Biology subjects

Nair, S.

Publications and source records attributed to Nair, S..

At least 55 records · Page 3Linked to original sources

bioRxiv2023

Yerba mate (Ilex paraguariensis) genome provides new insights into convergent evolution of caffeine biosynthesis

Yerba mate (Ilex paraguariensis) is an economically important crop marketed for the elaboration of mate, the third-most widely consumed caffeine-containing infusion worldwide. Here we report the first genome assembly of this species, which has a total length of 1.06 Gb and contains 53,390 protein-coding genes. Comparative analyses revealed that the large yerba mate genome size is partly due to a whole-genome duplication (Ip-) during the early evolutionary history of Ilex, in addition to the hexaploidization event ({gamma}) shared by core eudicots. Characterization of the genome allowed us to clone the genes encoding methyltransferase enzymes that catalyse multiple reactions required for caffeine production. To our surprise, this species has converged upon a different biochemical pathway compared to that of its relatives, coffee and tea. In order to gain insight into the structural basis for the convergent enzyme activities, we obtained a crystal structure for the terminal enzyme in the pathway that forms caffeine. The structure reveals that convergent solutions have evolved for substrate positioning because different amino acid residues facilitate a different substrate orientation such that efficient methylation occurs in the independently evolved enzymes in yerba mate and coffee. While our results show phylogenomic constraint limits the genes coopted for convergence of caffeine biosynthesis, the x-ray diffraction data suggests structural constraints are minimal for the convergent evolution of individual reactions.

plant biology↗

bioRxiv2023

Segment Anything for Microscopy

We present Segment Anything for Microscopy, a tool for interactive and automatic segmentation and tracking of objects in multi-dimensional microscopy data. Our method is based on Segment Anything, a vision foundation model for image segmentation. We extend it by training specialized models for microscopy data that significantly improve segmentation quality for a wide range of imaging conditions. We also implement annotation tools for interactive (volumetric) segmentation and tracking, that speed up data annotation significantly compared to established tools. Our work constitutes the first application of vision foundation models to microscopy, laying the groundwork for solving image analysis problems in these domains with a small set of powerful deep learning architectures.

bioinformatics↗

bioRxiv2023

PDBe CCDUtils: an RDKit-based toolkit for handling and analysing small molecules in the Protein Data Bank

While the Protein Data Bank (PDB) contains a wealth of structural information on ligands bound to macromolecules, their analysis can be challenging due to the large amount and diversity of data. Here, we present PDBe CCDUtils, a versatile toolkit for processing and analysing small molecules from the PDB in PDBx/mmCIF format. PDBe CCDUtils provides streamlined access to all the metadata for small molecules in the PDB and offers a set of convenient methods to compute various properties using RDKit, such as 2D depictions, 3D conformers, physicochemical properties, scaffolds, common fragments and cross-references to small molecule databases using UniChem. The toolkit also provides methods for identifying all the covalently attached chemical components in a macromolecular structure and calculating similarity among small molecules. By providing a broad range of functionality, PDBe CCDUtils caters to the needs of researchers in cheminformatics, structural biology, bioinformatics and computational chemistry.

bioinformatics↗

bioRxiv2023

Identifying Protein Conformational States in the PDB and Comparison to AlphaFold2 Predictions

Proteins, as molecular machines, are necessarily dynamic macromolecules that carry out essential cellular functions. Recognising their stable conformations is important for understanding the molecular mechanisms of disease. While AI-based computational methods have enabled protein structure prediction, the prediction of protein dynamics remains a challenge. Here, we present a deterministic pipeline that clusters experimentally determined protein structures to comprehensively recognise conformational states across the Protein Data Bank. Our approach clusters protein chains based on a GLObal CONformation (GLOCON) difference score, which is computed from pairwise C-alpha distances. By superposing the clustered structures, differences and similarities in conformational states can be observed. Additionally, we offer users the ability to superpose predicted models from the AlphaFold Database to the clusters of PDB structures. This clustering pipeline significantly advances researchers ability to explore the conformational landscape within the PDB. All clustered and superposed models can be viewed in Mol* on the PDBe Knowledge Base website, or accessed in as raw annotations via our GraphAPI and FTP server. The clustering package is made available as an open-source Python3 package under the Apache-2.0 license.

bioinformatics↗

bioRxiv2023

PICKLUSTER: A protein-interface clustering and analysis plug-in for UCSF ChimeraX

MotivationThe identification and characterization of interfaces in protein complexes is crucial for understanding the mechanisms of molecular recognition. These interfaces are also attractive targets for protein inhibition. However, targeting protein interfaces can be challenging for large interfaces that consist of multiple interacting regions. We present PICKLUSTER -a program for identifying sub-interfaces in protein-protein complexes using distance clustering. The division of the interface into smaller "sub-interfaces" offers a more focused approach for targeting protein-protein interfaces. Availability and implementationThe plug-in PICKLUSTER is implemented for the molecular visualization program UCSF ChimeraX 1.4 and subsequent versions and and is freely available in the ChimeraX toolshed or, together with the source code, from https://gitlab.com/topf-lab/pickluster.git). Contactmaya.topf@cssb-hamburg.de

bioinformatics↗

bioRxiv2023

Annotating Macromolecular Complexes in the Protein Data Bank: Improving the FAIRness of Structure Data

Macromolecular complexes are essential functional units in nearly all cellular processes, and their atomic-level understanding is critical for elucidating and modulating molecular mechanisms. The Protein Data Bank (PDB) serves as the global repository for experimentally determined structures of macromolecules. Structural data in the PDB offer valuable insights into the dynamics, conformation, and functional states of biological assemblies. However, the current annotation practices lack standardised naming conventions for assemblies in the PDB, complicating the identification of instances representing the same assembly. In this study, we introduce a method leveraging resources external to PDB, such as the Complex Portal, UniProt and Gene Ontology, to describe assemblies and contextualise them within their biological settings accurately. Employing the proposed approach, we assigned standard names and provided value-added annotations to over 90% of unique assemblies in the PDB. This standardisation of assembly data enhances the PDB, facilitating a deeper understanding of these cellular components. Furthermore, the data standardisation improves the PDBs FAIR attributes, fostering more effective basic and translational research and education across scientific disciplines.

bioinformatics↗

bioRxiv2023

Orthogonal CRISPR screens to identify transcriptional and epigenetic regulators of human CD8 T cell function

The clinical response to adoptive T cell therapies is strongly associated with transcriptional and epigenetic state. Thus, technologies to discover regulators of T cell gene networks and their corresponding phenotypes have great potential to improve the efficacy of T cell therapies. We developed pooled CRISPR screening approaches with compact epigenome editors to systematically profile the effects of activation and repression of 120 transcription factors and epigenetic modifiers on human CD8+ T cell state. These screens nominated known and novel regulators of T cell phenotypes with BATF3 emerging as a high confidence gene in both screens. We found that BATF3 overexpression promoted specific features of memory T cells such as increased IL7R expression and glycolytic capacity, while attenuating gene programs associated with cytotoxicity, regulatory T cell function, and T cell exhaustion. In the context of chronic antigen stimulation, BATF3 overexpression countered phenotypic and epigenetic signatures of T cell exhaustion. CAR T cells overexpressing BATF3 significantly outperformed control CAR T cells in both in vitro and in vivo tumor models. Moreover, we found that BATF3 programmed a transcriptional profile that correlated with positive clinical response to adoptive T cell therapy. Finally, we performed CRISPR knockout screens with and without BATF3 overexpression to define co-factors and downstream factors of BATF3, as well as other therapeutic targets. These screens pointed to a model where BATF3 interacts with JUNB and IRF4 to regulate gene expression and illuminated several other novel targets for further investigation.

genomics↗

bioRxiv2023

Antibody-directed extracellular proximity biotinylation reveals Contactin-1 regulates axo-axonic innervation of axon initial segments.

Axon initial segment (AIS) cell surface proteins mediate key biological processes in neurons including action potential initiation and axo-axonic synapse formation. However, few AIS cell surface proteins have been identified. Here, we used antibody-directed proximity biotinylation to define the cell surface proteins in close proximity to the AIS cell adhesion molecule Neurofascin. To determine the distributions of the identified proteins, we used CRISPR-mediated genome editing for insertion of epitope tags in the endogenous proteins. We found Contactin-1 (Cntn1) among the previously unknown AIS proteins we identified. Cntn1 is enriched at the AIS through interactions with Neurofascin and NrCAM. We further show that Cntn1 contributes to assembly of the AIS-extracellular matrix, and is required for AIS axo-axonic innervation by inhibitory basket cells in the cerebellum and inhibitory chandelier cells in the cortex.

neuroscience↗

bioRxiv2022

The origins of haplotype 58 (H58) Salmonella enterica serovar Typhi

Antimicrobial resistance (AMR) poses a serious threat to the clinical management of typhoid fever. AMR in Salmonella Typhi (S. Typhi) is associated with the H58 lineage, which arose comparatively recently before becoming globally disseminated. To better understand when and how this lineage emerged and became dominant, we performed detailed phylogenetic and phylodynamic analyses on contemporary genome sequences from S. Typhi isolated in the period spanning the emergence. Our dataset, which contains the earliest described H58 S. Typhi, indicates that the prototype H58 organisms were multi-drug resistant (MDR). These organisms emerged spontaneously in India in 1987 and became radially distributed throughout South Asia and then globally in the ensuing years. These early organisms were associated with a single long branch, possessing mutations associated with increased bile tolerance, suggesting that the first H58 organism was generated during chronic carriage. The subsequent use of fluoroquinolones led to several independent mutations in gyrA. The ability of H58 to acquire and maintain AMR genes continues to pose a threat, as extensively drug-resistant (XDR; MDR plus resistance to ciprofloxacin and third generation cephalosporins) variants, have emerged recently in this lineage. Understanding where and how H58 S. Typhi originated and became successful is key to understand how AMR drives successful lineages of bacterial pathogens. Additionally, these data can inform optimal targeting of typhoid conjugate vaccines (TCVs) for reducing the potential for emergence and the impact of new drug-resistant variants. Emphasis should also be placed upon the prospective identification and treatment of chronic carriers to prevent the emergence of new drug resistant variants with the ability to spread efficiently.

microbiology↗

bioRxiv2022

Host network-based discovery of critical regulators of innate immunity, virus growth, and pathogenesis in influenza virus infection

Innate immunity is protective against viruses, but also can facilitate pathological infection responses. Despite intensive research, our understanding of the mechanisms that regulate innate immunity in virus infection remains incomplete. Systems biology-based data-driven modeling approaches hold substantial promise toward discovery of crucial innate immune signaling regulators, yet model-derived predictions are almost completely unexplored. Here, we carried out systematic experimental validation of candidate regulators predicted by a transcriptional association network model of influenza virus-infected cells. We identified dozens of novel innate immune signaling regulators with potent effects on the replication of influenza and other viruses, and importantly, we established the biological relevance of a validated regulator in vivo. Collectively, these findings aid in clarifying mechanisms of influenza virus pathogenicity and might lead to innovative approaches for treating influenza virus disease. Similar data-driven modeling strategies may be applicable for the study of other pathogen systems or immunological disorders.

microbiology↗

bioRxiv2022

Analysis of Inbred Mouse strains' High-Impact Genotype-phenotype Hypotheses (AIMHIGH) reveals novel disease-causing candidate genes

Inbred mouse strains reveal the molecular basis of mammalian traits and diseases, particularly recessive ones. We utilized mouse community curated resources to set up an automated screen to discover novel testable gene function hypotheses. Using 11,832 community contributed strain-differentiating experiments and trait presence/absence scoring, we searched for all experiments where strains can be split by their phenotypic values (e.g., high vs. low responders). Then, using 48 sequenced strains, we found one or more candidate gene for each experiment where homozygous high-impact variants (such as stopgain, frameshifts) segregate strains into these same binary grouping. Our approach rediscovered 212 known gene-phenotype relationships, almost always highlighting potentially novel causal variants, as well as thousands of gene function hypotheses. To help find the most exciting hypotheses, we improved the state of the art in machine learning driven literature-based discovery (LBD). Reading on our top 3 ranked candidate genes per experiment reveals 80% of rediscovered relationships, compared to 5% reading at random. We proposed 1,842 novel gene-phenotype testable hypotheses using our approach. We built a web portal at aimhigh.stanford.edu to allow researchers to view all our testable hypotheses in detail. Our open-source code can be rerun as more sequenced strains and phenotyping experiments become available.

genomics↗

bioRxiv2022

PDB ProtVista: A reusable and open-source sequence feature viewer

The PDB ProtVista is a reusable and customisable sequence feature viewer that provides intuitive and detailed 2D visualisation of residue-level annotations while supporting interactive communication with 3D viewers. The Protein Data Bank in Europe (PDBe) team develops and maintains PDB ProtVista. Several public web services use it to display structural and functional annotations such as macromolecular interaction interfaces, intrinsic disorder predictions, sequence variants, and sequence conservation. The PDB ProtVista is freely available from https://github.com/PDBeurope/protvista-pdb. We provide extensive documentation and step-by-step user guides on integrating PDB ProtVista with existing web applications and 3D molecular viewers. We also offer examples of displaying the users custom data and functional annotations for PDB and UniProt entries powered by a rich set of PDBe API endpoints.

bioinformatics↗

bioRxiv2022

Comprehensive collection and prediction of ABC transmembrane protein structures in the AI era of structural biology

The number of unique transmembrane (TM) protein structures doubled in the last four years that can be attributed to the revolution of cryo-electron microscopy. In addition, AlphaFold2 (AF2) also provided a large number of predicted structures with high quality. However, if a specific protein family is the subject of a study, collecting the structures of the family members is highly challenging in spite of existing general and protein domain-specific databases. Here, we demonstrate this and assess the applicability and usability of automatic collection and presentation of protein structures via the ABC protein superfamily. Our pipeline identifies and classifies transmembrane ABC protein structures using PFAM search and also aims to determine their conformational states based on special geometric measures, conftors. Since the AlphaFold database contains structure predictions only for single polypeptide chains, we performed AF2-Multimer predictions for human ABC half transporters functioning as dimers. Our AF2 predictions warn of possibly ambiguous interpretation of some biochemical data regarding interaction partners and call for further experiments and experimental structure determination. We made our predicted ABC protein structures available through a web application, and we joined the 3D-Beacons Network to reach the broader scientific community through platforms such as PDBe-KB.

bioinformatics↗

bioRxiv2022

Integrative single-cell analysis of cardiogenesis identifies developmental trajectories and non-coding mutations in congenital heart disease

Congenital heart defects, the most common birth disorders, are the clinical manifestation of anomalies in fetal heart development - a complex process involving dynamic spatiotemporal coordination among various precursor cell lineages. This complexity underlies the incomplete understanding of the genetic architecture of congenital heart diseases (CHDs). To define the multi-cellular epigenomic and transcriptional landscape of cardiac cellular development, we generated single-cell chromatin accessibility maps of human fetal heart tissues. We identified eight major differentiation trajectories involving primary cardiac cell types, each associated with dynamic transcription factor (TF) activity signatures. We identified similarities and differences of regulatory landscapes of iPSC-derived cardiac cell types and their in vivo counterparts. We interpreted deep learning models that predict cell-type resolved, base-resolution chromatin accessibility profiles from DNA sequence to decipher underlying TF motif lexicons and infer the regulatory impact of non-coding variants. De novo mutations predicted to affect chromatin accessibility in arterial endothelium were enriched in CHD cases versus controls. We used CRISPR-based perturbations to validate an enhancer harboring a nominated regulatory CHD mutation, linking it to effects on the expression of a known CHD gene JARID2. Together, this work defines the cell-type resolved cis-regulatory sequence determinants of heart development and identifies disruption of cell type-specific regulatory elements as a component of the genetic etiology of CHD.

genomics↗

bioRxiv2022

The dynseq genome browser track enables visualization of context-specific, dynamic DNA sequence features at single nucleotide resolution

We introduce the dynseq genome browser track, which displays DNA nucleotide characters scaled by user-specified, base-resolution scores provided in the BigWig file format. The dynseq track enables visualization of context-specific, informative genomic sequence features. We demonstrate its utility in three popular genome browsers for interpreting cis-regulatory sequence syntax and regulatory variant interpretation by visualizing nucleotide importance scores derived from machine learning models of regulatory DNA trained on protein-DNA binding and chromatin accessibility experiments.

genomics↗

bioRxiv2022

JEDII: Juxtaposition Enabled DNA-binding Interface Identifier

The specific recognition of small stretches of the genomic sequence by their cognate binding protein partners is crucial for various biological processes. Traditionally the prediction of DNA-protein interactions has been treated as two separate problems - one where we predict the most probable DNA sequence that a given protein would bind to and another where we determine the amino acids constituting the DNA binding pocket on a protein. In this study, we introduce JEDII, a template-based method that combines these two aspects of DNA-protein interactions and predicts the residues, nucleotides and amino acids, that would mediate the interaction. Our computational method utilises known structures of DNA-protein complexes in a protocol that superimposes amino acid-nucleotide hydrogen-bonding donor and acceptors atoms on one another to identify the protein-DNA interface. The corner stone of the method is that specificity bestowing hydrogen-bonding interactions are structurally conserved. We validated the accuracy of our procedure on a dataset of 285 DNA-protein complexes where JEDII predicted the cognate DNA sequence with a 62% accuracy. It predicted the DNA-binding amino acids on the protein with 94 % accuracy and an MCC of 0.70. JEDII was also separately compared to other popular methods that predict the cognate DNA sequence and to methods that predict the DNA binding residues. The comparisons were done over four different datasets and JEDII outperformed most methods over all these data sets. JEDII is a robust method following a simple replicable algorithm to determine the molecular basis of DNA-protein specificity and could be instrumental in predicting DNA-protein complexes that are central to key biological phenomena.

bioinformatics↗

bioRxiv2022

Neuroinflammation in neuronopathic Gaucher disease: Role of microglia and NK cells

BackgroundNeuronopathic Gaucher Disease (nGD) is a rare neurodegenerative disorder caused by biallelic mutations in Gba, and buildup of glycosphingolipids in lysosomes. Neuronal injury and cell death are prominent pathological features, however the role of Gba in individual cell types and involvement of microglia, blood derived macrophages and immune infiltrates in nGD pathology remains enigmatic. MethodsHere, using single cell resolution of mouse nGD brains, we found induction of neuroinflammation pathways involving microglia, NK cells, astrocytes, and neurons. ResultsTargeted rescue of Gba in microglia and in neurons, respectively in Gba deficient, nGD mice reversed the buildup of glucosylceramide (GlcCer) and glucosylsphingosine (GlcSph), concomitant with amelioration of neuroinflammation, reduced level of serum neurofilament light chain (Nf-L) and improved survival. The levels of bioactive lipid, GlcSph was strongly correlated with serum Nf-L and ApoE in nGD mouse models as well as GD patients. Gba rescue in microglia/macrophage compartment prolonged survival, that was further enhanced upon treatment with brain permeant inhibitor of glucosylceramide synthase, effects mediated via improved glycosphingolipid homeostasis and reversal of neuroinflammation involving activation of microglia, brain macrophages and NK cells. ConclusionsTogether, our study delineates individual cellular effects of Gba deficiency in nGD brains, highlighting the central role of neuroinflammation driven by microglia activation and the role of brain permeant small molecule glucosylceramide inhibitor in reversing complex multidimensional pathophysiology of nGD. Our findings advance disease biology whilst identifying compelling biomarkers of nGD to improve patient management, enrich clinical trials and illuminate therapeutic targets. FundingResearch grant from Sanofi Genzyme; other support includes R01NS110354.Yale Liver Center P30DK034989, pilot project grant.

immunology↗

bioRxiv2022

Centrosome Loss And Cell Proliferation Defects Underlie Developmental Failure In Haploid Zebrafish Larvae

Haploid larvae in non-mammalian vertebrates are lethal with characteristic organ growth retardation collectively called "haploid syndrome." In contrast to mammals whose haploid intolerance is attributed to imprinting misregulation, the cellular principle of haploidy-linked defects in non-mammalian vertebrates remains unknown. Here, we investigated cellular defects that disrupt the ontogeny of gynogenetic haploid zebrafish larvae. Unlike diploid control, haploid larvae manifested unscheduled cell death at the organogenesis stage, attributed to haploidy-linked p53 upregulation. Moreover, we found that haploid larvae specifically suffered the gradual aggravation of mitotic spindle monopolarization during 1-3 days post fertilization, causing spindle assembly checkpoint-mediated mitotic arrest throughout the entire body. High-resolution imaging revealed that this mitotic defect accompanied the haploidy-linked centrosome loss occurring concomitantly with the gradual decrease in larval cell size. Either resolution of mitotic arrest or depletion of p53 partially improved organ growth in haploid larvae. Based on these results, we propose that haploidy-linked mitotic defects and cell death are parts of critical cellular causes shared among vertebrates that limit the larval growth in the haploid state, contributing to an evolutionary constraint on allowable ploidy status in the vertebrate life cycle.

developmental biology↗