Biology subjects

Raman, K.

Publications and source records attributed to Raman, K..

50 records · Page 3Linked to original sources

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↗

bioRxiv2021

picCASO: A Cellular Automaton for Spatial Organisation

Microbial consortia exhibit spatial patterning across diverse environments. Since probing the self-organization of natural microbial communities is limited by their inherent complexity, synthetic models have emerged as attractive alternatives. In this study, we develop novel frameworks of bacterial communication and explore the emergent spatiotemporal organization of microbes. Specifically, we build quorum sensing-mediated models of microbial growth that are utilized to characterize the dynamics of communities from arbitrary initial configurations and establish the effectiveness of our communication strategies in coupling the growth rates of microbes. Our simulations indicate that the behavior of quorum sensing-coupled consortia can be most effectively modulated by the rates of secretion of AHLs. Such a mechanism of control enables the construction of desired relative populations of constituent species in spatially organized populations. Our models accurately recapitulate previous experiments that have investigated pattern formation in synthetic multi-cellular systems. Additionally, our software tool enables the easy implementation and analysis of our frameworks for a variety of initial configurations and simplifies the development of sophisticated gene circuits facilitating distributed computing. Overall, we demonstrate the potential of spatial organization as a tunable parameter in synthetic biology by introducing a communication paradigm based on the location and strength of coupling of microbial strains. Author SummaryInteracting microorganisms that coexist in a given environment tend to have well-defined spatial arrangements. While the emergence of such organization is seen across different microbiomes in nature, it is hitherto not well understood. Decoding the inherent spatial patterning of microbes is constrained by the complexity of their natural habitats. Here, we take advantage of synthetic models of microbial communities to study the dynamics of emergent spatial organization. Our framework of bacterial communication utilizes modular synthetic devices to couple growth rates. In addition to uncovering potential principles of spatial organization, this work enables the construction of complex genetic circuits distributed across communicating strains. Additionally, we have developed a software tool, picCASO, that streamlines the investigation of microbial systems communicating through such frameworks.

systems biology↗

bioRxiv2021

Sequence neighborhoods enable reliable prediction of pathogenic mutations in cancer genomes

Identifying cancer-causing mutations from sequenced cancer genomes hold much promise for targeted therapy and precision medicine. "Driver" mutations are primarily responsible for cancer progression, while "passengers" are functionally neutral. Although several computational approaches have been developed for distinguishing between driver and passenger mutations, very few have concentrated on utilizing the raw nucleotide sequences surrounding a particular mutation as potential features for building predictive models. Using experimentally validated cancer mutation data in this study, we explored various string-based feature representation techniques to incorporate information on the neighborhood bases immediately 5 and 3 from each mutated position. Density estimation methods showed significant distributional differences between the neighborhood bases surrounding driver and passenger mutations. Binary classification models derived using repeated cross-validation experiments gave comparable performances across all window sizes. Integrating sequence features derived from raw nucleotide sequences with other genomic, structural and evolutionary features resulted in the development of a pan-cancer mutation effect prediction tool, NBDriver, which was highly efficient in identifying pathogenic variants from five independent validation datasets. An ensemble predictor obtained by combining the predictions from NBDriver with two other commonly used driver prediction tools (CONDEL and Mutation Taster) outperformed existing pan-cancer models in prioritizing a literature-curated list of driver and passenger mutations. Using the list of true positive mutation predictions derived from NBDriver, we identified a list of 138 known driver genes with functional evidence from various sources. Overall, our study underscores the efficacy of utilizing raw nucleotide sequences as features to distinguish between driver and passenger mutations from sequenced cancer genomes.

bioinformatics↗

bioRxiv2021

CASTLE: A database of synthetic lethal sets predicted from genome-scale metabolic networks

SummaryGenome-scale metabolic networks have been reconstructed for hundreds of organisms over the last two decades, with wide-ranging applications, including the identification of drug targets. Constraint-based approaches such as flux balance analysis have been effectively used to predict single and combinatorial drug targets in a variety of metabolic networks. We have previously developed Fast-SL, an efficient algorithm to rapidly enumerate all possible synthetic lethals from metabolic networks. Here, we introduce CASTLE, an online standalone database, which contains synthetic lethals predicted from the metabolic networks of over 130 organisms. These targets include single, double or triple lethal set of genes and reactions, and have been predicted using the Fast-SL algorithm. The workflow used for building CASTLE can be easily applied to other pathogenic models and used to identify novel therapeutic targets. AvailabilityCASTLE is available at https://ramanlab.github.io/CASTLE/ Contactkraman@iitm.ac.in

systems biology↗

bioRxiv2020

NetGenes: A database of essential genes predicted using features from interaction networks

Essential gene prediction models built so far are heavily reliant on sequence-based features and the scope of network-based features has been narrow. Previous work from our group demonstrated the importance of using network-based features for predicting essential genes with high accuracy. Here, we applied our approach for the prediction of essential genes to organisms from the STRING database and hosted the results in a standalone website. Our database, NetGenes, contains essential gene predictions for 2700+ bacteria predicted using features derived from STRING protein-protein functional association networks. Housing a total of 3.5M+ genes, NetGenes offers various features like essentiality scores, annotations and feature vectors for each gene. NetGenes is available at https://rbc-dsai-iitm.github.io/NetGenes/

bioinformatics↗

bioRxiv2020

Two-species community design of Lactic Acid Bacteria for optimal production of Lactate

Microbial communities that metabolise pentose and hexose sugars are useful in producing high-value chemicals, as this can result in the effective conversion of raw materials to the product, a reduction in the production cost, and increased yield. Here, we present a computational approach called CAMP (Co-culture/Community Analyses for Metabolite Production) that simulates and identifies appropriate communities to produce a metabolite of interest. To demonstrate this approach, we focus on optimal production of lactate from various Lactic Acid Bacteria. We used genome-scale metabolic models (GSMMs) belonging to Lactobacillus, Leuconostoc, and Pediococcus species from the Virtual Metabolic Human (VMH; https://vmh.life/) resource and well-curated GSMMs of L. plantarum WCSF1 and L. reuteri JCM 1112. We studied 1176 two-species communities using a constraint-based modelling method for steady-state flux-balance analysis of communities. Flux variability analysis was used to detect the maximum lactate flux in a community. Using glucose or xylose as substrates separately or in combination resulted in either parasitism, amensalism, or mutualism being the dominant interaction behaviour in the communities. Interaction behaviour between members of the community was deduced based on variations in the predicted growth rates of monocultures and co-cultures. Acetaldehyde, ethanol, NH4+, among other metabolites, were found to be cross-fed between community members. L. plantarum WCSF1 was a member of communities with high lactate yields. In silico community optimisation strategies to predict reaction knock-outs for improving lactate flux were implemented. Reaction knock-outs of acetate kinase, phosphate acetyltransferase, and fumarate reductase in the communities were found to enhance lactate production. ImportanceUnderstanding compatibility and interactions based on growth between the members of a microbial community is imperative to exploit these communities for biotechnological applications. Towards this goal, here, we introduce a computational analysis framework that evaluates all possible two-species communities generated from a given set of microbial species on single or multiple substrates to achieve optimal production of a target metabolite. As a case study, we analysed communities of Lactic Acid Bacteria to produce lactate. Lactate is a platform chemical produced experimentally from lignocellulosic biomass, which constitutes pentoses and hexoses, such as xylose and glucose. Metabolic engineering strategies, such as reaction knock-outs that can improve product flux while retaining the communitys viability are identified using in silico optimisation methods. Our approach can guide in the selection of most promising communities for experimental testing and validation to produce valuable bio-based chemicals.

systems biology↗

bioRxiv2020

Novel ratio-metric features enable the identification of new driver genes across cancer types

An emergent area of cancer genomics has been the identification of driver genes. Driver genes confer a selective growth advantage to the cell and push it towards tumorigenesis. Functionally, driver genes can be divided into two categories, tumour suppressor genes (TSGs) and oncogenes (OGs), which have distinct mutation type profiles. While several driver genes have been discovered, many remain undiscovered, especially those that are mutated at a low frequency across samples. The current methods are not sufficient to predict all driver genes because the underlying characteristics of these genes are not yet well understood. Thus, to predict novel genes, we need to define new features and models that are not biased and identify genes that might otherwise be overshadowed by mutation profiles of recurrent driver genes. In this study, we define new features and build a model to identify novel driver genes. We overcome overfitting and show that certain mutation types such as nonsense mutations are more important for classification. Some known cancer driver genes, which are predicted by the model as TSGs with high probability are ARID1A, TP53, and RB1. In addition to these known genes, potential driver genes predicted are CD36, ZNF750 and ARHGAP35 as TSGs and TAB3 as an oncogene. Overall, our approach surmounts the issue of low recall and bias towards genes with high mutation rates and predicts potential novel driver genes for further experimental screening.

cancer biology↗

bioRxiv2020

MINREACT: an efficient algorithm for identifying minimal metabolic networks

Genome-scale metabolic models are widely constructed and studied for understanding various design principles underlying metabolism, predominantly redundancy. Metabolic networks are highly redundant and it is possible to minimise the metabolic networks into smaller networks that retain the functionality of the original network. Here, we establish a new method, MO_SCPLOWINC_SCPLOWRO_SCPLOWEACTC_SCPLOW that systematically removes reactions from a given network to identify minimal reactome(s). We show that our method identifies smaller minimal reactomes than existing methods and also scales well to larger metabolic networks. Notably, our method exploits known aspects of network structure and redundancy to identify multiple minimal metabolic networks. We illustrate the utility of MO_SCPLOWINC_SCPLOWRO_SCPLOWEACTC_SCPLOW by identifying multiple minimal networks for 74 organisms from the BiGG database. We show that these multiple minimal reactomes arise due to the presence of compensatory reactions/pathways. We further employed MO_SCPLOWINC_SCPLOWRO_SCPLOWEACTC_SCPLOW for a case study to identify the minimal reactomes of different organisms in both glucose and xylose minimal environments. Identification of minimal reactomes of these different organisms elucidate that they exhibit varying levels of redundancy. A comparison of the minimal reactomes on glucose and xylose illustrate that the differences in the reactions required to sustain growth on either medium. Overall, our algorithm provides a rapid and reliable way to identify minimal subsets of reactions that are essential for survival, in a systematic manner. Author summaryAn organisms metabolism is routinely modelled by a metabolic network, which consists of all the enzyme-catalysed reactions that occur in the organism. These reactions are numerous, majorly due to the presence of redundant reactions that perform compensatory functions. Also, not all the reactions are functional in all environments and are unique to the environmental conditions. So, it is possible to minimise such large metabolic networks into smaller functional networks. Such minimal networks help in easier dissection of the capabilities of the network and also further our understanding of the various redundancies and other design principles occurring in these networks. Here, we have developed a new algorithm for identification of such minimal networks, that is efficient and superior to existing algorithms. We show the utility of our algorithm in identifying such minimal sets of reactions for many known metabolic networks. We have also shown a case study, using our algorithm to identify such minimal networks for different organisms in varied nutrient conditions.

systems biology↗

bioRxiv2019

Robust lysosomal rewiring in Mtb infected macrophages mediated by Mtb lipids restricts the intracellular bacterial survival

Intracellular pathogens commonly manipulate the host lysosomal system for their survival, however whether this affects the organization and functioning of the lysosomal system itself is not known. Here, we show using in vitro and in vivo infections that the lysosomal content and activity is globally elevated in M. tuberculosis infected macrophages. The enhanced lysosomal state is sustained over time and defines an adaptive homeostasis of the infected cell. Lysosomal alterations are caused by mycobacterial surface components, notably the cell wall lipid SL-1, which functions through the mTORC1-TFEB axis. Mtb mutant defective for SL-1 levels shows reduced lysosomal content and activity compared to wild type. Importantly, this phenotype is conserved during in vivo infection. The alteration in lysosomal phenotype in mutant Mtb lead to decreased lysosomal delivery of Mtb, and importantly, increased survival of intracellular Mtb. These results define the global alterations in the host lysosomal system as a crucial distinguishing feature of Mtb infected macrophages that is host protective and contribute to the containment of the pathogen.

cell biology↗

bioRxiv2019

Deciphering the metabolic capabilities of Bifidobacteria using genome-scale metabolic models

Bifidobacteria, the initial colonisers of breastfed infant guts, are considered as the key commensals that promote a healthy gastrointestinal tract. However, little is known about the key metabolic differences between different strains of these bifidobacteria, and consequently, their suitability for their varied commercial applications. In this context, the present study applies a constraint-based modelling approach to differentiate between 36 important bifidobacterial strains, enhancing their genome-scale metabolic models obtained from the AGORA (Assembly of Gut Organisms through Reconstruction and Analysis) resource. By studying various growth and metabolic capabilities in these enhanced genome-scale models across 30 different nutrient environments, we classified the bifidobacteria into three specific groups. We also studied the ability of the different strains to produce short chain fatty acids, finding that acetate production is niche- and strain-specific, unlike lactate. Further, we captured the role of critical enzymes from the bifid shunt pathway, which was found to be essential for a subset of bifidobacterial strains. Our findings underline the significance of analysing metabolic capabilities as a powerful approach to explore distinct properties of the gut microbiome. Overall, our study presents several insights into the nutritional lifestyles of bifidobacteria and could potentially be leveraged to design species/strain-specific probiotics or prebiotics.

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↗

bioRxiv2018

Uncovering novel pathways for enhancing hyaluronan synthesis in recombinant Lactococcus lactis: Genome-scale metabolic modelling and experimental validation

Hyaluronan (HA) is a naturally occurring high-value polysaccharide with important medical applications. HA is commercially produced from pathogenic microbial sources. HA-producing recombinant cell factories that are being developed with GRAS organisms are comparatively less productive than the best natural producers. The metabolism of these recombinant systems needs to be more strategically engineered to achieve significant improvement. Here, we use a genome-scale metabolic network model to account for the entire metabolic network of the cell to predict strategies for improving HA production. We here analyze the metabolic network of Lactococcus lactis adapted to produce HA, and identify non-conventional overexpression and knock-out strategies to enhance HA flux.\n\nTo experimentally validate our predictions, we identify an alternate route for enhancement of HA synthesis, originating from the nucleoside inosine, which has the capacity to function in parallel with the traditionally known route from glucose. Adopting this strategy resulted in a 2.8-fold increase in HA yield. The strategies identified and the experimental results show that the cell is capable of involving a larger subset of metabolic pathways in HA production. Apart from being the first report of the use of a nucleoside to improve HA production, our study shows how experimental results enable model refinement. Overall, we point out that well-constructed genome-scale metabolic models could be very potent tools to derive efficient strategies to improve biosynthesis of important high-value products.

bioengineering↗

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↗

bioRxiv2017

Predicting Novel Metabolic Pathways Through Subgraph Mining

The ability to predict pathways for biosynthesis of metabolites is very important in metabolic engineering. It is possible to mine the repertoire of biochemical transformations from reaction databases, and apply the knowledge to predict reactions to synthesize new molecules. However, this usually involves a careful understanding of the mechanism and the knowledge of the exact bonds being created and broken. There is clearly a need for a method to rapidly predict reactions for synthesizing new molecules, which relies only on the structures of the molecules, without demanding additional information such as thermodynamics or hand-curated information such as atom-atom mapping, which are often hard to obtain accurately.\n\nWe here describe a robust method based on subgraph mining, to predict a series of biochemical transformations, which can convert between two (even previously unseen) molecules. We first describe a reliable method based on subgraph edit distance to map reactants and products, using only their chemical structures. Having mapped reactants and products, we identify the reaction centre and its neighbourhood, the reaction signature, and store this in a reaction rule network. This novel representation enables us to rapidly predict pathways, even between previously unseen molecules. We also propose a heuristic that predominantly recovers natural biosynthetic pathways from amongst hundreds of possible alternatives, through a directed search of the reaction rule network, enabling us to provide a reliable ranking of the different pathways. Our approach scales well, even to databases with > 100,000 reactions. A Java-based implementation of our algorithms is available at https://github.com/RamanLab/ReactionMiner\n\nCCS CONCEPTS*Information systems [->]Data mining; *Applied computing [->]Bioinformatics;

systems biology↗