Biology subjects

Prabakaran, S.

Publications and source records attributed to Prabakaran, S..

8 recordsLinked to original sources

bioRxiv2021

Transcriptional variability accelerates pre-leukemia by cell diversification and perturbation of protein synthesis

Transcriptional variability facilitates stochastic cell diversification and can in turn underpin adaptation to stress or injury. We hypothesize that it may analogously facilitate progression of pre-malignancy to cancer. To investigate this, we initiated pre-leukemia in mouse cells with enhanced transcriptional variability due to conditional disruption of the histone lysine acetyltransferase gene Kat2a. By combining single-cell RNA-sequencing of pre-leukemia with functional analysis of transformation, we show that Kat2a loss results in global variegation of cell identity and accumulation of pre-leukemic cells. Leukemia progression is subsequently facilitated by destabilization of ribosome biogenesis and protein synthesis, which confer a transient transformation advantage. The contribution of transcriptional variability to early cancer evolution reflects a generic role in promoting cell fate transitions, which, in the case of well-adapted malignancies, contrastingly differentiates and depletes cancer stem cells. In other words, transcriptional variability confers forward momentum to cell fate systems, with differential multi-stage impact throughout cancer evolution. One-sentence summaryLoss of Kat2a enhances transcriptional variability of ribosome biosynthetic programs and transiently accelerates pre-leukemia

cancer biology↗

bioRxiv2020

Use of short-read RNA-Seq data to identify transcripts that can translate novel ORFs

Identification of as of yet unannotated or undefined novel open reading frames (nORFs) and exploration of their functions in multiple organisms has revealed that vast regions of the genome have remained unexplored or hidden. Present within both protein-coding and noncoding regions, these nORFs signify the presence of a much more diverse proteome than previously expected. Given the need to study nORFs further, proper identification strategies must be in place, especially because they cannot be identified using conventional gene signatures. Although Ribo-Seq and proteogenomics are frequently used to identify and investigate nORFs, in this study, we propose a workflow for identifying nORF containing transcripts using our precompiled database of nORFs with translational evidence, using sample transcript information. Further, we discuss the potential uses of this identification, the caveats involved in such a transcript identification and finally present a few representative results from our analysis of naive mouse B and T cells, human post-mortem brain and cichlid fish transcriptome. Our proposed workflow can identify noncoding transcripts that can potentially translate intronic, intergenic and several other classes of nORFs. One-line summaryA systematic workflow to identify nORF containing transcripts using sample transcript information.

genomics↗

bioRxiv2020

Rapid speciation of cichlids fishes may be explained by evolutionary divergence of novel open reading frames

Novel open reading frames (nORFs) with coding potential may arise from noncoding DNA. Not much is known about their emergence, functional role, fixation in a population or contribution to adaptive radiation. Cichlids fishes exhibit extensive phenotypic diversification and speciation. Encounters with new environments alone are not sufficient to explain this striking diversity of cichlid radiation because other taxa coexistent with the Cichlidae demonstrate lower species richness. Wagner et al analyzed cichlid diversification in 46 African lakes and reported that both extrinsic environmental factors and intrinsic lineage-specific traits related to sexual selection have strongly influenced the cichlid radiation 1, which indicates the existence of unknown molecular mechanisms responsible for rapid phenotypic diversification, such as emergence of novel open reading frames (nORFs). In this study, we integrated transcriptomic and proteomic signatures from two tissues of two cichlids species, identified nORFs and performed evolutionary analysis on these nORF regions. Our results suggest that the time scale of speciation of the two species and evolutionary divergence of these nORF genomic regions are similar and indicate a potential role for these nORFs in speciation of the cichlid fishes.

evolutionary biology↗

bioRxiv2019

Behavioural analysis of single-cell aneural ciliate, Stentor roeselii, using machine learning approaches

There is still a significant gap between our understanding of neural circuits and the behaviours they compute - i.e. the computations performed by these neural networks (Carandini 2012). Learning, behaviour, and memory formation, what used to only be associated with animals with neural systems, have been observed in many unicellular aneural species, namely Physarum, Paramecium, and Stentor (Tang & Marshall 2018). As these are fully functioning organisms, yet being unicellular, there is a much better chance to elucidate the detailed mechanisms underlying these learning processes in these organisms without the complications of highly interconnected neural circuits. An intriguing learning behaviour observed in Stentor roeselii (Jennings 1902) when stimulated with carmine has left scientists puzzled for more than a century. So far, none of the existing learning paradigm can fully encapsulate this particular series of five characteristic avoidant reactions. Although we were able to observe all responses described in literature and in a previous study (Dexter et al. 2019, manuscript in preparation), they do not conform to any particular learning model. We then investigated whether models based on machine learning approaches, including decision tree, random forest, and feed-forward neural networks could infer and predict the behavior of S. roeselii. Our results showed that an artificial neural network with multiple computational neurons is inefficient at modelling the single-celled ciliates avoidant reactions. This has highlighted the complexity of behaviours in aneural organisms. Additionally, this report will also discuss the significance of elucidating molecular details underlying learning and decision-making processes in these unicellular organisms, which could offer valuable insights that are applicable to higher animals.

animal behavior and cognition↗

bioRxiv2019

Translational products encoded by novel ORFs may form protein-like structures and have biological functions

Translation products encoded by non canonical or novel open reading frame (ORF) genomic regions are generally considered too small to play any significant biological role, and dismissed as inconsequential. In this study, we show that mutations mapping to novel ORFs have significantly higher pathogenicity scores than mutations in protein-coding regions. Importantly, novel ORFs can translate into protein-like structures with putative independent biological functions that can be of relevance in disease states, including cancer. We thus provide strong evidence to support the systematic study of novel ORFs to gain new insights into normal biological and disease processes.\n\nOne Sentence SummaryNon coding regions may encode protein-like products that are important to understand diseases.

genetics↗

bioRxiv2019

Proteins encoded by Novel ORFs have increased disorder but can be biochemically regulated and harbour pathogenic mutations

Recent evidence has suggested that protein or protein-like products can be encoded by previously uncharacterized Open Reading Frames (ORFs) that we define as Novel Open Reading Frames (nORFs)1,2. These nORFs are present in both coding and non coding regions of the human genome and the novel proteins that they encode have increased the number and complexity of cellular proteome from bacteria to humans. It is a conundrum whether these protein or protein-like products could play any significant functional biological role. But hopes have been raised to target them for anticancer and antimicrobial therapy3,4. To infer whether these novel proteins can perform biological functions, we used computational predictions to systematically investigate whether their amino acid sequences can form ordered or disordered structures. Our results indicated that that these novel proteins have significantly higher predicted disorder structure compared to all known proteins, yet we do not find any correlation between the pathogenicity of the mutations and whether they are present in the ordered and disordered regions of these novel proteins. This study reveals that we should investigate these novel proteins more systematically as they may be important to understand complex diseases.

genetics↗

bioRxiv2018

Loss Of Kat2a Enhances Transcriptional Noise And Depletes Acute Myeloid Leukemia Stem-Like Cells

Acute Myeloid Leukemia (AML) is an aggressive hematological malignancy with abnormal progenitor self-renewal and defective myelo-monocytic differentiation. Its pathogenesis comprises subversion of transcriptional regulation, through mutation and by hijacking normal chromatin regulation. Kat2a is a histone acetyltransferase central to promoter activity that we recently associated with stability of pluripotency networks, and identified as a genetic vulnerability in AML. Through combined chromatin profiling and single-cell transcriptomics, we demonstrate that Kat2a contributes to leukemia propagation through homogeneity of transcriptional programs and preservation of leukemia stem-like cells. Kat2a loss reduces transcriptional bursting frequency in a subset of gene promoters, generating enhanced variability of transcript levels but minimal effects on mean gene expression. Destabilization of target programs shifts cellular equilibrium out of self-renewal towards differentiation. We propose that control of transcriptional variability is central to leukemia stem-like cell propagation, and establish a paradigm exploitable in different tumors and at distinct stages of cancer evolution.

cancer biology↗

bioRxiv2017

Identification And Prioritisation Of Variants In The Short Open-Reading Frame Regions Of The Human Genome

As whole-genome sequencing technologies improve and accurate maps of the entire genome are assembled, short open-reading frames (sORFs) are garnering interest as functionally important regions that were previously overlooked. However, there is a paucity of tools available to investigate variants in sORF regions of the genome. Here we investigate the performance of commonly used tools for variant calling and variant prioritisation in these regions, and present a framework for optimising these processes. First, the performance of four widely used germline variant calling algorithms is systematically compared. Haplotype Caller is found to perform best across the whole genome, but FreeBayes is shown to produce the most accurate variant set in sORF regions. An accurate set of variants is found by taking the intersection of called variants. The potential deleteriousness of each variant is then predicted using a pathogenicity scoring algorithm developed here, called sORF-c. This algorithm uses supervised machine-learning to predict the pathogenicity of each variant, based on a holistic range of functional, conservation-based and region-based scores defined for each variant. By training on a dataset of over 130,000 variants, sORF-c outperforms other comparable pathogenicity scoring algorithms on a test set of variants in sORF regions of the human genome.\n\nList of Abbreviations

genetics↗