Biology subjects

Nair, N. U.

Publications and source records attributed to Nair, N. U..

At least 37 records · Page 2Linked to original sources

bioRxiv2021

Flow Cytometric Evaluation of Yeast-Bacterial Cell-Cell Interactions

Synthetic cell-cell interaction systems can be useful for understanding multicellular communities or for screening binding molecules. We adapt a previously characterized set of synthetic cognate nanobody-antigen pairs to a yeast-bacteria coincubation format and use flow cytometry to evaluate cell-cell interactions mediated by binding between surface-displayed molecules. We further use fluorescence-activated cell sorting (FACS) to enrich for a specific yeast-displayed nanobody within a mixed yeast-display population. Finally, we demonstrate that this system supports characterization of a therapeutically relevant nanobody-antigen interaction: a previously discovered nanobody that binds to the intimin protein expressed on the surface of enterohemorrhagic E. coli. Overall, our findings indicate that the yeast-bacteria format supports efficient evaluation of ligand-target interactions. With further development, this format may facilitate systematic characterization and high throughput discovery of bacterial surface-binding molecules.

synthetic biology↗

bioRxiv2021

Cheating the cheater: Suppressing false positive enrichment during biosensor-guided biocatalyst engineering

Transcription factor (TF)-based biosensors are very desirable reagents for high-throughput enzyme and strain engineering campaigns. Despite their potential, they are often difficult to deploy effectively as the small molecules being detected can leak out of high-producer cells, into low-producer cells, and activate the biosensor therein. This crosstalk leads to the overrepresentation of false positive/cheater cells in the enriched population. While the host cell can be engineered to minimize crosstalk (e.g., by deleting responsible transporters), this is not easily applicable to all molecules of interest, particularly those that can diffuse passively. One such biosensor recently reported for trans-cinnamic acid (tCA) suffers from crosstalk when used for phenylalanine ammonia-lyase (PAL) enzyme engineering by directed evolution. We report that desensitizing the biosensor (i.e., increasing the limit of detection, LOD) suppresses cheater population enrichment. Further we show that, if we couple the biosensor-based screen with an orthogonal pre-screen that eliminates a large fraction of true negatives, we can successfully reduce the cheater population during the fluorescence-activated cell sorting (FACS). Using the approach developed here, we were successfully able to isolate PAL variants with ~70% high kcat after a single sort. These mutants have tremendous potential in Phenylketonuria (PKU) treatment and flavonoid production.

synthetic biology↗

bioRxiv2021

Synthetic lethality-based prediction of anti-SARS-CoV-2 targets

Novel strategies are needed to identify drug targets and treatments for the COVID-19 pandemic. The altered gene expression of virus-infected host cells provides an opportunity to specifically inhibit viral propagation via targeting the synthetic lethal (SL) partners of such altered host genes. Pursuing this antiviral strategy, here we comprehensively analyzed multiple in vitro and in vivo bulk and single-cell RNA-sequencing datasets of SARS-CoV-2 infection to predict clinically relevant candidate antiviral targets that are SL with altered host genes. The predicted SL-based targets are highly enriched for infected cell inhibiting genes reported in four SARS-CoV-2 CRISPR-Cas9 genome-wide genetic screens. Integrating our predictions with the results of these screens, we further selected a focused subset of 26 genes that we experimentally tested in a targeted siRNA screen using human Caco-2 cells. Notably, as predicted, knocking down these targets reduced viral replication and cell viability only under the infected condition without harming non-infected cells. Our results are made publicly available, to facilitate their in vivo testing and further validation.

systems biology↗

bioRxiv2021

Sex biases in cancer and autoimmune disease incidence are correlated across human tissues

Cancer occurs more frequently in men while autoimmune diseases (AIDs) occur more frequently in women. To explore whether these sex biases have a common basis, we collected 167 AID incidence studies from many countries for tissues that have both a cancer type and an AID that arise from that tissue. Analyzing a total of 182 country-specific, tissue-matched cancer-AID incidence rate sex bias data pairs, we find that, indeed, the sex biases observed in the incidence of AIDs and cancers that occur in the same tissue are positively correlated across human tissues. The common key factor whose levels across human tissues are most strongly associated with these incidence rate sex biases is the sex bias in the expression of the 37 genes encoded in the mitochondrial genome.

cancer biology↗

bioRxiv2021

A landscape of synergistic drug combinations in non-small-cell lung cancer

Targeted therapeutics have advanced cancer treatment, but single agent activity remains limited by de novo and acquired resistance. Combining targeted drugs is broadly seen as a way to improve treatment outcome, motivating the ongoing search for efficacious combinations. To identify synergistic targeted therapy combinations and study the impact of tumor heterogeneity on combination outcome, we systematically tested over 5,000 two drug combinations at multiple doses across a collection of 81 non-small cancer cell lines. Both known and novel synergistic combinations were identified. Strikingly, very few combinations yield synergy across the majority of cell line models. Importantly, synergism mainly arises due to sensitization of single agent resistant models, rather than further sensitize already sensitive cell lines, frequently via dual targeting of a single or two highly interconnected pathways. This drug combinations resource, the largest of its kind should help delineate new synergistic regimens by facilitating the understanding of drug synergism in cancer.

cancer biology↗

bioRxiv2021

In-depth sequence-function characterization reveals multiple paths to enhance phenylalanine ammonia-lyase (PAL) activity.

Phenylalanine ammonia-lyases (PALs) deaminate L-phenylalanine to trans-cinnamic acid and ammonium and have idespread application in chemo-enzymatic synthesis, agriculture, and medicine. In particular, the PAL from Anabaena variabilis (Trichormus variabilis) has garnered significant attention as the active ingredient in Pegvaliase(R), the only FDA-approved drug treating classical phenylketonuria (PKU). Although an extensive body of literature exists on structure, substrate-specificity, and catalytic mechanism, protein-wide sequence determinants of function remain unknown, which limits the ability to rationally engineer these enzymes. Previously, we developed a high-throughput screen (HTS) for PAL, and here, we leverage it to create a detailed sequence-function landscape of PAL by performing deep mutational scanning (DMS). Our method revealed 79 hotspots that affected a positive change in enzyme fitness, many of which have not been reported previously. Using fitness values and structure-function analysis, we picked a subset of residues for comprehensive single- and multi-site saturation mutagenesis to improve the catalytic activity of PAL and identified combinations of mutations that led to improvement in reaction kinetics in cell-free and cellular contexts. To understand the mechanistic role of the most beneficial mutations, we performed QM/MM and MD and observed that different mutants confer improved catalytic activity via different mechanisms, including stabilizing first transition and intermediate states and improving substrate diffusion into the active site, and decreased product inhibition. Thus, this work provides a comprehensive sequence-function relationship for PAL, identifies positions that improve PAL activity when mutated and assesses their mechanisms of action.

bioengineering↗

bioRxiv2021

Cross-species identification of cancer-resistance associated genes uncovers their relevance to human cancer risk

Cancer is an evolutionarily conserved disease that occurs in a wide variety of species. We applied a comparative genomics approach to systematically characterize the genes whose conservation levels significantly correlates positively (PC) or negatively (NC) with a broad spectrum of cancer-resistance estimates, computed across almost 200 vertebrate species. PC genes are enriched in pathways relevant to tumor suppression including cell cycle, DNA repair, and immune response, while NC genes are enriched with a host of metabolic pathways. The conservation levels of the PC and NC genes in a species serve to build the first genomics-based predictor of its cancer resistance score. We find that PC genes are less tolerant to loss of function (LoF) mutations, are enriched in cancer driver genes and are associated with germline mutations that increase human cancer risk. Furthermore, their expression levels are associated with lifetime cancer risk across human tissues. Finally, their knockout in mice results in increased cancer incidence. In sum, we find that many genes associated with cancer resistance across species are implicated in human cancers, pointing to several additional candidate genes that may have a functional role in human cancer.

cancer biology↗

bioRxiv2021

Genome-scale metabolic modeling reveals SARS-CoV-2-induced host metabolic reprogramming and identifies metabolic antiviral targets

Tremendous progress has been made to control the COVID-19 pandemic caused by the SARS-CoV-2 virus. However, effective therapeutic options are still rare. Drug repurposing and combination represent practical strategies to address this urgent unmet medical need. Viruses, including coronaviruses, are known to hijack host metabolism to facilitate viral proliferation, making targeting host metabolism a promising antiviral approach. Here, we describe an integrated analysis of 12 published in vitro and human patient gene expression datasets on SARS-CoV-2 infection using genome-scale metabolic modeling (GEM), revealing complicated host metabolism reprogramming during SARS-CoV-2 infection. We next applied the GEM-based metabolic transformation algorithm to predict anti-SARS-CoV-2 targets that counteract the virus-induced metabolic changes. We successfully validated these targets using published drug and genetic screen data and by performing an siRNA assay in Caco-2 cells. Further generating and analyzing RNA-sequencing data of remdesivir-treated Vero E6 cell samples, we predicted metabolic targets acting in combination with remdesivir, an approved anti-SARS-CoV-2 drug. Our study provides clinical data-supported candidate anti-SARS-CoV-2 targets for future evaluation, demonstrating host metabolism-targeting as a promising antiviral strategy.

systems biology↗

bioRxiv2020

Analysis of Metabolic Network Disruption in Engineered Microbial Hosts due to Enzyme Promiscuity

BackgroundIncreasing understanding of metabolic and regulatory networks underlying microbial physiology has enabled creation of progressively more complex synthetic biological systems for biochemical, biomedical, agricultural, and environmental applications. However, despite best efforts, confounding phenotypes still emerge from unforeseen interplay between biological parts, and the design of robust and modular biological systems remains elusive. Such interactions are difficult to predict when designing synthetic systems and may manifest during experimental testing as inefficiencies that need to be overcome. Despite advances in tools and methodologies for strain engineering, there remains a lack of tools that can systematically identify incompatibilities between the native metabolism of the host and its engineered modifications. ResultsTransforming organisms such as Escherichia coli into microbial factories is achieved via a number of engineering strategies, used individually or in combination, with the goal of maximizing the production of chosen target compounds. One technique relies on suppressing or overexpressing selected genes; another involves on introducing heterologous enzymes into a microbial host. These modifications steer mass flux towards the set of desired metabolites but may create unexpected interactions. In this work, we develop a computational method, termed Metabolic Disruption Workflow (MDFlow), for discovering interactions and network disruption arising from enzyme promiscuity - the ability of enzymes to act on a wide range of molecules that are structurally similar to their native substrates. We apply MDFlow to two experimentally verified cases where strains with essential genes knocked out are rescued by interactions resulting from overexpression of one or more other genes. We then apply MDFlow to predict and evaluate a number of putative promiscuous reactions that can interfere with two heterologous pathways designed for 3-hydroxypropic acid (3-HP) production. ConclusionsUsing MDFlow, we can identify putative enzyme promiscuity and the subsequent formation of unintended and undesirable byproducts that are not only disruptive to the host metabolism but also to the intended end-objective of high biosynthetic productivity and yield. In addition, we show how enzyme promiscuity can potentially be responsible for the adaptability of cells to the disruption of essential pathways in terms of biomass growth.

systems biology↗

bioRxiv2020

Characterization of a membrane enzymatic complex for heterologous production of poly-γ-glutamate in E. coli

Poly-{gamma}-glutamic acid (PGA) produced by many Bacillus species is a polymer with many distinct and desirable characteristics. However, the multi-subunit enzymatic complex responsible for its synthesis, PGA Synthetase (PGS), has not been well characterized yet, in native nor in recombinant contexts. Elucidating structural and functional properties are crucial for future engineering efforts aimed at altering the catalytic properties of this enzyme. This study focuses on expressing the enzyme heterologously in the Escherichia coli membrane and characterizing localization, orientation, and activity of this heterooligomeric enzyme complex. In E. coli, we were able to produce high molecular weight PGA polymers with minimal degradation at titers of approximately 13 mg/L in deep-well microtiter batch cultures. Using fusion proteins, we observed, for the first time, the association and orientation of the different subunits with the inner cell membrane. These results elucidate provide fundamental structural information on this poorly studied enzyme complex and will aid future fundamental studies and engineering efforts. HIGHLIGHTSO_LISuccessfully expressed active poly-{gamma}-glutamate synthetase (PGS) in E. coli. C_LIO_LIConfirmed PGS localization at inner membrane of E. coli. C_LIO_LIElucidated topology of PGS components in E. coli membrane. C_LIO_LICulture and expression in microplates might allow future screening of a high number of samples. C_LIO_LIFaster production of poly-{gamma}-glutamate in E. coli supernatant compared to B. subtilis. C_LI

bioengineering↗

bioRxiv2020

Charged Gram-positive species sequester and decrease the potency of pediocin PA-1 in mixed microbial settings

Antimicrobial peptides (AMPs) have gained attention recently due to increasing antibiotic resistance amongst pathogens. Most AMPs are cationic in nature and their preliminary interactions with the negatively charged cell surface is mediated by electrostatic attraction. This is followed by pore formation, which is either receptor-dependent or -independent and leads to cell death. Typically, AMPs are characterized by their killing activity using bioactivity assays to determine host range and degree of killing. However, cell surface binding is independent from killing. Most of the studies performed to-date have attempted to quantify the peptide binding using artificial membranes. Here, we use the narrow-spectrum class IIa bacteriocin AMP pediocin PA-1 conjugated to a fluorescent dye as a probe to monitor cell surface binding. We developed a flow cytometry-based assay to quantify the strength of binding in target and non-target species. Through our binding assays, we found a strong positive correlation between cell surface charge and pediocin PA-1 binding. Interestingly, we also found inverse correlation between zeta potential and pediocin PA-1 binding, the correlation coefficient for which improved when only Gram-positives were considered. We also show the effect of the presence of protein, salt, polycationic species, and other non-target species on the binding of pediocin PA-1 to the target organism. We conclude that the of presence of highly charged non-target species, as well as solutes, can decrease the binding, and the apparent potency, of pediocin PA-1. Thus, these outcomes are highly significant to the use of pediocin PA-1 and related AMPs in mixed microbial settings such as those found in the gut microbiota.

molecular biology↗

bioRxiv2020

Engineered lactobacilli display anti-biofilm and growth suppressing activities against Pseudomonas aeruginosa

Biofilms are an emerging target for new therapeutics in the effort to address the continued increase in resistance and tolerance to traditional antimicrobials. In particular, the distinct nature of the biofilm growth state often means that traditional antimicrobials, developed to combat planktonic cells, are ineffective. Biofilm treatments are designed to both reduce pathogen load at an infection site and decrease the development of resistance by rendering the embedded organisms more susceptible to treatment at lower antimicrobial concentrations. In this work, we developed a new antimicrobial treatment modality by characterizing the natural capacity of two lactobacilli, L. plantarum and L. rhamnosus, to inhibit P. aeruginosa growth, biofilm formation, and biofilm viability. We further engineered these lactic acid bacteria (LAB) to secrete enzymes known to degrade P. aeruginosa biofilms and show that our best performing engineered LAB, secreting a pathogen-derived enzyme (PelAhyd), degrades up to 85 % of P. aeruginosa biofilm.

bioengineering↗

bioRxiv2020

A quantitative model for oral administration of living therapeutics

As medicine shifts toward precision-based and personalized therapeutics, utilizing more complex biomolecules to treat increasingly difficult and rare conditions, microorganisms provide an avenue for realizing the production and processing necessary for novel drug pipelines. More so, probiotic microbes can be co-opted to deliver therapeutics by oral administration as living drugs, able to survive and safely transit the digestive tract. As living therapeutics are in their nascency, traditional pharmacokinetic-pharmacodynamic (PK-PD) models for evaluating drug candidates are not appropriate for this novel platform. Using a living therapeutic in late-stage clinical development for phenylketonuria (PKU) as a case study, we adapt traditional oral drug delivery models to properly evaluate and inform the engineering of living therapeutics. We develop the adapted for living therapeutics compartmental absorption and transit (ALT-CAT) model to provide metrics for drug efficacy across nine age groups of PKU patients and evaluate model parameters that are influenced by patient physiology, microbe selection and therapeutic production, and dosing formulations. ImportanceThis work describes a kinetic model to study the behavior of orally delivered living therapeutics. Such therapeutics are becoming increasingly relevant and are an exciting mode of drug delivery that stems from the growing interest through the convergence of advances in synthetic biology of probiotics and gut microbes as well as microbiome science. In particular, this work describes the development of a mathematical framework (pharmacokinetic-pharmacodynamic, PK-PD) called ALT-CAT to model the behavior of orally delivered engineered bacteria that act as living therapeutics by adapting similar methods that have been developed and widely-used for small molecular drug delivery and absorption.

bioengineering↗

bioRxiv2020

Directed evolution of Anabaena variabilis phenylalanine ammonia-lyase (PAL) identifies mutants with enhanced activities

There is broad interest in engineering phenylalanine ammonia-lyase (PAL) for its biocatalytic applications in industry (fine-chemicals and natural product synthesis) and medicine (phenylketoruria/PKU and cancer treatment). While site-specific mutagenesis has been employed to improve PAL stability or substrate specificity, a more comprehensive mutational landscape has yet to be explored for this class of enzymes. Here, we report development of a directed evolution technique to engineer PAL enzymes. Central to this approach is a high-throughput enrichment that couples E. coli growth to PAL activity. Using the clinically-relevant PAL from Anabaena variabilis, which is used on the formulation of pegvaliase for PKU therapy, we identified mutations at residues previously unknown as relevant for function that increase turnover frequency almost twofold after only a single round of engineering. This work demonstrates the power our technique for ammonia-lyase enzyme engineering.

bioengineering↗

bioRxiv2020

Whole exome precision oncology targeting synthetic lethal vulnerabilities across the tumor transcriptome

Precision oncology has made significant advances in the last few years, mainly by targeting actionable mutations in cancer driver genes. However, the proportion of patients whose tumors can be targeted therapeutically remains limited. Recent studies have begun to explore the benefit of analyzing tumor transcriptomics data to guide patient treatment, raising the need for new approaches for systematically accomplishing that. Here we show that computationally derived genetic interactions can successfully predict patient response. Assembling a broad repertoire of 32 datasets spanning more than 1,500 patients and including both tumor transcriptomics and response data, we predicted the response in 17 out of 21 targeted and 8 out of 11 checkpoint therapy datasets across 8 different cancer types with considerable accuracy, without ever training on these datasets. Analyzing the recently published multi-arm WINTHER trial, we show that the fraction of patients benefitting from transcriptomic-based treatments could potentially be markedly increased from 15% to about 85% by targeting synthetic lethal vulnerabilities in their tumors. In summary, this is the first computational approach to obtain considerable predictive performance across many different targeted and immunotherapy datasets, providing a promising new way for guiding cancer treatment based on the tumor transcriptomics of cancer patients.

cancer biology↗

bioRxiv2019

Contribution of synthetic lethality to cancer risk, onset age, and tumor suppressor specificity across human tissues

Various characteristics of cancers exhibit tissue-specificity, including lifetime cancer risk, onset age and cancer driver genes. Previously, the large variation in cancer risk across human tissues was found to strongly correlate with the number of stem cell divisions and abnormal DNA methylation levels occurring in them. Here we study the role of another potentially important factor, synthetic lethality, in cancer risk. Analyzing transcriptomics data in the GTEx compendium we quantify the extent of co-inactivation of cancer synthetic lethal (cSL) gene pairs in normal tissues and find that normal tissues with more down-regulated cSL gene pairs have lower and delayed cancer risk. We also show that the tissue-specificity of numerous tumor suppressor genes is strongly associated with the expression of their cSL partner genes in the corresponding normal tissues. Overall, our findings uncover the role of synthetic lethality as a novel important factor involved in tumorigenesis.

cancer biology↗

bioRxiv2019

Quantifying and engineering probiotic bacterial adhesion to mucus

Mucus in the gastrointestinal (GI) tract is the primary point-of-interaction between humans and their gut microbiota. This not only intimates that mucus ensures protection against endogenous and ex-ogenous opportunists but provision for the human microbiota to reside and flourish. With the emergence of living therapeutics, engineered microbes can deliver and produce increasingly complex medicine, and controlling the mucoadhesive properties of different microbial chassis can dictate dose-response in a patient. Here we present a redesigned, in vitro, plate-based assay to measure the mucus adhesion of various probiotics. Cell-mucus interactions were isolated by immobilizing mucus to the plate surface. Binding parameters were derived for each probiotic strain by measuring cell adhesion over a wide range of cell concentrations, providing dose-dependent adhesion metrics. Surface proteins and cell components known to influence mucoadhesion were then heterologously expressed or altered in Lactococcus lactis MG1363 and E. coli Nissle 1917 to control mucus-binding capacity, avidity, and cooperativity.

bioengineering↗

bioRxiv2019

Towards creating an extended metabolic model (EMM) for E. coli using enzyme promiscuity prediction and metabolomics data

BackgroundMetabolic models are indispensable in guiding cellular engineering and in advancing our understanding of systems biology. As not all enzymatic activities are fully known and/or annotated, metabolic models remain incomplete, resulting in suboptimal computational analysis and leading to unexpected experimental results. We posit that one major source of unaccounted metabolism is promiscuous enzymatic activity. It is now well-accepted that most, if not all, enzymes are promiscuous - i.e., they transform substrates other than their primary substrate. However, there have been no systematic analyses of genome-scale metabolic models to predict putative reactions and/or metabolites that arise from enzyme promiscuity. ResultsOur workflow utilizes PROXIMAL - a tool that uses reactant-product transformation patterns from the KEGG database - to predict putative structural modifications due to promiscuous enzymes. Using iML1515 as a model system, we first utilized a computational workflow, referred to as Extended Metabolite Model Annotation (EMMA), to predict promiscuous reactions catalyzed, and metabolites produced, by natively encoded enzymes in E. coli. We predict hundreds of new metabolites that can be used to augment iML1515. We then validated our method by comparing predicted metabolites with the Escherichia coli Metabolome Database (ECMDB). ConclusionsWe utilized EMMA to augment the iML1515 metabolic model to more fully reflect cellular metabolic activity. This workflow uses enzyme promiscuity as basis to predict hundreds of reactions and metabolites that may exist in E. coli but have not been documented in iML1515 or other databases. Among these, we found that 17 metabolites have previously been documented in E. coli metabolomics studies. Further, 6 of these metabolites are not documented for any other E. coli metabolic model (e.g. KEGG, EcoCyc). The corresponding reactions should be added to iML1515 to create an Extended Metabolic Model (EMM). Other predicted metabolites and reactions can guide future experimental metabolomics studies. Further, our workflow can easily be applied to other organisms for which comprehensive genome-scale metabolic models are desirable.

systems biology↗