Biology subjects

Sharma, A.

Publications and source records attributed to Sharma, A..

22 records · Page 2Linked to original sources

bioRxiv2017

Inert and seed-competent tau monomers elucidate the structural origins of aggregation

Tauopathies feature progressive accumulation of tau amyloids. Pathology may begin when these amplify from a protein template, or seed, whose structure is unknown. We have purified and characterized distinct forms of tau monomer--inert (Mi) and seed-competent (Ms). Recombinant Ms triggered intracellular tau aggregation, induced tau fibrillization in vitro, and self-assembled. Ms from Alzheimers disease also seeded aggregation and self-assembled in vitro to form seed-competent multimers. We used crosslinking with mass spectrometry to probe structural differences in Mi vs. Ms. Crosslinks informed models of local peptide structure within the repeat domain which suggest relative inaccessibility of residues that drive aggregation (VQIINK/VQIVYK) in Mi, and exposure in Ms. Limited proteolysis supported this idea. Although tau monomer has been considered to be natively unstructured, our findings belie this assumption and suggest that initiation of pathological aggregation could begin with conversion of tau monomer from an inert to a seed-competent form.

neuroscience↗

bioRxiv2017

Engagement rules that underpin DBL-DARC interactions for ingress of Plasmodium knowlesi and Plasmodium vivax into human erythrocytes

The molecular mechanisms by which P. knowlesi and P. vivax invade human red blood cells have long been studied. Malaria parasite erythrocytic stages comprise of repeated bursts of parasites via cyclical invasion of host RBCs using dedicated receptor-ligand interactions. A family of erythrocyte-binding proteins (EBPs) from P. knowlesi and P. vivax attach to human Duffy antigen receptor for chemokines (DARC) via their Duffy binding-like domains (Pv-DBL and Pk-DBL respectively) for invasion. Here, we provide a comprehensive overview that presents new insights on the atomic resolution interactions that underpin the binding of human DARC with Pk/Pv-DBLs. Based on extensive structural and biochemical data, we provide a novel, testable and overarching interaction model that rationalizes even contradictory pieces of evidence that have so far existed in the literature on Pk/Pv-DBL/DARC binding determinants. We address the conundrum of how parasite-encoded Pk/Pv-DBLs recognize human DARC via its two sulfated tyrosine residues. We collate evidence for two distinct DARC integration sites on Pk/Pv-DBLs that together likely engage the DARCs sulfated extracellular domain. These analyses are important for both malaria vaccine and inhibitor development efforts that are targeted at abrogating Pk/Pv-DBL/DARC coupling as one avenue to prevent invasion of P. vivax into human red blood cells.

microbiology↗

bioRxiv2017

The multiplex network of human diseases

Untangling the complex interplay between phenotype and genotype is crucial to the effective characterization and subtyping of diseases. Here we build and analyze the multiplex network of 779 human diseases, which consists of a genotype-based layer and a phenotype-based layer. We show that diseases with common genetic constituents tend to share symptoms, and uncover how phenotype information helps boost genotype information. Moreover, we offer a flexible classification of diseases that considers their molecular underpinnings alongside their clinical manifestations. We detect cohesive groups of diseases that have high intra-group similarity at both the molecular and the phenotypic level. Inspecting these disease classes, we demonstrate the underlying pathways that connect diseases mechanistically. We observe monogenic disorders grouped together with complex diseases for which they increase the risk factor. We propose potentially new disease associations that arise as a unique feature of the information flow within and across the two layers.

bioinformatics↗

bioRxiv2016

Binary Relation Extraction from Biomedical Literature using Dependency Trees and SVMs

The goal of Biomedical relation extraction is to uncover high-quality relations from life science literature with diverse applications in the fields of Biology and Medicine. In the last decade, several methods can be found in published literature ranging from binary to complex relation extraction. In this work, we present a binary relation extraction system that relies on sentence level dependency features. We use a novel approach to map dependency tree based rules to feature vectors that can be used to train a classifier. We build a SVM classifier using these feature vectors and our experimental results show that it outperforms simple co-occurrence and rule-based systems. Through our experiments, using two 'real-world' examples, we quantify the positive impact of improved relation extraction on Literature Based Discovery.

bioinformatics↗