Genome, Environment, Microbiome and Metabolome in Autism (GEMMA) Study Design: Biomarkers Identification for Precision Treatment and Primary Prevention of Autism Spectrum Disorders by an Integrated Multi-Omics Systems Biology Approach.

Autism Spectrum Disorder (ASD) affects approximately 1 child in 54, with a 35-fold increase since 1960. Selected studies suggest that part of the recent increase in prevalence is likely attributable to an improved awareness and recognition, and changes in clinical practice or service availability. However, this is not sufficient to explain this epidemiological phenomenon. Research points to a possible link between ASD and intestinal microbiota because many children with ASD display gastro-intestinal problems. Current large-scale datasets of ASD are limited in their ability to provide mechanistic insight into ASD because they are predominantly cross-sectional studies that do not allow evaluation of perspective associations between early life microbiota composition/function and later ASD diagnoses. Here we describe GEMMA (Genome, Environment, Microbiome and Metabolome in Autism), a prospective study supported by the European Commission, that follows at-risk infants from birth to identify potential biomarker predictors of ASD development followed by validation on large multi-omics datasets. The project includes clinical (observational and interventional trials) and pre-clinical studies in humanized murine models (fecal transfer from ASD probands) and in vitro colon models. This will support the progress of a microbiome-wide association study (of human participants) to identify prognostic microbiome signatures and metabolic pathways underlying mechanisms for ASD progression and severity and potential treatment response.

One byte at a time: evidencing the quality of clinical service next-generation sequencing for germline and somatic variants.

Next-generation sequencing (NGS) is replacing other molecular techniques to become the de facto gene diagnostics approach, transforming the speed of diagnosis for patients and expanding opportunities for precision medicine. Consequently, for accredited laboratories as well as those seeking accreditation, both objective measures of quality and external review of laboratory processes are required. External quality assessment (EQA), or Proficiency Testing (PT), can assess a laboratory's service through an independent external agency, the EQA provider. The analysis of a growing number of genes and whole exome and genomes is now routine; therefore, an EQA must be delivered to enable all testing laboratories to participate. In this paper, we describe the development of a unique platform and gene target independent EQA scheme for NGS, designed to scale from current to future requirements of clinical diagnostic laboratories testing for germline and somatic variants. The EQA results from three annual rounds indicate that clinical diagnostic laboratories are providing an increasingly high-quality NGS service and variant calling abilities are improving. From an EQA provider perspective, challenges remain regarding delivery and performance criteria, as well as in analysing similar NGS approaches between cohorts with meaningful metrics, sample sourcing and data formats.

Toll-like receptor 5 in obesity: the role of gut microbiota and adipose tissue inflammation.

OBJECTIVE: This study aimed at establishing bacterial flagellin-recognizing toll-like receptor 5 (TLR5) as a novel link between gut microbiota composition, adipose tissue inflammation, and obesity. METHODS: An adipose tissue microarray database was used to compare women having the highest (n = 4, H-TLR) and lowest (n = 4, L-TLR) expression levels of TLR5-signaling pathway genes. Gut microbiota composition was profiled using flow cytometry and FISH. Standard laboratory techniques were used to determine anthropometric and clinical variables. In vivo results were verified using cultured human adipocytes. RESULTS: The H-TLR group had higher flagellated Clostridium cluster XIV abundance and Firmicutes-to-Bacteroides ratio. H-TLR subjects had obese phenotype characterized by greater waist circumference, fat %, and blood pressure (P < 0.05 for all). They also had higher leptin and lower adiponectin levels (P < 0.05 for both). Six hundred and sixty-eight metabolism- and inflammation-related adipose tissue genes were differentially expressed between the groups. In vitro studies confirmed that flagellin activated TLR5 inflammatory pathways, decreased insulin signaling, and increased glycerol secretion. CONCLUSIONS: The in vivo findings suggest that flagellated Clostridium cluster XIV bacteria contribute to the development of obesity through distorted adipose tissue metabolism and inflammation. The in vitro studies in adipocytes show that the underlying mechanisms of the human findings may be due to flagellin-activated TLR5 signaling.

Gut-adipose tissue axis in hepatic fat accumulation in humans.

BACKGROUND & AIMS: Recent evidence suggests that in animals gut microbiota composition (GMC) affects the onset and progression of hepatic fat accumulation. The aim of this study was to investigate in humans whether subjects with high hepatic fat content (HHFC) differ in their GMC from those with low hepatic fat content (LHFC), and whether these differences are associated with body composition, biomarkers and abdominal adipose tissue inflammation. METHODS: Hepatic fat content (HFC) was measured using proton magnetic resonance spectroscopy ((1)H MRS). Fecal GMC was profiled by 16S rRNA fluorescence in situ hybridization and flow cytometry. Adipose tissue gene expression was analyzed using Affymetrix microarrays and quantitative PCR. RESULTS: The HHFC group had unfavorable GMC described by lower amount of Faecalibacterium prausnitzii (FPrau) (p<0.05) and relatively higher Enterobacteria than the LHFC group. Metabolically dysbiotic GMC associated with HOMA-IR and triglycerides (p<0.05 for both). Several inflammation-related adipose tissue genes were differentially expressed and correlated with HFC (p<0.05). In addition, the expression of certain genes correlated with GMC dysbiosis, i.e., low FPrau-to-Bacteroides ratio. CONCLUSIONS: HHFC subjects differ unfavorably in their GMC from LHFC subjects. Adipose tissue inflammation may be an important link between GMC, metabolic disturbances, and hepatic fat accumulation.

Draft Genome Sequence of the Hydrogen- and Ethanol-Producing Anaerobic Alkalithermophilic Bacterium Caloramator celer.

Caloramator celer strain JW/YL-NZ35 is a Gram-positive thermophilic, alkalitolerant, and strictly anaerobic bacterium capable of producing hydrogen and ethanol under extreme conditions. The draft genome sequence presented here will provide valuable information to further explore the physiology of this species and its potential for biofuel production.

Genome Sequence of Halanaerobium saccharolyticum subsp. saccharolyticum Strain DSM 6643T, a Halophilic Hydrogen-Producing Bacterium.

Halanaerobium saccharolyticum is a halophilic anaerobic fermentative bacterium capable of producing hydrogen, a potential future energy carrier molecule. The high-quality draft genome of H. saccharolyticum subsp. saccharolyticum strain DSM 6643(T) consists of 24 contigs for 2,873,865 bp with a G+C content of 32.3%.

Tropical mangrove sediments as a natural inoculum for efficient electroactive biofilms.

Chronoamperometry is known to be an efficient way to form electroactive biofilms (EAB) on conductive electrodes. For the first time, tropical mangrove sediments are analyzed as a potential inoculum to form MFC anodes with the use of acetate as substrate. The performance of the EAB-coated carbon cloth electrodes are evaluated according to the maximal current density, the coulombic efficiency and the cyclic voltammogramms. Working electrodes (WE) polarized at -0.2V/SCE gave better results compared to -0.4V/SCE and 0.0 V/SCE. The maximal current density attained was 12A/m(2) with a CE of 24%. Contributions of the EAB in the generation of current were discussed and mechanisms of electronic transfer by the bacteria were discussed. Epifluorescence and SEM images showed the evolution of the biofilms on the electrode surface and the heterogeneity of the structure.

Reconstruction and validation of RefRec: a global model for the yeast molecular interaction network.

Molecular interaction networks establish all cell biological processes. The networks are under intensive research that is facilitated by new high-throughput measurement techniques for the detection, quantification, and characterization of molecules and their physical interactions. For the common model organism yeast Saccharomyces cerevisiae, public databases store a significant part of the accumulated information and, on the way to better understanding of the cellular processes, there is a need to integrate this information into a consistent reconstruction of the molecular interaction network. This work presents and validates RefRec, the most comprehensive molecular interaction network reconstruction currently available for yeast. The reconstruction integrates protein synthesis pathways, a metabolic network, and a protein-protein interaction network from major biological databases. The core of the reconstruction is based on a reference object approach in which genes, transcripts, and proteins are identified using their primary sequences. This enables their unambiguous identification and non-redundant integration. The obtained total number of different molecular species and their connecting interactions is approximately 67,000. In order to demonstrate the capacity of RefRec for functional predictions, it was used for simulating the gene knockout damage propagation in the molecular interaction network in approximately 590,000 experimentally validated mutant strains. Based on the simulation results, a statistical classifier was subsequently able to correctly predict the viability of most of the strains. The results also showed that the usage of different types of molecular species in the reconstruction is important for accurate phenotype prediction. In general, the findings demonstrate the benefits of global reconstructions of molecular interaction networks. With all the molecular species and their physical interactions explicitly modeled, our reconstruction is able to serve as a valuable resource in additional analyses involving objects from multiple molecular -omes. For that purpose, RefRec is freely available in the Systems Biology Markup Language format.

State space modeling of yeast gene expression dynamics.

Combined interaction of all the genes forms a central part of the functional system of a cell. Thus, especially the data-based modeling of the gene expression network is currently one of the main challenges in the field of systems biology. However, the problem is an extremely high-dimensional and complex one, so that normal identification methods are usually not applicable specially if aiming at dynamic models. We propose in this paper a subspace identification approach, which is well suited for high-dimensional system modeling and the presented modified version can also handle the underdetermined case with less data samples than variables (genes). The algorithm is applied to two public stress-response data sets collected from yeast Saccharomyces cerevisiae. The obtained dynamic state space model is tested by comparing the simulation results with the measured data. It is shown that the identified model can relatively well describe the dynamics of the general stress-related changes in the expression of the complete yeast genome. However, it seems inevitable that more precise modeling of the dynamics of the whole genome would require experiments especially designed for systemic modeling.

Exploratory modeling of yeast stress response and its regulation with gCCA and associative clustering.

We model dependencies between m multivariate continuous-valued information sources by a combination of (i) a generalized canonical correlations analysis (gCCA) to reduce dimensionality while preserving dependencies in m - 1 of them, and (ii) summarizing dependencies with the remaining one by associative clustering. This new combination of methods avoids multiway associative clustering which would require a multiway contingency table and hence suffer from curse of dimensionality of the table. The method is applied to summarizing properties of yeast stress by searching for dependencies (commonalities) between expression of genes of baker's yeast Saccharomyces cerevisiae in various stressful treatments, and summarizing stress regulation by finally adding data about transcription factor binding sites.

The mitochondrial ribosome-specific MrpL55 protein is essential in Drosophila and dynamically required during development.

We report on the essential Drosophila mRpL55 gene conserved exclusively in metazoans. Null mRpL55 mutants did not grow after hatching, moved slowly and died as first instar larvae. MrpL55 is similar to mammalian MRPL55, a protein that, in a large-scale mass spectrometry study, has been found as a mitoribosome-specific large subunit protein. We showed that MrpL55 was localised to the mitochondrion in S2 cells and tissues and was enriched in cells with a higher protein synthesis activity. The MrpL55 protein contains a KOW-like motif present in proteins with a role in transcriptional anti-termination and regulation of translation. Modulation of mRpL55 expression level is critical for development. Somatic clonal analysis showed that MrpL55 was not required in larval eye imaginal discs but required in pupal discs apparently during the second mitotic wave. Therefore, our results showed that the MrpL55 protein acts dynamically in the cell during development. We propose that MrpL55 is involved in Drosophila mitochondrial biogenesis and G2/M phase cell cycle progression.

Associative clustering for exploring dependencies between functional genomics data sets.

High-throughput genomic measurements, interpreted as cooccurring data samples from multiple sources, open up a fresh problem for machine learning: What is in common in the different data sets, that is, what kind of statistical dependencies are there between the paired samples from the different sets? We introduce a clustering algorithm for exploring the dependencies. Samples within each data set are grouped such that the dependencies between groups of different sets capture as much of pairwise dependencies between the samples as possible. We formalize this problem in a novel probabilistic way, as optimization of a Bayes factor. The method is applied to reveal commonalities and exceptions in gene expression between organisms and to suggest regulatory interactions in the form of dependencies between gene expression profiles and regulator binding patterns.

Drosophila non-muscle alpha-actinin is localized in nurse cell actin bundles and ring canals, but is not required for fertility.

The single copy Drosophila alpha-actinin gene is alternatively spliced to generate three different isoforms that are expressed in larval muscle, adult muscle and non-muscle cells, respectively. We have generated novel alpha-actinin alleles, which specifically remove the non-muscle isoform. Homozygous mutant flies are viable and fertile with no obvious defects. Using a monoclonal antibody that recognizes all three splice variants, we compared alpha-actinin distribution in wild type and mutant embryos and ovaries. We found that non-muscle alpha-actinin was present in young embryos and in the embryonic central nervous system. In ovaries, non-muscle alpha-actinin was localized in the nurse cell subcortical cytoskeleton, cytoplasmic actin cables and ring canals. In the mutant, alpha-actinin expression remained in muscle tissues, but also in a subpopulation of epithelial cells in both embryos and ovaries. This suggests that various populations of non-muscle cells regulate alpha-actinin expression in different ways. We also show that ectopically expressed adult muscle-specific alpha-actinin localizes to all F-actin containing structures in the nurse cells in the absence of endogenous non-muscle alpha-actinin.

Cloning and expression of murine enzymes involved in the salvage pathway of GDP-L-fucose.

In the salvage pathway of GDP-L-fucose, free cytosolic fucose is phosphorylated by L-fucokinase to form L-fucose-L-phosphate, which is then further converted to GDP-L-fucose in the reaction catalyzed by GDP-L-fucose pyrophosphorylase. We report here the cloning and expression of murine L-fucokinase and GDP-L-fucose pyrophosphorylase. Murine L-fucokinase is expressed as two transcripts of 3057 and 3270 base pairs, encoding proteins of 1019 and 1090 amino acids with predicted molecular masses of 111 kDa and 120 kDa respectively. Only the longer splice variant of L-fucokinase was enzymatically active when expressed in COS-7 cells. Murine GDP-L-fucose pyrophosphorylase has an open reading frame of 1773 base pairs encoding a protein of 591 amino acids with a predicted molecular mass of 65.5 kDa. GDP-L-fucose, the reaction product of GDP-L-pyrophosphorylase, was identified by HPLC and MALDI-TOF MS analysis. The tissue distribution of murine L-fucokinase and GDP-L-fucose pyrophosphorylase was investigated by quantitative real time PCR, which revealed high expression of L-fucokinase and GDP-L-fucose pyrophosphorylase in various tissues. The wide expression of both enzymes can also be observed from the large amount of data collected from a number of expressed sequence tag libraries, which indicate that not only the de novo pathway alone, but also the salvage pathway, could have a significant role in the synthesis of GDP-L-fucose in the cytosol.

Activation of the nodA promoter by the nodD genes of Rhizobium galegae induced by synthetic flavonoids or Galega orientalis root exudate.

Rhizobial nodD genes produce transcriptional regulators that, together with appropriate inducer compounds, activate the other symbiotic nodulation (nod) genes and initiate the nodule formation process. Two nodD homologues, nodD1 and nodD2, are present in the Rhizobium galegae strain HAMBI 1174. In this work we analysed their ability to induce the nodA promoter with synthetic inducers known to activate nod genes in other rhizobia. According to phylogenetic analysis, the inducer-specific carboxy-terminal part of the R. galegae nodD protein sequence groups together with those of Rhizobium leguminosarum and Sinorhizobium meliloti. However, the respective inducer compounds for their NodD proteins are not highly effective with R. galegae nodD products. The best inducer discovered with R. galegae nodD1 was the root exudate of the host plant of R. galegae, Galega orientalis. HPLC analyses revealed the presence of many divergent flavonoid compounds in the G. orientalis root exudate. The most effective HPLC fractions induced R. galegae nodD1 up to the level obtained by intact G. orientalis root exudate while apigenin and luteolin, which were also present in the root exudate, were only moderate inducers. A UV-Vis diode array spectrum of the most active peak indicated that the main inducer present in the G. orientalis root exudate is an unidentified chalcone-type compound. In the Galega-R. galegae interaction the first recognition between the NodD protein and the flavonoid inducer secreted from the roots of Galega is specific for these organisms, and thus partly responsible of the strict host specificity of this symbiosis.

The Drosophila hugin gene codes for myostimulatory and ecdysis-modifying neuropeptides.

In a genomic screen we isolated the Drosophila gene hugin (hug, cytology 87C1-2) by cross-hybridisation to a human glial cell line-derived neurotrophic factor cDNA. Upon cDNA sequence analysis and in vitro expression assays, the hugin gene was found to encode a signal peptide containing proprotein that was further processed in Schneider-2 cells into peptides similar to known neuropeptides. Two of the peptides were similar to FXPRL-amides (pyrokinins) and to the ecdysis-triggering hormone, respectively. The former displayed myostimulatory activity in a bioassay on the cockroach hyperneural muscle preparation, as well as in the Drosophila heart muscle assay. Hugin is expressed during the later half of embryogenesis and during larval stages in a subgroup of neurosecretory cells of the suboesophageal ganglion. Ubiquitous ectopic hugin expression resulted in larval death predominantly at or shortly after ecdysis from second to third instar, suggesting that at least one of the posttranslational cleavage products affects molting of the larva by interfering with the regulation of ecdysis.

Functional expression of Pseudomonas aeruginosa GDP-4-keto-6-deoxy-D-mannose reductase which synthesizes GDP-rhamnose.

Pseudomonas aeruginosa is an opportunistic Gram-negative bacterium that causes severe infections in a number of hosts from plants to mammals. A-band lipopolysaccharide of P. aeruginosa contains d-rhamnosylated O-antigen. The synthesis of GDP-D-rhamnose, the d-rhamnose donor in d-rhamnosylation, starts from GDP-D-mannose. It is first converted by the GDP-mannose-4,6-dehydratase (GMD) into GDP-4-keto-6-deoxy-D-mannose, and then reduced to GDP-D-rhamnose by GDP-4-keto-6-deoxy-D-mannose reductase (RMD). Here, we describe the enzymatic characterization of P. aeruginosa RMD expressed in Saccharomyces cerevisiae. Previous success in functional expression of bacterial gmd genes in S. cerevisiae allowed us to convert GDP-D-mannose into GDP-4-keto-6-deoxy-D-mannose. Thus, coexpression of the Helicobacter pylori gmd and P. aeruginosa rmd genes resulted in conversion of the 4-keto-6-deoxy intermediate into GDP-deoxyhexose. This synthesized GDP-deoxyhexose was confirmed to be GDP-rhamnose by HPLC, matrix-assisted laser desorption/ionization time-of-flight MS, and finally NMR spectroscopy. The functional expression of P. aeruginosa RMD in S. cerevisiae will provide a tool for generating GDP-rhamnose for in vitro rhamnosylation of glycoprotein and glycopeptides.