Biology subjects

Agarwal, P.

Publications and source records attributed to Agarwal, P..

47 records · Page 3Linked to original sources

bioRxiv2021

Mechanism of nucleotide discrimination by the translesion synthesis polymerase Rev1

Rev1 is a translesion DNA synthesis (TLS) polymerase involved in the bypass of adducted-guanine bases and abasic sites during DNA replication. During damage bypass, Rev1 utilizes a protein-template mechanism of DNA synthesis, where the templating DNA base is evicted from the Rev1 active site and replaced by an arginine side chain that preferentially binds incoming dCTP. Here, we utilize X-ray crystallography and molecular dynamics simulations to obtain structural insight into the dCTP specificity of Rev1. We show the Rev1 R324 protein-template forms sub-optimal hydrogen bonds with incoming dTTP, dGTP, and dATP that prevents Rev1 from adopting a catalytically competent conformation. Additionally, we show the Rev1 R324 protein-template forms optimal hydrogen bonds with incoming rCTP. However, the incoming rCTP adopts an altered sugar pucker, which prevents the formation of a catalytically competent Rev1 active site. This work provides novel insight into the mechanisms for nucleotide discrimination by the TLS polymerase Rev1.

biochemistry↗

bioRxiv2021

Actin capping protein regulates actomyosin contractility to maintain germline architecture in C. elegans

Actin dynamics play an important role in the morphogenesis of cells and tissues, yet the control of actin filament growth takes place at the molecular level. A challenge in the field is to link the molecular function of actin regulators with their physiological function. Here, we report the in vivo role of the actin capping protein CAP-1 in the C. elegans germline. We show that CAP-1 is associated with actomyosin structures in the cortex and rachis, where it keeps the level of contractility in check. A 60% reduction in the level of CAP-1 leads to a 2-fold increase in F-actin and non-muscle myosin II and only a 30% increase in Arp2/3. CAP-1 depletion leads to severe structural defects in the syncytial germline and oocytes, which can be rescued by reducing myosin activity. Thus, we uncover a physiological role for actin capping protein in maintaining C. elegans fertility by regulating the level of actomyosin contractility.

cell biology↗

bioRxiv2021

Integrated Systems-Analysis of the Human and Murine Pancreatic Cancer Glycomes Reveal a Tumor Promoting Role for ST6GAL1

Pancreatic ductal adenocarcinoma (PDA) is the 3rd leading cause of cancer-death in the U.S.. Glycans, such as CA-19-9, are biomarkers of PDA and are emerging as important modulators of cancer phenotypes. Herein, we utilized a systems-based approach integrating glycomic analysis of human PDA and the well-established KC mouse model, with transcriptomic data to identify and probe the functional significance of aberrant glycosylation in pancreatic cancer. We observed both common and distinct patterns of glycosylation in pancreatic cancer across species. Common alterations included increased levels of -2,3- and -2,6-sialic acids, bisecting GlcNAc and poly-LacNAc. However, core fucose, which was increased in human PDAC, was not seen in the mouse, indicating that not all human glycomic changes can be modeled in the KC mouse. In silico a nalysis of bulk and single cell sequencing data identified ST6GAL1, which underlies -2,6-sialic acid, as overexpressed in human PDA, concordant with histological data. Enzymes levels correlated with the stage of clinical disease. To test whether ST6GAL1 promotes pancreatic cancer we created a novel mouse in which a pancreas-specific genetic deletion of this enzyme overlays the KC mouse model. Analysis of our new model showed delayed cancer formation and a significant reduction in fibrosis. Our results highlight the importance of a strategic systems-approach to identifying glycans whose functions can be modeled in mouse, a crucial step in the development of therapeutics targeting glycosylation in pancreatic cancer. SIGNIFICANCEPancreatic ductal adenocarcinoma (PDA) is the 3rd leading cause of cancer-death in the U.S.. Glycosylation is emerging as an important modulator of cancer phenotype. Herein we use a systems-approach integrating glycomics of human PDA and a well-established PDA mouse model with transcriptomic data to identify ST6GAL1, the enzyme underlying -2,6-sialic acid, as a potential cancer promoter. A pancreatic specific ST6GAL1 knockout in the KC mouse showed delayed cancer formation and a reduction in fibrosis. Our results highlight the importance of a strategic systems-approach to identifying glycans whose functions can be modeled in mouse, a crucial step in the development of therapeutics targeting glycosylation in pancreatic cancer.

cancer biology↗

bioRxiv2021

The Cardiolipin Transacylase Tafazzin Regulates Basal Insulin Secretion and Mitochondrial Function in Pancreatic Islets from Mice

ObjectiveTafazzin (TAZ) is a cardiolipin (CL) biosynthetic enzyme important for maintaining mitochondrial function. TAZ impacts both the species and content of CL in the inner mitochondrial membrane which are essential for normal cellular respiration. In pancreatic {beta}-cells, mitochondrial function is closely associated with insulin secretion. However, the role of TAZ and CL in the secretion of insulin from pancreatic islets remains unknown. MethodsMale 4-month-old doxycycline-inducible TAZ knock-down (TAZ KD) mice and wild-type littermate controls were utilized. Immunohistochemistry was used to assess {beta}-cell morphology in whole pancreas sections, while ex vivo insulin secretion, CL content, RNA-Seq analysis and mitochondrial oxygen consumption were measured from isolated islet preparations. ResultsEx vivo insulin secretion under non-stimulatory low-glucose concentrations was reduced [~]52% from islets isolated from TAZ KD mice. Mitochondrial oxygen consumption under low-glucose conditions was also reduced [~]58% in islets from TAZ KD animals. TAZ-deficiency in pancreatic islets was associated with significant alteration in CL molecular species and reduced oxidized CL content. In addition, RNA-Seq of isolated islets showed that TAZ KD increased expression of extracellular matrix genes which are linked to pancreatic fibrosis, activated stellate cells and impaired {beta}-cell function. ConclusionThese data indicate a novel role for TAZ in regulating normal {beta}-cell function, particularly under low-glucose conditions.

physiology↗

bioRxiv2020

Visualizing Rev1 Catalyze Protein-template DNA Synthesis

During DNA replication, replicative DNA polymerases may encounter DNA lesions, which can stall replication forks. One way to prevent replication fork stalling is through the recruitment of specialized translesion synthesis (TLS) polymerases that have evolved to incorporate nucleotides opposite DNA lesions. Rev1 is a specialized TLS polymerase that bypasses abasic sites as well as minor-groove and exocyclic guanine adducts. It does this by using a unique protein-template mechanism in which the template base is flipped out of the DNA helix and the incoming dCTP hydrogen bonds with an arginine side chain. To observe Rev1 catalysis at the atomic level, we employed time-lapse X-ray crystallography. We found that Rev1 flips out the template base prior to binding the incoming nucleotide. Binding the incoming nucleotide changes the conformation of the DNA substrate to orient it for nucleotidyl transfer, and this is not coupled to large structural changes in the protein like those observed with other DNA polymerases. Moreover, we found that following nucleotide incorporation, Rev1 converts the pyrophosphate product to two mono-phosphates, which drives the reaction in the forward direction. Following nucleotide incorporation, the hydrogen bonds between the incorporated nucleotide and the arginine side chain are broken, but the templating base remains extrahelical. These post-catalytic changes prevent potentially mutagenic processive synthesis by Rev1 and facilitate dissociation of the DNA product from the enzyme.

biochemistry↗

bioRxiv2019

Developing synergistic drug combinations to restore antibiotic sensitivity in drug-resistant Mycobacterium tuberculosis

Tuberculosis (TB) is a leading global cause of mortality owing to an infectious agent, accounting for almost one-third of antimicrobial resistance (AMR) deaths annually. We aimed to identify synergistic anti-TB drug combinations with the capacity to restore therapeutic efficacy against drug-resistant mutants of the causative agent, Mycobacterium tuberculosis. We investigated combinations containing the known translational inhibitors, spectinomycin (SPT) and fusidic acid (FA), or the phenothiazine, chlorpromazine (CPZ), which disrupts mycobacterial energy metabolism. Potentiation of whole-cell drug efficacy was observed in SPT-CPZ combinations. This effect was lost against an M. tuberculosis mutant lacking the major facilitator superfamily (MFS) efflux pump, Rv1258c. Notably, the SPT-CPZ combination restored SPT efficacy against an SPT-resistant mutant carrying a g1379t point mutation in rrs, encoding the mycobacterial 16S ribosomal RNA. Combinations of SPT with FA, which targets the mycobacterial elongation factor G, exhibited potentiating activity against wild-type M. tuberculosis. Moreover, this combination produced a marginal potentiating effect against both FA-monoresistant and SPT-monoresistant mutants. Finally, combining SPT with the frontline anti-TB agents, rifampicin (RIF) and isoniazid, resulted in enhanced activity in vitro and ex vivo against both drug-susceptible M. tuberculosis and a RIF-monoresistant rpoB S531L mutant.These results support the utility of novel potentiating drug combinations in restoring antibiotic susceptibility of M. tuberculosis strains carrying genetic resistance to any one of the partner compounds.

microbiology↗

bioRxiv2019

Effect of topical soluble epoxide hydrolase inhibition in a preclinical diabetic wound healing model.

The objective of this study was to test a topical formulation of EPHX2 inhibitor, GSK2256294, in a dermal wound diabetic (db/db) mouse model. Comparisons were made between groups treated with EPHX2 inhibitor, matching vehicle control and a currently approved treatment for diabetic ulcers, becaplermin/Regranex, as a positive control which is highly efficacious in this model. Leptin receptor-deficient (db/db) male mice were sequentially assigned to treatment groups (N=10 per group) based on blood glucose levels and body weight. Mice were given a single 8 mm diameter full thickness wound on the back. Wounds were photographed and traced, and fresh test materials applied periodically with fresh dressing. Of ten mice treated with GSK2256294, three had completely healed wounds at the study conclusion, and 8/10 mice reached at least 50% closure. In the vehicle group, no mice achieved complete closure, and only 6/10 reached at least 50% closure by the study conclusion. Nine of ten wounds achieved complete closure in the positive control group by 21 days. Although the EPHX2 inhibitor results were indicative of efficacy, the single-sided p-value criterion of 0.05 was not met in this study.

physiology↗

bioRxiv2018

Pathway analysis of GWAS loci identifies novel drug targets and repurposing opportunities

Genome-wide association studies (GWAS) have made considerable progress and there is emerging evidence that genetics-based targets can lead to 28% more launched drugs. However, translating the results of GWAS for drug discovery remains challenging. We analyzed 1,589 GWAS across 1,456 protein interaction pathways to translate these often, imprecise genetic loci into therapeutic hypotheses for 182 diseases. We validate these pathway-based genetic targets by testing if current drug targets are enriched in the pathway space of the same indication. Remarkably, 30% of diseases have significantly more targets in these pathways than expected by chance; the comparable number for GWAS alone (without using pathway analysis) is zero. Although pathway analysis is routine for GWAS, this study shows that the routine analysis can often enrich for drug targets, by performing a systematic global analysis to translate genetic findings into therapeutic hypotheses for new drug discovery and repositioning opportunities for current drugs.

genomics↗

bioRxiv2018

Olfactory marker protein regulates refinement of the glomerular map

The olfactory glomerulus is the anatomical and functional unit of the olfactory bulb, defined by convergent input from olfactory sensory neuron (OSN) axons expressing the same type of odorant receptor (OR). A key marker of mature OSNs is the olfactory marker protein (OMP), whose deletion has been associated with deficits in OSN signal transduction and odor discrimination. Here, we have investigated glomerular odor responses and anatomical architecture in mice in which one or both alleles of OMP were replaced by the fluorescent synaptic activity reporter, synaptopHluorin (OMP+/- and OMP-/- mice, respectively). Functionally heterogeneous glomeruli, that is, ones with micro-domains with distinct odor responses were rare in OMP+/- mice, but occurred frequently in OMP-/- mice. Genetic targeting of single ORs revealed that these micro-domains arise from anomalous co-innervation of individual glomeruli by OSNs expressing different ORs. The glomerular mistargeting of OSNs in the absence of OMP is restricted to a local neighborhood of a few glomerular diameters. Our studies document functional heterogeneity in sensory input within individual glomeruli and uncover its anatomical correlate, revealing an unexpected role for OMP in the formation and refinement of the glomerular olfactory map.

neuroscience↗

bioRxiv2018

Predicting clinically promising therapeutic hypotheses using tensor factorization

Determining which target to pursue is a challenging and error-prone first step in developing a therapeutic treatment for a disease, where missteps are potentially very costly given the long-time frames and high expenses of drug development. We identified examples of successes and failures of target-indication pairs in clinical trials across 875 targets and 574 disease indications to build a gold-standard data set of 6,140 known clinical outcomes. We used information from Open Targets and others databases that covered 17 different sources of evidence for target-indication association and represented the data as a matrix of 21,437x2,211x17 with over two million non-null values. We designed and executed three benchmarking strategies to examine the performance of multiple machine learning models: Logistic Regression, Elasticnet, Random Forest, Tensor Factorization and Gradient Boosting Machine. With ten-fold cross validation, tensor factorization achieved AUROC=0.82{+/-}0.02 and AUPRC=0.71{+/-}0.03. Across multiple validation schemes, this was comparable or better than other methods. Tensor factorization is a general form of matrix factorization that has been successfully exploited in recommendation systems that suggest items to users based on their existing preference on a small number of items. Our application, using Bayesian probabilistic modelling, extends the capacity of matrix factorization to model multiple relationships between and among targets and indications. We use the model to show that our predicted probabilities of success correlate with clinical phases, and within clinical phase we can predict which trials are most likely to succeed.

bioinformatics↗

bioRxiv2017

Systematic interrogation of diverse Omic data reveals interpretable, robust, and generalizable transcriptomic features of clinically successful therapeutic targets

Target selection is the first and pivotal step in drug discovery. An incorrect choice may not manifest itself for many years after hundreds of millions of research dollars have been spent. We collected a set of 332 targets that succeeded or failed in phase III clinical trials, and explored whether Omic features describing the target genes could predict clinical success. We obtained features from the recently published comprehensive resource: Harmonizome. Nineteen features appeared to be significantly correlated with phase III clinical trial outcomes, but only 4 passed validation schemes that used bootstrapping or modified permutation tests to assess feature robustness and generalizability while accounting for target class selection bias. We also used classifiers to perform multivariate feature selection and found that classifiers with a single feature performed as well in cross-validation as classifiers with more features (AUROC=0.57 and AUPR=0.81). The two predominantly selected features were mean mRNA expression across tissues and standard deviation of expression across tissues, where successful targets tended to have lower mean expression and higher expression variance than failed targets. This finding supports the conventional wisdom that it is favorable for a target to be present in the tissue(s) affected by a disease and absent from other tissues. Overall, our results suggest that it is feasible to construct a model integrating interpretable target features to inform target selection. We anticipate deeper insights and better models in the future, as researchers can reuse the data we have provided to improve methods for handling sample biases and learn more informative features. Code, documentation, and data for this study have been deposited on GitHub at https://github.com/arouillard/omic-features-successful-targets.\n\nAUTHOR SUMMARYDrug discovery often begins with a hypothesis that changing the abundance or activity of a target--a biological molecule, usually a protein--will cure a disease or ameliorate its symptoms. Whether a target hypothesis translates into a successful therapy depends in part on the characteristics of the target, but it is not completely understood which target characteristics are important for success. We sought to answer this question with a supervised machine learning approach. We obtained outcomes of target hypotheses tested in clinical trials, scoring targets as successful or failed, and then obtained thousands of features (i.e. properties or characteristics) of targets from dozens of biological datasets. We statistically tested which features differed between successful and failed targets, and built a computational model that used these features to predict success or failure of targets in clinical trials. We found that successful targets tended to have more variable mRNA abundance from tissue to tissue and lower average abundance across tissues than failed targets. Thus, it is probably favorable for a target to be present in the tissue(s) affected by a disease and absent from other tissues. Our work demonstrates the feasibility of predicting clinical trial outcomes from target features.

bioinformatics↗