Biology subjects

Gautam, A.

Publications and source records attributed to Gautam, A..

At least 19 recordsLinked to original sources

bioRxiv2026

ProFinder: Candidate constitutive promoter prediction across the bacterial tree for genetic tool development in non-model species

Reliable constitutive promoters for non-model bacteria are lacking, resulting in a bottleneck for genetic tool development. Existing computational promoter predictors are trained on model Gammaproteobacteria and degrade on phylogenetically distant or compositionally atypical genomes. Here we present ProFinder, a computational method that integrates three biologically informed criteria for promoter prediction: a conserved 98-gene constitutive marker panel, an empirically derived intergenic search window, and a 72-motif {sigma}70 motif catalogue built from diverse lineages. From an input bacterial assembly, ProFinder returns a ranked shortlist of putative constitutive promoters. It matches the precision of full-genome classifiers and is more robust to GC extremes. Applied to 56,742 bacterial genomes spanning 149 phyla, ProFinder returned candidate promoters for 99.76%, delivering a pan-bacterial catalogue of pre-computed constitutive promoter parts to accelerate chassis development across the bacterial tree. ProFinder can be accessed at: https://plabase.cs.uni-tuebingen.de/profinder/.

microbiology↗

bioRxiv2026

High-throughput spheroid profiling reveals BMP-driven rewiring of glioma cell death responses

The mechanisms regulating glioma cell death are poorly understood. Here, we developed a high-throughput method to study cell death in patient-derived glioblastoma (GBM) and diffuse intrinsic pontine glioma (DIPG) spheroids. Using this method, we systematically profiled how extracellular ligands modulate compound-induced cell death. We find that bone morphogenetic protein 2 (BMP2) and BMP4 potently rewire cell death sensitivity. These ligands suppress killing by standard-of-care DNA alkylating agents and kinase inhibitors by inhibiting cell cycle progression. Simultaneously, BMP2/4 prime spheroids for lipid-dependent necrosis (LiDN), a palmitate-dependent form of non-apoptotic cell death that can be triggered by the clinical drug candidate tegavivint. Activating mutations in the BMP receptor ACVR1, found in ~25% of DIPG tumors, are sufficient to prime cells for LiDN in the absence of BMP ligand. Together, these findings identify a cell death switch that can be activated in glioma cells by BMP signaling.

cancer biology↗

bioRxiv2026

DNA Polymerase beta Accelerates Cellular DNA Base Excision Repair by Suppressing Excessive PARP1 Engagement

DNA polymerase beta (POL{beta}) is required for rapid rates of cellular DNA base excision repair (BER). However, the reason for this requirement is unclear, because other DNA polymerases can replace POL{beta}, in vitro. Here, we have identified the essential role of POL{beta} during cellular BER. As expected, POL{beta} deletion in human RPE-1 cells resulted in the rapid accumulation of DNA strand break intermediates during incubation with the monofunctional alkylating agent, methyl methanesulphonate (MMS). However, this accumulation was not detected in cells that also lack PARP1, indicating that POL{beta} is required for BER only if PARP1 is present. This result is reminiscent of the essential role of XRCC1 during BER, which is to suppress the excessive engagement and activity of PARP1 at BER intermediates and thereby enable their access and repair by other enzymes. Indeed, we found that POL{beta} is required to prevent excessive PARP1 engagement and activity during BER, and that XRCC1 and POL{beta} fulfil this function together. Finally, similar to XRCC1, loss of POL{beta} leads to persistent transcriptional suppression during MMS-induced BER, and this suppression is alleviated by treatment with PARP inhibitor. In summary, we show here that the essential role of POL{beta} during cellular BER is to suppress excessive PARP1 engagement and activity, and thereby maintain rapid rates of this important DNA repair process.

molecular biology↗

bioRxiv2026

Ethanol-induced activation of BMP signaling and reprogramming of cardiomyocytes' transcriptome

Congenital heart disease (CHD) comprises a diverse group of structural heart defects present at birth due to complex interactions between genetic and environmental factors. Prenatal alcohol exposure (PAE) is a known environmental factor that disrupts fetal cardiogenesis and increases the risk of CHD. However, the molecular mechanisms behind ethanol (EtOH)-induced CHD remain obscure. This study investigated the effects of EtOH on bone morphogenetic protein (BMP) signaling and transcriptomic reprograming in HL-1 cardiomyocytes. HL-1 cells were treated with varying concentrations of EtOH (25, 50, and 100 mM) for 24 h. 100 mM of EtOH exposure significantly enhanced SMAD1/5 phosphorylation and upregulated BMP-responsive genes, namely Id1, Gata4, Mef2c, and Nkx2.5. Increased histone acetyltransferase activity further validated activation of BMP signaling through histone hyperacetylation. These effects were reversed by the BMP pathway inhibitor LDN-193189, confirming pathway-specific activation. Further, transcriptome analysis following 100 mM EtOH treatment identified 3,876 differentially expressed genes. KEGG enrichment analysis revealed significant dysregulation of cardiogenic pathways, including TGF-{beta}, Hedgehog, PI3K-Akt, Notch, FoxO, and calcium signaling pathways, along with extracellular matrix-receptor interaction and focal adhesion pathways. Gene Ontology analysis highlighted disturbances in heart development, cellular differentiation, apoptosis, extracellular matrix (ECM) organization, and chromatin regulation. Network analysis identified key hub genes, viz. Kras, Fn1, Col1a1, Prkaca, Fbn1, Col6a1, Col6a2, Ccnd1, Col1a2 and Myc which are upregulated and Hsp90aa1, Mdm2, Jun, Hras, Il6, Hsp90ab1, Pdgfra, Cdkn1a, Pparg, Fos and Hspa8 are downregulated which were subsequently validated by qRT-PCR. Collectively, these findings provide novel insights into the molecular basis of EtOH-induced CHD and identify potential biomolecule candidates for future therapeutic investigation.

cell biology↗

bioRxiv2026

Industrialization restructures the domestic dog gut microbiome while preserving host specificity

Industrialization has reshaped human gut microbiomes, but its effects on other human-associated mammals remain poorly understood. Domestic dogs provide an informative comparative system because they have shared human environments and food systems for millennia yet retain distinct host biology. However, most canine microbiome studies have focused on industrialized companion animals, limiting our understanding of the ecological range of the domestic dog gut microbiome. We analyzed fecal 16S rRNA gene profiles from 261 dogs sampled across Nepal, Thailand, the United Arab Emirates, and the United States, spanning forager, agrarian, pastoralist, urban, and industrialized lifestyles; 257 dogs remained after excluding recent antibiotic exposure. Lifestyle was the strongest measured correlate of canine gut microbiome composition, and this structure persisted in restricted analyses of mature, non-shelter dogs sampled from temperate climate regions. Industrialized dogs differed from non-industrialized dogs through directional genus-level turnover, restructuring of VANISH- and BloSSUM-like microbial guilds, and shifts in predicted functional potential. Non-industrialized dogs were not microbiologically uniform: pastoralist dogs carried non-industrialized microbiome profiles but diverged from a simple forager-to-industrialized continuum. Cross-species comparisons with humans sampled across matched lifestyle categories showed parallel lifestyle-associated restructuring in both hosts, but host species remained the dominant axis of variation and the genera responding to industrialization were largely host-specific. These findings expand the ecological baseline for the domestic dog gut microbiome and identify industrialization as a major axis of microbiome restructuring in a long-term human-associated mammal. More broadly, they show that shared lifestyle transitions can impose parallel ecological pressures across host species without overriding host-specific community assembly.

microbiology↗

bioRxiv2026

Dissecting Complex Interactions Between Ferroptosis and the Proteasome

The proteasome is an essential multiprotein complex whose inhibition can lead to apoptosis. Ferroptosis is a non-apoptotic cell death mechanism whose fundamental regulation continues to be elucidated. How proteasome function regulates ferroptosis sensitivity is poorly understood and difficult to study given the essential nature of the proteasome. Here, we isolated the effects of proteasome inhibition on ferroptosis by combining direct cell death imaging, cell death pathway-specific inhibitors, and mathematical modeling. We find that proteasome inhibition enhances sensitivity to ferroptosis induced by glutathione peroxidase 4 (GPX4) inhibition while simultaneously promoting resistance to ferroptosis induced by system x - inhibition. Sensitization to GPX4 inhibition requires protein synthesis but not the apoptosis execution machinery and is opposed by the activating transcription factor 4 (ATF4) stress response pathway. This work demonstrates a complex role for proteasome function in ferroptosis regulation and establishes new methods to dissect cross-talk between ferroptosis and essential cellular processes.

cell biology↗

bioRxiv2026

Mettl3-catalyzed m6A methylation determines CD8+ T cell differentiation fate in tumor

The heterogeneity in patient responses to immune checkpoint blockade (ICB) is dictated by the relative abundance of exhausted CD8 T cell (Tex) subsets with distinct therapeutic responsiveness. Progenitor exhausted (pTex) cells remain sensitive to ICB, whereas terminally exhausted (tTex) cells are refractory; however, the molecular cues that bias differentiation toward these divergent fates remain poorly defined. Here, we identify the RNA methyltransferase Mettl3 as a central regulator of Tex fate. Across murine tumor models, human T cells, and adoptive transfer systems, Mettl3 expression is selectively enriched in tTex cells and inversely correlated with TCF1 pTex populations. Mechanistically, Mettl3 drives terminal exhaustion by stabilizing DNMT3B transcripts via mA modification, enforcing CpG methylation and chromatin compaction at memory-associated loci. Inhibition of the Mettl3-Dnmt3b axis reprograms chromatin accessibility toward memory-like states, thereby preserving progenitor potential and effector function. Consequently, T cells lacking Mettl3-Dnmt3b activity persist longer, mount robust recall responses, and achieve superior tumor control with enhanced responsiveness to PD-1 blockade. These findings establish the Mettl3-mA-Dnmt3b axis as a molecular rheostat of CD8 T cell fate, coupling epitranscriptomic regulation to epigenetic remodeling, and reveal a tractable pathway to improve the durability of cancer immunotherapy.

immunology↗

bioRxiv2025

MMonitor: Software for Real-Time Monitoring of Microbial Communities Using Long Reads

Real-time monitoring of microbial communities offers valuable insights into microbial dynamics across diverse environments. However, many existing metagenome analysis tools require advanced computational expertise and are not designed for monitoring. We present MMonitor, an open source software platform for the real-time analysis and visualization of metagenomic Oxford Nanopore Technologies (ONT) sequencing data. MMonitor includes two components: a desktop application for running bioinformatics pipelines through a graphical user interface (GUI) or command-line interface (CLI), and a web-based dashboard for interactive result inspection. The dashboard provides taxonomic composition over time, quality scores, diversity indices, and taxonomy-metadata correlations. Integrated pipelines enable automated de-novo assembly and reconstruction of metagenome-assembled genomes (MAGs). To validate MMonitor, we tracked human gut microbial populations in three bioreactors using 16S rRNA gene sequencing, and applied it to whole-genome sequencing (WGS) data to generate high-quality annotated MAGs and reveal functional insights. We also compare MMonitor to other software for real-time metagenomic analysis, highlighting the strengths and limitations of each tool for this use case. By performing automated time-series analyzes, sample management, and updating of reference databases, MMonitor addresses current limitations and supports dynamic microbiome research in various fields. IMPORTANCEMetagenome monitoring is essential for understanding and managing microbial communities in dynamic environments. Rapid and accurate analysis of these communities allows for timely interventions in biologically active environments, for example, in industrial biotechnology, environmental surveillance, and medical diagnostics. Metagenome monitoring tools should have real-time capabilities, intuitive interfaces, convenient setup and distribution, and suitable visual representations for effective monitoring. MMonitor addresses these challenges by offering a platform that combines real-time analysis of nanopore sequencing data with customizable pipelines and advanced visualization tools. It is designed to be accessible to researchers of all levels of computational skill. It supports efficient taxonomic and functional analyses based on popular metagenome software and automates taxonomic profiling, genome assembly, binning, and annotation. The dashboard helps to interpret data and present results. MMonitor is designed to track metagenomes and we hope that it can impact microbiome research and applications in health, biotechnology, agricultural, and environmental sciences.

bioinformatics↗

bioRxiv2025

Growth Hormone Excess Drives Liver Aging via increased Glycation stress

Growth hormone (GH) plays a crucial role in various physiological functions, with its secretion tightly regulated by complex endocrine mechanisms. Pathological conditions such as acromegaly or pituitary tumors result in elevated circulating GH levels, which have been implicated in a spectrum of metabolic disorders, potentially by regulating liver metabolism. In this study, we focused on the liver, a key organ in metabolic regulation and a primary target of GH, to investigate the impact of high circulating GH on liver metabolism. We used bovine GH overexpressing transgenic (bGH-Tg) mice to conduct a comprehensive transcriptomic analysis of hepatic tissues. The bGH-Tg mouse livers exhibit dysregulated fatty acid metabolism and heightened inflammatory responses. Notably, the transcriptomic profile of young bGH-Tg mouse livers resembled that of aged livers and displayed markers of increased cellular senescence. Furthermore, these mice exhibited a significant accumulation of advanced glycation end products (AGEs). Intervention with glycation-lowering compounds effectively reversed the insulin resistance and aberrant transcriptomic signatures in the liver that are associated with elevated GH levels. These findings underscore the potential therapeutic value of glycation-lowering agents in mitigating the deleterious effects of chronic GH overexpression. HighlightsO_LIOverexpression of bovine growth hormone impacts transcriptional changes in liver fat metabolism and inflammatory response in mice. C_LIO_LIHigh circulating growth hormone leads to transcriptional changes that suggest enhanced liver aging and induce cellular senescence. C_LIO_LIDetoxification pathways in bGH-Tg mice are inhibited, leading to the accumulation of Advanced Glycation End (AGE) products. C_LIO_LIGlycation-lowering compounds can mitigate pathologies associated with high GH levels. C_LI

physiology↗

bioRxiv2024

Ecological factors that drive microbial communities in culturally diverse fermented foods

Fermented foods are increasingly recognized for their health benefits. Historically, cultures worldwide have relied on fermentation to preserve foods and enhance their digestibility, flavor, aromas, and taste. Despite the abundance of global diversity of fermented foods, the microbial communities in traditionally fermented non-European foods remain largely understudied. Here, we characterized the bacterial and fungal communities in 90 plant and animal based fermented foods from Nepal, South Korea, Ethiopia, and Kazakhstan, all traditionally prepared for household consumption. Our results reveal that these foods host diverse and intricately interconnected ecosystems of bacteria and fungi. Beyond the well-known fermenters such as lactic acid bacteria (LABs), Bacillales, and yeasts (Saccharomycetales), these foods contain additional microbes whose roles in fermentation are not well understood. While the microbial compositions of fermented foods vary by geography and preparation methods, the type of food substrate has the most significant effect on differentiating bacterial communities. Vegetable-based ferments harbor bacterial communities consisting primarily of LABs and potential pathways associated with carbohydrates degradation. Contrastingly, legumes and animal-based fermented foods are enriched with Bacillales and protein and lipid degradation pathways. Moreover, the microbial interactions, characterized via bacteria- bacteria and bacteria-fungi co-occurrence networks, differ significantly across traditionally fermented plants, legumes, and dairy products, indicating that microbial ecosystems vary between traditional fermented foods derived from different substrates. Our findings highlight the underexplored diversity of microbial communities in traditional fermented foods and underscore the need to understand the entire microbial communities present in these foods and their functions when evaluating their effect on nutrition and health.

microbiology↗

bioRxiv2024

Personality assessment of synanthropic rhesus macaques: implications and challenges

"What makes animals thrive in human-dominated environments?" is a question that has been extensively researched transcending disciplines, but findings remain inconclusive. Consistent inter-individual differences or personalities can potentially explain the functional significance of habitat-specific traits and their variations that help animals successfully coexist with humans. Rhesus macaques (Macaca mulatta) are the most successful non-human primate in the Anthropocene, living in diverse climatic and environmental conditions. Studying the personalities of synanthropic rhesus macaques can provide insights into the biological traits that facilitate their success in human-dominated environments. We employed a multi-method bottom-up approach of behavioral observations and novelty experiments, standardized for assessing captive non-human primates, to evaluate the personalities of synanthropic adult rhesus macaques (N=52). To our surprise, novelty experiments encountered significant challenges, limiting their effectiveness. However, behavioral observations in the form of focal sampling revealed two repeatable traits: sociability and cautiousness. We found an effect of sex on sociability, where males were more sociable than females. In an additional analysis, we found that individuals who obtained food through contact provisioning had higher cautiousness scores than individuals who obtained food through non-contact provisioning. We discuss how the observed personality traits and their variations potentially offer adaptive advantages in human-dominated environments, where rhesus macaques face both benefits, such as anthropogenic subsidies and reduced predation, and costs, like exposure to anthropogenic stressors. We also emphasize that protocols designed for captive conditions may not be directly applicable to free-living animals. Thus, the study underscores the need to reconsider experimental designs to obtain comparable empirical evidence between captive and non-captive populations to enhance the ecological validity of personality assessments. Nevertheless, empirically identifying traits using observations in synanthropic species like rhesus macaques can still provide valuable insights into the mechanisms that enable certain animals to thrive amidst a rapid expansion of anthropogenic activities. Research highlightsStudying personalities of synanthropic animals can shed light on the biological traits that help them thrive in human-dominated environments. A bottom-up multi-method approach, standardized for assessing captive NHP personalities, was employed to study a population of synanthropic rhesus macaques. Behavioral observations captured two repeatable traits, namely sociability and cautiousness, where comparatively more cautious individuals obtained food through contact provisioning. Novelty experiments faced significant challenges.

animal behavior and cognition↗

bioRxiv2024

mgPGPT: Metagenomic analysis of plant growth-promoting traits

In a recent publication, we introduce the PGPT ontology and PGPT-db of bacterial plant growth-promotion traits and associated database of protein sequences, and provide several tools for bacterial genome analysis on the PLaBAse server. Here, we extend the scope of the PGPT ontology to perform PGPT analysis of metagenomic datasets. First, we introduce mgPGPT-db, an extended database of 39, 582, 183 protein sequences obtained computationally by including proteins from AnnoTree. With this, we have integrated the PGPT ontology into our metagenome analysis tool MEGAN and provide mapping files to identify PGPT-related genes using the results of a DIAMOND alignment of reads against either the new mgPGPT-db database, the NCBI-nr protein database, or the AnnoTree protein database. We demonstrate and compare these different approaches in detail on an example data set and evince the improvement compared to the PGPT-db. We also compare the inferred PGPT content of several samples taken from different environments and reveal plant specific PGPT clustering. IMPORTANCEA deeper understanding of plant growth-promoting traits of bacteria is important to enlight and enhance the native plant-beneficial bacterial functional diversity regarding the environmental stress adaption or the ability to suppress even food-borne pathogens by strain inoculation dedicated to agriculture and other plant production systems. The work presented here extends recent work beyond the analysis of individual to allow the assessment of the PGPT potential of metagenomes obtained from environmental samples.

bioinformatics↗

bioRxiv2023

A mechanistic study on the tolerance of PAM distal end mismatch by SpCas9

CRISPR-Cas9 is the newest technology available for targeted genome editing. It is very efficient and cheap compared to other genome editing techniques. However, its therapeutic application is limited due to its off-target activity. To have a better understanding of this off-target effect, we concentrated our efforts on its mismatch-prone PAM distal end. Current off-target prediction algorithms use RNA-DNA complementation derived energy as a major factor in predicting off-target effect. RNA-DNA complementation derived energy drives Cas9 conformational change, which in turn drives its functional activity. In the case of lower RNA-DNA complementarity, a partial conformational change occurs resulting in a slower reaction rate and partial activity. However, extensive mismatches are often tolerated despite lower complementation derived energy available from RNA: DNA duplex formation. Thus, the off-target activity of Cas9 depends directly on the nature of mismatches which in turn result in deviation of the active site of the enzyme due to structural instability in the duplex strand. In order to test the hypothesis, we have designed an array of mismatched target sites and performed in vitro and cell line-based experiments to assess the effects of PAM distal mismatches in Cas9 activity. For further mechanistic validation, Molecular dynamics simulation was performed and it revealed that certain mismatch mutations induced pronounced conformational instability within the RNA-DNA duplex, leading to elevated root mean square deviation (RMSD) values. We found that, target sites having mismatches in the 18th to 16th position upstream of the PAM showed no to little activity.

genomics↗

bioRxiv2023

Improving few-shot learning-based protein engineering with evolutionary sampling

Designing novel functional proteins remains a slow and expensive process due to a variety of protein engineering challenges; in particular, the number of protein variants that can be experimentally tested in a given assay pales in comparison to the vastness of the overall sequence space, resulting in low hit rates and expensive wet lab testing cycles. In this paper, we propose a few-shot learning approach to novel protein design that aims to accelerate the expensive wet lab testing cycle and is capable of leveraging a training dataset that is both small and skewed ({approx} 105 datapoints, < 1% positive hits). Our approach is composed of two parts: a semi-supervised transfer learning approach to generate a discrete fitness landscape for a desired protein function and a novel evolutionary Monte Carlo Markov Chain sampling algorithm to more efficiently explore the fitness landscape. We demonstrate the performance of our approach by experimentally screening predicted high fitness gene activators, resulting in a dramatically improved hit rate compared to existing methods. Our method can be easily adapted to other protein engineering and design problems, particularly where the cost associated with obtaining labeled data is significantly high. We have provided open source code for our method at https://github.com/SuperSecretBioTech/evolutionary_monte_carlo_search.

bioinformatics↗

bioRxiv2023

Microbiome Metabolome Integration Platform (MMIP): a web-based platform for microbiome and metabolome data integration and feature identification.

A microbial community maintains its ecological dynamics via metabolite crosstalk. Hence knowledge of the metabolome, alongside its populace, would help us understand the functionality of a community and also predict how it will change in atypical conditions. Methods that employ low-cost metagenomic sequencing data can predict the metabolic potential of a community, that is, its ability to produce or utilize specific metabolites. These, in turn, can potentially serve as markers of biochemical pathways that are associated with different communities. We developed MMIP (Microbiome Metabolome Integration Platform), a web-based analytical and predictive tool that can be used to compare the taxonomic content, diversity variation and the metabolic potential between two sets of microbial communities from targeted amplicon sequencing data. MMIP is capable of highlighting statistically significant taxonomic, enzymatic and metabolic attributes as well as learning-based features associated with one group in comparison with another. Further MMIP can predict linkages among species or groups of microbes in the community, specific enzyme profiles, compounds or metabolites associated with such a group of organisms. With MMIP, we aim to provide a user-friendly, online web-server for performing key microbiome-associated analyses of targeted amplicon sequencing data, predicting metabolite signature, and using learning-based linkage analysis, without the need for initial metabolomic analysis, and thereby helping in hypothesis generation.

bioinformatics↗

bioRxiv2023

MuLan-Methyl - Multiple Transformer-based Language Models for Accurate DNA Methylation Prediction

Transformer-based language models are successfully used to address massive text-related tasks. DNA methylation is an important epigenetic mechanism and its analysis provides valuable insights into gene regulation and biomarker identification. Several deep learning-based methods have been proposed to identify DNA methylation and each seeks to strike a balance between computational effort and accuracy. Here, we introduce MuLan-Methyl, a deep-learning framework for predicting DNA methylation sites, which is based on five popular transformer-based language models. The framework identifies methylation sites for three different types of DNA methylation, namely N6-adenine, N4-cytosine, and 5-hydroxymethylcytosine. Each of the employed language models is adapted to the task using the "pre-train and fine-tune" paradigm. Pre-training is performed on a custom corpus of DNA fragments and taxonomy lineages using self-supervised learning. Fine-tuning aims at predicting the DNA-methylation status of each type. The five models are used to collectively predict the DNA methylation status. We report excellent performance of MuLan-Methyl on a benchmark dataset. Moreover, we argue that the model captures characteristic differences between different species that are relevant for methylation. This work demonstrates that language models can be successfully adapted to applications in biological sequence analysis and that joint utilization of different language models improves model performance. Mulan-Methyl is open source and we provide a web server that implements the approach. Key pointsO_LIMuLan-Methyl aims at identifying three types of DNA-methylation sites. C_LIO_LIIt uses an ensemble of five transformer-based language models, which were pre-trained and fine-tuned on a custom corpus. C_LIO_LIThe self-attention mechanism of transformers give rise to importance scores, which can be used to extract motifs. C_LIO_LIThe method performs favorably in comparison to existing methods. C_LIO_LIThe implementation can be applied to chromosomal sequences to predict methylation sites. C_LI

bioinformatics↗

bioRxiv2022

Megan Server: facilitating interactive access to metagenomic data on a server

MotivationMetagenomic projects of large sequencing datasets (totaling hundreds of gigabytes of data). Thus, computational preprocessing and analysis are usually performed on a server rather than on a personal computer. The results of such analyses are then usually explored interactively. One approach is to use MEGAN, an interactive program that allows analysis and comparison of metagenomic datasets. Previous releases of MEGAN have required that the user first download the computed data from the server, an increasingly time-consuming process. Here we present Megan Server, a stand-alone program that serves MEGAN files to the web, using a RESTful API, facilitating interactive analysis without downloading the complete data. We describe a number of different application scenarios. AvailabilityMegan Server is provided as a standalone program tools/megan-server in the Megan6 software suite, available at https://software-ab.cs.uni-tuebingen.de/download/megan6.

bioinformatics↗

bioRxiv2022

Major Contribution of Myeloid Cells In TB specific Host Gene Signature: Revelations from Re-Analysis of Publicly Available Datasets

IntroductionInteraction of human host and its pathogen M.tuberculosis drives tuberculosis disease, resulting in dysregulation of host gene expression. We re-analyzed host gene expression datasets of TB to identify and validate a cellular circuit by interlinking the DEGs with their target miRNAs, GWAS hits associated with immunological phenotypes mapping to the DEGs and associated cellular subtypes through bioinformatic and experimental approaches. MethodologyDEGs were identified systematically through re-analysis of whole blood host transcriptomic datasets of treatment-naive TB patients, obtained from public repositories having at least 1.2 fold change of expression and FDR corrected p-value <0.05. Using well characterized M.tb antigens: Ag85 complex, LAM, CFP10 and ESAT6, we evaluated their effect on the expression of a subset of the top DEGs with at least two fold change of expression in a monocytic cell line THP1 with or without differentiation into a macrophage-like state with PMA and a T cell line Jurkat E6-1. ResultsWe discovered 305 DEGs (236 up and 69 down-regulated genes) out of which 23 (21 up and 2 down-regulated genes) were top DEGs. Overall, innate immune and myeloid cell associated pathways were enriched for up-regulated genes while T cell associated pathways were enriched for down-regulated genes. Among top DEGs, EPSTI1 was predominantly up-regulated in macrophages while SERPING1 was universally up-regulated in the monocyte model by all antigens. The down-regulation of gene expression was replicated by ESAT6 in T cell line by significantly down-regulating a top down-regulated gene LRRN3.

immunology↗