Biology subjects

Madan, S.

Publications and source records attributed to Madan, S..

13 recordsLinked to original sources

bioRxiv2025

ELITE: E3 Ligase Inference for Tissue specific Elimination: A LLM Based E3 Ligase Prediction System for Precise Targeted Protein Degradation

Targeted protein degradation (TPD) has transformed modern drug discovery by harnessing the ubiquitin-proteasome system to eliminate disease-driving proteins previously deemed undruggable. However, current approaches predominantly rely on a narrow set of ubiquitously expressed E3 ligases, such as Cereblon (CRBN) and Von Hippel-Lindau (VHL), which limits tissue specificity, increases systemic toxicity, and fosters resistance. Here, we present an AI-driven framework for the rational identification of tissue-specific E3 ligases suitable for precision-targeted degradation. Our model leverages an BERT-based protein language architecture trained on billions of sequences to generate contextual embeddings that capture structural and functional motifs relevant for E3-substrate compatibility. By integrating these embeddings with tissue-resolved protein-protein interaction data, the framework predicts ligase-target interactions that are both biologically plausible and context-restricted. This enables the prioritization of ligases capable of driving selective degradation of pathogenic proteins within disease-relevant tissues. The proposed approach offers a scalable path to expand the E3 ligase repertoire and advance TPD toward true precision medicine. The AI architecture used in this study builds upon a well-established, state-of-the-art protein language modeling framework rather than introducing a fundamentally new model design. The principal contribution instead lies in the deliberate engineering of a task-specific dataset that integrates large-scale protein sequence embeddings with tissue-resolved expression and interaction information tailored to targeted protein degradation. By curating and harmonizing these heterogeneous data modalities for the explicit purpose of identifying tissue-selective E3 ligases, we enable a generalizable foundation model to be effectively adapted to a previously unexplored application context. This data-centric advance underpins the frameworks ability to generate biologically meaningful, context-aware predictions and represents a central contribution of the present work. SignificanceCurrent computational strategies for targeted protein degradation (TPD) largely ignore biological context, relying on global E3-substrate datasets that overlook tissue specificity and thus cannot anticipate off-target toxicity or resistance. Our work introduces a context-aware, AI-driven framework that integrates large-scale protein language model embeddings with tissue-resolved protein-protein interaction data to predict compatible and tissue-selective E3 ligases for any pathogenic target. By learning biochemical compatibilities directly from sequence space, this approach transcends the limitations of curated interaction databases and enables generalization to novel ligases or disease mutations. The resulting ligand-target rankings combine biochemical plausibility with spatial expression constraints, providing a scalable foundation for designing degraders that act precisely within disease-relevant tissues. This represents a conceptual and technical advance toward precision degradomics - a next generation of targeted protein degradation where therapeutic selectivity is defined not only by molecular affinity but also by cellular and tissue context. Subsequent steps will include structural integration for ternary complex modeling, biochemical validation of predicted pairs, and the deployment of a public platform to guide tissue-specific degrader discovery. Data science maturityDSML3: Development/pre-production: Data science output has been rolled out/validated across multiple domains/problems

bioinformatics↗

bioRxiv2025

Mitochondrial fission regulates ROS for ventral furrow formation in Drosophila gastrulation

Mitochondrial dynamics are vital for embryogenesis, but their role in morphogenesis is largely unexplored. In this study, we elucidate the role of mitochondrial dynamics and function in regulating apical constriction at the ventral furrow in Drosophila gastrulation. We find that spatiotemporal redistribution of mitochondria from basal to apically constricting regions is coincident with Myosin II accumulation in ventral epithelial cells. This apical enrichment depends on intact microtubules and is regulated by Dorsal/NFkb signaling pathway. Embryos expressing mitochondrial fission protein, Drp1 mutants, contain larger, basally accumulated mitochondria, decreased apical constriction and ventral furrow defects. Transcriptomic and proteomic profiling of Drp1 mutants shows no change in Dorsal signaling, but an upregulation of antioxidant enzymes, decreased respiratory complexes, and reduced reactive oxygen species (ROS), confirming loss of mitochondrial activity and a reduced environment. Depleting mitochondrial SOD2 in Drp1 mutant embryos restores levels of antioxidant proteins and increases ROS, leading to an oxidized environment. These embryos also show rescue in apical mitochondrial migration and Myosin II accumulation, thereby enabling proper ventral furrow formation. Our study reveals the functional importance of localized mitochondrial dynamics, regulated by the Dorsal signaling pathway, and mitochondrial activity in a spatiotemporal manner for apical constriction during gastrulation.

developmental biology↗

bioRxiv2025

Single-cell-guided identification of logic-gated antigen combinations for designing effective and safe CAR therapy

Chimeric antigen receptor (CAR) T-cell therapy has revolutionized the treatment of hematological malignancies. However, its application in solid tumors remains limited because single targets are unlikely to suffice due to tumor antigen heterogeneity and off-tumor toxicities. To overcome these obstacles, we developed LogiCAR designer, a computational approach that utilizes single-cell transcriptomics data from patient tumors to systematically identify the cancer-specific antigen circuits with logic gates ("AND," "OR," and "NOT") that target the majority of cancer cells in a tumor while sparing normal cells and tissues as much as possible. LogiCAR designer efficiently scales to higher-order antigen combinations involving up to five genes. Applied to a large-scale dataset encompassing approximately 2 million cells (including > 620k tumor cells) from 342 clinical patient samples across all major breast cancer subtypes, LogiCAR designer identified antigen circuits with enhanced tumor-targeting efficacy and improved safety profiles compared to both previously reported circuits and single-target therapies in clinical trials. However, even these optimized shared circuits still proved insufficient for some patients. We hence systematically studied LogiCAR designers ability to identify highly effective CAR circuits that are individualized to each patient. Remarkably, such personalized CAR circuits provide estimated tumor-targeting efficacy tantamount to complete response in 76% of patients and partial response for all patients. Taken together, this analysis is the first systematic quantification of the efficacy and safety of all possible CAR circuits, showing that: (a) the quality of existing solutions leaves much to be desired; (b) the ability of shared circuits optimized across many patients is moderate, and finally, (c) individually tailored circuits offer significantly higher tumor-targeting efficacies for patients. LogiCAR designer offers a rigorous, data-driven way to facilitate the rational design of safe and effective CAR-based immunotherapies for cancer. Statement of Significance Development of a computational approach that efficiently identifies logic-gated CAR target combinations, called circuits, from single-cell transcriptomics, addressing a critical unmet clinical need. Application to the largest ensemble of breast cancer datasets to date, comprising [~]2 million cells (> 620k tumor cells) from 17 clinical cohorts, to identify CAR circuits predicted to be effective. Comprehensive safety profiling of candidate circuits spanning major tissues at both RNA and protein levels. Logic-gated CAR circuits generated by our pipeline address tumor heterogeneity and achieve efficacy and safety scores that surpass clinical trial and previously computationally identified circuits. Individualized rational CAR design offers a transformative approach to deliver precision-engineered CAR therapies with unprecedented efficacy.

bioinformatics↗

bioRxiv2024

IMMClock reveals immune aging and T cell function at single-cell resolution

The aging of the immune system substantially impacts individual immune responses, yet accurately quantifying immune age remains a complex challenge. Here we developed IMMClock, a novel immune aging clock that uses gene expression data to predict the biological age of individual CD8 T cells, CD4 T cells, and NK cells. The accuracy of IMMClock is first validated across multiple independent datasets, demonstrating its robustness. Second, utilizing the IMMClock, we find that intrinsic cellular aging processes are more strongly altered during immune aging than differentiation processes. Thirdly, our analysis confirms the strong associations between immune aging and established processes such as cellular senescence, exhaustion, and telomere length at the single cell level. Furthermore, immune aging is accelerated under several disease conditions such as type 2 diabetes, heart disease, and cancer. Finally, we apply IMMClock to analyze a perturb-seq gene activation screen of T cell functionality. We find that the post-perturbation immune age of individual T cells is strongly correlated with their pre-perturbation immune age. Furthermore, the immune age at resting state of individual T cells is strongly predictive of their post-stimulation activation state. Overall, IMMClock advances our understanding of immune aging by providing precise, single-cell level age estimations. Its future applications hold promise for identifying interventions that concomitantly rejuvenate and activate T cells, potentially enhancing efforts to counteract age-related immune decline.

bioinformatics↗

bioRxiv2024

Complement C5a receptor 1 blockade reverses cognitive deficits following cranialradiation therapy for brain cancer

Cranial radiation therapy (RT) for brain cancers leads to an irreversible decline in cognitive function without an available remedy. Radiation-induced cognitive deficits (RICD) are a particularly pressing problem for the survivors of pediatric and low grade glioma (LGG) cancers who often live long post-RT lives. Radiation-induced elevated neuroinflammation and gliosis, triggered by the detrimental CNS complement cascade, lead to excessive synaptic and cognitive loss. Using intact and brain cancer-bearing mouse models, we now show that targeting anaphylatoxin complement C5a receptor (C5aR1) is neuroprotective against RICD. We used a genetic knockout, C5aR1 KO mouse, and a pharmacologic approach, employing the orally active, brain penetrant C5aR1 antagonist PMX205 to reverse RICD. Irradiated C5aR1 KO and WT mice receiving PMX205 showed significant neurocognitive improvements in object recognition memory and memory consolidation tasks. Inhibiting C5a/C5aR1 axis reduced microglial activation, astrogliosis, and synaptic loss in the irradiated brain. Importantly, C5aR1 blockage in two syngeneic, orthotopic glioblastoma-bearing mice protected against RICD without interfering with the therapeutic efficacy of RT to reduce tumor volume in vivo. PMX205 clinical trials with healthy individuals and amyotrophic lateral sclerosis (ALS) patients showed no toxicity, drug-related adverse events, or infections. Thus, C5aR1 inhibition is a translationally feasible approach to address RICD, an unmet medical need. SIGNIFICANCECranial radiotherapy for brain cancers activates CNS complement cascade, leading to cognitive decline. Ablation of the complement C5a/C5aR1 axis alleviates radiation-induced neuroinflammation, synaptic loss, and cognitive dysfunction, providing a novel tractable approach.

neuroscience↗

bioRxiv2023

Charting the transcriptomic landscape of primary and metastatic cancers in relation to their origin and target normal tissues

Metastasis is a leading cause of cancer-related deaths, yet understanding how metastatic tumors adapt from their origin to target tissues is challenging. To address this, we assessed whether primary and metastatic tumors resemble their tissue of origin or target tissue in terms of gene expression. We analyzed gene expression profiles from various cancer types, including single-cell and bulk RNA-seq data, in both paired and unpaired primary and metastatic patient cohorts. We quantified the transcriptomic distances between tumor samples and their normal tissues, revealing that primary tumors are more similar to their tissue of origin, while metastases shift towards the target tissue. Pathway-level analysis highlighted critical transcriptomic changes during metastasis. Notably, primary cancers exhibited higher activity in cancer hallmarks, including Activating Invasion and Metastasis, compared to metastatic cancers. This comprehensive landscape analysis provides insight into how cancer tumors adapt to their metastatic environments, providing a transcriptome-wide view of the processes involved.

cancer biology↗

bioRxiv2023

Analyzing bacterial community compositions by GroEL-taxoproteomics

Profiling bacterial populations in mixed communities is a common task in microbiology. 16S rRNA-based sequencing is a widely accepted and functional approach but relies on amplification primers and cannot quantify isotope incorporation. Tandem mass spectrometry proteotyping is an effective alternative for taxonomically profiling microorganisms. We suggest that targeted proteotyping approaches can complement traditional population analyses. Therefore, we describe an approach to assess bacterial community compositions at the family level using the taxonomic marker protein GroEL, which is ubiquitously found in bacteria, except few obligate intracellular species. We refer to our method as GroEL-proteotyping. GroEL-proteotyping is based on high-resolution tandem mass spectrometry of GroEL peptides and identification of GroEL-derived taxa via a Galaxy workflow and a subsequent Python-based analysis script. Its advantage is that it can be performed with a curated and extendable sample-independent database and that GroEL can be pre-separated by SDS-PAGE to reduce sample complexity, improving GroEL identification while simultaneously decreasing the instrument time. GroEL-proteotyping was validated by employing it on a comprehensive raw data set obtained through a metaproteome approach from synthetic microbial communities as well as real human gut samples. Our data show that GroEL-proteotyping enables fast and straightforward profiling of highly abundant taxa in bacterial communities at reasonable taxonomic resolution.

microbiology↗

bioRxiv2022

MultiGML: Multimodal Graph Machine Learning for Prediction of Adverse Drug Events

Adverse drug events constitute a major challenge for the success of clinical trials. Several computational strategies have been suggested to estimate the risk of adverse drug events in preclinical drug development. While these approaches have demonstrated high utility in practice, they are at the same time limited to specific information sources and thus neglect a wealth of information that is uncovered by fusion of different data sources, including biological protein function, gene expression, chemical compound structure, cell-based imaging, etc. In this work we propose an integrative and explainable Graph Machine Learning approach (MultiGML), which fuses knowledge graphs with multiple further data modalities to predict drug related adverse events. MultiGML demonstrates excellent prediction performance compared to alternative algorithms, including various knowledge graph embedding techniques. MultiGML distinguishes itself from alternative techniques by providing in-depth explanations of model predictions, which point towards biological mechanisms associated with predictions of an adverse drug event. MotivationAdverse drug events are a major risk for failure of late-stage clinical trials. Attempts to prevent adverse drug events in preclinical drug development include experimental procedures for measuring liver-toxicity, cardio-toxicity, etc. Yet these procedures are costly and cannot fully guarantee success in later clinical studies, specifically in situations without a reliable animal model. Computational approaches developed for adverse event prediction have shown to be valuable, but are mostly limited to single data sources. Our approach successfully integrates various data sources on protein functions, gene expression, chemical compound structures and more, into the prediction of adverse events. A main distinguishing characteristic is the explainability of our model predictions which allow further insight into biological mechanisms.

bioinformatics↗

bioRxiv2022

Accurate Prediction of Virus-Host Protein-Protein Interactions via a Siamese Neural Network Using Deep Protein Sequence Embeddings

Prediction and understanding of tissue-specific virus-host interactions have relevance for the development of novel therapeutic interventions strategies. In addition, virus-like particles (VLPs) open novel opportunities to deliver therapeutic compounds to targeted cell types and tissues. Given our incomplete knowledge of virus-host interactions on one hand and the cost and time associated with experimental procedures on the other, we here propose a novel deep learning approach to predict virus-host protein-protein interactions (PPIs). Our method (Siamese Tailored deep sequence Embedding of Proteins - STEP) is based on recent deep protein sequence embedding techniques, which we integrate into a Siamese neural network architecture. After evaluating the high prediction performance of STEP in comparison to an existing method, we apply it to two use cases, SARS-CoV-2 and John Cunningham polyomavirus (JCV), to predict virus protein to human host interactions. For the SARS-CoV-2 spike protein our method predicts an interaction with the sigma 2 receptor, which has been suggested as a drug target. As a second use case, we apply STEP to predict interactions of the JCV VP1 protein showing an enrichment of PPIs with neurotransmitters, which are known to function as an entry point of the virus into glial brain cells. In both cases we demonstrate how recent techniques from the field of Explainable AI (XAI) can be employed to identify those parts of a pair of sequences, which most likely contribute to the protein-protein interaction. Altogether our work highlights the potential of deep sequence embedding techniques originating from the field of natural language processing as well as XAI methods for the analysis of biological sequences. We have made our method publicly available via GitHub. The bigger pictureDevelopment of novel cell and tissue specific therapies requires a profound knowledge about protein-protein interactions (PPIs). Identifying these PPIs with experimental approaches such as biochemical assays or yeast two-hybrid screens is cumbersome, costly, and at the same time difficult to scale. Computational approaches can help to prioritize huge amounts of possible PPIs by learning from biological sequences plus already-known PPIs. In this work, we developed a novel approach (Siamese Tailored deep sequence Embedding of Proteins - STEP) that is based on recent deep protein sequence embedding techniques, which we integrate into a Siamese neural network architecture. We use this approach to train models by utilizing protein sequence information and known PPIs. After evaluating the high prediction performance of STEP in comparison to an existing method, we apply it to two use cases, SARS-CoV-2 and John Cunningham polyomavirus (JCV), to predict virus protein to human host interactions. Altogether our work highlights the potential of deep sequence embedding techniques originating from the field of natural language processing as well as Explainable AI methods for the analysis of biological sequence data. HighlightsO_LIA novel deep learning approach (STEP) predicts virus protein to human host protein interactions based on recent deep protein sequence embedding and a Siamese neural network architecture C_LIO_LIPrediction of protein-protein interactions of the JCV VP1 protein and of the SARS-CoV-2 spike protein C_LIO_LIIdentification of parts of sequences that most likely contribute to the protein-protein interaction using Explainable AI (XAI) techniques C_LI Data Science MaturityDSML 3: Development/Pre-production: Data science output has been rolled out/validated across multiple domains/problems

bioinformatics↗

bioRxiv2021

Repurposing CAR targets in solid tumors via a pan-cancer single-cell transcriptome analysis

BACKGROUND: Chimeric antigen receptor (CAR) T cell therapies have yielded transformative clinical success for patients with blood tumors, but their full potential remains to be unleashed against solid tumors. One challenge is finding selective targets: cell surface proteins that are expressed widely by cancer cells and minimally by healthy cells in the tumor microenvironment and other normal tissues. METHODS: Analyzing pan-cancer patient tumor single cell transcriptomics data, we first define and quantify selectivity and safety scores of existing CAR targets for indications in which they are in clinical trials or approved. Selectivity scores are computed by the ability of a given surfaceome gene to classify tumor from nontumor cells in the tumor microenvironment. Safety scores are computed by mining healthy tissue transcriptomics and proteomics atlas data. Second, we identify new candidate cell surface CAR targets that have better selectivity and safety scores than the leading targets among those currently being tested, in an indication-specific manner. RESULTS: Remarkably, in almost all cancer types, we cannot find such better targets, testifying to the overall near optimality of the current target space. However, in HPV-negative head and neck squamous cell carcinoma (HNSC), for which there is currently a dearth of existing CAR targets, we find five new targets that have both superior selectivity and safety scores. Among the HNSC new targets, we find a few that additionally are strongly essentiality in HNSC cell lines.

bioinformatics↗

bioRxiv2021

Using a recently approved tumor mutational burden biomarker to stratify patients for immunotherapy may introduce a sex bias

The U.S. Food and Drug Administration (FDA) recently approved the treatment with pembrolizumab, an immune checkpoint inhibitor (ICI) targeting PD1 (anti-PD1), for patients with advanced solid tumors with a high tumor mutational burden (TMB) (defined as TMB [≥]10 mutations/Mb). However, following recent studies suggest that TMB levels and response to ICI treatment may differ between male and female melanoma patients, we investigated whether using this high-TMB threshold for selecting patients for anti-PD1 treatment may induce a sex-dependent bias. We analyzed a large ICI cohort of 1,286 patients across nine cancer types treated with anti-PD1/PDL1. We find that using this threshold would indeed result in an unwarranted sex bias in melanoma, successfully stratifying female but not male patients. While this threshold is currently not a regulatory prerequisite for ICI treatment in melanoma, it is important to raise awareness to this bias. Notably, no sex-dependent significant differences were observed in the response of melanoma patients to anti-CTLA4 therapies, different chemotherapies or combination therapies. Beyond melanoma, the high-TMB threshold additionally introduces a sex bias of considerable magnitude in glioblastoma and in patients with cancers of unknown origin, however, these results are not statistically significant. A power analysis shows that these biases may become significant with larger sample size, warranting further careful testing in larger cohorts.

cancer biology↗

bioRxiv2020

Cocculus hirsutus -derived phytopharmaceutical drug has potent anti-dengue activity

BackgroundDengue is a serious public health concern worldwide, with ~3 billion people at risk of contracting dengue virus (DENV) infections. Currently, no effective vaccine or drug is available for the prevention or treatment of dengue, which leaves only anti-mosquito strategies to combat this disease. The present study was initiated to determine the in-vitro and in vivo protective effects of a plant-derived phytopharmaceutical drug for the treatment of dengue. Methodology/Principal FindingsIn our previous report, we had identified methanolic extract of the aerial parts of Cissampelos pareira to exhibit in vitro and in vivo anti-dengue activity against all the four DENV serotypes. In the current study, we have identified another Indian medicinal plant, Cocculus hirsutus, which has a more potent anti-dengue activity than C. pareira. The activity has been evaluated through flow-cytometry-based virus inhibition assay. Interestingly, the stem of C. hirsutus was found to be more potent than the aerial part irrespective of the extraction solvent used viz., denatured spirit, hydro-alcohol (50:50) and water. Hence, the aqueous extract of stem of C. hirsutus (AQCH) was further advanced for investigations because of greater regulatory acceptance. The AQCH exhibited dose-dependent inhibition of release of DENV and its secretory antigen, NS1. Five chemical markers viz. Sinococuline, 20-Hydroxyecdysone, Makisterone-A, Magnoflorine and Coniferyl alcohol were identified as the major chemical ingredients of the AQCH extract. These chemicals were subsequently used for extract standardisation. Importantly, AQCH completely protected AG129 mice at 25 mg/kg/dose body weight when fed 4 times a day post-infection with a lethal dose of DENV-2 S221 strain. Because of its potential as an effective phytopharmaceutical drug against dengue, AQCH, has been formulated into tablets for further pre-clinical and clinical developments. Conclusions/SignificanceWe provide evidence of the pan anti-dengue potential of C. hirsutus-based phytopharmaceutical drug as determined through in vitro and in vivo experiments. We have also characterized five chemical entities in the drug substance, which provides means for standardization of drug substance and drug product. Based on these findings, a program to develop a safe and effective C. hirsutus-derived phytopharmaceutical drug for the treatment of dengue has been initiated. Author summaryThere is an urgent need to develop a safe and effective drug against dengue, which is a rapidly expanding mosquito-borne viral disease. Half of the worlds population has been estimated to be at risk of contracting this disease and the situation remains grim due to lack of an approved drug. We aimed to develop an ethnopharmacological drug against dengue by exploring traditional Indian medicinal science, Ayurveda. This led us to identify a creeper, Cocculus hirsutus, as a more potent anti-dengue plant than Cissampelos pareira, reported in our earlier published study. The stem part of C. hirsutus was found to be more efficacious in inhibiting the propagation of dengue viruses (DENVs) in cell culture than its aerial part. Hence, we chose to advance aqueous extract of stem of C. hirsutus (AQCH) for further studies. Importantly, AQCH also protected immune-compromised mice from lethal DENV infection, which is suggestive of its potential clinical relevance. We have identified five chemical marker compounds in AQCH to gauge the quality and consistency of extract preparation and its formulation into stable tablets. Based on the findings of this study, we have undertaken the development of a safe and effective C. hirsutus-derived phytopharmaceutical drug for the treatment of dengue.

microbiology↗

bioRxiv2017

SEA: The Small RNA Expression Atlas

We present the Small RNA Expression Atlas (SEA), a web application that allows for the interactive querying, visualization, and analysis of known and novel small RNAs across ten organisms. It contains sRNA and pathogen expression information for over 4,200 published samples with standardized search terms and ontologies. In addition, SEA allows for the interactive visualization and re-analysis of 879 differential expression and 514 classification comparisons. SEAs user model enables sRNA researchers to compare and re-analyze user-specific and published datasets, highlighting common and distinct sRNA expression patterns.\n\nWe provide evidence for SEAs fidelity by (i) generating a set of 591 tissue specific miRNAs across 30 tissues, (ii) finding known and novel bacterial and viral infections across diseases, and (iii) determining a Parkinsons disease-specific blood biomarker signature using novel data.\n\nWe believe that SEAs simple semantic search interface, the flexible interactive reports, and the user model with rich analysis capabilities will enable researchers to better understand the potential function and diagnostic value of sRNAs or pathogens across tissues, diseases, and organisms.\n\nAvailability and ImplementationSEA is implemented in Java, J2EE, spring, Django, html5, css3, JavaScript, Bootstrap, Vue.js, D3, mongodb and neo4j. It is freely available at http://sea.ims.bio/.

bioinformatics↗