Enterotypes of the human gut microbiome.

Our knowledge of species and functional composition of the human gut microbiome is rapidly increasing, but it is still based on very few cohorts and little is known about variation across the world. By combining 22 newly sequenced faecal metagenomes of individuals from four countries with previously published data sets, here we identify three robust clusters (referred to as enterotypes hereafter) that are not nation or continent specific. We also confirmed the enterotypes in two published, larger cohorts, indicating that intestinal microbiota variation is generally stratified, not continuous. This indicates further the existence of a limited number of well-balanced host-microbial symbiotic states that might respond differently to diet and drug intake. The enterotypes are mostly driven by species composition, but abundant molecular functions are not necessarily provided by abundant species, highlighting the importance of a functional analysis to understand microbial communities. Although individual host properties such as body mass index, age, or gender cannot explain the observed enterotypes, data-driven marker genes or functional modules can be identified for each of these host properties. For example, twelve genes significantly correlate with age and three functional modules with the body mass index, hinting at a diagnostic potential of microbial markers.

Adaptation to environmental factors shapes the organization of regulatory regions in microbial communities.

BACKGROUND: It has been shown in a number of metagenomic studies that the addition and removal of specific genes have allowed microbiomes to adapt to specific environmental conditions by losing and gaining specific functions. But it is not known whether and how the regulation of gene expression also contributes to adaptation. RESULTS: We have here characterized and analyzed the metaregulome of three different environments, as well as their impact in the adaptation to particular variable physico-chemical conditions. For this, we have developed a computational protocol to extract regulatory regions and their corresponding transcription factors binding sites directly from metagenomic reads and applied it to three well known environments: Acid Mine, Whale Fall, and Waseca Farm. Taking the density of regulatory sites in promoters as a measure of the potential and complexity of gene regulation, we found it to be quantitatively the same in all three environments, despite their different physico-chemical conditions and species composition. However, we found that each environment distributes their regulatory potential differently across their functional space. Among the functions with highest regulatory potential in each niche, we found significant enrichment of processes related to sensing and buffering external variable factors specific to each environment, like for example, the availability of co-factors in deep sea, of oligosaccharides in soil and the regulation of pH in the acid mine. CONCLUSIONS: These results highlight the potential impact of gene regulation in the adaptation of bacteria to the different habitats through the distribution of their regulatory potential among specific functions, and point to critical environmental factors that challenge the growth of any microbial community.

Co-PATHOgenex web application for assessing complex stress responses in pathogenic bacteria.

IMPORTANCE: Unveiling gene co-expression networks in bacterial pathogens has the potential for gaining insights into their adaptive strategies within the host environment. Here, we developed Co-PATHOgenex, an interactive and user-friendly web application that enables users to construct networks from gene co-expressions using custom-defined thresholds (https://avicanlab.shinyapps.io/copathogenex/). The incorporated search functions and visualizations within the tool simplify the usage and facilitate the interpretation of the analysis output. Co-PATHOgenex also includes stress stimulons for various bacterial species, which can help identify gene products not previously associated with a particular stress condition.

Identification of a potent and selective σ1 receptor agonist potentiating NGF-induced neurite outgrowth in PC12 cells.

Herein we report the synthesis, drug-likeness evaluation, and in vitro studies of new sigma (σ) ligands based on arylalkenylaminic scaffold. For the most active olefin the corresponding arylalkylamine was studied. Novel arylalkenylamines generally possess high σ(1) receptor affinity (K(i) values <25 nM) and good σ(1)/σ(2) selectivity (K(i)σ(2) >100). Particularly, the piperidine derivative (E)-17 and its arylalkylamine analog (R,S)-33 were observed to be excellent σ(1) receptor ligands (K(i)=0.70 and 0.86 nM, respectively) and to display significantly high selectivity over σ(2), µ-, and κ-opioid receptors and phencyclidine (PCP) binding site of the N-methyl-d-aspartate (NMDA) receptors. Moreover in PC12 cells (R,S)-33 promoted the nerve growth factor (NGF)-induced neurite outgrowth and elongation. Co-administration of the selective σ(1) receptor antagonist BD-1063 totally counteracted this effect, confirming that σ(1) receptors are involved in the (R,S)-33 modulation of the NGF effect in PC12 cells and suggesting a σ(1) agonist profile. As a part of our work, a threedimensional σ(1) pharmacophore model was also developed employing GALAHAD methodology. Only active compounds were used for deriving this model. The model included two hydrophobes and a positive nitrogen as relevant features and it was able to discriminate between molecules with and without affinity toward σ(1) receptor subtype.

Genetic algorithm optimization in drug design QSAR: Bayesian-regularized genetic neural networks (BRGNN) and genetic algorithm-optimized support vectors machines (GA-SVM).

Many articles in "in silico" drug design implemented genetic algorithm (GA) for feature selection, model optimization, conformational search, or docking studies. Some of these articles described GA applications to quantitative structure-activity relationships (QSAR) modeling in combination with regression and/or classification techniques. We reviewed the implementation of GA in drug design QSAR and specifically its performance in the optimization of robust mathematical models such as Bayesian-regularized artificial neural networks (BRANNs) and support vector machines (SVMs) on different drug design problems. Modeled data sets encompassed ADMET and solubility properties, cancer target inhibitors, acetylcholinesterase inhibitors, HIV-1 protease inhibitors, ion-channel and calcium entry blockers, and antiprotozoan compounds as well as protein classes, functional, and conformational stability data. The GA-optimized predictors were often more accurate and robust than previous published models on the same data sets and explained more than 65% of data variances in validation experiments. In addition, feature selection over large pools of molecular descriptors provided insights into the structural and atomic properties ruling ligand-target interactions.

Comprehensive dataset of shotgun metagenomes from oxygen stratified freshwater lakes and ponds.

Stratified lakes and ponds featuring steep oxygen gradients are significant net sources of greenhouse gases and hotspots in the carbon cycle. Despite their significant biogeochemical roles, the microbial communities, especially in the oxygen depleted compartments, are poorly known. Here, we present a comprehensive dataset including 267 shotgun metagenomes from 41 stratified lakes and ponds mainly located in the boreal and subarctic regions, but also including one tropical reservoir and one temperate lake. For most lakes and ponds, the data includes a vertical sample set spanning from the oxic surface to the anoxic bottom layer. The majority of the samples were collected during the open water period, but also a total of 29 samples were collected from under the ice. In addition to the metagenomic sequences, the dataset includes environmental variables for the samples, such as oxygen, nutrient and organic carbon concentrations. The dataset is ideal for further exploring the microbial taxonomic and functional diversity in freshwater environments and potential climate change impacts on the functioning of these ecosystems.

Diazotroph Genomes and Their Seasonal Dynamics in a Stratified Humic Bog Lake.

Aquatic N-fixation is generally associated with the growth and mass development of Cyanobacteria in nitrogen-deprived photic zones. However, sequenced genomes and environmental surveys suggest active aquatic N-fixation also by many non-cyanobacterial groups. Here, we revealed the seasonal variation and genomic diversity of potential N-fixers in a humic bog lake using metagenomic data and nif gene clusters analysis. Groups with diazotrophic operons were functionally divergent and included Cholorobi, Geobacter, Desulfobacterales, Methylococcales, and Acidobacteria. In addition to nifH (a gene that encodes the dinitrogenase reductase component of the molybdenum nitrogenase), we also identified sequences corresponding to vanadium and iron-only nitrogenase genes. Within the Chlorobi population, the nitrogenase (nifH) cluster was included in a well-structured retrotransposon. Furthermore, the presence of light-harvesting photosynthesis genes implies that anoxygenic photosynthesis may fuel nitrogen fixation under the prevailing low-irradiance conditions. The presence of rnf genes (related to the expression of H+/Na+-translocating ferredoxin: NAD+ oxidoreductase) in Methylococcales and Desulfobacterales suggests that other energy-generating processes may drive the costly N-fixation in the absence of photosynthesis. The highly reducing environment of the anoxic bottom layer of Trout Bog Lake may thus also provide a suitable niche for active N-fixers and primary producers. While future studies on the activity of these potential N-fixers are needed to clarify their role in freshwater nitrogen cycling, the metagenomic data presented here enabled an initial characterization of previously overlooked diazotrophs in freshwater biomes.

Classification of conformational stability of protein mutants from 3D pseudo-folding graph representation of protein sequences using support vector machines.

This work reports a novel 3D pseudo-folding graph representation of protein sequences for modeling purposes. Amino acids euclidean distances matrices (EDMs) encode primary structural information. Amino Acid Pseudo-Folding 3D Distances Count (AAp3DC) descriptors, calculated from the EDMs of a large data set of 1363 single protein mutants of 64 proteins, were tested for building a classifier for the signs of the change of thermal unfolding Gibbs free energy change (DeltaDeltaG) upon single mutations. An optimum support vector machine (SVM) with a radial basis function (RBF) kernel well recognized stable and unstable mutants with accuracies over 70% in crossvalidation test. To the best of our knowledge, this result for stable mutant recognition is the highest ever reported for a sequence-based predictor with more than 1000 mutants. Furthermore, the model adequately classified mutations associated to diseases of human prion protein and human transthyretin.

Classification of voltage-gated K(+) ion channels from 3D pseudo-folding graph representation of protein sequences using genetic algorithm-optimized support vector machines.

Voltage-gated K(+) ion channels (VKCs) are membrane proteins that regulate the passage of potassium ions through membranes. This work reports a classification scheme of VKCs according to the signs of three electrophysiological variables: activation threshold voltage (V(t)), half-activation voltage (V(a50)) and half-inactivation voltage (V(h50)). A novel 3D pseudo-folding graph representation of protein sequences encoded the VKC sequences. Amino acid pseudo-folding 3D distances count (AAp3DC) descriptors, calculated from the Euclidean distances matrices (EDMs) were tested for building the classifiers. Genetic algorithm (GA)-optimized support vector machines (SVMs) with a radial basis function (RBF) kernel well discriminated between VKCs having negative and positive/zero V(t), V(a50) and V(h50) values with overall accuracies about 80, 90 and 86%, respectively, in crossvalidation test. We found contributions of the "pseudo-core" and "pseudo-surface" of the 3D pseudo-folded proteins to the discrimination between VKCs according to the three electrophysiological variables.

Amino acid sequence autocorrelation vectors and Bayesian-regularized genetic neural networks for modeling protein conformational stability: gene V protein mutants.

Development of novel computational approaches for modeling protein properties from their primary structure is the main goal in applied proteomics. In this work, we reported the extension of the autocorrelation vector formalism to amino acid sequences for encoding protein structural information with modeling purposes. Amino acid sequence autocorrelation (AASA) vectors were calculated by measuring the autocorrelations at sequence lags ranging from 1 to 15 on the protein primary structure of 48 amino acid/residue properties selected from the AAindex data base. A total of 720 AASA descriptors were tested for building predictive models of the change of thermal unfolding Gibbs free energy change (delta deltaG) of gene V protein upon mutation. In this sense, ensembles of Bayesian-regularized genetic neural networks (BRGNNs) were used for obtaining an optimum nonlinear model for the conformational stability. The ensemble predictor described about 88% and 66% variance of the data in training and test sets respectively. Furthermore, the optimum AASA vector subset not only helped to successfully model unfolding stability but also well distributed wild-type and gene V protein mutants on a stability self-organized map (SOM), when used for unsupervised training of competitive neurons.

Protein radial distribution function (P-RDF) and Bayesian-Regularized Genetic Neural Networks for modeling protein conformational stability: chymotrypsin inhibitor 2 mutants.

Development of novel computational approaches for modeling protein properties is a main goal in applied Proteomics. In this work, we reported the extension of the radial distribution function (RDF) scores formalism to proteins for encoding 3D structural information with modeling purposes. Protein-RDF (P-RDF) scores measure spherical distributions on protein 3D structure of 48 amino acids/residues properties selected from the AAindex data base. P-RDF scores were tested for building predictive models of the change of thermal unfolding Gibbs free energy change (DeltaDeltaG) of chymotrypsin inhibitor 2 upon mutations. In this sense, an ensemble of Bayesian-Regularized Genetic Neural Networks (BRGNNs) yielded an optimum nonlinear model for the conformational stability. The ensemble predictor described about 84% and 70% variance of the data in training and test sets, respectively.

Proteometric study of ghrelin receptor function variations upon mutations using amino acid sequence autocorrelation vectors and genetic algorithm-based least square support vector machines.

Functional variations on the human ghrelin receptor upon mutations have been associated with a syndrome of short stature and obesity, of which the obesity appears to develop around puberty. In this work, we reported a proteometrics analysis of the constitutive and ghrelin-induced activities of wild-type and mutant ghrelin receptors using amino acid sequence autocorrelation (AASA) approach for protein structural information encoding. AASA vectors were calculated by measuring the autocorrelations at sequence lags ranging from 1 to 15 on the protein primary structure of 48 amino acid/residue properties selected from the AAindex database. Genetic algorithm-based multilinear regression analysis (GA-MRA) and genetic algorithm-based least square support vector machines (GA-LSSVM) were used for building linear and non-linear models of the receptor activity. A genetic optimized radial basis function (RBF) kernel yielded the optimum GA-LSSVM models describing 88% and 95% of the cross-validation variance for the constitutive and ghrelin-induced activities, respectively. AASA vectors in the optimum models mainly appeared weighted by hydrophobicity-related properties. However, differently to the constitutive activity, the ghrelin-induced activity was also highly dependent of the steric features of the receptor.

Proteochemometric modeling of the inhibition complexes of matrix metalloproteinases with N-hydroxy-2-[(phenylsulfonyl)amino]acetamide derivatives using topological autocorrelation interaction matrix and model ensemble averaging.

A target-ligand QSAR approach using autocorrelation formalism was developed for modeling the inhibitory potency (pIC(50)) toward matrix metalloproteinases (MMP-1, MMP-2, MMP-3, MMP-9, and MMP-13) of N-hydroxy-2-[(phenylsulfonyl)amino]acetamide derivatives. Target and ligand structural information was encoded in the Topological Autocorrelation Interaction matrix calculated from 2D topological representation of inhibitors and protein sequences. The relevant Topological Autocorrelation Interaction descriptors were selected by genetic algorithm-based multilinear regression analysis and Bayesian-regularized genetic neural network approaches. A model ensemble strategy was employed for achieving robust and reliable linear and non-linear predictors having nine topological autocorrelation interaction descriptors with square correlation coefficients of ensemble test-set fitting (R(2)(test)) about 0.80 and 0.87, respectively. Electrostatic and hydrophobicity/hydrophilicity properties were the most relevant on the optimum models. In addition, the distribution of the inhibition complexes on a self-organized map depicted target dependence rather than an inhibitor similarity pattern.

Amino Acid Sequence Autocorrelation vectors and ensembles of Bayesian-Regularized Genetic Neural Networks for prediction of conformational stability of human lysozyme mutants.

Development of novel computational approaches for modeling protein properties from their primary structure is a main goal in applied proteomics. In this work, we reported the extension of the autocorrelation vector formalism to amino acid sequences for encoding protein structural information with modeling purposes. Amino Acid Sequence Autocorrelation (AASA) vectors were calculated by measuring the autocorrelations at sequence lags ranging from 1 to 15 on the protein primary structure of 48 amino acid/residue properties selected from the AAindex database. A total of 720 AASA descriptors were tested for building predictive models of the thermal unfolding Gibbs free energy change of human lysozyme mutants. In this sense, ensembles of Bayesian-Regularized Genetic Neural Networks (BRGNNs) were used for obtaining an optimum nonlinear model for the conformational stability. The ensemble predictor described about 88% and 68% variance of the data in training and test sets, respectively. Furthermore, the optimum AASA vector subset was shown not only to successfully model unfolding thermal stability but also to distribute wild-type and mutant lysozymes on a stability Self-organized Map (SOM) when used for unsupervised training of competitive neurons.

Chemical glycosidation of trypsin with O-carboxymethyl-poly-beta-cyclodextrin: catalytic and stability properties.

The polysaccharide O-carboxymethyl-poly-beta-cyclodextrin was synthesized (molecular mass 13,000 Da, 40% carboxy groups) and attached to the surface of bovine pancreatic trypsin. The resulting neoglycoenzyme retained high proteolytic and esterolytic activity and contained approx. 1.0 mol of polymer/mol of enzyme. The optimum temperature for trypsin activity was increased by 10 degrees C after this transformation. Thermostability of the polymer-enzyme complex was increased by about 14 degrees C over 10 min incubation. The conjugate was also more resistant to thermal inactivation at different temperatures, ranging from 45 to 60 degrees C, demonstrating the influence of supramolecular and polymer-protein electrostatic interactions on trypsin thermostabilization. Additionally, the conjugate was 36-fold more resistant to the action of the anionic surfactant SDS. This modification also protected the enzyme from autolysis at alkaline pH.