Biology subjects

Ravikrishnan, A.

Publications and source records attributed to Ravikrishnan, A..

12 recordsLinked to original sources

bioRxiv2026

Host recovery after skin barrier disruption is individual-specific and associated with microbial functions

The human skin is repeatedly exposed to mechanical and environmental stress, particularly in common skin diseases such as eczema, and yet the determinants of recovery remain poorly understood. Using longitudinal, multimodal profiling of skin physiology, structure (Raman spectroscopy), and microbial communities (shotgun metagenomics), we investigated in a human cohort (n=36 subjects, x2 sites, x6 timepoints) how host-microbe interactions could jointly shape recovery. Despite baseline variability in physiological parameters, we established that our protocol enables a defined disruption of the stratum corneum. While recovery trajectories for host attributes were notably consistent across age groups and body sites, individual-specific differences in recovery timelines were observed. To assess the role of the skin microbiome, several key time-dependent changes in microbial species were identified including enrichment of select Cutibacterium and Staphylococcus species and depletion of Corynebacterium and Malassezia species. Clustering of microbiome stability profiles across subjects and sites identified 6 distinct groups which associate with varying host-recovery patterns and microbial functions. Finally, joint hazards modelling of recovery timing revealed significant contributions from microbial taxa, functions and stability groups, highlighting the under-appreciated role of host-microbial interactions in response to skin stress and in the recovery process.

genomics↗

bioRxiv2025

Malassezia and the Asian menopausal skin

BackgroundPost-menopausal women undergo significant dermatological changes, including thinning skin and reduced sebaceous gland activity, alongside increased incidence of dermatological diseases and hair loss. These changes reshape the skins ecological niche, influencing the skin mycobiome composition and behavior. Malassezia, a lipid-dependent human pathobiont and dominant fungal resident of skin, has been implicated in several dermatological disorders. We hypothesize that shifts in Malassezia populations contribute to post-menopausal skin disorders through altered host-microbe interactions. ResultsShotgun metagenomics of facial and scalp skin from 345 Asian women were stratified by menopausal stage (pre- [N=171], peri- [N=36], and post-menopausal [N=138]) and revealed the presence of seven out of the seventeen recognized Malassezia species: M. globosa, M. restricta, M. arunalokei, M. furfur, M. dermatis, M. japonica, and M. sympodialis. Detection frequencies of several species varied markedly across menopausal groups. Notably, M. globosa was detected 20% more frequently on the scalp of post- versus pre-menopausal women. Reduced sebum concentration on post-menopausal womens skin correlated with increased M. globosa abundance. In vitro co-culture of keratinocytes with Malassezia spp. showed cells tolerated fungal loads up to 104.5 CFU/cm{superscript 2}, but severe cytotoxicity was observed at [≥]105.5 CFU/cm{superscript 2}. M. globosa elicited the highest cytotoxicity towards keratinocytes. All Malassezia spp. tested invaded keratinocytes and triggered strong pro-inflammatory responses. Notably, IL-1, IL-1{beta}, IL-6, IL-8, IL-21, TNF-, GM-CSF, G-CSF, and MMP1 were significantly overproduced. Transcriptomics of keratinocytes exposed to toxic fungal loads revealed a gene expression profile characteristic of hyperproliferative and undifferentiated cells, alongside elevated expression of NLRP3, a key inflammasome sensor involved in pyroptosis. ConclusionsMenopause is associated with distinct shifts in Malassezia spp. prevalence and abundance. Reduced skin lipids and thickness may increase fungal burden relative to host cells, promoting inflammation and barrier dysfunction. Malassezias ability to invade keratinocytes suggests a mechanism for immune evasion and induction of chronic inflammation. Furthermore, keratinocytes exposed to high fungal loads exhibited a transcriptomic profile indicative of hyperproliferation and impaired differentiation, resembling patterns observed in psoriasis, seborrheic dermatitis, and other inflammatory skin conditions. Our co-culture model provides mechanistic insight into Malassezia-driven skin inflammation and offers a platform to develop targeted therapies for post-menopausal skin disorders.

microbiology↗

bioRxiv2025

Large-scale skin metagenomics reveals extensive prevalence, coordination, and functional adaptation of skin microbiome dermotypes across body sites

While skin microbiome studies have increasingly highlighted its importance in health and disease, our understanding of inter-individual heterogeneity in structure and function remains limited, impacting the ability to develop microbiome-based stratification and therapeutics. Powered by comprehensive skin microbiome characterization in a multi-ethnic population-based cohort (>3,550 shotgun metagenomes across 18 sampling sites), we established significant undescribed inter-individual heterogeneity and the extensive prevalence of distinct microbial configurations (17 species-resolution dermotypes) in seven out of nine body sites. Combining functional in silico and in vitro studies revealed insights into how these dermotypes assemble as a function of niche-dependent microbial interactions (e.g. hypoxia-dependent inhibition of S. hominis by S. epidermidis/M. luteus) and metabolic resource utilization (e.g. differential galactose and histidine metabolism). Integration of demographic, skin physiological, and behavioral data further identified >30 significant associations with host attributes. Cross-site analysis revealed remarkable coordination across disparate skin regions (predictive AUC-ROC>0.8) and bilateral consistency (Pearson {pi}>0.95), emphasizing the role of specific microbial and host factors in shaping dermotypes. Finally, we provide multiple lines of evidence that dermotype states impact the risk for skin discomfort (e.g. irritation, itch) and diseases (e.g. eczema), that when combined with our highly accurate dermotype classifiers (AUC-ROC>0.98), provide a new paradigm for understanding skin microbiome function and stratifying patients in the context of skin and other diseases.

genomics↗

bioRxiv2024

Skin metatranscriptomics reveals landscape of variation in microbial activity and gene expression across the human body

The skin microbiome plays an important role in immune homeostasis and skin health, and yet our understanding of in vivo microbial gene activity is hindered by the lack of a robust, non-invasive protocol for metatranscriptomics across skin sites. Circumventing the challenges of low microbial biomass, host contamination, and RNA stability, we developed a clinically tractable skin metatranscriptomics workflow that provides high technical reproducibility of profiles (Pearson r>0.95), uniform coverage across gene bodies, and strong enrichment of microbial mRNAs (2.5-40x). Applying this protocol to a cohort of healthy adults (n=27) across five different skin sites (n=102, paired metatranscriptomes and metagenomes), identified a striking divergence between transcriptomic and genomic abundances, with Staphylococcus species and the skin fungi Malassezia having an outsized contribution to the metatranscriptomic landscape at most sites despite their modest representation in metagenomes. Species-level analysis showed skin site-specific enrichment of gene expression (e.g. increased levels of secreted fungal phospholipase C on cheeks relative to scalp), and revealed how key pathways were transcriptionally active in vivo (e.g. propionate and 4-aminobutyrate metabolism, potentially impacting skin barrier function). Gene-level analysis identified diverse antimicrobial genes transcribed by skin commensals in situ, including several uncharacterized bacteriocins, some of which are expressed at levels comparable to known antimicrobial genes. Correlation of microbial gene expression with organismal abundances uncovered >20 genes that putatively mediate interactions between microbes (e.g. a secreted Malassezia restricta protein with strongly negative in vivo association with Cutibacterium acnes; Spearman {rho}>0.7). This work showcases the potential for leveraging skin metatranscriptomics to identify microbes whose activities play an outsized role in the community, and for uncovering pivotal microbial pathways and biomarkers linked to skin health and disease.

genomics↗

bioRxiv2024

Gut metagenomes of Asian octogenarians reveal metabolic potential expansion and distinct microbial species associated with aging phenotypes

While rapid demographic changes in Asia are driving the incidence of chronic diseases related to aging, the limited availability of high-quality in vivo data hampers our ability to understand complex multi-factorial contributions, including gut microbial, to healthy aging. Leveraging the availability of a well-phenotyped cohort of community-living octogenarians in Singapore, we used deep shotgun metagenomic sequencing to do high-resolution taxonomic and functional characterization of their gut microbiomes (n=234). Joint species-level analysis with other Asian cohorts identified a distinct age-associated shift in Asian gut metagenomes, characterized by a reduction in microbial richness, and enrichment of specific Alistipes and Bacteroides species (e.g. Alistipes shahii and Bacteroides xylanisolvens). Functional pathway analysis confirmed that these changes correspond to a metabolic potential expansion in aging towards alternate pathways that synthesize and utilize amino-acid precursors, relative to the dominant microbial guilds that typically produce butyrate in the gut from pyruvate (e.g. Faecalibacterium prausnitzii, Roseburia inulinivorans). Extending these observations to key clinical markers helped identify >10 robust gut microbial associations to inflammation, cardiometabolic and liver health, including potential probiotic species such as Parabacteroides goldsteinii and pathobionts such as Klebsiella pneumoniae, highlighting the role of the microbiome as biomarkers and potential intervention targets for promoting healthy aging.

genomics↗

bioRxiv2024

Life stage impact on the human skin ecosystem: lipids and the microbial community

While research into gut-microbe interactions is common and advanced, with multiple defined impacts on human health, studies exploring the significance of skin-microbe interactions remain underrepresented. Skin is the largest human organ, has a vast surface area, and is inhabited by a plethora of microorganisms which metabolise sebaceous lipids. Sebaceous free fatty acids are metabolized into bioactive lipid mediators with immune-modulatory properties by skin-resident microbes, including Malassezia. Intriguingly, many of the same lipid mediators are also found on human skin, implying these compounds may have microbial or mixed microbial/human origin. To support this hypothesis, we isolated lipids and microbial DNA from the skin of prepubescent, adult, pre- and post-menopausal volunteers and performed correlational analyses using skin lipidomics and metagenomics to compare lipid mediator profiles and microbiome compositions on skin with either low or high sebaceous gland activity. We found that specific microbial taxonomies were positively and negatively correlated with skin lipid mediator species with high statistical significance. 2D in vitro co-cultures with Malassezia and keratinocytes also directly linked the production of specific lipid mediators, detected on healthy human skin, to secretion of immuno-stimulatory cytokines. Together, these findings further support the hypothesis that microbial-derived skin lipid mediators influence healthy skin homeostasis and skin disease development and progression, thereby spotlighting the relevance of the skin microbiomes footprint on human health.

microbiology↗

bioRxiv2022

16S rRNA sequencing for profiling microbial communities in idiopathic granulomatous mastitis

BackgroundIdiopathic granulomatous mastitis (IGM) is a rare, chronic, and benign inflammatory breast disease with ambiguous aetiology. Contrastingly, lactational mastitis (LM) is commonly diagnosed in breastfeeding women. To investigate IGM aetiology, we profiled the microbial flora of pus and skin in IGM and LM patients. MethodsIGM patients were recruited from 2018 to 2020; LM patients were recruited as controls. 16S rRNA sequencing libraries were constructed from 16S rRNA gene amplified from total DNA extracted from pus and skin swabs. Metagenomic analysis was conducted using modified microbiome abundance analysis-suite customised R-resource for paired pus and skin samples. Microbiome multivariable association analyses were performed using linear models. Results21 IGM and 3 LM paired pus and skin samples underwent metagenomic analysis. Bray-Curtis ecological dissimilarity distance showed dissimilarity across four sample types (IGM-pus, IGM-skin, LM-pus, LM-skin; PERMANOVA, p<0.001). No characteristic dominant genus was observed across IGM samples. IGM pus samples were more diverse than corresponding IGM skin samples (Shannon and Simpson index; Wilcoxon paired signed-rank tests, p=0.022 and p=0.07). Corynebacterium kroppenstedtii, reportedly associated with IGM in literature, was higher in IGM pus samples than paired skin sample (Wilcoxon, p=0.022). Three other species and nineteen genera were statistically significant in paired IGM pus-skin comparison after antibiotic treatment adjustment, and multiple comparisons correction. ConclusionsMicrobial profiles are unique between IGM and LM patients. Inter-patient variability and polymicrobial IGM pus samples cannot implicate specific genus or species as an infectious cause for IGM.

microbiology↗

bioRxiv2022

Long-term ecological and evolutionary dynamics in the gut microbiomes of carbapenemase-producing Enterobacteriaceae colonized subjects

Long-term colonization of the gut microbiome by carbapenemase-producing Enterobacteriaceae (CPE) is a growing area of public health concern as it can lead to community transmission and rapid increase in cases of life-threatening CPE infections. Leveraging the observation that many subjects are decolonized without interventions within a year, we used longitudinal shotgun metagenomics (up to 12 timepoints) for detailed characterization of ecological and evolutionary dynamics in the gut microbiome of a cohort of CPE-colonized subjects and family members (n=46; 361 samples). Subjects who underwent decolonization exhibited a distinct ecological shift marked by recovery of microbial diversity, key commensals and anti-inflammatory pathways. In addition, colonization was marked by elevated but unstable Enterobacteriaceae abundances, which exhibited distinct strain-level dynamics for different species (Escherichia coli and Klebsiella pneumoniae). Finally, comparative analysis with whole genome sequencing data from CPE isolates (n=159) helped identify sub-strain variation in key functional genes and the presence of highly similar E. coli and K. pneumoniae strains with variable resistance profiles and plasmid sharing. These results provide an enhanced view into how colonization by multi-drug resistant bacteria associates with altered gut ecology and can enable transfer of resistance genes, even in the absence of overt infection and antibiotic usage.

genomics↗

bioRxiv2022

Genome-centric analysis of short and long read metagenomes reveals uncharacterized microbiome diversity in Southeast Asians

Despite extensive efforts to address it, the vastness of uncharacterized dark matter microbial genetic diversity can impact short-read sequencing based metagenomic studies. Population-specific biases in genomic reference databases can further compound this problem. Leveraging advances in long-read and Hi-C technologies, we deeply characterized 109 gut microbiomes from three ethnicities in Singapore to comprehensively reconstruct 4,497 medium and high-quality metagenome assembled genomes, 1,708 of which were missing in short-read only analysis and with >28x N50 improvement. Species-level clustering identified 70 (>10% of total) novel gut species out of 685, improved reference genomes for 363 species (53% of total), and discovered 3,413 strains that are unique to these populations. Among the top 10 most abundant gut bacteria in our study, one of the species and >80% of all strains were not represented in existing databases. Annotation of biosynthetic gene clusters (BGCs) uncovered more than 27,000 BGCs with a large fraction (36-88%) not represented in current databases, and with several unique clusters predicted to produce bacteriocins that could significantly alter microbiome community structure. These results reveal the significant uncharacterized gut microbial diversity in Southeast Asian populations and highlight the utility of hybrid metagenomic references for bioprospecting and disease-focused studies.

genomics↗

bioRxiv2021

Unraveling microbial interactions in the gut microbiome

The human gut comprises trillions of microorganisms that take part in several critical functions of the body. Within this structured habitat, the microbes synergise with one another mainly mediated through metabolic exchanges. In this study, we used 52 most commonly occurring microbial species in the gut and identified the interactions between them. Using our previously developed graph-based algorithm, MetQuest, we enumerate several biosynthetic pathways, mainly involved in amino acid biosynthesis and spanning across a pair of organisms. We compute the Metabolic Support Index (MSI), which captures the extent of support/interaction between a pair of microbes, based on the incremental change in metabolic capabilities achieved in a community vis-a-vis the individual organisms. Our results from these analyses are four-fold. Firstly, we show that dependencies between the gut microorganisms largely vary with respect to the environmental conditions. We observe that few phyla such as Firmicutes, Fusobacteria and Proteobacter showed significantly higher metabolic support on DMEM conditions compared to HF medium. Secondly, we infer a microbial association network based on the MSI values and found that the gut organisms are arranged in trophic-levels, with B. bifidum and R. torques acting as central nodes with the highest betweenness centrality. Thirdly, we find that species belonging to Lactobacillus genus, especially L. mucosae and L. reuteri show an enriched amino acid synthesising ability in the presence of many other gut organisms on a minimal glucose medium. Finally, through pathway analyses, we observe that metabolic exchanges are medium-dependent, and many of the metabolites are involved in energy metabolism, nucleotide, and vitamin biosynthesis. Overall, this study sheds light on the astonishing variety of underlying interactions between microorganisms in the gut. Author SummaryMicroorganisms are ubiquitous and exist in evolutionarily and metabolically diverse communities around us. In practically every ecosystem, microbes form complex dynamic assemblages--such as in the human gut, where they outnumber human cells. Community structure in these microbiomes is dictated by a complex web of interactions, where metabolic interactions are known to predominate. In this study, we employ computational tools to interrogate community metabolic networks and unravel the dependencies between microbes in the gut. We used 52 important and commonly occurring microbial species in the gut, as identified in previous studies. Through our analyses, we show that the dependencies between gut organisms vary in different environmental conditions. Further, we show that these organisms are arranged in multiple trophic levels, where a select few of them acting as central nodes. We also identify the metabolic interactions and demonstrate how synergistic interactions can enhance the amino acid biosynthesis in gut bacteria. These results, taken together, help us better understand the mechanism behind these interactions, which would pave the way for the rational design of pre- and pro-biotic formulations.

systems biology↗

bioRxiv2019

Investigating metabolic interactions in a microbial co-culture through integrated modelling and experiments

Microbial co-cultures have been used in several biotechnological applications. Within these co-cultures, the micro-organisms tend to interact with each other and perform complex actions vis-a-vis a single organism. Investigating metabolic interactions in microbial co-cultures is crucial in designing microbial consortia tailored for specific applications. In this study, we present a pipeline integrating modelling and experimental approaches to understand metabolic interactions between organisms in a community. We define a new index named Metabolic Support Index (MSI), which quantifies the benefits derived by each organism in the presence of the other when grown as a co-culture. We computed MSI for several experimentally demonstrated co-culture systems and showed that MSI, as a metric, accurately identifies the organism that derives the maximum benefit. We also computed MSI for a commonly used yeast co-culture consisting of Saccharomyces cerevisiae and Pichia stipitis and observed that the latter derives higher benefit from the interaction. Further, we designed two-stage experiments to study mutual interactions and showed that P. stipitis indeed derives the maximum benefit from the interaction, as shown from our computational predictions. Also, using our previously developed computational tool MetQuest, we identified all the metabolic exchanges happening between these organisms by analysing the pathways spanning the two organisms. By analysing the HPLC profiles and studying the isotope labelling, we show that P. stipitis consumes the ethanol produced by S. cerevisiae when grown on glucose-rich medium under aerobic conditions, as also indicated by our in silico pathway analyses. Our approach represents an important step in understanding metabolic interactions in microbial communities through an integrating framework of modelling and experiments.

systems biology↗

bioRxiv2017

Enumerating all possible biosynthetic pathways from metabolic networks

Exhaustive identification of all alternate possible pathways that exist within metabolic networks can provide valuable insights into cellular metabolism. With the growing number of metabolic reconstructions, there is a need for an efficient method to enumerate pathways, which can also scale well to large metabolic networks, such as those corresponding to microbial communities.\n\nWe developed MetQuest, an efficient graph-theoretic algorithm to enumerate all possible pathways of a particular length between a given set of source and target molecules. Our algorithm employs a guided breadth-first search to identify all feasible reactions based on the availability of the precursor molecules, followed by a novel dynamic-programming based enumeration, which assembles these reactions into pathways producing the target from the source. We demonstrate several interesting applications of our algorithm, ranging from predicting amino acid biosynthesis pathways to identifying the most diverse pathways involved in degradation of complex molecules. We also illustrate the scalability of our algorithm, by studying larger graphs such as those corresponding to microbial communities, and identify several metabolic interactions happening therein.\n\nA Python-based implementation of MetQuest is available at https://github.com/RamanLab/MetQuest

systems biology↗