Publications by authors named "Navneet Rai"

Food insecurity - when individuals or households have difficulty accessing sufficient, safe, culturally appropriate and nutritious food due to lack of money or other resources - is a global public health concern. Levels of food insecurity have increased across the UK in recent years, due in part to a decade of austerity, widespread loss of income during the COVID-19 pandemic and the more recent cost-of-living crisis, leading to rising use of food banks. The stress of living with uncertain access to food and going periods without food is damaging to physical and mental health.

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Microorganisms often live in complex habitats, where changes in the environment are predictable, providing an opportunity for microorganisms to learn, anticipate the upcoming environmental changes and prepare in advance for better survival and growth. One such environment is the mammalian intestine, where the abundance of different carbon sources is spatially distributed. In this study, we identified seven spatially distributed carbon sources in the mammalian intestine and tested whether exhibits phenotypes that are consistent with an anticipatory response given their spatial order and abundance within the mammalian intestine.

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We present a machine learning framework to automate knowledge discovery through knowledge graph construction, inconsistency resolution, and iterative link prediction. By incorporating knowledge from 10 publicly available sources, we construct an Escherichia coli antibiotic resistance knowledge graph with 651,758 triples from 23 triple types after resolving 236 sets of inconsistencies. Iteratively applying link prediction to this graph and wet-lab validation of the generated hypotheses reveal 15 antibiotic resistant E.

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Glutaraldehyde is a widely used biocide on the market for about 50 years. Despite its broad application, several reports on the emergence of bacterial resistance, and occasional outbreaks caused by poorly disinfection, there is a gap of knowledge on the bacterial adaptation, tolerance, and resistance mechanisms to glutaraldehyde. Here, we analyze the effects of the independent selection of mutations in the transcriptional regulator for biological replicates of cells subjected to adaptive laboratory evolution (ALE) in the presence of glutaraldehyde.

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How to design experiments that accelerate knowledge discovery on complex biological landscapes remains a tantalizing question. We present an optimal experimental design method (coined OPEX) to identify informative omics experiments using machine learning models for both experimental space exploration and model training. OPEX-guided exploration of Escherichia coli's populations exposed to biocide and antibiotic combinations lead to more accurate predictive models of gene expression with 44% less data.

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Food and human health are inextricably linked. As such, revolutionary impacts on health have been derived from advances in the production and distribution of food relating to food safety and fortification with micronutrients. During the past two decades, it has become apparent that the human microbiome has the potential to modulate health, including in ways that may be related to diet and the composition of specific foods.

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Microbial fermentation is an essential process for research and industrial applications, yet our understanding of cellular dynamics during long-term fermentation is limited. Here, we report a reproducible phenomenon of abrupt population collapse followed by a rapid population rescue that was observed during long-term chemostat cultivations, for various strains of Escherichia coli in minimal media. Through genome resequencing and whole-genome transcriptional profiling of replicate runs over time, we identified that changes in the tRNA and carbon catabolic genes are the genetic basis of this phenomenon.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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Synthetic biologists engineer systems with desired properties from simple and well-characterized biological parts. Among the most popular and versatile parts are tunable promoters and the transcription factors (TFs) that regulate them. Individual TFs can transduce physical or chemical signals to regulate gene expression; networks of TFs regulating each other's expression can filter signals, reduce noise, store memories, and oscillate.

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