Biology subjects

Chaudhary, S. U.

Publications and source records attributed to Chaudhary, S. U..

8 recordsLinked to original sources

bioRxiv2025

Conservation of the hydrogen-bond network in bacterial response regulators

The bacterial response regulator (RR) superfamily is activated by single aspartyl phosphorylation to modulate a distant target binding surface for diverse functions. The enteric CheY RRs, which represent the chemotaxis subfamily, have been extensively characterized. Their native, chemical or genetically-altered crystal structures have revealed an essential role for water-mediated hydrogen bonds (H-bonds) in activation. Here, we use molecular dynamics (MD) to compare the protein-water H-bond network in basal and in-silico phosphorylated conformations. We supplement the MD with energy frustration profiles for atomic structures and models from selected RR superfamily representatives. The energetically frustrated phosphorylation pocket consists of the conserved aspartate triad for phosphorylation, plus associated structural waters and residues for Mg2+ ion coordination. It orchestrates the H-bond network characterized here in atomic detail. The network has an energetically stable core. Its plastic nodes switch bonding states coupled to loop flexibility and sidechain rotations. Mutual information revealsthat the long-range, dynamic networks respond to single H-bond transitions. The network centrality of the phosphorylation pocket, connected to the target binding surface by water-mediated channels via the conserved switch residues (T87, K109), increases upon phosphorylation. Analysis of other RR representatives suggests this design is a generic feature of RR allostery with subtle, function-dependent differences. The water contribution may prove critical for the design of specific RR sub-family specific, allosteric inhibitors.

biophysics↗

bioRxiv2024

Causality Mapping Using Resting-state fMRI Reveals Hyperactivity and Hypoconnectivity in Schizophrenia Patients

Schizophrenia (SZ) is a debilitating disorder in which patients exhibit psychotic behavior due to aberrant connectivity between different regions of the brain. Advances in neuroimaging have now enabled the diagnosis and analysis of SZ in order to elucidate the whole brain functional connectivity networks. In the present study, we have used resting-state functional magnetic resonance imaging (rs-fMRI) to elucidate the causal relationships amongst the differentially activated brain regions between SZ patients (n=10) and healthy controls (n=10). Vector auto-regression (VAR) model and Granger causality (GC) were then applied to construct a functional connectivity network and analyze the causal effects in SZ patients. Our results revealed that the average voxel activation in the frontal lobe (FL), basal ganglia (BG), and ventricular system (VS) was significantly higher in patients indicating hyper-activity as compared to controls. Conversely, cerebellum white matter (CBWM) showed higher activation in the controls as compared to patients. A higher Pearson correlation was observed between the controls as compared to patients while VAR and GC showed higher functional connectivity among all the regions of interest (ROIs) along with more causal relations in the controls. Finally, mediation analysis showed that right middle superior frontal gyrus acts as a strong partial mediator between left accumbens area and left middle superior frontal gyrus. Taken together, this study decodes the dysregulated brain activity in schizophrenia showing hyperactivation in patients when compared with the healthy controls which leads to alterations in neural connections resulting in hypoconnectivity.

neuroscience↗

bioRxiv2022

The Solvation of the E. coli CheY Phosphorylation SiteMapped by XFMS

The Escherichia coli CheY protein belongs to a large bacterial response regulator superfamily. X-ray hydroxy radical foot-printing with mass spectroscopy (XFMS) has shown that allosteric activation of CheY by its motor target triggers a concerted internalization of aromatic sidechains. We reanalyzed the XFMS data to compare polar versus non-polar CheY residue positions. The polar residues around and including the 57D phosphorylated site had an elevated hydroxy radical reactivity. Bioinformatic measures revealed that a water-mediated hydrogen bond network connected this ring of residues with the central 57D. These residues solvated 57D to energetically stabilize the apo-CheY fold. The abundance of these reactive residues was reduced upon activation. This result was supported by the bioinformatics and consistent with the previously reported activation-induced increase in core hydrophobicity. It further illustrated XFMS detection of structural waters. Direct contacts between the ring residues and the phosphorylation site would stabilize the aspartyl phosphate. In addition, we report that the ring residue, 18R, is a constant central node in the 57D solvation network and that 18R non-polar substitutions determine CheY diversity as assessed by its evolutionary trace in bacteria with well-studied chemotaxis. These results showcase the importance of structured water dynamics for phosphorylation-mediated signal transduction.

biochemistry↗

bioRxiv2022

CanSeer: A Method for Development and Clinical Translation of Personalized Cancer Therapeutics

Computational modeling and analysis of biomolecular network models annotated with cancer patient-specific multi-omics data can enable the development of personalized therapies. Current endeavors aimed at employing in silico models towards personalized cancer therapeutics remain to be fully translated. In this work, we present "CanSeer" a novel multi-stage methodology for developing in silico models towards clinical translation of personalized cancer therapeutics. The proposed methodology integrates state-of-the-art dynamical analysis of biomolecular network models with patient-specific genomic and transcriptomic data to assess the individualized therapeutic responses to targeted drugs and their combinations. CanSeers translational approach employs transcriptomic data (RNA-seq based gene expressions) with genomic profile (CNVs, SMs, and SVs). Specifically, patient-specific cancer driver genes are identified, followed by the selection of druggable and/or clinically actionable targets for therapeutic interventions. To exemplify CanSeer, we have designed three case studies including (i) lung squamous cell carcinoma, (ii) breast invasive carcinoma, and (iii) ovarian serous cystadenocarcinoma. The case study on lung squamous cell carcinoma concluded that restoration of Tp53 activity together with an inhibition of EGFR as an efficacious combinatorial treatment for patients with Tp53 and EGFR cancer driver genes. The findings from the cancer case study helped identify personalized treatments including APR-246, APR-246+palbociclib, APR-246+osimertinib, APR-246+afatinib, APR-246+osimertinib+dinaciclib, and APR-246+afatinib+dinaciclib. The second case study on breast invasive carcinoma revealed CanSeers potential to elucidate drug resistance against targeted drugs and their combinations including KU-55933, afuresertib, ipatasertib, and KU-55933+afuresertib. Lastly, the ovarian cancer case study revealed the combinatorial efficacy of APR-246+carmustine, and APR-246+dinaciclib for treating ovarian serous cystadenocarcinoma. Taken together, CanSeer outlines a novel method for systematic identification of optimal tailored treatments with mechanistic insights into patient-to-patient variability of therapeutic response, drug resistance mechanism, and cytotoxicity profiling towards personalized medicine.

systems biology↗

bioRxiv2021

Navigating Multi-scale Cancer Systems Biology towards Model-driven Personalized Therapeutics

Rapid advancements in high-throughput omics technologies and experimental protocols have led to the generation of vast amounts of biomolecular data on cancer that now populates several online databases and resources. Cancer systems biology models built on top of this data have the potential to provide specific insights into complex multifactorial aberrations underpinning tumor initiation, development, and metastasis. Furthermore, the annotation of these single- or multi-scale models with patient data can additionally assist in designing personalized therapeutic interventions as well as aid in clinical decision-making. Here, we have systematically reviewed the emergence and evolution of (i) repositories with scale-specific and multiscale biomolecular cancer data, (ii) systems biology models developed using this data, (iii) associated simulation software for development of personalized cancer therapeutics, and (iv) translational attempts to pipeline multi-scale panomics data for data-driven in silico clinical oncology. The review concludes by highlighting that the absence of a generic, zero-code, panomics-based multi-scale modeling pipeline and associated software framework, impedes the development and seamless deployment of personalized in silico multi-scale models in clinical settings.

systems biology↗

bioRxiv2021

TISON: a next-generation multi-scale modeling theatre for in silico systems oncology

Multi-scale models integrating biomolecular data from genetic, transcriptional, and translational levels, coupled with extracellular microenvironments can assist in decoding the complex mechanisms underlying system-level diseases such as cancer. To investigate the emergent properties and clinical translation of such cancer models, we present Theatre for in silico Systems Oncology (TISON, https://tison.lums.edu.pk), a next-generation web-based multi-scale modeling and simulation platform for in silico systems oncology. TISON provides a "zero-code" environment for multi-scale model development by seamlessly coupling scale-specific information from biomolecular networks, microenvironments, cell decision circuits, in silico cell lines, and organoid geometries. To compute the temporal evolution of multi-scale models, a simulation engine and data analysis features are also provided. Furthermore, TISON integrates patient-specific gene expression data to evaluate patient-centric models towards personalized therapeutics. Several literature-based case studies have been developed to exemplify and validate TISONs modeling and analysis capabilities. TISON provides a cutting-edge multi-scale modeling pipeline for scale-specific as well as integrative systems oncology that can assist in drug target discovery, repositioning, and development of personalized therapeutics.

systems biology↗

bioRxiv2020

Causality Mapping Using Resting-State fMRI reveals Suppressed Functional Connectivity in Schizophrenia Patients

Schizophrenia is a psychotic brain disorder in which patients exhibit aberrant connectivity between different regions of the brain. Neuroimaging is a state-of-the-art technique that is now increasingly been employed in clinical investigation of Schizophrenia. In the present study, we have used resting-state functional magnetic resonance neuroimaging (rsfMRI) to elucidate the cause-and-effect relationships among four regions of the brain including occipital, temporal, and frontal lobes and hippocampus in Schizophrenia. For that, we have employed independent component analysis, a seed-based temporal correlation analysis, and Granger causality analysis for measuring causal relationships amongst four regions of the brain in schizophrenia patients. Eighteen subjects with nine patients and nine controls were evaluated in the study. Our results show that Schizophrenia patients exhibit significantly different activation patterns across the selected regions of the brain in comparison with the control. In addition to that, we also observed an aberrant causal relationship between these four regions of the brain. In particular, the temporal and frontal lobes of patients with schizophrenia had a significantly lowered causal relationship with the other areas of the brain. Taken together, the study elucidates the dysregulated brain activity in Schizophrenia patients, decodes its causal mapping and provides novel insights towards employment in clinical evaluation of Schizophrenia.

neuroscience↗

bioRxiv2020

In silico Drosophila Patient Model Reveals Optimal Combinatorial Therapies for Colorectal Cancer

In silico models of biomolecular regulation in cancer, annotated with patient-specific gene expression data can aid in the development of novel personalized cancer therapeutics strategies. Drosophila melanogaster is a well-established animal model that is increasingly being employed to evaluate preclinical personalized cancer therapies. Here, we report five Boolean network models of biomolecular regulation in cells lining the Drosophila midgut epithelium and annotate them with patient-specific mutation data to develop an in silico Drosophila Patient Model (DPM). The network models were validated against cell-type-specific RNA-seq gene expression data from the FlyGut-seq database and through three literature-based case studies on colorectal cancer. The results obtained from the study help elucidate cell fate evolution in colorectal tumorigenesis, validate cytotoxicity of nine FDA-approved cancer drugs, and devise optimal personalized drug treatment combinations. The proposed personalized therapeutics approach also helped identify synergistic combinations of chemotherapy (paclitaxel) with targeted therapies (pazopanib, or ruxolitinib) for treating colorectal cancer. In conclusion, this work provides a novel roadmap for decoding colorectal tumorigenesis and in the development of personalized cancer therapeutics through a DPM.

systems biology↗