Biology subjects

Raman, K.

Publications and source records attributed to Raman, K..

At least 19 recordsLinked to original sources

bioRxiv2026

Decoding Tumour-Specific Rewiring and Synthetic Lethality Through Genome-Scale Metabolic Models

Cancer cells rapidly rewire their metabolism, from efficient energy production toward anabolic processes, to sustain uncontrolled growth. Decoding such metabolic shifts is essential for uncovering novel therapeutic targets. To map systems-level metabolic changes across cancer types, we built context-specific genome-scale metabolic models for eight tissues (lung, thyroid, stomach, prostate, liver, kidney, colon, and breast) using gene expression data from The Cancer Genome Atlas (TCGA). Applying constraint-based modelling, we then identified differentially regulated pathways through flux enrichment analysis, revealing tissue-specific rewiring: branched chain amino acid metabolism was suppressed in breast cancer; sphingolipid metabolism was downregulated in colon, kidney, and thyroid but upregulated in breast. We further propose a model-driven pipeline to identify and characterise metabolic vulnerabilities. We first identify synthetic lethal reactions in normal tissues and their corresponding single lethal counterparts in cancers, thereby enabling the identification of metabolic "collateral lethal" reaction pairs for each cancer. Model-predicted collateral lethal gene pairs, including CMPK1-AK in colon, ALDOA-PGD in prostate, and SLC25A26-UQCRB in liver models, were supported through computational validation using DepMap data on gene essentiality. Subsequently, we show how to interpret metabolic rewiring in cancer tissues while accounting for any collateral lethal pairs. In summary, our results establish a systemic framework for decoding metabolic rewiring and synthetic lethal vulnerabilities in cancer.

systems biology↗

bioRxiv2026

Impact of retroactivity on information flows in engineered synthetic biological circuits

In biological networks, retroactivity describes the feedback from downstream components that can influence and alter the behavior of upstream systems. This effect poses a major challenge to the modular design of synthetic circuits, where upstream modules are expected to function independently of their connections. Beyond disrupting dynamics, retroactivity can also interfere with how information is transmitted through a network, acting as a bottleneck that reduces the fidelity of signal propagation. Here, we combine stochastic biochemical modeling with information-theoretic analysis to quantify how retroactivity constrains upstream signaling, even in strongly amplified feedback architectures, particularly in the presence of molecular noise. At the same time, we identify parameter regimes in which retroactivity can be exploited as a functional mechanism: downstream loading can trigger controllable state transitions, enabling circuits that respond to changes in their environment or interconnections. These findings suggest design principles for harnessing retroactivity for programmable signal processing and decision-making in cellular computation. Finally, we evaluate feedback-gain tuning as a mitigation strategy and demonstrate that increasing gain alone is insufficient under noisy conditions. We therefore propose complementary approaches to reduce retroactivity and delineate the operating regimes in which each strategy is most effective.

synthetic biology↗

bioRxiv2025

Chlorotonils exhibit potent activity against Mycobacterium tuberculosis, while resistance is mediated by MmpR5-MmpL5

Treatment of Mycobacterium tuberculosis (Mtb) is challenging and requires administration of at least four different antibiotics. Unfortunately, multi-drug resistant Mtb strains continue to emerge, undermining the effectiveness of current treatment regimens and highlighting the urgent need for new therapeutics. In this study, we evaluated the potential of natural product-derived chlorotonils as anti-Mtb agents. We demonstrate that chlorotonils exhibit nanomolar potency against a range of attenuated and virulent Mtb strains. Mechanistic studies and resistance profiling in Mtb revealed that chlorotonils affect both lipid and energy metabolism. Through systems biology approaches, including the construction of an Mtb CRISPRi library specifically designed for chemical-genomic profiling, we identified MmpR5/MmpL5 as major driver of chlorotonil-resistance in Mtb leading also to cross-resistance with bedaquiline. Our findings highlight chlorotonils as valuable chemical tools to further dissect the role and function of the MmpS5-MmpL5 efflux pump in drug-resistant Mtb.

microbiology↗

bioRxiv2025

Genome-scale metabolic model guided metabolic flux analysis in the endophyte Alternaria burnsii NCIM1409

Camptothecin (CPT), a potent anticancer alkaloid, is traditionally derived from plants like Camptotheca acuminata and Nothapodytes nimmoniana, but sustainable production remains challenging. This study explores the metabolic network of the fungal endophyte Alternaria burnsii NCIM1409, known for camptothecin production, using genome sequencing, genome scale metabolic modeling, and 13C-based pathway mapping. A genome-scale metabolic model (AltGEM iDD1552) was reconstructed, comprising 2188 reactions, 2148 metabolites, and 1552 genes, along with manual curation to include camptothecin biosynthesis pathways. Flux balance analysis identified key enzymatic targets, including secologanin synthase and tryptophan decarboxylase, for enhancing CPT production. Further, metabolic analysis of A. burnsii subjected to 20%[U-13C6] glucose or 99% [1-13C] glucose revealed active glycolysis, the pentose phosphate pathway, and the TCA cycle. This integrative approach provides insights into A. burnsiis metabolic capabilities and highlights strategies for optimizing camptothecin biosynthesis, offering a foundation for sustainable production methods.

systems biology↗

bioRxiv2025

NPM1 mislocalization mediated by RNA Pol I inhibition alters chromatin landscape

RNA polymerase I inhibition affects rRNA synthesis from rDNA clusters residing in nucleolar organizer regions (NORs). Here we have demonstrated RNA Pol I inhibition disrupts nucleolar architecture, NPM1 localization, and alters the chromatin landscape using coordinated eraser and writer enzymes. siRNA mediated depletion or dissociation of NPM1 allows HDAC1 loading on the chromatin. HDAC1 mediated deacetylation of H3K9ac creates H3K9 that undegoes SUV39H1-mediated methylation. The stripping of active histone marks leads to enrichment of repressive H3K9me3 in the genome. These altered chromatin landscape corroborates with loss of genome-wide chromatin accessibility and DNA hypermethylation mediated by DNMT1. Chromatin architectural analysis revealed disrupted nucleolar associated domains (NADs) transforming to lamin associated domains (LADs) with specific histone signatures and repressive states. The 3D nuclear architecture was remodeled by A/B compartments reorganization and loss of Hi-C loops at the H3K9ac depleted sites.

molecular biology↗

bioRxiv2025

Plasmidome, resistome, and virulence-associated genes characterization of Acinetobacter johnsonii in NASA cleanrooms and a clinical setting.

Evidence shows persistence of non-spore-forming Acinetobacter johnsonii in high-stakes controlled and nutrient-limited environments. This study aims to explore the mechanisms underpinning such adaptability through a comprehensive genomic analysis of 22 isolates of A. johnsonii from NASAs Payload Hazardous Servicing Facility (PHSF) and one carbapenem-resistant strain (E154408A) from patient colonization in Ireland. Core-genome phylogeny revealed clustering of PHSF-originating isolates in a monophyletic clade divergent from the main species lineage. Species-wide virulence-associated genes and metabolic profiling indicated the unique presence in PHSF-originating isolates of two complete efflux pumps and of a conserved allantoin racemase, suggesting adaptability for multiple environmental stresses. Observed ubiquity of blaOXA in investigated genomes (n=112) and phenotypically-validated multidrug-resistant profile of E154408A strain highlight A. johnsoniis potential as antimicrobial resistance (AMR) reservoir. Plasmidome analysis suggested gain/loss events across the monophyletic population and potential AMR acquisition pathways. Genome-to-metagenome mapping identified genomic signatures of A. johnsonii in PHSF >10 years post initial isolation. ImportanceAcinetobacter johnsonii is increasingly recognized as an emerging human pathogen, with growing evidence of its ability to persist in controlled, high-stakes environments, posing risks as both persisting environmental contaminant and antimicrobial resistance (AMR) reservoir. Yet, gaps remain in our understanding of its AMR profile and the mechanisms that enable its enhanced environmental adaptability. This knowledge is necessary in contexts where biological cleanliness is a priority such as clinical settings and spacecraft assembly facilities cleanrooms, where contamination of hardware with terrestrial microorganisms is concerning. In this study, we aim to address some of key knowledge gaps by providing genomic insights into a rare multi-drug resistant clinical isolate and 22 NASA cleanroom isolates that persisted for over a decade in extremely clean conditions. Our findings will help evaluate the contamination risk of A. johnsonii in high-stakes environments and ultimately strengthen our ability to manage this microbial contaminant across terrestrial and extraterrestrial settings. HighlightsO_LICleanrooms-derived A. johnsonii genomes show favorable traits for increased adaptability C_LIO_LIGenomic signatures of A. johnsonii persisted in the cleanrooms for >10 years C_LIO_LIblaOXA is ubiquitously found in the genome of all A. johnsonii C_LIO_LIE154408A is the first patient colonization by carbapenem-resistant A. johnsonii in Europe C_LI

genomics↗

bioRxiv2025

Deciphering Global Patterns of Marine Microbial Community Assembly and Network Stability

Microbial community assembly processes can help us understand ecology, evolution, and climatic influences on community composition while presenting opportunities for biotechnological applications and improved marine conservation. In this study, we have investigated species richness patterns, community assembly mechanisms, and interaction patterns of marine microbial communities by analysing 16S rRNA amplicon sequencing data from 4,611 samples collected from major ocean microbiome projects. Using neutral community models, the iCAMP framework, and co-occurrence network analyses, we showed that stochastic processes drive microbial community assembly across all latitude zones. Drift was the primary driver of community assembly in polar communities. The polar microbial communities exhibited the highest modularity and network robustness, but were more vulnerable to hub removal. While previous studies have attributed higher stability of the polar communities to environmental filtering, our analyses reveal that the resilience of the community is dependent on a few central taxa. By classifying the genera as generalists and specialists, we further highlight the role played by the specialist taxa in maintaining the stability of the marine microbial community, especially under the pressures of climate change and global warming. Overall, our findings offer a latitudinal perspective on ocean microbial community stability and the effect of anthropogenic disturbances on these communities.

systems biology↗

bioRxiv2025

PURE: Policy-guided Unbiased REpresentations for structure-constrained molecular generation

Structure-constrained molecular generation (SCMG) generates novel molecules that are structurally similar to a given molecule and have optimized properties. Deep learning solutions for SCMG are limited in that they are pre-disposed towards existing knowledge, and they suffer from a natural impedance mismatch problem due to the discrete nature of molecules, while deep learning methods for SCMG often operate in continuous space. Moreover, many task-specific evaluation metrics used during training often bias the model towards a particular metric -"metric-leakage". To overcome these shortcomings, we propose Policy-guided Unbiased REpresentations (PURE) for SCMG that learn within a framework simulating molecular transformations for drug synthesis. PURE combines self-supervised learning with a policy-based reinforcement-learning (RL) framework, thereby avoiding the need for external molecular metrics while learning high-quality representations that incorporate an inherent notion of similarity specific to the given task. Along with a semi-supervised training design, PURE utilizes template-based molecular simulations to better explore and navigate the discrete molecular search space. Despite the lack of metric biases, PURE achieves competitive or superior performance than state-of-the-art methods on multiple benchmarks. Our study emphasizes the importance of reevaluating current approaches for SCMG and developing strategies that naturally align with the problem. Finally, we illustrate how our methodology can be applied to combat drug resistance, by identifying sorafenib-like compounds as a case study.

biochemistry↗

bioRxiv2025

Extensive Benchmarking of Community Detection Algorithms

The detection of clusters or community in networks is an important problem in network science. We systematically evaluate many widely used community detection algorithms and their variants to identify clusters in complex networks. As the ground truth for assessing accuracy, we use artificial networks modeled on power-law distributions and real-world social networks. In addition, we also performed gene enrichment analysis on human and yeast protein-protein interaction networks to evaluate algorithms on their ability to uncover enriched communities. We implement and adapt an extensive suite of classical algorithms and their modern variants, classified into five types: stochastic, kernel-based, modularity-based, hierarchical, and local search-based. The algorithms are benchmarked primarily using the Normalized Mutual Information metric, with additional analyses focused on granularity by examining cluster ratio and computational time complexity. We find that decreasing the modularity of networks leads to a consistent decline in performance that follows a sigmoidal trajectory as communities become less defined. Algorithms with greater granularity remain stable when community structures are less distinct, while computation time remains independent of network modularity. Additionally, algorithms tend to perform poorly on smaller networks, and higher accuracy often requires a time complexity trade-off for specific high-performing methods. However, as the analysis expands to more extensive networks, this trade-off becomes more pronounced, highlighting the need for efficient scalability. Based on our benchmark and gene enrichment analysis results, we also present recommendations to practitioners. Our robust Python package, complete with a user-friendly command-line interface, empowers users to easily apply these algorithms to their datasets. Author summaryIn the era of big data, clustering has become an essential tool for processing and analyzing vast amounts of information. By dividing large data sets into smaller, meaningful clusters, we can simplify complex data structures, parallelize tasks, and reveal hidden patterns. This has become an essential preprocessing step that is widely used in various computational domains. Although traditional clustering algorithms remain popular, our work implements range of hybrid techniques that combine classical methods and some novel approaches. We also benchmark the performance of these approaches, their efficiency and efficacy in handling different types of networks.

systems biology↗

bioRxiv2025

Microbial communities on station and train surfaces in Chennai Metro: Insights into urban transit microbiome

BackgroundUrban public transport systems, particularly metro networks, serve as key hubs for microbial transmission, yet the urban microbiome in densely populated regions like India remains poorly characterized. These environments harbour diverse microbial communities, including beneficial and pathogenic species, which can influence public health. The COVID-19 pandemic has further underscored the need to monitor microbial ecosystems, particularly with respect to antimicrobial resistance (AMR) genes, which may have escalated due to the increased use of antibiotics during health crises. In a first-of-its-kind study in India, we comprehensively characterized microbial communities and the prevalence of AMR genes in the Chennai Metro system. We collected 96 surface swab samples from 12 stations across two metro lines, targeting four surfaces: bannisters, kiosks, rods, and ticket counters. Forty-seven samples passed quality control and were analyzed using whole-genome metagenomic sequencing. We employed taxonomic classification and conducted comparative and diversity analyses, as well as differential taxa profile analyses, across the sample collection objects. We studied the prevalence and abundance of AMR genes using AMR annotations from the CARD database. We performed pangenome analysis by constructing Metagenome Assembled Genomes (MAGs) from the collected samples and comparing them with the NCBI reference genome. ResultsComparative analysis with global urban microbiome datasets revealed distinct microbial profiles, including nine species that are differentially prevalent in Chennai samples. Surface type significantly influenced microbial diversity, with kiosks exhibiting the highest diversity. We successfully reconstructed several high-quality MAGs, providing insights into the genomic potential and adaptability of dominant taxa in this environment. While the overall prevalence of AMR genes was minimal, genes associated with Sulfonamide and Rifamycin resistance were detected. ConclusionThese findings highlight unique microbial signatures and emphasize the need for ongoing surveillance and targeted interventions to mitigate microbial transmission risks in densely populated urban areas.

ecology↗

bioRxiv2025

A Unified Framework to Dissect Robustness, Plasticity, Evolvability and Canalisation of Biological Function

A unique balance of seemingly contradictory properties like robustness and plasticity, or evolvability and functional canalisation, characterises biological systems. To understand the basis of these properties, we investigate gene regulation, which is at the core of biological function. We simulate dynamical models of over 190 million genetic circuits covering all possible three-gene circuit structures. Our computational pipeline classifies these circuits into functional clusters by matching their temporal response shapes. Thus, we generate a dataset linking circuit structure, parameters and a corresponding functional label. Our key finding is a finite list of 20 functions that three-node genetic circuits can perform. Moreover, the structure-parameter space for these circuits tends to be primed for responses that stabilise over time following a perturbation. Every structure exhibits potential for multifunctionality with a range of 2-17 functions contingent upon parameters. We quantify network degeneracy, showing that many structural changes can be made to circuits without altering function. We define three quantities: structural, parametric, and functional diversities. Using these diversities, we construct a UNified FramewOrk for reguLatory Dynamics (UNFOLD) to analyse four key biological properties--robustness, plasticity, evolvability, and functional canalisation. Using UNFOLD, we identify that only 6.5% of network structures are non-plastic, while parameter sets enabling parametric robustness exist for every three-node network. We identify functionally canalised circuits from structure pairs that can be interchanged for a large number of parameter sets without a change in function. Overall, our framework offers insights into the fundamental organisation of biological networks by thorough analysis of three-node networks. Significance StatementBiological systems exhibit remarkable properties like robustness, plasticity, evolvability, and canalisation. This study presents a unified computational framework to understand these properties by exhaustively exploring the design space of three-node genetic circuits, identifying that only 20 functions are achievable, and revealing a natural bias toward stability. We uncover key principles of network degeneracy and multifunctionality, highlighting the versatility of genetic circuits. By analysing structural, parametric, and functional diversities, we identify structural and parametric changes that can transition a genetic circuit from robust behaviour to plasticity or from being canalised to becoming evolvable. Our work advances theoretical insights into biological function. It provides a method to identify alternate designs and parametric conditions for genetic circuits, paving the way for the design of reliable synthetic genetic circuits.

systems biology↗

bioRxiv2025

An improved framework for grey-box identification of biological processes

Developing models from observations is at the heart of empirical science. Grey-box Modeling combines the insights gained from the results obtained from first principles with observational data. When the model turns out to be unsatisfactory, the goodness of such grey-box models in terms of predictability and parameter estimates largely depends on either modifying the model structure obtained from the first principles or conducting new experiments. Unfortunately, in the context of biological models, where the model structures are usually nonlinear ODEs with a large number of states and parameters along with sparse and noisy experimental data, traditional identification protocols have to go through several iterations to identify the source of the issue. Even after multiple iterations, they may still arrive at sub-optimal solutions. In this work, we propose an improved framework with a new set of tools to resolve this issue unambiguously with a minimum round of iterations.

systems biology↗

bioRxiv2025

Investigating the impact of carbamazepine on tomato plant metabolism using genome-scale metabolic modelling

A comprehensive mechanistic analysis of emerging pharmaceutical pollutants stress response in plants is needed to understand its chronic impact on food-chain contamination and agricultural productivity. To unravel this at systems-level, the current study employs insights from green-liver concept and establishes the utility of constraint-based modelling approach for elucidating perturbations in a plants metabolism due to pharmaceutical stress. In this study, the stress response of an emerging recalcitrant anticonvulsant pollutant, carbamazepine (CBZ), was simulated in tomato crop under phototrophic conditions. For this, an updated genome-scale metabolic model of tomato leaf (CBZ_iSL3433) was developed and augmented with CBZ transformation reactions based on the green-liver concept. The model was able to capture energy and co-factor competition-induced biomass reduction in presence of CBZ stress. Further, the study provides an in silico mechanistic proof for abiotic stress response induced by CBZ in tomato with altered flux states in nutrient assimilation, synthesis of key precursors of leaf biomass and secondary metabolites. Additionally, to extend the applicability of model, potential ameliorative effects of biostimulants such as proline, spermine, glycerol, and ethanol were investigated through model predictions. Through systematic computational analysis, 154 significantly altered reactions were identified in the presence of CBZ stress, of which 92 % of reactions were ameliorated with biostimulants. Amino acid biosynthesis was found to be the most significantly altered pathway under CBZ stress in the presence of biostimulants. Overall, the proposed framework can aid in screening and developing rational strategies to maintain agricultural yields amid rising plant stress due to such anthropogenic pollutants.

plant biology↗

bioRxiv2025

Metagenomic Profiling of Drinking Water Microbiomes: Insights into Microbial Diversity and Antimicrobial Resistance

Monitoring microbial components in drinking water is as essential as tracking its chemical composition. Although traditional culture-based methods provide valuable insight into microbial morphology and behaviour, their scope is restricted to culturable species. With the advent of high-throughput sequencing, we can now detect a wider range of microbes in any ecosystem, along with efficient insights into their functional potential and metabolic capabilities. In this study, metagenomic analyses were performed to fully understand the microbiome of drinking water supplied through public distribution systems in an Indian city. Our findings identified bacteria from the phyla Pseudomonadota, Planctomycetota, Bacteroidota, and Actinomycetota, consistent with previous studies of drinking water microbiomes of other countries. At the species level, Afipia carboxidovorans, Klebsiella pneumoniae, Pseudomonas aeruginosa, Sphingopyxis macrogoltabida, and Variovorax paradoxus were identified as members of the core microbiome. It was observed that the temperature of the water samples, even as little as a 5{o}C increase, influenced the composition and diversity of the microbial communities. No significant correlation was detected between the abundance of microbial species and the metal concentration in the sample. In addition, we traced the distribution of antibiotic resistance genes (ARGs), finding widespread resistance to aminoglycosides, tetracyclines, and macrolides in samples. In particular, ARGs such as adeF and ermR, which are known to be associated with multidrug resistance, were detected. Although this study did not directly assess the pathogenicity or mobility of these genes, their presence in potable water raises potential public health concerns due to the possibility of horizontal gene transfer (HGT) in environmental settings. Therefore, continuous monitoring of antibiotic resistance genes (ARGs) is imperative to accurately evaluate long-term risks and to guide evidence-based water quality management strategies. In summary, this study provides a comprehensive metagenomic overview of drinking water microbiota, ARGs, and water quality, offering a foundation for future surveillance and risk mitigation strategies. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=106 SRC="FIGDIR/small/636216v3_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@819c0org.highwire.dtl.DTLVardef@1d29372org.highwire.dtl.DTLVardef@1ce3cf9org.highwire.dtl.DTLVardef@1050997_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

Unveiling Hidden Microbial Diversity in Mars2020 Mission Assembly Cleanrooms withMolecular Insights into the Persistent andPerseverance of Novel Species DefyingMetagenome Sequencing

NASA cleanrooms, where space mission components are assembled, maintain stringent cleaning protocols and nutrient-poor environments, resulting in low yet persistent microbial loads. Although these oligotrophic extremophiles are reported in small numbers, their resistance to environmental stresses, sparse presence, and difficulty in extracting biomolecules often lead to their omission, even with advanced sequencing technologies. Traditional metagenomic approaches fail to detect these rare species due to challenges in lysing robust microbial cells and isolating minute amounts of DNA from dominant microorganisms. Additionally, the absence of database references for novel extremophiles limits their identification. Over a six month period of monitoring Mars 2020 mission cleanrooms, 182 bacterial strains from 19 families were identified using advanced molecular techniques. This included 14 novel Gram-positive species, eight of which were spore-formers. Despite being present at only about 0.001% abundance in metagenomic sequencing data, they were successfully cultured. Functional studies revealed their capabilities in nitrogen cycling, carbohydrate metabolism, and radiation resistance. Furthermore, 12 biosynthetic gene clusters, including those linked to ectoine and{varepsilon} -poly-L-lysine production, underscore their biotechnological potential. These findings emphasize the hidden microbial diversity in spacecraft assembly cleanrooms and highlight the need for advanced detection methods to uncover extremophiles with potential applications in biotechnology and space exploration. SynopsisUnderstanding extremophiles in NASA spacecraft assembly cleanrooms aids contaminant management in confined habitats, ensuring sustainability and safety in future space missions.

genomics↗

bioRxiv2025

An integrated systems biology approach establishes arginine biosynthesis as a metabolic weakness in Candida albicans during host infection

Candida albicans (CAL), one of the leading causes of fungal infections affecting nearly 70% of the population, poses a significant global health threat. With the emergence of drug-resistant strains, mortality rates have reached a staggering 63.6% in severe cases, complicating treatment options and demanding the discovery of novel therapeutic targets. To address this pressing need, we employed a unique multidisciplinary approach to elucidate the metabolic pathways that enable CAL to switch from a commensal to a virulent state. Condition-specific genome-scale metabolic models (GSMMs), along with a novel integrated host-CAL model developed in this study, highlighted the central role of arginine (Arg) metabolism and uncovered ALT1, an arginine biosynthesis enzyme, as a critical metabolic vulnerability in CAL virulence. Heightened expression of arginine biosynthesis genes indicated that increased arginine synthesis mainly occurs through proline intermediates during host interaction. Significantly impaired virulence and in vivo pathogenicity of ALT1-deleted CAL highlighted the potential of targeting arginine metabolism as a novel strategy to combat antifungal resistance and underscored the power of integrating systems biology with experimental approaches in identifying new therapeutic targets. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=129 SRC="FIGDIR/small/632533v2_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@ac1b89org.highwire.dtl.DTLVardef@143c3d1org.highwire.dtl.DTLVardef@1ecfd26org.highwire.dtl.DTLVardef@1dfe98d_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2024

Tumor-Infiltrating Lymphocytes Display Prognostic Signatures Associated with Chemotherapy Response in TNBC Patients

Triple-negative breast cancer (TNBC) is an aggressive subtype often marked by resistance to neoadjuvant chemotherapy (NAC), making treatment particularly challenging. Tumor-infiltrating lymphocytes (TILs), crucial players in the immune landscape of tumors, have been associated with treatment outcomes, but the prognostic potential of TIL-derived gene markers in pre-NAC samples from TNBC patients remains understudied. In this research, we analyzed the single-cell transcriptional profiles of approximately 5,000 cells from four chemosensitive and four chemoresistant TNBC patients using publicly available datasets. Leveraging standard single-cell analysis, we identified differentially expressed gene signatures within the TIL subpopulation, highlighting significant immune activation pathways differentiating chemoresistant from chemosensitive tumors. By employing robust feature selection and repeated cross-validation across microarray and RNA-seq datasets, we developed a stable set of 30 TIL-based gene markers with notable prognostic relevance for NAC response in TNBC. These markers achieved an AUROC of 0.78 in the training set and validated with AUROCs of 0.8, 0.658, and 0.736 across five independent test datasets, demonstrating consistency across diverse platforms and sequencing technologies. Furthermore, increased expression of these gene signatures correlated with improved recurrence-free survival (RFS) in a cohort of 220 TNBC patients. This study enhances our understanding of the TIL transcriptional landscape in NAC response, identifying potential biomarkers and therapeutic targets for improving treatment outcomes in TNBC.

cancer biology↗

bioRxiv2024

COSMOS: COmmunity and Single Microbe Optimisation System

Bioprocessing harnesses the potential of microbial communities and monocultures to convert renewable resources like agricultural by-products and wastewater into valuable products. While monocultures offer simplicity and control, microbial communities provide metabolic diversity, enabling complex substrate breakdown and cooperative biosynthesis. However, balancing efficiency with stability requires a nuanced approach, as each system presents unique advantages and limitations, particularly under different environmental conditions. To systematically explore the potential of these systems, we developed the COmmunity and Single Microbe Optimisation System (COSMOS), a dynamic computational framework that compares microbial monocultures and communities across diverse fermentation conditions. COSMOS identifies optimal microbial systems tailored to specific carbon sources and environments. Our findings highlight the impact of key factors such as environment, microbial interactions, and carbon sources on the biosynthetic capabilities of both communities and monocultures. A key result is the identification of the Shewanella oneidensis - Klebsiella pneumoniae community as the most efficient producer of 1,3-propanediol under anaerobic conditions. Notably, this aligns with previous experimental findings, with COSMOS accurately predicting the optimal carbon source concentration and inoculum ratio used. Additional findings highlight the value of communities for nutrient-limited processes and emphasise the importance of computational screening to balance productivity with ease of control. The insights gained from this study offer a roadmap toward optimising microbial systems for sustainable bioprocesses and circular bio-economies. ImportanceThe transition to sustainable biomanufacturing is vital for reducing reliance on fossil-based resources in industries such as biofuels, wastewater treatment, and pharmaceuticals. While monocultures are preferred for their predictability, microbial communities offer greater metabolic versatility, particularly in nutrient-limited environments. However, their complexity and the lack of systematic evaluation tools have hindered wider adoption. Identifying the most efficient microbial system--whether monoculture or community--and optimizing it for resource efficiency is key to sustainability and aligns with the UN Sustainable Development Goals (SDG 9 and SDG 12). COSMOS addresses this gap by providing a computational framework to assess microbial systems, minimizing the need for extensive experimental testing. By enabling data-driven selection of scalable and efficient bioprocessing strategies, COSMOS supports the transition to circular bio-economies and sustainable manufacturing, ensuring resource-efficient production while reducing environmental impact.

systems biology↗