We are searching data for your request:
Upon completion, a link will appear to access the found materials.
Project Goals and Objectives
The Human Skin Microbiome Project is an application of the principles and practices of classic microbiological investigation. Then, you will apply an expanding array of microbiology laboratory skills to grow and investigate the colonial, cellular, and metabolic properties of one of the bacterial species from your skin culture.
More specifically, you will:
- prepare a primary culture from your skin, and observe the colonial and cellular properties of the bacteria that grow on it;
- identify one skin isolate that you would like to investigate further, and maintain it in a pure culture for an extended period of time;
- use microbiological methods to investigate the cellular and metabolic properties of your skin isolate;
- understand basic principles of taxonomy and how to apply the information in Bergey’s Manual of Determinative Bacteriology to presumptively identify your skin isolate.
While it may seem somewhat ironic that bacteria you have been carrying around on your skin forever are now going to be classified as a potential biohazard and subjected to risk assessment and laboratory containment practices, it is nonetheless an important consideration.
Of the various types of bacteria that might be encountered during the primary culture stage of this project, most are BSL-1, and some BSL-2. To minimize the risks of working with bacteria of unknown identity, this project will be limited to only Gram positive bacteria, and BSL-2 practices will be employed while working with cultures of your isolate.
The definitive reference book for bacterial taxonomy and identification is Bergey’s Manual. There are two versions: the manual of Systematic Bacteriology, which is concerned with issues of bacterial taxonomy and systematics (arranging bacteria into taxa according to similarities/differences in DNA), and the manual of Determinative Bacteriology, which deals specifically with the identification aspects of bacterial taxonomy. The latter book will be our primary resource for this project. For an overview of the manual and how to use it, read Chapters I, II, III, IV, and V.
The Human Skin Microbiome
The bacteria and other microbes that live on human skin are those that are best adapted to survive the prevailing conditions. Regions of the human body can be thought of as different ecosystems. Exposed, dry areas of the skin, such as the forearm, are akin to an arid desert environment, which is a preferred environment for many Gram-positive bacteria. Skin sites that are generally dark, warm, and moist, such as the underarm or perineum, are similar to temperate or tropical ecosystems; they tend to harbor more microbes in general and are more likely to have a larger percentage of Gram-negative bacteria.
The quantitative differences found at these sites may relate to the amount of moisture, body temperature, and varying concentrations of skin surface lipids. Most microorganisms live in the superficial layers of the skin (the stratum corneum) and in the upper parts of the hair follicles. Some bacteria, however, reside in the deeper areas of the hair follicles, where they may be beyond the reach of ordinary disinfection procedures (like washing your face with soap/water or an antibacterial product). These out-of-reach bacteria serve as a reservoir for recolonization of the skin environment after the surface bacteria are removed.
Figure 1 illustrates the types of bacteria that are commonly found on various regions of the human skin. Not all of these bacteria are culturable, because the growth conditions necessary for their survival are difficult to replicate in an artificial environment. Using the culture conditions established for this laboratory, the bacteria grown in your primary culture will most likely be Actinobacteria (Micrococcus, Corynebacterium, Mycobacterium), Firmicutes (Staphylococcus or other Gram-positive bacteria), or Proteobacteria (Gram negative bacteria).
Figure 1. Types of bacteria found on human skin
The skin microbiome may include fungi such as yeasts and molds as well as bacteria. While interesting, these eukaryotic microorganisms are outside of the scope of this project. Molds form very distinctive colonies that will be easy to identify as fungal in origin, and thus, easy to avoid. Yeasts, however, produce colonies that resemble those of bacteria, although typically smaller and different in color. When you select the colony and make a pure culture, avoid colonies that have the appearance of either a mold (furry, fuzzy, or powdery) or a yeast (very small, very slow growing—only appearing after a week or more of incubation, and brightly colored—red, orange, pink, or even bright white colonies).
Over the course of several weeks, you will maintain your bacterial strain in pure culture while performing tests to determine its colonial, cellular, and metabolic properties, and ultimately its “presumptive” identity. The term “presumptive” is used because phenotypic methods are less exact than those that rely on a direct analysis of DNA.
Record all observations, test outcomes, and interpretations on the Human Skin Microbiome Project Worksheets, according to the instructions provided by your instructor.
Identification of an Unknown Bacterium
Bergey’s Manual contains an enormous amount of information about the characteristics of all known bacterial species, mostly presented in table form. You will use the information in these tables to determine the identity of your skin isolate. To aid in this process, and as a way to demonstrate how you ruled out all other possible bacterial species, a taxonomic tool called a dichotomous key (also called a diagnostic key or sequential key) will be used to narrow down the possibilities.
Figure 2. Example of a dichotomous key
A dichotomous key is a sequence of questions with two possible mutually exclusive answers (a “couplet,” such as yes or no, positive or negative, cocci or bacilli). Starting with a large group of bacteria (in this case, all of the possible Gram-positive bacteria that can be found on the human skin), the first question should relate to an observable characteristic for which there are two choices, followed by additional questions until the possibilities are narrowed to a single choice. A brief example is shown in Figure 2.
Bergey’s Manual of Determinative Bacteriology is designed to facilitate identification, and not classification, of bacteria based primarily on phenotypic observations. In Bergey’s, the broadest grouping is represented by the four Major Categories. The primary criterion to establish the Major Category for your skin microbe will be the nature of its cell wall. Remember that you are being limited to choosing a bacterium that is Gram positive; therefore, the starting point for this project will be bacteria that are in Major Category II: Gram-positive eubacteria that have cell walls. Because of the culture methods and conditions we will employ in this project, it is highly unlikely that your isolate would be from Categories III (eubacteria lacking cell walls) or IV (archaeobacteria); thus, those are excluded.
At this point, you have narrowed your options down from all bacteria to only those classified as Category II. The next step is to identify the Group, within the Major Category, to which your isolate belongs. Chapter V in Bergey’s provides a list and brief summary of the bacteria in the Groups within each Category; Table V.2 refers specifically to Category II bacteria. Cellular morphology (for example, cell shape and presence of endospores) and physiology (particularly with regard to your isolate’s physiological oxygen requirements and relative results) will help you decide on a Group. Once you made that decision, you can follow the directions given in Table V.2 to find the first page of the identification tables in Bergey’s.
Continue to narrow your choices down to a single Genus within the Group, and once you’ve decided on a genus, locate and refer to the identification table that specifies the different Species within that genus. The tables also list the laboratory tests routinely performed to differentiate among the species, and the expected outcomes of those tests for each bacterial species in the genus.
You may not be able to conduct all of the tests listed in Bergey’s. Different tests may be available for you to perform, depending on the laboratory in which you work. For the purpose of identifying your skin microbe, below is a list of the tests that are available for you to use in the ID process.
- Gram stain and endospore stain
- 7.5% salt tolerance (MSA)
- Mannitol fermentation (MSA for salt-tolerant bacteria)
- Bile tolerance and esculin hydrolysis (BE)
- Hemolysis on blood agar
- Glucose fermentation (MR; VP; TSI)
- Lactose/sucrose fermentation (TSI)
- Fermentation of other carbohydrates (see instructor)
- Susceptibility or resistance to antibiotics
- Acetoin production (VP)
- Production of H2S (TSI; SIM)
- Indole (SIM)
- Motility (SIM)
- Citrate utilization
- Nitrate reduction
- Coagulase (for staphylococci ONLY)
Other tests may be available, as indicated by your instructor.
Observations, Outcomes, and Next Steps
The following tasks will be performed over the course of several weeks:
Project Step 1: Make a primary culture from your skin on a TSA plate and incubate it for up to a week at room temperature.
Project Step 2: After incubation, the TSA plate that contains the primary culture from your skin will very likely hold many, hopefully well isolated, colonies.
From among these, choose 3 colonies for subculture and further examination. Remember to avoid colonies that appear to be a mold (typically large, green or brown, and fuzzy) or yeast (very small and vibrantly white or a shade of red). With a sterile inoculation loop, subculture each colony to a section of a TSA plate, to create a pure culture, as you did previously and illustrated in Figure 3. Incubate the plate at RT until bacterial growth is abundant.
Figure 3. Subcultures on different sections of a TSA plate
Project Step 3: Gram stain each of the three pure cultures. Choose one that is Gram-positive (bacilli or cocci) as your project bacterium (your skin microbe, or HSM). Subculture your HSM to a TSA slant and incubate it. Once there is abundant growth on the surface, store the TSA slant culture in the refrigerator. Make sure the slant is clearly labelled with your name.
Complete the Colonial and Cellular Morphology (CCM) Worksheet.
Project Step 4: From the observations you’ve made and the results of lab tests,use Bergey’s Manual as a reference to determine the Major Category, Group and then Genus of your HSM.
Complete the Genus Identification (GID) Worksheet.
Project Step 5: Once you’ve narrowed your options down to a single genus, locate the specific table in Bergey’s Manual for identification of individual species. Based on the information in the table, compile a list of appropriate tests that will facilitate species identification. Select those that are available for you to perform, as specified by your lab instructor. Perform the appropriate tests and record the results.
Project Step 6: Compare your results with the expected test outcomes from Bergey’s Manual to determine the Species of your human skin microbe.
Completethe Species Identification (SID) Worksheet.
Name __________________________________________ Date due _________________
Record the observations/results obtained so far below. NOTE: your instructor may make this worksheet available to you electronically through a course management system, and may request that you type your answers into the worksheet before printing and handing it in.
1. Name the region of your body from which you obtained the specimen for the primary culture.
2. Based on colony appearance, approximately how many different types of bacteria from your skin are represented on the TSA streak plate of your primary culture?
3. Based on the appearance of an isolated colony and using appropriate microbiology terminology, describe the colonial morphology of each of the three bacterial subcultures.
|Colony Type 1|
|Form (shape, margin, elevation)|
|Colony Type 2|
|Form (shape, margin, elevation)|
|Colony Type 3|
|Form (shape, margin, elevation)|
4. For each of the three bacterial pure cultures, describe the outcome of the Gram stain using appropriate microbiology terminology:
|Gram Stain Outcome||Cell shape||Arrangement|
5. Of the three bacteria you investigated, choose one that is Gram-positive as your project bacterium. Below, indicate which of the three you chose and restate the Gram staining result.
Colony # and Gram stain result (including cell shape and arrangement):
6. Compare and contrast the chemical composition and structure of the cell wall of a Gram-positive bacterium such as your isolate, with the cell wall of a Gram negative bacterium.
7. Briefly discuss why Gram-positive cells appear purple, and Gram-negative cells appear pink, after the Gram stain process is applied.
8. Briefly discuss how bacterial cells produce “arrangements” that we can observe with a microscope.
9. State whether it will be necessary for you to perform an endospore stain on your isolate, and give a specific reason to explain why you should, or should not, use this staining method to identify your isolate.
10. Briefly explain why it is necessary to include a mixture of iodine and potassium iodide (Gram’s Iodine) in the overall Gram stain procedure.
In addition to this worksheet, you may also be asked to prepare and provide to your instructor a Gram stained slide of a smear prepared from your environmental isolate to evaluate your technique. If evaluated, the following criteria will be used: single layer of cells is achieved, cell morphology and arrangement are easily determined, all cells appear the same color, shape, and arrangement, and there are no visible contaminants.
Instructor Evaluation of Gram Stained Slide:
Gram stain result as observed by instructor: _______________________________________
Evaluation of technique: _______________________________________________________
Criteria: Single layer of cells is achieved; cell morphology and arrangement are easily determined; all cells appear the same color, shape, and arrangement; and there are no visible contaminants. Degree of concurrence with instructor’s description of cellular morphology will also be noted.
Name __________________________________________ Date due _________________
1. From your CCM worksheet, restate the Gram stain results (reaction, morphology, and arrangement) for your skin isolate:
2. Taxonomic Classification: State the Domain and Phylum for your isolate, based on the evidence you have accumulated so far.
3. Report the results of the following physiological tests performed on your environmental isolate:
|Test||Describe in detail the outcomes of the following tests (meaning, what you directly observe: such as bubbles after H2O2 was added; red color on the slant and yellow on the butt with cracks; etc.)||Interpretation of observed outcome (for example; pos or neg; K/A, gas, etc)|
4. From your observations of bacterial growth characteristics and physiological tests up to this point, state ALL energy metabolism pathway(s) used by your skin bacterium to make ATP. Then provide convincing evidence from among your observations and test results to support your determination.
|Energy Metabolism pathway||Do your observations and/or results of the tests above indicate that your EI uses this pathway? (YES or NO)||STATE one observation and/or test result that provides scientific evidence for that pathway, and explain why/how the result indicates that your EI bacterium uses this pathway.|
5. Growth Category for Oxygen
Based on the observed growth patterns and test results above, state the physiological oxygen requirement (strict aerobe, microaerophile, strict anaerobe, facultative anaerobe, or aerotolerant anaerobe) for your EI bacterium.
6. Bergey’s Group
Table V in Bergey’s is divided into four sections, one for each Major Category of bacteria. Remember that you were directed to select a Gram-positive bacterium as your EI, so look at Table V.2 to determine to which Group, within Major Category II, your EI should be assigned. Based on the characteristics of your EI bacterium that you have observed up to this point:
(a) State the Group (group number and name) for your EI according to the Bergey’s Manual identification system.
(b)State TWO observations that provide scientific evidence to support your choice of Group designation from (a) above.
7. Tests to assign Genus
If necessary (and it may not be necessary at this point), perform additional tests to determine the genus of your environmental isolate. Describe those tests and their outcomes in the table below. If you can determine the genus without additional tests, don’t put anything in this table.
|Test/Observation||Describe in detail the outcomes of the following tests (meaning, what you directly observe): such as bubbles after H2O2 was added; red color on the slant and yellow on the butt with cracks; etc.)||Interpretation of observed outcome (for example; pos or neg; K/A, gas, etc)|
8. Genus ID Flowchart (dichotomous key)
A brief example of how to construct a dichotomous key was provided previously in this lab. Note that the key you develop will be used by your instructor to review the process and logic of your choice of genus.
Some advice on how to proceed: Remember that your goal is to RULE OUT genera that are not consistent with the characteristics you’ve observed for your EI bacterium. Start by listing ALL of the possible genera in the Bergey’s Group. Then look at the characteristics that distinguish the various genera from one another (such as cell shape, endospore production, growth on human skin, etc.). As your first couplet, choose a feature that your isolate exhibits and the majority of other genera lack. Then, for the genera that were not ruled out, choose as the next couplet a feature that again rules out as many genera as possible. Continue until there is only a single genus left that is consistent with all of your observations made up to this point.
State the presumptive genus of your EI bacterium: ____________________________________
Name ____________________________________________Date Due _______________
1. Review of observations/characteristics of your EI determined so far:
|Pigmentation of colonies (color ONLY)|
|Gram stain reaction|
|Endospores observed (yes or no)|
|Result of catalase test (+ or -)|
|Result of the oxidase test (+ or -)|
|Result of TSI|
|Result of nitrate reduction test (+ or -)|
|List ALL energy pathways indicated|
|Growth category for oxygen|
|Bergey’s Group (# and name)|
|Name of Genus|
2. Tests for Species ID
Locate the specific Table in Bergey’s Manual that shows the species within the genus and the tests needed for the differentiation and identification of your isolate. Below, list the tests you will need to perform (in addition to those already done) to presumptively identify your isolate. Cross reference the list of tests available in your laboratory (provided previously by your instructor).
3. Complete the table below with the tests/outcomes you observed for your EI bacterium. The number of rows in the table is arbitrary – only do as many tests as needed to ID your isolate. Add additional rows, if necessary).
|Name of test||Direct observation of the outcome of test(s) (how did the media, slide, tube, etc. APPEAR when you looked at it?)||Outcome/Result (pos or neg, K/A, etc.)|
4. As you did previously, construct a dichotomous key to show the process and logic used to presumptively identify your environmental isolate. Begin by listing ALL species within the genus, below. Note that subspecies (if there are any) should be listed individually.
5. Complete EITHER A or B below:
A. If you were able to identify a single species after completing all possible tests: Write the full binomial name (using scientific nomenclature) for your environmental isolate below:
B. If you were NOT able to discriminate a single species after completing all your tests: Write the full binomial name of ALL remaining species and provide the reason why you were unable to assign a single species to your EI.
6. Growth Characteristics
Use biology/microbiology terminology to state the specific category related to the following growth characteristics for your EI. Provide your reasoning for the choice of each category, including specific examples of growth patterns observed for your isolate from among your observations and tests.
|Physiological Category||Supporting evidence from among your observations/test results|
|Osmotic (salt) tolerance|
7. Fully classify your isolate by providing the following information, using appropriate terminology and scientific nomenclature:
|Taxon||Classification for your skin isolate|
The Skin Microbiome of Cohabiting Couples
Distinct microbial communities inhabit individuals as part of the human skin microbiome and are continually shed to the surrounding environment. Microbial communities from 17 skin sites of 10 sexually active cohabiting couples (20 individuals) were sampled to test whether cohabitation impacts an individual's skin microbiome, leading to shared skin microbiota among partner pairs. Amplified 16S rRNA genes of bacteria and archaea from a total of 340 skin swabs were analyzed by high-throughput sequencing, and the results demonstrated that cohabitation was significantly associated with microbial community composition, although this association was greatly exceeded by characteristics of body location and individuality. Random forest modeling demonstrated that the partners could be predicted 86% of the time (P < 0.001) based on their skin microbiome profiles, which was always greater than combinations of incorrectly matched partners. Cohabiting couples had the most similar overall microbial skin communities on their feet, according to Bray-Curtis distances. In contrast, thigh microbial communities were strongly associated with biological sex rather than cohabiting partner. Additional factors that were associated with the skin microbiome of specific body locations included the use of skin care products, pet ownership, allergies, and alcohol consumption. These baseline data identified links between the skin microbiome and daily interactions among cohabiting individuals, adding to known factors that shape the human microbiome and, by extension, its relation to human health. IMPORTANCE Our work characterizes the influence of cohabitation as a factor influencing the composition of the skin microbiome. Although the body site and sampled individual were stronger influences than other factors collected as metadata in this study, we show that modeling of detected microbial taxa can help with correct identifications of cohabiting partners based on skin microbiome profiles using machine learning approaches. These results show that a cohabiting partner can significantly influence our microbiota. Follow-up studies will be important for investigating the implications of shared microbiota on dermatological health and the contributions of cohabiting parents to the microbiome profiles of their infants.
Keywords: cohabitation high-throughput sequencing human skin microbiome random forest modeling.
Microbial diversity of the 10…
Microbial diversity of the 10 body locations sampled. (A) Pie charts illustrating the…
Ordinations (PCoA) generated by using…
Ordinations (PCoA) generated by using the Bray-Curtis dissimilarity metric for each of the…
Samples were matched with another…
Samples were matched with another sample in the data set that possessed the…
Heatmap summarizing the significant (…
Heatmap summarizing the significant ( P < 0.05) metadata factors that were collected…
Bar plots of the data set with the correct couple composition compared to…
The NIH Microbiome Cloud Project
A New Resource to Help Researchers Explore Microbiome Data
The NIH Microbiome Cloud Project (MCP), led by the National Institute of Allergy and Infectious Diseases (NIAID) and the National Human Genome Research Institute (NHGRI), addresses one of the greatest challenges facing microbiome scientists: large-scale data analyses. A team of scientists from NIH, academia, and industry is developing a cloud, or Internet-based, platform that brings together Human Microbiome Project (HMP) data and analysis tools. The HMP produced 14 terabytes of genetic information about the microbes that naturally colonize our bodies. That’s enough data to fill more than 3,000 standard DVDs.
“The amount of data that can now be generated is orders of magnitude higher than what could be done just a few years ago,” said John Tsang, chief of the Systems Genomics and Bioinformatics Unit in NIAID’s Laboratory of Systems Biology. Tsang’s research focuses on understanding biological interactions such as those between the microbiota and the human body. He plans to use the cloud platform to analyze his laboratory’s experimental microbiome sequencing data and compare them with HMP data.
Mining microbiome datasets promises to help scientists better understand the role of the microbiota in health and disease and identify new targets for drugs and vaccines, but scientists need proper tools to make sense of these complex data. Many researchers do not have access to the high-performance computing resources, data-analysis tools, or the technical expertise required to assemble and study large-scale microbiome datasets. By bringing together the data and tools in the cloud, the MCP will give researchers access to vast amounts of data with high-performance computing power.
“A cloud platform that provides access to centralized data-analysis resources is a great step forward,” Tsang said. “The availability of an established set of tools used by multiple investigators will facilitate our ability to explore large datasets.”
In September 2013, NIAID and NHGRI launched the first phase of the MCP, which makes a five-terabyte portion of HMP sequencing data publicly available on the Amazon Web Services cloud. Cloud storage facilitates analysis by reducing the need for time-consuming data downloads. The MCP team is developing the next phase of the project, which will add analysis tools, more data, and supporting documentation such as online tutorials. Before the platform is released to the public, a team of NIH scientists and extramural researchers will evaluate it and provide feedback on its functionality and usability.
The MCP’s cloud environment promises to encourage greater collaboration and data sharing among NIH intramural and extramural researchers and their colleagues. Scientists’ experiences with the MCP also will help inform NIH best practices for using cloud technologies for biomedical research.
Trait composition of the human skin microbiome
Figure 1a presents binary traits for skin microbes. Spore formation is uncommon, particularly among abundant species, which are five times less likely to sporulate than skin microbes in general. By contrast, over half of skin taxa produce at least one pigment. Enzyme activities are varied. Whereas catalase is present in just under half of skin bacteria, oxidase, urease, alkaline phosphatase, gelatinase, and aesculin hydrolysis are less common, while acid phosphatase, α-galactosidase, arylsulfatase, pyrazinamidase, and tellurite reductase are rare. Catalase is the only enzyme more prevalent in abundant taxa. Gas production by skin bacteria is limited: almost no microbes generate methane, although a small fraction produces hydrogen sulfide and indole. Nitrate reduction is relatively common. This is in keeping with previous findings that skin commensals frequently reduce the nitrate in sweat [49, 50].
Proportion of all taxa (> 0.001% of reads in at least one sample white) and abundant taxa (> 0.1% of reads in at least one sample gray) in the human skin microbiome that exhibit (a) a range of different binary traits, (b) different types of oxygen use, (c) different types of motility, (d) different shapes, (e) different Gram stains and (f) different patterns of aggregation
Figure 1b–f presents categorical traits for skin microbes. The majority of skin microbes are facultatively anaerobic, although there are sizeable fractions of strictly aerobic and strictly anaerobic organisms as well. Most skin microbes are also non-motile, and this is particularly true of abundant taxa. Still, an unexpectedly large proportion—approximately 40%—have flagella. No other forms of motility are strongly represented. Most skin bacteria are rod-shaped and occur in clumps. Overall, skin microbes are predominantly Gram-negative, although abundant bacteria are split equally between Gram-negative and Gram-positive taxa.
Quantitative microbial traits are given in Table 1. Optimal temperature for growth is between 33.2 and 35.0 °C, which is close to the range of mean skin surface temperature, at 32.5–35.5 °C . Optimal pH is near to neutral, even for abundant bacterial species. This is surprising, because the skin is an acidic environment, with pH values ranging from 4.0 to 7.0, but generally concentrated around pH
5.0 [52,53,54]. In fact, low pH is thought to benefit commensal skin microbes, which adhere better to the skin surface under acidic conditions . Optimal salt concentrations and salt concentration ranges are, likewise, well above salt concentrations measured in sweat . We hypothesize that this may be explained by sweat evaporation at the skin surface, which can concentrate the salt from sweat. Mean GC content is approximately 50%.
Figure 2 shows use of carbon substrates by skin bacteria. Here, we include all forms of use, including hydrolysis and fermentation. A wide range of carbon substrates are consumed by multiple skin taxa. This is particularly true of amino acids, with > 50% of the amino acids in our database used by > 70% of abundant skin taxa. Rates of use of monosaccharides and organic acids are lower, but still appreciable, with
40% used by > 70% of abundant skin taxa. Use of alcohols and oligosaccharides/polysaccharides is less widely distributed, with 22% of oligosaccharides and no (0%) alcohols used by > 70% of abundant taxa. Of the carbon compounds considered, the substrates used most often by abundant taxa are glutamate (95%), asparagine (95%), valerate (92%), and glucose (91%). Footnote 1 The substrates used least are gelatin (3%), urea (17%), and xylitol (17%).
Proportion of all taxa (> 0.001% of reads in at least one sample white) and abundant taxa (> 0.1% of reads in at least one sample gray) in the human skin microbiome that utilize particular (a) organic acids, (b) amino acids, (c) monosaccharides, (d) oligosaccharides and polysaccharides, (e) alcohols and (f) other compounds
Comparing abundant versus rare skin bacteria, abundant taxa are more likely to use amino and organic acids. Eight amino acids (alanine, asparagine, aspartate, glutamate, glycine, leucine, proline, and serine see Additional file 1: Supplemental Information II Table S2.3) are used more by abundant microbes than by the skin community as whole. Similarly, nine organic acids (acetate, citrate, formate, gluconate, malate, malonate, pyruvate, succinate, and valerate see Additional file 1: Supplemental Information II Table S2.3) are used more by abundant microbes. For both amino acids and organic acids, all significant differences indicate that abundant skin taxa use these compounds more than skin taxa as a whole. Differences in consumption of other compounds, including alcohols and saccharides, are less biased toward overuse by abundant species. Indeed, two complex sugars (xylose and cellobiose) are used less by abundant taxa. Glucose, a simple sugar, on the other hand, is used more by abundant taxa (see Additional file 1: Supplemental Information II Table S2.3).
It is well known that certain taxonomic groups, for example Actinobacteria, are overrepresented among skin microbes and, in particular, among abundant skin microbes. While these groups are likely overrepresented because they have traits that make them uniquely adapted to the skin environment, it is possible that the traits that are important for living on skin are not those that we measured. Instead, the skin relevant traits may be other traits and the differences that we observe in the traits that we did measure may merely exist as a result of phylogenetic conservation. For this reason, we performed an additional analysis regressing the probability of a taxon being abundant versus rare against each trait individually, both for a naïve logistic regression and for a regression where phylogenetic relatedness was accounted for using the phylolm package in R . To test the overall significance of a fitted regression, we compared it to a null model using a likelihood ratio test. In general, we found that many of the differences between abundant and rare taxa were preserved when phylogeny was accounted for. For instance, oxygen use, spore formation, Gram stain, type of motility, H2S production, the presence of catalase, aesculin hydrolysis and urease, and use of succinate, acetate, gluconate (organic acids), serine, proline, and glutamate (amino acids) were significantly different among abundant and rare taxa, whether or not phylogeny was considered. A few traits were not significant once phylogeny was included, for example cell shape, the presence of alkaline phosphatase, pyrazinamidase and gelatinase, and use of xylose, glucose, cellobiose (saccharides), malonate, formate, valerate, pyruvate, citrate, aspartate (organic acids), asparagine, alanine, leucine, and glycine (amino acids). Finally, use of 2-ketogluconate (organic acid) and the ability to perform nitrate reduction were only significant when accounting for phylogeny (see Additional file 1: Supplemental Information II, Table S2.1–S2.3).
Trait overrepresentation on human skin
Without comparison to prevalence in the world as a whole, it is impossible to know which traits are generally common versus preferentially selected for in skin environments. Figure 3a presents a comparison of binary traits among abundant skin bacteria versus bacteria more broadly (see “Materials and methods” section see also Additional file 1: Supplemental Information III Fig. S3.1). Although there is a correlation between prevalence of a trait on skin and in the world as a whole, several traits are underrepresented among abundant skin taxa. Spore formation, for example is 7.5 times less likely among skin taxa as compared to general bacteria. Meanwhile, there is a 4.5-fold reduction in the likelihood of a skin taxon possessing acid phosphatase and a 1.5-fold reduction in the likelihood of a skin taxon possessing alkaline phosphatase as compared to bacteria more broadly. General bacteria are also 23% more likely to produce a pigment, 21% more likely to possess catalase, and 87% more likely to possess oxidase. For categorical traits, we again see significant differences between skin taxa and taxa from the world more broadly. Abundant skin bacteria (see Fig. 3b) are approximately half as likely to be aerobic, favoring, instead, a more flexible, facultative strategy. Likewise, abundant skin bacteria are 8-fold less likely to exhibit gliding motility, and none possess axial filaments, whereas these occur in
0.1% of bacteria overall. Abundant skin taxa are also less likely to be spirillum or rod-shaped, whereas the fraction of cocci and coccibacilli on skin is inflated more than 2-fold. Finally, abundant skin bacteria are half as likely to grow in chains, preferring to aggregate as clumps instead.
Qualitative trait comparison for abundant taxa (> 0.1% of reads in at least one sample see also Supplementary Information I). a Proportion of taxa with a specific, qualitative trait in skin microbial communities (x-axis) versus the world as a whole (y-axis). Filled symbols represent traits that are significantly different in skin environments open circles represent traits that are not significantly different marker size reflects significance. b Plots of trait proportions among skin bacteria (pink) and world bacteria (green). Open red circles denote traits that are overrepresented on skin filled green circles denote traits that are overrepresented in the world (underrepresented on skin)
Figure 4 compares quantitative traits among world and skin bacteria (see also Additional file 1: Supplemental Information III, Figure S3.2). Abundant skin bacteria have more difficulty at high pH, tolerating, on average, a pH maximum of 7.97 versus 9.03 for the world in general. Abundant skin taxa also have a smaller range of pH values (2.41 versus 3.38) over which growth occurs. We speculate that this is because skin is a largely acidic environment with a relatively stable pH. Interestingly, however, optimal pH values for skin microbes do not reflect pH ranges measured on skin. Abundant skin bacteria also prefer warmer temperatures, can tolerate warmer temperatures, and have more difficulty at cold temperatures (with all three skin metrics being
+ 2 °C) as compared to bacteria more broadly. Again, we hypothesize that this is because the skin is, at least relatively speaking, a warmer environment . With respect to salt requirements, abundant skin bacteria are much less resilient to hypotonic conditions, requiring on average 1.1% NaCl, whereas average requirements in the world as a whole are closer to 0.02%. We speculate that this is because the skin is subject to constant excretion of salts through sweating. Finally, skin bacteria have a lower GC content (see also Additional file 1: Supplemental Information I, Figure S2), consistent with previous findings that host-associated organisms are AT-rich [57, 58].
Boxplots comparing quantitative traits among skin bacteria (pink) and bacteria from the world in general (green) for abundant skin microbes (> 0.1% of reads in at least one sample see also Supplemental Information I). Blue stars are used to denote significant differences between a trait value in the world versus on skin. Box width indicates the relative number of microbes used for the comparison
We do not consider differences in carbon substrate usage between skin and the world because this information was collected differently in the skin database relative to the world database, making comparison impossible (see “Materials and methods” section).
Phylum level differences
As suggested above, one explanation for observed trends in functional traits on human skin is that these result from certain phyla (Actinobacteria, Bacteroidetes, Firmicutes, and Proteobacteria) being the predominant constituents of the skin microbiome. To address this possibility, we used two separate approaches. First, we determined whether differences in functional traits between skin microbes and microbes more broadly persist when considering each phylum separately (see Tables 2, 3, and 4 and Additional file 1: Supplemental Information IV). For many traits—specifically, spore formation, pigment production, acid phosphatase, catalase (except for Actinobacteria), oxidase (see Table 2, Additional file 1: Table S4.1–S4.3), oxygen requirements, cell aggregation (see Table 3, Additional file 1: Table S4.4–S4.6), GC content, pH, and temperature requirements (see Table 4, Additional file 1: Table S4.7–S4.9)—biases that were apparent at the kingdom level are also apparent across multiple phyla. For other traits—for example, alkaline phosphatase, aeculin hydrolysis, and α-galactosidase (see Table 2, Additional file 1: Table S4.1–S4.3)—biases from the global composition appear driven by a single phylum, usually Proteobacteria, which is the most diverse phylum (see Additional file 1: Table S1.2) and thus most likely to impact overall results. Finally, for a few traits—most notably H2S and indole production (see Table 2, Additional file 1: Table S4.1–S4.3), motility, Gram stain, and cell shape (see Table 3, Additional file 1: Table S4.4–S4.6)—trends vary among phyla. Second, similar to our comparison of abundant versus rare taxa, we regressed the probability of a taxon being on the skin versus in the world more broadly against each trait individually using both a naïve logistic regression and a regression where phylogenetic relatedness was accounted for . We then tested the overall significance of a fitted regression based on a null model using a likelihood ratio test. This analysis showed that all traits significantly over/underrepresented on skin relative to the world remained significant when accounting for phylogeny, while three traits (urease, pyrazinamidase, and nitrate reduction) were only significant under phylogenetic correction (see Additional file 1: Supplemental Information IV, Figure S4.10 and S4.11).
Trait differences among skin sites
Human skin microbiomes generally structure according to skin environment, with three environments—dry, moist, and sebaceous—represented (see Additional file 1: Supplemental Information I, Table S1.1). Because taxonomic composition differs among these three environments, functional diversity may vary as well. To test this hypothesis, we performed pairwise comparisons (dry vs. moist, dry vs. sebaceous, and moist vs. sebaceous) for all traits and substrate utilizations in our database (see Supplemental Information V). Surprisingly, not one difference emerged among skin environments for enzyme activities, gas production, spore formation, pigment production, nitrate reduction, Gram stain, cell aggregation, or pH, temperature, and NaCl requirements (see Additional file 1: Figure S5.1i, iii, S5.2i, iii, S5.3i, iii). Abundant bacteria at sebaceous sites are less likely to be rods as compared to abundant taxa at moist sites (49% versus 68%, see Additional file 1: Figure S5.3iv). As well, anaerobes are slightly underrepresented at dry sites as compared to sebaceous sites (see Additional file 1: Figure S5.2ii), and GC content is slightly lower at dry sites as compared to moist sites (see Additional file 1: Figure S5.5), although these latter two trends only emerge when considering the full skin microbiome, not just abundant taxa. Unfortunately, when accounting for phylogeny, the model for cell shape was degenerate for abundant taxa. However, variation in oxygen use between dry and sebaceous sites was observed even with phylogenetic correction. We did not attempt to control for phylogeny for GC content, since this was a quantitative trait.
Substrate usage (see Additional file 1: Supplementary Information V, Figure S5.6–S5.11) is similarly constant among skin environments, and what few differences do exist only occur between moist and sebaceous sites. Specifically, bacterial use of three organic acids—quinate, malonate, and caprate—as well as glucosamine (a monosaccharide) is overrepresented at sebaceous sites. By contrast, bacterial use of three saccharides—rhamnose, xylose, and cellobiose—as well as glycine (an amino acid) and urea are overrepresented at moist sites.
Our finding of high similarity among skin sites is in keeping with previous studies , but contrasts with a KEGG analysis performed in Oh et al. . The discrepancy between our trait database analysis and the KEGG analysis may be because we considered a different set of functions. Alternatively, it may be because of differences in our definition of function prevalence. In particular, Oh et al.  quantified commonness of pathways across samples, whereas we quantify commonness of functions across taxa. Defining prevalence across species is not possible using pathway analysis, highlighting a distinction and benefit of our trait-based approach.
Skin microbiome and formulation of cosmetics with Pro-, Pre-, and Post-biotics ingredients – Definitions and key considerations. Opportunities and challenges for cosmetic formulators and companies
The human skin is inhabited by large and diverse microbial communities (bacteria, fungi, viruses). Advanced analytical study approaches such as metagenomics and recent research projects have increased our understanding of their composition and dynamics. The human skin microbiome and microbiota have been in recent years the subject of a lot of interest as key components of a healthy skin. Sometimes presented as the “second barrier”, the skin microbiome is an exciting area for the cosmetic industry as it leads to a new generation of products and marketing opportunities. Translating our better understanding, formulations of cosmetic products with pro-, pre-, and post-biotics ingredients are meant to protect, optimize, and restore skin microbial balance for skin health. After defining the microbiome, microbiota, and these ingredients, the article provides key considerations about them. The article also reviews the challenges associated with formulating and marketing this new branch of cosmetics.
This article follows a presentation given at the 2018 Society of Cosmetic Chemists Annual Scientific Meeting, NYC.
The skin is critical to our general health and an effective “healthy” skin acts as a protective physical and biological barrier between the environment and the inside of the body (1). It reduces the effects of harmful UV radiation, prevents the entry of hazardous substances (allergens, chemicals, pollution particles, etc.) and the invasion of pathogens into the skin, while controlling the loss of water and nutrients from the skin.
A healthy skin surface has an acidic pH and maintaining its level is critical for an effective skin barrier function and defensive mechanism against pathogenic microorganisms (2-4). It is a complex and dynamic biology system involving a wide array of components in order to maintain the skin homeostasis.
SKIN MICROBIOME & MICROBIOTA AND SKIN HEALTH
Part of this biology system is the skin microbiota, a highly diverse collection of microorganisms (bacteria, fungi, viruses) that densely colonizes the skin, and a co .
The main focus of our research was to identify the dynamics of the skin microbiota and recolonization behavior after skin damage. In addition to these findings, our study provides a first overview of the specific bacterial players involved, which should be considered as leads for larger and more detailed analysis. Based on our findings we here present a working model that there is a short-lived recolonization of the damaged skin with microbial constituents (frequently Propionibacterium) from the surrounding superficial skin layer (bacteria from adjacent skin) and that this transient microbiome is replaced by the microbiome that inhabits the deeper layers of the stratum corneum (Figure 8). During the recolonization process the microbial communities of the host and invading bacteria from the environment trigger the skin to express antimicrobial proteins and inflammatory molecules. These host-specific innate immune responses may help the skin in closing the wound, resulting in a restored barrier function in which epidermal keratinocytes are in homeostasis with the local microbiome.
Model for skin injury, microbial recolonization, and host response. Details in Discussion and Conclusion sections. Recolonization is done by bacteria from the deeper layers of the adjacent skin (black arrows). During this process the microbial communities of the host and invading bacteria from the environment trigger the skin to express antimicrobial proteins and inflammatory molecules (yellow arrows). SC = stratum corneum SG = stratum granulosum SS = stratum spinosum.
In a recent review by Virgin and Todd , it was hypothesized that microbial communities influence our resistance and susceptibility to multifactorial inflammatory diseases like type 1 diabetes, ulcerative colitis and Crohn's disease. It was postulated that disease genetics may be combinatorial with different host-gene-microbial interactions, contributing to the pathogenesis of disease in subsets of patients. It is known that there are different pathways to the same diagnosis in complex diseases, which is supported by the observation that subsets of patients respond differently to mechanistically distinct interventions. These considerations also apply to common skin diseases such as atopic dermatitis and psoriasis. Both diseases show clinical variability with respect to disease manifestation and therapeutic responses, which could be linked to differential involvement of skin microbiota (Staphylococcus aureus and group A streptococci, respectively). Understanding of host-gene-microbial interactions could possibly lead to the identification of mechanistically important interactions that enable more accurate interpretation of genetics, pathogenesis, and therapeutic success . The finding that null alleles of the epidermis-expressed filaggrin (FLG) gene are a major risk factor for atopic dermatitis , and our observation that copy number variation of epidermis-expressed genes such as β-defensins and LCE3B/C predispose to psoriasis [51, 55], indicate that epidermal biology and stratum corneum homeostasis play an important role in these common inflammatory diseases. Our present study demonstrates quantitative and qualitative differences between the microbiomes of the various stratum corneum layers. Most bacteria reside in the upper layers as bacterial DNA was undetectable in the deeper layers (> 15 times stripping Additional file 9). Whereas intact stratum corneum appears to be an effective barrier to colonization of the deeper stratum corneum layers, this may be quite different in skin conditions with disturbed barrier function (for example, atopic dermatitis and psoriasis). The question is if genetically determined variation of stratum corneum properties leads to shifts in bacterial communities at a high hierarchical level (for example, phylum) or rather favors the colonization by particular species. Microbiota may be differently distributed or even qualitatively different, as suggested by a recent study on psoriasis . Speculatively, this may lead to abnormal exposure of the epidermal keratinocytes or Langerhans cells to live bacteria or bacterial components. Continuous exposure to pathogen-associated molecular patterns (PAMPs) may lead to uncontrolled stimulation of pattern recognition receptors (PRR) some of which were shown to be abnormally expressed in lesional psoriasis skin . Stimulation of the innate and adaptive immune system by PAMPs or by specific antigens could be a driving force for the chronic inflammatory process, but such a scenario clearly requires experimental confirmation.
The study we present here provides essential leads and suggestions for the design of more in-depth studies. Such investigations could include targeted identification of microbial taxa and host factors that regulate the microbiome composition in normal skin, but also during wound healing or in skin diseases. Such studies will contribute to understand the relationship between host and microorganisms, and may lead to novel strategies in prevention and development of new therapies for treatment. Selective modulation of skin microbiota composition by pre- and/or probiotica  could be interesting future strategies to achieve beneficial effects in patients.
Xeroderma (Dry Skin)
Multiple studies have demonstrated that ceramides play an important role in keeping the skin hydrated and intact in a healthy stratum corneum. 2 6 It has been proposed that damage to the stratum corneum correlated with aging could be the result of a ceramide deficiency. 26 Streptococcus thermophilus has been shown to enhance levels of ceramides in keratinocytes. 2 7 A study investigating the topical treatment of a S thermophilus-containing cream in elderly women demonstrated an improved lipid barrier and increased resistance to aging-related dry skin. 2 8
Microorganism identification methods: culture vs. nonculture tools
The culture of microorganisms is a historical method for studying their characteristics and properties. With recent advances in molecular biology, this fundamental tool has been shelved in favor of next-generation sequencing methods, which are more sensitive and faster than culture. However, next-generation sequencing does not provide all the information needed to understand the habits of microorganisms in vivo for example, it provides no information about the viability of the detected organisms . A goal that is as important as the improvement of sampling and storage methods is the improvement of culture parameters in efforts aimed at isolating the viable and culturable fraction of the skin microbiome, which presents its own particularities and shows certain consistent traits . For example, Myles et al.  showed that when using a low-nutrient culture medium (R2A), inhibition of the gram-positive fraction by treating the sample with vancomycin and a reduced incubation temperature led to the isolation of the gram-negative fraction of the skin microbiota. Moreover, other parameters of the protocol could be adjusted to obtain more efficient culture media for the growth of diverse skin microorganisms and to improve the methods of colony identification [101, 102]. In these efforts, the culturomics method was improved by Lagier et al. , which allowed the discovery of multiple unknown bacteria. By using these methods (i.e., the combination of multiple culture media and conditions), Timm et al.  collected more than 800 strains, including more than 30 bacterial genera and 14 fungal genera. However, because this technique requires fastidious and time-consuming work, an increasing number of scientific teams have reinstated this method uniquely or with the use of complementary metagenomic tools [30, 105, 106].
The democratization of metagenomic technologies has induced a shift in interest related to human-associated microorganisms. The skin microbiota has been largely underestimated in terms of diversity, which has persisted because of culture techniques that induce bias due to the growth of microbes in artificial settings . To apply this kind of method for skin microbiome analyses, particular attention is needed at each step of the protocol, including the DNA extraction method, library construction, sequencing step (e.g., primer selection, the chosen platform , and the use of blanks and controls), and subsequent analysis (e.g., the selected database and software) [27, 106,107,108]. Furthermore, advanced methods to isolate and cultivate difficult strains by reverse genomics have been recently proposed .
Gill, S. R. et al. Metagenomic analysis of the human distal gut microbiome. Science 312, 1355–1359 (2006).
The Committee on Metagenomics. The New Science of Metagenomics: Revealing the Secrets of Our Microbial Planet (The National Academies Press, Washington DC, 2007).
MacArthur, R. H. & Wilson, E. O. The Theory of Island Biogeography (Princeton Univ. Press, Princeton, 1967).
Ambramsky, Z. & Rosenzweig, M. L. The productivity diversity relationship: Tilman's pattern reflected in rodent communities. Nature 309, 150–151 (1984).
Ley, R. E. et al. Obesity alters gut microbial ecology. Proc. Natl Acad. Sci. USA 102, 11070–11075 (2005).
Rawls, J. F., Mahowald, M. A., Ley, R. E. & Gordon, J. I. Reciprocal gut microbiota transplants from zebrafish and mice to germ-free recipients reveal host habitat selection. Cell 127, 423–433 (2006).
Ludwig, W. et al. ARB: a software environment for sequence data. Nucleic Acids Res. 32, 1363–1371 (2004).
Cole, J. R. et al. The Ribosomal Database Project (RDP-II): sequences and tools for high-throughput rRNA analysis. Nucleic Acids Res. 33, D294–D296 (2005).
Schloss, P. D. & Handelsman, J. DOTUR, a computer program for defining operational taxonomic units and estimating species richness. Appl. Environ. Microbiol. 71, 1501–1506 (2005).
DeSantis, T. Z. et al. NAST: a multiple sequence alignment server for comparative analysis of 16S rRNA genes. Nucleic Acids Res. 34, W394–W399 (2006).
Lozupone, C., Hamady, M. & Knight, R. UniFrac — an online tool for comparing microbial community diversity in a phylogenetic context. BMC Bioinformatics 7, 371 (2006).
Gao, Z. et al. Molecular analysis of human forearm superficial skin bacterial biota. Proc. Natl Acad. Sci. USA 104, 2927–2932 (2007).
Pei, Z. et al. Bacterial biota in the human distal esophagus. Proc. Natl Acad. Sci. USA 101, 4250–4255 (2004).
Bik, E. M. et al. Molecular analysis of the bacterial microbiota in the human stomach. Proc. Natl Acad. Sci. USA 103, 732–737 (2006).
Eckburg, P. B. et al. Diversity of the human intestinal microbial flora. Science 308, 1635–1638 (2005).
Ley, R. E., Turnbaugh, P. J., Klein, S. & Gordon, J. I. Microbial ecology: Human gut microbes associated with obesity. Nature 444, 1022–1023 (2006).
Hyman, R. W. et al. Microbes on the human vaginal epithelium. Proc. Natl Acad. Sci. USA 102, 7952–7957 (2005).
Hubbell, S. P. Neutral theory and the evolution of ecological equivalence. Ecology 87, 1387–1398 (2006).
Turnbaugh, P. J. et al. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 444, 1027–1031 (2006).
Tringe, S. G. et al. Comparative metagenomics of microbial communities. Science 308, 554–557 (2005).
Venter, J. C. et al. Environmental genome shotgun sequencing of the Sargasso Sea. Science 304, 66–74 (2004).
Kanehisa, M., Goto, S., Kawashima, S., Okuno, Y. & Hattori, M. The KEGG resource for deciphering the genome. Nucleic Acids Res. 32, D277–D280 (2004).
Gordon, J. I. et al. Extending our view of self: the Human Gut Microbiome Initiative (HGMI). National Human Genome Research Institute &lthttp://www.genome.gov/Pages/Research/Sequencing/SeqProposals/HGMISeq.pdf (2005).
Xu, J. et al. Evolution of symbiotic bacteria in the distal human intestine. PLoS Biol. 5, e156 (2007).
Podar, M. et al. Targeted access to the genomes of low abundance organisms in complex microbial communities. Appl. Environ. Microbiol. 73, 3205–3214 (2007).
Zengler, K. et al. Cultivating the uncultured. Proc. Natl Acad. Sci. USA 99, 15681–15686 (2002).
Teeling, H. et al. TETRA: a web-service and stand-alone program for the analysis and comparison of tetranucleotide usage patterns in DNA sequences. BMC Bioinformatics 5, 163 (2004).
McHardy, A. C. et al. Accurate phylogenetic classification of variable-length DNA fragments. Nature Methods 4, 63–72 (2007).
von Mering, C. et al. Quantitative phylogenetic assessment of microbial communities in diverse environments. Science 315, 1126–1130 (2007).
International HapMap Consortium. A haplotype map of the human genome. Nature 437, 1299–1320 (2005).
Lozupone, C. & Knight, R. UniFrac: a new phylogenetic method for comparing microbial communities. Appl. Environ. Microbiol. 71, 8228–8235 (2005).
Lozupone, C. A., Hamady, M., Kelley, S. T. & Knight, R. Quantitative and qualitative β diversity measures lead to different insights into factors that structure microbial communities. Appl. Environ. Microbiol. 73, 1576–1585 (2007).
Wu, L. et al. Development and evaluation of functional gene arrays for detection of selected genes in the environment. Appl. Environ. Microbiol. 67, 5780–5790 (2001).
Gentry, T. J. et al. Microarray application in microbial ecology research. Microb. Ecol. 52, 159–175 (2006).
Gao, H. et al. Microarray-based analysis of microbial community RNAs by whole-community RNA amplification. Appl. Environ. Microbiol. 73, 563–571 (2007).
Poretsky, R. S. et al. Analysis of microbial gene transcripts in environmental samples. Appl. Environ. Microbiol. 71, 4121–4126 (2005).
Grant, S. et al. Identification of eukaryotic open reading frames in metagenomic cDNA libraries made from environmental samples. Appl. Environ. Microbiol. 72, 135–143 (2006).
Ram, R. J. et al. Community proteomics of a natural microbial biofilm. Science 308, 1915–1920 (2005).
Wishart, D. S. et al. HMDB: the human metabolome database. Nucleic Acids Res. 35, D521–D526 (2007).
de Hoon, M. J., Imoto, S., Nolan, J. & Miyano, S. Open source clustering software. Bioinformatics 20, 1453–1454 (2004).
Saldanha, A. J. Java Treeview — extensible visualization of microarray data. Bioinformatics 20, 3246–3248 (2004).
Backhed, F. et al. The gut microbiota as an environmental factor that regulates fat storage. Proc. Natl Acad. Sci. USA 101, 15718–15723 (2004).
Backhed, F., Manchester, J. K., Semenkovich, C. F. & Gordon, J. I. Mechanisms underlying the resistance to diet-induced obesity in germ-free mice. Proc. Natl Acad. Sci. USA 104, 979–984 (2007).
Martin, F. J. et al. A top-down systems biology view of microbiome–mammalian metabolic interactions in a mouse model. Mol. Syst. Biol. 3, doi:10.1038/msb4100153 (2007).
Sidhu, H., Allison, M. J., Chow, J. M., Clark, A. & Peck, A. B. Rapid reversal of hyperoxaluria in a rat model after probiotic administration of Oxalobacter formigenes . J. Urol. 166, 1487–1491 (2001).
Chu, F. F. et al. Bacteria-induced intestinal cancer in mice with disrupted Gpx1 and Gpx2 genes. Cancer Res. 64, 962–968 (2004).
Pull, S. L., Doherty, J. M., Mills, J. C., Gordon, J. I. & Stappenbeck, T. S. Activated macrophages are an adaptive element of the colonic epithelial progenitor niche necessary for regenerative responses to injury. Proc. Natl Acad. Sci. USA 102, 99–104 (2005).
Hooper, L. V., Stappenbeck, T. S., Hong, C. V. & Gordon, J. I. Angiogenins: a new class of microbicidal proteins involved in innate immunity. Nature Immunol. 4, 269–273 (2003).
Mazmanian, S. K., Liu, C. H., Tzianabos, A. O. & Kasper, D. L. An immunomodulatory molecule of symbiotic bacteria directs maturation of the host immune system. Cell 122, 107–118 (2005).
Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447, 661–678 (2007).
Cash, H. L., Whitham, C. V., Behrendt, C. L. & Hooper, L. V. Symbiotic bacteria direct expression of an intestinal bactericidal lectin. Science 313, 1126–1130 (2006).
Braun-Fahrlander, C. et al. Environmental exposure to endotoxin and its relation to asthma in school-age children. N. Engl. J. Med. 347, 869–877 (2002).
Kozyrskyj, A. L., Ernst, P. & Becker, A. B. Increased risk of childhood asthma from antibiotic use in early life. Chest 131, 1753–1759 (2007).
Wostmann, B. S., Bruckner-Kardoss, E. & Pleasants, J. R. Oxygen consumption and thyroid hormones in germfree mice fed glucose–amino acid liquid diet. J. Nutr. 112, 552–559 (1982).
2.4: The Human Skin Microbiome Project - Biology
A new understanding has emerged of links between human physiology and skin microbiota.
Microbial diversity is governed by genetics and diverse skin environments.
Host and microbe interactions shape and influence skin health.
Commensal microorganisms exhibit unique behaviors that permit tolerance.
An abundant and diverse collection of bacteria, fungi, and viruses inhabits the human skin. These microorganisms vary between individuals and between different sites on the skin. The factors responsible for the unique variability of the skin microbiome are only partly understood, but results suggest that host genetic and environmental influences play a major role. Today, the steady accumulation of data describing the skin microbiome, combined with experiments designed to test the biological functions of surface microbes, has provided new insights into links between human physiology and skin microbiota. This review describes some of the current information regarding the skin microbiome and its impact on human health. Specifically, we summarize the present understanding of the function of microbe–host interactions on the skin and highlight some unique features that distinguish skin commensal organisms from pathogenic microbes.