Biology subjects

Murali, R.

Publications and source records attributed to Murali, R..

24 records · Page 2Linked to original sources

bioRxiv2021

Evolution of the cytochrome-bd type oxygen reductase superfamily and the function of cydAA in Archaea

Cytochrome bd-type oxygen reductases (cytbd) belong to one of three enzyme superfamilies that catalyze oxygen reduction to water. They are widely distributed in Bacteria and Archaea, but the full extent of their biochemical diversity is unknown. Here we used phylogenomics to identify 3 families and several subfamilies within the cytbd superfamily. The core architecture shared by all members of the superfamily consists of four transmembrane helices that bind two active site hemes, which are responsible for oxygen reduction. While previously characterized cytochrome bd-type oxygen reductases use quinol as an electron donor to reduce oxygen, sequence analysis shows that only one of the identified families has a conserved quinol binding site. The other families are missing this feature, suggesting that they use an alternative electron donor. Multiple gene duplication events were identified within the superfamily, resulting in significant evolutionary and structural diversity. The CydAA cytbd, found exclusively in Archaea, is formed by the co-association of two superfamily paralogs. We heterologously expressed CydAA from Caldivirga maquilingensis and demonstrated that it performs oxygen reduction with quinol as an electron donor. Strikingly, CydAA is the first isoform of cytbd containing only b-type hemes shown to be active when isolated, demonstrating that oxygen reductase activity in this superfamily is not dependent on heme d.

biochemistry↗

bioRxiv2020

RIPK2 Stabilizes c-Myc and is an Actionable Target for Inhibiting Prostate Cancer Metastasis

Despite advances in diagnosis and treatment, metastatic prostate cancer remains incurable and is associated with high mortality rates. Thus, novel actionable drug targets are urgently needed for therapeutic interventions in advanced prostate cancer. Here we report receptor-interacting protein kinase 2 (RIPK2) as an actionable drug target for suppressing prostate cancer metastasis. RIPK2 is frequently amplified in lethal prostate cancers and its overexpression is associated with disease progression and aggressiveness. Genetic and pharmacological inhibition of RIPK2 significantly suppressed prostate cancer progression in vitro and metastasis in vivo. Multi-level proteomic analysis revealed that RIPK2 strongly regulates c-Myc protein stability and activity, largely by activating the MKK7/JNK/c-Myc phosphorylation pathway--a novel, non-canonical RIPK2 signaling pathway. Targeting RIPK2 inhibits this phosphorylation pathway, and thus promotes the degradation of c-Myc--a potent oncoprotein for which no drugs have been approved for clinical use yet. These results support targeting RIPK2 for personalized therapy in prostate cancer patients towards improving survival.

cancer biology↗

bioRxiv2019

Structural studies of the Tudor domain from the Bombyx homolog of Drosophila PAPI: Implication to piRNA biogenesis

PIWI proteins and their associated PIWI-interacting RNAs (piRNAs) play crucial roles in proper gametogenesis in animal gonads. Partner of PIWIs (PAPI) is one of the important piRNA biogenesis factors. PAPI contains a Tudor domain and tandem KH domains. The tudor domain specifically recognizes symmetrical-dimethylarginines (sDMAs) on PIWI proteins. BmPAPI, a Bombyx mori homolog of PAPI, is localized at the outer membrane of mitochondria and supports exonucleolytic trimming of piRNA precursors to form mature 3-end of piRNAs. To understand the structural basis of piRNA processing by BmPAPI, we present crystal structures of the apo- and sDMA-liganded Tudor domain of BmPAPI.

biophysics↗

bioRxiv2019

Targeting the Id1-Kif11-Aurka axis in triple negative breast cancer using combination therapy

Evidence points to breast cancer following a hierarchical model, with Cancer Stem Cells (CSCs) driving critical phenotypes of the bulk tumor. Chemoresistant CSCs are not an abstract concept but have clinical consequences as they drive relapse and ultimately lead to mortality in patients, making it imperative to understand how these subpopulations of cells survive. Our previous work (1-2) has demonstrated that the bHLH transcription factor, Inhibitor of Differentiation 1 (Id1) and its closely related family member Id3, have an important role in maintaining the CSC phenotype in the Triple Negative breast cancer (TNBC) subtype. A genetic screen conducted to further elucidate the molecular mechanism underlying the Id (Id1/3) mediated CSC phenotypes in TNBC revealed critical cell cycle genes such as Kif11 and Aurka as putative Id targets. We take this work forward by investigating how alteration in Kif11 and Aurka via Id proteins promotes the CSC phenotype in TNBC. Cells lacking Id are poised in a state of G0/G1 arrest from which they can re-enter the cell cycle. Intriguingly, depletion of Kif11 and Aurka independently did not phenocopy the G0/G1 arrest observed in Id knockdown (Id KD) cells. We have further explored the hypothesis that we can deplete the chemo resistant Id expressing CSC population by combining chemotherapy with targeted therapy using existing small molecule inhibitors (against Id target Kif11) to more effectively debulk the entire tumor. This work opens up exciting new possibilities of targeting Id targets like Kif11, in the TNBC subtype which is currently refractory to chemotherapy.

cancer biology↗

bioRxiv2019

FIND: Identifying Functionally and Structurally Important Features in Protein Sequences with Deep Neural Networks

The ability to correctly predict the functional role of proteins from their amino acid sequences would significantly advance biological studies at the molecular level by improving our ability to understand the biochemical capability of biological organisms from their genomic sequence. Existing methods that are geared towards protein function prediction or annotation mostly use alignment-based approaches and probabilistic models such as Hidden-Markov Models. In this work we introduce a deep learning architecture (Function Identification with Neural Descriptions or FIND) which performs protein annotation from primary sequence. The accuracy of our methods matches state of the art techniques, such as protein classifiers based on Hidden Markov Models. Further, our approach allows for model introspection via a neural attention mechanism, which weights parts of the amino acid sequence proportionally to their relevance for functional assignment. In this way, the attention weights automatically uncover structurally and functionally relevant features of the classified protein and find novel functional motifs in previously uncharacterized proteins. While this model is applicable to any database of proteins, we chose to apply this model to superfamilies of homologous proteins, with the aim of extracting features inherent to divergent protein families within a larger superfamily. This provided insight into the functional diversification of an enzyme superfamily and its adaptation to different physiological contexts. We tested our approach on three families (nitrogenases, cytochrome bd-type oxygen reductases and heme-copper oxygen reductases) and present a detailed analysis of the sequence characteristics identified in previously characterized proteins in the heme-copper oxygen reductase (HCO) superfamily. These are correlated with their catalytic relevance and evolutionary history. FIND was then applied to discover features in previously uncharacterized members of the HCO superfamily, providing insight into their unique sequence features. This modeling approach demonstrates the power of neural networks to recognize patterns in large datasets and can be utilized to discover biochemically and structurally important features in proteins from their amino acid sequences.\n\nAuthor summary

bioinformatics↗

bioRxiv2018

Id proteins promote a cancer stem cell phenotype in triple negative breast cancer via Robo1-dependent c-Myc activation

Breast cancers display phenotypic and functional heterogeneity and several lines of evidence support the existence of cancer stem cells (CSCs) in certain breast cancers, a minor population of cells capable of tumor initiation and metastatic dissemination. Identifying factors that regulate the CSC phenotype is therefore important for developing strategies to treat metastatic disease. The Inhibitor of Differentiation Protein 1 (Id1) and its closely related family member Inhibitor of Differentiation 3 (Id3) (collectively termed Id) are expressed by a diversity of stem cells and are required for metastatic dissemination in experimental models of breast cancer. In this study, we show that ID1 is expressed in rare neoplastic cells within ER-negative breast cancers. To address the function of Id1 expressing cells within tumors, we developed two independent murine models of Triple Negative Breast Cancer (TNBC) in which a genetic reporter permitted the prospective isolation of Id1+ cells. Id1+ cells are enriched for self-renewal in tumorsphere assays in vitro and for tumor initiation in vivo. Conversely, depletion of Id1 and Id3 in the 4T1 murine model of TNBC demonstrates that Id1/3 are required for cell proliferation and self-renewal in vitro, as well as primary tumor growth and metastatic colonization of the lung in vivo. Using combined bioinformatic analysis, we have defined a novel mechanism of Id protein function via negative regulation of the Roundabout Axon Guidance Receptor Homolog 1 (Robo1) leading to activation of a Myc transcriptional programme.

cancer biology↗