Navneet Rai

An integrative, multi‐scale, genome‐wide model reveals the phenotypic landscape of Escherichia coli

Given the vast behavioral repertoire and biological complexity of even the simplest organisms, accurately predicting phenotypes in novel environments and unveiling their biological organization is a challenging endeavor. Here, we present an integrative modeling methodology that unifies under a common framework the various biological processes and their interactions across multiple layers. We trained this methodology on an extensive normalized compendium for the gram‐negative bacterium Escherichia coli, which incorporates gene expression data for genetic and environmental perturbations, transcriptional regulation, signal transduction, and metabolic pathways, as well as growth measurements. Comparison with measured growth and high‐throughput data demonstrates the enhanced ability of the integrative model to predict phenotypic outcomes in various environmental and genetic conditions, even in cases where their underlying functions are under‐represented in the training set. This work paves the way toward integrative techniques that extract knowledge from a variety of biological data to achieve more than the sum of their parts in the context of prediction, analysis, and redesign of biological systems.

Multi-omics integration accurately predicts cellular state in unexplored conditions for Escherichia coli.

A significant obstacle in training predictive cell models is the lack of integrated data sources. We develop semi-supervised normalization pipelines and perform experimental characterization (growth, transcriptional, proteome) to create Ecomics, a consistent, quality-controlled multi-omics compendium for Escherichia coli with cohesive meta-data information. We then use this resource to train a multi-scale model that integrates four omics layers to predict genome-wide concentrations and growth dynamics. The genetic and environmental ontology reconstructed from the omics data is substantially different and complementary to the genetic and chemical ontologies. The integration of different layers confers an incremental increase in the prediction performance, as does the information about the known gene regulatory and protein-protein interactions. The predictive performance of the model ranges from 0.54 to 0.87 for the various omics layers, which far exceeds various baselines. This work provides an integrative framework of omics-driven predictive modelling that is broadly applicable to guide biological discovery.

Prediction by Promoter Logic in Bacterial Quorum Sensing

Quorum-sensing systems mediate chemical communication between bacterial cells, coordinating cell-density-dependent processes like biofilm formation and virulence-factor expression. In the proteobacterial LuxI/LuxR quorum sensing paradigm, a signaling molecule generated by an enzyme (LuxI) diffuses between cells and allosterically stimulates a transcriptional regulator (LuxR) to activate its cognate promoter (pR). By expressing either LuxI or LuxR in positive feedback from pR, these versatile systems can generate smooth (monostable) or abrupt (bistable) density-dependent responses to suit the ecological context. Here we combine theory and experiment to demonstrate that the promoter logic of pR – its measured activity as a function of LuxI and LuxR levels – contains all the biochemical information required to quantitatively predict the responses of such feedback loops. The interplay of promoter logic with feedback topology underlies the versatility of the LuxI/LuxR paradigm: LuxR and LuxI positive-feedback systems show dramatically different responses, while a dual positive/negative-feedback system displays synchronized oscillations. These results highlight the dual utility of promoter logic: to probe microscopic parameters and predict macroscopic phenotype.

Interactions among Quorum Sensing Inhibitors

Many pathogenic bacteria use quorum sensing (QS) systems to regulate the expression of virulence genes in a density-dependent manner. In one widespread QS paradigm the enzyme LuxI generates a small diffusible molecule of the acyl-homoserine lactone (AHL) family; high cell densities lead to high AHL levels; AHL binds the transcription factor LuxR, triggering it to activate gene expression at a virulence promoter. The emergence of antibiotic resistance has generated interest in alternative anti-microbial therapies that target QS. Inhibitors of LuxI and LuxR have been developed and tested in vivo, and can act at various levels: inhibiting the synthesis of AHL by LuxI, competitively or non-competitively inhibiting LuxR, or increasing the turnover of LuxI, LuxR, or AHL. Here use an experimentally validated computational model of LuxI/LuxR QS to study the effects of using inhibitors individually and in combination. The model includes the effect of transcriptional feedback, which generates highly non-linear responses as inhibitor levels are increased. For the ubiquitous LuxI-feedback virulence systems, inhibitors of LuxI are more effective than those of LuxR when used individually. Paradoxically, we find that LuxR competitive inhibitors, either individually or in combination with other inhibitors, can sometimes increase virulence by weakly activating LuxR. For both LuxI-feedback and LuxR-feedback systems, a combination of LuxR non-competitive inhibitors and LuxI inhibitors act multiplicatively over a broad parameter range. In our analysis, this final strategy emerges as the only robust therapeutic option.

4-Hydroxy-trans-2-nonenal (4-HNE) induces neuronal SH-SY5Y cell death via hampering ATP binding at kinase domain of Akt1

Inhibition mechanism(s) of protein kinase B/Akt1 and its consequences on related cell signaling were investigated in human neuroblastoma SH-SY5Y cells exposed to 4-hydroxy-trans-2-nonenal (4-HNE), one of the most reactive aldehyde by-products of lipid peroxidation. In silico data indicate that 4-HNE interacts with kinase domain of Akt1 with the total docking score of 6.0577 and also forms H-bond to Glu234 residue similar to highly potent Akt1 inhibitor imidazopiperidine analog 8b, in which the protonated imidazole nitrogen involves in two hydrogen bonds between Glu234 and Asp292. The strong hydrogen bonding with Glu234 and hydrophobic interactions with several residues, namely Leu156, Gly157, Val164, Ala177, Tyr229, Ala230, Met281 and Thr291, at the vicinity which is normally occupied by the ribose of ATP, appear to be the main causes of Akt1 inhibition and lead to the significant conformational change on this region of protein. Results of mutational docking prove that Glu234 plays a major role in 4-HNE-mediated Akt1 inhibition. In silico data on Akt inhibition were further validated by observing the down-regulated levels of phosphorylated (Thr308/Ser493) Akt1 as well as the altered levels of the downstream targets of pAkt, namely downregulated levels of pGSK3β (Ser9), β-catenin, Bcl2 and upregulated levels of pro-apoptotic markers, namely Bad, Bax, P53 and caspase-9/3. The cellular fate of such pAkt inhibition was evidenced by increased reactive oxygen species, degraded nuclei, transferase dUTP nick end labeling positive cells and upregulated levels of pJNK1/2. We identified that 4-HNE-mediated Akt1 inhibition was due to the competitive inhibition of ATP by 4-HNE at the kinase domain of ATP binding sites.

The Genetic and Transcriptional Basis of Short and Long Term Adaptation across Multiple Stresses in Escherichia coli

Microbes exhibit short and long term responses when exposed to challenging environmental conditions. To what extent these responses are correlated, what their evolutionary potential is and how they translate to cross-stress fitness is still unclear. In this study, we comprehensively characterized the response of Escherichia coli populations to four abiotic stresses (n-butanol, osmotic, acidic, and oxidative) and their combinations by performing genome-scale transcriptional analysis and growth profiling. We performed an analysis of their cross-stress behavior which identified 15 cases of cross- protection and one case of cross vulnerability. To elucidate the evolutionary potential of stress responses to individual stresses and stress combinations, we re-sequenced E. coli populations evolved in those four environments for 500 generations. We developed and applied a network-driven method that integrates mutations and differential expression to identify core and stress-specific gene communities that are likely to have a phenotypic impact. Our results suggest that beyond what is expected from the general stress response mechanisms, cross-stress behavior arises both from common pathways, several including metal ion binding and glycolysis/gluconeogenesis, and stress-specific expression programs. The stress-specific dependences uncovered, argue that cross-stress behavior is ubiquitous and central to understanding microbial physiology under stressful conditions.

RiboTALE: A modular, inducible system for accurate gene expression control

A limiting factor in synthetic gene circuit design is the number of independent control elements that can be combined together in a single system. Here, we present RiboTALEs, a new class of inducible repressors that combine the specificity of TALEs with the ability of riboswitches to recognize exogenous signals and differentially control protein abundance. We demonstrate the capacity of RiboTALEs, constructed through different combinations of TALE proteins and riboswitches, to rapidly and reproducibly control the expression of downstream targets with a dynamic range of 243.7 ± 17.6-fold, which is adequate for many biotechnological applications.

Quorum Sensing Biosensors

Quorum sensing is a cell density-dependent phenomenon, which at high cell density induces expression of target genes in a bacterial population. In bacteria, quorum sensing is facilitated by cell-to-cell signaling molecules referred as autoinducers (AIs). Among Gram-negative bacteria, quorum sensing is mediated primarily by two classes of AIs: AI-1 and AI-2. Further, AI-1 has tens of subtypes and each bacterium responds to a very limited subtypes of AI-1. These signaling molecules, at high cell density, regulate several physiological processes among bacteria, including bioluminescence, biofilm, and virulence.

Quorum Sensing in Competence and Sporulation

In several Gram-positive bacteria, competence and sporulation are few of several physiological processes controlled by quorum sensing (QS). Competence is a phenomenon wherein a bacterium acquires extracellular DNA for its maintenance. Only a fraction of cells (10–20 %), in a population, develop competence, at a particular window of growth phase, and in response upregulate expression of genes involved in the uptake and processing of extracellular DNA. Sporulation, second QS-controlled phenotype, occurs under extreme stress and nutritional scarcity. Prolonged nutrient deprivation compels the cell to enter the process of sporulation, the outcome of which is the production of a metabolically dormant endospore that resumes growth once the conditions become favorable again. Spore formation is a complex and tightly regulated phenomenon, where several hundred genes are directly and indirectly involved. Regulation of competence and sporulation is a complex and temporally regulated process. In present chapter, we will discuss QS driven regulation of competence and sporulation in different Gram-positive bacteria.

A network-based model for drug repurposing in Rheumatoid Arthritis

Background The identification of drug repositioning targets through computational methods has the potential to provide a fast, inexpensive alternative to traditional drug discovery process. Diseases where complicated pathophysiology evolves over time are excellent targets for such methods. One such disease is Rheumatoid Arthritis (RA), a chronic inflammatory autoimmune disease, where the drug survival at 5-years is less than 50% for patients treated with disease-modifying anti-rheumatic drugs (DMARDs). Methods and Findings We have developed a network-based approach for drug repositioning that takes into account the human interactome network, proximity measures between drug targets and disease-associated genes, potential side-effects, genome-wide gene expression and disease modules that emerge through pertinent analysis. We found that all DMARDs, except for hydroxychloroquine (HCQ), were found to be significantly proximal to RA-related genes. Application of the method on anti-diabetic agents, statins and H2 receptor blockers identified anti-diabetic agents – gliclazide, sitagliptin and metformin – that have similar network signatures with the DMARDs. Subsequent in-vitro experiments on mouse fibroblast NIH-3T3 cells validated the findings and the down regulation of six key RA-related inflammatory genes. Our analysis further argues that the combination of HCQ and/or sulfasalazine with methotrexate (MTX) is predicted to have an additive synergistic effect in treatment based on network complementarity. Similarly, leflunomide and tofacitinib were found to be suitable alternatives upon chemoresistance to MTX-based double/triple therapy, given the complementary network signatures and overlapping critical target hubs. Conclusions Our results corroborate that computational methods that are based on network proximity, among other contextual information can help narrow down the drug candidates for drug repositioning, as well as support decisions for combinatorial drug treatment that is tailored to patient’s needs. Author summary The network-based proximity between drug targets and disease genes can provide novel insights on the repercussion, interplay, and reposition of drugs in the context of disease. Disease-modifying anti-rheumatic drugs (DMARDs) located significantly close to rheumatoid arthritis (RA)-associated genes and RA-relevant pathways. Three anti-diabetic agents were identified to have an anti-inflammatory effect like DMARDs. We built RA disease module encompassing the emerging small-molecule targets and functional neighbors, which better explained the RA pathophysiology. By proximity and network robustness, tofacitinib and tocilizumab were the most potent for RA disease module as a single agent, and this is consistent with clinical observation. Side effects of clinical importance are predictable by measuring network-based proximity between drug targets and side effect protein. Network-based drug-disease proximity offers a novel and clinically actionable information about drugs and opens the new possibility to drug combination and reposition.