U.S. patent application number 17/492367 was filed with the patent office on 2022-03-31 for predicting skin age based on the analysis of skin flora and lifestyle data.
The applicant listed for this patent is ProdermIQ, Inc.. Invention is credited to Sasan AMINI, Dana HOSSEINI, Eveie SCHWARTZ.
Application Number | 20220101942 17/492367 |
Document ID | / |
Family ID | |
Filed Date | 2022-03-31 |
United States Patent
Application |
20220101942 |
Kind Code |
A1 |
HOSSEINI; Dana ; et
al. |
March 31, 2022 |
PREDICTING SKIN AGE BASED ON THE ANALYSIS OF SKIN FLORA AND
LIFESTYLE DATA
Abstract
The present invention relates to a combination of experimental
and computational workflows that allow characterization of specific
molecular mechanisms by which the microbiome contribute to skin
health and skin age.
Inventors: |
HOSSEINI; Dana; (San Diego,
CA) ; AMINI; Sasan; (Redwood City, CA) ;
SCHWARTZ; Eveie; (San Diego, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
ProdermIQ, Inc. |
San Diego |
CA |
US |
|
|
Appl. No.: |
17/492367 |
Filed: |
October 1, 2021 |
Related U.S. Patent Documents
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
|
|
16860456 |
Apr 28, 2020 |
11211143 |
|
|
17492367 |
|
|
|
|
15760813 |
Mar 16, 2018 |
10679725 |
|
|
PCT/US2016/052161 |
Sep 16, 2016 |
|
|
|
16860456 |
|
|
|
|
62220072 |
Sep 17, 2015 |
|
|
|
International
Class: |
G16B 5/00 20060101
G16B005/00; G16B 20/00 20060101 G16B020/00; G16H 50/30 20060101
G16H050/30; A61B 34/10 20060101 A61B034/10; G16B 20/20 20060101
G16B020/20; G16H 20/30 20060101 G16H020/30; A61B 10/02 20060101
A61B010/02; C12Q 1/689 20060101 C12Q001/689 |
Claims
1.-20. (canceled)
21. A method for treating an individual based on a skin age of the
individual, comprising: a. receiving metadata related to the
individual and a microbiome of the individual, wherein the metadata
comprises a parameter selected from the group consisting of
lifestyle information of the individual, an ethnicity of the
individual, a duration of sleep of the individual, a duration of
sun exposure of the individual, a diet of the individual, an
antibiotic used by the individual, and a skin care product used by
the individual; b. computer processing the metadata related to the
individual and the microbiome of the individual to determine the
skin age of the individual; c. determining, based at least in part
on the skin age of the individual, a treatment to provide to the
individual to improve the skin age of the individual; and d.
treating the individual with the treatment.
22. The method of claim 21, wherein the metadata further comprises
a gender of the individual.
23. The method of claim 21, wherein the treatment comprises a
modification of the lifestyle of the individual, a modification of
the duration of sleep of the individual, a modification of the
duration of sun exposure of the individual, a modification of the
diet of the individual, a modification of the antibiotic used by
the individual, or a modification of the skin care product used by
the individual.
24. The method of claim 21, wherein the skin age of the individual
is different from a chronological age of the individual.
25. The method of claim 21, further comprising obtaining a sample
from the individual, and assaying the sample to determine the
microbiome of the individual.
26. The method of claim 25, wherein the sample is obtained from a
skin or subcutaneous tissue of the individual via swipe, scrape,
swab, biopsy, or tape.
27. The method of claim 25, wherein the microbiome comprises a
plurality of microorganisms selected from the group consisting of
bacteria, fungi, and any combination thereof.
28. The method of claim 25, wherein the microbiome comprises a
bacteria or a fungi selected from the group consisting of
Alphaproteobacteria, Betaproteobacteria, Gammaproteobacteria,
Propionibacteria, Proteobacteria, Bacteroidetes, Corynebacteria,
Actinobacteria, Clostridiales, Lactobacillales, Staphylococcus,
Bacillus, Micrococcus, Streptococcus, Bacteroidales,
Flavobacteriales, Firmicutes, Enterococcus, Pseudomonas,
Malassezia, Maydida, Debaroyomyces, and Cryptococcus.
29. The method of claim 25, wherein the microbiome comprises a
bacteria of the genus Propionibacteria, Staphylococci,
Corynebacteria, or Acenitobacteria.
30. The method of claim 25, wherein the microbiome comprises a
bacteria selected from the group consisting of Propionibacterium
acnes, Corynebacterium kroppenstedtii, Neisseria meningitides, and
Staphylococcus epidermidis.
31. The method of claim 25, wherein the assaying comprises
sequencing nucleic acids of the sample to determine the
microbiome.
32. The method of claim 31, wherein the sequencing is selected from
the group consisting of whole genome sequencing, next-generation
sequencing, Sanger-sequencing, 16S ribosomal deoxyribonucleic acid
(rDNA) sequencing, and 16S ribosomal ribonucleic acid (rRNA)
sequencing.
33. The method of claim 21, wherein the treatment comprises a
substance that modifies a microbiome of the individual.
34. The method of claim 21, wherein determining the treatment to
provide to the individual comprises determining an effect of an
antibiotic compound, an antioxidant compound, or an
anti-inflammatory compound on a microbiome of the individual.
35. The method of claim 34, wherein the treatment comprises the
antibiotic compound, the antioxidant compound, or the
anti-inflammatory compound that is determined to improve the skin
age of the individual.
36. The method of claim 35, wherein the treatment comprises the
antibiotic compound, the antioxidant compound, or the
anti-inflammatory compound that is determined to improve the skin
age of the individual along with another recommendation to improve
the skin age of the individual.
37. The method of claim 21, wherein the computer processing
comprises a regression model.
38. The method of claim 21, wherein the computer processing
comprises a Bayesian network model.
39. The method of claim 21, wherein the computer processing
comprises a random forest model.
40. The method of claim 39, wherein the random forest model
determines the skin age of the individual based at least in part on
analyzing at least one feature of microbial species or metadata
selected from the group listed in FIG. 6.
41. The method of claim 40, wherein the random forest model
determines the skin age of the individual based at least in part on
analyzing at least 5 features of microbial species or metadata
selected from the group listed in FIG. 6.
42. The method of claim 21, wherein the parameter comprises the
duration of sleep of the individual, the duration of sun exposure
of the individual, the diet of the individual, the antibiotic used
by the individual, or the skin care product used by the
individual.
43. The method of claim 42, wherein the parameter comprises the
duration of sleep of the individual or the duration of sun exposure
of the individual.
44. The method of claim 43, wherein the parameter comprises the
duration of sleep of the individual.
45. The method of claim 44, wherein the parameter comprises the
duration of sleep of the individual and the duration of sun
exposure of the individual.
Description
CROSS-REFERENCE
[0001] This application is a continuation of U.S. application Ser.
No. 16/860,456, filed Apr. 28, 2020, which is a continuation of
U.S. application Ser. No. 15/760,813, filed Mar. 16, 2018, now U.S.
Pat. No. 10,679,725, issued Jun. 9, 2020, which is a U.S. National
Stage Entry of International Application No. PCT/US2016/052161,
filed Sep. 16, 2016, which claims the benefit of U.S. Provisional
Application No. 62/220,072, filed Sep. 17, 2015, each of which is
incorporated by reference herein in its entirety.
BACKGROUND OF THE INVENTION
Field of the Invention
[0002] The invention relates generally to computational methods and
more specifically to methods and a system for characterizing skin
age as a function of skin flora.
Background Information
[0003] About 100 trillion microorganisms live in and on the human
body vastly outnumbering the body's approximately 10 trillion human
cells. These normally harmless viruses, bacteria and fungi are
referred to as commensal or mutualistic organisms. Commensal and
mutualistic organisms help keep our bodies healthy in many ways:
they help us to digest foods and acquire nutrients such as vitamins
B and K, encourage the immune system to develop and prevent the
colonization of, for example, bacterial pathogens that cause
disease by competing with them. Together all of the microorganisms
living in and on the body--commensal, mutualistic and
pathogenic--are referred to as the microbiome and their equilibrium
and associated metabolome is closely linked to an individual's
health status and vice-versa.
[0004] Next generation sequencing (NGS) has created an opportunity
to quickly and accurately identify and profile the microbiome
inhabiting the skin and subcutaneous tissue. The optimal flora also
interacts with the host immune system in a synergistic way further
propagating its health benefits. The associated metabolome of
individuals can also be profiled either by a mass-spectrometry
based system or using genomics-based metabolome modeling and
flux-balance analysis and used to make a healthy metabolome
profile. All these methodologies can be used to dissect the
complexity of microbial communities.
[0005] The highly dynamic microbial communities than live on the
skin are important to skin health. While the importance of skin
microbiome makes it an appealing target for promoting skin health,
this inherent variability in these communities makes it difficult
to identify the underlying molecular mechanisms that link
microbiome structure to human fitness. One possible reason for this
high level of population diversity is that there is a significant
functional redundancy in the population. While a large variety of
possible population structures may be functionally equivalent in
their aggregate metabolic capacities, the specific assembly of
molecular functions would be the key indicator of a microbial
community's capacity to influence human host state.
[0006] Aging is the accumulation of changes in an organism or
object over time. Aging in humans refers to a multidimensional
process of physical, biological, psychological, and social change.
One of the human organs widely studied within the context of aging
is skin. Human skin ages over time, but the specifics of that
process, the pace, and extent varies drastically among different
individuals, and is a complex interplay between genetic elements,
and environmental factors, including microbiome and lifestyle
characteristics. Understanding this dynamic is critical for better
controlling the aging process.
SUMMARY OF THE INVENTION
[0007] The invention relates generally to identifying the specific
molecular mechanisms within which microbiome contributes to skin
health. To this end, a unique and richly contextualized dataset of
skin microbiomes has been assembled for analysis. Using
computational biology and machine learning techniques, molecular
information from population structure data are extrapolated and the
information is used to identify the important links between
microbiome and skin health.
[0008] Accordingly, in one embodiment, the invention provides a
method of identifying the specific molecular mechanisms within
which microbiome contributes to skin age. To this end, a unique and
richly contextualized dataset of skin microbiomes has been
assembled for analysis. Using computational biology and machine
learning techniques, molecular information from population
structure data are extrapolated and the information is used to
identify the important links between microbiome and skin age.
[0009] In another embodiment, the invention provides a method of
characterizing microbial communities, associated enzymatic
activities, and metabolites that can impact skin health and skin
age.
[0010] In yet another embodiment, the present invention provides a
method of identifying microbiome feature targets that influence
skin age and interactions with donor parameters like sleep, sun
exposure, and antibiotic use.
[0011] In still another embodiment, the present invention provides
a method for determining a skin age which include analyzing a
microbiome of a skin sample from a donor subject and determining
the skin age, wherein analyzing includes classifying the microbiome
utilizing microbiome taxonomy information.
[0012] In another embodiment, the present invention provides a
method for determining a customized therapy. The method includes
predicting and/or determining a skin age for a subject using the
methods of the invention, and prescribing a customized treatment
including, but not limited to, oral or topical medications, skin
creams, lifestyle recommendations, or a combination thereof, to the
subject based on the determined skin age with, or without, the
intention of improving overall skin quality.
[0013] In yet another embodiment, the present invention provides a
method for recommending a lifestyle or product based on a predicted
skin age of a subject. The method includes predicting a skin age
for a subject using the methods of the invention, and prescribing
to the subject one or more lifestyle or product recommendations. In
embodiments, the lifestyle or product recommendation is associated
with prescription or non-prescription skin care products, sun
exposure limits, antibiotic use (e.g., type and quantity), sleep
(e.g., daily recommended average hours of sleep, diet, exercise
(e.g., type, frequency and/or exertion level), medications, pet
ownership (e.g., type of pet), probiotics (e.g., use and type),
vitamin and supplement use, or combinations thereof.
[0014] In another embodiment, the invention provides a
non-transitory computer-readable medium for predicting skin age.
The medium includes instructions stored thereon, that when executed
on a processor, perform the steps of: a) analyzing microbiome data;
and b) generating a predicted skin age.
BRIEF DESCRIPTION OF THE DRAWINGS
[0015] FIG. 1 is a graphical representation of data. Forehead
microbiome profiles of multiple individuals were compared using the
Bray-Curtis dissimilarity measure. The result was demonstrated as a
heat-plot with individual microbiomes segregated to males (in blue)
and females (in red) across both rows and columns, sorted in
ascending age order. In the heatplot, more similar microbiomes are
color-coded in red (dark grey) and less similar examples are shown
in green (light grey). The red (grey) halo across the diagonal line
proves that age is strong influence on community structure.
[0016] FIG. 2 is a graphical representation of data. Two models
were generated to predict skin age based on the microbiome data
(i.e. taxa only) or a combination of microbiome plus enzyme
activities and metabolites. The taxa only (orange) predicts age
correlations with actual age with a R.sup.2=0.48. Mixed model
(blue) predicts age with greater accuracy and R.sup.2=0.63.
[0017] FIG. 3 is a graphical representation of data showing the
effect of different environmental factors on skin age. A
computational model is built to predict the impact of microbiome
and lifestyle parameters on skin age. The generated model can next
be used to predict how any lost hour of sleep or extra hour of
sun-exposure ages the skin.
[0018] FIG. 4 is a graphical representation of data using Bayesian
Network Inference to identify statistically significant casual
links between donor parameters and skin microbiome features. In the
network figure, donor parameters are shown with grey nodes,
bacterial taxa are shown as yellow nodes, enzyme activities are
shown as purple nodes and metabolites are shown as blue nodes. All
predicted causal relationships between parameters are shown in
blue.
[0019] FIG. 5 is a series of graphical representations generated
using a random forest model built to predict biological age from
skin microbiome alone (left panel) or from a combination of skin
microbiome, plus metadata collected from individuals (right
panel).
[0020] FIG. 6 is a graphical representation showing the importance
of different variables deconstructed in the skin age prediction
model. The y-axis shows a list of the variables (i.e. microbial
species or metadata) and the x-axis is the IncNodePurity, a measure
of how impurity changes in a random forest model when variables are
randomly permuted.
DETAILED DESCRIPTION OF THE INVENTION
[0021] It is now well established that about 100 trillion
microorganisms live in and on the human body vastly outnumbering
the approximately 10 trillion human cells. These normally harmless
viruses, bacteria and fungi are referred to either as commensals
(are not harmful to their host) or mutualistic (offer a benefit).
Commensal and mutualistic organisms help keep our bodies healthy in
many ways: they help us to digest foods and acquire nutrients such
as vitamins B and K, encourage our immune system to develop and
prevent the colonization of, for example, bacterial pathogens that
cause disease by competing with them. Together all of the
microorganisms--commensal, mutualistic and pathogenic--are referred
to as the body's microbiome and their equilibrium and associated
microbiome is closely linked to an individual's health status and
vice-versa.
[0022] The present invention relates to a combination of
experimental and computational workflows that allow
characterization of specific molecular mechanisms by which the
microbiome contribute to skin health and skin age. A skin profiling
platform was used to characterize skin microbiome of multiple
individuals at different age groups. Using computational biology
and machine learning techniques, molecular information was
extrapolated from population structure data and the information was
used to identify the important links between microbiome and skin
age. In particular, embodiments of the methods and the associated
computational platform provided herein relate to collecting a
unique and highly contextualized skin microbiome dataset and
generating metagenomic predictions and calculating metabolic models
from the microbiome community structures. Using these data,
computational models were developed for donor age as function of
donor parameters and microbiome features. Using this model,
microbiome feature targets that influence skin age and interactions
with donor parameters like sleep, sun exposure, and antibiotic use
were identified. Not only will this lead to specific,
microbiome-based hypotheses for intervention for skin health, but
also will become a powerful data analysis pipeline for the
computational modeling and interpretation of future microbiome
data.
[0023] The invention provides a method of identifying the specific
molecular mechanisms within which microbiome contributes to skin
age. To this end, a unique and richly contextualized dataset of
skin microbiomes has been assembled for analysis. Using
computational biology and machine learning techniques, molecular
information from population structure data are extrapolated and the
information is used to identify the important links between
microbiome and skin age
[0024] The term "skin" or "subcutaneous tissue" refers to the outer
protective covering of the body, consisting of the epidermis
(including the stratum corneum) and the underlying dermis, and is
understood to include sweat and sebaceous glands, as well as hair
follicle structures and nails. Throughout the present application,
the adjective "cutaneous" and "subcutaneous" can be used, and
should be understood to refer generally to attributes of the skin,
as appropriate to the context in which they are used. The epidermis
of the human skin comprises several distinct layers of skin tissue.
The deepest layer is the stratum basalis layer, which consists of
columnar cells. The overlying layer is the stratum spinosum, which
is composed of polyhedral cells. Cells pushed up from the stratum
spinosum are flattened and synthesize keratohyalin granules to form
the stratum granulosum layer. As these cells move outward, they
lose their nuclei, and the keratohyalin granules fuse and mingle
with tonofibrils. This forms a clear layer called the stratum
lucidum. The cells of the stratum lucidum are closely packed. As
the cells move up from the stratum lucidum, they become compressed
into many layers of opaque squamae. These cells are all flattened
remnants of cells that have become completely filled with keratin
and have lost all other internal structure, including nuclei. These
squamae constitute the outer layer of the epidermis, the stratum
corneum. At the bottom of the stratum corneum, the cells are
closely compacted and adhere to each other strongly, but higher in
the stratum they become loosely packed, and eventually flake away
at the surface.
[0025] As used in this specification and the appended claims, the
singular forms "a", "an", and "the" include plural references
unless the context clearly dictates otherwise. Thus, for example,
references to "the method" includes one or more methods, and/or
steps of the type described herein which will become apparent to
those persons skilled in the art upon reading this disclosure and
so forth.
[0026] 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 this invention belongs. Although
any methods and materials similar or equivalent to those described
herein can be used in the practice or testing of the invention, the
preferred methods and materials are now described.
[0027] The invention relates generally to using microbiome
community structures to predict microbiome metagenomes, the genes
and gene abundances present in a microbial community. Predicted
metagenomes are comprised of 2055 enzyme functions. From predicted
metagenomes, community metabolomes are modeled. Model community
metabolomes are comprised of 2893 metabolites, 4481 enzyme
function-mediated interactions, and 1346 enzyme functions. From
prior experimental result, predicted metagenomes and metabolome
correlate well with biological observations.
[0028] To further characterize the association of age with
microbiome, the forehead microbiome profiles of multiple
individuals were compared using the Bray-Curtis dissimilarity
measure. The result was demonstrated as a heat-plot with individual
microbiomes segregated to males (in blue) and females (in red),
sorted in ascending age order. In the heatplot, more similar
microbiomes are color-coded in red and less similar examples are
shown in green. The red halo across the diagonal line proves that
age is strong influence on community structure.
[0029] A machine-learning approach was used to generate
computational models that predict donor age as a function of donor
parameters (e.g. gender, ethnicity, hours of sleep, hours of sun
exposure) and microbiome features (population structure, predicted
metagenome). To this end, a statistics-based evolutional algorithm
using symbolic regression was used to search the space of
mathematical equations to find a model that best fits the data
provided, varying both the form and parameters of possible models.
Two models were generated. The first used only population structure
data for microbiome features, the second incorporated predicted
metagenomes and models metabolome in addition to population
structure. All the microbiome features, lifestyle information and
parameters were collected from human subjects.
[0030] The term "subject" as used herein refers to any individual
or patient to which the subject methods are performed. Generally
the subject is human, although as will be appreciated by those in
the art, the subject may be an animal. Thus other animals,
including mammals such as rodents (including mice, rats, hamsters
and guinea pigs), cats, dogs, rabbits, farm animals including cows,
horses, goats, sheep, pigs, etc., and primates (including monkeys,
chimpanzees, orangutans and gorillas) are included within the
definition of subject.
[0031] Computational models were validated in one of two ways. The
first validation method, the correlation between predicted and
actual donor age was considered (FIG. 2). Here, the mixed model
(Pearson Correlation Coefficient=0.81) outperformed the taxa-only
model (Pearson Correlation Coefficient=0.72). While both
predictions are strong, the mixed model has the advantage of not
only being more accurate, but also the mixed model has the capacity
to provide greater insight into the molecular mechanisms than link
skin microbiome with skin age. In the second validation method, we
predicted the effects of reduced sleep, increasing sun exposure,
and use of antibiotics of skin "age" (FIG. 3). As expected, these
parameters has a negative effect of predicted skin age. As
predicted from computational model, every extra hour of lost sleep
"ages" skin .about.0.98 years, every extra hour of sun "ages" skin
.about.0.46 years, and the use of antibiotics "ages" skin
.about.0.54 years.
[0032] Accordingly, in one aspect, the invention provides a method
of characterizing the age of skin or subcutaneous tissue of a
subject. The method includes: a) obtaining a sample comprising a
plurality of microorganisms from the skin or subcutaneous tissue of
the subject; and b) analyzing and classifying the plurality of
microorganisms of (a) to characterize the microbiome of the
subject, thereby characterizing the microbiome of the subject; and
c) use the microbiome information to predict skin or subcutaneous
tissue age.
[0033] As used herein, the terms "sample" and "biological sample"
refer to any sample suitable for the methods provided by the
present invention. A sample of cells can be any sample, including,
for example, a skin or subcutaneous tissue sample obtained by
non-invasive techniques such as tape stripping, scraping, swabbing,
or more invasive techniques such as biopsy of a subject. In one
embodiment, the term "sample" refers to any preparation derived
from skin or subcutaneous tissue of a subject. For example, a
sample of cells obtained using the non-invasive method described
herein can be used to isolate nucleic acid molecules or proteins
for the methods of the present invention. Samples for the present
invention may be taken from an area of the skin shown to exhibit a
disease or disorder, which is suspected of being the result of a
disease or a pathological or physiological state, such as psoriasis
or dermatitis, or the surrounding margin or tissue. As used herein,
"surrounding margin" or "surrounding tissue" refers to tissue of
the subject that is adjacent to the skin shown to exhibit a disease
or disorder, but otherwise appears to be normal.
[0034] Accordingly, in one aspect, the invention proposes a model
that describes how the microbiome can potentially protect skin from
aging effects. A Baysian Network (BN) model is generated for donor
parameters and microbiome features to identify the potential causal
links between them. BN are probabilistic graphical models of
conditional dependencies between random variables in the form of a
directed acyclic graph. Directed edges are relationships between
nodes inferred from data such that the state of a child node is
dependent on the states of its parent nodes. In the generated
network, no node was permitted to be the parent node of a donor
parameter. Networks for microbiome taxa, predicted metagenomes, and
community metabolomes were generated independently, and then final
networks were combined into a composite network (FIG. 4). From this
network, a number of potential molecular mechanisms can be
predicted, linked to antioxidant activities, antimicrobial
activities, and production of anti-inflammatory compounds.
[0035] Accordingly, in one aspect, the invention proposes a model
that can predict skin age from skin microbiome composition. The
model has been built using a random forest approach that can take
the microbiome composition as the only input (FIG. 5, left panel)
with a R-squared value of 0.89. The model can be improved further
by including other metadata including average hours of sun
exposure, average hours of sleep, skin microbiome balance, skin
microbiome diversity, and skin happiness. The new model which
includes the microbiome composition and all above-mentioned
metadata (FIG. 5, right panel) has an improved performance with a
R-squared value of 0.93.
[0036] Contribution of different variables, microbial species,
microbial genera, or metadata to skin age can be deconstructed from
the model (FIG. 6). The list of variables in the order of their
contribution to skin age model are listed in the y-axis from top to
bottom. As shown in the graph, Corynebacterium kroppenstedtii,
hours of sleep, Propionibacterium acnes, Neisseria meningitides,
and Staphylococcus epidermidis are the top variables with maximum
contribution to the predicted skin age.
[0037] Accordingly, in one aspect, the invention provides a method
of characterizing skin age for healthy or disease samples.
[0038] As used herein "healthy" refers to a sample from a subject
that is free from disease or disorder, a skin disorder, any
particular undesirable phenotype or risk thereof. The term healthy
skin refers to skin that is devoid of a skin condition, disease or
disorder, including, but not limited to inflammation, rash,
dermatitis, atopic dermatitis, eczema, psoriasis, dandruff, acne,
cellulitis, rosacea, warts, seborrheic keratosis, actinic
keratosis, tinea versicolor, viral exantham, shingles, ringworm,
and cancer, such as basal cell carcinoma, squamous cell carcinoma,
and melanoma.
[0039] Additionally, as used herein, a "disease" or "disorder" is
intended to generally refer to any skin associated disease, for
example, but in no way limited to, inflammation, rash, dermatitis,
atopic dermatitis, eczema, psoriasis, dandruff, acne, cellulitis,
rosacea, warts, seborrheic keratosis, actinic keratosis, tinea
versicolor, viral exantham, shingles, ringworm, and cancer, such as
basal cell carcinoma, squamous cell carcinoma, and melanoma.
[0040] The term "cancer" as used herein, includes any malignant
tumor including, but not limited to, carcinoma, melanoma and
sarcoma. Cancer arises from the uncontrolled and/or abnormal
division of cells that then invade and destroy the surrounding
tissues. As used herein, "proliferating" and "proliferation" refer
to cells undergoing mitosis. As used herein, "metastasis" refers to
the distant spread of a malignant tumor from its sight of origin.
Cancer cells may metastasize through the bloodstream, through the
lymphatic system, across body cavities, or any combination thereof.
The term "cancerous cell" as provided herein, includes a cell
afflicted by any one of the cancerous conditions provided herein.
The term "carcinoma" refers to a malignant new growth made up of
epithelial cells tending to infiltrate surrounding tissues, and to
give rise to metastases. The term "melanoma" refers to a malignant
tumor of melanocytes which are found predominantly in skin but also
in bowel and the eye. "Melanocytes" refer to cells located in the
bottom layer, the basal lamina, of the skin's epidermis and in the
middle layer of the eye. Thus, "melanoma metastasis" refers to the
spread of melanoma cells to regional lymph nodes and/or distant
organs (e.g., liver, brain, breast, prostate, etc.).
[0041] The microbiome profiles can be generated by any method and
platform that utilizes analysis of a nucleic acid molecule, such as
sequencing a nucleic acid molecule. Sequencing methods may include
whole genome sequencing, next generation sequencing,
Sanger-sequencing, 16S rDNA sequencing and 16S rRNA sequencing.
Further, such methods and platforms described herein may utilize
mass-spectrometry, quantitative PCR, immunofluorescence, in situ
hybridization, a microbial staining based platform, or combination
thereof.
[0042] In embodiments, the input to the identification platform can
be any nucleic acid, including DNA, RNA, cDNA, miRNA, mtDNA, single
or double-stranded. This nucleic acid can be of any length, as
short as oligos of about 5 bp to as long a megabase or even longer.
As used herein, the term "nucleic acid molecule" means DNA, RNA,
single-stranded, double-stranded or triple stranded and any
chemical modifications thereof. Virtually any modification of the
nucleic acid is contemplated. A "nucleic acid molecule" can be of
almost any length, from 10, 20, 30, 40, 50, 60, 75, 100, 125, 150,
175, 200, 225, 250, 275, 300, 400, 500, 600, 700, 800, 900, 1000,
1500, 2000, 2500, 3000, 3500, 4000, 4500, 5000, 6000, 7000, 8000,
9000, 10,000, 15,000, 20,000, 30,000, 40,000, 50,000, 75,000,
100,000, 150,000, 200,000, 500,000, 1,000,000, 1,500,000,
2,000,000, 5,000,000 or even more bases in length, up to a
full-length chromosomal DNA molecule. For methods that analyze
expression of a gene, the nucleic acid isolated from a sample is
typically RNA.
[0043] Micro-RNAs (miRNA) are small single stranded RNA molecules
an average of 22 nucleotides long that are involved in regulating
mRNA expression in diverse species including humans (reviewed in
Bartel 2004). The first report of miRNA was that of the lin-4 gene,
discovered in the worm C. elegans (Lee, Feinbaum et al. 1993).
Since then hundreds of miRNAs have been discovered in flies, plants
and mammals. miRNAs regulate gene expression by binding to the
3'-untranslated regions of mRNA and catalyze either i) cleavage of
the mRNA; or 2) repression of translation. The regulation of gene
expression by miRNAs is central to many biological processes such
as cell development, differentiation, communication, and apoptosis
(Reinhart, Slack et al. 2000; Baehrecke 2003; Brennecke, Hipfner et
al. 2003; Chen, Li et al. 2004). Recently it has been shown that
miRNA are active during embryogenesis of the mouse epithelium and
play a significant role in skin morphogenesis (Yi, O'Carroll et al.
2006).
[0044] Given the role of miRNA in gene expression it is clear that
miRNAs will influence, if not completely specify the relative
amounts of mRNA in particular cell types and thus determine a
particular gene expression profile (i.e., a population of specific
mRNAs) in different cell types. In addition, it is likely that the
particular distribution of specific miRNAs in a cell will also be
distinctive in different cell types. Thus, determination of the
miRNA profile of a tissue may be used as a tool for expression
profiling of the actual mRNA population in that tissue.
Accordingly, miRNA levels and/or detection of miRNA mutations are
useful for the purposes of disease detection, diagnosis, prognosis,
or treatment-related decisions (i.e., indicate response either
before or after a treatment regimen has commenced) or
characterization of a particular disease in the subject.
[0045] As used herein, the term "protein" refers to at least two
covalently attached amino acids, which includes proteins,
polypeptides, oligopeptides and peptides. A protein may be made up
of naturally occurring amino acids and peptide bonds, or synthetic
peptidomimetic structures. Thus "amino acid", or "peptide residue",
as used herein means both naturally occurring and synthetic amino
acids. For example, homo-phenylalanine, citrulline and noreleucine
are considered amino acids for the purposes of the invention.
"Amino acid" also includes imino acid residues such as proline and
hydroxyproline. The side chains may be in either the (R) or the (S)
configuration.
[0046] A "probe" or "probe nucleic acid molecule" is a nucleic acid
molecule that is at least partially single-stranded, and that is at
least partially complementary, or at least partially substantially
complementary, to a sequence of interest. A probe can be RNA, DNA,
or a combination of both RNA and DNA. It is also within the scope
of the present invention to have probe nucleic acid molecules
comprising nucleic acids in which the backbone sugar is other that
ribose or deoxyribose. Probe nucleic acids can also be peptide
nucleic acids. A probe can comprise nucleolytic-activity resistant
linkages or detectable labels, and can be operably linked to other
moieties, for example a peptide.
[0047] A single-stranded nucleic acid molecule is "complementary"
to another single-stranded nucleic acid molecule when it can
base-pair (hybridize) with all or a portion of the other nucleic
acid molecule to form a double helix (double-stranded nucleic acid
molecule), based on the ability of guanine (G) to base pair with
cytosine (C) and adenine (A) to base pair with thymine (T) or
uridine (U). For example, the nucleotide sequence 5'-TATAC-3' is
complementary to the nucleotide sequence 5'-GTATA-3'.
[0048] As used herein "hybridization" refers to the process by
which a nucleic acid strand joins with a complementary strand
through base pairing. Hybridization reactions can be sensitive and
selective so that a particular sequence of interest can be
identified even in samples in which it is present at low
concentrations. In an in vitro situation, suitably stringent
conditions can be defined by, for example, the concentrations of
salt or formamide in the prehybridization and hybridization
solutions, or by the hybridization temperature, and are well known
in the art. In particular, stringency can be increased by reducing
the concentration of salt, increasing the concentration of
formamide, or raising the hybridization temperature. For example,
hybridization under high stringency conditions could occur in about
50% formamide at about 37.degree. C. to 42.degree. C. Hybridization
could occur under reduced stringency conditions in about 35% to 25%
formamide at about 30.degree. C. to 35.degree. C. In particular,
hybridization could occur under high stringency conditions at
42.degree. C. in 50% formamide, 5.times.SSPE, 0.3% SDS, and 200
mg/ml sheared and denatured salmon sperm DNA. Hybridization could
occur under reduced stringency conditions as described above, but
in 35% formamide at a reduced temperature of 35.degree. C. The
temperature range corresponding to a particular level of stringency
can be further narrowed by calculating the purine to pyrimidine
ratio of the nucleic acid of interest and adjusting the temperature
accordingly. Variations on the above ranges and conditions are well
known in the art.
[0049] As used herein, the term "skin flora" or "microbiome" refers
to microorganisms, including bacteria, viruses, and fungi that
inhabit the skin or subcutaneous tissues of the subject.
[0050] As used herein, the terms microbial, microbe, or
microorganism refer to any microscopic organism including
prokaryotes or eukaryotes, bacterium, archaebacterium, fungus,
virus, or protist, unicellular or multicellular.
[0051] As used herein, the term "ameliorating" or "treating" means
that the clinical signs and/or the symptoms associated with the
cancer or melanoma are lessened as a result of the actions
performed. The signs or symptoms to be monitored will be
characteristic of a particular cancer or melanoma and will be well
known to the skilled clinician, as will the methods for monitoring
the signs and conditions. Thus, a "treatment regimen" refers to any
systematic plan or course for treating a disease or cancer in a
subject.
[0052] In embodiments, nucleic acid molecules can also be isolated
by lysing the cells and cellular material collected from the skin
sample by any number of means well known to those skilled in the
art. For example, a number of commercial products available for
isolating polynucleotides, including but not limited to, RNeasy.TM.
(Qiagen, Valencia, Calif.) and TriReagent.TM. (Molecular Research
Center, Inc, Cincinnati, Ohio) can be used. The isolated
polynucleotides can then be tested or assayed for particular
nucleic acid sequences, including a polynucleotide encoding a
cytokine. Methods of recovering a target nucleic acid molecule
within a nucleic acid sample are well known in the art, and can
include microarray analysis.
[0053] As discussed further herein, nucleic acid molecules may be
analyzed in any number of ways known in the art that may assist in
determining the microbiome and/or metabolome associated with an
individual's skin. For example, the presence of nucleic acid
molecules can be detected by DNA-DNA or DNA-RNA hybridization or
amplification using probes or fragments of the specific nucleic
acid molecule. Nucleic acid amplification based assays involve the
use of oligonucleotides or oligomers based on the nucleic acid
sequences to detect transformants containing the specific DNA or
RNA.
[0054] In another embodiment, antibodies that specifically bind the
expression products of the nucleic acid molecules of the invention
may be used to characterize the skin lesion of the subject. The
antibodies may be used with or without modification, and may be
labeled by joining them, either covalently or non-covalently, with
a reporter molecule.
[0055] A wide variety of labels and conjugation techniques are
known by those skilled in the art and may be used in various
nucleic acid and amino acid assays. Means for producing labeled
hybridization or PCR probes for detecting sequences related to the
nucleic acid molecules of Tables 1-6 include oligolabeling, nick
translation, end-labeling or PCR amplification using a labeled
nucleotide. Alternatively, the nucleic acid molecules, or any
fragments thereof, may be cloned into a vector for the production
of an mRNA probe. Such vectors are known in the art, are
commercially available, and may be used to synthesize RNA probes in
vitro by addition of an appropriate RNA polymerase such as T7, T3,
or SP6 and labeled nucleotides. These procedures may be conducted
using a variety of commercially available kits (Pharmacia &
Upjohn, (Kalamazoo, Mich.); Promega (Madison Wis.); and U.S.
Biochemical Corp., Cleveland, Ohio). Suitable reporter molecules or
labels, which may be used for ease of detection, include
radionuclides, enzymes, fluorescent, chemiluminescent, or
chromogenic agents as well as substrates, cofactors, inhibitors,
magnetic particles, and the like.
[0056] PCR systems usually use two amplification primers and an
additional amplicon-specific, fluorogenic hybridization probe that
specifically binds to a site within the amplicon. The probe can
include one or more fluorescence label moieties. For example, the
probe can be labeled with two fluorescent dyes: 1) a
6-carboxy-fluorescein (FAM), located at the 5'-end, which serves as
reporter, and 2) a 6-carboxy-tetramethyl-rhodamine (TAMRA), located
at the 3'-end, which serves as a quencher. When amplification
occurs, the 5'-3' exonuclease activity of the Taq DNA polymerase
cleaves the reporter from the probe during the extension phase,
thus releasing it from the quencher. The resulting increase in
fluorescence emission of the reporter dye is monitored during the
PCR process and represents the number of DNA fragments generated.
In situ PCR may be utilized for the direct localization and
visualization of target nucleic acid molecules and may be further
useful in correlating expression with histopathological
finding.
[0057] Means for producing specific hybridization probes for
nucleic acid molecules of the invention include the cloning of the
nucleic acid sequences into vectors for the production of mRNA
probes. Such vectors are known in the art, commercially available,
and may be used to synthesize RNA probes in vitro by means of the
addition of the appropriate RNA polymerases and the appropriate
labeled nucleotides. Hybridization probes may be labeled by a
variety of reporter groups, for example, radionuclides such as 32P
or 35S, or enzymatic labels, such as alkaline phosphatase coupled
to the probe via avidin/biotin coupling systems, and the like.
[0058] The term "skin care product" or "personal care product"
refers to skin care products and includes, but is not limited to,
cleansing products, shampoo, conditioner, toners or creams, topical
ointments and gels, as well as localized (e.g. under eye) gel, all
of which may be formulated to contain ingredients specifically
designed to shift microbial population to a healthy profile with or
without a commensal or mutualistic organism or mixture of commensal
or mutualistic organisms in either an active or dormant state. Such
skin care products may further include therapeutic agents,
vitamins, antioxidants, minerals, skin toning agents, polymers,
excipients, surfactants, probiotics or fraction thereof,
microorganism or product from the culture thereof, such a bacteria,
fungi and the like, either living, dormant or inactive.
[0059] "Skin commensal microorganisms" means both prokaryotes and
eukaryotes that may colonize (i.e., live and multiply on human
skin) or temporarily inhabit human skin in vitro, ex vivo and/or in
vivo. Exemplary skin commensal microorganisms include, but are not
limited to, Alphaproteobacteria, Betaproteobacteria,
Gammaproteobacteria, Propionibacteria, Corynebacteria,
Actinobacteria, Clostridiales, Lactobacillales, Staphylococcus,
Bacillus, Micrococcus, Streptococcus, Bacteroidales,
Flavobacteriales, Enterococcus, Pseudomonas, Malassezia, Maydida,
Debaroyomyces, and Cryptococcus.
[0060] P. acnes is a commensal, non-sporulating bacilliform
(rod-shaped), gram-positive bacterium found in a variety of
locations on the human body including the skin, mouth, urinary
tract and areas of the large intestine. P. acnes can consume skin
oil and produce byproducts such as short-chain fatty acids and
propionic acid, which are known to help maintain a healthy skin
barrier. Propionibacteria such as P. acnes also produce
bacteriocins and bacteriocin-like compounds (e.g., propionicin
P1G-1, jenseniin G, propionicins SM1, SM2 T1, and acnecin), which
are inhibitory toward undesirable lactic acid-producing bacteria,
gram-negative bacteria, yeasts, and molds. In embodiments, a
subject having skin identified as having P. acnes may be treated
with a personal care product designed to inhibit growth and
proliferation of P. acnes.
[0061] In an embodiment, the invention provides a method of
characterizing skin and/or subcutaneous tissue comprising
collecting a sample from a subject containing skin or subcutaneous
tissue flora. Skin and subcutaneous tissue flora of healthy
individuals can be collected using swiping, scraping, swabbing,
using tape strips or any other effective microbial collection
method. The harvested sample can be profiled on a NGS,
Sanger-sequencing, mass-spectrometry, quantitative PCR,
immunofluorescence, in situ hybridization, or microbial staining
based platform. For sequencing-based platforms, this can be done
either using a whole-genome sequencing approach, or via targeted
applications, a prominent example of which is 16S rDNA sequencing.
All the above-mentioned identification methods can be implemented
on samples directly collected from individuals without any
proliferation step. This way, minimal bias is introduced toward
identification of a mixture of culturable and unculturable
microorganisms. A proprietary analysis algorithm can be used to
identify species composition of each individual. A consensus
healthy profile may be constructed from the healthy cohort. The
healthy profile may be updated real time as more samples are
collected over time. The healthy profile will serve as the
reference for comparing all individual samples, i.e. profiles.
Examples of identified bacteria belong to any phylum, including
Actinobacteria, Firmicutes, Proteobacteria, Bacteroidetes. It will
typically include common species such as Propionibacteria,
Staphylococci, Corynebacteria, and Acenitobacteria species.
[0062] In an embodiment, the invention provides a platform or
method for characterizing skin and subcutaneous tissue microbial
flora of individuals with skin conditions. Skin and subcutaneous
tissue flora of individuals with skin conditions that are
considered to be suboptimal can be collected using swiping,
swabbing, tape strips or any other effective microbial collection
method. Collected microbial sample can be profiled on a NGS,
Sanger-sequencing, mass-spectrometry, quantitative PCR,
immunofluorescence, in situ hybridization, or microbial staining
based platform. For the sequencing based platforms, this can be
done either using a whole-genome sequencing approach, or via
targeted applications, a prominent example of which is 16S rDNA
sequencing. All the identification methods can be implemented on
samples directly collected from individuals without any
proliferation step. This way, minimal bias is introduced toward
identification of a mixture of culturable and unculturable
microorganisms. A personal skin and subcutaneous tissue flora
profile can be generated for each individual. Individuals, based on
their phenotypic characteristics, can be placed under specific skin
condition categories as well. Such clustering effort will help to
identify biological significant patterns which are characteristic
of each cohort. The microbial composition of the affected cohort is
distinct from the healthy profile. Microbial species which are
associated with any given skin condition can be used as early
diagnostic markers for individuals who have not developed a visual
skin condition but may be prone to that. Examples of identified
bacteria belong to any phylum, including Actinobacteria,
Firmicutes, Proteobacteria, Bacteroidetes. It will typically
include common species, such as Propionibacteria, Staphylococci,
Corynebacteria, and Acenitobacteria species. Damaged skin can
impact the composition of bacterial flora or can cause
nonpathogenic bacteria to become pathogenic.
[0063] In an embodiment, the invention provides a platform or
method for characterizing a consensus healthy skin and subcutaneous
tissue metabolite profile. The metabolome associated with skin and
subcutaneous tissue flora can also be profiled either by a
mass-spectrometry based system or using genomics-based metabolome
modeling and flux-balance analysis. Extraction can be done on
samples collected by using swiping, swabbing, tape strips or any
other effective microbial collection method. Alternatively, those
metabolites and biochemical, specifically the extracellular ones,
can be directly isolated from any individual without going through
any cell harvesting. Characterization can be done on the whole
metabolome or only be focused on a subset of metabolites, which are
known or may be shown to be of significance in a particular disease
pathology. All the above-mentioned identification methods can be
implemented on samples directly collected from individuals without
any proliferation step. This way, minimal bias is introduced in the
population composition. A proprietary analysis algorithm may be
used to identify metabolite composition of each individual's skin
flora. A consensus healthy profile may be constructed from the
healthy cohort. The healthy profile may be updated real time as
more samples are collected over time. The healthy profile will
serve as the reference for comparing all individual samples, i.e.
profiles.
[0064] In an embodiment, the invention provides a platform or
method for characterizing skin and subcutaneous tissue microbial
flora of individuals with skin conditions. Metabolite composition
of skin and subcutaneous tissue flora of individuals with skin
conditions that are considered to be suboptimal can be profiled
either by a mass-spectrometry based system or using genomics-based
metabolome modeling and flux-balance analysis. Extraction can be
done on samples collected by using swiping, swabbing, tape strips
or any other effective microbial collection method. Alternatively,
those metabolites and biochemical, specifically the extracellular
ones, can be directly isolated from any individual without going
through any cell harvesting. Characterization can be done on the
whole metabolome or only be focused on a subset of metabolites,
which are known or may be shown to be of significance. All the
above-mentioned identification methods can be implemented on
samples directly collected from individuals without any
proliferation step. This way, minimal bias is introduced in the
population composition. A personal profile can be generated for
each individual that reflects the metabolite composition of the
skin and subcutaneous tissue flora. Individuals, based on their
phenotypic characteristics, can be placed under specific skin
condition categories as well. Such clustering effort will help to
identify biological significant patterns that are characteristic of
each cohort. The metabolite composition of the affected cohort is
distinct from the healthy profile. Metabolites which are associated
with any given skin condition can be used as early diagnostic
markers for individuals who have not developed a visual skin
condition but may be prone to that.
[0065] In an embodiment, the platform or method described herein
may be provided as a test for profiling the skin flora of any
individual, either healthy or with a skin condition and also their
associated metabolome. Such test would be sensitive to characterize
the dominant skin flora and metabolites of any individual. A
customized or personalized evaluation of any individual's flora may
be conducted and identified skin and subcutaneous tissue flora and
metabolites may be compared to healthy and also affected skin
profiles. A customized or personalized report may be generated
which will specify species composition of the individual's skin and
subcutaneous tissue flora and also its associated metabolites. Such
report will enlist the beneficial and commensal species that are
depleted or over-represented in each individual. It will also
include the list of beneficial or undesired metabolites that are
either depleted or over-represented in each individual. This may be
used for formulation of the customized or personalized skin care or
personal care product. Such report may also form the basis of a
recommendation engine that generates clinical, lifestyle and
product recommendations that may improve the health of the skin
either directly as a result of a change in the diversity or
composition of an individual's skin flora, or a change in both
diversity and composition of an individual's skin flora.
Alternatively, the test can be administered to assess the
performance of other skin care and personal care products,
therapies, or evaluate any disruption of the normal skin flora or
metabolites. The test can be performed before, during, and after
any skin treatment in order to monitor the efficacy of that
treatment regimen on skin flora or its associated metabolites. The
test can also be used for early diagnostic of skin conditions that
are associated with a signature microbial profile or their
accompanying metabolites. The sensitivity of the test allows early
diagnostic of such skin conditions before their phenotypic
outbreak. In an aspect, the invention provides a method for
generating, or a customized or personalized skin care or personal
care product formulated for a particular individual. The customized
or personalized product contains one or more beneficial or
commensal microorganisms or a set of chemicals and metabolites
which may be depleted in any given individual. Regular
administration of such skin care products and personal care
products should shift the suboptimal profile towards a healthy
equilibrium. Skin care product may be applied after cleansing the
existing flora with a proprietary lotion that will enhance the
efficacy of colonization of skin care product microorganisms or its
constituent metabolites. Any customized or personalized skin care
or personal care product can contain one or more microorganisms,
culturable or unculturable. The customized or personalized product
can alternatively be a substrate and nutrients that favor the
establishment or proliferation of mutualistic or commensal
organisms and/or suppression of pathogenic organisms. Those
chemicals and metabolites are either synthesized in vitro or
purified from a microorganism.
[0066] The present invention is described partly in terms of
functional components and various processing steps. Such functional
components and processing steps may be realized by any number of
components, operations and techniques configured to perform the
specified functions and achieve the various results. For example,
the present invention may employ various biological samples,
biomarkers, elements, materials, computers, data sources, storage
systems and media, information gathering techniques and processes,
data processing criteria, statistical analyses, regression analyses
and the like, which may carry out a variety of functions. In
addition, although the invention is described in the medical
diagnosis context, the present invention may be practiced in
conjunction with any number of applications, environments and data
analyses; the systems described herein are merely exemplary
applications for the invention.
[0067] Methods for data analysis according to various aspects of
the present invention may be implemented in any suitable manner,
for example using a computer program operating on the computer
system. An exemplary analysis system, according to various aspects
of the present invention, may be implemented in conjunction with a
computer system, for example a conventional computer system
comprising a processor and a random access memory, such as a
remotely-accessible application server, network server, personal
computer or workstation. The computer system also suitably includes
additional memory devices or information storage systems, such as a
mass storage system and a user interface, for example a
conventional monitor, keyboard and tracking device. The computer
system may, however, comprise any suitable computer system and
associated equipment and may be configured in any suitable manner.
In one embodiment, the computer system comprises a stand-alone
system. In another embodiment, the computer system is part of a
network of computers including a server and a database.
[0068] The software required for receiving, processing, and
analyzing biomarker information may be implemented in a single
device or implemented in a plurality of devices. The software may
be accessible via a network such that storage and processing of
information takes place remotely with respect to users. The
analysis system according to various aspects of the present
invention and its various elements provide functions and operations
to facilitate microbiome analysis, such as data gathering,
processing, analysis, reporting and/or diagnosis. The present
analysis system maintains information relating to microbiomes and
samples and facilitates analysis and/or diagnosis, For example, in
the present embodiment, the computer system executes the computer
program, which may receive, store, search, analyze, and report
information relating to the microbiome. The computer program may
comprise multiple modules performing various functions or
operations, such as a processing module for processing raw data and
generating supplemental data and an analysis module for analyzing
raw data and supplemental data to generate a models and/or
predictions.
[0069] The epigenetic analysis system may also provide various
additional modules and/or individual functions. For example, the
epigenetic analysis system may also include a reporting function,
for example to provide information relating to the processing and
analysis functions. The epigenetic analysis system may also provide
various administrative and management functions, such as
controlling access and performing other administrative
functions.
[0070] Although the invention has been described with reference to
the above examples, it will be understood that modifications and
variations are encompassed within the spirit and scope of the
invention. Accordingly, the invention is limited only by the
following claims.
* * * * *