Inflammatory Protein Signatures as Predictive Disease-Specific Markers for Non-Alcoholic Steatohepatitis (NASH).

Non-alcoholic fatty liver disease (NAFLD) is the most prevalent chronic disease worldwide, consisting of a broad spectrum of diseases such as simple steatosis (NAFL), non-alcoholic steatohepatitis (NASH), fibrosis, cirrhosis, and hepatocellular carcinoma. Hepatic inflammation plays a key role in the pathophysiology of NAFLD. Inflammatory mediators such as cytokines and chemokines are considered as contributing factors to NAFLD development and progression. In the present study, we aimed to investigate the inflammatory protein signatures as predictive disease-specific markers for non-alcoholic fatty liver disease (NAFLD). This cross-sectional study included healthy control (n = 64), NAFL (n = 109), and NASH (n = 60) human subjects. Serum concentrations of various cytokines and chemokines were evaluated using sensitive multiplex assays. We used principal component analysis (PCoA) to reveal distinct differences in the levels of cytokines and chemokines between each of the study groups. Further, a random forest classification model was developed to identify the panel of markers that could predict diseases. The protein-protein network analysis was performed to determine the various signaling pathways associated with the disease-specific panel of markers. Serum concentrations of TNF-α, IL-1ß, IL-1ra, G-CSF, PDGF-BB, MCP-1, MIP-1a, MIP-1b, RANTES, eotaxin, IL-8 and IP-10 were significantly increased in NASH group as compared to control group. Furthermore, serum concentrations of IL-9 and IL-13 were significantly lower in the NASH group, whereas IL-2 levels were significantly decreased in the NAFL group when compared to the control group. PCoA results demonstrated statistically significant differences in cytokines and chemokines between each of the study groups (PERMANOVA p = 0.001; R2 = 0.102). RANTES, IL-1ra, MIP-1b, IL-2, and G-CSF could differentiate the NAFL group from the controls; G-CSF, IL-1ra, TNF-α, RANTES, and IL-9 could differentiate the NASH group from the controls; and G-CSF, IL-9, IL-13, eotaxin, and TNF- α could differentiate the NASH group from the NAFL group. Our protein-protein network revealed that these markers are involved in cytokine-cytokine receptor interaction, Th1 and Th2 cell differentiation, TNF, chemokine, JAK/STAT, P13K/Akt, TLR, NOD-like receptor, NF-kB, and adipocytokine signaling pathways which might be responsible for disease pathogenesis. Our study findings revealed a set of distinct cytokine and chemokine markers and they might be considered as biomarkers in distinguishing NASH from NAFL. Future multicentre studies with larger sample size are recommended to determine the potential utility of these panels of markers.

Associations between host microbiome and inflammation suggest role for host microbiome in driving COVID-19 disease severity.

Systemic inflammation and innate immune activation are associated with COVID-19 disease severity. Knowledge gaps remain in the relationships between microbiome, inflammation and COVID-19 disease severity. To better characterise these associations, we performed 16SrDNA analysis of stool samples in COVID-19 subjects to explore diversity and taxanomic composition. We correlated these to host inflammatory profiles, derived from soluble plasma biomarkers measured by bead-based fluorescence and electrochemiluminescence immunoassays. Associations of microbial diversity and inflammatory biomarkers on maximal COVID-19 severity (mild, moderate v severe/critical) was explored using logistic regression and weighted gene correlation network analysis (WGCNA). Of 79 subjects, 58% were male and 88% were Caucasian with 36% experiencing mild disease, 22% moderate disease and 40% critical/severe COVID-19. Hierarchical clustering and principal component analysis (PCo) revealed distinct inflammatory clusters that were found to correlate with 4 modules of microbiome profiles. Modules 3 and 4 were associated with both older age and severe/critical disease outcomes. These modules were enriched in pathogenic and inflammatory bacteria that mapped to a pro-inflammatory biomarker cluster. In contrast, module 1 exhibited enrichment of anti-inflammatory bacteria, was associated with younger age and mild/moderate disease outcomes and mapped to a less-inflamed biomarker cluster. This study provides further insights into links between host microbiome, inflammatory responses to SARS-CoV-2 infection and clinical COVID-19 disease severity, suggesting a role for the microbiome in shaping distinct host inflammatory responses to infection.

Comparative diet-gut microbiome analysis in Crohn's disease and Hidradenitis suppurativa.

Introduction: The chronic inflammatory skin disease Hidradenitis suppurativa (HS) is strongly associated with Crohn's Disease (CD). HS and CD share clinical similarities and similar inflammatory pathways are upregulated in both conditions. Increased prevalence of inflammatory disease in industrialised nations has been linked to the Western diet. However, gut microbiota composition and diet interaction have not been compared in HS and CD. Methods: Here we compared the fecal microbiota (16S rRNA gene amplicon sequencing) and habitual diet of previously reported subjects with HS (n = 55), patients with CD (n = 102) and controls (n = 95). Results and discussion: Patients with HS consumed a Western diet similar to patients with CD. Meanwhile, habitual diet in HS and CD was significantly different to controls. Previously, we detected differences in microbiota composition among patients with HS from that of controls. We now show that 40% of patients with HS had a microbiota configuration similar to that of CD, characterised by the enrichment of pathogenic genera (Enterococcus, Veillonella and Escherichia_Shigella) and the depletion of putatively beneficial genera (Faecalibacterium). The remaining 60% of patients with HS harboured a normal microbiota similar to that of controls. Antibiotics, which are commonly used to treat HS, were identified as a co-varying with differences in microbiota composition. We examined the levels of several inflammatory markers highlighting that growth-arrest specific 6 (Gas6), which has anti-inflammatory potential, were significantly lower in the 40% of patients with HS who had a CD microbiota configuration. Levels of the pro-inflammatory cytokine IL-12, which is a modulator of intestinal inflammation in CD, were negatively correlated with the abundance of health-associated genera in patients with HS. In conclusion, the fecal microbiota may help identify patients with HS who are at greater risk for development of CD.

Robust cross-cohort gut microbiome associations with COVID-19 severity.

Although many recent studies have examined associations between the gut microbiome and COVID-19 disease severity in individual patient cohorts, questions remain on the robustness across international cohorts of the biomarkers they reported. Here, we performed a meta-analysis of eight shotgun metagenomic studies of COVID-19 patients (comprising 1,023 stool samples) and 23 > 16S rRNA gene amplicon sequencing (16S) cohorts (2,415 total stool samples). We found that disease severity (as defined by the WHO clinical progression scale) was associated with taxonomic and functional microbiome differences. This alteration in gut microbiome configuration peaks at days 7-30 post diagnosis, after which the gut microbiome returns to a configuration that becomes more similar to that of healthy controls over time. Furthermore, we identified a core set of species that were consistently associated with disease severity across shotgun metagenomic and 16S cohorts, and whose abundance can accurately predict disease severity category of SARS-CoV-2 infected subjects, with Actinomyces oris abundance predicting population-level mortality rate of COVID-19. Additionally, we used relational diet-microbiome databases constructed from cohort studies to predict microbiota-targeted diet patterns that would modulate gut microbiota composition toward that of healthy controls. Finally, we demonstrated the association of disease severity with the composition of intestinal archaeal, fungal, viral, and parasitic communities. Collectively, this study has identified robust COVID-19 microbiome biomarkers, established accurate predictive models as a basis for clinical prognostic tests for disease severity, and proposed biomarker-targeted diets for managing COVID-19 infection.

Weighted Gene Co-Expression Network Analysis Identifies a Functional Guild and Metabolite Cluster Mediating the Relationship between Mucosal Inflammation and Adherence to the Mediterranean Diet in Ulcerative Colitis.

Diet influences the pathogenesis and clinical course of inflammatory bowel disease (IBD). The Mediterranean diet (MD) is linked to reductions in inflammatory biomarkers and alterations in microbial taxa and metabolites associated with health. We aimed to identify features of the gut microbiome that mediate the relationship between the MD and fecal calprotectin (FCP) in ulcerative colitis (UC). Weighted gene co-expression network analysis (WGCNA) was used to identify modules of co-abundant microbial taxa and metabolites correlated with the MD and FCP. The features considered were gut microbial taxa, serum metabolites, dietary components, short-chain fatty acid and bile acid profiles in participants that experienced an increase (n = 13) or decrease in FCP (n = 16) over eight weeks. WGCNA revealed ten modules containing sixteen key features that acted as key mediators between the MD and FCP. Three taxa (Faecalibacterium prausnitzii, Dorea longicatena, Roseburia inulinivorans) and a cluster of four metabolites (benzyl alcohol, 3-hydroxyphenylacetate, 3-4-hydroxyphenylacetate and phenylacetate) demonstrated a strong mediating effect (ACME: -1.23, p = 0.004). This study identified a novel association between diet, inflammation and the gut microbiome, providing new insights into the underlying mechanisms of how a MD may influence IBD. See clinicaltrials.gov (NCT04474561).

Human Milk Microbiome of Healthy Indian Mothers is Dominated by Genus Pseudomonas.

BACKGROUND: The composition of the human milk microbiome is highly variable and multifactorial. Milk microbiota from various countries show striking differences. There is a paucity of data from healthy lactating Indian mothers. RESEARCH AIM: To describe the milk microbiota of healthy North Indian women, using a culture-independent, targeted metagenomic approach. METHODS: We recruited exclusively breastfeeding mothers (N = 22) who had vaginally delivered full-term singleton infants in a tertiary care hospital less than 1 week previously and had not recently consumed systemic antibiotics. Milk samples (5 ml) were collected aseptically, and microbial deoxyribonucleic acid was extracted. Microbial composition and diversity were determined using a 454-pyrosequencing platform. Core genera were identified, and their relative abundances ranked. Heatmaps showing the variation of the ranked abundances and Shannon index were obtained using R. RESULTS: Participants (all exclusively vegetarian) had a mean (SD) age of 27.2 (3.4) years, postnatal age of 3.9 (1.6) days and gestation 38 (1.2) weeks. The dominant phylum was Proteobacterium (relative abundance 84%) and dominant genus Pseudomonas (relative abundance 61.78%). Eleven species of Pseudomonas were identified, all generally considered nonpathogenic. Based on abundance patterns of the core genera, the milk samples could be grouped: (a) dominated by Pseudomonas with low diversity; (b) less Pseudomonas and high diversity; and (c) dominated by Pseudomonas but high diversity. All neonates were healthy and gaining weight well at 1 month of age. CONCLUSIONS: Healthy, lactating, vegetarian, North Indian women who deliver at term gestation and have no recent exposure to antibiotics, have a unique milk microbiome dominated by Pseudomonas.

Emerging tools for understanding the human microbiome.

Recent advances in sequencing technologies, experimental protocols and approaches in data generation and analysis have enabled us to investigate the human microbiome at an unprecedented level of resolution. The current chapter aims to provide an understanding of the different computational and bioinformatic strategies adopted to answer the different questions of a typical microbiome investigation and how the upstream DNA sequencing methodologies can affect this. The chapter enlist the state-of-the-art in metagenomic data analysis along with the available strategies to perform an integrated investigation of the human microbiome along with other data layers.

A high-risk gut microbiota configuration associates with fatal hyperinflammatory immune and metabolic responses to SARS-CoV-2.

Protection against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and associated clinical sequelae requires well-coordinated metabolic and immune responses that limit viral spread and promote recovery of damaged systems. However, the role of the gut microbiota in regulating these responses has not been thoroughly investigated. In order to identify mechanisms underpinning microbiota interactions with host immune and metabolic systems that influence coronavirus disease 2019 (COVID-19) outcomes, we performed a multi-omics analysis on hospitalized COVID-19 patients and compared those with the most severe outcome (i.e. death, n = 41) to those with severe non-fatal disease (n = 89), or mild/moderate disease (n = 42), that recovered. A distinct subset of 8 cytokines (e.g. TSLP) and 140 metabolites (e.g. quinolinate) in sera identified those with a fatal outcome to infection. In addition, elevated levels of multiple pathobionts and lower levels of protective or anti-inflammatory microbes were observed in the fecal microbiome of those with the poorest clinical outcomes. Weighted gene correlation network analysis (WGCNA) identified modules that associated severity-associated cytokines with tryptophan metabolism, coagulation-linked fibrinopeptides, and bile acids with multiple pathobionts, such as Enterococcus. In contrast, less severe clinical outcomes are associated with clusters of anti-inflammatory microbes such as Bifidobacterium or Ruminococcus, short chain fatty acids (SCFAs) and IL-17A. Our study uncovered distinct mechanistic modules that link host and microbiome processes with fatal outcomes to SARS-CoV-2 infection. These features may be useful to identify at risk individuals, but also highlight a role for the microbiome in modifying hyperinflammatory responses to SARS-CoV-2 and other infectious agents.

The gut microbiome as a modulator of healthy ageing.

The gut microbiome is a contributory factor in ageing-related health loss and in several non-communicable diseases in all age groups. Some age-linked and disease-linked compositional and functional changes overlap, while others are distinct. In this Review, we explore targeted studies of the gut microbiome of older individuals and general cohort studies across geographically distinct populations. We also address the promise of the targeted restoration of microorganisms associated with healthier ageing.

Colorectal microbiota after removal of colorectal cancer.

The colonic microbiome has been implicated in the pathogenesis of colorectal cancer (CRC) and intestinal microbiome alterations are not confined to the tumour. Since data on whether the microbiome normalises or remains altered after resection of CRC are conflicting, we studied the colonic microbiota of patients after resection of CRC. We profiled the microbiota using 16S rRNA gene amplicon sequencing in colonic biopsies from patients after resection of CRC (n = 63) in comparison with controls (n = 52), subjects with newly diagnosed CRC (n = 93) and polyps (i = 28). The colonic microbiota after surgical resection remained significantly different from that of controls in 65% of patients. Genus-level profiling and beta-diversity confirmed two distinct groups of patients after resection of CRC: one with an abnormal microbiota similar to that of patients with newly diagnosed CRC and another similar to non-CRC controls. Consumption levels of several dietary ingredients and cardiovascular drugs co-varied with differences in microbiota composition suggesting lifestyle factors may modulate differential microbiome trajectories after surgical resection. This study supports investigation of the colonic microbiota as a marker of risk for development of CRC.

Toward an improved definition of a healthy microbiome for healthy aging.

The gut microbiome is a modifier of disease risk because it interacts with nutrition, metabolism, immunity and infection. Aging-related health loss has been correlated with transition to different microbiome states. Microbiome summary indices including alpha diversity are apparently useful to describe these states but belie taxonomic differences that determine biological importance. We analyzed 21,000 fecal microbiomes from seven data repositories, across five continents spanning participant ages 18-107 years, revealing that microbiome diversity and uniqueness correlate with aging, but not healthy aging. Among summary statistics tested, only Kendall uniqueness accurately reflects loss of the core microbiome and the abundance and ranking of disease-associated and health-associated taxa. Increased abundance of these disease-associated taxa and depletion of a coabundant subset of health-associated taxa are a generic feature of aging. These alterations are stronger correlates of unhealthy aging than most microbiome summary statistics and thus help identify better targets for therapeutic modulation of the microbiome.

Gut microbiome diversity in acute severe colitis is distinct from mild to moderate ulcerative colitis.

BACKGROUND AND AIM: Although the gut microbiome of patients with ulcerative colitis (UC) has been characterized, no study has characterized the gut microbiome in acute severe colitis (ASC). We compared the gut microbiome of patients with UC, ASC, and healthy controls (HCs). METHODS: Patients with mild to moderate UC (n = 24), ASC (n = 19 with 21 episodes) and HCs (n = 50) were recruited prospectively. A 16SrDNA amplicon approach was used to explore gut microbial diversity and taxonomic repertoires. UC was diagnosed using European Crohn's and Colitis Organization guidelines, and ASC was diagnosed using Truelove and Witts' criteria. RESULTS: The normalized alpha diversity was significantly lower in ASC than mild-moderately active UC (P < 0.05) or HC (P < 0.001). The gut microbiome in ASC was highly unstable, as characterized by high intracohort variation (analyzed using J-divergence measure), which was significantly greater than in UC or HC. On principal coordinate analysis, the microbiome of HC and UC were similar, with the ASC cohort being distinct from both. Comparison of ranked abundances identified four distinct clusters of genera (G1, G2, G3, and G4), with specific trends in their abundance across three groups: G1/G2A clusters had the least, whereas G3 had the highest abundance in the ASC cohort. CONCLUSIONS: Gut microbial diversity is lower in ASC than mild-moderate UC or HCs. Gut microbiome composition is increasingly unstable in ASC, with a distinct abundance of specific genera varying between HCs and ASC. Mild-moderate UC lies within the spectrum.

Understanding the impact of age-related changes in the gut microbiome on chronic diseases and the prospect of elderly-specific dietary interventions.

Ageing is associated with characteristic changes including a gradual decline of physiological functions, inflamm-aging, sarcopenia, and the associated onset of multiple diseases. Another factor potentially contributing to enhanced susceptibility to multiple diseases is aging-associated alteration in the gut microbiome. These alterations include a loss of commensals and gain of disease-associated pathobionts, and are accelerated by lifestyle factors like medication, reduced mobility and restricted diet. Several studies suggest that supplementation or modification of the habitual diet may help to address age-related frailty and comorbidities, aided by microbiota modulation. In this review, we comprehensively summarize recent investigations of microbiota alterations during aging and age-related diseases and the possibilities for altering the microbiome as a therapeutic approach.

Metagenomic analysis reveals distinct patterns of gut lactobacillus prevalence, abundance, and geographical variation in health and disease.

Lactobacilli are exploited extensively for food fermentation and biotechnology. Some food and gut isolates have been developed as probiotics, for which species that may be commensal to the human host are considered desirable. However, the robustness of defining original niches for lactobacilli - food, environment, the gut - is questionable, and culture-independent analyses of prevalence in different human populations is lacking. Here we analyzed the abundance of lactobacilli in 6,154 subjects from a database of highly curated fecal shotgun metagenomics data spanning 25 nationalities, with ages ranging from infancy to 102 years. Twenty-five species were detected, which we assigned into low, medium, and high prevalence groups. The microbiome of apparently healthy individuals could be categorized into 6 clusters or Lactobacillotypes (LbTypes), with three of the Lbtypes being dominated by L.delbrueckii, L.ruminis, L.casei, and the other three comprising a combination of different species. These Lactobacillus clusters exhibit distinct global abundance patterns. The cluster prevalences also display distinct age-specific trends influenced by geography, with overall lactobacillus prevalence increasing significantly with age in North America and Europe but declining with age in non-Westernized societies. Regression analysis stratified by regional location identified distinct associations of the Lactobacillotypes with age, BMI, and gender. Cirrhosis, fatty-liver, , IBD and T2D were characterized by net gain of lactobacilli, whereas hypertension patients harbored depleted lactobacillus levels. Collectively these data indicate that the species abundance of gut lactobacilli is moderated by geography, diet, and interaction with the whole microbiome, and has strong interactions with diseases associated with a western lifestyle.

Microbiome and health implications for ethnic minorities after enforced lifestyle changes.

Modern lifestyles increase the risk of chronic diseases, in part by modifying the microbiome, but the health effects of lifestyles enforced on ethnic minorities are understudied1-3. Lifestyle affects the microbiome early in life, when the microbiome is assembled and the immune system is undergoing maturation4-6. Moreover, the influence of lifestyle has been separated from genetic and geographic factors by studies of genetically similar populations and ethnically distinct groups living in the same geographic location7-11. The lifestyle of Irish Travellers, an ethnically distinct subpopulation, changed with legislation in 2002 that effectively ended nomadism and altered their living conditions. Comparative metagenomics of gut microbiomes shows that Irish Travellers retain a microbiota similar to that of non-industrialized societies. Their microbiota is associated with non-dietary factors and is proportionately linked with risk of microbiome-related metabolic disease. Our findings suggest there are microbiome-related public health implications when ethnic minorities are pressured to change lifestyles.

Metagenome-wide association analysis identifies microbial determinants of post-antibiotic ecological recovery in the gut.

Loss of diversity in the gut microbiome can persist for extended periods after antibiotic treatment, impacting microbiome function, antimicrobial resistance and probably host health. Despite widespread antibiotic use, our understanding of the species and metabolic functions contributing to gut microbiome recovery is limited. Using data from 4 discovery cohorts in 3 continents comprising >500 microbiome profiles from 117 individuals, we identified 21 bacterial species exhibiting robust association with ecological recovery post antibiotic therapy. Functional and growth-rate analysis showed that recovery is supported by enrichment in specific carbohydrate-degradation and energy-production pathways. Association rule mining on 782 microbiome profiles from the MEDUSA database enabled reconstruction of the gut microbial 'food web', identifying many recovery-associated bacteria as keystone species, with the ability to use host- and diet-derived energy sources, and support repopulation of other gut species. Experiments in a mouse model recapitulated the ability of recovery-associated bacteria (Bacteroides thetaiotaomicron and Bifidobacterium adolescentis) to promote recovery with synergistic effects, providing a boost of two orders of magnitude to microbial abundance in early time points and faster maturation of microbial diversity. The identification of specific species and metabolic functions promoting recovery opens up opportunities for rationally determining pre- and probiotic formulations offering protection from long-term consequences of frequent antibiotic usage.

Vaginal Microbiome of Pregnant Indian Women: Insights into the Genome of Dominant Lactobacillus Species.

The trillions of microorganisms residing in the human body display varying degrees of compositional and functional diversities within and between individuals and contribute significantly to host physiology and susceptibility to disease. Microbial species present in the vaginal milieu of reproductive age women showed a large personal component and varies widely in different ethnic groups at the taxonomic, genomic, and functional levels. Lactobacillus iners, L. crispatus, L. gasseri, L. jensenii, and L. johnsonii are most frequently detected bacterial species in the vaginal milieu of reproductive age women. However, we currently lack (i) an understanding of the baseline vaginal microbiota of reproductive age Indian women, (ii) the extent of taxonomic and functional variations of vaginal microbiota between individuals and (iii) the genomic repertoires of the dominant vaginal microbiota associated with the Indian subjects. In our study, we analyzed the metagenome of high vaginal swab (HVS) samples collected from 40 pregnant Indian women enrolled in the GARBH-Ini cohort. Composition and abundance of bacterial species was characterized by pyrosequencing 16S rRNA gene. We identified 3067 OTUs with ≥ 10 reads from four different bacterial phyla. Several species of lactobacilli were clustered into three community state types (CSTs). L. iners, L. crispatus, L. gasseri, and L. jensenii are the most frequently detected Lactobacillus species in the vaginal environment of Indian women. Other than Lactobacillus, several species of Halomonas were also identified in the vaginal environment of most of the women sampled. To gain genomic and functional insights, we isolated several Lactobacillus species from the HVS samples and explored their whole genome sequences by shotgun sequencing. We analyzed the genome of dominant Lactobacillus species, L. iners, L. crispatus, L. gasseri, and L. paragesseri to represent the CSTs and identify functions that may influence the composition of complex vaginal microbial ecology. This study reports for the first time the vaginal microbial ecology of Indian women and genomic insights into L. iners, L. crispatus, L. gasseri, and L. paragesseri commonly found in the genital tract of reproductive age women.

IPCO: Inference of Pathways from Co-variance analysis.

BACKGROUND: Key aspects of microbiome research are the accurate identification of taxa and the profiling of their functionality. Amplicon profiling based on the 16S ribosomal DNA sequence is a ubiquitous technique to identify and profile the abundance of the various taxa. However, it does not provide information on their encoded functionality. Predictive tools that can accurately extrapolate the functional information of a microbiome based on taxonomic profile composition are essential. At present, the applicability of these tools is limited due to requirement of reference genomes from known species. We present IPCO (Inference of Pathways from Co-variance analysis), a new method of inferring functionality for 16S-based microbiome profiles independent of reference genomes. IPCO utilises the biological co-variance observed between paired taxonomic and functional profiles and co-varies it with the queried dataset. RESULTS: IPCO outperforms other established methods both in terms of sample and feature profile prediction. Validation results confirmed that IPCO can replicate observed biological associations between shotgun and metabolite profiles. Comparative analysis of predicted functionality profiles with other popular 16S-based functional prediction tools showed significantly lower performances with predicted functionality showing little to no correlation with paired shotgun features across samples. CONCLUSIONS: IPCO can infer functionality from 16S datasets and significantly outperforms existing tools. IPCO is implemented in R and available from https://github.com/IPCO-Rlibrary/IPCO.