Biology subjects

Singh, N. P.

Publications and source records attributed to Singh, N. P..

12 recordsLinked to original sources

bioRxiv2026

Hyperactivity preferentially disrupts scotopic retinal circuits in the early stages of diabetic retinopathy

Diabetic retinopathy is a leading cause of blindness worldwide. In this disease, visual impairments result from neurovascular dysfunction in the retina. However, the specific retinal circuits and the mechanisms underlying their dysfunction remain unclear. Here, in mouse models of diabetes, we evaluated scotopic (night vision) and photopic (day vision) retinal circuit function in the early stages of the disease. Using a combination of immunohistochemical, behavioral and electrophysiological techniques, we show that vision impairments early in the disease are not associated with widespread degeneration of the retinal circuitry but with abnormalities localized to scotopic circuits. Specifically, we observed swelling of axon terminals in rod but not cone bipolar cells. Importantly, these abnormalities were closely linked with bipolar cell activity, as rod bipolar cells showed higher intrinsic activity than cone bipolar cells, and inducing hyperactivity in control retinas was sufficient to recapitulate the pathology in diabetic rod bipolar cells. Further, the swollen rod bipolar cell terminals exhibited an increase in oxidative stress, consistent with cellular dysfunction. In line with this notion, electrophysiological measurements demonstrated that the contrast sensitivity of retinal ganglion cells was preferentially attenuated under scotopic conditions. Finally, behavioral experiments showed that visual sensitivity under scotopic conditions was affected earlier in disease progression compared to photopic conditions. Together, these findings reveal that scotopic circuits are preferentially affected in the early stages of diabetic retinopathy and that hyperactivity is sufficient to drive these abnormalities.

neuroscience↗

bioRxiv2024

Alevin-fry-atac enables rapid and memory frugal mapping of single-cell ATAC-seq data using virtual colors for accurate genomic pseudoalignment

Ultrafast mapping of short reads via lightweight mapping techniques such as pseudoalignment has significantly accelerated transcriptomic and metagenomic analyses, often with minimal accuracy loss compared to alignment-based methods. However, applying pseudoalignment to large genomic references, like chromosomes, is challenging due to their size and repetitive sequences. We introduce a new and modified pseudoalignment scheme that partitions each reference into "virtual colors.... These are essentially overlapping bins of fixed maximal extent on the reference sequences that are treated as distinct "colors" from the perspective of the pseudoalignment algorithm. We apply this modified pseudoalignment procedure to process and map single-cell ATAC-seq data in our new tool alevin-fry-atac. We compare alevin-fry-atac to both Chromap and Cell Ranger ATAC. Alevin-fry-atac is highly scalable and, when using 32 threads, is approximately 2.8 times faster than Chromap (the second fastest approach) while using approximately one third of the memory and mapping slightly more reads. The resulting peaks and clusters generated from alevin-fry-atac show high concordance with those obtained from both Chromap and the Cell Ranger ATAC pipeline, demonstrating that virtual colorenhanced pseudoalignment directly to the genome provides a fast, memory-frugal, and accurate alternative to existing approaches for single-cell ATAC-seq processing. The development of alevin-fry-atac brings single-cell ATAC-seq processing into a unified ecosystem with single-cell RNA-seq processing (via alevin-fry) to work toward providing a truly open alternative to many of the varied capabilities of CellRanger. Furthermore, our modified pseudoalignment approach should be easily applicable and extendable to other genome-centric mapping-based tasks and modalities such as standard DNA-seq, DNase-seq, Chip-seq and Hi-C.

bioinformatics↗

bioRxiv2024

A replicable and modular benchmark for long-read transcript quantification methods

We provide a replicable benchmark for long-read transcript quantification, and evaluate the performance of some recently-introduced tools on several synthetic long-read RNA-seq datasets. This benchmark is designed to allow the results to be easily replicated by other researchers, and the structure of the underlying Snakemake workflow is modular to make the addition of new tools or new data sets relatively easy. In analyzing previously assessed simulations, we find discrepancies with recently-published results. We also demonstrate that the robustness of certain approaches hinge critically on the quality and "cleanness" of the simulated data. AvailabilityThe Snakemake scripts for the benchmark are available at https://github.com/COMBINE-lab/lr_quant_benchmarks, the data used as input for the benchmarks (reference sequences, annotations, and simulated reads) are available at https://doi.org/10.5281/zenodo.13130623.

bioinformatics↗

bioRxiv2023

Tree-based differential testing using inferentialuncertainty for RNA-Seq

Identifying differentially expressed transcripts poses a crucial yet challenging problem in transcriptomics. Substantial uncertainty is associated with the abundance estimates of certain transcripts which, if ignored, can lead to the exaggeration of false positives and, if included, may lead to reduced power. Given a set of RNA-Seq samples, TreeTerminus arranges transcripts in a hierarchical tree structure that encodes different layers of resolution for interpretation of the abundance of transcriptional groups, with uncertainty generally decreasing as one ascends the tree from the leaves. We introduce mehenDi, which utilizes the tree structure from TreeTerminus for differential testing. The nodes output by mehenDi, called the selected nodes are determined in a data-driven manner to maximize the signal that can be extracted from the data while controlling for the uncertainty associated with estimating the transcript abundances. The identified selected nodes can include transcripts and inner nodes, with no two nodes having an ancestor/descendant relationship. We evaluated our method on both simulated and experimental datasets, comparing its performance with other tree-based differential methods as well as with uncertainty-aware differential transcript/gene expression methods. Our method detects inner nodes that show a strong signal for differential expression, which would have been overlooked when analyzing the transcripts alone.

bioinformatics↗

bioRxiv2023

Fulgor: A fast and compact k-mer index for large-scale matching and color queries

The problem of sequence identification or matching -- determining the subset of references from a given collection that are likely to contain a query nucleotide sequence -- is relevant for many important tasks in Computational Biology, such as metagenomics and pan-genome analysis. Due to the complex nature of such analyses and the large scale of the reference collections a resource-efficient solution to this problem is of utmost importance. The reference collection should therefore be pre-processed into an index for fast queries. This poses the threefold challenge of designing an index that is efficient to query, has light memory usage, and scales well to large collections. To solve this problem, we describe how recent advancements in associative, order-preserving, k-mer dictionaries can be combined with a compressed inverted index to implement a fast and compact colored de Bruijn graph data structure. This index takes full advantage of the fact that unitigs in the colored de Bruijn graph are monochromatic (all k-mers in a unitig have the same set of references of origin, or "color"), leveraging the order-preserving property of its dictionary. In fact, k-mers are kept in unitig order by the dictionary, thereby allowing for the encoding of the map from k-mers to their inverted lists in as little as 1 + o(1) bits per unitig. Hence, one inverted list per unitig is stored in the index with almost no space/time overhead. By combining this property with simple but effective compression methods for inverted lists, the index achieves very small space. We implement these methods in a tool called Fulgor. Compared to Themisto, the prior state of the art, Fulgor indexes a heterogeneous collection of 30,691 bacterial genomes in 3.8x less space, a collection of 150,000 Salmonella enterica genomes in approximately 2 x less space, is at least twice as fast for color queries, and is 2 - 6x faster to construct. 2012 ACM Subject ClassificationApplied computing [->] Bioinformatics

bioinformatics↗

bioRxiv2023

The bromodomain inhibitor JQ1 is a molecular glue targeting centromeres

Centromeres are the position on each chromosome that orchestrates the accurate partitioning of the genome during cell division. Centromere-dependent cell-cycle checkpoints are maintained by cancer cells to prevent catastrophic chromosome segregation defects in dividing cells1, 2, making centromeric chromatin a valuable target for anti-cancer therapeutics. However, no compounds have been identified that specifically target centromeric chromatin using standard drug discovery approaches. Here we develop a big-data approach to identify the protein composition of repetitive DNA loci, including centromeres, and screen candidate small molecules that act on centromeric chromatin composition. We discover that the BET bromodomain protein BRD4 localises to centromeres and regulates centromeric cohesion. We further show that the bromodomain inhibitor JQ1 affects centromeric BRD4 by stabilising a direct interaction between BRD4 and Centromere Protein B (CENP-B), acting as a molecular-glue that promotes centromere cohesion in a CENP-B-dependent manner. Strikingly, CENP-B transitions from a non-essential protein in JQ1-sensitive cells to the most significant determinant of cell-proliferation in JQ1-resistant cells. Our observations demonstrate a completely overlooked role for BRD4 and JQ1 in directly targeting the centromere, with important consequences for JQ1-derivatives currently entering clinical use3.

molecular biology↗

bioRxiv2023

An associative transcriptomics study on rice bean (Vigna umbellata) provides new insights into genetic basis and candidate genes governing flowering, maturity and seed weight

Rice bean is an underrated legume with significant potential to support food and nutritional security worldwide, being a rich source of proteins, minerals, and essential fatty acids. Therefore, we considered three pivotal production traits of rice bean; flowering, maturity and seed weight, to identify associated candidate genes. One-hundred diverse genotypes out of 1800 evaluated rice bean accessions from the Indian National Genebank were considered for phenotypic data collection and genotyping by transcriptome sequencing approach. Association analysis involving various GWAS models was conducted to identify significant marker-trait associations. The results revealed association of 82 markers on 48 transcripts for flowering, 26 markers on 22 transcripts for maturity and 22 markers on 21 transcripts for seed weight. The annotation of associated transcripts unraveled the functional genes related to the considered traits. Among the significant candidate genes identified, HSC80, P-II PsbX, phospholipid-transporting-ATPase-9, pectin-acetylesterase-8 and E3-ubiquitin-protein-ligase-RHG1A were found associated with flowering. Further, associations of WRKY1 and DEAD-box-RH27 with seed weight, PIF3 and pentatricopeptide-repeat-containing-gene with maturity & seed weight and aldo-keto-reductase with flowering & maturity have been revealed. The present investigation provides insights into the genetic mechanisms governing economically-essential traits like flowering, maturity and seed weight that can be potentially utilized for rice bean improvement. HighlightsThe present investigation is the first associative transcriptomics approach implemented on Vigna umbellata. Based on the marker-trait association analysis, candidate genes for flowering, maturity and seed weight traits are reported.

bioinformatics↗

bioRxiv2022

TreeTerminus - Creating transcript trees using inferential replicate counts

The accuracy and robustness of many types of analyses performed using RNA-seq data are directly impacted by the quality of the transcript and gene abundance estimates inferred from this data. However, a certain degree of uncertainty is always associated with the transcript abundance estimates. This uncertainty may make many downstream analyses, such as differential testing, difficult for certain transcripts. Conversely, gene-level analysis, though less ambiguous, is often too coarse-grained. To circumvent this problem, methods have proposed grouping transcripts together into distinct inferential units that should be used as a base unit for analysis. However, these methods dont take downstream analysis into account. We introduce TreeTerminus, a data-driven approach for grouping transcripts into a tree structure where leaves represent individual transcripts and internal nodes represent an aggregation of a transcript set. TreeTerminus constructs trees such that, on average, the inferential uncertainty decreases as we ascend the tree topology. The tree provides the flexibility to analyze data at nodes that are at different levels of resolution in the tree and can be tuned depending on the analysis of interest. To obtain fixed groups for the downstream analysis, we provide a dynamic programming (DP) approach that can be used to find a cut through the tree that optimizes one of several different objectives. We evaluated TreeTerminus on two simulated and two experimental datasets, and observed an improved performance compared to transcripts (leaves) and other methods under several different metrics.

bioinformatics↗

bioRxiv2022

Detecting isoform-level allelic imbalance accounting for inferential uncertainty

Allelic imbalance (AI) of gene expression in heterozygous individuals is a hallmark of cis-genetic regulation, revealing mechanisms underlying the association of non-coding genetic variation with downstream traits, as in GWAS. Most methods for detecting AI from RNA-sequencing (RNA-seq) data examine allelic expression per exonic SNP, which may obscure imbalance in expression of individual isoforms. Detecting AI at the isoform level requires accounting for inferential uncertainty (IU) of expression estimates, caused by multi-mapping of RNA-seq reads to isoforms and alleles. Swish, a method developed previously for differential transcript expression accounting for IU, can be applied in a paired setting to detect AI. However, in AI analysis, most transcripts will have high IU across alleles such that even methods like Swish will lose power. Our proposed method, SEESAW, offers AI analysis at various level of resolution, including gene level, isoform level, and optionally aggregating isoforms within a gene based on their transcription start site (TSS). This TSS-based aggregation strategy strengthens the signal for transcripts that may have high IU with respect to allelic quantification. SEESAW is primarily designed for experiments with multiple replicates or conditions of organisms with the same genotype, as in an F1 cross or time course experiments of cell lines. Additionally, we introduce a new test for detecting AI that changes across a continuous covariate, as in a time course experiment. The SEESAW suite of methods is evaluated both on simulated data and applied to an RNA-seq dataset of differentiating F1 mouse osteoblasts.

bioinformatics↗

bioRxiv2022

The extracellular RNA pool within Zea mays apoplast: composition and differential expression during Ustilago maydis infection

The existence of an extracellular pool of RNA (exRNA) has been documented in both animal and plant cells in a number of instances. These exRNA species play important role in host response against different environmental stimuli. The mechanism of their function however remains largely unknown. In this study we report the composition of the exRNA pool within the leaf apoplast of Z. mays under normal growth condition. We could detect RNA transcripts originating from both the genic as well as the intergenic regions of the nuclear, mitochondrial and chloroplast genomes of maize in our exRNA sequencing data. Our data showed increased abundance of about 75% of the exRNA transcripts during infection with a basidiomycete smut fungi, Ustilago maydis. Functional classification of the differentially abundant exRNA transcripts within U. maydis SG200 WT infected maize apoplast with respect to uninfected apoplast revealed significant enrichment of the exRNA transcripts corresponding to the ribosome biogenesis pathway. Data related to the effect of two extracellular T2 type ribonucleases, Nuc1 and Nuc2 from U. maydis on the composition of exRNA pool of maize is also presented.

plant biology↗

bioRxiv2021

Identification of oxalyl-CoA synthetase gene (LsAAE3) and its regulatory role in β-ODAP biosynthesis in grasspea (Lathyrus sativus L.)

Grasspea is a popular pulse crop due to its hardiness and low cost of production. Presence of anti-nutritive factor {beta}-ODAP in its seeds and other plant parts hinder its widespread cultivation and usage. Oxalyl-CoA synthetase is one of the key enzyme of {beta}-ODAP biosynthesis pathway, catalyses the conversion of oxalate to oxalyl-CoA. ACYL ACTIVATING ENZYME 3 (AAE3) gene has been characterised to encode an oxalyl-CoA synthetase enzyme in many plant species. We report here the isolation of full length AAE3 homolog in grasspea with a combination of PCR based strategy and in silico analysis. We first identified AAE3 homolog by PCR using degenerate primers. The partial LsAAE3 sequence showed 88% amino acids sequence identity with the characterised AAE3 gene of M. truncatula. We then predicted the full length AAE3 sequence using the publically available transcriptome datasets of grasspea. Determination of LsAAE3 gene and protein structure and phylogenetic relationship analysis strongly suggested that LsAAE3 is a true homolog of AAE3 gene. Expression profiling of LsAAE3 in grasspea varieties with contrast in {beta}-ODAP content revealed its inverse relationship with the {beta}-ODAP content, LsAAE3 thus negatively regulates the synthesis of {beta}-ODAP. Involvement of AAE3 encoded oxalyl-CoA synthetase in a CoA-dependent pathway of oxalate degradation is well proven in many plant species. We also identified the CoA-dependent pathway of oxalate degradation in grasspea. Based on these observations, we hypothesized that LsAAE3 may regulate {beta}-ODAP content, possibly, by CoA-dependent pathway of oxalate degradation in grasspea. If this hypothesis is substantiated, genetic manipulation of LsAAE3 presents viable option for reducing {beta}-ODAP content in grass pea.

molecular biology↗

bioRxiv2020

Genome-wide binding analyses of HOXB1 revealed a novel DNA binding motif associated with gene repression

Knowledge of the diverse DNA binding specificities of transcription factors is important for understanding their specific regulatory functions in animal development and evolution. We have examined the genome-wide binding properties of the mouse HOXB1 protein in ES cells differentiated into neural fates. Unexpectedly, only a small number of HOXB1 bound regions (7%) correlate with binding of the known HOX cofactors PBX and MEIS. In contrast, 22% of the HOXB1 binding peaks display co-occupancy with the transcriptional repressor REST. Analyses revealed that co-binding of HOXB1 with PBX correlates with active histone marks and high levels of expression, while co-occupancy with REST correlates with repressive histone marks and repression of the target genes. Analysis of HOXB1 bound regions uncovered enrichment of a novel 15 base pair HOXB1 binding motif HB1RE (HOXB1 response element). In vitro template binding assays showed that HOXB1, PBX1 and MEIS can bind to this motif. In vivo, this motif is sufficient to direct expression of a reporter gene and over-expression of HOXB1 selectively represses this activity. Our analyses suggest that HOXB1 has evolved an association with REST in gene regulation and the novel HB1RE motif contributes to HOXB1 function in part through a repressive role in gene expression.

developmental biology↗