Characterization of the Gut Microbiota in Urban Thai Individuals Reveals Enterotype-Specific Signature.

Gut microbiota play vital roles in human health, utilizing indigestible nutrients, producing essential substances, regulating the immune system, and inhibiting pathogen growth. Gut microbial profiles are dependent on populations, geographical locations, and long-term dietary patterns resulting in individual uniqueness. Gut microbiota can be classified into enterotypes based on their patterns. Understanding gut enterotype enables us to interpret the capability in macronutrient digestion, essential substance production, and microbial co-occurrence. However, there is still no detailed characterization of gut microbiota enterotype in urban Thai people. In this study, we characterized the gut microbiota of urban Thai individuals by amplicon sequencing and classified their profiles into enterotypes, including Prevotella (EnP) and Bacteroides (EnB) enterotypes. Enterotypes were associated with lifestyle, dietary habits, bacterial diversity, differential taxa, and microbial pathways. Microbe-microbe interactions have been studied via co-occurrence networks. EnP had lower α-diversities than those in EnB. A correlation analysis revealed that the Prevotella genus, the predominant taxa of EnP, has a negative correlation with α-diversities. Microbial function enrichment analysis revealed that the biosynthesis pathways of B vitamins and fatty acids were significantly enriched in EnP and EnB, respectively. Interestingly, Ruminococcaceae, resistant starch degraders, were the hubs of both enterotypes, and strongly correlated with microbial diversity, suggesting that traditional Thai food, consisting of rice and vegetables, might be the important drivers contributing to the gut microbiota uniqueness in urban Thai individuals. Overall findings revealed the biological uniqueness of gut enterotype in urban Thai people, which will be advantageous for developing gut microbiome-based diagnostic tools.

Gene Co-Expression Analysis Inferring the Crosstalk of Ethylene and Gibberellin in Modulating the Transcriptional Acclimation of Cassava Root Growth in Different Seasons.

Cassava is a crop of hope for the 21st century. Great advantages of cassava over other crops are not only the capacity of carbohydrates, but it is also an easily grown crop with fast development. As a plant which is highly tolerant to a poor environment, cassava has been believed to own an effective acclimation process, an intelligent mechanism behind its survival and sustainability in a wide range of climates. Herein, we aimed to investigate the transcriptional regulation underlying the adaptive development of a cassava root to different seasonal cultivation climates. Gene co-expression analysis suggests that AP2-EREBP transcription factor (ERF1) orthologue (D142) played a pivotal role in regulating the cellular response to exposing to wet and dry seasons. The ERF shows crosstalk with gibberellin, via ent-Kaurene synthase (D106), in the transcriptional regulatory network that was proposed to modulate the downstream regulatory system through a distinct signaling mechanism. While sulfur assimilation is likely to be a signaling regulation for dry crop growth response, calmodulin-binding protein is responsible for regulation in the wet crop. With our initiative study, we hope that our findings will pave the way towards sustainability of cassava production under various kinds of stress considering the future global climate change.

Inferring transcriptional gene regulation network of starch metabolism in Arabidopsis thaliana leaves using graphical Gaussian model.

BACKGROUND: Starch serves as a temporal storage of carbohydrates in plant leaves during day/night cycles. To study transcriptional regulatory modules of this dynamic metabolic process, we conducted gene regulation network analysis based on small-sample inference of graphical Gaussian model (GGM). RESULTS: Time-series significant analysis was applied for Arabidopsis leaf transcriptome data to obtain a set of genes that are highly regulated under a diurnal cycle. A total of 1,480 diurnally regulated genes included 21 starch metabolic enzymes, 6 clock-associated genes, and 106 transcription factors (TF). A starch-clock-TF gene regulation network comprising 117 nodes and 266 edges was constructed by GGM from these 133 significant genes that are potentially related to the diurnal control of starch metabolism. From this network, we found that ß-amylase 3 (b-amy3: At4g17090), which participates in starch degradation in chloroplast, is the most frequently connected gene (a hub gene). The robustness of gene-to-gene regulatory network was further analyzed by TF binding site prediction and by evaluating global co-expression of TFs and target starch metabolic enzymes. As a result, two TFs, indeterminate domain 5 (AtIDD5: At2g02070) and constans-like (COL: At2g21320), were identified as positive regulators of starch synthase 4 (SS4: At4g18240). The inference model of AtIDD5-dependent positive regulation of SS4 gene expression was experimentally supported by decreased SS4 mRNA accumulation in Atidd5 mutant plants during the light period of both short and long day conditions. COL was also shown to positively control SS4 mRNA accumulation. Furthermore, the knockout of AtIDD5 and COL led to deformation of chloroplast and its contained starch granules. This deformity also affected the number of starch granules per chloroplast, which increased significantly in both knockout mutant lines. CONCLUSIONS: In this study, we utilized a systematic approach of microarray analysis to discover the transcriptional regulatory network of starch metabolism in Arabidopsis leaves. With this inference method, the starch regulatory network of Arabidopsis was found to be strongly associated with clock genes and TFs, of which AtIDD5 and COL were evidenced to control SS4 gene expression and starch granule formation in chloroplasts.

Genome-scale metabolic models of Saccharomyces cerevisiae.

Systematic analysis of Saccharomyces cerevisiae metabolic functions and pathways has been the subject of extensive studies and established in many aspects. With the reconstruction of the yeast genome-scale metabolic (GSM) network and in silico simulation of the GSM model, the nature of the underlying cellular processes can be tested and validated with the increasing metabolic knowledge. GSM models are also being exploited in fundamental research studies and industrial applications. In this chapter, the principle concepts for construction, simulation and validation of GSM models, progressive applications of the yeast GSM models, and future perspectives are described. This will support and encourage researchers who are interested in systemic analysis of yeast metabolism and systems biology.

The genome-scale metabolic model iIN800 of Saccharomyces cerevisiae and its validation: a scaffold to query lipid metabolism.

BACKGROUND: Up to now, there have been three published versions of a yeast genome-scale metabolic model: iFF708, iND750 and iLL672. All three models, however, lack a detailed description of lipid metabolism and thus are unable to be used as integrated scaffolds for gaining insights into lipid metabolism from multilevel omic measurement technologies (e.g. genome-wide mRNA levels). To overcome this limitation, we reconstructed a new version of the Saccharomyces cerevisiae genome-scale model, iIN800 that includes a more rigorous and detailed description of lipid metabolism. RESULTS: The reconstructed metabolic model comprises 1446 reactions and 1013 metabolites. Beyond incorporating new reactions involved in lipid metabolism, we also present new biomass equations that improve the predictive power of flux balance analysis simulations. Predictions of both growth capability and large scale in silico single gene deletions by iIN800 were consistent with experimental data. In addition, 13C-labeling experiments validated the new biomass equations and calculated intracellular fluxes. To demonstrate the applicability of iIN800, we show that the model can be used as a scaffold to reveal the regulatory importance of lipid metabolism precursors and intermediates that would have been missed in previous models from transcriptome datasets. CONCLUSION: Performing integrated analyses using iIN800 as a network scaffold is shown to be a valuable tool for elucidating the behavior of complex metabolic networks, particularly for identifying regulatory targets in lipid metabolism that can be used for industrial applications or for understanding lipid disease states.

Detection of nonribosomal peptide synthetase genes in Xylaria sp. BCC1067 and cloning of XyNRPSA.

Nonribosomal peptides, synthesized by nonribosomal peptide synthetases (NRPS), are an important group of diverse bioactive fungal metabolites. Xylaria sp. BCC1067, which is known to produce a variety of biologically active metabolites, was studied for gene encoding NRPS by two different PCR-based methods and seven different NRPS fragments were obtained. In addition, screening a genomic library with an amplified NRPS fragment as a probe identified a putative NRPS gene named XyNRPSA. The functionality of XyNRPSA for the production of a corresponding metabolite was probed by gene insertion inactivation. Comparing the disrupting metabolite profile with that of the wild type led to the identification of a speculated metabolite. The crude extract of Xylaria sp. BCC1067 also exhibits antifungal activity against the human pathogens Candida albicans and Trichophyton mentagrophytes. However, the evaluation of biological activity of the XyNRPSA product suggests that it is neither a compound with antifungal activity nor a siderophore. In the vicinity of XyNRPSA, a second gene (named XyPtB) was identified. Its localization and homology to orfB of the ergot alkaloid biosynthetic gene cluster suggests that XyPtB may be involved in XyNRPSA product biosynthesis.

Identification of flux regulation coefficients from elementary flux modes: A systems biology tool for analysis of metabolic networks.

Within a metabolic network, the elementary flux modes enables a unique description of different operations of the network. Thus, the metabolic fluxes can be specified as convex combinations of the elementary flux modes. Here, we describe an approach to identify the set of elementary flux modes that operates in a given metabolic network through the use of measurements of macroscopic fluxes, that is, fluxes in and out of the cell. Besides enabling estimation of the metabolic fluxes, the parameters of the linear combinations of the elementary flux modes provide valuable physiological information; we call these parameters flux regulation coefficients (FRCs). These coefficients indicate which enzyme subsets are important at different growth conditions. We demonstrate how FRCs can be used to map the operation of the metabolic network of the yeast Saccharomyces sp. under different growth conditions.

Nylon fibers as supporting media in anaerobic hybrid reactors: it's effects on system's performance and microbial distribution.

The performances of three anaerobic hybrid reactors with various nylon fiber densities per packed bed volume (33, 22, and 11 kg/m(3) in R1, R2, and R3, respectively) as supporting media were evaluated through their ability to remove organic compounds in cassava starch wastewater. In addition, the distributions of non-methanogenic and methanogenic population in the reactors were investigated. During a 6-month operation, the organic loading rate was increased in stepwise from 0.5 to 4.0 kg COD/m3/day and the hydraulic retention time (HRT) shortened to 5.4 days. The COD removal efficiency was more favorable in R1 (87%) and R2 (84%) than in R3 (70%). The total biomass in the reactors with greater nylon fiber densities was also higher and increased from 20.4 to 67.3 g VSS and to 57.5 g VSS in R1 and R2, respectively. When the HRT was further shortened to 3 days, however, the efficiency of both reactors demonstrated a declining trend and reached 74% in R1 and 61% in R2. The distribution of microbial populations involved in the reactors was determined using the Most Probable Number technique. The result showed the lowest number of methanogens in R3 which correlated well to its relatively low efficiency. The number of non-methanogens in all reactors was, nonetheless, comparable. By shortening the HRT to 3 days, the methanogenic population in R2 diminished in both attached and suspended biomass whereas a slight reduction was detected only in the attached biomass of R1.