Biology subjects

Leshchiner, D.

Publications and source records attributed to Leshchiner, D..

7 recordsLinked to original sources

bioRxiv2026

In Vivo Transcriptomics-Based Framework for Systematic Indication Expansion

Capturing the systemic effects of drugs in vivo remains a fundamental challenge for drug repurposing strategies. Here, we present an in vivo transcriptomics-based drug repurposing framework that profiles multi-organ RNA-seq from drug-treated rodents to model systemic drug responses and match tissue-specific drug signatures against a compendium of disease signatures for indication expansion. We applied this framework to semaglutide, a GLP-1 receptor agonist originally developed for diabetes and subsequently expanded for several other indications. By harmonizing more than 900 human disease transcriptomic signatures via agentic AI and using Z-transformed reversal of gene expression scoring to quantify drug-disease relationships, our approach successfully recovered established semaglutide indications while deprioritizing Alzheimers disease, an indication for which semaglutide was not effective. Top predictions for common diseases were supported by real-world electronic health record data from the Mount Sinai Health System and several predictions for rare indications are proposed for further investigation. This framework provides a scalable strategy for indication expansion across diverse therapeutic agents, especially investigational compounds for which real-world evidence remains sparse.

systems biology↗

bioRxiv2026

A pretrained unified model enables cellular functional profile prediction and multi-objective virtual drug screening

Cells are characterized by molecular states, coordinated molecular interactions, regulatory programs, and responses to perturbations. Systematic mapping of these cellular functional profiles across biological contexts remains experimentally costly and fragmented. Here we present InsilicoCell, a pretrained multi-modal, multi-task model that unifies prediction of cellular functional profiles spanning molecular states, molecular interactions, and perturbation-induced responses. Built on a supervised transformer architecture and pretrained on more than 88 million measurements across seven tasks, including drug sensitivity, drug-induced gene expression, and drug-protein binding, InsilicoCell learns a shared representation that links molecular profiles to cellular phenotypes, improves performance over task-specific models, and generalizes to unseen entities, contexts, and conditions. InsilicoCell extends beyond cell line systems to patient, spatial and single-cell settings, and enables multi-objective virtual drug screening. It identifies novel candidate compounds with experimental validation, including c-Myc activity inhibitors, antifibrotic agents and stemness-inducing compounds. Together, InsilicoCell provides a scalable framework for predictive cellular biology and therapeutic discovery.

bioinformatics↗

bioRxiv2026

Automating scientific annotations for open transcriptomic profiles via multi-stage agents

Public transcriptomic repositories contain millions of samples, yet their large-scale reuse is hindered by heterogeneous and inconsistently reported metadata. In the Gene Expression Omnibus (GEO), key biological information is often distributed across study- and sample-level records, requiring context-dependent interpretation. Here we present GEOMeta, a large language model (LLM)-based multi-stage workflow with task-specialized agents for automated GEO metadata curation. The pipeline separates metadata retrieval, task-specific information extraction, field standardization, ontology mapping and quality control. Using GEOMeta, we generated standardized annotations for approximately 600,000 human bulk RNA-seq samples. To demonstrate its utility, we benchmarked transcriptome representation models for predicting sex, age, tissue and disease from transcriptome embeddings. We further prospectively annotated newly submitted GEO studies and evaluated 22 frontier LLMs. Recent open-source Flash models achieved annotation quality comparable to leading reasoning models while reducing costs by an order of magnitude. GEOMeta provides a scalable resource and reproducible framework for metadata curation.

bioinformatics↗

bioRxiv2026

Deep nanoparticle protein corona plasma proteomics resolves a stage-specific peripheral signature of Alzheimer's disease

Structured AbstractO_ST_ABSINTRODUCTIONC_ST_ABSAlzheimers disease (AD) progresses over decades, yet plasma biomarkers that resolve disease stage rather than simply detect disease remain scarce. This distinction is clinically consequential because effective AD intervention depends on identifying patients before disease biology has progressed beyond a therapeutically responsive stage. METHODSWe used small-molecule-modulated protein corona proteomics to profile plasma from 90 individuals in the Australian Imaging, Biomarker and Lifestyle cohort, stratified by Centiloid (CL) A{beta}-amyloid burden (30 amyloid- negative, CL < 15; 30 moderate-to-high, CL 26 to 100; 30 very high, CL > 100). We quantified 3,176 proteins and applied differential abundance and actual causality analyses to identify stage-specific and candidate causal proteins. RESULTSDifferential protein abundance was exclusively captured during the moderate-to-high AD transition, revealing a discrete proteomic "switch." The switch was marked by accumulation of the autophagy receptor CALCOCO1, together with coordinated depletion of the S100A8/S100A9 calprotectin complex and core erythroid-cytoskeletal network structural markers (e.g., SPTA1, SPTB, ANK1). Adhesion G protein-coupled receptor G6 (ADGRG6) showed a significant moderate positive monotonic association with absolute CL burden, providing a proportional molecular anchor for cumulative disease burden. Actual causality analysis identified COL6A2, FOXRED2, P3H1, PRR4, and GOLGA5 as candidate upstream drivers linking matrix remodeling, Golgi trafficking, and collagen processing to AD progression. DISCUSSIONThese findings suggest a candidate blood-accessible framework for staging AD by active disease biology, which, if replicated in independent cohorts, may have implications for therapeutic selection and mechanism-guided clinical trials. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=92 SRC="FIGDIR/small/740710v1_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@1ead718org.highwire.dtl.DTLVardef@cfa523org.highwire.dtl.DTLVardef@62a367org.highwire.dtl.DTLVardef@1d5c9a5_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2023

CRISPRi-TnSeq: A genome-wide high-throughput tool for bacterial essential-nonessential genetic interaction mapping

Genetic interaction networks can help identify functional connections between genes and pathways, which can be leveraged to establish (new) gene function, drug targets, and fill pathway gaps. Since there is no optimal tool that can map genetic interactions across many different bacterial strains and species, we develop CRISPRi-TnSeq, a genome-wide tool that maps genetic interactions between essential genes and nonessential genes through the knockdown of a targeted essential gene (CRISPRi) and the simultaneous knockout of individual nonessential genes (Tn-Seq). CRISPRi-TnSeq thereby identifies, on a genome-wide scale, synthetic and suppressor-type relationships between essential and nonessential genes, enabling the construction of essential-nonessential genetic interaction networks. To develop and optimize CRISPRi-TnSeq, CRISPRi strains were obtained for 13 essential genes in Streptococcus pneumoniae, involved in different biological processes including metabolism, DNA replication, transcription, cell division and cell envelope synthesis. Transposon-mutant libraries were constructed in each strain enabling screening of [~]24,000 gene-gene pairs, which led to the identification of 1,334 genetic interactions, including 754 negative and 580 positive genetic interactions. Through extensive network analyses and validation experiments we identify a set of 17 pleiotropic genes, of which a subset tentatively functions as genetic capacitors, dampening phenotypic outcomes and protecting against perturbations. Furthermore, we focus on the relationships between cell wall synthesis, integrity and cell division and highlight: 1) how essential gene knockdown can be compensated by rerouting flux through nonessential genes in a pathway; 2) the existence of a delicate balance between Z-ring formation and localization, and septal and peripheral peptidoglycan (PG) synthesis to successfully accomplish cell division; 3) the control of c-di-AMP over intracellular K+ and turgor, and thereby modulation of the cell wall synthesis machinery; 4) the dynamic nature of cell wall protein CozEb and its effect on PG synthesis, cell shape morphology and envelope integrity; 5) functional dependency between chromosome decatenation and segregation, and the critical link with cell division, and cell wall synthesis. Overall, we show that CRISPRi-TnSeq uncovers genetic interactions between closely functionally linked genes and pathways, as well as disparate genes and pathways, highlighting pathway dependencies and valuable leads for gene function. Importantly, since both CRISPRi and Tn-Seq are widely used tools, CRISPRi-TnSeq should be relatively easy to implement to construct genetic interaction networks across many different microbial strains and species.

genetics↗

bioRxiv2023

Bulk RNA-Sequencing of small airway cell cultures from IPF and post-COVID lung fibrosis patients illustrates disease signatures and differential responses to TGF-β1 treatment

IPF is a condition in which an injury to the lung leads to the accumulation of scar tissue. This fibrotic tissue reduces lung compliance and impairs gas exchange. Studies have shown that infection with COVID-19 significantly worsens the clinical outcomes of IPF patients. The exact etiology of IPF is unknown, but recent evidence suggests that the distal small airways, (those having a diameter less than 2 mm in adults), play a role in the early pathogenesis of IPF. TGF-{beta}1 is a main driver of fibrosis in a variety of tissues; the binding of TGF-{beta}1 to its receptor triggers a signaling cascade that results in inflammatory signaling, accumulation of collagen and other components of the extracellular matrix, and immune system activation. This study aimed to investigate possible mechanisms that contribute to worsening lung fibrosis in IPF patients after being diagnosed with COVID-19, with a particular focus on the role of TGF-{beta}1. Small airway cell cultures derived from IPF and post-COVID-19 IPF patient transplant tissues were submitted for RNA-sequencing and differential gene expression analysis. The genetic signatures for each disease state were determined by comparing the differentially expressed genes present in the cells cultured under control conditions to cells cultured with TGF-{beta}1. The genes shared between the culture conditions laid the framework for determining the genetic signatures of each disease. Our data found that genes associated with pulmonary fibrosis appeared to be more highly expressed in the post-COVID fibrosis samples, under both control and TGF-{beta}1-treated conditions. A similar trend was noted for genes involved in the TGF-{beta}1 signaling pathway; the post-COVID fibrosis cell cultures seemed to be more responsive to treatment with TGF-{beta}1. Gene expression analysis, RT-PCR, and immunohistochemistry confirmed increased levels of BMP signaling in the IPF small airway cell cultures. These findings suggest that TGF-{beta}1 signaling in IPF small airway cells could be inhibited by BMP signaling, leading to the differences in genetic signatures between IPF and post-COVID fibrosis.

cell biology↗

bioRxiv2022

A genome-wide atlas of antibiotic susceptibility targets and pathways to tolerance.

Detailed knowledge on how bacteria evade antibiotics and eventually develop resistance could open avenues for novel therapeutics and diagnostics. It is thereby key to develop a comprehensive genome-wide understanding of how bacteria process antibiotic stress, and how modulation of the involved processes affects their ability to overcome said stress. Here we undertake a comprehensive genetic analysis of how the major human pathogen Streptococcus pneumoniae responds to 20 antibiotics. We built a genome-wide atlas of drug susceptibility determinants and generate a genetic interaction network that connects cellular processes and genes of unknown function, which we show can be used as therapeutic targets. Pathway analysis reveals a genome-wide "tolerome", defined by cellular processes that can make a bacterium less susceptible, and often tolerant, in an antibiotic specific manner. Importantly, modulation of these processes confers fitness benefits during active infections under antibiotic selection. Moreover, screening of sequenced clinical isolates demonstrates that mutations in tolerome genes readily evolve and are frequently associated with resistant strains, indicating such mutations may be an important harbinger for the emergence of antibiotic resistance.

microbiology↗