Biology subjects

Agarwal, V.

Publications and source records attributed to Agarwal, V..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

Use of Stem Cell-Derived Cardiomyocyte and Nasal Epithelium Models to Establish a Multi-Tissue Model Platform to Validate Repurposed Drugs Against SARS-CoV-2 Infection

The novel coronavirus disease (COVID-19) and any future coronavirus outbreaks will require more affordable, effective and safe treatment options to complement current ones such as Paxlovid. Drug repurposing can be a promising approach if we are able to find a rapid, robust and reliable way to down-select and screen candidates using in silico and in vitro approaches. With repurposed drugs, ex vivo models could offer a rigorous route to human clinical trials with less time invested into nonclinical animal (in vivo) studies. We have previously shown the value of commercially available ex vivo/3D airway and alveolar tissue models, and this paper takes this further by developing and validating human nasal epithelial model and embryonic stem cells derived cardiomyocyte model. Five shortlisted candidates (fluvoxamine, everolimus, pyrimethamine, aprepitant and sirolimus) were successfully compared with three control drugs (remdesivir, molnupiravir, nirmatrelvir) when tested against key variants of the SARS-CoV-2 virus including Delta and Omicron, and we were able to reconfirm our earlier finding that fluvoxamine can induce antiviral efficacy in combination with other drugs. Scalability of this high-throughput screening approach has been demonstrated using a liquid handling robotic platform for future Disease-X outbreaks.

microbiology↗

bioRxiv2023

MPRAbase: A Massively Parallel Reporter Assay Database

Massively parallel reporter assays (MPRAs) represent a set of high-throughput technologies that measure the functional effects of thousands of sequences/variants on gene regulatory activity. There are several different variations of MPRA technology and they are used for numerous applications, including regulatory element discovery, variant effect measurement, saturation mutagenesis, synthetic regulatory element generation or characterization of evolutionary gene regulatory differences. Despite their many designs and uses, there is no comprehensive database that incorporates the results of these experiments. To address this, we developed MPRAbase, a manually curated database that currently harbors 129 experiments, encompassing 17,718,677 elements tested across 35 cell types and 4 organisms. The MPRAbase web interface (http://www.mprabase.com) serves as a centralized user-friendly repository to download existing MPRA data for independent analysis and is designed with the ability to allow researchers to share their published data for rapid dissemination to the community.

genomics↗

bioRxiv2023

Angiopoietin-like protein 2 mediates vasculopathy driven fibrogenesis in a mouse model of systemic sclerosis

Vasculopathy is a common hallmark of various fibrotic disorders including systemic sclerosis (SSc), yet its underlying etiology and contribution to fibrogenesis remain ill-defined. In SSc the vasculopathy typically precedes the onset of fibrosis and we observed that this phenomenon is recapitulated in the Snail transgenic mouse model of SSc. The vascular anomalies manifest as deformed vessels, endothelial cell dysfunction and vascular leakage. Our investigation into the underlying mechanism of this phenotype revealed that Angiopoietin-like protein 2 (Angptl2), secreted by the Snail transgenic keratinocytes, is a principal driver of fibrotic vasculopathy. In endothelial cells, Angptl2 upregulates pro-fibrotic genes, downregulates the tight junction protein Claudin 5, and prompts the acquisition of mesenchymal characteristics. Inhibiting endothelial cell junctional instability and consequently vascular leakage with a synthetic analog of the microbial metabolite Urolithin A (UAS03) effectively mitigated the vasculopathy and inhibited fibrogenesis. Thus, Angptl2 emerges as a promising early biomarker of the disease and inhibiting the vasculopathy inducing effects of this protein with agents such as UAS03 presents an appealing therapeutic avenue to reduce disease severity. These insights hold the potential to revolutionize the approach to the treatment of fibrotic diseases by targeting the vascular defects.

pathology↗

bioRxiv2023

CodonBERT: Large Language Models for mRNA Design and Optimization

AO_SCPLOWBSTRACTC_SCPLOWmRNA based vaccines and therapeutics are gaining popularity and usage across a wide range of conditions. One of the critical issues when designing such mRNAs is sequence optimization. Even small proteins or peptides can be encoded by an enormously large number of mRNAs. The actual mRNA sequence can have a large impact on several properties including expression, stability, immunogenicity, and more. To enable the selection of an optimal sequence, we developed CodonBERT, a large language model (LLM) for mRNAs. Unlike prior models, CodonBERT uses codons as inputs which enables it to learn better representations. CodonBERT was trained using more than 10 million mRNA sequences from a diverse set of organisms. The resulting model captures important biological concepts. CodonBERT can also be extended to perform prediction tasks for various mRNA properties. CodonBERT outperforms previous mRNA prediction methods including on a new flu vaccine dataset.

bioinformatics↗

bioRxiv2023

Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation

Sequence-based machine learning models trained on genome-scale biochemical assays improve our ability to interpret genetic variants by providing functional predictions describing their impact on the cis-regulatory code. Here, we introduce a new model, Borzoi, which learns to predict cell- and tissue-specific RNA-seq coverage from DNA sequence. Using statistics derived from Borzois predicted coverage, we isolate and accurately score variant effects across multiple layers of regulation, including transcription, splicing, and polyadenylation. Evaluated on QTLs, Borzoi is competitive with, and often outperforms, state-of-the-art models trained on individual regulatory functions. By applying attribution methods to the derived statistics, we extract cis-regulatory patterns driving RNA expression and post-transcriptional regulation in normal tissues. The wide availability of RNA-seq data across species, conditions, and assays profiling specific aspects of regulation emphasizes the potential of this approach to decipher the mapping from DNA sequence to regulatory function.

genomics↗

bioRxiv2023

Massively parallel characterization of transcriptional regulatory elements in three diverse human cell types

The human genome contains millions of candidate cis-regulatory elements (CREs) with cell-type-specific activities that shape both health and myriad disease states. However, we lack a functional understanding of the sequence features that control the activity and cell-type-specific features of these CREs. Here, we used lentivirus-based massively parallel reporter assays (lentiMPRAs) to test the regulatory activity of over 680,000 sequences, representing a nearly comprehensive set of all annotated CREs among three cell types (HepG2, K562, and WTC11), finding 41.7% to be functional. By testing sequences in both orientations, we find promoters to have significant strand orientation effects. We also observe that their 200 nucleotide cores function as non-cell-type-specific on switches providing similar expression levels to their associated gene. In contrast, enhancers have weaker orientation effects, but increased tissue-specific characteristics. Utilizing our lentiMPRA data, we develop sequence-based models to predict CRE function with high accuracy and delineate regulatory motifs. Testing an additional lentiMPRA library encompassing 60,000 CREs in all three cell types, we further identified factors that determine cell-type specificity. Collectively, our work provides an exhaustive catalog of functional CREs in three widely used cell lines, and showcases how large-scale functional measurements can be used to dissect regulatory grammar.

genomics↗

bioRxiv2022

Barcoding and its application for visualizing ecological dynamics

Time is perceived to be unidirectional and continuous in the philosophy of science. This continuity can play a crucial role in time series analysis as events are generally seen as an outcome of the past, or subject to events that occurred previously in time. In this study, we describe an ordinal approach to perceiving ecological time series - one that relies on pattern formation with both antecedent and future events. Our approach defines a limited set of structural shapes that can occur for past, present, and future time points. Such a library of all possible shapes can then be used for novel approaches to data visualization and time series analysis. We applied this method to simple ecological models and then to natural time series data for measles cases in London and the phytoplankter Pseudo-nitzschia spp. in Narragansett Bay, Rhode Island. Alternative perspectives on time series representation can strengthen our ability to identify important patterns in dynamics and effectively discriminate between similar time series. When used in conjunction with conventional line-plots, barcodes can be tailored to demonstrate the presence or absence of specific structural patterns or features. Our results show that data exploration without the assumption of time series continuity can yield important and novel insight into the behavior of ecological systems.

ecology↗

bioRxiv2022

The genetic and biochemical determinants of mRNA degradation rates in mammals

BackgroundDegradation rate is a fundamental aspect of mRNA metabolism, and the factors governing it remain poorly characterized. Understanding the genetic and biochemical determinants of mRNA half-life would enable a more precise identification of variants that perturb gene expression through post-transcriptional gene regulatory mechanisms. ResultsHere, we establish a compendium of 54 human and 27 mouse transcriptome-wide mRNA decay rate datasets. A meta-analysis of these data identified a prevalence of technical noise and measurement bias, induced partially by the underlying experimental strategy. Correcting for these biases allowed us to derive more precise, consensus measurements of half-life which exhibit enhanced consistency between species. We trained substantially improved statistical models based upon genetic and biochemical features to better predict half-life and characterize the factors molding it. Our state-of-the-art model, Saluki, is a hybrid convolutional and recurrent deep neural network which relies only upon an mRNA sequence annotated with coding frame and splice sites to predict half-life (r=0.77). Saluki predicts the impact of RNA sequences and genetic mutations therein on mRNA stability, in agreement with functional measurements derived from massively parallel reporter assays. ConclusionsOur work produces a more robust "ground truth" with regards to transcriptome-wide mRNA half-lives in mammalian cells. Using these consolidated measurements, we trained a model that is over 50% more accurate in predicting half-life from sequence than existing models. Our best model, Saluki, succinctly captures many of the known determinants of mRNA half-life and can be rapidly deployed to predict the functional consequences of arbitrary mutations in the transcriptome.

genomics↗

bioRxiv2021

Multiplex genomic recording of enhancer and signal transduction activity in mammalian cells

Measurements of gene expression and signal transduction activity are conventionally performed with methods that require either the destruction or live imaging of a biological sample within the timeframe of interest. Here we demonstrate an alternative paradigm, termed ENGRAM (ENhancer-driven Genomic Recording of transcriptional Activity in Multiplex), in which the activity and dynamics of multiple transcriptional reporters are stably recorded to DNA. ENGRAM is based on the prime editing-mediated insertion of signal- or enhancer-specific barcodes to a genomically encoded recording unit. We show how this strategy can be used to concurrently genomically record the relative activity of at least hundreds of enhancers with high fidelity, sensitivity and reproducibility. Leveraging synthetic enhancers that are responsive to specific signal transduction pathways, we further demonstrate time- and concentration-dependent genomic recording of Wnt, NF-{kappa}B, and Tet-On activity. Finally, by coupling ENGRAM to sequential genome editing, we show how serially occurring molecular events can potentially be ordered. Looking forward, we envision that multiplex, ENGRAM-based recording of the strength, duration and order of enhancer and signal transduction activities has broad potential for application in functional genomics, developmental biology and neuroscience.

genomics↗

bioRxiv2021

Effective gene expression prediction from sequence by integrating long-range interactions

The next phase of genome biology research requires understanding how DNA sequence encodes phenotypes, from the molecular to organismal levels. How noncoding DNA determines gene expression in different cell types is a major unsolved problem, and critical downstream applications in human genetics depend on improved solutions. Here, we report substantially improved gene expression prediction accuracy from DNA sequence through the use of a new deep learning architecture called Enformer that is able to integrate long-range interactions (up to 100 kb away) in the genome. This improvement yielded more accurate variant effect predictions on gene expression for both natural genetic variants and saturation mutagenesis measured by massively parallel reporter assays. Notably, Enformer outperformed the best team on the critical assessment of genome interpretation (CAGI5) challenge for noncoding variant interpretation with no additional training. Furthermore, Enformer learned to predict promoter-enhancer interactions directly from DNA sequence competitively with methods that take direct experimental data as input. We expect that these advances will enable more effective fine-mapping of growing human disease associations to cell-type-specific gene regulatory mechanisms and provide a framework to interpret cis-regulatory evolution. To foster these downstream applications, we have made the pre-trained Enformer model openly available, and provide pre-computed effect predictions for all common variants in the 1000 Genomes dataset. One-sentence summaryImproved noncoding variant effect prediction and candidate enhancer prioritization from a more accurate sequence to expression model driven by extended long-range interaction modelling.

genomics↗

bioRxiv2021

The landscape of alternative polyadenylation in single cells of the developing mouse embryo

3' untranslated regions (3' UTRs) post-transcriptionally regulate mRNA stability, localization, and translation rate. While 3'-UTR isoforms have been globally quantified in limited cell types using bulk measurements, their differential usage among cell types during mammalian development remains poorly characterized. In this study, we examined a dataset comprising [~]2 million cells spanning E9.5-E13.5 of mouse embryonic development to quantify transcriptome-wide changes in alternative polyadenylation (APA). We observe a global lengthening of 3' UTRs across embryonic stages in all cell types, although we detect shorter 3' UTRs in hematopoietic lineages and longer 3' UTRs in neuronal cell types within each stage. An analysis of RBP dynamics identifies ELAV-like family members, which are concomitantly induced in neuronal lineages and developmental stages experiencing 3'-UTR lengthening, as putative regulators of APA. By measuring 3'-UTR isoforms in an expansive single cell dataset, our work provides a transcriptome-wide and organism-wide map of the dynamic landscape of alternative polyadenylation during mammalian organogenesis.

genomics↗

bioRxiv2020

In silico validation and fabrication of matrix diffusion based polymeric transdermal films for repurposing gabapentin hydrochloride in oncogenic neuropathic pain.

BackgroundGabapentin (GBP) is an FDA approved drug for the treatment of partial and secondary generalized seizures, apart from also being used for diabetic neuropathy. GBP displays highly intricate mechanism of action and its inhibitory response in elevated antagonism of NMDA (N-methyl-D-aspartate receptor) receptor and has potential in controlling neuropathic pain of cancer origin. ObjectiveTherefore, in the present study, we have selected BCATc (Pyridoxal 5-phosphate dependent branched-chain aminotransferase cytosolic) enzyme that is highly expressed in neuropathic stress conditions and have analysed the GBP as its competitive inhibitor by modeling, docking and checking its pharmacokinetic suitability through ADMET. Though in this study the results exhibited higher efficacy of GBP in controlling neuropathic pain, the drug shows certain potential therapeutic limitations like shorter half-life, repetitive dosing, high inter subjective variability. MethodsTherefore, a suitable and equally efficacious drug delivery method was also designed and developed by loading GBP transdermal patches (GBP-TDP) by solvent evaporation method using PVP and HPMC in ratio of 2:1 as a polymer base for reservoir type of TDP. Also, PEG 400 was used as a plasticizer and PVA (4%) was taken for backing membrane preparation and then the optimized GBP-TDP was subjected for physical characterization, optimization and ex vivo release kinetics. Results and conclusionThe results showed desired specifications with uneven and flaky surface appearance giving avenue for controlled release of the drugs with 75.58% of drug release in 12 hrs., further suggesting that GBP-TDP can be used as an effective tool against diabetic neuropathy pain.

neuroscience↗

bioRxiv2020

Tracking changes in behavioural dynamics using prediction error

Automated analysis of video can now generate extensive time series of pose and motion in freely-moving organisms. This requires new quantitative tools to characterize behavioural dynamics. For the model roundworm Caenorhabditis elegans, body pose can be accurately quantified from video as coordinates in a single low-dimensional space. We focus on this well-established case as an illustrative example and propose a method to reveal subtle variations in behaviour at high time resolution. Our data-driven method, based on empirical dynamic modeling, quantifies behavioural change as prediction error with respect to a time-delay-embedded attractor of behavioural dynamics. Because this attractor is constructed from a user-specified reference data set, the approach can be tailored to specific behaviours of interest at the individual or group level. We validate the approach by detecting small changes in the movement dynamics of C. elegans at the initiation and completion of delta turns. We then examine an escape response initiated by an aversive stimulus and find that the method can track return to baseline behaviour in individual worms and reveal variations in the escape response between worms. We suggest that this general approach - defining dynamic behaviours using reference attractors and quantifying dynamic changes using prediction error - may be of broad interest and relevance to behavioural researchers working with video-derived time series.

bioinformatics↗

bioRxiv2020

Genome-wide strand asymmetry in massively parallel reporter activity favors genic strands

Massively parallel reporter assays (MPRAs) are useful tools to discover and characterize regulatory elements in human genomes. Partly because enhancer function is assumed to be orientation independent with respect to each strand of the DNA helix, most reported MPRA results ignore stranded information. However, we find pervasive strand asymmetry of MPRA signals in datasets from multiple reporter configurations and in both published and newly reported data. These effects are reproducible across different cell types and in different treatments within a cell type, and are observed both within and outside of annotated regulatory elements. From elements in gene bodies, MPRA strand asymmetry favors the sense strand, suggesting that biological function related to endogenous transcription is driving the phenomenon. Similarly, within Alu mobile element insertions, we find that strand asymmetry favors the transcribed strand of the ancestral retrotransposon. The effect is consistent across the multiplicity of Alu elements in human genomes, and is more pronounced in younger, less diverged Alu elements. We find sequence features driving MPRA strand asymmetry and demonstrate its prediction from sequence alone. We see some evidence for both RNA stabilization and transcriptional activation mechanisms, and hypothesize that the effect is driven by natural selection favoring efficient transcription. Our results indicate that strand asymmetry, as a pervasive and reproducible feature, should be accounted for in analysis of MRPA data. More importantly, the fact that MPRA asymmetry favors naturally transcribed strands suggests that it stems from preserved biological functions that have a substantial, global impact on gene and genome evolution.

genomics↗

bioRxiv2020

Designing of Epitope-Based Vaccine from the Conserved Region of the Spike Glycoprotein of SARS-CoV-2

The emergence of COVID-19 as a pandemic with a high morbidity rate is posing serious global concern. There is an urgent need to design a suitable therapy or vaccine that could fight against SARS-CoV-2 infection. As spike glycoprotein of SARS-CoV-2 plays a crucial role in receptor binding and membrane fusion inside the host, it could be a suitable target for designing of an epitope-based vaccine. SARS-CoV-2 is an RNA virus and thus has a property to mutate. So, a conserved peptide region of spike glycoprotein was used for predicting suitable B cell and T cell epitopes. 4 T cell epitopes were selected based on stability, antigenicity, allergenicity and toxicity. Further, MHC-I were found from the immune database that could best interact with the selected epitopes. Population coverage analysis was also done to check the presence of identified MHC-I, in the human population of the affected countries. The T cell epitope that binds with the respective MHC-I with highest affinity was chosen. Molecular dynamic simulation results show that the epitope is well selected. This is an in-silico based study that predicts a novel T cell epitope from the conserved spike glycoprotein that could act as a target for designing of the epitope-based vaccine. Further, B cell epitopes have also been found but the main work focuses on T cell epitope as the immunity generated by it is long lasting as compared to B cell epitope.

bioinformatics↗

bioRxiv2020

α-Synuclein aggregation intermediates form fibril polymorphs with distinct prion-like properties

-Synuclein (-Syn) amyloid fibrils in synucleinopathies (such as Parkinsons disease (PD), multiple system atrophy (MSA)) are structurally and functionally different, reminiscent of prion-like strains. However, how a single protein can form different fibril polymorphs in various synucleinopathies is not known. Here, we demonstrate the structure-function relationship of two distinct -Syn fibril polymorphs, the pre-matured fibrils (PMF) and helix-matured fibrils (HMF) based on -Syn aggregation intermediates. These polymorphs not only display the structural differences, including their fibril core structure as demonstrated by solid-state nuclear magnetic resonance (NMR) spectroscopy and H/D-exchange coupled with mass spectrometry but also possess different cellular activities such as seeding, cellular internalization, and cell-to-cell transmission. The HMF with a compact core structure exhibits low seeding potency in cells but readily internalizes and transmits from one cell to another. Whereas the less structured PMF lacks the cell-to-cell transmission ability but induces abundant -Syn pathology and triggers the formation of aggresomes in cells. Overall, the study highlights how the conformational heterogeneity in the aggregation pathway may lead to fibril polymorphs with distinct prion-like behavior in PD.

neuroscience↗

bioRxiv2020

Gjd2b-mediated gap junctions promote glutamatergic synapse formation and dendritic elaboration in Purkinje neurons

Gap junctions between neurons serve as electrical synapses, in addition to conducting metabolites and signaling molecules. These functions of gap junctions have led to the idea that during development, gap junctions could prefigure chemical synapses. We present evidence for this idea at a central, glutamatergic synapse and provide some mechanistic insights. Here, we show that reduction or loss of Gjd2b-containing gap junctions led to a decrease in glutamatergic synapse density in cerebellar Purkinje neurons (PNs) in larval zebrafish. Gjd2b-/- larvae exhibited faster mEPSCs and a consistent decrease in dendritic arbor size. These PNs also showed decreased branch elongations but normal rate of branch retractions. Further, the dendritic growth deficits in gjd2b-/- mutants were rescued by expressing full length Gjd2b in single PNs. This suggests that Gjd2b may form heterotypic channels with other connexins in gjd2b-/- larvae, though it is not clear if PNs in wild type animals make homotypic or heterotypic gap junction channels. Dendritic growth deficits were not rescued by expressing a deletion mutant of Gjd2b unable to form functional channels. Finally, the expression levels of five isoforms of camkii were increased in gjd2b-/- larvae and inhibition of CaMKII restored dendritic arbor lengths of mutant larvae to wild type levels. These results suggest a link between signaling via Gjd2b-containing gap junctions, CaMKII function and dendritic growth. In sum, our results demonstrate that Gjd2b-mediated gap junctions are key regulators of glutamatergic synapse formation and dendritic elaboration in PNs.

neuroscience↗

bioRxiv2019

A systematic evaluation of the design, orientation, and sequence context dependencies of massively parallel reporter assays

Massively parallel reporter assays (MPRAs) functionally screen thousands of sequences for regulatory activity in parallel. Although MPRAs have been applied to address diverse questions in gene regulation, there has been no systematic comparison of how differences in experimental design influence findings. Here, we screen a library of 2,440 sequences, representing candidate liver enhancers and controls, in HepG2 cells for regulatory activity using nine different approaches (including conventional episomal, STARR-seq, and lentiviral MPRA designs). We identify subtle but significant differences in the resulting measurements that correlate with epigenetic and sequence-level features. We also test this library in both orientations with respect to the promoter, validating en masse that enhancer activity is robustly independent of orientation. Finally, we develop and apply a novel method to assemble and functionally test libraries of the same putative enhancers as 192-mers, 354-mers, and 678-mers, and observe surprisingly large differences in functional activity. This work provides a framework for the experimental design of high-throughput reporter assays, suggesting that the extended sequence context of tested elements, and to a lesser degree the precise assay, influence MPRA results.

genomics↗