Biology subjects

Lakshmanan, M.

Publications and source records attributed to Lakshmanan, M..

11 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↗

bioRxiv2025

A community-consensus reconstruction of Chinese Hamster metabolism enables structural systems biology analyses to decipher metabolic rewiring in lactate-free CHO cells

Genome-scale metabolic models (GEMs) are indispensable for studying and engineering cellular metabolism. Here, we present iCHO3K, a community-consensus, manually-curated reconstruction of the Chinese Hamster metabolic network. In addition to accounting for 11004 reactions associated with 3597 genes, iCHO3K includes 3489 protein structures and structural descriptors for >70% of its 7377 metabolites, enabling deeper exploration of the link between molecular structure and cellular metabolism. We used iCHO3K to contextualize transcriptomics and metabolomics data from a CHO cell line in which lactate secretion is abolished. We found the reduced glycolytic flux and enhanced TCA cycle flux were accompanied by an elevated NADH and PEP levels in these cells, consistent with experimental measurements. Leveraging iCHO3Ks structural annotations, we identified candidate binding interactions of NADH and PEP with glycolytic enzymes showing model-predicted differential flux, suggesting novel allosteric regulation associated with the observed decrease in glucose uptake and glycolysis. Overall, iCHO3K offers a valuable framework for systematic integration of omics data, improved flux predictions, and structure-guided insights, thus advancing CHO cell engineering and enhancing biomanufacturing efficiency.

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

Sweetwater: an underrated crude glycerol for sustainable lipid production in non-conventional yeasts

Sweetwater, a methanol-free by-product of the fat splitting process, is an emerging alternative feedstock for microbial utilization of crude glycerol. Yeasts are promising candidates in this context due to their versatility in delivering a wide range of value-added products including lipids. To enhance sweetwater utilization, we investigated the growth and lipid production of 21 oleaginous yeast strains in sweetwater and found that nutrient limitation and the unique carbon composition of sweetwater boosted significant lipid accumulation in several strains, in particular Rhodosporidium toruloides NRRL Y-6987. To decipher the underlying mechanism, the transcriptomic changes of R. toluroides NRRL Y-6987 were further analyzed, indicating potential sugars and oligopeptides in sweetwater supporting growth and lipid accumulation as well as exogenous fatty acid uptake leading to the enhanced lipid accumulation. Our comparative study successfully demonstrated sweetwater as a cost-effective feedstock and suggested potential sweetwater type and strain engineering targets for increasing microbial lipid production. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=139 SRC="FIGDIR/small/558176v1_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@1ad5738org.highwire.dtl.DTLVardef@139227eorg.highwire.dtl.DTLVardef@7a8e1org.highwire.dtl.DTLVardef@e072c6_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2023

Super-silencer perturbation by EZH2 and REST inhibition leads to large loss of chromatin interactions and reduction in cancer growth

Human silencers have been shown to exist and regulate developmental gene expression. However, the functional importance of human silencers needs to be elucidated, such as whether they can form "super-silencers" and whether they are linked to cancer progression. Here, through interrogating two putative silencer components of FGF18 gene, we found that two nearby silencers can cooperate via compensatory chromatin interactions to form a "super-silencer". Furthermore, double knockout of two silencers exhibited synergistic upregulation of FGF18 expression and changes of cell identity. To perturb the "super-silencers", we applied combinational treatment of an EZH2 inhibitor GSK343, and a REST inhibitor, X5050 ("GR"). We found that GR led to severe loss of TADs and loops, while the use of one inhibitor by itself only showed mild changes. Such changes in TADs and loops were associated with reduced CTCF and TOP2A mRNA levels. Moreover, GSK343 and X5050 synergistically upregulated super-silencer-controlled genes related to cell cycle, apoptosis and DNA damage, leading to anticancer effects both in vitro and in vivo. Overall, our data demonstrated the first example of a "super-silencer" and showed that combinational usage of GSK343 and X5050 to disrupt "super-silencers" could potentially lead to cancer ablation.

molecular biology↗

bioRxiv2022

Super-silencers regulated by chromatin interactions control apoptotic genes

Human silencers have been shown to exist and regulate developmental gene expression. However, the functional importance of human silencers needs to be elucidated such as the working mechanism and whether they can form "super-silencers". Here, through interrogating two putative silencer components of FGF18 gene, we found that two silencers can cooperate via compensated chromatin interactions to form a "super-silencer". Furthermore, double knock-out of two silencers exhibited synergistic upregulation of FGF18 expression and changes of cell identity. To disturb the "super-silencers", we applied combinational treatment of an EZH2 inhibitor GSK343, and a REST inhibitor, X5050 ("GR"). We found that GR led to severe loss of TADs and loops, while the use of just one inhibitor by itself only showed mild changes. Such changes of TADs and loops may due to reduced CTCF protein level observed upon GR treatment. Moreover, GSK343 and X5050 worked together synergistically to upregulate the apoptotic genes controlled by super-silencers, and thus gave rise to antitumor effects including apoptosis, cell cycle arrest and tumor growth inhibition. Overall, our data demonstrated the first example of a "super-silencer" and showed that combinational usage of GSK343 and X5050 could potentially lead to cancer ablation through disruption of "super-silencers".

genomics↗

bioRxiv2021

Systematic evaluation of genome-wide metabolic landscapes in lactic acid bacteria reveals diet-induced and strain-specific probiotic idiosyncrasies

Lactic acid bacteria (LAB) naturally occur in animal and plant niches and are well-known to elicit several health benefits in humans. Yet, how they adapt their functional metabolic landscapes to diverse nutrient environments and synthesize relevant bioactive compounds remain unexplored across genera, species and strains. Hence, presented herein is a systematic framework for comprehensively characterizing the genome-wide metabolisms of six representative LAB by combining multi-omics data with in silico modeling. We analyse the differences in their growth and cellular fitness, biosynthetic capability of health-relevant compounds, i.e., postbiotics, and probable interactions with 15 common gut microbiota under 11 virtual dietary regimes, and show such attributes are diet- and species-specific. Particularly, some LAB exhibit a desirable balance between synthesis of beneficial postbiotic compounds, positive interactions with beneficial gut commensals, and the ability to colonize and persist in gut environment. We also observe that "high fat-low carb" diets likely lead to detrimental outcomes in most LAB. Our results clearly highlight that probiotics are not "one size fits all" commodities and need to be formulated in a personalised manner for their use as dietary supplements and live biotherapeutics. Overall, the proposed framework will systematize the probiotic administration and could also widen the strain repertoire.

systems biology↗

bioRxiv2019

Enzyme capacity-based genome scale modelling of CHO cells

Chinese hamster ovary (CHO) cells are most prevalently used for producing recombinant therapeutics in biomanufacturing. Recently, more rational and systems approaches have been increasingly exploited to identify key metabolic bottlenecks and engineering targets for cell line engineering and process development based on the CHO genome-scale metabolic model which mechanistically characterizes cell culture behaviours. However, it is still challenging to quantify plausible intracellular fluxes and discern metabolic pathway usages considering various clonal traits and bioprocessing conditions. Thus, we newly incorporated enzyme kinetic information into the updated CHO genome-scale model (iCHO2291) and added enzyme capacity constraints within the flux balance analysis framework (ecFBA) to significantly reduce the flux variability in biologically meaningful manner, as such improving the accuracy of intracellular flux prediction. Interestingly, ecFBA could capture the overflow metabolism under the glucose excess condition where the usage of oxidative phosphorylation is limited by the enzyme capacity. In addition, its applicability was successfully demonstrated via a case study where the clone- and media-specific lactate metabolism was deciphered, suggesting that the lactate-pyruvate cycling could be beneficial for CHO cells to efficiently utilize the mitochondrial redox capacity. In summary, iCHO2296 with ecFBA can be used to confidently elucidate cell cultures and effectively identify key engineering targets, thus guiding bioprocess optimization and cell engineering efforts as a part of digital twin model for advanced biomanufacturing in future.

systems biology↗

bioRxiv2019

On the impact of biomass composition in constraint-based flux analysis

The biomass equation is a critical component in genome-scale metabolic models (GEMs): it is used as the de facto objective function in flux balance analysis (FBA). This equation accounts for the quantities of all known biomass precursors that are required for cell growth based on the macromolecular and monomer compositions measured at certain conditions. However, it is often reported that the macromolecular composition of cells could change across different environmental conditions; the use of the same single biomass equation in FBA, under multiple conditions, is questionable. Thus, we first investigated the qualitative and quantitative variations of macromolecular compositions of three representative host organisms, Escherichia coli, Saccharomyces cerevisiae and Cricetulus griseus, across different environmental/genetic variations. While macromolecular building blocks such as DNA, RNA, protein, and lipid composition vary notably, variations in fundamental biomass monomer units such as nucleotides and amino acids are not appreciable. We further observed that while macromolecular compositions are similar across taxonomically closer species, certain monomers, especially fatty acids, vary substantially. Based on the analysis results, we subsequently propose a new extension to FBA, named "Flux Balance Analysis with Ensemble Biomass (FBAwEB)", to embrace the natural variation in selected components of the biomass equation. The current study clearly highlights that certain components of the biomass equation are very sensitive to different conditions, and the ensemble representation of biomass equation in the FBA framework enables us to account for such natural variations accurately during GEM-guided in silico simulations.

systems biology↗

bioRxiv2019

Multi-omics profiling of CHO parental hosts reveals cell line-specific variations in bioprocessing traits

Chinese hamster ovary (CHO) cells are the most prevalent mammalian cell factories for producing recombinant therapeutic proteins due to their ability to synthesize human-like post-translational modifications and ease of maintenance in suspension cultures. Currently, a wide variety of CHO host cell lines have been developed; substantial differences exist in their phenotypes even when transfected with the same target vector. However, relatively less is known about the influence of their inherited genetic heterogeneity on phenotypic traits and production potential from the bioprocessing point of view. Herein, we present a global transcriptome and proteome profiling of three commonly used parental cell lines (CHO-K1, CHO-DXB11 and CHO-DG44) in suspension cultures and further report their growth-related characteristics, and N- and O-glycosylation patterns of host cell proteins (HCPs). The comparative multi-omics analysis indicated that some physiological variations of CHO cells grown in the same media are possibly originated from the genetic deficits, particularly in the cell cycle progression. Moreover, the dihydrofolate reductase deficient DG44 and DXB11 possess relatively less active metabolism when compared to K1 cells. The protein processing abilities and the N- and O-glycosylation profiles also differ significantly across the host cell lines, suggesting the need to select host cells in a rational manner for the cell line development on the basis of recombinant protein being produced.

systems biology↗

bioRxiv2018

Memote: A community-driven effort towards a standardized genome-scale metabolic model test suite

Several studies have shown that neither the formal representation nor the functional requirements of genome-scale metabolic models (GEMs) are precisely defined. Without a consistent standard, comparability, reproducibility, and interoperability of models across groups and software tools cannot be guaranteed.\n\nHere, we present memote (https://github.com/opencobra/memote) an open-source software containing a community-maintained, standardized set of metabolic model tests. The tests cover a range of aspects from annotations to conceptual integrity and can be extended to include experimental datasets for automatic model validation. In addition to testing a model once, memote can be configured to do so automatically, i.e., while building a GEM. A comprehensive report displays the models performance parameters, which supports informed model development and facilitates error detection.\n\nMemote provides a measure for model quality that is consistent across reconstruction platforms and analysis software and simplifies collaboration within the community by establishing workflows for publicly hosted and version controlled models.

systems biology↗