Biology subjects

Shah, F.

Publications and source records attributed to Shah, F..

8 recordsLinked to original sources

bioRxiv2026

A Time-Resolved Single-Cell Atlas Reveals Infection-Status, Age-, and Sex-Dependent Immune Responses Drive Viral Disease Severity

The majority of mortality during viral infections occurs in older males; however, underlying mechanisms by which age and sex shape antiviral immunity and pathological inflammatory responses remain incompletely understood. Here, we performed time-resolved single-cell RNA sequencing across 16 conditions spanning age, sex, and four stages of influenza infection in mice, generating a high-resolution atlas. Aged mice demonstrate delayed antiviral and inflammatory responses in multiple myeloid cells, impairing viral clearance, which delays recovery. Similarly, endothelial cells from aging mice show prolonged inflammatory and antiviral gene signatures. Altered gene signatures in immune and endothelial cells result in a shift in endothelial-immune interactions in the aged lung. Further, the infection status of the cell is a major driver of transcriptional state, with infected myeloid cells exhibiting broad upregulation of genes, including interferon-stimulated, inflammatory, complement, and oxidative stress-related genes. To assess whether these age-associated transcriptional patterns are conserved in humans, we examined BAL cells obtained from healthy individuals and COVID-19 patients, and found that immune cells from aged COVID-19 patients had elevated antiviral and pro-inflammatory gene expression compared to cells from young patients. Our analyses of sex differences identified that multiple myeloid cell types in aged male mice, but not in young male mice, show persistent inflammatory responses at later stages of infection, a likely mechanism contributing to elevated mortality in older males. These data reveal how infection status of the cell, age, and sex interact to drive persistent inflammation and impaired resolution, providing a foundational resource for designing age- and sex-specific therapeutic strategies.

immunology↗

bioRxiv2025

Reconstructing Ambient Temperature in Forensic Death Time Estimation

Ambient temperature TA has a strong impact on temperature based time since death estimation (TTDE). Frequently TA is lowered instantaneously at a time t0 from a start value TA0 to TA1 < TA0 e.g. by window or door opening. We focus on reconstructing TA0 and t0. TTDE literature suggests temperature measurements in closed compartments as e.g. cupboards or neighboring rooms, where TA0 could have been preserved after t0. We aim to estimate t0 and TA0 from temperature measurements TZ(t) in closed compartments Z at times t > t0. We obtained promising results assuming Newtonian cooling for boxes filled with air, with heaps of clothes or even with books in two different experimental scenarios. Two different parameter estimators, (t0^, TA0^) forsingle quadruple temperature measurement and (t0*, TA0*) for N quadruple measurements were tested. Our results in a climate chamber were partially appropriate for TTDE input. A decline at time t0 from TA0 = 22.5{degrees}C {downarrow} TA1 = 14{degrees}C was reconstructed at t = t0 + 95min with relative deviations {rho}t0^ = 27% and {rho}TA0^ = 19% relative to t - t0 and TA0 - TA1 respectively, for N = 1 quadruple measurement with span {Delta}t = 50min. For N = 200 quadruple measurements in [t = t0 + 95min, t = t0 + 295min] we found {rho}t0^ = 5% and {rho}TA0^ = 11% with the same {Delta}t. Further research is necessary to guarantee applicability in routine casework. We will investigate more elaborate cooling models, estimation algorithms, and evaluation localization.

biophysics↗

bioRxiv2024

scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery

PurposeSingle-cell RNA sequencing (scRNA-seq) is producing vast amounts of individual cell profiling data. Analysis of such datasets presents a significant challenge in accurately annotating cell types and their associated biomarkers. scRNA-seq datasets analysis will help us understand diseases such as Alzheimers, Cancer, Diabetes, Coronavirus disease 2019 (COVID-19), Systemic Lupus Ery-thematosus (SLE), etc. Recently different pipelines based on machine learning (ML) and Deep Neural Network (DNN) methods have been employed to tackle these issues utilizing scRNA-seq datasets. These pipelines have arisen as a promising resource and are capable of extracting meaningful and concise features from noisy, diverse, and high-dimensional data to enhance annotations and subsequent analysis. Existing tools require high computational resources to execute large sample datasets. MethodsWe have developed a cutting-edge platform known as scaLR (Single Cell Analysis using Low Resource) that efficiently processes data in batches, and reduces the required resources for processing large datasets and running NN models. scaLR is equipped with data processing, feature extraction, training, evaluation, and downstream analysis. The data processing module consists of sample-wise & standard scaler normalization and splitting of data. Its novel feature extraction algorithm, first trains the model on a feature subset and stores feature importance for all the features in that subset. At the end of this process, top K features are selected based on their importance. The model is trained on top K features, its performance evaluation and associated downstream analysis provide significant biomarkers for different cell types and diseases/traits. ResultsTo showcase the capabilities of scaLR, we utilized several scRNA-seq datasets of Peripheral Blood Mononuclear Cells (PBMCs), Alzheimers patients, and large datasets from human and mouse embryonic development. Our findings indicate that scaLR offers comparable prediction accuracy and requires less model training time and compute resources than existing Python-based pipelines and frameworks. Moreover, scaLR efficiently handles large sample datasets (>11.4 million cells) with minimal resource usage (29GB RAM, 12GB GPU, and 8 CPUs) while maintaining high prediction accuracy and being capable of ranking the biomarker association with specific cell types and diseases. ConclusionWe present scaLR a Python-based platform, engineered to utilize minimal computational resources while maintaining comparable execution times to existing frameworks. It is highly scalable and capable of efficiently handling datasets containing millions of cell samples and providing their classification and important biomarkers.

bioinformatics↗

bioRxiv2021

Cholinergic control of striatal GABAergic microcircuits

AO_SCPLOWBSTRACTC_SCPLOWCholinergic interneurons (CINs) are essential elements of striatal circuits and behaviors. While acetylcholine signaling via muscarinic receptors (mAChRs) have been well studied, more recent data indicate that postsynaptic nicotinic receptors (nAChRs) located on GABAergic interneurons (GINs) are equally critical. One demonstration is that CINs stimulation induces large disynaptic inhibition of SPNs mediated by nAChR activation of striatal GINs. While these circuits are ideally positioned to modulate striatal output activity, the neurons involved are not definitively identified due largely to an incomplete mapping of CINs-GINs interconnections. Here, we show that CINs optogenetic activation evokes an intricate dual mechanism involving co-activation of pre- and postsynaptic mAChRs and nAChRs on four GINs populations. Using multi-optogenetics, we demonstrate the participation of tyrosine hydroxylase-expressing GINs in the disynaptic inhibition of SPNs likely via heterotypic electrical coupling with neurogliaform interneurons. Altogether, our results highlight the importance of CINs in regulating GINs microcircuits via complex synaptic/heterosynaptic mechanisms.

neuroscience↗

bioRxiv2020

Connexin 43 confers chemoresistance through activating PI3K

Circumventing chemoresistance is crucial for effectively treating glioblastoma due to limited therapeutic options. The gap junction protein connexin 43 (Cx43) renders glioblastoma resistant to the frontline chemotherapy temozolomide; however, targeting Cx43 is difficult because mechanisms underlying Cx43-mediated chemoresistance remain elusive. Here we show that Cx43, but not other connexins, is highly expressed in glioblastoma and strongly correlates with poor patient prognosis and chemoresistance, making Cx43 the prime therapeutic target among all connexins. The intracellular carboxyl terminus of Cx43 binds to phosphatidylinositol 3-kinase (PI3K) catalytic subunit {beta} (PIK3CB, also called PI3K{beta} or p110{beta}), thereby activating PI3K signaling independent of Cx43-channels and subsequently inducing temozolomide resistance. A combination of CT1, a Cx43-targeting peptide inhibitor, and PIK3CB-selective inhibitors restores temozolomide sensitivity in vitro and in vivo. This study not only reveals novel mechanistic insights into chemoresistance in glioblastoma, but also demonstrates that targeting Cx43 and PIK3CB/p110{beta} is an effective approach for overcoming chemoresistance.

cancer biology↗

bioRxiv2020

Galaxy-SynBioCAD: Synthetic Biology Design Automation tools in Galaxy workflows

Many computer-aided design tools are available for synthetic biology and metabolic engineering. Yet, these tools can be difficult to apprehend, sometimes requiring a level of expertise that limits their use by a wider community. Furthermore, some of the tools, although complementary, rely on different input and output formats and cannot communicate with one another. Scientific workflows address these shortcomings while offering a novel design strategy. Among the workflow systems available, Galaxy is a web-based platform for performing findable and accessible data analyses for all scientists regardless of their informatics expertise, along with interoperable and reproducible computations regardless of the particular platform that is being used. Here, we introduce the Galaxy-SynBioCADa portal, the first Galaxy toolshed for synthetic biology and metabolic engineering. It allows one to easily create workflows or use those already developed by the community. The portal is a growing community effort where developers can add new tools and users can evaluate the tools performing design for their specific projects. The tools and workflows currently shared on the Galaxy-SynBioCAD portal cover an end-to-end metabolic pathway design process from the selection of strain and target to the calculation of DNA parts to be assembled to build libraries of strains to be engineered to produce the target. Standard formats are used throughout to enforce the compatibility of the tools. These include SBML for strain and pathway and SBOL for genetic layouts. The portal has been benchmarked on 81 literature pathways, overall, we find we have a 65% (and 88%) success rate in retrieving the literature pathways among the top 10 (50) pathways predicted and generated by the workflows.

bioengineering↗

bioRxiv2019

Neuropilin 2 signaling mediates corticostriatal transmission, spine maintenance, and goal-directed learning in mice

The striatum represents the main input structure of the basal ganglia, receiving massive excitatory input from the cortex and the thalamus. The development and maintenance of cortical input to the striatum is crucial for all striatal function including many forms of sensorimotor integration, learning and action control. The molecular mechanisms regulating the development and maintenance of corticostriatal synaptic transmission are unclear. Here we show that the guidance cue, Semaphorin 3F and its receptor Neuropilin 2 (Nrp2), influence dendritic spine maintenance, corticostriatal short-term plasticity, and learning in adult male and female mice. We found that Nrp2 is enriched in adult layer V pyramidal neurons, corticostriatal terminals, and in developing and adult striatal spiny projection neurons (SPNs). Loss of Nrp2 increases SPN excitability and spine number, reduces short-term facilitation at corticostriatal synapses, and impairs goal-directed learning in an instrumental task. Acute deletion of Nrp2 selectively in adult layer V cortical neurons produces a similar increase in the number of dendritic spines and presynaptic modifications at the corticostriatal synapse in the Nrp2-/- mouse, but does not affect the intrinsic excitability of SPNs. Furthermore conditional loss of Nrp2 impairs sensorimotor learning on the accelerating rotarod without affecting goal-directed instrumental learning. Collectively, our results identify Nrp2 signaling as essential for the development and maintenance of the corticostriatal pathway and may shed novel insights on neurodevelopmental disorders linked to the corticostriatal pathway and semaphorin signaling.\n\nSignificance StatementThe corticostriatal pathway controls sensorimotor, learning and action control behaviors and its dysregulation is linked to neurodevelopmental disorders, such as autism spectrum disorder (ASD). Here we demonstrate that neuropilin 2 (Nrp2), a receptor for the axon-guidance cue semaphorin 3F, has important and previously unappreciated functions in the development and adult maintenance of dendritic spines on striatal spiny projection neurons (SPNs), corticostriatal short-term plasticity, intrinsic physiological properties of SPNs and learning in mice. Our findings, coupled with Nrp2s association with ASD in human populations, suggest that Nrp2 may play an important role in ASD pathophysiology. Overall, our work demonstrates Nrp2 as a key regulator of corticostriatal development, maintenance and function, and may lead to better understanding of neurodevelopmental disease mechanisms.

neuroscience↗

bioRxiv2018

LTMG (Left truncated mixture Gaussian) based modeling of transcriptional regulatory heterogeneities in single cell RNA-seq data - a perspective from the kinetics of mRNA metabolism

A key challenge in modeling single-cell RNA-seq (scRNA-seq) data is to capture the diverse gene expression states regulated by different transcriptional regulatory inputs across single cells, which is further complicated by a large number of observed zero and low expressions. We developed a left truncated mixture Gaussian (LTMG) model that stems from the kinetic relationships between the transcriptional regulatory inputs and metabolism of mRNA and gene expression abundance in a cell. LTMG infers the expression multi-modalities across single cell entities, representing a genes diverse expression states; meanwhile the dropouts and low expressions are treated as left truncated, specifically representing an expression state that is under suppression. We demonstrated that LTMG has significantly better goodness of fitting on an extensive number of single-cell data sets, comparing to three other state of the art models. In addition, our systems kinetic approach of handling the low and zero expressions and correctness of the identified multimodality are validated on several independent experimental data sets. Application on data of complex tissues demonstrated the capability of LTMG in extracting varied expression states specific to cell types or cell functions. Based on LTMG, a differential gene expression test and a co-regulation module identification method, namely LTMG-DGE and LTMG-GCR, are further developed. We experimentally validated that LTMG-DGE is equipped with higher sensitivity and specificity in detecting differentially expressed genes, compared with other five popular methods, and that LTMG-GCR is capable to retrieve the gene co-regulation modules corresponding to perturbed transcriptional regulations. A user-friendly R package with all the analysis power is available at https://github.com/zy26/LTMGSCA.

bioinformatics↗