Biology subjects

Jain, S.

Publications and source records attributed to Jain, S..

At least 73 records · Page 4Linked to original sources

bioRxiv2025

Non-invasive brain stimulation protects cognitive impairment in i.c.v.STZ injected rats: role of adult neurogenesis

In Alzheimers like dementia, neurodegeneration and synaptic dysfunction is known to the critical player in explaining the cognitive impairment. Adult neurogenesis, normally a chronic and quite process is explained to have potential in the field of Alzheimers therapy. Previous research on the non-invasive brain stimulation showed that controlling pattern of stimulation externally we can regulate/ entrain neuronal activity, possibly altering the structural changes in the circuit level. However, literature investigating if non-invasive brain stimulation could hold any potential to initiate process of adult neurogenesis are scarce. In the present study, with the use of behavioural, microscopic and biochemical tools we found that extremely low frequency magnetic field at an intensity of 17.96{micro}T with sinusoidal wave of 50Hz for 2hr daily for a period of two week in streptozotocin induced animal model of sporadic Alzheimers disease can cause improvement in spatial and reference memory, influencing their swimming strategy in water maze. Which is caused by stimulation in immature neural pluripotent stem cells, with additional redox balance and mitigation of glial aggravation in brain areas like olfactory bulb, prefrontal cortex and hippocampus. These changes were accompanied by neuroprotection as observed in the granular layer of dentate gyrus. Taken together, present study explains plausible mechanism of action on protection of neurodegeneration in Alzheimers disease through non-invasive brain stimulation. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=91 SRC="FIGDIR/small/666022v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@bc7830org.highwire.dtl.DTLVardef@176cc01org.highwire.dtl.DTLVardef@4b869dorg.highwire.dtl.DTLVardef@72e703_HPS_FORMAT_FIGEXP M_FIG Graphical illustration representing external magnetic field causing cognitive improvement eliciting adult neurogenesis in animal model of Alzheimers disease C_FIG

neuroscience↗

bioRxiv2025

SCOPE: Revealing Hidden Mechanisms in Phenotypic Screens Through Target and Pathway Enrichment

Phenotypic screening enables discovery of small molecules without requiring predefined targets, but mechanistic interpretation remains challenging due to polypharmacology and pathway complexity. We developed SCOPE (Screening Compound Ontology for Pathway Enrichment), a KNIME-based computational framework that resolves the molecular drivers of phenotypic activity by linking compound-level screening data to annotated targets and pathways. SCOPE integrates multi-source target annotations and performs statistical enrichment to identify shared mechanisms of action. Applied to a high-throughput screen for modulators of ER-stress induced secretion of endoplasmic reticulum (ER) resident proteins, a process known as exodosis, SCOPE identified calcium signaling as the most enriched KEGG pathway without prior biological context. Target enrichment revealed G protein-coupled receptors (GPCRs) involved in inositol 1,4,5-trisphosphate receptors (IP3Rs)-mediated signaling, with widespread antagonism among hit compounds implicating this pathway in the regulation of exodosis. Notably, SCOPE uncovered a novel role for the histamine receptor HRH1, which was validated by RNAi knockdown and pharmacological inhibition, implicating HRH1 as a potential therapeutic target in ER stress-related disorders. These results highlight SCOPEs potential to deconvolute phenotypic screens and uncover actionable mechanisms in complex cellular systems.

bioinformatics↗

bioRxiv2025

Seasonal frost improves probiotic and nutrient availability in fermented vegetables

Climate change is shifting seasonal patterns in temperate regions, increasing the likelihood of early frost and raising questions about its impact on food production and quality. In this study, we tested how a single seasonal frost event influences microbial communities, fermentation dynamics, and the nutritional profile of naturally fermented cabbage and carrots, two cold-tolerant crops commonly grown in the Northeast US Using microbial sequencing, metagenomic analysis, and targeted vitamin assays, we found that frost exposure had no negative impact on microbial diversity or fermentation outcomes. Instead, it was associated with subtle shifts in microbial composition and increased abundance of genes involved in vitamin biosynthesis, including those linked to vitamin K{square}, B{square}{square}, and threonine. These genetic changes corresponded to higher concentrations of vitamins A and E in fermented carrots and vitamin K{square} in fermented cabbage. Our findings suggest that frost can enhance the nutritional and sensory qualities of fermented vegetables, offering a model for a new climate-resilient strategy for producing value-added, regionally distinctive food products.

microbiology↗

bioRxiv2025

Serum exosome miR-150-5p in rheumatic heart disease-associated angiogenesis

The focus of this study is on Rheumatic heart disease (RHD), an autoimmune consequence of rheumatic fever leading to mitral valve damage. Aim is to explore the role of exosomes derived from RHD patients in promoting angiogenesis during RHD pathogenesis. Exosomes were isolated via column chromatography from the pericardial fluid (PF) and serum of RHD patients, pericardial fluid from non-RHD patients undergoing coronary artery bypass grafting (CABG) surgery, and serum of healthy individuals. These exosomes were characterized by transmission electron microscopy (TEM) and western blotting. Human umbilical vein endothelial cells (HUVECs) cultured from umbilical cord(s), were treated with purified exosomes from RHD, CABG patients or healthy individuals. Exosome treatment induced morphological changes in HUVECs, including elongation and tube-like structure formation. The qRT-PCR gene expression analysis revealed significant upregulation of angiogenic markers (VEGF-A 2.7-fold, MMP2 1.5-fold) and inflammatory cytokine (IL-4 15.17-fold), in HUVECs stimulated with RHD serum exosomes. Conversely, there was downregulation of anti- angiogenic genes (RASA-1 0.65-fold and endostatin 0.4-fold). Notably, elevated levels of miR- 150-5p (34.29 + 9.524-fold mean difference; p= 0.0007) were detected in serum-derived exosomes from RHD patients compared to healthy controls. Stimulation of HUVECs with RHD serum exosomes resulted in reduction in the expression of TP53 (0.227-fold; p= 0.006), a known target of miR-150-5p. Furthermore, transfection of HUVECs with miR-150-5p mimics induced the formation of angiogenic endothelial tubes, underscoring the role of miR-150-5p in promoting angiogenesis. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=128 SRC="FIGDIR/small/659484v1_ufig1.gif" ALT="Figure 1000"> View larger version (27K): org.highwire.dtl.DTLVardef@1520e92org.highwire.dtl.DTLVardef@3aa03aorg.highwire.dtl.DTLVardef@ed6e1dorg.highwire.dtl.DTLVardef@1d85231_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2025

In vitro plasticity between ureteric epithelial and distal nephron identity and maturity is controlled by extracellular signals.

Several studies have described human pluripotent stem cell (hPSC)-derived ureteric epithelium, which in the embryo arises from the nephric duct and forms the collecting ducts of the kidney. However, hPSC-derived distal nephron epithelium can also adopt a ureteric phenotype, despite this not occurring during embryogenesis. In this study, RETtdTomato and GATA3mCherry reporter lines were used to further investigate this plasticity. Induction of anterior intermediate mesoderm resulted in the spontaneous formation of an epithelial plexus with a nephric duct-like identity. Subsequent addition of RSPO1 induced patterning of distalized nephrons, including distal convoluted tubule and thick ascending limb of loop of Henle but lacking proximal segments or glomeruli. This epithelium showed a capacity to adopt ureteric epithelial or nephric duct-like states in ex vivo co-culture in response to external cues. The same epithelium seeded as single cells in Matrigel formed epithelial spheroids and adopted a RET+ ureteric tip identity. This in vitro continuum between nephric duct, ureteric epithelium and distal nephron illustrates the role of the microenvironment in cellular identity. Highlights and eTOCO_ST_ABSHighlightsC_ST_ABSO_LIDistal renal epithelium shows substantial plasticity in vitro C_LIO_LIIdentity can be shifted towards distal nephron, including distal convoluted tubule and loop of Henle, in response to prolonged RSPO1 C_LIO_LIDistal epithelial identity can shift in ex vivo culture in response to surrounding signals C_LIO_LICollecting duct is supported by Matrigel culture C_LI eTOC blurbKairath and colleagues investigate the plasticity of induced renal epithelium in response to external signalling cues in vitro and ex vivo. Initial patterning generates a plastic epithelium able to pattern to specific distal nephron. The same epithelium, cultured in Matrigel patterns to ureteric epithelium.

developmental biology↗

bioRxiv2025

IL4Pred2: Prediction of Interleukin-4 Inducing Peptides in Human and Mouse

In 2013, our group developed IL4pred, a host-independent method for predicting interleukin-4 (IL-4) inducing peptides, which has been widely used by the scientific community. In this study, we present a second-generation method, IL4Pred2, which is a host-specific approach designed to predict IL-4 inducing peptides separately for human and mouse hosts. All models were trained, tested, and benchmarked on experimentally validated data obtained from the IEDB. We employed a wide range of state-of-the-art techniques for prediction, including similarity-based approaches, machine learning, deep learning methods, and large language models. Our best model achieved highest AUC 0.80 with MCC 0.45 for human and AUC 0.82 with MCC 0.50 for mouse on independent set of main datasets. All models were trained, test and optimized on training dataset. We validate our final model on an independent dataset which is not used in training or hyperparameter optimization of models. In this study, we developed models on three types of datasets called Main, Alternate1 and Alternate2 for predicting IL-4 inducing peptides. This abstract show performance of our models on Main dataset, performance of models on other datasets have been discussed in manuscript. One of the major objectives of this study is to facilitate research community in the area of immunotherapy and vaccine development. Thus, we developed, a web server and standalone software IL4pred2 for predicting, designing and scanning IL-4 inducing peptides in proteins (https://webs.iiitd.edu.in/raghava/il4pred2/ and https://github.com/raghavagps/il4pred2).

bioinformatics↗

bioRxiv2025

Scaling unlocks broader generation and deeper functional understanding of proteins

Generative protein language models (PLMs) are powerful tools for designing proteins purpose-built to solve problems in medicine, agriculture, and industrial processes. Recent work has trained ever larger language models, but there has been little systematic study of the optimal training distributions and the influence of model scale on the sequences generated by PLMs. We introduce the ProGen3 family of sparse generative PLMs, and we develop compute-optimal scaling laws to scale up to a 46B-parameter model pre-trained on 1.5T amino acid tokens. Pro-Gen3s pre-training data is sampled from an optimized data distribution over the Profluent Protein Atlas v1, a carefully curated dataset of 3.4B full-length proteins. We evaluate for the first time in the wet lab the influence of model scale on the sequences generated by PLMs, and we find that larger models generate viable proteins for a much wider diversity of protein families. Finally, we find both computationally and experimentally that larger models are more responsive to alignment with laboratory data, resulting in improved protein fitness prediction and sequence generation capabilities. These results indicate that larger PLMs like ProGen3-46B trained on larger, well-curated datasets are powerful foundation models that push the frontier of protein design.1

synthetic biology↗

bioRxiv2025

In Silico Tool for Predicting and Scanning Rheumatoid Arthritis-Inducing Peptides in an Antigen

Rheumatoid arthritis (RA) is an autoimmune disorder in which the immune system mounts an abnormal response to self-antigens, leading to chronic inflammation and joint damage. Therefore, identifying antigenic regions in a protein that trigger RA is crucial for developing protein-based therapeutics. In this study, we developed models for predicting RA-inducing peptides using a dataset comprising of 291 experimentally confirmed RA-inducing peptides and 165 RA non-inducing peptides. Our initial analysis revealed that certain residues, such as glycine, proline, and tyrosine, are significantly enriched in RA-inducing peptides. While alignment-based techniques like BLAST and MERCI offered high precision, they suffered from limited coverage. We developed machine/deep learning based prediction and obtained highest performance (AUC = 0.75) using XGboost on an independent dataset. We also developed prediction methods using large language models and achieved highest performance (AUC 0.72) using ProtBERT. Our ensemble model achieved highest performance (AUC = 0.80 & MCC = 0.45) on an independent dataset that combine XGBoost and MERCI-derived motifs. All models were rigorously evaluated on an independent dataset not used during training or testing of models. This study will be valuable for assessing the risk of proteins used in probiotics, genetically modified foods, and protein-based therapeutics. Our most effective approach has been implemented in RAIpred, a web server and standalone software tool for predicting and scanning RA-inducing peptides. (https://webs.iiitd.edu.in/raghava/raipred/). HighlightsO_LIRheumatoid arthritis (RA), an incurable chronic joint disorder with diverse systemic complications. C_LIO_LIAn attempt to identify antigenic regions in a protein which trigger this severe disease. C_LIO_LIUtilizing sequence composition based features for developing models. C_LIO_LIImplementation of ML, DL and LLM based models for prediction of RA-inducing peptides. C_LIO_LIDevelopment of webserver, standalone, pypi and GitHub package for users. C_LI

bioinformatics↗

bioRxiv2025

NTxPred2: A large language model for predicting neurotoxic peptides and neurotoxins

Most of the existing methods including our NTxPred, use a single model to predict both neurotoxic peptides and neurotoxic proteins (neurotoxins). In this study, we developed distinct models for predicting neurotoxic peptides and neurotoxins. Our peptide dataset consists of 877 neurotoxic and 877 non-neurotoxic peptides, while our protein dataset includes 775 neurotoxins and 775 non-neurotoxins. Preliminary analysis reveals that certain residues, such as cysteine, are more prevalent in both neurotoxic peptides and proteins, though their abundance differs in magnitude. First, we developed machine learning models using composition and binary profiles, achieving a maximum AUC of 0.97 for peptides and 0.85 for proteins. The performance for proteins improved from an AUC of 0.85 to 0.89 when evolutionary information was incorporated. Next, we built machine learning models using embeddings from protein language models, attaining an AUC of 0.96 for peptides and 0.94 for proteins. We also developed protein language models and achieved an AUC of 0.98 for peptides using esm2-t30 and 0.91 for proteins using esm2-t6. All models were trained and tested using 5-fold cross-validation, and final models were evaluated on an independent dataset not used in training. We further assessed protein models on the peptide dataset and vice versa, highlighting the necessity of separate models. The proposed models outperform existing methods on independent datasets. Our neurotoxicity prediction models will aid in the safety assessment of genetically modified foods and therapeutic proteins by minimizing the need for animal testing. To support the scientific community, we developed a standalone software and web server NTxPred2, for predicting and scanning neurotoxins (https://webs.iiitd.edu.in/raghava/ntxpred2/, https://github.com/raghavagps/ntxpred2/). HighlightsO_LIDevelopment of models for predicting neurotoxic peptides and neurotoxins C_LIO_LIML Models Using LLM Embeddings for Neurotoxin Prediction C_LIO_LIProtein language models esm2-t30 for predicting neurotoxic peptides C_LIO_LIImportant for risk assessment of genetically modified foods/proteins C_LIO_LINTxPred2 is an updated version of NTxPred for predicting neurotoxicity C_LI

bioinformatics↗

bioRxiv2025

Discovering Symbolic Cognitive Models from Human and Animal Behavior

Symbolic models play a key role in cognitive science, expressing computationally precise hypotheses about how the brain implements a cognitive process. Identifying an appropriate model typically requires a great deal of effort and ingenuity on the part of a human scientist. Here, we adapt FunSearch Romera-Paredes et al. (2024), a recently developed tool that uses Large Language Models (LLMs) in an evolutionary algorithm, to automatically discover symbolic cognitive models that accurately capture human and animal behavior. We consider datasets from three species performing a classic reward-learning task that has been the focus of substantial modeling effort, and find that the discovered programs outperform state-of-the-art cognitive models for each. The discovered programs can readily be interpreted as hypotheses about human and animal cognition, instantiating interpretable symbolic learning and decision-making algorithms. Broadly, these results demonstrate the viability of using LLM-powered program synthesis to propose novel scientific hypotheses regarding mechanisms of human and animal cognition.

animal behavior and cognition↗

bioRxiv2025

VIVIDHA: Variant Prediction and Visualization Interface for Dynamic High-throughput Analysis

Large scale genome sequencing projects have produced huge datasets that pose challenges of high processing times especially for variant calling, a significant downstream analysis step. Efficient utilization of computational resources for accurate variant prediction in a timely manner is possible using Hadoop MapReduce framework. We have developed VIVIDHA (Variant Prediction and Visualization Interface for Dynamic High-throughput Analysis), a high throughput methodology for prediction of variants based on splitting the alignment file using overlapping regions using Hadoop MapReduce framework. The size of overlap region is user-defined. Three variant callers viz. GATK, VarScan2 and BCFTools have been included to predict variants using a consensus approach. Speed-up observed provides the rationale of better performance as number of compute cores and file size are increased. VIVIDHA is available in both GUI as well as command-line modes and can be downloaded from URL: https://github.com/bioinformatics-cdac/VividhaInstaller

bioinformatics↗

bioRxiv2025

Druggable genome CRISPRi screen in 3D hydrogels reveals regulators of cortactin-driven actin remodeling in invading glioblastoma cells

To identify new therapeutic targets that limit glioblastoma (GBM) invasion, we applied druggable-genome CRISPR screens to patient-derived GBM cells in micro-dissectible biomimetic 3D hydrogel platforms that permit separation and independent analysis of core vs. invasive fractions. We identified 12 targets whose suppression limited invasion, of which ACP1 (LMW-PTP) and Aurora Kinase B (AURKB) were validated in neurosphere assays. Proximity labeling analysis identified cortactin as an ACP1- AURKB link, as cortactin undergoes serine phosphorylation by AURKB and tyrosine dephosphorylation by ACP1. Suppression of ACP1 or AURKB in culture and in vivo shifted the balance of cortactin phosphorylation in GBM and reduced actin polymerization and actin-cortactin co-localization. Additional biophysical analysis implicated AURKB in GBM cell adhesion and cortical stiffness, and ACP1 in resistance to mechanical stress and shape plasticity needed for 3D migration. These findings reveal a novel targetable axis that balances kinase and phosphatase activities to regulate actin polymerization during GBM invasion.

cancer biology↗

bioRxiv2025

Breaking the Phalanx: Overcoming Bacterial Drug Resistance with Quorum Sensing Inhibitors that Enhance Therapeutic Activity of Antibiotics

Antibiotic-resistant bacterial infections loom over humanity as an increasingly deadly threat. There exists a dire need for new treatments, especially those that synergize with our existing arsenal of antibiotic drugs, to help overcome the gap in antibiotic efficacy and attenuate the development of new antibiotic resistance in the most dangerous pathogens. Quorum-sensing systems in bacteria drive the formation of biofilms, increase surface motility, and enhance other virulence factors, making these systems attractive targets for the discovery of novel antibacterials. Quorum-sensing inhibitors (QSIs) are hypothesized to synergize with existing antibiotics, making bacteria more sensitive to the effects of these drugs. In this study, we aimed to find the synergistic combinations between the QSIs and known antibiotics to combat the two deadliest hospital infections - Pseudomonas aeruginosa and Acinetobacter baumannii. We mined biochemical activity databases and literature to identify known, high-efficacy QSIs against these bacteria. We used these data to develop and validate a Quantitative Structure-Activity Relationship (QSAR) model for predicting QSI activity and then employed this model to identify new potential QSIs from the Inxight database of approved and investigational drugs. We then tested binary mixtures of the identified QSIs with 11 existing antibiotics using a combinatorial matrix screening approach with ten (five of each) clinical isolates of P. aeruginosa and A. baumannii. Amongst explored drug combinations, 22 exhibited a synergistic effect. Although no mixture inhibiting all the strains was found, piperacillin combined with ketoprofen, indomethacin, and piroxicam demonstrated the broadest antimicrobial action. We anticipate that further preclinical investigation of these combinations of novel repurposed QSIs with a known antibiotic may lead to novel clinical candidates. Table of Content Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=121 SRC="FIGDIR/small/633658v3_ufig1.gif" ALT="Figure 1"> View larger version (52K): org.highwire.dtl.DTLVardef@17ee291org.highwire.dtl.DTLVardef@14cc242org.highwire.dtl.DTLVardef@65a9e2org.highwire.dtl.DTLVardef@743ac9_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

Foundational Architecture Enabling Federated Learning for Training Space Biomedical Machine Learning Models between the International Space Station and Earth

The public and commercial space industries are planning longer duration and more distant space missions, including the establishment of a habitable lunar base and crewed missions to Mars. To support Earth-independent scientific and medical operations, such missions can leverage artificial intelligence and machine learning models to assist with crew healthcare, spacecraft maintenance, and other critical tasks. However, transferring large volumes of data between Earth and space for model development consumes valuable bandwidth, is vulnerable to communication disruptions, and may compromise crew safety and data privacy. Federated learning enables model training while keeping data in situ and only transferring model parameters. In this work, we present a flexible, resilient federated learning framework that provides the secure transmission of model updates between Earth and the International Space Station. On March 15, 2024, this framework pioneered the deployment of federated learning in a spaceflight setting, training classifier models between Earth and the ISS using both real biomedical research data and synthetically generated data.

bioinformatics↗

bioRxiv2025

Perceived risk of forward versus backward balance disturbances while walking in young and older adults

Falls, which often result from trips or slips, pose a major health concern, particularly among older adults. Experiencing falls or near-falls from balance disturbances can shape subsequent gait-related decisions, as individuals may avoid situations they perceive as risky. Here, we explore whether perceptions of risk are sensitive to the direction of previously experienced balance disturbances - forward or backward - and whether these perceptions change with age. Twenty young and twenty older adults walked on a split-belt treadmill while performing a two-alternative forced-choice task, where they indicated their preference between a forward-falling trip and a backward-falling slip. We varied the perturbation magnitudes using an adaptive staircase algorithm to obtain multiple trip-slip equivalence points. Using a mixed- effects linear model, we estimated the slope of the trip-slip relationship, which quantified the direction and strength of the sensitivity of perceived risk to perturbation type. To assess reliability, we repeated the procedure on a second day. Additionally, we investigated two potential reasons underlying any observed sensitivity - 1) emotional responses measured by state anxiety, and 2) physical responses measured by peak center of mass velocity. We found that both young and older adults perceived slips to be riskier than trips, with no group difference in sensitivity. The relative sensitivity to slips versus trips was moderately reliable across two days of testing, though most participants were less sensitive to perturbation direction on the second day. Neither state anxiety responses nor peak center of mass (CoM) velocity explained the directional sensitivity, though CoM velocity was higher during slips than trips for both age groups. These results suggest that the characteristics of experienced balance disturbances may influence behavioral decisions. Objectively measuring individual differences in the perceived risk of balance disturbances and the ability to recover from these disturbances, can potentially improve fall risk assessment and inform personalized training interventions.

neuroscience↗

bioRxiv2025

NfκBin: A Machine Learning based Method for Screening Nf-κB Inhibitors.

Nuclear Factor kappa B (NF-{kappa}B) is a transcription factor whose upregulation is associated in chronic inflammatory diseases, including rheumatoid arthritis, inflammatory bowel disease, and asthma. In order to develop therapeutic strategies targeting NF-{kappa}B-related diseases, we developed a computational approach to predict drugs capable of inhibiting NF-{kappa}B signaling pathways. In this study, we utilized a dataset comprising 1,149 inhibitors and 1,332 non-inhibitors retrieved from PubChem. Chemical descriptors were computed using the PaDEL software, and relevant features were selected using advanced feature selection techniques. Initially, machine learning models were constructed using 2D descriptors, 3D descriptors, and molecular fingerprints, achieving maximum AUC values of 0.66, 0.56, and 0.66, respectively. To improve feature selection, we applied univariate analysis and SVC-L1 regularization to identify features that can effectively differentiate inhibitors from non-inhibitors. Using these selected features, we developed machine learning models, our support vector classifier achieved an AUC of 0.75 on the validation dataset. All models were trained using five-fold cross-validation and rigorously evaluated on an independent validation dataset, which was not used during training or hyperparameter optimization. Finally, our best-performing model was employed to screen FDA-approved drugs for potential NF-{kappa}B inhibitors. Notably, most of the predicted inhibitors corresponded to drugs previously identified as inhibitors in experimental studies, underscoring the models predictive reliability. Our best- performing models have been integrated into a standalone software and web server, Nf{kappa}Bin, to enable the scientific community to perform high-throughput screening of Nf-{kappa}B inhibitors from chemical libraries. (https://webs.iiitd.edu.in/raghava/nfkbin/). Highlights1) Inhibitors of Nf-{kappa}B can be used to manage disease like rheumatoid arthritis, asthma. 2) A novel tool Nf{kappa}Bin developed for screening Nf-{kappa}B inhibitors from chemical library. 3) Cutting-edge feature selection techniques used to identify the most relevant chemical descriptors. 4) Models were developed and validated on a robust dataset of inhibitors and non-inhibitors. 5) Integrated best-performing models into the Nf{kappa}Bin software and web server.

bioinformatics↗

bioRxiv2024

Integration of spatial multiplexed protein imaging and transcriptomics in the human kidney tracks the regenerative potential timeline of proximal tubules

The organizational principles of nephronal segments are based on longstanding anatomical and physiological attributes that are closely linked to the homeostatic functions of the kidney. Novel molecular approaches have recently uncovered layers of deeper signatures and states in tubular cells that arise at various timepoints on the spectrum between health and disease. For example, a dedifferentiated state of proximal tubular cells with mesenchymal stemness markers is frequently seen after injury. The persistence of such a state is associated with failed repair. Here, we introduce a novel analytical pipeline applied to highly multiplexed spatial protein imaging to characterize proximal tubular subpopulations and neighborhoods in reference and disease human kidney tissue. The results were validated and extended through integration with spatial and single cell transcriptomics. We demonstrate that, in reference tissue, a large proportion of S1 and S2 proximal tubular epithelial cells express THY1, a mesenchymal stromal and stem cell marker that regulates differentiation. Kidney disease is associated with loss of THY1 and transition towards expression of PROM1, another stem cell marker shown recently to be linked to failed repair. We demonstrate that the trajectory of proximal tubular cells to THY1 expression is clearly distinct from that of PROM1, and that a state with PROM1 expression is associated with niches of inflammation. Our data support a model in which the interplay between THY1 and PROM1 expression in proximal tubules associates with their regenerative potential and marks the timeline of disease progression.

bioinformatics↗

bioRxiv2024

Industrialization of three-dimensional hiPSC-cardiac microtissues for high-throughput cardiac safety and drug discovery screening

Current cardiac cell models for drug screening often face a trade-off between cellular maturity and achieving high throughput. While three-dimensional human induced pluripotent stem cell-based heart models typically exhibit more adult-like features, their application is hindered by the need for large cell numbers or complex equipment. Here, we developed cost-effective methods to scale up production of three-dimensional cardiac microtissues (cMTs) containing three cardiac cell types, and assess calcium transients and action potential metrics for high-throughput screening (HTS). Automating the procedure revealed reproducible drug responsiveness and predictive accuracy in a reference compound screen. Furthermore, an arrhythmic phenotype was reliably triggered in cMTs containing cardiomyocytes with a RYR2 mutation. A screen of FDA-approved drugs identified 17 drugs that rescued the arrhythmic phenotype. Our findings underscore the scalability of cMTs and their utility in disease modelling and HTS. The advanced "technology-readiness-level" of cMTs supports their regulatory uptake and acceptance within the pharmaceutical industry.

cell biology↗