Biology subjects

Arora, A.

Publications and source records attributed to Arora, A..

49 records · Page 3Linked to original sources

bioRxiv2022

RNA localization mechanisms transcend cell morphology

RNA molecules are localized to specific subcellular regions through interactions between RNA regulatory elements and RNA binding proteins (RBPs). Generally, our knowledge of the mechanistic details behind the localization of a given RNA is restricted to a particular cell type. Here, we show that RNA/RBP interactions that regulate RNA localization in one cell type predictably regulate localization in other cell types with vastly different morphologies. To determine transcriptome-wide RNA spatial distributions across the apicobasal axis of human intestinal epithelial cells, we used our recently developed RNA proximity labeling technique, Halo-seq. We found that mRNAs encoding ribosomal proteins (RP mRNAs) were strongly localized to the basal pole of these cells. Using reporter transcripts and single molecule RNA FISH, we found that pyrimidine-rich TOP motifs in the 5' UTRs of RP mRNAs were su[ffi]cient to drive basal RNA localization. Interestingly, the same TOP motifs were also su[ffi]cient to drive RNA localization to the neurites of mouse neuronal cells. In both cell types, the regulatory activity of the TOP motif was dependent on it being at the extreme 5' end of the transcript, was abolished upon perturbation of the TOP-binding protein LARP1, and was reduced upon inhibition of kinesins. To extend these findings, we compared subcellular RNAseq data from neuronal and epithelial cells. We found that the basal compartment of epithelial cells and the projections of neuronal cells were enriched for highly similar sets of RNAs, indicating that broadly similar mechanisms may be transporting RNAs in both cell types. These findings identify the first RNA element known to regulate RNA localization across the apicobasal axis of epithelial cells, establish LARP1 as an RNA localization regulator, and demonstrate that RNA localization mechanisms cut across cell morphologies.

molecular biology↗

bioRxiv2022

Machine learning guided signal enrichment for ultrasensitive plasma tumor burden monitoring

In solid tumor oncology, circulating tumor DNA (ctDNA) is poised to transform care through accurate assessment of minimal residual disease (MRD) and therapeutic response monitoring. To overcome the sparsity of ctDNA fragments in low tumor fraction (TF) settings and increase MRD sensitivity, we previously leveraged genome-wide mutational integration through plasma whole genome sequencing (WGS). We now introduce MRD-EDGE, a composite machine learning-guided WGS ctDNA single nucleotide variant (SNV) and copy number variant (CNV) detection platform designed to increase signal enrichment. MRD-EDGE uses deep learning and a ctDNA-specific feature space to increase SNV signal to noise enrichment in WGS by 300X compared to our previous noise suppression platform MRDetect. MRD-EDGE also reduces the degree of aneuploidy needed for ultrasensitive CNV detection through WGS from 1Gb to 200Mb, thereby expanding its applicability to a wider range of solid tumors. We harness the improved performance to track changes in tumor burden in response to neoadjuvant immunotherapy in non-small cell lung cancer and demonstrate ctDNA shedding in precancerous colorectal adenomas. Finally, the radical signal to noise enrichment in MRD-EDGE enables de novo mutation calling in melanoma without matched tumor, yielding clinically informative TF monitoring for patients on immune checkpoint inhibition.

genomics↗

bioRxiv2021

Nfeature: A platform for computing features of nucleotide sequences

In the past few decades, public repositories on nucleotides have increased with exponential rates. This pose a major challenge to researchers to predict the structure and function of nucleotide sequences. In order to annotate function of nucleotide sequences it is important to compute features/attributes for predicting function of these sequences using machine learning techniques. In last two decades, several software/platforms have been developed to elicit a wide range of features for nucleotide sequences. In order to complement the existing methods, here we present a platform named Nfeature developed for computing wide range of features of DNA and RNA sequences. It comprises of three major modules namely Composition, Correlation, and Binary profiles. Composition module allow to compute different type of compositions that includes mono-/di-tri-nucleotide composition, reverse complement composition, pseudo composition. Correlation module allow to compute various type of correlations that includes auto-correlation, cross-correlation, pseudo-correlation. Similarly, binary profile is developed for computing binary profile based on nucleotides, mono-nucleotides, di-/tri-nucleotide properties. Nfeature also allow to compute entropy of sequences, repeats in sequences and distribution of nucleotides in sequences. In addition to compute feature in whole sequence, it also allows to compute features from part of sequence like split, start, end, and rest. In a nutshell, Nfeature amalgamates existing features as well as number of novel features like nucleotide repeat index, distance distribution, entropy, binary profile, and properties. This tool computes a total of 29217 and 14385 features for DNA and RNA sequence, respectively. In order to provide, a highly efficient and userfriendly tool, we have developed a standalone package and web-based platform (https://webs.iiitd.edu.in/raghava/nfeature).

bioinformatics↗

bioRxiv2021

Harbingers of Aggressive Prostate Cancer: Precision Oncology Case Studies

Primary prostate cancer (PCa) can show marked molecular heterogeneity. However, systematic analyses comparing primary PCa and matched metastases in individual patients are lacking. We aimed to address the molecular aspects of metastatic progression while accounting for heterogeneity of primary PCa. In this pilot study, we collected 12 radical prostatectomy (RP) specimens from men who subsequently developed metastatic castration-resistant prostate cancer (mCRPC). We used histomorphology (Gleason grade, focus size, stage) and immunohistochemistry (IHC) (ERG and p53) to identify independent tumors and/or distinct subclones of primary PCa. We then compared molecular profiles of these primary PCa areas to matched metastatic samples using whole exome sequencing (WES) and amplicon-based DNA and RNA sequencing. Based on combined pathology and molecular analysis, seven (58%) RP specimens harbored monoclonal and topographically continuous disease, albeit with some degree of intra-tumor heterogeneity; four (33%) specimens showed true multifocal disease; and one displayed monoclonal disease with discontinuous topography. Early (truncal) events in primary PCa included SPOP p.F133V (one patient), BRAF p.K601E (one patient), and TMPRSS2:ETS rearrangements (nine patients). Activating AR alterations were seen in eight (67%) mCRPC patients, but not in matched primary PCa. Hotspot TP53 mutations, found in metastases from three patients, were readily present in matched primary disease. Alterations in genes encoding epigenetic modifiers were observed in several patients (either shared between primary foci and metastases or in metastatic samples only). WES-based phylogenetic reconstruction and/or clonality scores were consistent with the index focus designated by pathology review in six out of nine (67%) cases. The three instances of discordance pertained to monoclonal, topographically continuous tumors, which would have been considered as unique disease in routine practice. Overall, our results emphasize pathologic and molecular heterogeneity of primary PCa, and suggest that comprehensive IHC-assisted pathology review and genomic analysis are highly concordant in nominating the "index" primary PCa area.

cancer biology↗

bioRxiv2021

High-throughput identification of RNA localization elements reveals a regulatory role for A/G rich sequences

Hundreds of RNAs are enriched in the projections of neuronal cells. For the vast majority of them, though, the sequence elements within them that regulate their localization are unknown. To identify RNA elements capable of directing transcripts to neurites, we designed and deployed a massively parallel reporter assay that tested the localization regulatory ability of thousands of sequence fragments drawn from endogenous mouse 3' UTRs. We identified peaks of regulatory activity within several 3' UTRs and found that sequences derived from these peaks were both necessary and sufficient for RNA localization to neurites in mouse cells as well as active in human neurons. The active localization elements were enriched in adenosine and guanosine residues and were tens to hundreds of nucleotides long as shortening of the elements led to significantly reduced activity. Using RNA affinity and mass spectrometry, we found that the RNA-binding protein Unk was associated with the active localization elements. Depletion of Unk in cells reduced the neurite-enrichment of the elements, indicating a functional requirement for Unk in their trafficking. These results provide a framework for the unbiased, high-throughput identification of RNA elements and mechanisms that govern transcript localization.

molecular biology↗

bioRxiv2021

Free spermidine evokes superoxide radicals that manifest toxicity

Spermidine and other polyamines alleviate oxidative stress, yet excess spermidine seems toxic to Escherichia coli unless it is neutralized by SpeG, an enzyme for the spermidine N-acetyl transferase function. Besides, a specific mechanism of SpeG function conferring pathogenic fitness to Staphylococcus aureus USA300 strain is unknown. Here, we provide evidence that although spermidine mitigates oxidative stress by lowering hydroxyl radical and hydrogen peroxide levels, excess of it simultaneously triggers the production of superoxide radicals, thereby causing toxicity in the E. coli {Delta}speG strain as well as naturally SpeG-deficient S. aureus RN4220 strain. However, wild-type E. coli and S. aureus USA300 with a horizontally-acquired speG gene tolerate applied exogenous spermidine stress. Furthermore, we demonstrate that while RNA-bound spermidine inhibits iron oxidation, free spermidine interacts and oxidizes the iron to evoke superoxide radicals directly. Superoxide radicals thus generated, further affects redox balance and iron homeostasis. Since iron acquisition and metabolism in the host tissues is a challenging task for S. aureus, the current findings helped us explain that the evolutionary gain of SpeG function by S. aureus USA300 strain allows it to neutralize exogenous spermidine- and spermine-mediated toxicity towards iron metabolism inside the host tissues.

biochemistry↗

bioRxiv2021

Cooperative regulation of adherens junction elongation through Epidermal Growth Factor Receptor (EGFR) activation.

The mechanisms controlling the elongation rates of adherens junctions are poorly characterized and lag behind our understanding of those controlling their static properties. In suspended cell aggregates, we found that the speed of de novo junction formation between two cells increases with the number of junctions that the cells are already engaged in. This cooperative effect is driven by the transient activation of the Epidermal Growth Factor Receptor (EGFR) upon junction formation. EGFR activation then regulates the actin cytoskeleton turnover while cortical tension remains unaltered. Overall, we show that EGFR activation regulates the elongation speed of junctions (kinetype) without affecting their final size (phenotype) in such aggregates.

biophysics↗

bioRxiv2020

Pan-cancer identification of clinically relevant genomic subtypes using outcome-weighted integrative clustering

Molecular phenotypes of cancer are complex and influenced by a multitude of factors. Conventional unsupervised clustering of heterogeneous cancer patient populations is inevitably driven by the dominant variation from major factors such as cell-of-origin or histology. Drawing from ideas in supervised text classification, we developed survClust, an outcome-weighted clustering algorithm for integrative patient stratification. We show survClust outperforms unsupervised clustering in identifying cancer patient subpopulations characterized by specific genomic phenotypes with more aggressive clinical behavior. The algorithm and tools we developed have direct utility toward clinically relevant patient stratification based on tumor genomics to inform clinical decision-making.

genomics↗

bioRxiv2020

A Method to sort heterogenous cell populations based on migration in 2D and 3D environments

Whereas phenotypic assays such as Boyden chambers and wound healing assays can easily be employed to characterize the migratory potential of cells at the population level, few methods exist that can sort subpopulations of cells based on their migratory behaviour from an initial heterogeneous pool. In this paper, we present an approach to sort migratory cancer and immune cells based on their spontaneous migration in 2D and 3D microenvironments. Using this method, which is easy to implement and readily scalable, millions of live cells can be sorted based on their migratory characteristics and then subjected to downstream genomic, molecular and functional tests. We reveal that enrichment of the most migratory cytotoxic T lymphocytes yields a pool of cells with enhanced cytotoxicity against cancer cells. This new functional sorting method opens new avenues for the precise characterization of the mechanisms underlying hitherto unexplained heterogeneities in migratory phenotypes within a cell population, and for the targeted enrichment of the most potent migratory leukocytes in immunotherapies.

cell biology↗

bioRxiv2019

Type-2 diabetes with low LDL-C: genetic insights into a unique phenotype

Although hyperlipidemia is traditionally considered a risk factor for type-2 diabetes (T2D), evidence has emerged from statin trials and candidate gene investigations suggesting that lower LDL-C increases T2D risk. We thus sought to comprehensively examine the phenotypic and genotypic relationships of LDL-C with T2D. Using data from the UK Biobank, we found that LDL-C was negatively associated with T2D (OR=0.43[0.41, 0.45] per mmol/L unit of LDL-C), despite positive associations of LDL-C with HbA1c and BMI. We then performed the first genome-wide exploration of variants simultaneously associated with lower LDL-C and increased T2D risk, using data on LDL-C from the UK Biobank (n=431,167) and the GLGC consortium (n=188,577), and T2D from the DIAGRAM consortium (n=898,130). We identified 31 loci associated with lower LDL-C and increased T2D, capturing several potential mechanisms. Seven of these loci have previously been identified for this phenotype, and 9 have previously been implicated in non-alcoholic fatty liver disease. Finally, two-sample Mendelian randomization analyses suggest that low LDL-C causes T2D, although causal interpretations are challenging due to pleiotropy. Our findings extend our current understanding of the higher T2D risk among individuals with low LDL-C, and of the underlying mechanisms, including those underlying the diabetogenic effect of LDL-C-lowering medications.

genetics↗

bioRxiv2019

Single-cell analysis of EphA clustering phenotypes to probe cancer cell heterogeneity

Eph receptors, a family of receptor tyrosine kinases, play a crutial role in the assembly and maintenance of healthy tissues. Dysfunction in Eph signaling are causally and correlatively associated with cancer progression. In breast cancer cells, dysregulated Eph signaling has been largely linked to alterations in receptor clustering abilities. In the present study, we implemented a single-cell assay and a scoring scheme to systematically probe the spatial organization of activated EphA receptor in carcinoma cells of different origin. Using this assay, we found that cancer cells retained EphA clustering phenotype upon cell division for several generations and degree of clustering reported for population as well as single-cell migration potential. Finally, using patient-derived cancer cell lines, we probed the evolution of EphA signalling in cancer cell populations that underwent metastatic transformation and acquisition of drug resistance. Taken together, our simple and scalable approach provides a reliable quantitation of EphA associated gene expression and phenotypes in multiple carcinomas and can assay the heterogeneity of cancer cell populations in a cost- and time-effective manner.

cancer biology↗

bioRxiv2018

Stimulation of the posterior cingulate impairs episodic memory encoding

Neuroimaging experiments implicate the posterior cingulate cortex (PCC) in episodic memory processing, making it a potential target for responsive neuromodulation strategies outside of the hippocampal network. However, causal evidence for the role PCC plays in memory encoding is lacking. In patients undergoing seizure mapping, we investigated functional properties of the PCC using deep brain stimulation (DBS) and stereotactic electroencephalography (stereo EEG). These techniques allow precise targeting of deep cortical structures including the PCC, and simultaneous acquisition of oscillatory recordings from neighboring regions such as the hippocampus. We used a free recall experiment in which PCC was stimulated during item encoding period of half of the study lists, while no stimulation was applied during encoding period of the remaining lists. We evaluated if stimulation affected memory and/or modulated hippocampal activity. Results revealed four main findings. (i) Stimulation during encoding impaired memory for early items on the study lists. (ii) Stimulation increased hippocampal gamma band power. (iii) Stimulation-induced gamma power predicted memory impairment. (iv) Functional connectivity between the hippocampus and PCC predicted the degree of stimulation effect on memory. Our findings offer the first causal evidence implicating the PCC in episodic memory encoding. Importantly, results highlight that stimulation targeted outside of the temporal lobe can modulate hippocampal activity with implications on behavior. Furthermore, a-priori measures of connectivity between brain regions within a functional network can be informative in predicting behavioral effects of stimulation. Our findings have significant implications for developing therapie to treat diseases of memory loss and cognitive impairment using DBS.\n\nSignificance StatementCognitive impairment and memory loss are critical public health challenges. Deep brain stimulation (DBS) is a promising tool for developing strategies to ameliorate memory disorders by targeting brain regions involved in mnemonic processing. Using DBS, our study sheds light on the lesser-known role of the posterior cingulate cortex (PCC) in memory encoding. Stimulating the PCC during encoding impairs subsequent recall memory. The degree of impairment is predictedby stimulation-induced hippocampal gamma oscillations and functional connectivity between PCC and hippocampus. Our findings provide the first causal evidence implicating PCC in memory encoding and highlight the PCC as a favorable target for neuromodulation strategies, using a-priori connectivity measures to predict stimulation effects. This has significant implications for developing therapies for memory diseases.

neuroscience↗

bioRxiv2018

Genetic determinants of risk and survival in pulmonary arterial hypertension

BackgroundPulmonary arterial hypertension (PAH) is a rare disorder leading to premature death. Rare genetic variants contribute to disease etiology but the contribution of common genetic variation to disease risk and outcome remains poorly characterized.\n\nMethodsWe performed two separate genome-wide association studies of PAH using data across 11,744 European-ancestry individuals (including 2,085 patients), one with genotypes from 5,895 whole genome sequences and another with genotyping array data from 5,849 further samples. Cross-validation of loci reaching genome-wide significance was sought by meta-analysis. We functionally annotated associated variants and tested associations with duration of survival.\n\nFindingsA locus at HLA-DPA1/DPB1 within the class II major histocompatibility (MHC) region and a second near SOX17 were significantly associated with PAH. The SOX17 locus contained two independent signals associated with PAH. Functional and epigenomic data indicate that the risk variants near SOX17 alter gene regulation via an enhancer active in endothelial cells. PAH risk variants determined haplotype-specific enhancer activity and CRISPR-inhibition of the enhancer reduced SOX17 expression. Analysis of median survival showed that PAH patients with two copies of the HLA-DPA1/DPB1 risk variant had a two-fold difference (>16 years versus 8 years), compared to patients homozygous for the alternative allele.\n\nInterpretationWe have found that common genetic variation at loci in HLA-DPA1/DPB1 and an enhancer near SOX17 are associated with PAH. Impairment of Sox17 function may be more common in PAH than suggested by rare mutations in SOX17. Allelic variation at HLA-DPB1 stratifies PAH patients for survival following diagnosis, with implications for future therapeutic trial design.\n\nFundingUK NIHR, BHF, UK MRC, Dinosaur Trust, NIH/NHLBI, ERS, EMBO, Wellcome Trust, EU, AHA, ACClinPharm, Netherlands CVRI, Dutch Heart Foundation, Dutch Federation of UMC, Netherlands OHRD and RNAS, German DFG, German BMBF, APH Paris, Inserm, Universite Paris-Sud, and French ANR.

genetics↗