Biology subjects

Misra, S.

Publications and source records attributed to Misra, S..

28 records · Page 2Linked to original sources

bioRxiv2022

3'UTR-dependent dynamic changes in TP53 mRNA localization regulate p53 tumor suppressor activity

The tumor suppressor p53 triggers senescence in response to oncogenic stress in primary cells. However, the mechanisms by which tumor cells retaining p53 bypass senescence are not fully understood. Here we report that p53 cytostatic activity is inhibited in tumor cells by the 3 untranslated region (3UTR) of its mRNA, without altering p53 levels. 3UTR inhibition requires a long U-rich element (URE) and its binding protein, HuR. The 3UTR excluded TP53 mRNAs from a perinuclear compartment containing the CK2 kinase, suppressing p53 phosphorylation on an activating CK2 site, Ser392. In primary cells undergoing oncogene-induced senescence and tumor cells treated with genotoxic agents, TP53 mRNAs became concentrated in the perinuclear cytoplasm, coinciding with p53 phosphorylation and activation by CK2. In both cases, perinuclear re-localization of TP53 transcripts required AMPK2-dependent HuR nuclear translocation. ATM kinase activity was essential for DNA damage-induced spatial reprogramming of TP53 mRNAs, likely through phosphorylation and inactivation of MDM2. MDM2 was required for peripheral localization of TP53 transcripts and negatively regulated levels of the AMPK2 activating kinase, CaMKK{beta}. Our findings reveal a critical role for 3UTR sequences in suppressing p53 protein activity and provide a new mechanistic framework for p53 activation by DNA damaging agents.

cancer biology↗

bioRxiv2022

3'UTR-directed, kinase proximal mRNA decay inhibits C/EBPβ phosphorylation/activation to suppress senescence in tumor cells

C/EBP{beta} is a potent regulator of oncogene-induced senescence (OIS) and the SASP. C/EBP{beta} is post-translationally activated in OIS cells by the effector kinases ERK1/2 and CK2. However, in tumor cells C/EBP{beta} activation is suppressed by its 3UTR. 3'UTR regulation of protein activity (UPA) requires a G/U-rich element (GRE) and its cognate binding protein, HuR. These components segregate CEBPB transcripts away from a perinuclear compartment harboring ERK1/2 and CK2, restricting C/EBP{beta} from its activating kinases. We report here that the mRNA decay proteins UPF1 and Staufen1/2 are essential UPA factors enriched within the perinuclear cytoplasm. STAU1/2 and UPF1 overlap with CK2 on perinuclear signaling endosomes where they promote localized CEBPB mRNA decay. UPF1 or STAU1/2 depletion in tumor cells increased CEBPB transcripts adjacent to CK2 foci, coinciding with C/EBP{beta} activation and senescence. The GRE and an adjacent STAU binding site independently suppress C/EBP{beta}-mediated senescence, while a distinct 3UTR region inhibits its SASP-inducing activity. KrasG12D-driven lung tumors in mice carrying a Cebpb GRE deletion rarely progressed to malignant adenocarcinomas, demonstrating the importance of UPA to enable tumor progression in vivo. Thus, kinase-proximal mRNA decay is a novel mechanism that inhibits C/EBP{beta} activation in tumor cells to facilitate senescence bypass.

cancer biology↗

bioRxiv2021

Accelerating Identification of Chromatin Accessibility from noisy ATAC-seq Data using Modern CPUs

Identifying accessible chromatin regions is a fundamental problem in epigenomics with ATAC-seq being a commonly used assay. Exponential rise in single cell ATAC-seq experiments has made it critical to accelerate processing of ATAC-seq data. ATAC-seq data can have a low signal-to-noise ratio for various reasons including low coverage or low cell count. To denoise and identify accessible chromatin regions from noisy ATAC-seq data, use of deep learning on 1D data - using large filter sizes, long tensor widths, and/or dilation - has recently been proposed. Here, we present ways to accelerate the end-to-end training performance of these deep learning based methods using CPUs. We evaluate our approach on the recently released AtacWorks toolkit. Compared to an Nvidia DGX-1 box with 8 V100 GPUs, we get up to 2.27x speedup using just 16 CPU sockets. To achieve this, we build an efficient 1D dilated convolution layer and demonstrate reduced precision (BFloat16) training.

genomics↗

bioRxiv2021

Accelerating long-read analysis on modern CPUs

Long read sequencing is now routinely used at scale for genomics and transcriptomics applications. Mapping of long reads or a draft genome assembly to a reference sequence is often one of the most time consuming steps in these applications. Here, we present techniques to accelerate minimap2, a widely used software for mapping. We present multiple optimizations using SIMD parallelization, efficient cache utilization and a learned index data structure to accelerate its three main computational modules, i.e., seeding, chaining and pairwise sequence alignment. These result in reduction of end-to-end mapping time of minimap2 by up to 1.8 x while maintaining identical output.

genomics↗

bioRxiv2020

LISA: Learned Indexes for DNA Sequence Analysis

Next Generation Sequencing (NGS) is transforming fields like genomics, transcriptomics, and epigenetics with rapidly increasing throughput at reduced cost. This also demands overcoming performance bottlenecks in the downstream analysis of the sequencing data. A key performance bottleneck is searching for exact matches of entire or substrings of short DNA/RNA sequence queries in a long reference sequence database. This task is typically performed by using an index of the reference - such as FM-index, suffix arrays, suffix trees, hash tables, or lookup tables. In this paper, we propose accelerating this sequence search by substituting or enhancing the indexes with machine learning based indexes - called learned indexes - and present LISA (Learned Indexes for Sequence Analysis). We evaluate LISA through a number of case studies - that cover widely used software tools; short and long reads; human, animal, and plant genome datasets; DNA and RNA sequences; various traditional indexing techniques (FM-indexes, hash tables and suffix arrays) - and demonstrate significant performance benefits in a majority of them. For example, our experiments on real datasets show that LISA achieves speedups of up to 2.2 fold and 4.7 fold over the state-of-the-art FM-index based implementations for exact sequence search modules in popular tools bowtie2 and BWA-MEM2, respectively. Code availabilityLISA-based FM-index: https://github.com/IntelLabs/Trans-Omics-Acceleration-Library/tree/master/src/LISA-FMI LISA-based hash-table: https://github.com/IntelLabs/Trans-Omics-Acceleration-Library/tree/master/src/LISA-hash LISA applied to BWA-MEM2: https://github.com/bwa-mem2/bwa-mem2/tree/bwa-mem2-lisa.

bioinformatics↗

bioRxiv2020

Allosteric regulation of lysosomal enzyme recognition by the cation-independent mannose 6-phosphate receptor

The cation-independent mannose 6-phosphate receptor (CI-MPR), also known as the IGF2 receptor or CD222, is a multifunctional type I transmembrane glycoprotein ubiquitously expressed in most eukaryotic cell types. Through the receptors ability to bind a variety of unrelated extracellular and intracellular ligands, it is involved in a wide array of functions including protein trafficking, lysosomal biogenesis, internalization, regulation of cell growth, cell migration and apoptosis. CI-MPR has a large extracellular region comprised of 15 contiguous domains, four of which interact with phosphorylated glycans on lysosomal enzymes. Here we present a series of biophysical studies, along with crystal structures, providing information on how the N-terminal 5 domains of this receptor work in concert to bind and release carbohydrates. High-resolution electron microscopy as well as hydroxyl radical protein footprinting (HRPF) of this multifunctional multidomain construct demonstrates dynamic conformational changes occur as a consequence of ligand binding and different pH conditions, These data, coupled with surface plasmon resonance studies and molecular modeling, allow us to propose a bi-dentate oligosaccharide binding model, which could explain how high affinity carbohydrate binding is achieved through allosteric domain cooperativity.

biochemistry↗

bioRxiv2020

The Drosophila seminal Sex Peptide can associate with rival as well as own sperm and provide function for SP in polyandrous females

In populations in which females tend to mate with more than one male, sperm competition and cryptic female choice can occur, triggering biases in sperm use and influencing males paternity share outcome of the mating. This competition occurs in the context of molecules and cells of male and female working interdependently towards the common goal of optimal fertilization. For example, a males seminal fluid molecules modify the females physiology to increase reproductive success. However, since some of these modifications induce long-term changes in female physiology, this can indirectly benefit rival males. Indeed, rival males can tailor their ejaculates accordingly, minimizing the energy cost of mating. Here we investigate the direct benefits that seminal fluid proteins from an ejaculate of one male can confer to sperm of a rival. We report that Sex Peptide (SP) that a female receives from one male can bind to sperm from a prior mate, that were already stored in the female. Moreover, the second males SP can restore fertility and facilitate efficient sperm release or utilization of sperm received from the first male that had been stored in the female. Thus, SP from one male can directly benefit another and as such is a key molecular component in the process of inter-ejaculate interaction.

evolutionary biology↗

bioRxiv2019

A Higher Proportion of Craniosynostosis Genes Are Cancer Driver Genes

Craniosynostosis (CRS) is a congenital abnormality deformity with a heterogenous genetic contribution. Previously, there are two attempts to collect genes that are genetically associated with craniosynostosis and some related syndromes with 57 (Twigg and Wilkie, 2015) and 39 (Goos and Mathijssen, 2019) genes identified, respectively. We expanded this list of craniosynostosis genes by adding another 17 genes with an updated literature search. These genes are shown to be more likely to be intolerant to functional mutations. Of these 113 craniosynostosis genes, 21 (19% vs. 1.5% baseline frequency) are cancer driver genes, a 14-fold enrichment. The cancer-craniosynostosis connection is further validated by an over-representation analysis of craniosynostosis genes in KEGG cancer pathway and several cancer related gene-sets. Many cancer-craniosynostosis overlapping genes participate in intracellular signaling pathways, which play a role in both development and cancer. This connection can be viewed from the oncogenesis recapitulates ontogenesis framework. Nineteen craniosynostosis genes are transcription factor genes (16.8% vs. 8.2% baseline), and craniosynostosis genes are also enriched in targets of certain transcription factors or micro RNAs.

genetics↗

bioRxiv2019

Accelerating Sequence Alignment to Graphs

Aligning DNA sequences to an annotated reference is a key step for genotyping in biology. Recent scientific studies have demonstrated improved inference by aligning reads to a variation graph, i.e., a reference sequence augmented with known genetic variations. Given a variation graph in the form of a directed acyclic string graph, the sequence to graph alignment problem seeks to find the best matching path in the graph for an input query sequence. Solving this problem exactly using a sequential dynamic programming algorithm takes quadratic time in terms of the graph size and query length, making it difficult to scale to high throughput DNA sequencing data. In this work, we propose the first parallel algorithm for computing sequence to graph alignments that leverages multiple cores and single-instruction multiple-data (SIMD) operations. We take advantage of the available inter-task parallelism, and provide a novel blocked approach to compute the score matrix while ensuring high memory locality. Using a 48-core Intel Xeon Skylake processor, the proposed algorithm achieves peak performance of 317 billion cell updates per second (GCUPS), and demonstrates near linear weak and strong scaling on up to 48 cores. It delivers significant performance gains compared to existing algorithms, and results in run-time reduction from multiple days to three hours for the problem of optimally aligning high coverage long (PacBio/ONT) or short (Illumina) DNA reads to an MHC human variation graph containing 10 million vertices.\n\nAvailabilityThe implementation of our algorithm is available at https://github.com/ParBLiSS/PaSGAL. Data sets used for evaluation are accessible using https://alurulab.cc.gatech.edu/PaSGAL.

bioinformatics↗

bioRxiv2018

Identification of Significant Computational Building Blocks through Comprehensive Deep Dive of NGS Secondary Analysis Methods

Rapid advancements in next generation sequencing technologies have greatly improved the throughput of sequencing and reduced the cost to under $1000 per genome propelling ambitious projects across the globe that are pursuing sequencing million or more genomes. In addition, the sequencing throughput is increasing and the cost is decreasing at a rate much faster than the Moores law. This necessitates equivalent rate of acceleration of NGS secondary analysis that assembles the reads into full genomes and identifies variants between genomes. Conventional improvement in hardware can at best help accelerate this according to the Moores law if the corresponding software is able to use the hardware efficiently. This is currently not the case for majority of the dozens of software tools used for NGS secondary analysis. Thus, to keep pace with the rate of advancement of sequencers, we need - 1) hardware that is designed taking into account the computational requirements of NGS secondary analysis and 2) software tools that use the hardware efficiently.\n\nIn this work, we take the first step towards that goal by identifying the computational requirements of NGS secondary analysis. We surveyed dozens of software tools from all the three major problems in secondary analysis - sequence mapping, de novo assembly, and variant calling - to select seven popular tools and a workflow for an in depth analysis. We performed runtime profiling of the tools using multiple real datasets to find that the majority of the runtime is dominated by just four building blocks - Smith Waterman alignment, FM-index based sequence search, Debruijn graph construction and traversal and pairwise hidden markov model algorithm. Together, these building blocks cover 80.5%-98.2% of the runtime for sequence mapping, 63.9%-99.4% of the runtime for De novo assembly, and 72%-93% of the runtime for variant calling. The beauty of this result is that by just tailoring our software and hardware for these building blocks, we can get a major performance improvement of NGS secondary analysis.

genomics↗