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Kiran Raosaheb Patil

Publications and source records attributed to Kiran Raosaheb Patil.

6 recordsLinked to original sources
PubMed2026

Common xenobiotics modulate gut microbial responses to low‑calorie sweeteners in vitro.

The gut microbiota is implicated in adverse effects associated with low-calorie sweeteners. Yet, the direct impact of sweeteners on gut bacteria remains largely uncharacterized. Here, we report interactions between 25 phylogenetically diverse gut bacterial strains and 39 commercially used sweeteners. We tested these sweeteners individually and in combination with four commonly co-consumed compounds, viz., advantame, caffeine, vanillin, and duloxetine. Three-quarters of the tested sweeteners individually impacted the growth of at least one tested bacterial strain. Further, over 100 interactions were found between sweeteners and the four co-consumed compounds. Isosteviol, a commonly used sweetener-component, and duloxetine, an antidepressant, synergistically inhibited Roseburia intestinalis, a bacterium previously linked to glucose homeostasis, and Parabacteroides merdae, a prevalent commensal linked to healthy microbiota. Proteomic, metabolomic, and genetic analyses indicate altered small molecule transport underpinning this sweetener-drug synergy. The isosteviol-duloxetine combination also modulated metabolism of a synthetic gut bacterial community, leading to increased toxicity to HeLa cells and altered secretion of inflammation-modulatory cytokines IL-6 and IL-8 by Caco-2 cells. Our data warrant further studies on interactions between low-calorie sweeteners and common xenobiotics.

Humans↗
PubMed2006

Integration of metabolome data with metabolic networks reveals reporter reactions.

Interpreting quantitative metabolome data is a difficult task owing to the high connectivity in metabolic networks and inherent interdependency between enzymatic regulation, metabolite levels and fluxes. Here we present a hypothesis-driven algorithm for the integration of such data with metabolic network topology. The algorithm thus enables identification of reporter reactions, which are reactions where there are significant coordinated changes in the level of surrounding metabolites following environmental/genetic perturbations. Applicability of the algorithm is demonstrated by using data from Saccharomyces cerevisiae. The algorithm includes preprocessing of a genome-scale yeast model such that the fraction of measured metabolites within the model is enhanced, and thus it is possible to map significant alterations associated with a perturbation even though a small fraction of the complete metabolome is measured. By combining the results with transcriptome data, we further show that it is possible to infer whether the reactions are hierarchically or metabolically regulated. Hereby, the reported approach represents an attempt to map different layers of regulation within metabolic networks through combination of metabolome and transcriptome data.

Algorithms↗
PubMed2006

Hap4 is not essential for activation of respiration at low specific growth rates in Saccharomyces cerevisiae.

In Saccharomyces cerevisiae, the heme-activated protein complex Hap2/3/4/5 plays a major role in the transcription of genes involved in respiration. Thus, overexpression of HAP4 has been shown to result in a 10% increase in the respiratory capacity. Here the physiology of a HAP4-deleted S. cerevisiae strain was investigated, and we found that the hap4delta S. cerevisiae exhibited poor growth on ethanol, although the growth rate on glucose was indifferent from the wild type in aerobic as well as anaerobic cultures. Moreover, it exhibited a large (75%) reduction in the critical glucose uptake rate at which fermentative metabolism is onset, indicating a substantial reduction in respiratory capacity. We also performed whole genome transcription analysis for the hap4delta and the wild type, grown in carbon-limited chemostat cultures operated at a dilution rate of 0.05 h(-1). Although both strains exhibited respiratory metabolism, there was significant change in expression of many genes in the hap4delta strain. These genes are involved in several different parts of the metabolism, including oxidative stress response, peroxisomal functions, and energy generation. This study strongly indicates that Hap4 activation only occurs at intermediate specific growth rates, below which the transcription of genes responsible for respiration is dependent on the Hap2/3/5 complex and above which the Hap4 protein augments the transcription. Furthermore, statistical analysis of the transcription data and integration of the data with a genome scale metabolic network provided new insight and evidence for the role of Hap4 in transcriptional regulation of mitochondrial respiration.

Biomass↗
PubMed2005

Evolutionary programming as a platform for in silico metabolic engineering.

BACKGROUND: Through genetic engineering it is possible to introduce targeted genetic changes and hereby engineer the metabolism of microbial cells with the objective to obtain desirable phenotypes. However, owing to the complexity of metabolic networks, both in terms of structure and regulation, it is often difficult to predict the effects of genetic modifications on the resulting phenotype. Recently genome-scale metabolic models have been compiled for several different microorganisms where structural and stoichiometric complexity is inherently accounted for. New algorithms are being developed by using genome-scale metabolic models that enable identification of gene knockout strategies for obtaining improved phenotypes. However, the problem of finding optimal gene deletion strategy is combinatorial and consequently the computational time increases exponentially with the size of the problem, and it is therefore interesting to develop new faster algorithms. RESULTS: In this study we report an evolutionary programming based method to rapidly identify gene deletion strategies for optimization of a desired phenotypic objective function. We illustrate the proposed method for two important design parameters in industrial fermentations, one linear and other non-linear, by using a genome-scale model of the yeast Saccharomyces cerevisiae. Potential metabolic engineering targets for improved production of succinic acid, glycerol and vanillin are identified and underlying flux changes for the predicted mutants are discussed. CONCLUSION: We show that evolutionary programming enables solving large gene knockout problems in relatively short computational time. The proposed algorithm also allows the optimization of non-linear objective functions or incorporation of non-linear constraints and additionally provides a family of close to optimal solutions. The identified metabolic engineering strategies suggest that non-intuitive genetic modifications span several different pathways and may be necessary for solving challenging metabolic engineering problems.

Algorithms↗
PubMed2005

Uncovering transcriptional regulation of metabolism by using metabolic network topology.

Cellular response to genetic and environmental perturbations is often reflected and/or mediated through changes in the metabolism, because the latter plays a key role in providing Gibbs free energy and precursors for biosynthesis. Such metabolic changes are often exerted through transcriptional changes induced by complex regulatory mechanisms coordinating the activity of different metabolic pathways. It is difficult to map such global transcriptional responses by using traditional methods, because many genes in the metabolic network have relatively small changes at their transcription level. We therefore developed an algorithm that is based on hypothesis-driven data analysis to uncover the transcriptional regulatory architecture of metabolic networks. By using information on the metabolic network topology from genome-scale metabolic reconstruction, we show that it is possible to reveal patterns in the metabolic network that follow a common transcriptional response. Thus, the algorithm enables identification of so-called reporter metabolites (metabolites around which the most significant transcriptional changes occur) and a set of connected genes with significant and coordinated response to genetic or environmental perturbations. We find that cells respond to perturbations by changing the expression pattern of several genes involved in the specific part(s) of the metabolism in which a perturbation is introduced. These changes then are propagated through the metabolic network because of the highly connected nature of metabolism.

Algorithms↗
PubMed2004

Use of genome-scale microbial models for metabolic engineering.

Metabolic engineering serves as an integrated approach to design new cell factories by providing rational design procedures and valuable mathematical and experimental tools. Mathematical models have an important role for phenotypic analysis, but can also be used for the design of optimal metabolic network structures. The major challenge for metabolic engineering in the post-genomic era is to broaden its design methodologies to incorporate genome-scale biological data. Genome-scale stoichiometric models of microorganisms represent a first step in this direction.

Algorithms↗