Biology subjects

Palanikumar, I.

Publications and source records attributed to Palanikumar, I..

4 recordsLinked to original sources

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

FROG Analysis Ensures the Reproducibility of Genome Scale Metabolic Models

Genome-scale metabolic models (GEMs) and other constraint-based models (CBMs) play a pivotal role in understanding biological phenotypes and advancing research in areas like metabolic engineering, human disease modelling, drug discovery, and personalized medicine. Despite their growing application, a significant challenge remains in ensuring the reproducibility of GEMs, primarily due to inconsistent reporting and inadequate model documentation of model results. Addressing this gap, we introduce FROG analysis, a community-driven initiative aimed at standardizing reproducibility assessments of CBMs and GEMs. The FROG framework encompasses four key analyses--Flux variability, Reaction deletion, Objective function, and Gene deletion--to produce standardized, numerically reproducible FROG reports. These reports serve as reference datasets, enabling model evaluators, curators, and independent researchers to verify the reproducibility of GEMs systematically. BioModels, a leading repository of systems biology models, has integrated FROG analysis into its curation workflow, enhancing the reproducibility and reusability of submitted GEMs. In our study evaluating 65 GEM submissions from the community, approximately 40% reproduced without intervention, 28% requiring minor adjustments, and 32% needing input from authors. The standardization introduced by FROG analysis facilitated the detection and resolution of issues, ultimately leading to the successful reproduction of all models. By establishing a standardized and comprehensive approach to evaluating GEM reproducibility, FROG analysis significantly contributes to making CBMs and GEMs more transparent, reusable, and reliable for the broader scientific community.

systems biology↗

bioRxiv2023

Panera: A novel framework for surmounting uncertainty in microbial community modelling using Pan-genera metabolic models

Over the last decade, microbiome research has witnessed exponential growth, largely driven by the widespread availability of metagenomic data. Despite this influx of data, 16S targeted amplicon sequencing, which offers relatively lower resolution, still dominates the landscape over whole-genome shotgun sequencing. Existing algorithms for constructing metabolic models of microbial communities primarily rely on whole-genome sequences and do not fully harness the potential of 16S datasets. In this study, we report Panera, a novel framework designed to model microbial communities under uncertainty and yet perform inferences by building pan-genus metabolic models. We tested the performance of the models from our approach by analysing their ability to capture the functionality of the entire genus and individual species within a genus. We further exercise the model to explore the comprehensive metabolic abilities of a genus, shedding light on metabolic commonalities between microbial groups. Furthermore, we showcase its application in characterising microbial community models using 16S data. Our hybrid community models, which combine both GSMM and pan-genus metabolic models, exhibit a 10% reduction in prediction error, with error rates diminishing as community size increases. Overall, the Panera framework represents a potent and effective approach for metabolic modelling, enabling robust predictions of the metabolic phenotypes of microbial communities, even when working with limited 16S data. This advancement has the potential to greatly impact the field of microbiome research, offering new insights into the metabolic dynamics of diverse microbial ecosystems.

systems biology↗

bioRxiv2023

Designing function-specific minimal microbiomes from large microbial communities

MotivationMicroorganisms thrive in large communities of diverse species, exhibiting various functionalities. The mammalian gut microbiome, for instance, has the functionality of digesting dietary fibre and producing different short-chain fatty acids. Not all microbes present in a community contribute to a given functionality; it is possible to find a minimal microbiome, which is a subset of the large microbiome, that is capable of performing the functionality while maintaining other community properties such as growth rate. Such a minimal microbiome will also contain keystone species for SCFA production in that community. In the wake of perturbations of the gut microbiome that result in disease conditions, cultivated minimal microbiomes can be administered to restore lost functionalities. ResultsIn this work, we present a systematic algorithm to design a minimal microbiome from a large community for a user-proposed function. We employ a top-down approach with sequential deletion followed by solving a mixed-integer linear programming problem with the objective of minimising the L1-norm of the membership vector. We demonstrate the utility of our algorithm by identifying the minimal microbiomes corresponding to model communities of the gut, and discuss their validity based on the presence of the keystone species in the community. Our approach is generic and finds application in studying a variety of microbial communities. AvailabilityThe algorithm is available from https://github.com/RamanLab/minMicrobiome Author SummaryMicroorganisms are ubiquitous in nature. They survive in communities by interacting with each other and influence the biosphere by carrying out specific functions. For instance, the mammalian digestive system is heavily dependent on microbial communities in the gut (known as gut microbiome) to digest dietary fibres which are otherwise indigestible. The capability of gut microbes to convert dietary fibres to short-chain fatty acids help the host by regulating the functionality of the gut epithelial barrier. Oftentimes, some members of a community have redundant functions. Hence, it is possible to find a smaller subset of organisms that is capable of a given functionality, while also maintaining the required growth rate. We call them a minimal microbiome. Knowledge of such function-specific minimal microbiomes is useful for constructing communities for laboratory study and for designing treatment strategies for medical conditions caused by microbiome disruption. We present an optimization algorithm for identifying function-specific minimal microbiomes from a large community. We also demonstrate the performance of the algorithm by analysing minimal microbiomes obtained from some known communities. Overall, our research work highlights the significance of function-specific minimal microbiomes and provides an efficient computational tool for their identification.

systems biology↗