Biology subjects

Rastogi, R.

Publications and source records attributed to Rastogi, R..

13 recordsLinked to original sources

bioRxiv2026

Systematic mapping of enhancers, silencers, and topological elements with prime editing deletion screens

Noncoding regulatory elements in the human genome can control gene expression through distinct mechanisms, including acting as enhancers, silencers, or topological elements. However, we lack tools to identify all such classes systematically and quantify their effects on gene expression in an endogenous genomic context. Here we develop Swap-seq, a method that combines pooled twin prime editing with flow-sorting to delete hundreds of endogenous regulatory sequences and measure their quantitative effects on the expression of a nearby gene. We apply Swap-seq to dissect the regulatory elements that control PPIF expression in THP-1 monocytes. We quantify the effects of distal and intronic enhancers, characterize CTCF insulators, and discover a silencer that regulates distal genes through histone deacetylase (HDAC)-mediated repression of neighboring enhancers. Hundreds of other genomic elements share its chromatin signatures and transcription factor binding sites, suggesting that this silencer represents a broader class of repressive elements. We leverage Swap-seq data to benchmark sequence-to-function models and show that they often capture the local activity of regulatory elements but fail to link them to distal target genes. Swap-seq thus provides a generalizable tool to discover regulatory elements across mechanistic classes, characterize their effects on gene expression, and generate large-scale datasets for evaluating sequence-to-function models.

genomics↗

bioRxiv2026

A Cytokine Receptor Signaling Atlas Reveals How STAT Mosaics Fine-Tune T Cell Function

The extent to which JAK/STAT cytokine signaling is functionally redundant or selective remains debated. Here we engineered a double orthogonal IL-2/IL-2R{beta}/{gamma}c ternary system enabling programmable, interference-free activation of each of the 36 mammalian cytokine receptors, and their downstream six STATs, in T cells. At the membrane-proximal level, comprehensive phospho-signaling profiling revealed that while each receptor activates a dominant STAT, unique STAT activation fingerprints derived from combinatorial biases fine-tune nuanced T cell fates. At the membrane-distal level, single-cell transcriptomic atlas of all cytokine receptors confirmed that these STAT mosaics sensitively specify non-redundant transcriptional programs. STAT5-dominant receptors drove proliferative expansion at the expense of stemness; STAT3-driven programs instructed a continuum from stem cell memory to terminal effector states with preserved cytotoxic capacity and mediated superior curative antitumor responses; while other STATs specified highly restricted phenotypes. These findings decode a STAT signaling vocabulary that defines the intrinsic functional bandwidth of natural cytokines.

immunology↗

bioRxiv2026

More isn't always better: Too much exoskeleton torque can disrupt balance

Wearable exoskeletons are a promising tool for augmenting balance and reducing fall risk. Recent work suggests that active ankle exoskeletons need to act faster than the human to improve reactive balance control. However, the magnitude of exoskeleton torque that is best for improving reactive balance remains unknown. Drawing from the optimal torque for minimizing metabolic expenditure, we hypothesized that reactive balance would improve with increased exoskeleton torque. Participants wearing bilateral ankle exoskeletons were instructed to maintain standing balance during 15cm backward support-surface perturbations. Three exoskeleton plantarflexion torque conditions were tested: NO (Off), LOW (15Nm), or HIGH (30Nm). LOW torque improved balance performance compared to NO torque (p<0.001), with a 7{+/-}3% decrease in peak center of mass (CoM) displacement. Although HIGH torque caused a 9{+/-}11% decrease in peak CoM displacement compared to NO torque (p=0.12), it was not significant due to high intersubject variability. Whereas LOW torque decreased peak CoM displacement in all (range: -0.2 to -1.6cm), HIGH torque only decreased it in some (range = 1.2 to -2.6cm). The change in CoM displacement from LOW to HIGH torque was associated with balance ability, quantified by the narrowing beam test (R2=0.29, p=0.06), while this relationship didnt meet conventional statistical significance, likely due to the small sample size, it suggests that higher levels of exoskeleton torque may hinder balance performance in individuals with better balance ability. Taken together, more exoskeleton torque is not always better for balance, highlighting a potential need to personalize exoskeleton torque for balance augmentation.

bioengineering↗

bioRxiv2025

Single-Nucleus Analysis of Human White Adipose Tissue Reveals Adipocyte Subsets with Distinct Metabolic Profiles

Anatomic location of white adipose tissue is a determinant of cardiometabolic risk. To understand differences within/between adipose depots, we generated 65,668 single-nucleus transcriptomes from human subcutaneous or intraabdominal adipose tissue (SAT/IAT). Unsupervised analysis revealed 26 adipose-resident cell clusters including two subpopulations of mature adipocytes, characterized by high vs. low expression of adipocyte maturation genes (ADIPOMAThi vs. ADIPOMATlo). ADIPOMATlo adipocytes demonstrate a low-differentiation, pro-inflammatory, and pro-fibrotic transcriptome. IAT-resident ADIPOMATlo were more abundant in higher BMI donors, while SAT-resident ADIPOMATlo associated with impaired glycemia. TSHZ3 was identified as a candidate regulator of ADIPOMATlo transcriptome. TSHZ3 knockdown in adipogenic progenitors inhibited differentiation, with downregulation of early adipogenic regulators (e.g. CEBPA/B, PPARG) and mature adipocyte genes. Heterozygous deletion of Tshz3 in mice reduced SAT and IAT weight. Here, we show that adipocyte subsets with distinct transcriptomic signature reside in human WAT; altered TSHZ3-mediated transcriptional regulation may contribute to low-maturation subpopulation linked to metabolic disease.

cell biology↗

bioRxiv2024

Fine-tuning sequence-to-expression models onpersonal genome and transcriptome data

Genomic sequence-to-expression deep learning models, which are trained to predict gene expression and other molecular phenotypes across the reference genome, have recently been shown to have poor out-of-the-box performance in predicting gene expression variation across individuals based on their personal genome sequences. Here we explore whether additional training (fine-tuning) on paired personal genome and transcriptome data improves the performance of such sequence-to-expression models. Using Enformer as a representative pretrained model, we explore various fine-tuning strategies. Our results show that fine-tuning improves cross-individual prediction performance over the baseline Enformer model for held-out individuals on genes seen during fine-tuning, with comparable performance to variant-based linear models commonly used in transcriptome-wide association studies. However, fine-tuning does not improve model generalizability on held-out genes, which contain sequences and variants unseen during fine-tuning, highlighting a remaining open challenge in the field.

genomics↗

bioRxiv2024

Critical assessment of missense variant effect predictors on disease-relevant variant data

Regular, systematic, and independent assessment of computational tools used to predict the pathogenicity of missense variants is necessary to evaluate their clinical and research utility and suggest directions for future improvement. Here, as part of the sixth edition of the Critical Assessment of Genome Interpretation (CAGI) challenge, we assess missense variant effect predictors (or variant impact predictors) on an evaluation dataset of rare missense variants from disease-relevant databases. Our assessment evaluates predictors submitted to the CAGI6 Annotate-All-Missense challenge, predictors commonly used by the clinical genetics community, and recently developed deep learning methods for variant effect prediction. To explore a variety of settings that are relevant for different clinical and research applications, we assess performance within different subsets of the evaluation data and within high-specificity and high-sensitivity regimes. We find strong performance of many predictors across multiple settings. Meta-predictors tend to outperform their constituent individual predictors; however, several individual predictors have performance similar to that of commonly used meta-predictors. The relative performance of predictors differs in high-specificity and high-sensitivity regimes, suggesting that different methods may be best suited to different use cases. We also characterize two potential sources of bias. Predictors that incorporate allele frequency as a predictive feature tend to have reduced performance when distinguishing pathogenic variants from very rare benign variants, and predictors supervised on pathogenicity labels from curated variant databases often learn label imbalances within genes. Overall, we find notable advances over the oldest and most cited missense variant effect predictors and continued improvements among the most recently developed tools, and the CAGI Annotate-All-Missense challenge (also termed the Missense Marathon) will continue to assess state-of-the-art methods as the field progresses. Together, our results help illuminate the current clinical and research utility of missense variant effect predictors and identify potential areas for future development.

genetics↗

bioRxiv2023

Characterizing uncertainty in predictions of genomic sequence-to-activity models

Genomic sequence-to-activity models are increasingly utilized to understand gene regulatory syntax and probe the functional consequences of regulatory variation. Current models make accurate predictions of relative activity levels across the human reference genome, but their performance is more limited for predicting the effects of genetic variants, such as explaining gene expression variation across individuals. To better understand the causes of these shortcomings, we examine the uncertainty in predictions of genomic sequence-to-activity models using an ensemble of Basenji2 model replicates. We characterize prediction consistency on four types of sequences: reference genome sequences, reference genome sequences perturbed with TF motifs, eQTLs, and personal genome sequences. We observe that models tend to make high-confidence predictions on reference sequences, even when incorrect, and low-confidence predictions on sequences with variants. For eQTLs and personal genome sequences, we find that model replicates make inconsistent predictions in >50% of cases. Our findings suggest strategies to improve performance of these models.

genomics↗

bioRxiv2023

Ectopic transcription due to inappropriately inherited histone methylation may interfere with the ongoing function of terminally differentiated cells

How mutations in histone modifying enzymes lead to neurodevelopmental disorders is unknown. We took advantage of the invariant embryonic lineage and adult nervous system in C. elegans to investigate a double mutant between spr-5/Lsd1/Kdm1a (H3K4me1/2 demethylase) and met-2/Setdb1 (H3K9 methyltransferase). We demonstrate that spr-5; met-2 double mutant worms have a severe chemotaxis defect caused by the ectopic expression of germline genes in somatic tissues. Despite this behavioral defect, we observe few embryonic lineage alterations and an intact adult nervous system. This raises the possibility that the abnormal chemotaxis behavior may be due to ongoing defects in terminally differentiated cells rather than alterations in development. Remarkably, we found that shutting off the ectopic germline expression rescues normal chemotaxis in the same spr-5; met-2 adult worms that had a chemotaxis defect earlier. This suggests that ongoing inappropriate transcription can block normal behavior in an intact nervous system. Based on these data, it is possible that the intellectual disability and altered behavior observed in human neurodevelopmental syndromes caused by mutations in histone modifying enzymes could be due to ongoing ectopic transcription and may be reversible.

developmental biology↗

bioRxiv2023

Personal transcriptome variation is poorly explained by current genomic deep learning models

Genomic deep learning models can predict genome-wide epigenetic features and gene expression levels directly from DNA sequence. While current models perform well at predicting gene expression levels across genes in different cell types from the reference genome, their ability to explain expression variation between individuals due to cis-regulatory genetic variants remains largely unexplored. Here we evaluate four state-of-the-art models on paired personal genome and transcriptome data and find limited performance when explaining variation in expression across individuals.

genomics↗

bioRxiv2022

Cross-protein transfer learning substantially improves zero-shot prediction of disease variant effects

Genetic variation in the human genome is a major determinant of individual disease risk, but the vast majority of missense variants have unknown etiological effects. Here, we present a robust learning framework for leveraging saturation mutagenesis experiments to construct accurate computational predictors of proteome-wide missense variant pathogenicity. We train cross-protein transfer (CPT) models using deep mutational scanning data from only five proteins and achieve state-of-the-art performance on clinical variant interpretation for unseen proteins across the human proteome. High sensitivity is crucial for clinical applications and our model CPT-1 particularly excels in this regime. For instance, at 95% sensitivity of detecting human disease variants annotated in ClinVar, CPT-1 improves specificity to 68%, from 27% for ESM-1v and 55% for EVE. Furthermore, for genes not used to train REVEL, a supervised method widely used by clinicians, we show that CPT-1 compares favorably with REVEL. Our framework combines predictive features derived from general protein sequence models, vertebrate sequence alignments, and AlphaFold2 structures, and it is adaptable to the future inclusion of other sources of information. We find that vertebrate alignments, albeit rather shallow with only 100 genomes, provide a strong signal for variant pathogenicity prediction that is complementary to recent deep learning-based models trained on massive amounts of protein sequence data. We release predictions for all possible missense variants in 90% of human genes. Our results demonstrate the utility of mutational scanning data for learning properties of variants that transfer to unseen proteins.

genetics↗

bioRxiv2021

High Resolution Ancestry Deconvolution for Next Generation Genomic Data

As genome-wide association studies and genetic risk prediction models are extended to globally diverse and admixed cohorts, ancestry deconvolution has become an increasingly important tool. Also known as local ancestry inference (LAI), this technique identifies the ancestry of each region of an individuals genome, thus permitting downstream analyses to account for genetic effects that vary between ancestries. Since existing LAI methods were developed before the rise of massive, whole genome biobanks, they are computationally burdened by these large next generation datasets. Current LAI algorithms also fail to harness the potential of whole genome sequences, falling well short of the accuracy that such high variant densities can enable. Here we introduce Gnomix, a set of algorithms that address each of these points, achieving higher accuracy and swifter computational performance than any existing LAI method, while also enabling portable models that are particularly useful when training data are not shareable due to privacy or other restrictions. We demonstrate Gnomix (and its swift phase correction counterpart Gnofix) on worldwide whole-genome data from both humans and canids and utilize its high resolution accuracy to identify the location of ancient New World haplotypes in the Xoloitzcuintle, dating back over 100 generations. Code is available at https://github.com/AI-sandbox/gnomix.

genomics↗

bioRxiv2020

PRAK-03202: A triple antigen VLP vaccine candidate against SARS CoV-2

The rapid development of safe and effective vaccines against SARS CoV-2 is the need of the hour for the coronavirus outbreak. Here, we have developed PRAK-03202, the worlds first triple antigen VLP vaccine candidate in a highly characterized S. cerevisiae-based D-CryptTM platform, which induced SARS CoV-2 specific neutralizing antibodies in BALB/c mice. Immunizations using three different doses of PRAK-03202 induces antigen specific (Spike, envelope and membrane proteins) humoral response and neutralizing potential. PBMCs from convalescent patients, when exposed to PRAK-03202, showed lymphocyte proliferation and elevated IFN-{gamma} levels suggestive of conservation of epitopes and induction of T helper 1 (Th1)-biased cellular immune responses. These data support the clinical development and testing of PRAK-03202 for use in humans.

immunology↗

bioRxiv2020

Multi-Antigenic Virus-like Particle of SARS CoV-2 produced in Saccharomyces cerevisiae as a vaccine candidate

Spike, Envelope and Membrane proteins from the SARS CoV-2 virus surface coat are important vaccine targets. We hereby report recombinant co-expression of the three proteins (Spike, Envelope and Membrane) in a engineered Saccharomyces cerevisiae platform (D-Crypt) and their self-assembly as Virus-like particle (VLP). This design as a multi-antigenic VLP for SARS CoV-2 has the potential to be a scalable vaccine candidate. The VLP is confirmed by transmission electron microscopy (TEM) images of the SARS CoV-2, along with supportive HPLC, Dynamic Light Scattering (DLS) and allied analytical data. The images clearly outline the presence of a "Corona" like morphology, and uniform size distribution.

biochemistry↗