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Chandrasekaran, S.

Publications and source records attributed to Chandrasekaran, S..

At least 19 recordsLinked to original sources

Multi-Tissue Metabolomic Signatures of Five Longevity Interventions Converge on Ergothioneine and Lipid Remodeling in Male UM-HET3 Mice

The pace of aging can be delayed by mutations, dietary manipulations, and drugs, yet the metabolic mechanisms underlying longevity interventions remain poorly understood. Here we present a multi-tissue metabolomic analysis of male UM-HET3 mice treated from 4 to 12 months of age with five validated longevity interventions: rapamycin, acarbose, 17-estradiol, canagliflozin, or caloric restriction. Using a feature-stabilized XGBoost pipeline applied to seven tissues, we show that metabolomic profiles can identify treated mice as likely recipients of a lifespan-extending intervention well before survival differences emerge. A leave-one-intervention-out procedure confirmed that models trained on any four interventions successfully classified mice from a fifth, unseen intervention, implying shared metabolic alterations across mechanistically distinct treatments. The most influential metabolites -- defined as the minimum set explaining 50% of cumulative model gain -- differed substantially across tissues. Only ergothioneine, a dietary antioxidant, ranked highly in more than two tissues: it was elevated by all five interventions in plasma and brain, and by four of five in muscle. Enrichment analyses further identified coordinated remodeling of lipid classes in plasma, perigonadal fat, and kidney. These findings reveal tissue-specific metabolic reprogramming shared across mechanistically distinct longevity interventions and, pending validation against interventions that do not extend lifespan, suggest a path toward metabolomic screening of candidate anti-aging drugs.

Molecular Biology↗

Cryptococcus neoformans rewires the conserved Wee1-CDK checkpoint through two divergent kinases required for replication-stress tolerance and virulence

Cell-cycle checkpoints couple cell division to environmental and intracellular stress. Here, we show that the human fungal pathogen Cryptococcus neoformans possesses two divergent Wee1-family kinases, CnSwe1 and CnSwe102, that retain conserved CDK-inhibitory activity but function differently from their counterparts in the canonical Saccharomyces cerevisiae morphogenesis checkpoint. The swe1{Delta} and swe102{Delta} mutants displayed distinct stress-response defects, and genetic analyses suggested a dosage-sensitive genetic interaction between SWE1 and SWE102. Although both proteins promoted Cdc28 tyrosine phosphorylation and elongated-cell morphology when expressed in S. cerevisiae, neither localized to the mother-bud neck in C. neoformans. Altered SWE1 dosage in the absence of SWE102 increased sensitivity to replicative and DNA-damaging stresses and perturbed cell-cycle progression under genotoxic conditions. Importantly, loss of SWE1 nearly abolished virulence in a murine infection model, and SWE102 also contributed to pathogenicity. Together, these findings indicate that the conserved Wee1-CDK module acts in C. neoformans as a dosage-sensitive checkpoint that promotes stress adaptation and fungal virulence.

microbiology↗

Signaling mechanism of the transmembrane energy receptor Aer

The E. coli aerotaxis receptor Aer is a bacterial chemoreceptor that senses intracellular redox changes via an N terminal PAS domain bound to a flavin adenine dinucleotide (FAD) cofactor. Distinct from canonical methyl-accepting chemotaxis proteins (MCPs) such as Tar/Tsr, Aer lacks a periplasmic ligand-binding domain and adaptive methylation, transmitting conformational signals laterally from the PAS domain to the HAMP domain and the methylation helix-like cap (MHL cap) of the kinase control domain (KCD). To elucidate the Aer signalling mechanism, we determined cryo electron microscopy (cryo EM) structures of full length Aer in oxidized flavin quinone (kinase on) and reduced semiquinone (kinase off) states. Structural comparison reveals redox linked rearrangements of the FAD binding pocket, reorientation of PAS-HAMP interactions, and strikingly altered MHL cap stability. PAS-MHL-cap contact in the oxidized state compresses the receptor and stabilized proximal KCD helices, whereas reduction disrupts these contacts, increasing KCD flexibility. To probe distal effects on KCD architecture, we performed nanodisc reconstitution and pulse dipolar ESR spectroscopy on spin labelled positions along the four helix bundle. Distance distributions indicate redox dependent changes in helix separation, particularly at the C terminal MHL2 region, consistent with PAS driven loosening of KCD packing in kinase off states. These data support a model in which FAD redox chemistry reorganizes flavin pocket residues that in turn subtly alter PAS conformation to influence PAS-HAMP and PAS-MHL-cap packing and hence KCD conformational stability. The findings reveal an Aer specific signaling axis distinct from periplasmic ligand binding MCPs that has adapted MCP architecture for lateral PAS input and cytoplasmic redox sensing.

biochemistry↗

Copper transport is required for metabolism and persistence of Toxoplasma gondii

Copper is a conserved cofactor, required for essential processes including aerobic respiration. Pathogens subvert host copper; however the mechanisms of copper uptake are not well understood. Here, we identify and validate two copper transporters of Toxoplasma gondii and determine the role of copper in parasite metabolism and pathogenesis. We show that Ctr1 is required for copper uptake. Deletion leads to undetectable parasite-associated copper, a significant growth defect and a metabolic shift from mitochondrial respiration with to glycolysis. Absence of Crt1 was fully and rapidly rescued by exogenous copper, which we believe is transported through the lower-affinity transporter Ctr2. Ctr2 is dispensable in rapid growth, however, has a role in parasite persistence in chronic infection, both in vitro and in vivo. Together, these findings reveal for the first time a critical role for copper uptake in shaping metabolic plasticity, and highlights the importance of nutrient availability in regulating apicomplexan metabolism.

cell biology↗

A Modular Machine Learning Framework for Small-Molecule Drug Repurposing Based on Organ Permeability, Target Binding, and Biomarker Modulation

With nearly 90% of drug candidates failing in clinical trials due to poor efficacy or toxicity, drug repurposing has emerged as a vital strategy to accelerate the delivery of life-saving treatments. However, most current drug repurposing approaches fail to account for the physiological barriers and downstream biological impacts that dictate therapeutic success. To bridge this gap, we present SCOUT (Screening Candidates via Organ Uptake and Target-binding), a modular machine learning-driven framework for drug repurposing by simultaneously modeling organ permeability, drug-target binding, and biomarker modulation. Unlike conventional repurposing efforts that rely on single-point predictions or disconnected steps, SCOUT integrates these factors into a unified predictive funnel, significantly reducing trial-and-error and prioritizing candidates with the highest probability of therapeutic relevance. As a proof of concept, we applied SCOUT to Alzheimers disease, where blood-brain barrier (BBB) penetration is an important hurdle. The framework first predicts the unbound brain-to-plasma partition coefficient, Kpuu, using SMILES-based embeddings and achieved robust performance (Accuracy: 0.90, Recall: 0.94), then integrates machine learning to predict binding affinity with BACE1, a key Alzheimers target that was used in this work as a proof of concept. By combining these modules, SCOUT reduced the candidate search space by 99.9% and identified hits with diverse mechanisms of action. Critically, SCOUT extends beyond hit identification by employing mechanistic modeling to simulate how these candidates might modulate specific disease biomarkers (e.g. Amyloid-beta (A{beta}) peptides). This hybrid approach ensures repurposed candidates are not only chemically viable but also physiologically active, providing a rational and resource-efficient pipeline for drug development.

systems biology↗

Modeling Microbiome Modulation of Tumor Metabolic Networks to Predict Synergistic Therapies

Differences in microbiome composition profoundly influence drug response, yet methods to model the metabolic interplay between tumors, microbes, and therapeutics remain limited. We present a generalizable framework combining machine-learning and genome-scale metabolic modeling to prioritize combination therapies for colorectal cancer (CRC) in the presence of Fusobacterium nucleatum (Fn) and other pathogenic, probiotic, and commensal microbes. Trained on 6,514 drug combinations in microbe-free CRC cell lines, the model predicted synergistic combinations in both microbe-free and microbe-associated contexts and generalized to immunotherapy-associated conditions. Predictions were validated using an asymmetric co-culture system that mimics the colons normoxic-anaerobic gradient, confirming synergistic combinations in HCT116 cells with Fn, including drugs not typically used in CRC therapy. Mechanistic analysis and targeted pharmacological perturbations revealed phospho-inositol metabolism and cysteine transport as key determinants of Fn-dependent drug synergy. Together, this work introduces a scalable, microbiome-aware framework to enable discovery of context-specific combination therapies.

bioinformatics↗

A Heterogeneous Graph Framework for Inference of Metabolite-Protein-Drug Interaction Networks

Metabolite-protein interactions (MPIs) are essential for coordinating cellular metabolism and signaling. Yet, MPIs remain incompletely characterized due to the limited scalability of experimental methods and the complexity of tissue-specific regulatory mechanisms. Existing computational approaches often focus on direct interactions and overlook higher-order associations and effect of drug perturbations. Here we introduce TopoMPI, a graph-based framework that integrates five types of biological relationships--metabolite-metabolite (MMI), protein-protein (PPI), metabolite-protein (MPI), drug-protein (DPI), and drug-drug (DDI)--into a heterogeneous network structure. It comprises three complementary sub-models targeting direct interaction prediction, high-order association discovery, and drug-protein-metabolite triplet interaction inference. Comprehensive evaluation across 24 tissue-specific MPI networks, protein-metabolite association studies and pharmacological metabolomic datasets confirm the biological relevance, robustness and generalizability of TopoMPI for MPI prediction with AUCs ranging from 0.79 to 0.86. TopoMPI provides a scalable framework for systems-level characterization of metabolic regulation and drug mode-of-action.

systems biology↗

Discrimination of Normal from Slow-Aging Mice by Plasma Metabolomic and Proteomic Features

Tests that can predict whether a drug is likely to extend mouse lifespan could speed up the search for anti-aging drugs. We have applied a machine learning algorithm, XGBoost regression, to seek sets of plasma metabolites that can discriminate control mice from mice treated with an anti-aging diet (caloric restriction) or any of four anti-aging drugs. When the model is trained on any four of these five interventions, it predicts significantly higher lifespan extension in mice exposed to the intervention which was not included in the training set. Plasma peptide data sets also succeed at this task. Models trained on drug-treated normal mice also discriminate long-lived mutant mice from their respective controls, and models trained on males can discriminate drug-treated from control females. Triglycerides are over-represented among the most influential features in the regression models. Triglycerides with longer fatty acid chains tend to be higher in the slow-aging mice, while triglycerides with shorter fatty acid chains tend to decrease. Plasma metabolite patterns may help to select the most promising anti-aging drugs in mice or in humans, and may give new leads into physiological and enzymatic targets relevant to discovery of new anti-aging drugs.

bioinformatics↗

Flavoproteins as native and genetically encoded spin probes for in cell ESR spectroscopy

Flavin cofactors are attractive Electron Spin Resonance (ESR) probes for proteins because cellular reductants and light can generate their semiquinone states. We have used ESR spectroscopy to study the bacterial transmembrane aerotaxis receptor (Aer) in its native Escherichia coli membrane environment. Optimization of the spectroscopic (electronic relaxation times) and cell growth (isotopic labeling) conditions allowed for measurements of Aer with its partners - the histidine kinase (CheA) and the coupling protein (CheW) - in native signaling arrays. Continuous-wave ESR measurements at room temperature showed a rigid Aer flavin immobilized in the cofactor pocket and Q-band electron nuclear double resonance (ENDOR) measurements identified a predominant anionic semiquinone radical state in cell. Q-band four-pulse double electron-electron resonance (4P-DEER) measurements indicated a 4.1 nm distance between the two flavins of an Aer homodimer, consistent with previous in vitro measurements, but also revealed additional separations in cell indicative of chemoreceptor arrays, not previously observed for Aer. For general application, we further developed a genetically encoded Light-Oxygen and Voltage (LOV) domain for incorporation into target proteins as an ESR probe of structural properties in cell. This approach provides a framework to elucidate protein oligomeric states and conformations that are difficult to reproduce in vitro.

biochemistry↗

Ultra-high-throughput screening of antimicrobial combination therapies using a two-stage transparent machine learning model

Here, we present M2D2, a two-stage machine learning (ML) pipeline that identifies promising antimicrobial drug combinations, which are crucial for combating drug resistance. M2D2 addresses key challenges in drug combination discovery by predicting drug synergies using computationally generated drug-protein interaction data, thereby circumventing the need for expensive omics data. The model improves the accuracy of drug target identification using high-throughput experimental and computational methods via feedback between ML stages. M2D2s transparent framework provides mechanistic insights into drug interactions and was benchmarked against chemogenomics, transcriptomics, and metabolomics datasets. We experimentally validated M2D2 using high-throughput screening of 946 combinations of Food and Drug Administration (FDA)- approved drugs and antibiotics against Escherichia coli. We discovered synergy between a cerebrovascular drug and a widely used penicillin antibiotic and validated predicted mechanisms of action using genome-wide CRISPR inhibition screens. M2D2 offers a transparent ML tool for rapidly designing combination therapies and guides repurposing efforts while providing mechanistic insights.

systems biology↗

Rapid prediction of thermodynamically destabilizing tyrosine phosphorylations in cancers

Tyrosine phosphorylations are a prominent characteristic of numerous cancers, necessitating the use of computational tools to comprehensively analyze phosphoproteomes and identify potentially (dys)functional phosphorylations. Here we propose a machine learning-based method to predict the thermodynamic stability change resulting from tyrosine phosphorylation. Our approach, based on prediction of phosphomimetic delta-delta-G from structural features, strongly correlates with experimental mutational scanning cDNA proteolysis data (R = 0.71). We predicted the destabilizing effects of all 384,857 tyrosine residues from the Alphafold2 database. We then applied our approach to a pan-cancer phosphoproteomics dataset, comprising over 600 unique tyrosine phosphorylations across 11 cancer subtypes. We predict destabilizing phosphorylations in both oncogenes and tumor suppressors, where the former likely reflects a generalized relief of auto-inhibition or activating conformational change. We find that the number of circuit topological parallel relations with respect to residues contacting the phosphorylated site is greater for autoinhibited oncogenes than for other proteins (Wilcoxon p = 0.03). Utilizing an extreme gradient-boosting machine learning approach, we obtain an AUC of 0.85 for the prediction of autoinhibited phosphorylation states from circuit topological features. The top destabilized proteins from the pan-cancer data are enriched for chemical and oxidative stress pathways. Among metabolic proteins, highly destabilizing phosphorylations tend to occur in more peripheral proteins with lower network centrality measures (Wilcoxon p = 0.005). We predict 58% of recurrent tyrosine cancer phosphorylations to be destabilizing at the 1 kcal/mol threshold. Our approach can enable rapid screening of destabilizing phosphorylations and phosphomimetic mutations.

systems biology↗

Recon8D: A metabolic regulome network from oct-omics and machine learning

To explore multiomic regulation of the metabolome, we used machine learning to predict metabolomic variation across [~]1000 different cancer cell lines with matched omics data from 8 biomolecular classes: genomics (copy-number and mutations), epigenomics (histone post-translational modifications (PTMs) and DNA-methylation), transcriptomics and RNA splice variants, non-coding transcriptomics (miRNA and lncRNA), proteomics, and phosphoproteomics. Overall, the metabolome is tightly associated with the transcriptome, with coding and non-coding RNAs emerging as top predictors. Peripheral metabolites are predictable via levels of corresponding enzymes, while those in central metabolism require combinatorial predictors in signaling and redox pathways, and may not reflect corresponding pathway expression. We reconstruct multiomic interaction subnetworks for highly predictable metabolites, and YAP1 signaling emerged as a top global predictor across 4 omic layers. We prioritize predictive multiomic features for single-cell and spatial metabolomics assays. Top predictors were enriched for synthetic-lethal interactions and synergistic combination therapies that target compensatory metabolic modulators.

systems biology↗

Development, validation and application of an LC-MS/MS method quantifying free forms of the micronutrients queuine and queuosine in human plasma using a surrogate matrix approach

Queuosine (Q) is a hypermodified 7-deaza-guanosine nucleoside exclusively synthesized by bacteria. This micronutrient and its respective nucleobase form queuine (q) are salvaged by humans either from gut microflora, or digested food. Depletion of Q-tRNA in human or mouse cells causes protein misfolding that triggers endoplasmic reticular stress and activation of the unfolded protein responses. In vivo, this reduces neuronal architecture of the mouse brain affecting learning and memory. Herein, a sensitive method for quantifying free q and Q in human blood was developed, optimised and validated. After evaluating q/Q extraction efficiency in several different solid-phase sorbents, Bond Elut PBA (phenylboronic acid) cartridges were found to have the highest extraction recovery for q (82%) and Q (71%) from pooled human plasma. PBS with 4% BSA was used as surrogate matrix for method development and validation. An LC-MS/MS method was validated across the concentration range of 0.0003 - 1 {micro}M for both q and Q, showing excellent: linearity (r2 = 0.997 (q) and r2 = 0.998 (Q)), limit of quantification (0.0003 {micro}M), accuracy (100.39% - 125.71%) and precision (CV% < 15.68%). In a sampling of healthy volunteers (n = 44) there was no significant difference in q levels between male (n = 14; mean = 0.0068 {micro}M) and female (n = 30; mean = 0.0080 {micro}M) participants (p = 0.50). Q was not detected in human plasma. This validated method can now be used to further our understanding of the role of q/Q in nutrition, physiology and pathology.

biochemistry↗

Transfer learning predicts species-specific drug interactions in emerging pathogens

Machine learning (ML) algorithms are necessary to efficiently identify potent drug combinations within a large candidate space to combat drug resistance. However, existing ML approaches cannot be applied to emerging and under-studied pathogens with limited training data. To address this, we developed a transfer learning and crowdsourcing framework (TACTIC) to train ML models on data from multiple bacteria. TACTIC was built using 2,965 drug interactions from 12 bacterial strains and outperformed traditional ML models in predicting drug interaction outcomes for species that lack training data. Top TACTIC model features revealed genetic and metabolic factors that influence cross- species and species-specific drug interaction outcomes. Upon analyzing [~]600,000 predicted drug interactions across 9 metabolic environments and 18 bacterial strains, we identified a small set of drug interactions that are selectively synergistic against Gram- negative (e.g., A. baumannii) and non-tuberculous mycobacteria (NTM) pathogens. We experimentally validated synergistic drug combinations containing clarithromycin, ampicillin, and mecillinam against M. abscessus, an emerging pathogen with growing levels of antibiotic resistance. Lastly, we leveraged TACTIC to propose selectively synergistic drug combinations to treat bacterial eye infections (endophthalmitis).

systems biology↗

Licochalcone A from licorice reprograms metabolic and antioxidant pathways in the breast leading to a tumor preventive environment.

BackgroundAnti-estrogens have had limited impact on breast cancer (BC) prevention. Novel agents with better tolerability, and efficacy beyond estrogen receptor (ER) positive BC are needed. We studied licochalcone A (LicA) for ER-agnostic BC prevention. MethodsWe evaluated antiproliferative effects of LicA in seven breast cell lines and its suppression of ER+ and ER- xenograft tumors in mice. High-risk human breast tissue was treated with LicA ex vivo, followed by RNA sequencing and metabolism flux modeling. Confirmatory testing was performed in an independent specimen set and ER+/- BC cell lines using NanoString metabolic panel, proteomics, western blots, and spatiotemporally resolved cholesterol quantification in single cells. ResultsLicA suppressed proliferation in vitro and xenograft tumor growth in vivo. It downregulated pivotal steps in PI3K-AKT-SREBP1-dependent lipogenesis, suppressed PI3K and AKT phosphorylation, SREBP1 protein expression, and cholesterol levels in the plasma membrane inner leaflet, to the levels in normal breast cells. LicA also suppressed prostaglandin E2 synthesis and PRPS1-catalyzed de novo nucleotide biosynthesis, stalling proliferation; further evident by reduced MKI67 and BCL2 proteins. ConclusionsLicA targets SREBP1, a central regulator of lipogenesis and immune response, reducing pro-tumorigenic aberrations in lipid homeostasis and inflammation. It is a promising non-endocrine candidate for BC prevention.

cancer biology↗

The latent stage of Toxoplasma gondii is targeted by the immune response and host protective

Latency is a microbial strategy for persistence. For Toxoplasma gondii the ability of the bradyzoite stage to form long-lived cysts is critical for transmission, while their presence in neurons is considered important for immune evasion. Development of a mathematical model highlighted that immune pressure on bradyzoites should contribute to dynamics of cyst formation and reactivation. Experimental data demonstrated that a cyst-derived antigen was recognized by CD8+ T cells and that IFN-{gamma} signaling in neurons contributes to cyst control. In addition, modeling and the use of a parasite strain unable to form bradyzoites revealed that this stage was not required for long-term persistence, but the absence of cyst formation resulted in increased tachyzoite replication in the CNS with associated tissue damage and mortality. Thus, the latent form of T. gondii is under immune pressure, mitigates infection-induced damage, and promotes survival of host and parasite.

microbiology↗

Inferring Metabolic Objectives and Tradeoffs in Single Cells During Embryogenesis

While proliferating cells optimize their metabolism to produce biomass, the metabolic objectives of cells that perform non-proliferative tasks are unclear. The opposing requirements for optimizing each objective results in a trade-off that forces single cells to prioritize their metabolic needs and optimally allocate limited resources. To define metabolic objectives and tradeoffs in biological systems mathematically, we integrated bulk and single-cell omics data with a novel framework to infer cell objectives using metabolic modeling and machine learning. We validated this framework by identifying essential genes from CRISPR-Cas9 screens in embryonic stem cells, and by inferring the metabolic objectives of quiescent cells and during different cell-cycle phases. Applying this to embryonic cell states, we observed a decrease in metabolic entropy upon development. We further uncovered a trade-off between glutathione and biosynthetic precursors in 1-cell zygote, 2-cell embryo, and blastocyst cells, potentially representing a trade-off between pluripotency and proliferation.

systems biology↗

UNNT: A novel Utility for comparing Neural Net and Tree-based models

Using deep learning in cancer research to tackle scientific challenges is becoming an increasingly popular technique. Advances in enhanced data generation, machine learning algorithms, and compute infrastructure have led to an acceleration in the use of deep learning in various domains of cancer research such as drug response problems. In our study, we explored tree-based models to improve the accuracy of a single drug response model and demonstrate that tree-based models such as XGBoost have advantages over deep learning models, such as a convolutional neural network (CNN), for single drug response problems. However, comparing models is not a trivial task. To make training and comparing CNNs and XGBoost (eXtreme Gradient Boosting) more accessible to users, we developed an open-source library called UNNT (A novel Utility for comparing Neural Net and Tree-based models). The case studies, in this manuscript, focus on cancer drug response datasets however the application can be used on datasets from other domains, such as chemistry. Author summaryAdvancement in data science, machine learning (ML), and artificial intelligence (AI) methods has enabled extraction of meaningful information from large and complex datasets that has assisted in better understanding, diagnosing, and treating cancer. The understanding of the drug response domain in cancer research has been accelerated with developing ML models to aid in predicting the effectiveness of the drugs based on a specific genomic molecular feature. In this study we developed a novel robust framework called UNNT (A novel Utility for comparing Neural Net and Tree-based models) that trains and compares deep learning method such as CNN and tree-based method such as XGBoost on the user input dataset. We applied this software to single drug response problem in cancer to identify the best performing ML method based on the National Cancer Institute 60 (NCI60) dataset. In addition, we studied the computational aspects of training each of these models where our results show that neither is evidently superior on both CPUs and GPUs while training. This shows that when both models have similar error rates for a dataset the hardware available determines the model choice for training as discussed in tables 7 and 11. UNNT: https://github.com/vgutta/UNNT.git O_TBL View this table: org.highwire.dtl.DTLVardef@410860org.highwire.dtl.DTLVardef@18ce417org.highwire.dtl.DTLVardef@d2b43corg.highwire.dtl.DTLVardef@1370c16org.highwire.dtl.DTLVardef@fa7a00_HPS_FORMAT_FIGEXP M_TBL O_FLOATNOTable 7.C_FLOATNO O_TABLECAPTIONFastest training times for CNN and XGBoost on CPU and GPU (all features) C_TABLECAPTION C_TBL O_TBL View this table: org.highwire.dtl.DTLVardef@5f9fbborg.highwire.dtl.DTLVardef@1efecc1org.highwire.dtl.DTLVardef@a0813borg.highwire.dtl.DTLVardef@1845b46org.highwire.dtl.DTLVardef@8463e8_HPS_FORMAT_FIGEXP M_TBL O_FLOATNOTable 11.C_FLOATNO O_TABLECAPTIONFastest training times for CNN and XGBoost on CPU and GPU (FDA model) C_TABLECAPTION C_TBL

bioinformatics↗