Global pleiotropic effects in adaptively evolved Escherichia coli lacking CRP reveal molecular mechanisms that define the growth physiology.

Evolution facilitates emergence of fitter phenotypes by efficient allocation of cellular resources in conjunction with beneficial mutations. However, system-wide pleiotropic effects that redress the perturbations to the apex node of the transcriptional regulatory networks remain unclear. Here, we elucidate that absence of global transcriptional regulator CRP in Escherichia coli results in alterations in key metabolic pathways under glucose respiratory conditions, favouring stress- or hedging-related functions over growth-enhancing functions. Further, we disentangle the growth-mediated effects from the CRP regulation-specific effects on these metabolic pathways. We quantitatively illustrate that the loss of CRP perturbs proteome efficiency, as evident from metabolic as well as ribosomal proteome fractions, that corroborated with intracellular metabolite profiles. To address how E. coli copes with such systemic defect, we evolved Δcrp mutant in the presence of glucose. Besides acquiring mutations in the promoter of glucose transporter ptsG, the evolved populations recovered the metabolic pathways to their pre-perturbed state coupled with metabolite re-adjustments, which altogether enabled increased growth. By contrast to Δcrp mutant, the evolved strains remodelled their proteome efficiency towards biomass synthesis, albeit at the expense of carbon efficiency. Overall, we comprehensively illustrate the genetic and metabolic basis of pleiotropic effects, fundamental for understanding the growth physiology.

FSHR antagonists can trigger a PCOS-like state.

Over the recent years, FSHR has become an important target for development of fertility regulating agents, as impairment of FSH-FSHR interaction can lead to subfertility or infertility. In our previous study, we identified a 9-mer peptide (FSHß (89-97)) that exhibited FSHR antagonist activity. The histopathological and biochemical observations indicated, in addition to FSHR antagonism, a striking resemblance to a PCOS-like state. These observations led us to hypothesize that use of FSHR antagonists can trigger a PCOS-like state. In the present study, to validate this hypothesis, we performed qRT-PCR validation using ovarian tissue samples from our previous study. Expression of three genes known to be differentially expressed in PCOS was evaluated and found to be similar to the PCOS state. To further test the hypothesis, theoretical simulations were carried out by using the human menstrual cycle model available in the literature. Model simulations for FSHR antagonism were indicative of increased testosterone levels, increased ratio of luteinizing hormone/follicle stimulating hormone, and stockpiling of secondary follicles, which are typical characteristics of PCOS. The findings of this study will be relevant while reviewing the utility of FSHR antagonists for fertility regulation and reproductive medicine.Abbreviations: FSH: Follicle-stimulating hormone; FSHR: Follicle-stimulating hormone receptor; cAMP: Cyclic adenosine 3'5' monophosphate; PKA: Protein kinase A; PI3K: Phosphoinositide 3-kinase; PKB: protein kinase B; ERK1/2: Extracellular signal-regulated protein kinase 1/2; MAPK: Mitogen-activated protein kinases; T: testosterone; E2: estradiol; PCOS: Polycystic ovarian syndrome; LH: luteinizing hormone; Lhcgr: luteinizing hormone/choriogonadotropin receptor; CYP17A1: cytochrome P450 family 17 subfamily A member 1; Inhba: inhibin subunit beta A; qRT-PCR: Real-Time quantitative reverse transcription polymerase chain reaction; FSHß: Follicle-stimulating hormone ß subunit; Ct: Cycle threshold; Rn18s: Rattus norvegicus 18S ribosomal RNA.

MetGENE: gene-centric metabolomics information retrieval tool.

BACKGROUND: Biomedical research often involves contextual integration of multimodal and multiomic data in search of mechanisms for improved diagnosis, treatment, and monitoring. Researchers need to access information from diverse sources, comprising data in various and sometimes incongruent formats. The downstream processing of the data to decipher mechanisms by reconstructing networks and developing quantitative models warrants considerable effort. RESULTS: MetGENE is a knowledge-based, gene-centric data aggregator that hierarchically retrieves information about the gene(s), their related pathway(s), reaction(s), metabolite(s), and metabolomic studies from standard data repositories under one dashboard to enable ease of access through centralization of relevant information. We note that MetGENE focuses only on those genes that encode for proteins directly associated with metabolites. All other gene-metabolite associations are beyond the current scope of MetGENE. Further, the information can be contextualized by filtering by species, anatomy (tissue), and condition (disease or phenotype). CONCLUSIONS: MetGENE is an open-source tool that aggregates metabolite information for a given gene(s) and presents them in different computable formats (e.g., JSON) for further integration with other omics studies. MetGENE is available at https://bdcw.org/MetGENE/index.php.

Global Transcriptional Regulators Fine-Tune the Translational and Metabolic Efficiency for Optimal Growth of Escherichia coli.

Global transcriptional regulators coordinate complex genetic interactions that bestow better adaptability for an organism against external and internal perturbations. These transcriptional regulators are known to control an enormous array of genes with diverse functionalities. However, regulator-driven molecular mechanisms that underpin precisely tuned translational and metabolic processes conducive for rapid exponential growth remain obscure. Here, we comprehensively reveal the fundamental role of global transcriptional regulators FNR, ArcA, and IHF in sustaining translational and metabolic efficiency under glucose fermentative conditions in Escherichia coli By integrating high-throughput gene expression profiles and absolute intracellular metabolite concentrations, we illustrate that these regulators are crucial in maintaining nitrogen homeostasis, govern expression of otherwise unnecessary or hedging genes, and exert tight control on metabolic bottleneck steps. Furthermore, we characterize changes in expression and activity profiles of other coregulators associated with these dysregulated metabolic pathways, determining the regulatory interactions within the transcriptional regulatory network. Such systematic findings emphasize their importance in optimizing the proteome allocation toward metabolic enzymes as well as ribosomes, facilitating condition-specific phenotypic outcomes. Consequentially, we reveal that disruption of this inherent trade-off between ribosome and metabolic proteome economy due to the loss of regulators resulted in lowered growth rates. Moreover, our findings reinforce that the accumulations of intracellular metabolites in the event of proteome repartitions negatively affects the glucose uptake. Overall, by extending the three-partition proteome allocation theory concordant with multi-omics measurements, we elucidate the physiological consequences of loss of global regulators on central carbon metabolism restraining the organism to attain maximal biomass synthesis.IMPORTANCE Cellular proteome allocation in response to environmental or internal perturbations governs their eventual phenotypic outcome. This entails striking an effective balance between amino acid biosynthesis by metabolic proteins and its consumption by ribosomes. However, the global transcriptional regulator-driven molecular mechanisms that underpin their coordination remains unexplored. Here, we emphasize that global transcriptional regulators, known to control expression of a myriad of genes, are fundamental for precisely tuning the translational and metabolic efficiencies that define the growth optimality. Towards this, we systematically characterized the single deletion effect of FNR, ArcA, and IHF regulators of Escherichia coli on exponential growth under anaerobic glucose fermentative conditions. Their absence disrupts the stringency of proteome allocation, which manifests as impairment in key metabolic processes and an accumulation of intracellular metabolites. Furthermore, by incorporating an extension to the empirical growth laws, we quantitatively demonstrate the general design principles underlying the existence of these regulators in E. coli.

Multiomics Analysis and Systems Biology Integration Identifies the Roles of IL-9 in Keratinocyte Metabolic Reprogramming.

IL-9‒producing T cells are present in healthy skin as well as in the cutaneous lesions of inflammatory diseases and cancers. However, the roles of IL-9 in human skin during homeostasis and in the pathogenesis of inflammatory disorders remain obscure. In this study, we examined the roles of IL-9 in metabolic reprogramming of human primary keratinocytes (KCs). High-throughput quantitative proteomics revealed that IL-9 signaling in human primary KCs disrupts the electron transport chain by downregulating multiple electron transport chain proteins. Nuclear magnetic resonance-based metabolomics showed that IL-9 also reduced the production of tricarboxylic acid cycle intermediates in human primary KCs. An integration of multiomics data with systems-level analysis using the constraint-based MitoCore model predicted marked IL-9-dependent effects on central carbohydrate metabolism, particularly in relation to the glycolytic switch. Stable isotope metabolomics and biochemical assays confirmed increased glucose consumption and redirection of metabolic flux toward lactate by IL-9. Functionally, IL-9 inhibited ROS production by IFN-γ and promoted human primary KC survival by inhibiting apoptosis. In conclusion, our data reveal IL-9 as a master regulator of KC metabolic reprogramming and survival.

A steady-state modeling approach for simulation of antimicrobial peptide-cell membrane interaction.

Antimicrobial Peptides (AMPs) are host defense molecules that initiate microbial death by binding to the membrane. On membrane binding, AMPs undergo changes in conformation and aggregation state to enable killing action. Depending on the AMP and cell membrane characteristics, the nature of binding can be aggregating or non-aggregating, with high/low cooperativity, at single or multiple sites with high/low affinity leading to a unique killing action that needs to be studied individually. In the present study, a steady-state model that simulates AMP-membrane interaction was developed and was used to predict the mechanism of AMP binding. The predictions obtained from the model were validated with experimentally deciphered values available in literature. The model was further used to predict the mechanism for a set of designed AMPs with high sequence similarity to Myeloid Antimicrobial Peptide (MAP) family. Depending on the predicted mechanism, a unique half saturation constant and steepness of response (Hill coefficient) was obtained which was further validated with available data from literature. The model could reliably predict the mechanism, the half saturation constant and the Hill coefficient values. Further based on the analysis, it was observed that aggregation and oligomerization result in drastic killing action in a short range of peptide concentration owing to high Hill coefficient values. Mechanisms such as monomers binding at multiple sites with/without cooperativity result in antimicrobial activity at low half saturation constant though the killing action may not be steep. Thus, the methodology developed here can be used to develop hypothesis for studying AMP-membrane interaction mechanisms.

In-Silico Bioprospecting: Finding Better Enzymes.

Enzymes are essential biological macromolecules, which catalyse chemical reactions and have impacted the human civilization tremendously. The importance of enzymes as biocatalyst was realized more than a century ago by eminent scientists like Kuhne, Buchner, Payen, Sumner, and the last three decades has seen exponential growth in enzyme industry, mainly due to the revolution in tools and techniques in molecular biology, biochemistry and production. This has resulted in high demand of enzymes in various applications like food, agriculture, chemicals, pharmaceuticals, cosmetics, environment and research sector. The cut-throat competition also pushes the enzyme industry to constantly discover newer and better enzymes regularly. The conventional methods to discover enzymes are generally costly, time consuming and have low success rate. Exploring the exponentially growing biological databases with the help of various computational tools can increase the discovering process, with less resource consumption and higher success rate. Present review discusses this approach, known as in-silico bioprospecting, which broadly involves computational searching of gene/protein databases to find novel enzymes.

Role of phosphate limitation and pyruvate decarboxylase in rewiring of the metabolic network for increasing flux towards isoprenoid pathway in a TATA binding protein mutant of Saccharomyces cerevisiae.

BACKGROUND: Production of isoprenoids, a large and diverse class of commercially important chemicals, can be achieved through engineering metabolism in microorganisms. Several attempts have been made to reroute metabolic flux towards isoprenoid pathway in yeast. Most approaches have focused on the core isoprenoid pathway as well as on meeting the increased precursors and cofactor requirements. To identify unexplored genetic targets that positively influence the isoprenoid pathway activity, a carotenoid based genetic screen was previously developed and three novel mutants of a global TATA binding protein SPT15 was isolated for heightened isoprenoid flux in Saccharomyces cerevisiae. RESULTS: In this study, we investigated how one of the three spt15 mutants, spt15_Ala101Thr, was leading to enhanced isoprenoid pathway flux in S. cerevisiae. Metabolic flux analysis of the spt15_Ala101Thr mutant initially revealed a rerouting of the central carbon metabolism for the production of the precursor acetyl-CoA through activation of pyruvate-acetaldehyde-acetate cycle in the cytoplasm due to high flux in the reaction caused by pyruvate decarboxylase (PDC). This led to alternate routes of cytosolic NADPH generation, increased mitochondrial ATP production and phosphate demand in the mutant strain. Comparison of the transcriptomics of the spt15_Ala101Thr mutant cell with SPT15WT bearing cells shows upregulation of phosphate mobilization genes and pyruvate decarboxylase 6 (PDC6). Increasing the extracellular phosphate led to an increase in the growth rate and biomass but diverted flux away from the isoprenoid pathway. PDC6 is also shown to play a critical role in isoprenoid pathway flux under phosphate limitation conditions. CONCLUSION: The study not only proposes a probable mechanism as to how a spt15_Ala101Thr mutant (a global TATA binding protein mutant) could increase flux towards the isoprenoid pathway, but also PDC as a new route of metabolic manipulation for increasing the isoprenoid flux in yeast.

Steady state analysis of the genetic regulatory network incorporating underlying molecular mechanisms for anaerobic metabolism in Escherichia coli.

A Gene Regulatory Network (GRN) represents complex connections between genes in a cell which interact with each other through their RNA and protein expression products, thereby determining the expression levels of mRNA and proteins required for functioning of the cell. Microarray experiments yield the log fold change in mRNA abundance and quantify the expression levels for a GRN at the genome level. While Boolean or Bayesian modeling along with expression and location data are useful in analyzing microarray data, they lack underlying mechanistic details present in GRNs. Our objective is to understand the role of molecular mechanisms in quantifying a GRN. To that effect, we analyze under steady state, the complete GRN for the central metabolic pathway during anaerobiosis in Escherichia coli. We simulate the microarray experiments using a steady state gene expression simulator (SSGES) that models molecular mechanistic details such as dimerization, multiple-site binding, auto-regulation and feedback. Given a GRN, the SSGES provided the log fold change in mRNA expression values as the output, which can be compared to data from microarray experiments. We predict the log fold changes for mutants obtained by knocking out crucial transcriptional regulators such as FNR (F), ArcA (A), IHFA-B (I) and DpiA (D) and observe a high degree of correlation with previously reported experimental data. We also predict the microarray expression values for hitherto unknown combinations of deletion mutants. We hierarchically cluster the predicted log fold change values for these mutants and postulate that E. coli has evolved from a predominantly lactate secreting (FAID mutant) into a mixed acid secreting phenotype as seen in the wild type (WT) during anaerobiosis. Upon simulating a model without incorporating the mechanistic details, not only the correlation with the experimental data reduced considerably, but also the clustering of expression data indicated WT to be closer to the quadruple mutant FAID. This clearly demonstrates the significance of incorporating mechanistic data while quantifying the expression profile of a GRN which can help in predicting the effect of a gene mutant and understanding the evolution of transcriptional control.