Biology subjects

Shah, P. K.

Publications and source records attributed to Shah, P. K..

7 recordsLinked to original sources

bioRxiv2026

Variation in the embryonic cell lineages of Caenorhabditis elegans natural populations

Developmental variation has often been appreciated as a major phenotypic substrate of evolutionary innovation in multicellular organisms. Although quantification of developmental variation in isogenic populations has a long history, it has been difficult to accomplish across natural populations until recently. Given its invariant cell lineage, Caenorhabditis elegans represents an ideal model system for studying how natural diversity in development is expressed at the resolution of single cells, cell lineages, and the whole embryo. Using embGAN, a deep-learning pipeline that enables label-free lineage tracing without the bottleneck and potential confounding effects of generating transgenic strains, we reconstructed the early embryonic lineage of 78 embryos spanning eight genetic backgrounds, including six wild strains, and quantified cell-cycle timing with a lineage-aligned graph metric that separates global rescaling of developmental rate from localized changes in lineage structure. We found that cell-cycle timing differs reproducibly among genotypes, with between-strain variation consistently exceeding within-strain variation. The magnitude of this variation was itself genotype-specific and not correlated with levels of genetic divergence. Unlike the global shifts in the tempo of development that dominate divergence between species, these differences are predominantly in lineage patterning, changing the timing of specific divisions, and they align to cell fate. Thus, the invariant lineage of C. elegans is built on a structured, genotype-specific landscape of developmental variation, a potential source of trait variation from which drift in developmental systems can emerge.

developmental biology↗

bioRxiv2025

ANTIGEN-SPECIFIC CD8+ T-CELLS ARE INVOLVED IN HUMAN CAROTID ATHEROSCLEROTIC PLAQUES DESTABILIZATION

Immunization of ApoE-/- mice expressing human HLA-A 02:01 with p210, an apoB100-derived peptide, reduces atherosclerotic plaque development by inducing a p210-specific CD8+ T cell population. Studying Class-I MHC/CD8+ T cell signaling offers a promising approach to understanding the mechanism behind the athero-protective effects of p210 immunization. We aimed to identify a p210-specific CD8+ T cell population in human carotid atherosclerotic plaques from blood-positive HLA-A 02:01 patients undergoing surgical carotid endarterectomy (CEA). The study included 22 consecutive patients who were HLA-A 02:01 (+) out of 49 enrolled (reflecting an estimated prevalence of about 30% HLA-A02:01(+) in the Caucasian population). Immunohistochemistry staining used a PE-marked A 02:01-KTTKQSFDL Pentamer on fixed endarterectomy plaques. Both HLA-A 02:01 (+) and (-) patient plaques were used, with the latter serving as an internal negative control. Presence of pentamer (+) CD8 T cells indicated a p210-specific CD8+ T cell population. Patients positive for HLA-A 02:01 showed an average of 3.40 {+/-} 2.17 x 10^3 HPF p210-specific CD8+ T cells (61.80%, 3% of total CD3+) in the shoulders of atherosclerotic plaques post-CEA, significantly higher than in controls (p < 0.0001). The proportion of p210-specific CD8+ T cells was lower in plaques displaying morphological features of instability. This study, for the first time, identifies a p210-specific CD8+ T cell population in human carotid atherosclerotic plaques from HLA-A02:01(+) patients, suggesting a role for autoimmunity in atherosclerosis development and supporting the potential efficacy of p210 immunization in HLA-A02:01 (+) individuals to reduce atherosclerosis. The variation in this specific T cell population within human plaques correlates with plaque vulnerability, highlighting p210-specific CD8+ T cells as a potential target for future therapies.

immunology↗

bioRxiv2025

Long-term ex ovo culture of Caenorhabditis elegans embryos

While the genetic tractability, transparency and invariant development of the Caenorhabditis elegans embryo have led to its broad adoption as a model system for the study of cell and developmental biology, its impermeable eggshell has complicated the use of small-molecule reagents during embryogenesis. Existing genetic approaches for rendering the embryo permeable to acute small molecule treatment have increased the accessibility of early embryogenesis to pharmacological manipulation but compromise long-term viability, preventing their use in studies of later developmental processes or post-exposure physiology. Here, we describe the use of an optimized enzymatic eggshell digestion protocol coupled with a minimal, serum-free culture medium that supports the survival and normal development of ex ovo embryos through larval maturation and adulthood. We show that this approach renders embryos permeable to a wide range of small molecules, enabling precise temporal manipulation of developmental processes previously inaccessible through conventional genetic methods. We demonstrate the utility of this technique through the pharmacological modulation of cytoskeletal components including microtubules and actin, as well as the minus-end-directed microtubule motor protein dynein, highlighting applications for the study of cell division, morphogenesis, and neuronal development, especially at later stages of embryogenesis. As a proof of concept, we use acutely timed dynein inhibition to show that cytoplasmic dynein is required to transport the centriole into the dendrite of embryonically born sensory neurons. This approach expands the experimental toolkit available for labeling and manipulating developmental processes in C. elegans.

cell biology↗

bioRxiv2025

Mitochondria transported by Kinesin 3 prevent localized calcium spiking to inhibit caspase-dependent specialized cell death

Polarized cells (such as neurons), defined by distinct compartments are, like many cell types, subject to developmental elimination, as in neurite pruning. The molecular mechanism behind specialized elimination remains a largely open question. We previously introduced the "tri-partite" embryonic cell death program Compartmentalized Cell Elimination (CCE) in the scaffolding tail-spike epithelial cell and sex-specific CEM neurons of C. elegans. CCE is stereotyped and ordered, with three distinct programs eliminating three cell compartments, and bearing morphological hallmarks reminiscent of neurite pruning. Here we report first, that, prior to CCE onset, mitochondria undergo UNC-116/Kinesin 1-dependent irreversible retrograde transport; and second, that the caspase protease CED-3 promotes the completion of the proximal nicking event of CCE by helping inhibit the kinesin 3 homolog UNC-104. While known canonically to carry only synaptic vesicles, UNC-104/Kinesin 3, in the CCE context, and in the absence of CED-3/Caspase, can transport mitochondria anterogradely to the severing site. We observe both caspase activity and Ca2+ spiking at this site prior to nicking. Mitochondria appear to protect against nicking in the absence of CED-3 via their MCU-1 uniporter-dependent Ca2+ uptake capacity. Our study sheds light on the molecular machinery of specialized cell elimination and pruning, highlighting involvement of region-specific Ca2+ signaling, an anti-death function of mitochondria via local Ca2+ uptake, mitochondrial transport as a regulatory strategy, with UNC-116/kinesin 1-mediated retrograde transport important for cell elimination priming and UNC-104/kinesin 3 in a previously undescribed role as a non-canonical anterograde mitochondrial motor.

cell biology↗

bioRxiv2024

Automated Cell Lineage Reconstruction using Label-Free 4D Microscopy

Here we describe embGAN, a deep learning pipeline that addresses the challenge of automated cell detection and tracking in label-free 3D time lapse imaging. embGAN requires no manual data annotation for training, learns robust detections that exhibits a high degree of scale invariance and generalizes well to images acquired in multiple labs on multiple instruments.

developmental biology↗

bioRxiv2023

Novel metrics reveal new structure and unappreciated heterogeneity in C. elegans development

High throughput experimental approaches are increasingly allowing for the quantitative description of cellular and organismal phenotypes. Distilling these large volumes of complex data into meaningful measures that can drive biological insight remains a central challenge. In the quantitative study of development, for instance, one can resolve phenotypic measures for single cells onto their lineage history, enabling joint consideration of heritable signals and cell fate decisions. Most attempts to analyze this type of data, however, discard much of the information content contained within lineage trees. In this work we introduce a generalized metric, which we term the branch distance, that allows us to compare any two embryos based on phenotypic measurements in individual cells. This approach aligns those phenotypic measurements to the underlying lineage tree, providing a flexible and intuitive framework for quantitative comparisons between, for instance, Wild-Type (WT) and mutant developmental programs. We apply this novel metric to data on cell-cycle timing from over 1300 WT and RNAi-treated Caenorhabditis elegans embryos. Our new metric revealed surprising heterogeneity within this data set, including subtle batch effects in WT embryos and dramatic variability in RNAi-induced developmental phenotypes, all of which had been missed in previous analyses. Further investigation of these results suggests a novel, quantitative link between pathways that govern cell fate decisions and pathways that pattern cell cycle timing in the early embryo. Our work demonstrates that the branch distance we propose, and similar metrics like it, have the potential to revolutionize our quantitative understanding of organismal phenotype.

developmental biology↗

bioRxiv2023

Machine Learning Identifies Plasma Proteomic Signatures of Descending Thoracic Aortic Disease

BackgroundDescending thoracic aortic aneurysms and dissections can go undetected until severe and catastrophic, and few clinical indices exist to screen for aneurysms or predict risk of dissection. MethodsThis study generated a plasma proteomic dataset from 75 patients with descending type B dissection (Type B) and 62 patients with descending thoracic aortic aneurysm (DTAA). Standard statistical approaches were compared to supervised machine learning (ML) algorithms to distinguish Type B from DTAA cases. Quantitatively similar proteins were clustered based on linkage distance from hierarchical clustering and ML models were trained with uncorrelated protein lists across various linkage distances with hyperparameter optimization using 5-fold cross validation. Permutation importance (PI) was used for ranking the most important predictor proteins of ML classification between disease states and the proteins among the top 10 PI protein groups were submitted for pathway analysis. ResultsOf the 1,549 peptides and 198 proteins used in this study, no peptides and only one protein, hemopexin (HPX), were significantly different at an adjusted p-value <0.01 between Type B and DTAA cases. The highest performing model on the training set (Support Vector Classifier) and its corresponding linkage distance (0.5) were used for evaluation of the test set, yielding a precision-recall area under the curve of 0.7 to classify between Type B from DTAA cases. The five proteins with the highest PI scores were immunoglobulin heavy variable 6-1 (IGHV6-1), lecithin-cholesterol acyltransferase (LCAT), coagulation factor 12 (F12), HPX, and immunoglobulin heavy variable 4-4 (IGHV4-4). All proteins from the top 10 most important correlated groups generated the following significantly enriched pathways in the plasma of Type B versus DTAA patients: complement activation, humoral immune response, and blood coagulation. ConclusionsWe conclude that ML may be useful in differentiating the plasma proteome of highly similar disease states that would otherwise not be distinguishable using statistics, and, in such cases, ML may enable prioritizing important proteins for model prediction.

systems biology↗