. K. Pan, D.
N. Gaines, Geographic Expansion of Lyme Disease in the Southeastern
United States, 2000-2014. Open Forum Infect. Dis. 2, ofv143 (2015).
[0148] 27. C. R. Molins, L. V. Ashton, G. P. Wormser, A. M. Hess,
M. J. Delorey, S. Mahapatra, M. E. Schriefer, J. T. Belisle,
Development of a metabolic biosignature for detection of early Lyme
disease. Clin. Infect. Dis. 60, 1767-1775 (2015). [0149] 28. J.
Xia, I. V. Sinelnikov, B. Han, D. S. Wishart, MetaboAnalyst
3.0--making metabolomics more meaningful. Nucleic Acids Res. 43,
W251-257 (2015). [0150] 29. F. A. Iannotti, V. Di Marzo, S.
Petrosino, Endocannabinoids and endocannabinoid-related mediators:
Targets, metabolism and role in neurological disorders. Prog. Lipid
Res. 62, 107-128 (2016). [0151] 30. J. D. Forrester, M. Brett, J.
Matthias, D. Stanek, C. B. Springs, N. Marsden-Haug, H. Oltean, J.
S. Baker, K. J. Kugeler, P. S. Mead, A. Hinckley, Epidemiology of
Lyme disease in low-incidence states. Ticks Tick Borne Dis. 6,
721-723 (2015). [0152] 31. M. Dalle Carbonare, E. Del Giudice, A.
Stecca, D. Colavito, M. Fabris, A. D'Arrigo, D. Bernardini, M. Dam,
A. Leon, A saturated N-acylethanolamine other than N-palmitoyl
ethanolamine with anti-inflammatory properties: a neglected story.
J. Neuroendocrinol 20 Suppl 1, 26-34 (2008). [0153] 32. V. Di
Marzo, D. Melck, P. Orlando, T. Bisogno, O. Zagoory, M. Bifulco, Z.
Vogel, L. De Petrocellis, Palmitoylethanolamide inhibits the
expression of fatty acid amide hydrolase and enhances the
anti-proliferative effect of anandamide in human breast cancer
cells. Biochem. J. 358, 249-255 (2001). [0154] 33. J. Lo Verme, J.
Fu, G. Astarita, G. La Rana, R. Russo, A. Calignano, D. Piomelli,
The nuclear receptor peroxisome proliferator-activated
receptor-alpha mediates the anti-inflammatory actions of
palmitoylethanolamide. Mol. Pharmacol. 67, 15-19 (2005). [0155] 34.
H. B. Bradshaw, N. Rimmerman, S. S. Hu, V. M. Benton, J. M. Stuart,
K. Masuda, B. F. Cravatt, D. K. O'Dell, J. M. Walker, The
endocannabinoid anandamide is a precursor for the signaling lipid
N-arachidonoyl glycine by two distinct pathways. BMC Biochem. 10,
14 (2009). [0156] 35. B. F. Cravatt, D. K. Giang, S. P. Mayfield,
D. L. Boger, R. A. Lerner, N. B. Gilula, Molecular characterization
of an enzyme that degrades neuromodulatory fatty-acid amides.
Nature 384, 83-87 (1996). [0157] 36. E. B. Divito, M. Cascio,
Metabolism, physiology, and analyses of primary fatty acid amides.
Chem. Rev. 113, 7343-7353 (2013). [0158] 37. E. J. Masters, C. N.
Grigery, R. W. Masters, STARI, or Masters disease: Lone Star
tick-vectored Lyme-like illness. Infect. Dis. Clin. North Am. 22,
361-376, (2008). [0159] 38. C. f. D. C. a. Prevention,
Recommendations for test performance and interpretation from the
Second National Conference on Serologic Diagnosis of Lyme Disease.
Morb. Mortal. Wkly. Rep. 44, 590-591 (1995). [0160] 39. M. E.
Aguero-Rosenfeld, G. Wang, I. Schwartz, G. P. Wormser, Diagnosis of
lyme borreliosis. Clin. Microbiol. Rev. 18, 484-509 (2005). [0161]
40. E. J. Masters, Lyme-like illness currently deserves Lyme-like
treatment. Clin. Infect. Dis. 42, 580-581; author reply 581-582
(2006). [0162] 41. P. Comstedt, M. Hanner, W. Schuler, A. Meinke,
R. Schlegl, U. Lundberg, Characterization and optimization of a
novel vaccine for protection against Lyme borreliosis. Vaccine 33,
5982-5988 (2015). [0163] 42. N. Wressnigg, P. N. Barrett, E. M.
Pollabauer, M. O'Rourke, D. Portsmouth, M. G. Schwendinger, B. A.
Crowe, I. Livey, T. Dvorak, B. Schmitt, M. Zeitlinger, H.
Kollaritsch, M. Esen, P. G. Kremsner, T. Jelinek, R. Aschoff, R.
Weisser, I. F. Naudts, G. Aichinger, A Novel multivalent OspA
vaccine against Lyme borreliosis is safe and immunogenic in an
adult population previously infected with Borrelia burgdorferi
sensu lato. Clin. Vaccine Immunol. 21, 1490-1499 (2014). [0164] 43.
M. Gomes-Solecki, Blocking pathogen transmission at the source:
reservoir targeted OspA-based vaccines against Borrelia
burgdorferi. Front Cell Infect. Microbiol. 4, 136 (2014). [0165]
44. L. M. Richer, D. Brisson, R. Melo, R. S. Ostfeld, N. Zeidner,
M. Gomes-Solecki, Reservoir targeted vaccine against Borrelia
burgdorferi: a new strategy to prevent Lyme disease transmission.
J. Infect. Dis. 209, 1972-1980 (2014). [0166] 45. B. J. B. Johnson,
in Lyme Disease: An Evidence-based Approach. (CAB International,
Wallingford, Oxfordshire, UK; Cambridge, Mass. 2011), chap. 4, pp.
73-87. [0167] 46. G. P. Wormser, A. Levin, S. Soman, O. Adenikinju,
M. V. Longo, J. A. Branda, Comparative cost-effectiveness of
two-tiered testing strategies for serodiagnosis of lyme disease
with noncutaneous manifestations. J. Clin. Microbiol. 51, 4045-4049
(2013). [0168] 47. F. G. Strathmann, A. N. Hoofnagle, Current and
future applications of mass spectrometry to the clinical
laboratory. Am. J. Clin. Pathol. 136, 609-616 (2011). [0169] 48. N.
R. Kitteringham, R. E. Jenkins, C. S. Lane, V. L. Elliott, B. K.
Park, Multiple reaction monitoring for quantitative biomarker
analysis in proteomics and metabolomics. J. Chromatogr. BAnalyt.
Technol. Biomed. Life Sci. 877, 1229-1239 (2009). [0170] 49. D. S.
Dumlao, M. W. Buczynski, P. C. Norris, R. Harkewicz, E. A. Dennis,
High-throughput lipidomic analysis of fatty acid derived
eicosanoids and N-acylethanolamines. Biochim. Biophys. Acta 1811,
724-736 (2011). [0171] 50. S. Mahapatra, A. M. Hess, J. L. Johnson,
K. D. Eisenach, M. A. DeGroote, P. Gitta, M. L. Joloba, G. Kaplan,
G. Walzl, W. H. Boom, J. T. Belisle, A metabolic biosignature of
early response to anti-tuberculosis treatment. BMC Infect. Dis. 14,
53 (2014). [0172] 51. N. V. Voge, R. Perera, S. Mahapatra, L.
Gresh, A. Balmaseda, M. A. Lorono-Pino, A. S. Hopf-Jannasch, J. T.
Belisle, E. Harris, C. D. Blair, B. J. Beaty, Metabolomics-Based
Discovery of Small Molecule Biomarkers in Serum Associated with
Dengue Virus Infections and Disease Outcomes. PLoS Negl. Trop. Dis.
10, e0004449 (2016). [0173] 52. L. Tritten, J. Keiser, M.
Godejohann, J. Utzinger, M. Vargas, O. Beckonert, E. Holmes, J.
Saric, Metabolic profiling framework for discovery of candidate
diagnostic markers of malaria. Sci. Rep. 3, 2769 (2013). [0174] 53.
N. Vinayavekhin, G. Mahipant, A. S. Vangnai, P. Sangvanich,
Untargeted metabolomics analysis revealed changes in the
composition of glycerolipids and phospholipids in Bacillus subtilis
under 1-butanol stress. Appl. Microbiol. Biotechnol. 99, 5971-5983
(2015). [0175] 54. K. L. Clark, B. F. Leydet, C. Threlkeld,
Geographical and genospecies distribution of Borrelia burgdorferi
sensu lato DNA detected in humans in the USA. J. Med. Microbiol.
63, 674-684 (2014). [0176] 55. J. H. Oliver, Jr., T. Lin, L. Gao,
K. L. Clark, C. W. Banks, L. A. Durden, A. M. James, F. W.
Chandler, Jr., An enzootic transmission cycle of Lyme borreliosis
spirochetes in the southeastern United States. Proc. Natl. Acad.
Sci. USA. 100, 11642-11645 (2003). [0177] 56. H. M. Feder, Jr., D.
M. Hoss, L. Zemel, S. R. Telford, 3rd, F. Dias, G. P. Wormser,
Southern Tick-Associated Rash Illness (STARI) in the North: STARI
following a tick bite in Long Island, N.Y. Clin. Infect. Dis. 53,
e142-146 (2011). [0178] 57. M. F. Vaughn, P. D. Sloane, K. Knierim,
D. Varkey, M. A. Pilgard, B. J. Johnson, Practice-Based Research
Network Partnership with CDC to acquire clinical specimens to study
the etiology of southern tick-associated rash illness (STARI). J.
Am. Board Fam. Med. 23, 720-727 (2010). [0179] 58. W. Wang, H.
Zhou, H. Lin, S. Roy, T. A. Shaler, L. R. Hill, S. Norton, P.
Kumar, M. Anderle, C. H. Becker, Quantification of proteins and
metabolites by mass spectrometry without isotopic labeling or
spiked standards. Anal. Chem. 75, 4818-4826 (2003). [0180] 59. R.
M. Salek, C. Steinbeck, M. R. Viant, R. Goodacre, W. B. Dunn, The
role of reporting standards for metabolite annotation and
identification in metabolomic studies. Gigascience 2, 13 (2013).
[0181] 60. R. C. Team. (R Foundation for Statistical Computing,
Vienna, Austria, 2016). [0182] 61. A. L. a. M. Wiener,
Classification and Regression by randomForest. R News 3, 18-22
(2002). [0183] 62. J. Friedman, T. Hastie, R. Tibshirani,
Regularization Paths for Generalized Linear Models via Coordinate
Descent. J. Stat. Softw. 33, 1-22 (2010). [0184] 63. X. Robin, N.
Turck, A. Hainard, N. Tiberti, F. Lisacek, J. C. Sanchez, M.
Muller, pROC: an open-source package for R and S+ to analyze and
compare ROC curves. BMC Bioinformatics 12, 77 (2011). [0185] 64. D.
M. D. Adler, rgl: 3D Visualization Using OpenGL. R package version
0.96.0. (2016).
TABLE-US-00005 [0185] TABLE 1 Classification modeling using the 261
molecular feature biosignature list. RF, random forest; LASSO,
least absolute shrinkage and selection operator; MF; molecular
feature. RF (261 MFs) LASSO (38/82 MFs.sup..+-.) Test-Set Number
Number % Number % Classification Sample of Data Correctly
Classification Correctly Classification Model Group Files*
Predicted Accuracy Predicted Accuracy 1: Two-Way Early 60 58 97 59
98 Model Lyme Disease STARI 38 34 89 34 89 2: Three-Way Early 60 51
85 51 85 Model Lyme Disease STARI 38 35 92 35 92 Healthy 40 38 95
37 93 Controls *Samples were analyzed in duplicate by LC-MS.
.sup..+-.A total of 38 MFs were selected by the LASSO model for
two-way modeling and 82 MFs were selected by the LASSO for
three-way modeling.
TABLE-US-00006 TABLE 2 Serum samples used in the study Description
of Sample Sample Criteria for Sample State Sample Samples Nos.
Inclusion Purpose Collected Provider* Ref. Early Lyme Disease (n =
70) Age: 16-81 70 At least one EM present on Discovery NY NYMC (27)
Male (52) initial visit to the clinic. Training and Female (18)
Samples were collected at Test initial visit to the clinic and
pre-treatment. Positive culture and/or PCR test for B. burgdorferi.
Patients lived in an endemic area for LD. STARI (n = 55) STARI
Group 1 33 All patients had a Discovery NC, VA, CDC, Fort (57) Age:
4-82 physician-diagnosed Training and GA, KY Collins, Male (17)
erythema migrans-like rash Test TN, AL, IA CO Female (16) .gtoreq.5
cm and a recent history and NE of possible or verified STARI Group
2 22 exposure to Amblyomma MO NYMC (20) Age: 8-80 americanum (lone
star) Male (13) ticks before the onset of Female (9) symptoms.
Patients lived in a non-endemic area for LD with the exception of
three patients..sup..+-.Samples were standard two-tiered negative
for LD. Healthy Donors (n = 95) Healthy Group 1 28 No history of
tick-borne Discovery CO CDC, Fort -- Age: 18-unknown disease within
the last 12 Training and Collins, Male (8) months and lived in a
non- Test CO Female (20) endemic area for LD. Samples were standard
two-tiered negative for LD. Healthy Group 2.sup..dagger.# 30 No
history of Lyme disease NY NYMC -- Age: 18-74.sup..sctn. and lived
in an endemic area for LD. Samples were standard two-tiered
negative for LD. Healthy Group 3.sup..dagger. 37 No previous
diagnosis with Verification.sup. FL UCF (65) (65) Age: 18-60 and/or
treated for LD; and could not have lived within the past 10 years
in a state with a high incidence of LD (CT, DE, ME, MD, MA, MN, NH,
NJ, NY, PA, VT, VA and WI). Samples were standard two-tiered
negative for LD. NYMC, New York Medical College; CDC, Centers for
Disease Control and Prevention; UCF, University of Central Florida;
LD, Lime disease *Sample handling varied among laboratories that
provided samples. .sup..+-.Two patients were from southwest Iowa
and one was from southeast Virginia; both areas are considered to
have low risk for Lyme disease and a higher prevalence of A.
americanum as compared to I. scapularis. .sup..dagger.The gender of
these donors was approximately 50% females and? 50% males.
.sup.#The samples were obtained from the same geographic location
as the early Lyme disease samples. .sup..sctn.Age ranged from 18-74
for all donors (n = 100). Only a subset of 30 donors were used for
this study. .sup. Healthy controls from Florida were used to verify
that the dysregulation of MFs between EL and STARI were not due to
regional differences.
TABLE-US-00007 TABLE 3 261 MF biosignature list The experimentally
obtained mass of each MF was used to search against the Metlin
database and the Human Metabolome Database (HMDB). The predicted
chemical structures had to match to the MF mass within 15 ppm. MFs
could have matches to multiple chemical structures of within the
same classes of chemicals or to structures of a different chemical
class. The putative chemical structure data obtained by
interrogation against the HMDB were used to evaluate possible
metabolic pathways that differed between early Lyme disease and
STARI patients (see Table 4). Compound Predicted Formula Predicted
# of m/z Chemical Alternate Mass Structure (based Metabolite Level
Chemical 2 Way 3 Way Retention on accurate Class or of Structures
RF LASSO LASSO MF # Time mass) Pathway Iden. .+-.15 ppm Model Model
Model CSU/ 166.0852 C.sub.9H.sub.11NO.sub.2 Phenylalanine 1 >5 x
x x CDC- 165.078 Phenylalanine metabolism 001 1.86 CSU/ 239.0919
C.sub.12H.sub.14O.sub.5 Phenylpropanoid 4 5 x x CDC- 238.0844
Trans-2,3,4- and 002 11.66 trimethoxycinnamate polyketide
metabolism CSU/ 886.4296 -- -- 4 0 x x CDC- 1770.8438 003 12.18
CSU/ 181.0859 C.sub.10H.sub.12O.sub.3 Endogenous 4 >5 x x CDC-
180.0788 5'-(3'-Methoxy-4'- metabolite 004 14.7 hydroxyphenyI)-
associated gamma- with valerolactone microbiome CSU/ 223.0968
C.sub.12H.sub.14O.sub.4 -- 4 >5 x CDC- 222.0895 -- 005 14.69
CSU/ 286.1444 C.sub.17H.sub.19NO.sub.3 Alkaloid 1 >5 x x CDC-
285.1371 Piperine metabolism 006 16.08 CSU/ 286.1437
C.sub.17H.sub.19NO.sub.3 -- 4 >5 x CDC- 285.1364 -- 007 16.06
CSU/ 463.2339 C.sub.25H.sub.34O.sub.8 Peptide 4 >5 x x CDC-
462.2248 Ala Lys Met Asn 008 16.36 (SEQ ID NO: 6) CSU/ 242.2844
C.sub.16H.sub.35N -- 4 1 x x CDC- 241.2772 -- 009 17.1 CSU/
1112.6727 -- -- 4 0 x CDC- 1111.6663 010 17.86 CSU/ 454.2923
C.sub.21H.sub.44NO.sub.7P N-acyl 3 >5 x CDC- 453.2867
Glycerophospho- ethanolamine 011 18.08 N-Palmitoyl metabolism
Ethanolamine CSU/ 270.3156 C.sub.18H.sub.39N -- 4 1 x x x CDC-
269.3076 -- 012 18.02 CSU/ 284.3314 C.sub.19H.sub.41N -- 4 1 x x x
CDC- 283.3236 -- 013 18.13 CSU/ 300.6407
C.sub.33H.sub.37N.sub.5O.sub.6 Peptide 4 >5 x x x CDC- 599.268
Asp Phe Arg Tyr 014 18.27 (SEQ ID NO: 1) CSU/ 522.3580
C.sub.26H.sub.52NO.sub.7P Glycerophospholipid 3 >5 x CDC-
521.3483 PC(18:1) metabolism 015 18.5 CSU/ 363.2192
C.sub.21H.sub.30O.sub.5 Sterol 4 >5 x CDC- 362.2132 4,5.alpha.-
metabolism 016 18.58 dihydrocortisone CSU/ 590.4237 -- -- 4 0 x x
CDC- 589.4194 017 19.24 CSU/ 388.3939 -- -- 4 0 x CDC- 387.3868 018
19.53 CSU/ 300.2892 C.sub.18H.sub.37NO.sub.2 N-acyl 1 >5 x x x
CDC- 299.2821 Palmitoyl ethanolamine 019 19.66 ethanolamide
metabolism CSU/ 256.2632 C.sub.16H.sub.33NO Primary Fatty 1 1 x
CDC- 255.2561 Palmitic amide Acid Amide 020 20.08 Metabolism CSU/
394.3515 -- -- 4 0 x CDC- 376.3171 021 20.09 CSU/ 228.1955 -- -- 4
0 x CDC- 227.1885 022 20.99 CSU/ 284.2943 C.sub.18H.sub.37NO
Primary Fatty 1 1 x CDC- 283.2872 Stearamide Acid Amide 023 21.15
Metabolism CSU/ 338.3430 C.sub.22H.sub.43NO Primary Fatty 1 3 x
CDC- 337.3344 13Z- Acid Amide 024 22.14 Docosenamide Metabolism
(Erucamide) CSU/ 689.5604 C.sub.38H.sub.77N.sub.2O.sub.6P
Sphingolipid 3 >5 x CDC- 688.5504 SM(d18:115:0) / metabolism 025
22.52 SM (d18:1/14:1-OH) CSU/ 553.3904 C.sub.35H.sub.52O.sub.5
Endogenous 4 3 x x CDC- 552.3819 Furohyperforin metabolite - 026
23.38 derived from food CSU/ 432.2803 C.sub.25H.sub.37NO.sub.5
Peptide 4 >5 x CDC- 431.2727 Ala Ile Lys Thr 027 10.8 (SEQ ID
NO: 9) CSU/ 389.2174 C.sub.19H.sub.32O.sub.8 Fatty acid 4 >5 x x
CDC- 388.2094 Methyl metabolism 028 15.47 10,12,13,15-
bisepidioxy16- hydroperoxy-8E- octadecenoate CSU/ 385.2211
C.sub.16H.sub.28N.sub.6O.sub.5 Peptides 4 >5 x CDC- 384.2147 Lys
His Thr 029 15.84 CSU/ 399.2364 -- -- 4 0 x x CDC- 398.2313 030
16.23 CSU/ 449.3261 C.sub.46H.sub.89NO.sub.12S Sphingolipid 4 2 x
CDC- 879.6122 C22-OH Sulfatide metabolism 031 17.07 CSU/ 467.3821
C.sub.24H.sub.40O.sub.8 Prostaglandin 4 >5 x CDC- 444.2717
2-glyceryl-6-keto- metabolism 032 17.1 PGF1.alpha. CSU/ 836.5936
C.sub.44H.sub.85NO.sub.11S Sphingolipid 4 1 x CDC- 835.5845 C20
Sulfatide metabolism 033 17.15 CSU/ 792.5646
C.sub.42H.sub.82NO.sub.10P Glycerophosph 4 >5 x CDC- 791.5581
PS(36:0) olipid 034 17.17 metabolism CSU/ 356.2802 -- -- 4 0 x CDC-
355.2722 035 17.35 CSU/ 806.5798 C.sub.43H.sub.84NO.sub.10P
Glycerophospholipid 4 >5 x CDC- 805.5746 PS(37:0) metabolism 036
17.71 CSU/ 762.5582 C.sub.41H.sub.80NO.sub.9P Glycerophospholipid 4
>5 x CDC- 761.5482 PS-O(35:1) metabolism 037 17.79 CSU/ 718.5308
C.sub.39H.sub.73O.sub.8P Glycerophospholipid 4 >5 x CDC-
700.4946 PA(36:2) metabolism 038 17.88 CSU/ 734.5079 -- -- 4 0 x x
x CDC- 1449.9753 039 17.81 CSU/ 690.4825 -- -- 4 0 x CDC- 1361.924
040 17.95 CSU/ 426.1798 -- -- 4 0 x CDC- 425.1725 041 18.03 CSU/
580.4144 -- -- 4 0 x x CDC- 1158.8173 042 18.26 CSU/ 741.5154
C.sub.83H.sub.150O.sub.17P.sub.2 Glycerophospholipid 4 2 x CDC-
1481.0142 CL(74:6) metabolism 043 18.24 CSU/ 864.6245
C.sub.46H.sub.89NO.sub.11S Sphingolipid 4 2 x CDC- 863.6166 C22
Sulfatide metabolism 044 18.17 CSU/ 558.4017 -- -- 4 0 x CDC-
1080.7347 045 18.28 CSU/ 719.5012 -- -- 4 0 x CDC- 1402.9377 046
18.26 CSU/ 536.3897 -- -- 4 0 x CDC- 1053.7382 047 18.36 CSU/
538.8674 -- -- 4 0 x CDC- 1058.696 048 18.4 CSU/ 653.4619 -- -- 4 0
x CDC- 1270.8593 049 18.43 CSU/ 732.5450 C.sub.40H.sub.75O.sub.8P
Glycerophospholipid 4 >5 x CDC- 714.5092 PA(37:2) metabolism 050
18.47 CSU/ 748.5232 -- -- 4 0 x CDC- 1478.0059 051 18.58 CSU/
704.4985 -- -- 4 0 x x CDC- 1372.925 052 18.7 CSU/ 682.4841 -- -- 4
0 x CDC- 1328.9008 053 18.77 CSU/ 360.3615 -- -- 4 0 x CDC-
359.3555 054 18.89 CSU/ 441.2412 C.sub.20H.sub.32N.sub.4O.sub.7
Peptide 4 >5 x CDC- 440.2325 Pro Asp Pro Leu 055 19.09 (SEQ ID
NO: 10) CSU/ 638.4554 -- -- 4 0 x CDC- 1240.847 056 18.92 CSU/
755.5311 C.sub.83H.sub.144O.sub.17P.sub.2 Glycero- 4 2 x CDC-
1474.9941 CL(74:9) phospholipid 057 18.94 metabolism CSU/ 711.5023
-- -- 4 0 x CDC- 1386.9417 058 19.09 CSU/ 784.5530 -- -- 4 0 x CDC-
1567.0908 059 19.27 CSU/ 645.4660 -- -- 4 0 x CDC- 1271.8896 060
19.36 CSU/ 623.4521 -- -- 4 0 x x CDC- 1210.8362 061 19.55 CSU/
370.1837 C.sub.19H.sub.23N.sub.5O.sub.3 -- 4 1 x x x CDC- 369.1757
-- 062 19.7 CSU/ 300.2886 C.sub.18H.sub.34O.sub.2 Fatty acid 4
>5 x CDC- 282.2569 13Z-octadecenoic metabolism 063 19.84 acid
CSU/ 309.0981 C.sub.15H.sub.16O.sub.7 -- 4 3 x CDC- 308.0913 -- 064
2.06 CSU/ 561.2965 C.sub.54H.sub.88O.sub.24 Endogenous 4 5 x CDC-
1120.5778 Camellioside D metabolite - 065 11.7 derived from food
CSU/ 811.1942 C.sub.42H.sub.30N.sub.6O.sub.12 -- 4 1 x x x CDC-
810.1869 -- 066 12.07 CSU/ 947.7976 C.sub.62H.sub.106O.sub.6
Triacylglycerol 4 >5 x x x CDC- 946.7936 TAG(59:7) metabolism
067 14.55 CSU/ 1106.2625 -- -- 4 0 x CDC- 2209.5193 068 14.53 CSU/
371.2070 C.sub.15H.sub.26N.sub.6O.sub.7 Peptide 4 >5 x CDC-
370.1997 His Ser Lys 069 15.52 CSU/ 389.2178
C.sub.19H.sub.32O.sub.8 -- 4 >5 x x CDC- 388.2099 -- 070 15.52
CSU/ 443.2649 C.sub.19H.sub.34N.sub.6O.sub.6 Peptide 3 >5 x CDC-
442.256 Pro Gln Ala Lys 071 15.52 (SEQ ID NO: 11) CSU/ 410.2033 --
-- 4 3 x x x
CDC- 409.196 072 17.18 CSU/ 850.6093 C.sub.48H.sub.84NO.sub.9P
Glycero- 4 1 x CDC- 849.6009 PS-O(42:6) phospholipid 073 17.63
metabolism CSU/ 1111.6690 -- -- 4 0 x x CDC- 1110.6656 074 17.89
CSU/ 1487.0005 -- -- 4 0 x x x CDC- 1485.9987 075 18.17 CSU/
697.4896 -- -- 4 0 x CDC- 1358.909 076 18.32 CSU/ 439.8234 -- -- 4
0 x CDC- 877.6325 077 18.71 CSU/ 567.8897 -- -- 4 0 x CDC- 566.8818
078 18.73 CSU/ 435.2506 C.sub.21H.sub.39O.sub.7P Glycero- 4 >5 x
CDC- 434.243 Lyso-PA(18:2) phospholipid 079 19 metabolism CSU/
834.6136 C.sub.45H.sub.88NO.sub.10P Glycero- 4 >5 x CDC-
833.6057 PS(39:0) phospholipid 080 18.83 metabolism CSU/ 534.8834
-- -- 4 0 x CDC- 533.8771 081 18.82 CSU/ 468.8441 -- -- 4 0 x CDC-
467.8373 082 19.13 CSU/ 482.4040 -- -- 4 0 x x CDC- 481.3976 083
19.99 CSU/ 533.1929 C.sub.23H.sub.28N.sub.6O.sub.9 Peptide 4 >5
x x CDC- 532.1854 Asp His Phe Asp 084 20.84 (SEQ ID NO: 7) CSU/
312.3259 -- -- 4 0 x CDC- 311.319 085 22.05 CSU/ 137.0463
C.sub.4H.sub.8O.sub.5 Sugar 4 >5 x x x CDC- 136.0378 Threonate
metabolite 086 1.37 CSU/ 466.3152 C.sub.26H.sub.43NO.sub.6 Bile
acid 1 3 x x CDC- 465.3085 Glycocholic acid metabolism 087 14.73
CSU/ 228.1955 -- -- 4 0 x CDC- 227.1884 088 15.22 CSU/ 385.2211
C.sub.20H.sub.32O.sub.7 Peptide 4 >5 x CDC- 384.2143 Lys His Thr
089 15.83 CSU/ 403.2338 C.sub.16H.sub.30N.sub.6O.sub.6 Peptide 3
>5 x CDC- 402.2253 Lys Gln Gln 090 15.84 CSU/ 683.4728 -- -- 4 0
x x CDC- 1347.9062 091 17.56 CSU/ 675.4753 -- -- 4 0 x CDC-
1348.9377 092 18.37 CSU/ 682.4841 -- -- 4 0 x CDC- 1345.9257 093
18.76 CSU/ 762.5401 -- -- 4 0 x CDC- 1506.0367 094 19.36 CSU/
227.0897 C.sub.9H.sub.16O.sub.5 -- 4 2 x x CDC- 204.1002 -- 095
9.68 CSU/ 189.1122 C.sub.9H.sub.14O.sub.4 Fatty acid 1 >5 x CDC-
188.1049 Nonanedioic Acid metabolism 177 12.27 CSU/ 169.0860
C.sub.9H.sub.12O.sub.3 Endogenous 4 >5 x CDC- 168.0786
2,6-Dimethoxy-4- metabolite - 097 9.94 methylphenol derived from
food CSU/ 183.1016 C.sub.10H.sub.14O.sub.3 -- 4 >5 x x CDC-
182.0943 -- 098 10.89 CSU/ 476.3055 C.sub.26H.sub.41N.sub.3O.sub.5
-- 4 5 x x CDC- 475.2993 -- 099 11.09 CSU/ 276.1263
C.sub.15H.sub.17NO.sub.4 -- 4 3 x CDC- 275.1196 -- 100 11.16 CSU/
314.0672 C.sub.10H.sub.12N.sub.5O.sub.5P -- 4 1 x CDC- 313.06 --
101 11.56 CSU/ 201.1122 C.sub.10H.sub.16O.sub.4 Fatty acid 3 >5
x CDC- 200.1047 -- metabolism 102 11.56 CSU/ 115.0391
C.sub.5H.sub.6O.sub.3 Phenylalanine 4 >5 x CDC- 114.0318 --
metabolism 103 11.57 CSU/ 491.1569 C.sub.24H.sub.26O.sub.11 -- 4
>5 x CDC- 490.1504 -- 104 11.56 CSU/ 241.1054
C.sub.10H.sub.18O.sub.5 Fatty acid 4 3 x CDC- 218.1157 3-Hydroxy-
metabolism 105 11.57 sebacic acid CSU/ 105.0914 -- 4 0 x CDC-
104.0841 106 11.57 CSU/ 811.7965 -- -- 4 0 x x x CDC- 810.7882 107
12.07 CSU/ 311.1472 C.sub.18H.sub.20N.sub.2O.sub.4 Peptide 3 >5
x CDC- 328.1391 Phe Tyr 108 12.22 CSU/ 271.1543 -- -- 4 0 x CDC-
270.1464 109 12.24 CSU/ 169.0860 C.sub.9H.sub.12O.sub.3 Endogenous
4 >5 x CDC- 168.0787 2,6-Dimethoxy-4- metabolite - 110 12.24
methylphenol derived from food CSU/ 187.0967 C.sub.9H.sub.14O.sub.4
-- 4 4 x CDC- 186.0889 -- 111 12.24 CSU/ 215.1283
C.sub.11H.sub.18O.sub.4 Endogenous 4 4 x x CDC- 214.1209
alpha-Carboxy- metabolite - 112 12.32 delta-decalactone derived
from food CSU/ 475.1635 C.sub.25H.sub.22N.sub.4O.sub.6 Peptide 4
>5 x CDC- 474.1547 His Cys Asp Thr 113 12.25 (SEQ ID NO: 12)
CSU/ 129.0547 C.sub.6H.sub.8O.sub.3 Fatty acid 4 >5 x CDC-
128.0474 (4E)-2- metabolism 114 12.33 Oxohexenoic acid CSU/
519.1881 C.sub.20H.sub.30N.sub.4O.sub.12 Poly D- 4 >5 x x CDC-
518.1813 Poly-g-D- glutamate 115 12.33 glutamate metabolism CSU/
125.0599 C.sub.7H.sub.8O.sub.2 Catechol 3 >5 x CDC- 124.0527
4-Methylcatechol metabolism 116 13.12 CSU/ 247.1550
C.sub.12H.sub.22O.sub.5 Fatty acid 4 4 x CDC- 246.1469 3-Hydroxy-
metabolism 117 13.13 dodecanedioic acid CSU/ 517.2614
C.sub.21H.sub.36N.sub.6O.sub.9 Peptide 4 >5 x CDC- 516.2544 Gln
Glu Gln Ile 118 13.13 (SEQ ID NO: 13) CSU/ 301.0739
C.sub.16H.sub.12O.sub.6 Endogenous 4 >5 x CDC- 300.0658
Chrysoeriol metabolite - 119 13.14 derived from food CSU/ 327.1773
C.sub.16H.sub.24N.sub.4O.sub.2 -- 4 1 x CDC- 304.1885 -- 120 14.17
CSU/ 387.2023 C.sub.19H.sub.30O.sub.8 Endogenous 4 >5 x CDC-
386.1935 Citroside A metabolite - 121 14.51 derived from food CSU/
875.8451 -- -- 4 0 x CDC- 1749.684 122 14.55 CSU/ 737.5118
C.sub.42H.sub.73O.sub.8P Glycero- 4 >5 x CDC- 736.5056 PA(39:5)
phospholipid 123 14.52 metabolism CSU/ 1274.3497 -- -- 4 0 x CDC-
1273.3481 124 14.96 CSU/ 1274.2092 -- -- 4 0 x CDC- 1273.2 125
14.96 CSU/ 1486.5728 -- -- 4 0 x CDC- 2971.1328 126 14.95 CSU/
965.3818 -- -- 4 0 x CDC- 964.3727 127 15.37 CSU/ 1086.1800 -- -- 4
0 x x CDC- 2170.3435 128 15.38 CSU/ 1086.0562
C.sub.97H.sub.167N.sub.5O.sub.48 Sphingolipid 4 1 x CDC- 2170.0908
NeuAcalpha2- metabolism 129 15.38 3Galbeta1- 3GalNAcbeta1- 4(9-OAc-
NeuAcalpha2- 8NeuAcalpha2- 3)Galbeta1- 4Glcbeta- Cer(d18:1/18:0)
CSU/ 1086.4344 -- 4 0 x CDC- 2169.8474 130 15.39 CSU/ 1240.7800 --
-- 4 0 x CDC- 1239.7712 131 15.38 CSU/ 616.1776 -- -- 4 0 x x x
CDC- 615.1699 132 15.43 CSU/ 285.2061 C.sub.16H.sub.28O.sub.4 -- 4
1 x x CDC- 284.1993 -- 133 15.99 CSU/ 357.1363
C.sub.20H.sub.20O.sub.6 Endogenous 4 >5 x x CDC- 356.1284
Xanthoxylol metabolite - 134 15.98 derived from food CSU/ 317.1956
C.sub.12H.sub.24N.sub.6O.sub.4 Peptide 4 >5 x CDC- 316.1885 Arg
Ala Ala 135 16.24 CSU/ 299.1853 C.sub.16H.sub.26O.sub.5
Prostaglandin 4 >5 x x CDC- 298.1781 Tetranor-PGE1 metabolism
136 16.24 CSU/ 334.2580 -- -- 4 0 x x CDC- 333.2514 137 16.36 CSU/
317.2317 -- -- 4 0 x x CDC- 316.2254 138 16.63 CSU/ 299.2219
C.sub.17H.sub.30O.sub.4 Fatty acid 4 2 x CDC- 298.2148 8E-
metabolism 139 16.64 Heptadecenedioic acid CSU/ 748.5408
C.sub.40H.sub.78NO.sub.9P Glycerophospholipid 4 >5 x CDC-
747.5317 PS-O(34:1) metabolism 140 17.23 CSU/ 331.2471
C.sub.18H.sub.34O.sub.5 Fatty acid 4 >5 x x CDC- 330.2403
11,12,13- metabolism 141 17.26 trihydroxy-9- octadecenoic acid CSU/
712.4935 C.sub.79H.sub.140O.sub.17P.sub.2 Glycerophospholipid 4 1 x
CDC- 1422.9749 CL(70:7) metabolism 142 17.82 CSU/ 674.5013
C.sub.37H.sub.72NO.sub.7P Glycerophospholipid 4 >5 x CDC-
673.4957 PE-P(32:1) metabolism 143 17.99 CSU/ 583.3480
C.sub.27H.sub.46N.sub.6O.sub.8 Peptide 4 1 x x CDC- 582.3379 Leu
Lys Glu Pro 144 18.04 Pro (SEQ ID NO: 8) CSU/ 677.9537 -- -- 4 0 x
CDC- 676.9478 145 18.36 CSU/ 531.3522 C.sub.35H.sub.46O.sub.4 -- 4
2 x CDC- 530.3457 -- 146 18.4 CSU/ 585.2733
C.sub.33H.sub.36N.sub.4O.sub.6 Bilirubin 4 >5 x CDC- 584.2649
15,16- breakdown 147 18.39 Dihydrobiliverdin products - Porphyrin
metabolism CSU/ 513.3431 -- -- 4 0 x CDC- 512.3352 148 18.4 CSU/
611.9156 -- -- 4 0 x
CDC- 610.9073 149 18.59 CSU/ 549.0538 -- -- 4 0 x CDC- 531.0181 150
18.38 CSU/ 755.5311 -- -- 4 0 x CDC- 1509.0457 151 18.93 CSU/
713.4492 C.sub.38H.sub.65O.sub.10P Glycerophospholipid 4 4 x x x
CDC- 712.4391 PG(32:5) metabolism 152 19.35 CSU/ 599.4146
C.sub.40H.sub.54O.sub.4 lsoflavinoid 4 >5 x CDC- 598.4079
lsomytiloxanthin 153 19.59 CSU/ 762.5029 C.sub.43H.sub.72NO.sub.8P
Glycero- 4 >5 x CDC- 761.4919 PE(38:7) phospholipid 154 19.66
metabolism CSU/ 502.3376 C.sub.27H.sub.40N.sub.4O.sub.4 Peptide 4
>5 x x x CDC- 484.3039 Gln Leu Pro Lys 155 19.87 (SEQ ID NO: 2)
CSU/ 741.4805 C.sub.40H.sub.69O.sub.10P Glycero- 4 >5 x CDC-
740.4698 PG(34:5) phospholipid 156 19.96 metabolism CSU/ 648.4672
C.sub.34H.sub.66NO.sub.8P Glycero- 4 >5 x x CDC- 647.4609
PE(29:1) phospholipid 157 19.98 metabolism CSU/ 415.3045 -- -- 4 0
x x x CDC- 414.2978 158 20.19 CSU/ 516.3532
C.sub.23H.sub.42N.sub.6O.sub.6 Peptide 4 1 x CDC- 498.3199 Ala Leu
Ala Pro 159 20.27 Lys (SEQ ID NO: 14) CSU/ 769.5099
C.sub.42H.sub.73O.sub.10P Glycero- 4 >5 x CDC- 768.5018 PG(36:5)
phospholipid 160 20.53 metabolism CSU/ 862.5881 -- -- 4 0 x CDC-
861.5818 161 20.86 CSU/ 837.5358 C.sub.53H.sub.72O.sub.8 Endogenous
4 2 x CDC- 836.5274 Amitenone metabolite - 162 21.11 derived from
food CSU/ 558.3995 C.sub.26H.sub.48N.sub.6O.sub.6 Peptide 4 2 x
CDC- 540.367 Leu Ala Pro Lys 163 21.44 Ile (SEQ ID NO: 15) CSU/
366.3729 -- -- 4 0 x x x CDC- 365.3655 164 22.79 CSU/ 445.2880
C.sub.45H.sub.74O.sub.15 Endogenous 4 1 x x CDC- 854.5087
(3b,21b)-12-Oleanene- metabolite - 165 12.48 3,21,28-triol 28-
derived from [arabinosyl-(1->3)- food arabinosyl-(1->3)-
arabinoside] CSU/ 333.1446 C.sub.12H.sub.20N.sub.4O.sub.7 Peptide 4
>5 x x x CDC- 332.1373 Glu Gln Gly 166 12.89 CSU/ 1105.9305 --
-- 4 0 x CDC- 2209.8462 167 14.53 CSU/ 329.1049
C.sub.18H.sub.16O.sub.6 Phenylalanine 4 >5 x CDC- 328.0976
2-Oxo-3- metabolism 168 14.61 phenylpropanoic acid CSU/ 1241.2053
-- -- 4 0 x CDC- 1240.2 169 15.38 CSU/ 1088.6731 -- -- 4 0 x CDC-
1087.6676 170 17.85 CSU/ 667.4391 C.sub.37H.sub.63O.sub.8P Glycero-
4 >5 x CDC- 666.4323 PA(24:5) phospholipid 171 20.35 metabolism
CSU/ 133.0497 C.sub.5H.sub.8O.sub.4 Pantothenate 4 >5 x CDC-
132.0423 2-Acetolactic acid and CoA 172 11.57 Biosynthesis Pathway
CSU/ 259.1540 -- -- 4 0 x CDC- 258.1469 173 11.75 CSU/ 311.1472
C.sub.10H.sub.20N.sub.6O.sub.4 Dipeptide 4 >5 x CDC- 288.1574
Asn Arg 174 12.23 CSU/ 147.0652 C.sub.6H.sub.10O.sub.4 Pantothenate
4 >5 x CDC- 146.0579 .alpha.-Ketopantoic acid and CoA 175 12.33
Biosynthesis Pathway CSU/ 169.0860 C.sub.9H.sub.12O.sub.3
Endogenous 4 >5 x CDC- 168.0788 Epoxyoxophorone metabolite - 176
12.29 derived from food CSU/ 187.0965 C.sub.9H.sub.14O.sub.4
Endogenous 4 >5 x CDC- 186.0894 5-Butyltetrahydro- metabolite -
096 9.93 2-oxo-3- derived from food furancarboxylic acid CSU/
139.1116 C.sub.9H.sub.14O.sub.4 Endogenous 4 >5 x CDC- 138.1044
3,6-Nonadienal metabolite - 178 12.95 derived from food CSU/
515.2811 C.sub.26H.sub.42O.sub.10 Endogenous 4 >5 x CDC-
514.2745 Cofaryloside metabolite - 179 13.14 derived from food CSU/
283.1522 C.sub.25H.sub.42N.sub.2O.sub.7S Endogenous 4 >5 x CDC-
282.1444 Epidihydrophaseic metabolite - 180 13.93 acid derived from
food CSU/ 1486.7386 -- -- 4 0 x x CDC- 2971.4668 181 14.97 CSU/
285.2065 C.sub.16H.sub.28O.sub.4 -- 4 1 x x CDC- 284.1991 -- 182
16.02 CSU/ 668.4686 C.sub.16H.sub.28O.sub.4 Endogenous 4 1 x x CDC-
1317.8969 Omphalotin A metabolite - 183 18.04 derived from food
CSU/ 454.2924 C.sub.21H.sub.41O.sub.7P Glycero- 4 >5 x x CDC-
436.2587 Lyso-PA(18:1) phospholipid 184 18.1 metabolism CSU/
706.9750 -- -- 4 0 x CDC- 705.9684 185 18.7 CSU/ 607.9324 -- -- 4 0
x x CDC- 606.9246 186 19.01 CSU/ 834.5575 -- -- 4 0 x CDC- 833.5502
187 20.32 CSU/ 521.4202 -- -- 4 0 x x CDC- 503.3858 188 21.06 CSU/
683.4727 -- -- 4 0 x CDC- 1364.9294 189 17.54 CSU/ 728.9890 -- -- 4
1 x CDC- 1455.9633 190 18.63 CSU/ 726.5104
C.sub.81H.sub.144O.sub.17P.sub.2 Glycero- 4 2 x CDC- 1451.0035
CL(72:7) phospholipid 191 18.64 metabolism CSU/ 633.9280 -- -- 4 0
x CDC- 632.9206 192 18.47 CSU/ 176.0746 -- -- 4 0 x x CDC- 175.0667
193 2.31 CSU/ 596.9082 -- -- 4 0 x x CDC- 1191.8033 194 19.1 CSU/
209.0784 C.sub.17H.sub.24O.sub.3 Phenyl- 4 >5 x CDC- 208.0713
Benzylsuccinate propanoic acid 195 9.92 metabolism CSU/ 792.5483 --
-- 4 0 x CDC- 1566.055 196 18.46 CSU/ 618.9221 -- -- 4 0 x CDC-
1218.8083 197 19.02 CSU/ 549.0543 -- -- 4 0 x CDC- 531.0189 198
18.37 CSU/ 553.7262 -- -- 4 0 x CDC- 552.7188 199 18.74 CSU/
756.0320 -- -- 4 0 x CDC- 755.0266 200 18.95 CSU/ 639.6307 -- -- 4
0 x CDC- 638.6205 201 19.58 CSU/ 753.4414 C.sub.42H.sub.67O.sub.8P
Glycerophospholipid 4 2 x CDC- 730.4513 PA(39:8) metabolism 202
19.37 CSU/ 532.5606 -- -- 4 0 x x CDC- 531.5555 203 18.38 CSU/
279.1693 C.sub.15H.sub.22N.sub.2O.sub.3 Dipeptide 4 >5 x x CDC-
278.1629 Phe Leu 204 11.05 CSU/ 241.1069 C.sub.12H.sub.16O.sub.5
Fatty acid 1 >5 x x x CDC- 240.0996 3-Carboxy-4- metabolism 205
14.7 methyl-5-propyl-2- furanpropanoic acid (CMPF) CSU/ 337.1667
C.sub.12H.sub.24N.sub.4O.sub.7 -- 4 2 x x CDC- 336.1599 -- 206
20.67 CSU/ 328.3204 C.sub.20H.sub.41NO.sub.2 N-acyl 1 5 x CDC-
327.3148 Stearoyl ethanolamine 207 20.72 ethanolamide metabolism
CSU/ 514.3718 C.sub.56H.sub.99NO.sub.14 Sphingolipid 4 1 x CDC-
1009.7122 3-O-acetyl- metabolism 208 18.42 sphingosine-
2,3,4,6-tetra-O- acetyl- GalCer(d18:1/h22:0) CSU/ 630.4594 -- -- 4
0 x CDC- 1241.8737 209 19.95 CSU/ 415.1634
C.sub.8H.sub.9N.sub.5O.sub.2 Endogenous 4 2 x x CDC- 207.0784
6-Amino-9H- metabolite - 210 12.2 purine-9- derived from propanoic
acid food CSU/ 464.1916 C.sub.16H.sub.29N.sub.7O.sub.7S Peptide 4
>5 x x x CDC- 463.1849 Arg Asp Cys Ala 211 13.05 (SEQ ID NO: 3)
CSU/ 1249.2045 -- -- 4 0 x x x CDC- 1248.1993 212 15.31 CSU/
1248.9178 -- -- 4 0 x x x CDC- 1247.9141 213 15.3 CSU/ 244.2270
C.sub.14H.sub.29NO.sub.2 N-acyl 4 3 x CDC- 243.22 Lauroyl
ethanolamine 214 17.17 ethanolamide metabolism CSU/ 463.3426 -- --
4 0 x CDC- 924.6699 215 18.08 CSU/ 468.3892 C.sub.31H.sub.46O.sub.2
-- 4 1 x CDC- 450.3553 -- 216 19.17 CSU/ 438.3787 -- -- 4 0 x CDC-
420.3453 217 420.3453 CSU/ 364.3407 -- -- 4 0 x x CDC- 346.3068 218
20.72 CSU/ 158.1539 -- -- 4 0 x x x CDC- 157.1466 219 15.36 CSU/
792.0006 -- -- 4 0 x CDC- 790.995 220 12.04 CSU/ 792.2025 -- -- 4 0
x CDC- 791.1947 221 12.04 CSU/ 989.5004 -- -- 4 0 x x CDC-
1976.9858 222 12.03 CSU/ 791.6016 -- -- 4 0 x CDC- 790.594 223
12.04 CSU/ 819.6064 -- -- 4 0 x x CDC- 1635.8239 224 12.06 CSU/
1115.5593 -- -- 4 0 x CDC- 2228.1028 225 14.95 CSU/ 1486.9176 -- --
4 0 x
CDC- 2970.7976 226 14.96 CSU/ 529.3381
C.sub.24H.sub.44N.sub.6O.sub.7 Peptide 4 5 x x x CDC- 528.3296 Gln
Val Leu Leu 227 16.89 Gly (SEQ ID NO: 4) CSU/ 430.3161
C.sub.23H.sub.40O.sub.6 -- 4 1 x CDC- 412.2845 -- 228 20.23 CSU/
282.2776 C.sub.18H.sub.32O -- 4 >5 x x x CDC- 264.2456 -- 229
20.56 CSU/ 297.2793 C.sub.19H.sub.36O.sub.2 Oleic acid 1 >5 x
CDC- 296.2734 Methyloleate ester 230 20.66 CSU/ 714.3655 -- -- 4 0
x CDC- 1426.718 231 11.73 CSU/ 714.5306 -- -- 4 0 x CDC- 1427.0479
232 11.76 CSU/ 989.7499 -- -- 4 0 x CDC- 1977.4865 233 12.03 CSU/
221.0744 C.sub.7H.sub.12N.sub.2O.sub.6 Peptide 4 >5 x CDC-
220.0672 L-beta-aspartyl-L- 234 13.7 serine CSU/ 190.1260
C.sub.9H.sub.19NOS 2- 4 2 x x x CDC- 189.1187 8- oxocarboxylic 235
14.12 Methylthiooctanal acid doxime metabolism CSU/ 313.2734
C.sub.19H.sub.36O.sub.3 Fatty acid 4 5 x CDC- 312.2663 2-oxo-
metabolism 236 18.91 nonadecanoic acid CSU/ 286.2737
C.sub.17H.sub.35NO.sub.2 N-acyl 1 4 x x CDC- 285.2666 Pentadecanoyl
ethanolamine 237 19.08 ethanolamide metabolism CSU/ 382.3675
C.sub.24H.sub.47NO.sub.2 N-acyl 4 1 x x x CDC- 381.3603 Erucicoyl
ethanolamine 238 20.23 ethanolamide metabolism CSU/ 337.2712
C.sub.19H.sub.38O.sub.3 Fatty acid 4 2 x CDC- 314.282 2-Hydroxy-
metabolism 239 20.66 nonadecanoic acid CSU/ 441.3687
C.sub.30H.sub.48O.sub.2 Sterol 4 >5 x CDC- 440.3614
4,4-Dimethyl-14a- metabolism 240 21.26 formyl-5a-
cholesta-8,24-dien-3b-ol CSU/ 425.3735 C.sub.30H.sub.48O Sterol 4
>5 x CDC- 424.3666 Butyrospermone metabolism 241 21.5 CSU/
356.3517 C.sub.22H.sub.45NO.sub.2 N-acyl 1 2 x CDC- 355.3448
Eicosanoyl ethanolamine 242 21.67 ethanolamide metabolism CSU/
393.2970 C.sub.22H.sub.42O.sub.4 -- 4 3 x CDC- 370.3082 -- 243
22.46 CSU/ 477.2968 C.sub.31H.sub.40O.sub.4 Peptide 4 >5 x x x
CDC- 476.2898 Lys Lys Thr Thr 244 22.79 (SEQ ID NO: 5) CSU/
614.4833 -- -- 4 0 x x CDC- 613.4772 245 19.78 CSU/ 167.9935
C.sub.7H.sub.5NS.sub.2 -- 4 1 x CDC- 166.9861 -- 246 13.2 CSU/
714.6967 -- -- 4 0 x x CDC- 1427.3824 247 11.76 CSU/ 459.3968 -- --
4 0 x x x CDC- 458.3904 248 19.08 CSU/ 677.6170
C.sub.47H.sub.80O.sub.2 Sterol 4 >5 x CDC- 676.6095 Cholesterol
ester metabolism 249 20.71 (20:2) CSU/ 298.2740
C.sub.18H.sub.35NO.sub.2 Sphingolipid 2 >5 x x CDC- 297.2668
3-Ketospingosine metabolism 250 16.44 CSU/ 460.2695
C.sub.26H.sub.37NO.sub.6 -- 4 >5 x CDC- 459.2627 -- 251 16.87
CSU/ 1003.7020 -- -- 4 0 x x CDC- 1002.696 252 18.46 CSU/ 342.2635
C.sub.19H.sub.35NO.sub.4 -- 4 2 x x x CDC- 341.2565 -- 253 15.62
CSU/ 529.3827 -- -- 4 0 x x x CDC- 1022.6938 254 17.86 CSU/
630.4765 -- -- 4 0 x CDC- 612.4417 255 18.11 CSU/ 514.3734 -- -- 4
0 x CDC- 1026.7281 256 18.41 CSU/ 667.4754 -- -- 4 0 x CDC-
1315.916 257 19.28 CSU/ 459.2502 C.sub.23H.sub.39O.sub.7P Glycero-
2 >5 x x x CDC- 458.2429 Lyso PA(20:4) phospholipid 258 19.02
metabolism CSU/ 516.8549 -- -- 4 0 x CDC- 1031.6945 259 18.43 CSU/
740.5242 C.sub.83H.sub.148O.sub.17P.sub.2 Glycero- 4 2 x CDC-
1479.0334 CL(74:7) phospholipid 260 metabolism CSU/ 1104.0614 -- --
4 0 x CDC- 2206.1096 261 15.2
TABLE-US-00008 TABLE 4 MetaboAnalyst results Holm Pathway Hit Total
Expected Hits Raw p -log(p) adjust FDR Impact Glycerophospholipid
39 1.2638 .sup. 4.sup..dagger. 0.035545 3.337 1 1 0.33235
metabolism Sphingolipid 25 0.81014 .sup. 3.sup..+-. 0.045107 3.0987
1 1 0.15499 metabolism Valine, leucine and 27 0.87495 2 0.21724
1.5268 1 1 0.17117 isoleucine biosynthesis Phenylalanine 45 1.4582
1 0.77605 0.25353 1 1 0.11906 metabolism alpha-Linolenic acid 29
0.93976 2 0.24148 1.421 1 1 0 metabolism Glycosylphosphatidylino 14
0.45368 1 0.37027 0.99353 1 1 0.0439 sitol(GPI)-anchor biosynthesis
Linoleic acid 15 0.48608 1 0.39079 0.93957 1 1 0 metabolism
Riboflavin metabolism 21 0.68052 1 0.50079 0.69157 1 1 0
Phenylalanine, tyrosine 27 0.87495 1 0.59113 0.52572 1 1 0.00062
and tryptophan biosynthesis Pantothenate and CoA 27 0.87495 1
0.59113 0.52572 1 1 0.02002 biosynthesis Steroid hormone 99 3.2081
3 0.63116 0.4602 1 1 0.0382 biosynthesis Glycerolipid metabolism 32
1.037 1 0.65393 0.42476 1 1 0.01247 Ubiquinone and other 36 1.1666
1 0.69723 0.36064 1 1 0 terpenoid-quinone bios+A14:I29ynthesis
Nitrogen metabolism 39 1.2638 1 0.72615 0.32 1 1 0 Butanoate
metabolism 40 1.2962 1 0.73517 0.30766 1 1 0.04772 Ascorbate and
aldarate 45 1.4582 1 0.77605 0.25353 1 1 0.00802 metabolism Drug
metabolism - 86 2.7869 2 0.77721 0.25205 1 1 0.0176 cytochrome P450
Primary bile acid 47 1.5231 1 0.7906 0.23496 1 1 0.00846
biosynthesis Lysine degradation 47 1.5231 1 0.7906 0.23496 1 1
0.06505 Fatty acid biosynthesis 49 1.5879 1 0.80422 0.21788 1 1 0
Fatty acid metabolism 50 1.6203 1 0.81069 0.20986 1 1 0 Starch and
sucrose 50 1.6203 1 0.81069 0.20986 1 1 0.01265 metabolism Pentose
and 53 1.7175 1 0.82888 0.18768 1 1 0.009 glucuronate
interconversions Arachidonic acid 62 2.0091 1 0.87371 0.135 1 1 0
metabolism Aminoacyl-tRNA 75 2.4304 1 0.91874 0.084752 1 1 0
biosynthesis Purine metabolism 92 2.9813 1 0.95452 0.046547 1 1
0.00791 Porphyrin and 104 3.3702 1 0.96989 0.030577 1 1 0.01101
chlorophyll metabolism Total, the total number of compounds in the
pathway; Hits, the actual number of compounds in the pathway
matched from the 261 MF biosignature list; Raw p, the original p
value calculated from the enrichment analysis; Holm adjust, the
adjusted p value by the Holm-Bonferroni method; FDR, the p value
adjusted using False Discovery Rate; Impact, the pathway impact
value calculated from pathway topology analysis. The MetaboAnalyst
results were used to target specific MFs in the early Lyme
disease-STARI biosignature for structural identification.
.sup..dagger.The 4 hits in the glycerophospholipid metabolism
pathway were phosphatidic acid, phosphatidylethanolamine,
phosphatidylcholine and lysophosphotidylcholine. .sup..+-.The 3
hits in the sphingolipid metabolism pathway were in sphingosine,
dehydrosphinganine and sulfatide.
TABLE-US-00009 TABLE 5 Regression coefficients (.beta.) of the
LASSO two-way statistical model The regression coefficient for each
of the 38 MFs (CSU/CDC-#) used in the LASSO two-way classification
model are provided. The regression coefficients were generated with
data from the Training-Set samples, and applied in the
classification of the Test-Set samples as shown in Table 6. MF Id
Coefficient Intercept -0.5089 CSU/CDC-001 -0.3032 CSU/CDC-002
-0.0359 CSU/CDC-012 -0.31 CSU/CDC-013 -0.2256 CSU/CDC-014 0.05737
CSU/CDC-028 0.21447 CSU/CDC-039 0.29641 CSU/CDC-062 0.0152
CSU/CDC-066 -0.0559 CSU/CDC-067 0.63951 CSU/CDC-072 -0.1451
CSU/CDC-075 0.10409 CSU/CDC-086 0.71497 CSU/CDC-107 -0.2586
CSU/CDC-132 0.88577 CSU/CDC-152 -0.6125 CSU/CDC-155 -0.0083
CSU/CDC-158 -0.027 CSU/CDC-164 0.22005 CSU/CDC-166 -0.2033
CSU/CDC-182 -0.1077 CSU/CDC-204 -0.163 CSU/CDC-205 -0.8688
CSU/CDC-211 0.43327 CSU/CDC-212 -0.3513 CSU/CDC-213 -0.422
CSU/CDC-219 1.01872 CSU/CDC-227 0.43588 CSU/CDC-229 0.11674
CSU/CDC-235 0.3664 CSU/CDC-019 0.52461 CSU/CDC-238 0.7812
CSU/CDC-244 -0.7325 CSU/CDC-247 0.00621 CSU/CDC-248 0.38858
CSU/CDC-253 0.10575 CSU/CDC-254 0.27792 CSU/CDC-258 -0.5593
TABLE-US-00010 TABLE 6 LASSO and RF two-way model classification
probability scores The LASSO and RF probability scores are provided
for each patient sample tested in duplicate. These are probability
scores for the Test-Set samples. A probability score of .gtoreq.0.5
classified the samples as early Lyme disease (EL), and a
probability score of <0.5 resulted in the sample being
classified as STARI. LASSO Coded Prob- LASSO RF RF Sample ability
Class- Prob- Class- Sample Patient ID Score ification ability
ification ID Type Valb1618 0.9979 EL 0.8980 EL EDL134- EL 022315
Valb1591 0.9995 EL 0.8980 EL EDL134- EL 120214 Valb1454 0.9900 EL
0.6320 EL EDL135- EL 022315 Valb0820 0.5264 EL 0.8660 EL EDL135- EL
120214 Valb0989 0.9820 EL 0.8620 EL EDL136- EL 022315 Valb0546
0.8814 EL 0.8960 EL EDL136- EL 120214 Valb1573 0.9875 EL 0.5840 EL
EDL137- EL 022315 Valb1299 0.7198 EL 0.4380 STARI EDL137- EL 120214
Valb0477 0.9247 EL 0.7780 EL EDL138- EL 022315 Valb0160 0.9868 EL
0.9160 EL EDL138- EL 120214 Valb0813 0.7300 EL 0.4880 STARI EDL139-
EL 022315 Valb0443 0.8307 EL 0.7680 EL EDL139- EL 120214 Valb1412
0.9287 EL 0.7200 EL EDL140- EL 022315 Valb0886 0.9045 EL 0.8140 EL
EDL140- EL 120214 Valb0827 0.9846 EL 0.9040 EL EDL71- EL 022315
Valb0186 0.9609 EL 0.9180 EL EDL71- EL 120214 Valb1337 0.9417 EL
0.8200 EL EDL73- EL 022315 Valb0714 0.9836 EL 0.9000 EL EDL73- EL
120214 Valb1510 0.9773 EL 0.7720 EL EDL74- EL 022315 Valb0642
0.9986 EL 0.8520 EL EDL74- EL 120214 Valb1586 0.9995 EL 0.9020 EL
EDL75- EL 022315 Valb1402 1.0000 EL 0.9160 EL EDL75- EL 120214
Valb0593 0.9595 EL 0.8020 EL EDL76- EL 022315 Valb0608 0.6940 EL
0.7980 EL EDL76- EL 120214 Valb0808 0.9205 EL 0.8720 EL EDL77- EL
022315 Valb0750 0.9998 EL 0.7240 EL EDL77- EL 120214 Valb0907
0.9459 EL 0.6720 EL EDL78- EL 022315 Valb0585 0.9891 EL 0.9180 EL
EDL78- EL 120214 Valb1638 0.9832 EL 0.6000 EL EDL79- EL 022315
Valb1640 0.9906 EL 0.8500 EL EDL79- EL 120214 Valb1430 0.9812 EL
0.7580 EL ELL06- EL 022315 Valb1155 0.9995 EL 0.8080 EL ELL06- EL
120214 Valb1553 0.9783 EL 0.7780 EL ELL07- EL 022315 Valb1562
0.9999 EL 0.7920 EL ELL07- EL 120214 Valb1445 0.8085 EL 0.7160 EL
ELL08- EL 022315 Valb1188 0.9983 EL 0.7860 EL ELL08- EL 120214
Valb1613 0.9993 EL 0.8640 EL ELL09- EL 022315 Valb1514 1.0000 EL
0.8820 EL ELL09- EL 120214 Valb1479 0.3775 STARI 0.6320 EL ELL10-
EL 022315 Valb0933 0.9095 EL 0.8380 EL ELL10- EL 120214 Valb0923
0.7083 EL 0.8120 EL ELL16- EL 022315 Valb0338 0.7215 EL 0.8320 EL
ELL16- EL 120214 Valb0783 0.7849 EL 0.8880 EL ELL17- EL 022315
Valb0261 0.9862 EL 0.9120 EL ELL17- EL 120214 Valb1264 0.9418 EL
0.8240 EL ELL18- EL 022315 Valb0545 0.9738 EL 0.8480 EL ELL18- EL
120214 Valb1427 0.9704 EL 0.8480 EL ELL61- EL 022315 Valb1071
0.9664 EL 0.7620 EL ELL61- EL 120214 Valb1211 0.7950 EL 0.7360 EL
ELL62- EL 022315 Valb1217 0.7831 EL 0.8360 EL ELL62- EL 120214
Valb1414 0.9892 EL 0.9100 EL ELL63- EL 022315 Valb1104 0.9699 EL
0.8600 EL ELL63- EL 120214 Valb0736 0.9469 EL 0.9300 EL ELL64- EL
022315 Valb0384 0.9780 EL 0.9040 EL ELL64- EL 120214 Valb0672
0.9415 EL 0.7680 EL ELL65- EL 022315 Valb0300 0.9927 EL 0.8920 EL
ELL65- EL 120214 Valb1018 0.9093 EL 0.8320 EL ELL66- EL 022315
Valb0458 0.8905 EL 0.8480 EL ELL66- EL 120214 Valb1356 0.9174 EL
0.8100 EL ELL67- EL 022315 Valb0492 0.9747 EL 0.7260 EL ELL67- EL
120214 Valb1561 0.0313 STARI 0.4860 STARI M06A- STARI 022315
Valb1328 0.8608 EL 0.6060 EL M06A- STARI 120214 Valb0329 0.1613
STARI 0.2680 STARI M09A- STARI 022315 Valb0070 0.2476 STARI 0.4080
STARI MO9A- STARI 120214 Valb1052 0.0242 STARI 0.4060 STARI M13A-
STARI 022315 Valb0809 0.8461 EL 0.8340 EL M13A- STARI 120214B
Valb1256 0.0157 STARI 0.2900 STARI M16A- STARI 022315 Valb1100
0.3798 STARI 0.4120 STARI M16A- STARI 120214 Valb1236 0.2314 STARI
0.6800 EL M19A- STARI 022315 Valb0580 0.5508 EL 0.6140 EL M19A-
STARI 120214 Valb1525 0.7045 EL 0.4720 STARI M22A- STARI 022315
Valb0534 0.0496 STARI 0.4580 STARI M22A- STARI 120214 Valb0556
0.1448 STARI 0.3400 STARI M26A- STARI 022315 Valb0116 0.4234 STARI
0.2860 STARI M26A- STARI 120214 Valb0461 0.0037 STARI 0.2360 STARI
M27A- STARI 022315 Valb0266 0.1015 STARI 0.2080 STARI M27A- STARI
120214 Valb0447 0.0316 STARI 0.1220 STARI S03- STARI 022315
Valb0026 0.0060 STARI 0.1420 STARI S03- STARI 120214 Valb1114
0.0010 STARI 0.1760 STARI S09- STARI 022315 Valb0464 0.0254 STARI
0.2120 STARI S09- STARI 120214 Valb1292 0.0004 STARI 0.1280 STARI
S17- STARI 022315 Valb0754 0.0005 STARI 0.1020 STARI S17- STARI
120214 Valb0434 0.0257 STARI 0.2520 STARI S21- STARI 022315
Valb0044 0.0559 STARI 0.4300 STARI S21- STARI 120214 Valb0873
0.0173 STARI 0.1840 STARI S33- STARI 022315 Valb0352 0.0012 STARI
0.2200 STARI S33- STARI 120214 Valb1141 0.0001 STARI 0.1120 STARI
S39- STARI 022315 Valb0480 0.0002 STARI 0.1160 STARI S39- STARI
120214 Valb0618 0.0158 STARI 0.3220 STARI S43- STARI 022315
Valb0660 0.1493 STARI 0.3020 STARI S43- STARI 120214 Valb0223
0.0007 STARI 0.0960 STARI S47- STARI 022315 Valb0054 0.0095 STARI
0.0940 STARI S47- STARI 120214 Valb0335 0.0093 STARI 0.0660 STARI
S53- STARI 022315 Valb0197 0.0183 STARI 0.0360 STARI S53- STARI
120214 Valb0409 0.2080 STARI 0.2080 STARI S55- STARI 022315
Valb0060 0.0332 STARI 0.1280 STARI S55- STARI 120214 Valb0437
0.0004 STARI 0.0980 STARI S65- STARI 022315 Valb0093 0.0003 STARI
0.1500 STARI S65- STARI 120214
TABLE-US-00011 TABLE 7 Regression coefficients (.beta.) of the
LASSO three-way statistical model. The regression coefficient for
each of the 82 MFs (CSU/CDC-#) used in the LASSO three-way
classification model are provided. The regression coefficients were
generated with data from the Training-Set samples, and applied in
the classification of the Test-Set samples as shown in Table 8. MF
Id Early Lyme Disease Healthy Controls STARI Intercept 0.5755
-0.4927 -0.0828 CSU/CDC-001 0.37556 0 0 CSU/CDC-003 0 0.4377 0
CSU/CDC-004 0 0.00298 0 CSU/CDC-006 0.0704 0 0 CSU/CDC-008 -0.1193
0 0 CSU/CDC-009 0.22921 0 0 CSU/CDC-012 0.15307 0 -0.2457
CSU/CDC-013 0 0 -0.1007 CSU/CDC-014 0 0 0.72128 CSU/CDC-017 0.11117
0 0 CSU/CDC-026 0 -0.0633 0.05925 CSU/CDC-030 0 0.05795 0
CSU/CDC-039 0 -0.6065 0.06517 CSU/CDC-042 -0.4151 0.02856 0
CSU/CDC-052 0 0.05484 0 CSU/CDC-061 0 0.08714 0 CSU/CDC-062 0 0
0.60672 CSU/CDC-066 0 0 -0.3676 CSU/CDC-067 -1.1528 0 0 CSU/CDC-070
-0.5929 0.5531 0 CSU/CDC-072 0 0 -0.0857 CSU/CDC-074 0.01711 0 0
CSU/CDC-075 0 0 0.18553 CSU/CDC-083 0 -0.0872 0 CSU/CDC-084 0
-0.2013 0.21541 CSU/CDC-086 -1.1622 0 0.06776 CSU/CDC-087 0 0.03553
0 CSU/CDC-091 0 -0.6683 0 CSU/CDC-095 0 0 -0.0694 CSU/CDC-098 0
0.05396 0 CSU/CDC-099 0 -0.0398 0 CSU/CDC-107 0.36836 0 -0.1847
CSU/CDC-112 0 1.11724 0 CSU/CDC-115 0 0.12435 0 CSU/CDC-128 0
0.4206 -0.1927 CSU/CDC-132 0 0 1.0998 CSU/CDC-133 0.35613 -0.1349 0
CSU/CDC-134 0 -0.1009 0 CSU/CDC-136 0 -1.2108 0 CSU/CDC-137 0
-0.2512 0 CSU/CDC-138 -0.0183 0 0 CSU/CDC-141 0 0 0.2233
CSU/CDC-144 0 -0.1318 0 CSU/CDC-152 0.70277 0 0 CSU/CDC-155 0.27512
0 0 CSU/CDC-157 0 0 0.0505 CSU/CDC-158 0 1.89865 0 CSU/CDC-164
-0.2964 0 0 CSU/CDC-165 0 -0.4008 0 CSU/CDC-166 0.14382 0 0
CSU/CDC-181 0 1.3044 0 CSU/CDC-183 0 -0.7613 0.01014 CSU/CDC-184
0.35021 0 0 CSU/CDC-186 0 0.40861 0 CSU/CDC-188 0 0.5533 0
CSU/CDC-193 0 -1.2355 0 CSU/CDC-194 0 0.57412 0 CSU/CDC-203 -0.0308
0 0 CSU/CDC-205 0.50193 0 -0.3139 CSU/CDC-206 0 -0.0668 0
CSU/CDC-210 0 0 -0.218 CSU/CDC-211 -0.7208 0 0.20891 CSU/CDC-212 0
0 -0.0139 CSU/CDC-213 0 0 -0.2463 CSU/CDC-218 0 0.00722 0
CSU/CDC-219 -1.0252 0 0 CSU/CDC-222 0 -0.4632 0 CSU/CDC-224 0
-0.516 0 CSU/CDC-227 -0.4157 0 0.59261 CSU/CDC-229 0 0 0.86651
CSU/CDC-235 -0.9905 0 0 CSU/CDC-019 0 -0.0326 0.52245 CSU/CDC-237 0
0.62355 0 CSU/CDC-238 0 0 0.96539 CSU/CDC-244 1.5845 0 0
CSU/CDC-245 0 -1.3521 0 CSU/CDC-248 -0.0904 0 0.06017 CSU/CDC-250 0
0 -0.0882 CSU/CDC-252 0 -0.0646 0 CSU/CDC-253 0 0 0.16563
CSU/CDC-254 -0.1985 0 0 CSU/CDC-258 0 0 -0.7011
TABLE-US-00012 TABLE 8 LASSO and RF three-way model classification
probability scores. The LASSO and RF probability scores are
provided for each patient sample tested in duplicate. These are
probability scores for the Test-Set samples. Both the LASSO and RF
classifiers provided a probability score for a sample being early
Lyme disease patient (EL), healthy control (HC) and STARI. The
sample was classified based on the highest probability score for
membership in one of the three groups (EL, HC, or STARI). Coded
LASSO Probability LASSO RF Probability RF Sample Score for EL, HC,
Class- Score for EL, HC, Class- Sample Patient ID and STARI
ification and STARI ification ID Type Valb1618 0.9998 EL 0.8420 EL
EDL134- EL 0.0000 0.0560 022315 0.0002 0.1020 Valb1591 1.0000 EL
0.8600 EL EDL134- EL 0.0000 0.0320 120214 0.0000 0.1080 Valb1454
0.9978 EL 0.5140 EL EDL135- EL 0.0003 0.0840 022315 0.0019 0.4020
Valb0820 0.9798 EL 0.6560 EL EDL135- EL 0.0010 0.1140 120214 0.0192
0.2300 Valb0989 0.9765 EL 0.3620 HC EDL136- EL 0.0190 0.5660 022315
0.0045 0.0720 Valb0546 0.9184 EL 0.5760 EL EDL136- EL 0.0198 0.3360
120214 0.0618 0.0880 Valb1573 0.6006 EL 0.4640 EL EDL137- EL 0.3980
0.1620 022315 0.0015 0.3740 Valb1299 0.0350 STARI 0.4640 EL EDL137-
EL 0.0012 0.1380 120214 0.9639 0.3980 Valb0477 0.9823 EL 0.5760 EL
EDL138- EL 0.0001 0.2480 022315 0.0175 0.1760 Valb0160 0.9570 EL
0.5800 EL EDL138- EL 0.0284 0.3560 120214 0.0146 0.0640 Valb0813
0.7815 EL 0.3380 EL EDL139- EL 0.1288 0.3340 022315 0.0897 0.3280
Valb0443 0.1403 HC 0.5140 EL EDL139- EL 0.8550 0.3480 120214 0.0047
0.1380 Valb1412 0.9258 EL 0.5260 EL EDL140- EL 0.0010 0.1860 022315
0.0732 0.2880 Valb0886 0.6301 EL 0.4060 HC EDL140- EL 0.1495 0.4380
120214 0.2204 0.1560 Valb0827 0.9395 EL 0.5600 EL EDL71- EL 0.0600
0.3240 022315 0.0005 0.1160 Valb0186 0.9623 EL 0.5460 EL EDL71- EL
0.0341 0.3980 120214 0.0036 0.0560 Valb1337 0.9873 EL 0.6840 EL
EDL73- EL 0.0000 0.0480 022315 0.0127 0.2680 Valb0714 0.9991 EL
0.7480 EL EDL73- EL 0.0000 0.0740 120214 0.0009 0.1780 Valb1510
0.9795 EL 0.6700 EL EDL74- EL 0.0000 0.1140 022315 0.0205 0.2160
Valb0642 0.9990 EL 0.7280 EL EDL74- EL 0.1080 120214 0.1640
Valb1586 1.0000 EL 0.8180 EL EDL75- EL 0.0000 0.0920 022315 0.0000
0.0900 Valb1402 1.0000 EL 0.8460 EL EDL75- EL 0.0000 0.0640 120214
0.0000 0.0900 Valb0593 0.9699 EL 0.5380 EL EDL76- EL 0.0155 0.3180
022315 0.0146 0.1440 Valb0608 0.2554 HC 0.4000 HC EDL76- EL 0.4250
0.4320 120214 0.3197 0.1680 Valb0808 0.9747 EL 0.5080 EL EDL77- EL
0.0135 0.3480 022315 0.0118 0.1440 Valb0750 1.0000 EL 0.5600 EL
EDL77- EL 0.0000 0.2140 120214 0.0000 0.2260 Valb0907 0.9570 EL
0.5640 EL EDL78- EL 0.0006 0.1900 022315 0.0424 0.2460 Valb0585
0.8967 EL 0.5760 EL EDL78- EL 0.0837 0.3440 120214 0.0196 0.0800
Valb1638 0.9978 EL 0.5880 EL EDL79- EL 0.0000 0.0940 022315 0.0022
0.3180 Valb1640 0.9891 EL 0.8180 EL EDL79- EL 0.0000 0.0700 120214
0.0109 0.1120 Valb1430 0.9960 EL 0.6740 EL ELL06- EL 0.0000 0.0980
022315 0.0040 0.2280 Valb1155 0.9921 EL 0.7140 EL ELL06- EL 0.0073
0.1020 120214 0.0006 0.1840 Valb1553 0.9522 EL 0.4940 EL ELL07- EL
0.0308 0.3240 022315 0.0170 0.1820 Valb1562 0.9989 EL 0.6360 EL
ELL07- EL 0.0011 0.1900 120214 0.0000 0.1740 Valb1445 0.8847 EL
0.6300 EL ELL08- EL 0.0032 0.1880 022315 0.1122 0.1820 Valb1188
0.9871 EL 0.6260 EL ELL08- EL 0.0124 0.1600 120214 0.0005 0.2140
Valb1613 1.0000 EL 0.8320 EL ELL09- EL 0.0000 0.0740 022315 0.0000
0.0940 Valb1514 1.0000 EL 0.7780 EL ELL09- EL 0.0000 0.1120 120214
0.0000 0.1100 Valb1479 0.2786 STARI 0.5340 EL ELL10- EL 0.1610
0.2020 022315 0.5604 0.2640 Valb0933 0.5295 EL 0.6060 EL ELL10- EL
0.3586 0.2880 120214 0.1119 0.1060 Valb0923 0.6352 EL 0.5600 EL
ELL16- EL 0.1147 0.2900 022315 0.2501 0.1500 Valb0338 0.4277 STARI
0.4760 EL ELL16- EL 0.0788 0.4300 120214 0.4935 0.0940 Valb0783
0.8276 EL 0.5720 EL ELL17- EL 0.0090 0.3660 022315 0.1634 0.0620
Valb0261 0.9899 EL 0.6060 EL ELL17- EL 0.0038 0.3060 120214 0.0064
0.0880 Valb1264 0.7738 EL 0.5880 EL ELL18- EL 0.0116 0.2880 022315
0.2146 0.1240 Valb0545 0.1309 HC 0.5000 EL ELL18- EL 0.8465 0.3480
120214 0.0225 0.1520 Valb1427 0.9965 EL 0.5460 EL ELL61- EL 0.0022
0.3180 022315 0.0012 0.1360 Valb1071 0.9949 EL 0.5240 EL ELL61- EL
0.0040 0.3060 120214 0.0011 0.1700 Valb1211 0.6844 EL 0.4780 EL
ELL62- EL 0.3003 0.3280 022315 0.0153 0.1940 Valb1217 0.0136 HC
0.4560 EL ELL62- EL 0.9855 0.4140 120214 0.0009 0.1300 Valb1414
0.9456 EL 0.6260 EL ELL63- EL 0.0523 0.2680 022315 0.0020 0.1060
Valb1104 0.4263 HC 0.4460 HC ELL63- EL 0.5711 0.4700 120214 0.0026
0.0840 Valb0736 0.8514 EL 0.4700 HC ELL64- EL 0.1341 0.4880 022315
0.0145 0.0420 Valb0384 0.7501 EL 0.4000 HC ELL64- EL 0.2400 0.5680
120214 0.0100 0.0320 Valb0672 0.9502 EL 0.4200 HC ELL65- EL 0.0479
0.4660 022315 0.0019 0.1140 Valb0300 0.9441 EL 0.5220 EL ELL65- EL
0.4020 120214 0.0760 Valb1018 0.2340 HC 0.3360 HC ELL66- EL 0.7645
0.6140 022315 0.0015 0.0500 Valb0458 0.5250 EL 0.2980 HC ELL66- EL
0.4676 0.6620 120214 0.0074 0.0400 Valb1356 0.6663 EL 0.6480 EL
ELL67- EL 0.3313 0.1860 022315 0.0024 0.1660 Valb0492 0.7816 EL
0.5200 EL ELL67- EL 0.2169 0.3160 120214 0.0015 0.1640 Valb0408
0.0012 HC 0.0840 HC HCN07- HC 0.9984 0.8860 022315 0.0004 0.0300
Valb0311 0.0039 HC 0.0720 HC HCN07- HC 0.9653 0.8880 120214 0.0308
0.0400 Valb0440 0.0006 HC 0.1480 HC HCN08- HC 0.9993 0.8140 022315
0.0001 0.0380 Valb0123 0.0189 HC 0.1960 HC HCN08- HC 0.9758 0.7700
120214 0.0053 0.0340 Valb0327 0.0029 HC 0.1180 HC HCN09- HC 0.9970
0.8600 022315 0.0001 0.0220 Valb0112 0.0000 HC 0.0540 HC HCN09- HC
0.9995 0.9260 120214 0.0005 0.0200 Valb1108 0.0042 HC 0.3780 HC
HCN16- HC 0.9957 0.5120 022315 0.0001 0.1100 Valb0269 0.0724 HC
0.0700 HC HCN16- HC 0.9238 0.9120 120214 0.0039 0.0180 Valb0411
0.0243 HC 0.2760 HC HCN17- HC 0.9710 0.6700 022315 0.0047 0.0540
Valb0029 0.0491 HC 0.0620 HC HCN17- HC 0.9435 0.9220 120214 0.0074
0.0160 Valb0860 0.1211 HC 0.3560 HC HCN18- HC 0.8540 0.4300 022315
0.0250 0.2140 Valb0302 0.0198 HC 0.0240 HC HCN18- HC 0.9792 0.9720
120214 0.0010 0.0040 Valb0709 0.0060 HC 0.2980 HC HCN19- HC 0.9930
0.5740 022315 0.0010 0.1280 Valb0178 0.0024 HC 0.0480 HC HCN19- HC
0.9940 0.9260 120214 0.0036 0.0260 Valb0962 0.0978 HC 0.3700 HC
HCN25- HC 0.8543 0.4420 022315 0.0479 0.1880 Valb0418 0.6988 EL
0.2500 HC HCN25- HC 0.1304 0.5540 120214 0.1708 0.1960 Valb0632
0.0014 HC 0.1080 HC HCN28- HC 0.9982 0.8440 022315 0.0005 0.0480
Valb0124 0.0226 HC 0.0800 HC HCN28- HC 0.9655 0.8780 120214 0.0119
0.0420 Valb0690 0.9013 EL 0.5920 EL HCN29- HC
0.0929 0.3340 022315 0.0058 0.0740 Valb0066 0.0876 HC 0.1260 HC
HCN29- HC 0.8866 0.8560 120214 0.0257 0.0180 Valb1466 0.0038 HC
0.1860 HC HCW13- HC 0.9957 0.7800 022315 0.0005 0.0340 Valb0777
0.2406 HC 0.1320 HC HCW13- HC 0.7540 0.8560 120214 0.0054 0.0120
Valb1405 0.0021 HC 0.2540 HC HCW21- HC 0.9959 0.5900 022315 0.0019
0.1560 Valb0802 0.2993 HC 0.1660 HC HCW21- HC 0.6973 0.8180 120214
0.0034 0.0160 Valb1254 0.5258 EL 0.4020 EL HCW25- HC 0.4539 0.3720
022315 0.0203 0.2260 Valb0697 0.0064 HC 0.4060 HC HCW25- HC 0.9906
0.4180 120214 0.0031 0.1760 Valb1138 0.0005 HC 0.1720 HC HCW26- HC
0.9988 0.7260 022315 0.0007 0.1020 Valb0520 0.0041 HC 0.1580 HC
HCW26- HC 0.9956 0.7940 120214 0.0004 0.0480 Valb1119 0.0001 HC
0.2120 HC HCW28- HC 0.9998 0.7240 022315 0.0001 0.0640 Valb0572
0.1165 HC 0.1180 HC HCW28- HC 0.8831 0.8600 120214 0.0004 0.0220
Valb0943 0.0616 HC 0.2260 HC HCW29- HC 0.9320 0.5440 022315 0.0064
0.2300 Valb0419 0.3990 HC 0.2480 HC HCW29- HC 0.5992 0.6840 120214
0.0018 0.0680 Valb1282 0.0191 HC 0.2980 HC HCW34- HC 0.6025 0.4380
022315 0.3783 0.2640 Valb0719 0.0209 HC 0.0980 HC HCW34- HC 0.9768
0.8980 120214 0.0024 0.0040 Valb1535 0.0056 HC 0.2160 HC HCW37- HC
0.9885 0.5380 022315 0.0059 0.2460 Valb1091 0.0163 HC 0.2120 HC
HCW37- HC 0.9766 0.7280 120214 0.0071 0.0600 Valb1509 0.1004 HC
0.3080 HC HCW44- HC 0.8845 0.5860 022315 0.0151 0.1060 Valb0944
0.0532 HC 0.2300 HC HCW44- HC 0.9143 0.7280 120214 0.0325 0.0420
Valb1349 0.0037 HC 0.3080 HC HCW46- HC 0.9898 0.6100 022315 0.0066
0.0820 Valb0801 0.0039 HC 0.2640 HC HCW46- HC 0.9822 0.6500 120214
0.0139 0.0860 Valb1561 0.0005 STARI 0.0044 STARI M06A- STARI 0.0000
0.1788 022315 0.9995 0.8168 Valb1328 0.6469 EL 0.5180 EL M06A-
STARI 0.0097 0.0960 120214 0.3434 0.3860 Valb0329 0.2186 STARI
0.2140 STARI M09A- STARI 0.0048 0.0740 022315 0.7767 0.7120
Valb0070 0.0212 STARI 0.2480 STARI M09A- STARI 0.0066 0.0980 120214
0.9722 0.6540 Valb1052 0.0298 STARI 0.3840 STARI M13A- STARI 0.0061
0.1920 022315 0.9640 0.4240 Valb0809 0.9494 EL 0.6200 EL M13A-
STARI 0.0020 0.1560 120214 0.0486 0.2240 B Valb1256 0.0016 STARI
0.2340 STARI M16A- STARI 0.0002 0.1440 022315 0.9982 0.6220
Valb1100 0.0232 STARI 0.2400 STARI M16A- STARI 0.0055 0.0820 120214
0.9713 0.6780 Valb1236 0.1166 STARI 0.4740 EL M19A- STARI 0.0227
0.2340 022315 0.8607 0.2920 Valb0580 0.1942 STARI 0.4080 STARI
M19A- STARI 0.1003 0.1800 120214 0.7055 0.4120 Valb1525 0.9962 EL
0.3660 STARI M22A- STARI 0.0000 0.1700 022315 0.0038 0.4640
Valb0534 0.1791 STARI 0.3520 STARI M22A- STARI 0.0000 0.1880 120214
0.8208 0.4600 Valb0556 0.3684 STARI 0.3120 STARI M26A- STARI 0.1161
0.0300 022315 0.5155 0.6580 Valb0116 0.4121 STARI 0.1900 STARI
M26A- STARI 0.0005 0.0560 120214 0.5874 0.7540 Valb0461 0.0048
STARI 0.2000 STARI M27A- STARI 0.0293 0.0860 022315 0.9659 0.7140
Valb0266 0.0169 STARI 0.1300 STARI M27A- STARI 0.0001 0.0560 120214
0.9830 0.8140 Valb0447 0.0016 STARI 0.1280 STARI S03- STARI 0.1106
0.0780 022315 0.8877 0.7940 Valb0026 0.0005 STARI 0.1320 STARI S03-
STARI 0.0004 0.0640 120214 0.9992 0.8040 Valb1114 0.0013 STARI
0.1800 STARI S09- STARI 0.0004 0.2660 022315 0.9982 0.5540 Valb0464
0.1404 STARI 0.1320 STARI S09- STARI 0.0000 0.2000 120214 0.8596
0.6680 Valb1292 0.0002 STARI 0.1360 STARI S17- STARI 0.0000 0.1980
022315 0.9997 0.6660 Valb0754 0.0001 STARI 0.0980 STARI S17- STARI
0.0000 0.1480 120214 0.9999 0.7540 Valb0434 0.0209 STARI 0.1780
STARI S21- STARI 0.0896 0.2000 022315 0.8896 0.6220 Valb0044 0.0148
STARI 0.2560 STARI S21- STARI 0.0203 0.1920 120214 0.9649 0.5520
Valb0873 0.0079 STARI 0.1340 STARI S33- STARI 0.0169 0.2480 022315
0.9753 0.6180 Valb0352 0.0003 STARI 0.1280 STARI S33- STARI 0.0087
0.2180 120214 0.9910 0.6540 Valb1141 0.0000 STARI 0.1060 STARI S39-
STARI 0.0169 0.1100 022315 0.9831 0.7840 Valb0480 0.0000 STARI
0.0540 STARI S39- STARI 0.0002 0.0500 120214 0.9998 0.8960 Valb0618
0.0015 STARI 0.2640 STARI S43- STARI 0.0010 0.2060 022315 0.9975
0.5300 Valb0660 0.0018 STARI 0.2700 STARI S43- STARI 0.0008 0.1400
120214 0.9973 0.5900 Valb0223 0.0002 STARI 0.1080 STARI S47- STARI
0.0340 0.3080 022315 0.9658 0.5840 Valb0054 0.0023 STARI 0.0640
STARI S47- STARI 0.0168 0.0740 120214 0.9808 0.8620 Valb0335 0.0085
STARI 0.0660 STARI S53- STARI 0.0023 0.0440 022315 0.9893 0.8900
Valb0197 0.0050 STARI 0.0320 STARI S53- STARI 0.0001 0.0320 120214
0.9949 0.9360 Valb0409 0.0714 STARI 0.1680 STARI S55- STARI 0.0715
0.1420 022315 0.8571 0.6900 Valb0060 0.0119 STARI 0.1020 STARI S55-
STARI 0.0059 0.1180 120214 0.9821 0.7800 Valb0437 0.0001 STARI
0.0800 STARI S65- STARI 0.0078 0.1060 022315 0.9921 0.8140 Valb0093
0.0000 STARI 0.1060 STARI S65- STARI 0.0001 0.0720 120214 0.9999
0.8220
Sequence CWU 1
1
1514PRTArtificial SequenceSynthetic Sequence 1Asp Phe Arg
Tyr124PRTArtificial SequenceSynthetic Sequence 2Gln Leu Pro
Lys134PRTArtificial SequenceSynthetic Sequence 3Arg Asp Cys
Ala145PRTArtificial SequenceSynthetic Sequence 4Gln Val Leu Leu
Gly1 554PRTArtificial SequenceSynthetic Sequence 5Lys Lys Thr
Thr164PRTArtificial SequenceSynthetic Sequence 6Ala Lys Met
Asn174PRTArtificial SequenceSynthetic Sequence 7Asp His Phe
Asp185PRTArtificial SequenceSynthetic Sequence 8Leu Lys Glu Pro
Pro1 594PRTArtificial SequenceSynthetic Sequence 9Ala Ile Lys
Thr1104PRTArtificial SequenceSynthetic Sequence 10Pro Asp Pro
Leu1114PRTArtificial SequenceSynthetic Sequence 11Pro Gln Ala
Lys1124PRTArtificial SequenceSynthetic Sequence 12His Cys Asp
Thr1134PRTArtificial SequenceSynthetic Sequence 13Gln Glu Gln
Ile1145PRTArtificial SequenceSynthetic Sequence 14Ala Leu Ala Pro
Lys1 5155PRTArtificial SequenceSynthetic Sequence 15Leu Ala Pro Lys
Ile1 5
* * * * *