Microbiomic signatures of psoriasis: feasibility and methodology comparison

Altered skin microbiome, inflammation, and JAK/STAT signaling in Southeast Asian ichthyosis patients

BACKGROUND

Congenital ichthyosis (CI) is a collective group of rare hereditary skin disorders. Patients present with epidermal scaling, fissuring, chronic inflammation, and increased susceptibility to infections. Recently, there is increased interest in the skin microbiome; therefore, we hypothesized that CI patients likely exhibit an abnormal profile of epidermal microbes because of their various underlying skin barrier defects. Among recruited individuals of Southeast Asian ethnicity, we performed skin meta-genomics (i.e., whole-exome sequencing to capture the entire multi-kingdom profile, including fungi, protists, archaea, bacteria, and viruses), comparing 36 CI patients (representing seven subtypes) with that of 15 CI age-and gender-matched controls who had no family history of CI.

RESULTS

This case-control study revealed 20 novel and 31 recurrent pathogenic variants. Microbiome meta-analysis showed distinct microbial populations, decreases in commensal microbiota, and higher colonization by pathogenic species associated with CI; these were correlated with increased production of inflammatory cytokines and Th17- and JAK/STAT-signaling pathways in peripheral blood mononuclear cells. In the wounds of CI patients, we identified specific changes in microbiota and alterations in inflammatory pathways, which are likely responsible for impaired wound healing.

CONCLUSIONS

Together, this research enhances our understanding of the microbiological, immunological, and molecular properties of CI and should provide critical information for improving therapeutic management of CI patients.

Decrypting Skin Microbiome in Psoriasis: Current Status

Background

Psoriasis is an autoimmune, chronic, inflammatory skin condition of multifactorial etiology. Recent studies in human skin microbiome research have revealed the dysbiosis in lesional skin of psoriatic patients, as well as have established the association of dysbiosis in the elicitation of inflammatory response of psoriatic skin.

Objective

The present review aimed to recapitulate the insights of psoriasis lesional skin microbiome studies published in the last 2 decades, and to determine the most important bacterial genera that can be deployed as psoriatic skin microbial signature for therapeutic intervention.

Methods

To achieve the stated objectives, full-text analysis of literature selected through systematic search of digital literature databases has been carried out following PRISMA guidelines.

Results

Literature analysis suggests differential abundance of specific bacterial genera in the lesional psoriatic skin (LPS) compared to normal skin (NS) of psoriasis patients and skin from healthy subjects. These bacterial genera collectively can be utilized as potential biomarker for constructing lesional psoriatic skin specific microbial signature, and to explore the role of bacterial species in maintaining the skin homeostasis. The analysis further revealed that multiple bacterial species instead of a single bacterial species is important for understanding the psoriasis etiogenesis. Furthermore, decreased microbiome stability and increased diversity might have role in the exacerbation of lesions on skin of psoriatic patients.

Conclusion

Considering the importance of human skin microbiome dysbiosis in psoriasis, research efforts should be carried out to develop new therapeutic measures in addition to current therapies by exploiting the human and host-skin-associated microbial genomic and metabolomic knowledge.

Regulation of psoriasis, colitis, and the intestinal microbiota by clusterin

Psoriasis, a chronic and systemic inflammatory disorder characterized by activation of the interleukin (IL)-23/IL-17 axis, may be associated with the intestinal microbiota through the so-called "gut-skin axis." Clusterin is a glycoprotein ubiquitously distributed in mammalian tissues; however, its role in psoriasis is unclear. Therefore, we evaluated the role of clusterin in psoriatic skin inflammation, systemic inflammation, and colitis using a murine model of IMQ-induced psoriasis. In IMQ-treated clusterin-knockout (clusterin-/-) mice, the expressions of inflammatory cytokines in clusterin-silenced human keratinocytes and intestinal microbial composition were analyzed. We also examined clusterin expression in the skin tissues of patients with psoriasis. IMQ-induced psoriatic skin inflammation is suppressed in clusterin-/- mice. Long-term administration of IMQ induced systemic inflammation and colitis; however, both were alleviated by the genetic deletion of clusterin. Genetic silencing of clusterin in human keratinocytes inhibited the production of inflammatory cytokines involved in the initiation and progression of psoriasis. The composition of the intestinal microbiota in IMQ-treated clusterin-/- and wild-type mice was different. Genetic deletion of clusterin suppressed the increase in the Firmicutes/Bacteroidetes (F/B) ratio. Skin tissues of patients with psoriasis showed high clusterin expression. In conclusion, inhibition of clusterin decreased psoriatic skin inflammation, systemic inflammation, colitis, and altered the F/B ratio in an IMQ-induced murine psoriasis model.

Facial Skin Microbiome: Aging-Related Changes and Exploratory Functional Associations with Host Genetic Factors, a Pilot Study

In this exploratory study, we investigate the variation in the facial skin microbiome architecture through aging and their functional association with host genetic factors in a cohort of healthy women, living in the same area and without cutaneous diseases. Notably, facial skin microbiota (SM) samples were collected from a cohort of 15 healthy Caucasian females, firstly divided into three age groups (younger women aged 20-35 years old; middle aged women of 36-52 years old; and older women aged 53-68 years old). Then, the recruited cohort was divided into two groups based on their facial hydration level (dry and normal skin). The facial SM revealed a different composition in the three analyzed aging groups and between normal and dry skins. The middle-aged women also revealed functional variations associated with collagen biosynthesis and oxidative stress damage repair. Otherwise, the association between selected host SNPs (single nucleotide polymorphisms) and the facial SM profile showed significant associations, suggesting a negative correlation with collagen metabolism and ROS damage protection. Finally, the composition and functionality of the facial SM seemed to affect the aging process through the two aging-correlated pathways of host ROS damage repair and collagen metabolism. Our exploratory data could be useful for future studies characterizing the structure, function, and dynamics of the SM in the aging process to design personalized therapeutic agents focusing on potential genomic targets, microbes, and their metabolites.

The human microbiome in disease and pathology

This narrative review seeks to examine the relationships between bacterial microbiomes and infectious disease. This is achieved by detailing how different human host microbiomes develop and function, from the earliest infant acquisitions of maternal and environmental species through to the full development of microbiomes by adulthood. Communication between bacterial species or communities of species within and outside of the microbiome is a factor in both maintenance of homeostasis and management of threats from the external environment. Dysbiosis of this homeostasis is key to understanding the development of disease states. Several microbiomes and the microbiota within are used as prime examples of how changes in species composition, particularly at the phylum level, leads to such diverse conditions as inflammatory bowel disease (IBD), type 2 diabetes, psoriasis, Parkinson's disease, reflux oesophagitis and others. The review examines spatial relationships between microbiomes to understand how dysbiosis in the gut microbiome in particular can influence diseases in distant host sites via routes such as the gut-lung, gut-skin and gut-brain axes. Microbiome interaction with host processes such as adaptive immunity is increasingly identified as critical to developing the capacity of the immune system to react to pathogens. Dysbiosis of essential bacteria involved in modification of host substrates such as bile acid components can result in development of Crohn's disease, small intestine bacterial overgrowth, hepatic cancer and obesity. Interactions between microbiomes in distantly located sites are being increasingly being identified, resulting in a 'whole of body' effect by the combined host microbiome.

Advances in the human skin microbiota and its roles in cutaneous diseases

Skin is the largest organ in the human body, and the interplay between the environment factors and human skin leads to some skin diseases, such as acne, psoriasis, and atopic dermatitis. As the first line of human immune defense, skin plays significant roles in human health via preventing the invasion of pathogens that is heavily influenced by the skin microbiota. Despite being a challenging niche for microbes, human skin is colonized by diverse commensal microorganisms that shape the skin environment. The skin microbiota can affect human health, and its imbalance and dysbiosis contribute to the skin diseases. This review focuses on the advances in our understanding of skin microbiota and its interaction with human skin. Moreover, the potential roles of microbiota in skin health and diseases are described, and some key species are highlighted. The prevention, diagnosis and treatment strategies for microbe-related skin diseases, such as healthy diets, lifestyles, probiotics and prebiotics, are discussed. Strategies for modulation of skin microbiota using synthetic biology are discussed as an interesting venue for optimization of the skin-microbiota interactions. In summary, this review provides insights into human skin microbiota recovery, the interactions between human skin microbiota and diseases, and the strategies for engineering/rebuilding human skin microbiota.

Psoriasis: Interplay between dysbiosis and host immune system

With advancement in human microbiome research, an increasing number of scientific evidences have endorsed the key role of both gut and skin microbiota in the pathogenesis of psoriasis. Microbiome dysbiosis, characterized by altered diversity and composition, as well as rise of pathobionts, have been identified as possible triggers for recurrent episodes of psoriasis. Mechanistically, gut dysbiosis leads to "leaky gut syndrome" via disruption of epithelial bilayer, thereby, resulting in translocation of bacteria and other endotoxins to systemic circulation, which in turn, results in inflammatory response. Similarly, skin dysbiosis disrupts the cutaneous homeostasis, leading to invasion of bacteria and other pathogens to deeper layers of skin or even systemic circulation further enhanced by injury caused by pruritus-induced scratching, and elicit innate and adaptive inflammation. The present review explores the correlation of both skin and gut microbiota dysbiosis with psoriasis. Also, the studies highlighting the potential of bacteriotherapeutic approaches including probiotics, prebiotics, metabiotics, and fecal microbiota transplantation for the management of psoriasis have been discussed.

Microbiota differences of skin and pharyngeal microbiota between patients with plaque and guttate psoriasis in China

Psoriasis can be provoked or exacerbated by environmental exposures such as certain microbiomes. The distinction between plaque psoriasis (PP) and guttate psoriasis (GP) in the skin or pharyngeal microbiota is not yet clear. High-throughput sequencing using Illumina MiSeq was used in this study to characterize skin and pharyngeal microbial composition in patients with PP [large PP (LPP, n = 62), small PP (SPP, n = 41)] and GP (n = 14). The alpha- and beta-diversity of skin microbiota LPP was similar to that of the SPP group, but different from the GP group. There were no differences in pharyngeal microbiota among the groups. According to linear discriminant analysis effect size (LEfSe) analysis, Staphylococcus, Stenotrophomonas, Enhydrobacter, Brevundimonas, and Allorhizobium–Neorhizobium–Pararhizobium–Rhizobium were the dominant genera of skin microbiota in PP. Diversity of skin microbiota correlated with Psoriasis Area and Severity Index (PASI). Moderate-to-severe psoriasis and mild psoriasis have different microbiota compositions. The skin microbiota may be related to the pharyngeal microbiota. Furthermore, two microbiota-based models could distinguish psoriasis subtypes with area under the receiver-operating characteristic curve (AUC-ROC) of 0.935 and 0.836, respectively. In conclusion, the skin microbiota in patients with LPP is similar to that in patients with SPP, but displays variations compared to that of GP, no differences are noted between subtypes in pharyngeal microbiota. Skin microbiota diversity correlated with PASI.

Air pollution and endocrine disruptors induce human microbiome imbalances: A systematic review of recent evidence and possible biological mechanisms

A rich body of literature indicates that environmental factors interact with the human microbiome and influence its composition and functions contributing to the pathogenesis of diseases in distal sites of the body. This systematic review examines the scientific evidence on the effect of environmental toxicants, air pollutants and endocrine disruptors (EDCs), on compositional and diversity of human microbiota. Articles from PubMed, Embase, WoS and Google Scholar where included if they focused on human populations or the SHIME® model, and assessed the effects of air pollutants and EDCs on human microbiome. Non-human studies, not written in English and not displaying original research were excluded. The Newcastle-Ottawa Scale was used to assess the quality of individual studies. Results were extracted and presented in tables. 31 studies were selected, including 24 related to air pollutants, 5 related to EDCs, and 2 related to EDC using the SHIME® model. 19 studies focussed on the respiratory system (19), gut (8), skin (2), vaginal (1) and mammary (1) microbiomes. No sufficient number of studies are available to observe a consistent trend for most of the microbiota, except for streptococcus and veillionellales for which 9 out of 10, and 3 out of 4 studies suggest an increase of abundance with exposure to air pollution. A limitation of the evidence reviewed is the scarcity of existing studies assessing microbiomes from individual systems. Growing evidence suggests that exposure to environmental contaminants could change the diversity and abundance of resident microbiota, e.g. in the upper and lower respiratory, gastrointestinal, and female reproductive system. Microbial dysbiosis might lead to colonization of pathogens and outgrowth of pathobionts facilitating infectious diseases. It also might prime metabolic dysfunctions disrupting the production of beneficial metabolites. Further studies should elucidate the role of environmental pollutants in the development of dysbiosis and dysregulation of microbiota-related immunological processes.

The Gut Microbiome in Psoriasis and Crohn’s Disease: Is Its Perturbation a Common Denominator for Their Pathogenesis?

Psoriasis and inflammatory bowel disease (IBD), including ulcerative colitis (UC) and Crohn’s disease (CD), are interlinked. In fact, the prevalence of IBD is higher in patients with psoriasis, with a risk of ulcerative colitis of 1.6-times higher than in the general population. Analogously, patients with psoriasis have a greater risk of developing IBD. Furthermore, they share some clinical features and pathogenic mechanisms. Both are chronic inflammatory diseases with a relapsing-remitting condition that persists for the patient’s whole life and exhibit increased permeability of the mucosal barrier of skin and gut, allowing an increased interaction of pathogens with inflammatory receptors of the immune cells. A key element in the pathogenesis of these diseases is represented by the microbiota; in particular, the gut microbiota is an important driver of CD pathogenesis, while in psoriasis changes in gut and skin microbiota have been described without a defined pathogenic function. Furthermore, genetic predispositions or environmental factors contribute to disease manifestation, with a central role attributed to the immune responses and, in particular, to a dysregulated role played by T helper 17 cells both in psoriasis and IBD. The purpose of this review was to summarize present information about the links between psoriasis, inflammatory bowel disease, in particular Crohn’s disease, and changes in gut and/or skin microbiome.

Explainable AI reveals changes in skin microbiome composition linked to phenotypic differences

Alterations in the human microbiome have been observed in a variety of conditions such as asthma, gingivitis, dermatitis and cancer, and much remains to be learned about the links between the microbiome and human health. The fusion of artificial intelligence with rich microbiome datasets can offer an improved understanding of the microbiome's role in human health. To gain actionable insights it is essential to consider both the predictive power and the transparency of the models by providing explanations for the predictions. We combine the collection of leg skin microbiome samples from two healthy cohorts of women with the application of an explainable artificial intelligence (EAI) approach that provides accurate predictions of phenotypes with explanations. The explanations are expressed in terms of variations in the relative abundance of key microbes that drive the predictions. We predict skin hydration, subject's age, pre/post-menopausal status and smoking status from the leg skin microbiome. The changes in microbial composition linked to skin hydration can accelerate the development of personalized treatments for healthy skin, while those associated with age may offer insights into the skin aging process. The leg microbiome signatures associated with smoking and menopausal status are consistent with previous findings from oral/respiratory tract microbiomes and vaginal/gut microbiomes respectively. This suggests that easily accessible microbiome samples could be used to investigate health-related phenotypes, offering potential for non-invasive diagnosis and condition monitoring. Our EAI approach sets the stage for new work focused on understanding the complex relationships between microbial communities and phenotypes. Our approach can be applied to predict any condition from microbiome samples and has the potential to accelerate the development of microbiome-based personalized therapeutics and non-invasive diagnostics.

Spermidine-induced recovery of human dermal structure and barrier function by skin microbiome

An unbalanced microbial ecosystem on the human skin is closely related to skin diseases and has been associated with inflammation and immune responses. However, little is known about the role of the skin microbiome on skin aging. Here, we report that the Streptococcus species improved the skin structure and barrier function, thereby contributing to anti-aging. Metagenomic analyses showed the abundance of Streptococcus in younger individuals or those having more elastic skin. Particularly, we isolated Streptococcus pneumoniae, Streptococcus infantis, and Streptococcus thermophilus from face of young individuals. Treatment with secretions of S. pneumoniae and S. infantis induced the expression of genes associated with the formation of skin structure and the skin barrier function in human skin cells. The application of culture supernatant including Streptococcal secretions on human skin showed marked improvements on skin phenotypes such as elasticity, hydration, and desquamation. Gene Ontology analysis revealed overlaps in spermidine biosynthetic and glycogen biosynthetic processes. Streptococcus-secreted spermidine contributed to the recovery of skin structure and barrier function through the upregulation of collagen and lipid synthesis in aged cells. Overall, our data suggest the role of skin microbiome into anti-aging and clinical applications.

Gut-Skin Axis: Current Knowledge of the Interrelationship between Microbial Dysbiosis and Skin Conditions

The microbiome plays an important role in a wide variety of skin disorders. Not only is the skin microbiome altered, but also surprisingly many skin diseases are accompanied by an altered gut microbiome. The microbiome is a key regulator for the immune system, as it aims to maintain homeostasis by communicating with tissues and organs in a bidirectional manner. Hence, dysbiosis in the skin and/or gut microbiome is associated with an altered immune response, promoting the development of skin diseases, such as atopic dermatitis, psoriasis, acne vulgaris, dandruff, and even skin cancer. Here, we focus on the associations between the microbiome, diet, metabolites, and immune responses in skin pathologies. This review describes an exhaustive list of common skin conditions with associated dysbiosis in the skin microbiome as well as the current body of evidence on gut microbiome dysbiosis, dietary links, and their interplay with skin conditions. An enhanced understanding of the local skin and gut microbiome including the underlying mechanisms is necessary to shed light on the microbial involvement in human skin diseases and to develop new therapeutic approaches.

Skin microbiota in health and disease: From sequencing to biology

Microbiota live in a closely regulated interaction with their environment, and vice versa. The presence and absence of microbial entities is greatly influenced by features of the niche in which they thrive. Characteristic of this phenomenon is that different human skin sites harbor niche‐specific communities of microbes. Microbial diversity is considerable, and the current challenge lies in determining which microbes and (corresponding) functionality are of importance to a given ecological niche. Furthermore, as there is increasing evidence of microbial involvement in health and disease, the need arises to fundamentally understand microbiome processes for application in health care, nutrition and personal care products (e.g. diet, cosmetics, probiotics). This review provides a current overview of state‐of‐the‐art sequencing‐based techniques and corresponding data analysis methodology for profiling of complex microbial communities. Furthermore, we also summarize the existing knowledge regarding cutaneous microbiota and their human host for a wide range of skin diseases.

The cutaneous and intestinal microbiome in psoriatic disease

Psoriasis (PsO) and psoriatic arthritis (PsA) are chronic immune-mediated inflammatory diseases of multifactorial etiology. In addition to genetic and environmental factors, evidence supports involvement of a dysregulated human microbiome in the pathogenesis of psoriatic disease. In particular, alterations in the composition of the microbiome, termed dysbiosis, can result in downstream proinflammatory effects in the gut, skin, and joints. Both the cutaneous and intestinal microbial populations are implicated in the pathogenesis of psoriatic disease, although exact mechanisms are unclear. Herein, we review the relationship between the human microbiome and psoriatic disease. Further insight into the functions of the microbiome may allow for greater understanding of inflammatory disease processes and identification of additional therapeutic targets.

Microbiome succession with increasing age in three oral sites

The incidence of oral diseases is remarkably increased with age, and it may be related to oral microbiota. In this study, we systematically investigated the microbiota of gingival crevicular fluid (GCF), tongue back (TB) and saliva (SAL) from various age groups in healthy populations. The microbial diversity results indicated that the α-diversity of bacteria had a tendency to decrease in aging mouth, whereas the β-diversity showed an opposite increasing trend in all three sites. Next, the microbial structure exploration revealed a divergence in bacterial profile in three sites in response to aging, but the intersite differential bacteria demonstrated a uniform bell-shaped variation trend with age. Meanwhile, several age-differentiated genera were shared by GCF, SAL and TB sites, and the bacterial correlation analysis demonstrated a clear shift in the pattern of bacterial correlations with age. Moreover, both the intra- and intersite "core microbiome" showed significantly decreased bacterial diversities with age. Finally, the trending differential bacteria species were used as a biomarker to distinguish the different age groups, and the prediction accuracies in GCF were 0.998, 0.809, 0.668, 0.675 and 0.956. Our results revealed the characteristics of intra- and intersite bacterial succession with age, providing novel insights into senile oral diseases.

Robust Microbial Markers for Non-Invasive Inflammatory Bowel Disease Identification

Inflammatory Bowel Disease (IBD) is an umbrella term for a group of inflammatory diseases of the gastrointestinal tract, including Crohn's Disease and ulcerative colitis. Changes to the intestinal microbiome, the community of micro-organisms that resides in the human gut, have been shown to contribute to the pathogenesis of IBD. IBD diagnosis is often delayed due to its non-specific symptoms and because an invasive colonoscopy is required for confirmation, which leads to poor growth in children and worse treatment outcomes. Feature selection algorithms are often applied to microbial communities to identify bacterial groups that drive disease. It has been shown that aggregating Ensemble Feature Selection (EFS) can improve the robustness of feature selection algorithms, which is defined as the variation of feature selector output caused by small changes to the dataset. In this work, we apply a two-step filter and an EFS process to generate robust feature subsets that can non-invasively predict IBD subtypes from high-resolution microbiome data. The predictive power of the robust feature subsets is the highest reported in literature to date. Furthermore, we identify five biologically plausible bacterial species that have not previously been implicated in IBD aetiology.

Microbe-host interplay in atopic dermatitis and psoriasis

Despite recent advances in understanding microbial diversity in skin homeostasis, the relevance of microbial dysbiosis in inflammatory disease is poorly understood. Here we perform a comparative analysis of skin microbial communities coupled to global patterns of cutaneous gene expression in patients with atopic dermatitis or psoriasis. The skin microbiota is analysed by 16S amplicon or whole genome sequencing and the skin transcriptome by microarrays, followed by integration of the data layers. We find that atopic dermatitis and psoriasis can be classified by distinct microbes, which differ from healthy volunteers microbiome composition. Atopic dermatitis is dominated by a single microbe (Staphylococcus aureus), and associated with a disease relevant host transcriptomic signature enriched for skin barrier function, tryptophan metabolism and immune activation. In contrast, psoriasis is characterized by co-occurring communities of microbes with weak associations with disease related gene expression. Our work provides a basis for biomarker discovery and targeted therapies in skin dysbiosis.

Atopic dermatitis (AD) and psoriasis (PSO) are associated with dysbiosis. Here, by analyses of skin microbiome and host transcriptome of AD and PSO patients, the authors find distinct microbial and disease-related gene transcriptomic signatures that differentiate both diseases.

Rapid Inference of Direct Interactions in Large-Scale Ecological Networks from Heterogeneous Microbial Sequencing Data

The availability of large-scale metagenomic sequencing data can facilitate the understanding of microbial ecosystems in unprecedented detail. However, current computational methods for predicting ecological interactions are hampered by insufficient statistical resolution and limited computational scalability. They also do not integrate metadata, which can reduce the interpretability of predicted ecological patterns. Here, we present FlashWeave, a computational approach based on a flexible Probabilistic Graphical Model framework that integrates metadata and predicts direct microbial interactions from heterogeneous microbial abundance data sets with hundreds of thousands of samples. FlashWeave outperforms state-of-the-art methods on diverse benchmarking challenges in terms of runtime and accuracy. We use FlashWeave to analyze a cross-study data set of 69,818 publicly available human gut samples and produce, to the best of our knowledge, the largest and most diverse network of predicted, direct gastrointestinal microbial interactions to date. FlashWeave is freely available for download here: https://github.com/meringlab/FlashWeave.jl.

Psoriasis: Obesity and Fatty Acids

Psoriasis is chronic inflammatory skin disease affecting skin, joints, cardiovascular system, brain, and metabolism. The pathogenesis of psoriasis is mediated by a complex interplay between the immune system, inflammatory mediators of different pathways, e.g., TNF-alpha and the IL-23/IL-17 pathways, psoriasis-associated susceptibility loci, autoantigens, and multiple environmental factors. Psoriasis is triggered by the combination of genetic and environmental factors. A novel environmental risk factor with rising importance is obesity. Several studies proved that obesity is an independent risk factor for the onset and severity of psoriasis. Due to the dramatic increase of obesity worldwide this minireview focuses on obesity as a major environmental risk factor for psoriasis and the mechanisms of obesity-mediated exacerbation of psoriasis.

Combined culture and metagenomic analyses reveal significant shifts in the composition of the cutaneous microbiome in psoriasis

BACKGROUND

The treatment of psoriasis has been revolutionized by the development of biologic therapies. However, the pathogenesis of psoriasis, in particular the role of the cutaneous microbiome, remains incompletely understood. Moreover, skin microbiome studies have relied heavily on 16S rRNA sequencing data in the absence of bacterial culture.

OBJECTIVES

To characterize and compare the cutaneous microbiome in 20 healthy controls and 23 patients with psoriasis using metagenomic analyses and to determine changes in the microbiome during treatment.

METHODS

Swabs from lesional and nonlesional skin from patients with psoriasis, and from controls matched for site and skin microenvironment, were analysed using both 16S rRNA sequencing and traditional culture combined with mass spectrometry (MALDI-TOF) in a prospective study.

RESULTS

Psoriasis was associated with an increased abundance of Firmicutes and a corresponding reduction in Actinobacteria, most marked in lesional skin, and at least partially reversed during systemic treatment. Shifts in bacterial community composition in lesional sites were reflected in similar changes in culturable bacteria, although changes in the microbiota over repeated swabbing were detectable only with sequencing. The composition of the microbial communities varied by skin site and microenvironment. Prevotella and Staphylococcus were significantly associated with lesional skin, and Anaerococcus and Propionibacterium with nonlesional skin. There were no significant differences in the amount of bacteria cultured from the skin of healthy controls and patients with psoriasis.

CONCLUSIONS

Shifts in the cutaneous microbiome in psoriasis, particularly during treatment, may shed new light on the pathogenesis of the disease and may be clinically exploited to predict treatment response. What's already known about this topic? Alterations in the composition of the cutaneous microbiome have been described in psoriasis, although methodological differences in study design prevent direct comparison of results. To date, most cutaneous microbiome studies have focused on 16S rRNA sequencing data, including both living and dead bacteria. What does this study add? This prospective observational study confirms that changes in the composition of the cutaneous microbiome, detected by 16S rRNA sequencing, are consistent with those identified by bacterial culture and mass spectrometry. The changes in the microbiome during antipsoriasis therapy should be further investigated to determine whether these represent potential novel biomarkers of treatment response. What is the translational message? Characterization of cutaneous microbiota may ultimately move into the clinic to help facilitate treatment selection, not only by optimizing currently available treatments, but also by identifying new therapeutic targets.

Investigation of the skin microbiome: swabs vs. biopsies

BACKGROUND

Human skin is populated by diverse bacteria and there is increasing evidence that resident bacteria play a key role initiating immune responses in cutaneous diseases such as atopic dermatitis, psoriasis and hidradenitis suppurativa. Bacteria are present at all layers of the skin but many studies have relied on swabs to profile the skin microbiota.

OBJECTIVES

As the pathogenesis of many skin conditions is dermal, we wanted to compare the microbiota obtained in swabs (surface) and biopsies (dermis).

METHODS

Using 16S rRNA gene sequencing we established the microbial profiles of skin swabs and skin biopsies in 16 patients.

RESULTS

We found differences in both diversity and taxonomic composition of the microbiome obtained from swabs and biopsies of the same individual. Several taxa were found to be more abundant in the swabs, which displayed significantly higher community richness, but Clostridiales and Bacteroidetes were significantly enriched in the biopsies. Most published research on cutaneous microbiota has been based on skin swabs, which represent the surface of the skin.

CONCLUSIONS

Our study demonstrated a clear difference between the microbiome observed from skin swabs and skin biopsies. These findings may be highly relevant in disorders such as psoriasis where pathogenesis arises in the dermis. What's already known about this topic? 16S RNA gene sequencing has facilitated study of the skin microbiome. Several studies have sequenced the microbiome sampled by skin swabs. What does this study add? The microbiome data obtained using swabs and biopsies were different. Diseases that are predominantly dermal should be studied using both swabs and biopsies.

Psoriasis Pathogenesis and Treatment

Research on psoriasis pathogenesis has largely increased knowledge on skin biology in general. In the past 15 years, breakthroughs in the understanding of the pathogenesis of psoriasis have been translated into targeted and highly effective therapies providing fundamental insights into the pathogenesis of chronic inflammatory diseases with a dominant IL-23/Th17 axis. This review discusses the mechanisms involved in the initiation and development of the disease, as well as the therapeutic options that have arisen from the dissection of the inflammatory psoriatic pathways. Our discussion begins by addressing the inflammatory pathways and key cell types initiating and perpetuating psoriatic inflammation. Next, we describe the role of genetics, associated epigenetic mechanisms, and the interaction of the skin flora in the pathophysiology of psoriasis. Finally, we include a comprehensive review of well-established widely available therapies and novel targeted drugs.

Mechanisms of microbial pathogenesis and the role of the skin microbiome in psoriasis: A review

The pathogenesis of psoriasis may involve a breakdown of immune tolerance to cutaneous microorganisms. Psoriasis is associated with a higher incidence of Crohn disease and periodontitis, two diseases involving impaired tolerance and abnormal immune activation in response to intestinal and oral microbiota, respectively. In addition, guttate and chronic plaque psoriasis are associated with Streptococcus pyogenes colonization. The aim of this review is to characterize the microorganisms implicated in psoriasis by examining results of major association studies and possible mechanisms of pathogenesis. Although studies show relative increases in Streptococcus and Staphylococcus and decreases in Malassezia and Cutibacterium, they differ in methods of sampling and methods of microbial analysis. As such, no definitive associations between microbes and psoriasis have been found to date. It also remains unclear if changes in the microbiomal composition have a causal association with psoriasis or are simply a consequence of the inflammatory microenvironment. Techniques enabling strain-level analysis rather than species-level analysis of the skin microbiome are likely necessary to determine microbiomal signatures of psoriasis. Future investigations may lead to new diagnostic tests and novel treatments, such as probiotics or bacterial transplantation.

Ecologically informed microbial biomarkers and accurate classification of mixed and unmixed samples in an extensive cross-study of human body sites

BACKGROUND

The identification of body site-specific microbial biomarkers and their use for classification tasks have promising applications in medicine, microbial ecology, and forensics. Previous studies have characterized site-specific microbiota and shown that sample origin can be accurately predicted by microbial content. However, these studies were usually restricted to single datasets with consistent experimental methods and conditions, as well as comparatively small sample numbers. The effects of study-specific biases and statistical power on classification performance and biomarker identification thus remain poorly understood. Furthermore, reliable detection in mixtures of different body sites or with noise from environmental contamination has rarely been investigated thus far. Finally, the impact of ecological associations between microbes on biomarker discovery was usually not considered in previous work.

RESULTS

Here we present the analysis of one of the largest cross-study sequencing datasets of microbial communities from human body sites (15,082 samples from 57 publicly available studies). We show that training a Random Forest Classifier on this aggregated dataset increases prediction performance for body sites by 35% compared to a single-study classifier. Using simulated datasets, we further demonstrate that the source of different microbial contributions in mixtures of different body sites or with soil can be detected starting at 1% of the total microbial community. We apply a biomarker selection method that excludes indirect environmental associations driven by microbe-microbe associations, yielding a parsimonious set of highly predictive taxa including novel biomarkers and excluding many previously reported taxa. We find a considerable fraction of unclassified biomarkers ("microbial dark matter") and observe that negatively associated taxa have a surprisingly high impact on classification performance. We further detect a significant enrichment of rod-shaped, motile, and sporulating taxa for feces biomarkers, consistent with a highly competitive environment.

CONCLUSIONS

Our machine learning model shows strong body site classification performance, both in single-source samples and mixtures, making it promising for tasks requiring high accuracy, such as forensic applications. We report a core set of ecologically informed biomarkers, inferred across a wide range of experimental protocols and conditions, providing the most concise, general, and least biased overview of body site-associated microbes to date.

Minor taxa in human skin microbiome contribute to the personal identification

The human skin microbiome can vary over time, and inter-individual variability of the microbiome is greater than the temporal variability within an individual. The skin microbiome has become a useful tool to identify individuals, and one type of personal identification using the skin microbiome has been reported in a community of less than 20 individuals. However, identification of individuals based on the skin microbiome has shown low accuracy in communities larger than 80 individuals. Here, we developed a new approach for personal identification, which considers that minor taxa are one of the important factors for distinguishing between individuals. We originally established a human skin microbiome for 66 samples from 11 individuals over two years (33 samples each year). Our method could classify individuals with 85% accuracy beyond a one-year sampling period. Moreover, we applied our method to 837 publicly available skin microbiome samples from 89 individuals and succeeded in identifying individuals with 78% accuracy. In short, our results investigate that (i) our new personal identification method worked well with two different communities (our data: 11 individuals; public data: 89 individuals) using the skin microbiome, (ii) defining the personal skin microbiome requires samples from several time points, (iii) inclusion of minor skin taxa strongly contributes to the effectiveness of personal identification.

A systematic literature review of the human skin microbiome as biomarker for dermatological drug development

AIMS

To explore the potential of the skin microbiome as biomarker in six dermatological conditions: atopic dermatitis (AD), acne vulgaris (AV), psoriasis vulgaris (PV), hidradenitis suppurativa (HS), seborrhoeic dermatitis/pityriasis capitis (SD/PC) and ulcus cruris (UC).

METHODS

A systematic literature review was conducted according to the PRISMA guidelines. Two investigators independently reviewed the included studies and ranked the suitability microbiome implementation for early phase clinical studies in an adapted GRADE method.

RESULTS

In total, 841 papers were identified and after screening of titles and abstracts for eligibility we identified 42 manuscripts that could be included in the review. Eleven studies were included for AD, five for AV, 10 for PV, two for HS, four for SD and 10 for UC. For AD and AV, multiple studies report the relationship between the skin microbiome, disease severity and clinical response to treatment. This is currently lacking for the remaining conditions.

CONCLUSION

For two indications - AD and AV - there is preliminary evidence to support implementation of the skin microbiome as biomarkers in early phase clinical trials. For PV, UC, SD and HS there is insufficient evidence from the literature. More microbiome-directed prospective studies studying the effect of current treatments on the microbiome with special attention for patient meta-data, sampling methods and analysis methods are needed to draw more substantial conclusions.

Longitudinal Study of the Psoriasis-Associated Skin Microbiome during Therapy with Ustekinumab in a Randomized Phase 3b Clinical Trial

Plaque psoriasis, a chronic inflammatory disease primarily affecting the skin, is thought to have a multifactorial etiology, including innate immune system dysregulation, environmental triggers, and genetic susceptibility. We sought to further understand the role of skin microbiota in psoriasis pathogenesis, as well as their response to therapy. We systematically analyzed dynamic microbiota colonizing psoriasis lesions and adjacent nonlesional skin in 114 patients prior to and during ustekinumab treatment in a phase 3b clinical trial. By sequencing the bacterial 16S ribosomal RNA gene from skin swab samples obtained at six anatomical sites, we identified minor, site-specific differences in microbial diversity and composition between pretreatment lesional and nonlesional skin. During therapy, microbial communities within lesional and nonlesional skin diverged, and body-site dispersion increased, reflecting microbial skin site-specificity. Microbiota demonstrated greater pretreatment heterogeneity in psoriatic lesions than in nonlesional skin, and variance increased as treatment progressed. Microbiota colonizing recurrent lesions did not overlap with pretreatment lesional microbiota, suggesting colonization patterns varied between initial and recurrent psoriatic lesions. While plaque psoriasis does not appear to be associated with specific microbes and/or microbial diversity, this large dataset provides insight into microbial variation associated with (i) disease in different body locations, (ii) initial versus recurrent lesions, and (iii) anti-IL12/23 therapy.

Intestinal Microbiota Influences Non-intestinal Related Autoimmune Diseases

The human body is colonized by millions of microorganisms named microbiota that interact with our tissues in a cooperative and non-pathogenic manner. These microorganisms are present in the skin, gut, nasal, oral cavities, and genital tract. In fact, it has been described that the microbiota contributes to balancing the immune system to maintain host homeostasis. The gut is a vital organ where microbiota can influence and determine the function of cells of the immune system and contributes to preserve the wellbeing of the individual. Several articles have emphasized the connection between intestinal autoimmune diseases, such as Crohn's disease with dysbiosis or an imbalance in the microbiota composition in the gut. However, little is known about the role of the microbiota in autoimmune pathologies affecting other tissues than the intestine. This article focuses on what is known about the role that gut microbiota can play in the pathogenesis of non-intestinal autoimmune diseases, such as Grave's diseases, multiple sclerosis, type-1 diabetes, systemic lupus erythematosus, psoriasis, schizophrenia, and autism spectrum disorders. Furthermore, we discuss as to how metabolites derived from bacteria could be used as potential therapies for non-intestinal autoimmune diseases.

The role of the microbiome in psoriasis: moving from disease description to treatment selection?

BACKGROUND

With several million microbes per square centimetre of skin, the task of mapping the physiological cutaneous microbiome is enormous. Indeed, the reliance on bacterial culture to identify cutaneous bacterial communities has led to a systematic underappreciation of cutaneous microbial diversity, potentially limiting our understanding of common inflammatory skin diseases, including psoriasis. However, based heavily on developments in molecular biology and bioinformatics, including next-generation sequencing, the last decade has witnessed a marked increase in our understanding of the extent and composition of the cutaneous microbiome. It is already clear that skin-specific (skin site and skin microenvironment), individual-specific (hygiene, sex, age and hormonal status), disease-specific (atopic eczema, acne) and genetic factors can all influence the cutaneous microbiome, albeit to varying and, as yet, ill-defined extents.

OBJECTIVES

To investigate the role of the microbiome in psoriasis and to outline how microbiome studies can be harnessed to provide new insights into disease pathogenesis and treatment selection.

METHODS

This review briefly describes the process of 16S ribosomal RNA sequencing and then charts our current understanding of the cutaneous microbiome in health and the alterations (dysbiosis) associated with chronic inflammatory diseases, with particular reference to psoriasis.

RESULTS

The possibility and clinical relevance of intraindividual cross-talk between the various microbiomes is discussed and potential mechanisms underpinning the interactions between resident skin flora and the immune system are highlighted.

CONCLUSIONS

Ultimately, in the age of personalized medicine, the integration of cutaneous microbiome signatures and comprehensive disease and drug response endotypes will herald a novel approach in the clinical management of chronic multisystem inflammatory diseases.

Fragile skin microbiomes in megacities are assembled by a predominantly niche-based process

Given the higher incidence of skin diseases in more urbanized populations and its association with the skin microbiome, we questioned how the skin microbiome differed depending on the degree of urbanization. Skin microbiomes of 231 healthy subjects in five large cities in China varied mainly with environment and socioeconomic status of the cities in question. The differences among microbiomes could be explained by the predominantly niche-based assembly of microbial communities, which was supported by a dominance test, β-null deviation, and edge-length abundance distribution. Networks among microbes in larger cities were more fragile, which may contribute to the higher incidence of skin diseases in more urbanized environments. These results suggest that microbial ecological theory can provide a framework for understanding crucial health-associated features of the human microbiome.

Combining 16S rRNA gene variable regions enables high-resolution microbial community profiling

BACKGROUND

Most of our knowledge about the remarkable microbial diversity on Earth comes from sequencing the 16S rRNA gene. The use of next-generation sequencing methods has increased sample number and sequencing depth, but the read length of the most widely used sequencing platforms today is quite short, requiring the researcher to choose a subset of the gene to sequence (typically 16-33% of the total length). Thus, many bacteria may share the same amplified region, and the resolution of profiling is inherently limited. Platforms that offer ultra-long read lengths, whole genome shotgun sequencing approaches, and computational frameworks formerly suggested by us and by others all allow different ways to circumvent this problem yet suffer various shortcomings. There is a need for a simple and low-cost 16S rRNA gene-based profiling approach that harnesses the short read length to provide a much larger coverage of the gene to allow for high resolution, even in harsh conditions of low bacterial biomass and fragmented DNA.

RESULTS

This manuscript suggests Short MUltiple Regions Framework (SMURF), a method to combine sequencing results from different PCR-amplified regions to provide one coherent profiling. The de facto amplicon length is the total length of all amplified regions, thus providing much higher resolution compared to current techniques. Computationally, the method solves a convex optimization problem that allows extremely fast reconstruction and requires only moderate memory. We demonstrate the increase in resolution by in silico simulations and by profiling two mock mixtures and real-world biological samples. Reanalyzing a mock mixture from the Human Microbiome Project achieved about twofold improvement in resolution when combing two independent regions. Using a custom set of six primer pairs spanning about 1200 bp (80%) of the 16S rRNA gene, we were able to achieve ~ 100-fold improvement in resolution compared to a single region, over a mock mixture of common human gut bacterial isolates. Finally, the profiling of a Drosophila melanogaster microbiome using the set of six primer pairs provided a ~ 100-fold increase in resolution and thus enabling efficient downstream analysis.

CONCLUSIONS

SMURF enables the identification of near full-length 16S rRNA gene sequences in microbial communities, having resolution superior compared to current techniques. It may be applied to standard sample preparation protocols with very little modifications. SMURF also paves the way to high-resolution profiling of low-biomass and fragmented DNA, e.g., in the case of formalin-fixed and paraffin-embedded samples, fossil-derived DNA, or DNA exposed to other degrading conditions. The approach is not restricted to combining amplicons of the 16S rRNA gene and may be applied to any set of amplicons, e.g., in multilocus sequence typing (MLST).

Skin and fur bacterial diversity and community structure on American southwestern bats: effects of habitat, geography and bat traits

Microorganisms that reside on and in mammals, such as bats, have the potential to influence their host's health and to provide defenses against invading pathogens. However, we have little understanding of the skin and fur bacterial microbiota on bats, or factors that influence the structure of these communities. The southwestern United States offers excellent sites for the study of external bat bacterial microbiota due to the diversity of bat species, the variety of abiotic and biotic factors that may govern bat bacterial microbiota communities, and the lack of the newly emergent fungal disease in bats, white-nose syndrome (WNS), in the southwest. To test these variables, we used 16S rRNA gene 454 pyrosequencing from swabs of external skin and fur surfaces from 163 bats from 13 species sampled from southeastern New Mexico to northwestern Arizona. Community similarity patterns, random forest models, and generalized linear mixed-effects models show that factors such as location (e.g., cave-caught versus surface-netted) and ecoregion are major contributors to the structure of bacterial communities on bats. Bats caught in caves had a distinct microbial community compared to those that were netted on the surface. Our results provide a first insight into the distribution of skin and fur bat bacteria in the WNS-free environment of New Mexico and Arizona. More importantly, it provides a baseline of bat external microbiota that can be explored for potential natural defenses against pathogens.

Task Force on Design and Analysis in Oral Health Research: Host-Microbiome Interactions in Dysbiosis

Knowledge Transfer Statement: This article discusses the proceedings of the conference organized by the Task Force on Design and Analysis in Oral Health Research on the new advances in host-microbiome interactions, analytical methods, and their implication in inflammatory periodontal disease management.

Machine learning methods to predict child posttraumatic stress: a proof of concept study

BACKGROUND

The care of traumatized children would benefit significantly from accurate predictive models for Posttraumatic Stress Disorder (PTSD), using information available around the time of trauma. Machine Learning (ML) computational methods have yielded strong results in recent applications across many diseases and data types, yet they have not been previously applied to childhood PTSD. Since these methods have not been applied to this complex and debilitating disorder, there is a great deal that remains to be learned about their application. The first step is to prove the concept: Can ML methods - as applied in other fields - produce predictive classification models for childhood PTSD? Additionally, we seek to determine if specific variables can be identified - from the aforementioned predictive classification models - with putative causal relations to PTSD.

METHODS

ML predictive classification methods - with causal discovery feature selection - were applied to a data set of 163 children hospitalized with an injury and PTSD was determined three months after hospital discharge. At the time of hospitalization, 105 risk factor variables were collected spanning a range of biopsychosocial domains.

RESULTS

Seven percent of subjects had a high level of PTSD symptoms. A predictive classification model was discovered with significant predictive accuracy. A predictive model constructed based on subsets of potentially causally relevant features achieves similar predictivity compared to the best predictive model constructed with all variables. Causal Discovery feature selection methods identified 58 variables of which 10 were identified as most stable.

CONCLUSIONS

In this first proof-of-concept application of ML methods to predict childhood Posttraumatic Stress we were able to determine both predictive classification models for childhood PTSD and identify several causal variables. This set of techniques has great potential for enhancing the methodological toolkit in the field and future studies should seek to replicate, refine, and extend the results produced in this study.

Unexplored diversity and strain-level structure of the skin microbiome associated with psoriasis

Psoriasis is an immune-mediated inflammatory skin disease that has been associated with cutaneous microbial dysbiosis by culture-dependent investigations and rRNA community profiling. We applied, for the first time, high-resolution shotgun metagenomics to characterise the microbiome of psoriatic and unaffected skin from 28 individuals. We demonstrate psoriatic ear sites have a decreased diversity and psoriasis is associated with an increase in Staphylococcus, but overall the microbiomes of psoriatic and unaffected sites display few discriminative features at the species level. Finer strain-level analysis reveals strain heterogeneity colonisation and functional variability providing the intriguing hypothesis of psoriatic niche-specific strain adaptation or selection. Furthermore, we accessed the poorly characterised, but abundant, clades with limited sequence information in public databases, including uncharacterised Malassezia spp. These results highlight the skins hidden diversity and suggests strain-level variations could be key determinants of the psoriatic microbiome. This illustrates the need for high-resolution analyses, particularly when identifying therapeutic targets. This work provides a baseline for microbiome studies in relation to the pathogenesis of psoriasis.

The role of microbiota, and probiotics and prebiotics in skin health

During the past decade, scientists have made great strides in understanding the microbiome's role in human health. Today, the microbiome has become key in scientific research, therapeutic development, medical treatment, and as a news feature in the media. Most studies have focused on the microbiome of our gut, but recently researchers have turned their attention to other microbiomes, including that of the skin. These studies of gut and skin microbiomes are yielding very informative results, new treatment strategies, and the development of new prebiotic and probiotic products for the treatment of many skin conditions.

Shaping of cutaneous function by encounters with commensals

The skin is the largest organ in the human body and provides the first line of defence against environmental attack and pathogen invasion. It harbor multiple commensal microbial communities at different body sites, which play important roles in sensing the environment, protecting against colonization and infection of pathogens, and guiding the host immune system in response to foreign invasions. The skin microbiome is largely variable between individuals and body sites, with several core commensal members commonly shared among individuals at the healthy state. These microbial commensals are essential to skin health and can potentially lead to disease when their abundances and activities change due to alterations in the environment or in the host. While recent advances in sequencing technologies have enabled a large number of studies to characterize the taxonomic composition of the skin microbiome at various body sites and under different physiological conditions, we have limited understanding of the microbiome composition and dynamics at the strain level, which is highly important to many microbe-related diseases. Functional studies of the skin microbial communities and the interactions among community members and with the host are currently scant, warranting future investigations. In this review, we summarize the recent findings on the skin microbiome, highlighting the roles of the major commensals, including bacteria, fungi and bacteriophages, in modulating skin functions in health and disease. Functional studies of the skin microbiota at the metatranscriptomic and proteomic levels are also included to illustrate the interactions between the microbiota and the host skin.

Host-Microbiome Interaction and Cancer: Potential Application in Precision Medicine

It has been experimentally shown that host-microbial interaction plays a major role in shaping the wellness or disease of the human body. Microorganisms coexisting in human tissues provide a variety of benefits that contribute to proper functional activity in the host through the modulation of fundamental processes such as signal transduction, immunity and metabolism. The unbalance of this microbial profile, or dysbiosis, has been correlated with the genesis and evolution of complex diseases such as cancer. Although this latter disease has been thoroughly studied using different high-throughput (HT) technologies, its heterogeneous nature makes its understanding and proper treatment in patients a remaining challenge in clinical settings. Notably, given the outstanding role of host-microbiome interactions, the ecological interactions with microorganisms have become a new significant aspect in the systems that can contribute to the diagnosis and potential treatment of solid cancers. As a part of expanding precision medicine in the area of cancer research, efforts aimed at effective treatments for various kinds of cancer based on the knowledge of genetics, biology of the disease and host-microbiome interactions might improve the prediction of disease risk and implement potential microbiota-directed therapeutics. In this review, we present the state of the art of sequencing and metabolome technologies, computational methods and schemes in systems biology that have addressed recent breakthroughs of uncovering relationships or associations between microorganisms and cancer. Together, microbiome studies extend the horizon of new personalized treatments against cancer from the perspective of precision medicine through a synergistic strategy integrating clinical knowledge, HT data, bioinformatics, and systems biology.

Microbial biofilms and the human skin microbiome

The human skin microbiome plays an important role in both health and disease. Microbial biofilms are a well-characterized mode of surface-associated growth, which present community-like behaviors. Additionally, biofilms are a critical element in certain skin diseases. We review how the perception of the resident skin microbiota has evolved from the early linkages of certain microbes to disease states, to a more comprehensive and intricate understanding brought on by biofilm and microbiome revelations. Rapidly expanding arsenals of experimental methods are opening new horizons in the study of human-microbe and microbe-microbe interactions. Microbial community profiling has largely remained a separate discipline from that of biofilm research, yet the introduction of metatranscriptomics, metabolomics, and the ability to distinguish between dormant and active members of a community have all paved the road toward a convergent cognizance of the encounter between these two microbial disciplines.

Sebum and Hydration Levels in Specific Regions of Human Face Significantly Predict the Nature and Diversity of Facial Skin Microbiome

The skin microbiome varies across individuals. The causes of these variations are inadequately understood. We tested the hypothesis that inter-individual variation in facial skin microbiome can be significantly explained by variation in sebum and hydration levels in specific facial regions of humans. We measured sebum and hydration from forehead and cheek regions of healthy female volunteers (n = 30). Metagenomic DNA from skin swabs were sequenced for V3-V5 regions of 16S rRNA gene. Altogether, 34 phyla were identified; predominantly Actinobacteria (66.3%), Firmicutes (17.7%), Proteobacteria (13.1%) and Bacteroidetes (1.4%). About 1000 genera were identified; predominantly Propionibacterium (58.6%), Staphylococcus (8.6%), Streptococcus (4.0%), Corynebacterium (3.6%) and Paracoccus (3.3%). A subset (n = 24) of individuals were sampled two months later. Stepwise multiple regression analysis showed that cheek sebum level was the most significant predictor of microbiome composition and diversity followed by forehead hydration level; forehead sebum and cheek hydration levels were not. With increase in cheek sebum, the prevalence of Actinobacteria (p = 0.001)/Propionibacterium (p = 0.002) increased, whereas microbiome diversity decreased (Shannon Index, p = 0.032); this was opposite for other phyla/genera. These trends were reversed for forehead hydration levels. Therefore, the nature and diversity of facial skin microbiome is jointly determined by site-specific lipid and water levels in the stratum corneum.

How the microbiota shapes rheumatic diseases

The human gut harbours a tremendously diverse and abundant microbial community that correlates with, and even modulates, many health-related processes. The mucosal interfaces are particularly active sites of microorganism-host interplay. Growing insight into the characteristic composition and functionality of the mucosal microbiota has revealed that the microbiota is involved in mucosal barrier integrity and immune function. This involvement affects proinflammatory and anti-inflammatory processes not only at the epithelial level, but also at remote sites such as the joints. Here, we review the role of the gut microbiota in shaping local and systemic immune responses and how disturbances in the host-microorganism interplay can potentially affect the development and progression of rheumatic diseases. Increasing our understanding of how to promote host-microorganism homeostasis could therefore reveal novel strategies for the prevention or alleviation of rheumatic disease.

Investigating Oral Microbiome Profiles in Children with Cleft Lip and Palate for Prognosis of Alveolar Bone Grafting

In this study, we sought to investigate the oral microbiota structure of children with cleft lip and palate (CLP) and explore the pre-operative oral bacterial composition related to the prognosis of alveolar bone grafting. In total, 28 patients (19 boys, 9 girls) with CLP who were scheduled to undergo alveolar bone grafting for the first time were recruited. According to the clinical examination of operative sites at the third month after the operation, the individuals were divided into a non-inflammation group (n = 15) and an inflammation group (n = 13). In all, 56 unstimulated saliva samples were collected before and after the operation. The v3-v4 hypervariable regions of the 16S rRNA gene were sequenced using an Illumina MiSeq sequencing platform. Based on the beta diversity of the operational taxonomic units (OTUs) in the inflammation and non-inflammation samples, the microbial variation in the oral cavity differed significantly between the two groups before and after the operation (P < 0.05). Analysis of the relative abundances of pre-operative OTUs revealed 26 OTUs with a relative abundance higher than 0.01%, reflecting a significant difference of the relative abundance between groups (P < 0.05). According to a principal component analysis of the pre-operative samples, the inflammation-related OTUs included Tannerella sp., Porphyromonas sp., Gemella sp., Moraxella sp., Prevotella nigrescens, and Prevotella intermedia, most of which were enriched in the inflammation group and showed a significant positive correlation. A cross-validated random forest model based on the 26 different OTUs before the operation was able to fit the post-operative status of grafted sites and yielded a good classification result. The sensitivity and specificity of this classified model were 76.9% and 86.7%, respectively. These findings show that the oral microbiota profile before alveolar bone grafting may be related to the risk of post-operative inflammation at grafted sites.

The healthy human microbiome

Humans are virtually identical in their genetic makeup, yet the small differences in our DNA give rise to tremendous phenotypic diversity across the human population. By contrast, the metagenome of the human microbiome—the total DNA content of microbes inhabiting our bodies—is quite a bit more variable, with only a third of its constituent genes found in a majority of healthy individuals. Understanding this variability in the “healthy microbiome” has thus been a major challenge in microbiome research, dating back at least to the 1960s, continuing through the Human Microbiome Project and beyond. Cataloguing the necessary and sufficient sets of microbiome features that support health, and the normal ranges of these features in healthy populations, is an essential first step to identifying and correcting microbial configurations that are implicated in disease. Toward this goal, several population-scale studies have documented the ranges and diversity of both taxonomic compositions and functional potentials normally observed in the microbiomes of healthy populations, along with possible driving factors such as geography, diet, and lifestyle. Here, we review several definitions of a ‘healthy microbiome’ that have emerged, the current understanding of the ranges of healthy microbial diversity, and gaps such as the characterization of molecular function and the development of ecological therapies to be addressed in the future.

Body Site Is a More Determinant Factor than Human Population Diversity in the Healthy Skin Microbiome

We studied skin microbiota present in three skin sites (forearm, axilla, scalp) in men from six ethnic groups living in New York City. Methods. Samples were obtained at baseline and after four days following use of neutral soap and stopping regular hygiene products, including shampoos and deodorants. DNA was extracted using the MoBio Power Lyzer kit and 16S rRNA gene sequences determined on the IIlumina MiSeq platform, using QIIME for analysis. Results. Our analysis confirmed skin swabbing as a useful method for sampling different areas of the skin because DNA concentrations and number of sequences obtained across subject libraries were similar. We confirmed that skin location was the main factor determining the composition of bacterial communities. Alpha diversity, expressed as number of species observed, was greater in arm than on scalp or axilla in all studied groups. We observed an unexpected increase in α-diversity on arm, with similar tendency on scalp, in the South Asian group after subjects stopped using their regular shampoos and deodorants. Significant differences at phylum and genus levels were observed between subjects of the different ethnic origins at all skin sites. Conclusions. We conclude that ethnicity and particular soap and shampoo practices are secondary factors compared to the ecological zone of the human body in determining cutaneous microbiota composition.

Identification of cell types, tissues and pathways affected by risk loci in psoriasis

Many common variants have been found associated with the risk of psoriasis, but the underlying mechanism is still largely unknown, mostly owing to the difficulty in dissecting the mechanism of each variant using representative cell type and tissue in biological experiments. We applied an integrative method SNPsea which has been developed by investigators in Broad, to identify the most relevant cell types, tissues, and pathways to psoriasis by assessing the condition specificity affected by psoriasis genome-wide association studies-implicated genes. We employed this software on 89 single-nucleotide polymorphisms with genome-wide significance in Han Chinese and Caucasian populations. We found significant evidence for peripheral blood CD56 + NK cells (P = 1.30 × 10(-7)), Langerhans cells (P = 4.96 × 10(-6)) and CD14+ monocytes (P < 4.80 × 10(-5)) in psoriasis. We suggested that the DNase I hypersensitivity sites in CD14+ cells were active in psoriasis (P = 2.20 × 10(-16)). In addition, we discovered that biotic stimulus response, cytokine production and NF-κB pathways were significantly activated in psoriasis (P < 1.00 × 10(-5)). In conclusion, we found several innate immune cells and immune pathways in psoriasis that will help guide biological experiments for psoriasis risk variants in future.

Modeling the Context-Dependent Associations between the Gut Microbiome, Its Environment, and Host Health

Changes in the gut microbiome are often associated with disease. One of the major goals in microbiome research is determining which components of this complex system are responsible for the observed differences in health state. Most studies apply a reductionist approach, wherein individual organisms are evaluated independently of the surrounding context of the microbiome. While such methods have yielded valuable insights into the microbiome, they fail to identify patterns that may be obscured by contextual variation. A recent report by Schubert et al. [A. M. Schubert, H. Sinani, and P. D. Schloss, mBio 6(4):e00974-15, 2015, doi: 10.1128/mBio.00974-15] communicates an alternative approach to the study of the microbiome’s association with host health. By coupling a multifactored experimental design with regression modeling, the authors are able to profile context-dependent changes in the microbiome and predict health status. This work underscores the value of incorporating model-based procedures into the investigation of the microbiome and illustrates the potential clinical transformations that may arise through their use.