Background The European Community has adopted very restrictive policies regarding the dissemination and use of genetically modified organisms (GMOs). In fact, a maximum threshold of 0.9% of contaminating GMOs is tolerated for a "GMO-free" label. In recent years, imports of undescribed GMOs have been detected. Their sequences are not described and therefore not detectable by conventional approaches, such as PCR. Results We developed DUGMO, a bioinformatics pipeline for the detection of genetically modified (GM) bacteria, including unknown GM bacteria, based on Illumina paired-end sequencing data. The method is currently focused on the detection of GM bacteria with - possibly partial - transgenes in pure bacterial samples. In the preliminary steps, coding sequences (CDSs) are aligned through two successive BLASTN against the host pangenome with relevant tuned parameters to discriminate CDSs belonging to the wild type genome (wgCDS) from potential GM coding sequences (pgmCDSs). Then, Bray-Curtis distances are calculated between the wgCDS and each pgmCDS, based on the difference of genomic vocabulary. Finally, two machine learning methods, namely the Random Forest and Generalized Linear Model, are carried out to target true GM CDS(s), based on six variables including Bray-Curtis distances and GC content. Tests carried out on a GMBacillus subtilisshowed 25 positive CDSs corresponding to the chloramphenicol resistance gene and CDSs of the inserted plasmids. On a wild typeB. subtilis, no false positive sequences were detected. Conclusion DUGMO detects exogenous CDS, truncated, fused or highly mutated wild CDSs in high-throughput sequencing data, and was shown to be efficient at detecting GM sequences, but it might also be employed for the identification of recent horizontal gene transfers.

Statistical analysis of network is an active research area and the literature counts a lot of papers concerned with network models and statistical analysis of networks. However, very few papers deal with missing data in network analysis and we reckon that, in practice, networks are often observed with missing values. In this paper we focus on the Stochastic Block Model with valued edges and consider a MCAR setting by assuming that every dyad (pair of nodes) is sampled identically and independently of the others with probability ρ > 0. We prove that maximum likelihood estimators and its variational approximations are consistent and asymptotically normal in the presence of missing data as soon as the sampling probability ρ satisfies ρ log(n)/n.

Statistical analysis of network is an active research area and the literature counts a lot of papers concerned with network models and statistical analysis of networks. However, very few papers deal with missing data in network analysis and we reckon that, in practice, networks are often observed with missing values. In this paper we focus on the Stochastic Block Model with valued edges and consider a MCAR setting by assuming that every dyad (pair of nodes) is sampled identically and independently of the others with probability ρ > 0. We prove that maximum likelihood estimators and its variational approximations are consistent and asymptotically normal in the presence of missing data as soon as the sampling probability ρ satisfies ρ log(n)/n.

Aims Quality evaluation of fresh whitemouth croaker (Micropogonias furnieri ) by histamine determination using the HPLC‐DAD method and quantification of histamine‐forming bacteria using NGS and qPCR. Methods and Results The histamine content of fresh whitemouth croaker was detected by high performance liquid chromatography with diode array detector with a concentration ranging from 258·52 to 604·62 mg kg−1 being observed. The number of histidine decarboxylase (hdc gene) copies from Gram‐negative bacteria and the bacteria Morganella morganii and Enterobacter aerogenes were quantified by quantitative polymerase chain reaction. All samples were positive, with copy numbers of the hdc gene ranging from 4·67 to 12·01 log10 per g. The microbial community was determined by sequencing the V4 region of the 16S rRNA gene using the Ion Torrent platform. The bioinformatics data generated by frog software showed that the phylum Proteobacteria was the most abundant, with the family Moraxellaceae being more prevalent in samples collected in the summer, whereas the Pseudomonadaceae was more present in the winter. Conclusions All fish muscle samples analysed in this study presented histamine values higher than those allowed by CODEX Alimentarius. Additionally, a wide variety of spoilage micro‐organisms capable of expressing the enzyme histidine decarboxylase were detected. Thus, improvements in handling and processing are required to minimize the prevalence of histamine‐producing bacteria in fish. Significance and Impact of the Study Global fish production in 2016 was 171 million tons, with the largest consumer being China, followed by Indonesia and the USA. In Brazil, 1·3 million tons of fish are consumed per year, with whitemouth croaker being the main fish landed. Notably, cases associated with histamine poisoning are quite common. According to the European Food Safety Authority and European Centre for Disease Prevention and Control, a total of 599 HFP outbreaks were identified in the European Union during the period 2010–2017. In the USA, there were 333 outbreaks with 1383 people involved between 1998 and 2008.

Aims Quality evaluation of fresh whitemouth croaker (Micropogonias furnieri ) by histamine determination using the HPLC‐DAD method and quantification of histamine‐forming bacteria using NGS and qPCR. Methods and Results The histamine content of fresh whitemouth croaker was detected by high performance liquid chromatography with diode array detector with a concentration ranging from 258·52 to 604·62 mg kg−1 being observed. The number of histidine decarboxylase (hdc gene) copies from Gram‐negative bacteria and the bacteria Morganella morganii and Enterobacter aerogenes were quantified by quantitative polymerase chain reaction. All samples were positive, with copy numbers of the hdc gene ranging from 4·67 to 12·01 log10 per g. The microbial community was determined by sequencing the V4 region of the 16S rRNA gene using the Ion Torrent platform. The bioinformatics data generated by frog software showed that the phylum Proteobacteria was the most abundant, with the family Moraxellaceae being more prevalent in samples collected in the summer, whereas the Pseudomonadaceae was more present in the winter. Conclusions All fish muscle samples analysed in this study presented histamine values higher than those allowed by CODEX Alimentarius. Additionally, a wide variety of spoilage micro‐organisms capable of expressing the enzyme histidine decarboxylase were detected. Thus, improvements in handling and processing are required to minimize the prevalence of histamine‐producing bacteria in fish. Significance and Impact of the Study Global fish production in 2016 was 171 million tons, with the largest consumer being China, followed by Indonesia and the USA. In Brazil, 1·3 million tons of fish are consumed per year, with whitemouth croaker being the main fish landed. Notably, cases associated with histamine poisoning are quite common. According to the European Food Safety Authority and European Centre for Disease Prevention and Control, a total of 599 HFP outbreaks were identified in the European Union during the period 2010–2017. In the USA, there were 333 outbreaks with 1383 people involved between 1998 and 2008.

The undefined mixed starter culture (UMSC) is used in the manufacture of cheeses. Deciphering UMSC microbial diversity is important to optimize industrial processes. The UMSC was studied using culture-dependent and culture-independent based methods. MALDI-TOF MS enabled identification of species primarily from the Lactococcus genus. Comparisons of carbohydrate metabolism profiles allowed to discriminate five phenotypes of Lactococcus (n = 26/1616). The 16S sequences analysis (V1–V3, V3–V4 regions) clustered the UMSC microbial diversity into two Lactococcus operational taxonomic units (OTUs). These clustering results were improved with the DADA2 algorithm on the housekeeping purR sequences. Five L. lactis variants were detected among the UMSC. The whole-genome sequencing of six isolates allowed for the identification of the lactis subspecies using Illumina® (n = 5) and Pacbio® (n = 1) technologies. Kegg analysis confirmed the L. lactis species-specific niche adaptations and highlighted a progressive gene pseudogenization. Then, agar spot tests and agar well diffusion assays were used to assess UMSC antimicrobial activities. Of note, isolate supernatants (n = 34/1616) were shown to inhibit the growth of Salmonella ser. Typhimurium CIP 104115, Lactobacillus sakei CIP 104494, Staphylococcus aureus DSMZ 13661, Enterococcus faecalis CIP103015 and Listeria innocua CIP 80.11. Collectively, these results provide insightful information about UMSC L. lactis diversity and revealed a potential application as a bio-protective starter culture.

The undefined mixed starter culture (UMSC) is used in the manufacture of cheeses. Deciphering UMSC microbial diversity is important to optimize industrial processes. The UMSC was studied using culture-dependent and culture-independent based methods. MALDI-TOF MS enabled identification of species primarily from the Lactococcus genus. Comparisons of carbohydrate metabolism profiles allowed to discriminate five phenotypes of Lactococcus (n = 26/1616). The 16S sequences analysis (V1–V3, V3–V4 regions) clustered the UMSC microbial diversity into two Lactococcus operational taxonomic units (OTUs). These clustering results were improved with the DADA2 algorithm on the housekeeping purR sequences. Five L. lactis variants were detected among the UMSC. The whole-genome sequencing of six isolates allowed for the identification of the lactis subspecies using Illumina® (n = 5) and Pacbio® (n = 1) technologies. Kegg analysis confirmed the L. lactis species-specific niche adaptations and highlighted a progressive gene pseudogenization. Then, agar spot tests and agar well diffusion assays were used to assess UMSC antimicrobial activities. Of note, isolate supernatants (n = 34/1616) were shown to inhibit the growth of Salmonella ser. Typhimurium CIP 104115, Lactobacillus sakei CIP 104494, Staphylococcus aureus DSMZ 13661, Enterococcus faecalis CIP103015 and Listeria innocua CIP 80.11. Collectively, these results provide insightful information about UMSC L. lactis diversity and revealed a potential application as a bio-protective starter culture.

The undefined mixed starter culture (UMSC) is used in the manufacture of cheeses. Deciphering UMSC microbial diversity is important to optimize industrial processes. The UMSC was studied using culture-dependent and culture-independent based methods. MALDI-TOF MS enabled identification of species primarily from the Lactococcus genus. Comparisons of carbohydrate metabolism profiles allowed to discriminate five phenotypes of Lactococcus (n = 26/1616). The 16S sequences analysis (V1–V3, V3–V4 regions) clustered the UMSC microbial diversity into two Lactococcus operational taxonomic units (OTUs). These clustering results were improved with the DADA2 algorithm on the housekeeping purR sequences. Five L. lactis variants were detected among the UMSC. The whole-genome sequencing of six isolates allowed for the identification of the lactis subspecies using Illumina® (n = 5) and Pacbio® (n = 1) technologies. Kegg analysis confirmed the L. lactis species-specific niche adaptations and highlighted a progressive gene pseudogenization. Then, agar spot tests and agar well diffusion assays were used to assess UMSC antimicrobial activities. Of note, isolate supernatants (n = 34/1616) were shown to inhibit the growth of Salmonella ser. Typhimurium CIP 104115, Lactobacillus sakei CIP 104494, Staphylococcus aureus DSMZ 13661, Enterococcus faecalis CIP103015 and Listeria innocua CIP 80.11. Collectively, these results provide insightful information about UMSC L. lactis diversity and revealed a potential application as a bio-protective starter culture.

Anaerobic digestion (AD) is a microbial process that can efficiently degrade organic waste into renewable energies such as methane-rich biogas. However, the underpinning microbial mechanisms are highly vulnerable to a wide range of inhibitory compounds, leading to process failure and economic losses. High-throughput sequencing technologies enable the identification of microbial indicators of digesters inhibition and can provide new insights into the key phylotypes at stake during AD process. But yet, current studies have used different inocula, substrates, geographical sites and types of reactors, resulting in indicators that are not robust or reproducible across independent studies. In addition, such studies focus on the identification of a single microbial indicator that is not reflective of the complexity of AD. Our study proposes the first analysis of its kind that seeks for a robust signature of microbial indicators of phenol and ammonia inhibitions, whilst leveraging on 4 independent in-house and external AD microbial studies. We applied a recent multivariate integrative method on two-in-house studies to identify such signature, then predicted the inhibitory status of samples from two datasets with more than 90% accuracy. Our study demonstrates how we can efficiently analyze existing studies to extract robust microbial community patterns, predict AD inhibition, and deepen our understanding of AD towards better AD microbial management.

Anaerobic digestion (AD) is a microbial process that can efficiently degrade organic waste into renewable energies such as methane-rich biogas. However, the underpinning microbial mechanisms are highly vulnerable to a wide range of inhibitory compounds, leading to process failure and economic losses. High-throughput sequencing technologies enable the identification of microbial indicators of digesters inhibition and can provide new insights into the key phylotypes at stake during AD process. But yet, current studies have used different inocula, substrates, geographical sites and types of reactors, resulting in indicators that are not robust or reproducible across independent studies. In addition, such studies focus on the identification of a single microbial indicator that is not reflective of the complexity of AD. Our study proposes the first analysis of its kind that seeks for a robust signature of microbial indicators of phenol and ammonia inhibitions, whilst leveraging on 4 independent in-house and external AD microbial studies. We applied a recent multivariate integrative method on two-in-house studies to identify such signature, then predicted the inhibitory status of samples from two datasets with more than 90% accuracy. Our study demonstrates how we can efficiently analyze existing studies to extract robust microbial community patterns, predict AD inhibition, and deepen our understanding of AD towards better AD microbial management.

The undefined mixed starter culture (UMSC) is used in the manufacture of cheeses. Deciphering UMSC microbial diversity is important to optimize industrial processes. The UMSC was studied using culture-dependent and culture-independent based methods. MALDI-TOF MS enabled identification of species primarily from the Lactococcus genus. Comparisons of carbohydrate metabolism profiles allowed to discriminate five phenotypes of Lactococcus (n = 26/1616). The 16S sequences analysis (V1-V3, V3-V4 regions) clustered the UMSC microbial diversity into two Lactococcus operational taxonomic units (OTUs). These clustering results were improved with the DADA2 algorithm on the housekeeping purR sequences. Five L. lactis variants were detected among the UMSC. The whole-genome sequencing of six isolates allowed for the identification of the lactis subspecies using Illumina® (n = 5) and Pacbio® (n = 1) technologies. Kegg analysis confirmed the L. lactis species-specific niche adaptations and highlighted a progressive gene pseudogenization. Then, agar spot tests and agar well diffusion assays were used to assess UMSC antimicrobial activities. Of note, isolate supernatants (n = 34/1616) were shown to inhibit the growth of Salmonella ser. Typhimurium CIP 104115, Lactobacillus sakei CIP 104494, Staphylococcus aureus DSMZ 13661, Enterococcus faecalis CIP103015 and Listeria innocua CIP 80.11. Collectively, these results provide insightful information about UMSC L. lactis diversity and revealed a potential application as a bio-protective starter culture.

Limousin, a renowned beef breed originating from central France, has been selectively bred over the last 100 years to improve economically important traits. We used whole-genome sequencing data from 10 unrelated Limousin bull calves to detect polymorphisms and identify regions under selection. A total of 13 943 766 variants were identified. Moreover, 311 852 bi-allelic SNPs and 92 229 indels located on autosomes were fixed for the alternative allele in all sequenced animals, including the previously reported missense deleterious F94L mutation inMSTN. We performed a whole-genome screen to discover genomic regions with excess homozygosity, using the pooled heterozygosity score and identified 171 different candidate selective sweeps. In total, 68 candidate genes were found in only 57 of these regions, indicating that a large fraction of the genome under selection might lie in non-coding regions and suggesting that a majority of adaptive mutations might be regulatory in nature. Many QTL were found within candidate selective sweep regions, including QTL associated with shear force or carcass weight. Among the putative selective sweeps, we located genes (MSTN,NCKAP5,RUNX2) that potentially contribute to important phenotypes in Limousin. Several candidate regions and genes under selection were also found in previous genome-wide selection scans performed in Limousin. In addition, we were able to pinpoint candidate causative regulatory polymorphisms inGRIK3andRUNX2that might have been under selection. Our results will contribute to improved understanding of the mechanisms and targets of artificial selection and will facilitate the interpretation of GWASs performed in Limousin.

Limousin, a renowned beef breed originating from central France, has been selectively bred over the last 100 years to improve economically important traits. We used whole-genome sequencing data from 10 unrelated Limousin bull calves to detect polymorphisms and identify regions under selection. A total of 13 943 766 variants were identified. Moreover, 311 852 bi-allelic SNPs and 92 229 indels located on autosomes were fixed for the alternative allele in all sequenced animals, including the previously reported missense deleterious F94L mutation inMSTN. We performed a whole-genome screen to discover genomic regions with excess homozygosity, using the pooled heterozygosity score and identified 171 different candidate selective sweeps. In total, 68 candidate genes were found in only 57 of these regions, indicating that a large fraction of the genome under selection might lie in non-coding regions and suggesting that a majority of adaptive mutations might be regulatory in nature. Many QTL were found within candidate selective sweep regions, including QTL associated with shear force or carcass weight. Among the putative selective sweeps, we located genes (MSTN,NCKAP5,RUNX2) that potentially contribute to important phenotypes in Limousin. Several candidate regions and genes under selection were also found in previous genome-wide selection scans performed in Limousin. In addition, we were able to pinpoint candidate causative regulatory polymorphisms inGRIK3andRUNX2that might have been under selection. Our results will contribute to improved understanding of the mechanisms and targets of artificial selection and will facilitate the interpretation of GWASs performed in Limousin.

The genus Gallus is distributed across a large part of Southeast Asia and has received special interest because the domestic chicken, Gallus gallus domesticus, has spread all over the world and is a major protein source for humans. There are four species: the red junglefowl (G. gallus), the green junglefowl (G. varius), the Lafayette's junglefowl (G. lafayettii) and the grey junglefowl (G. sonneratii). The aim of this study is to reconstruct the history of these species by a whole genome sequencing approach and resolve inconsistencies between well supported topologies inferred using different data and methods. Using deep sequencing, we identified over 35 million SNPs and reconstructed the phylogeny of the Gallus genus using both distance (BioNJ) and maximum likelihood (ML) methods. We observed discrepancies according to reconstruction methods and genomic components. The two most supported topologies were previously reported and were discriminated by using phylogenetic and gene flow analyses, based on ABBA statistics. Terminology fix requested by the deputy editor led to support a scenario with G. gallus as the earliest branching lineage of the Gallus genus, instead of G. varius. We discuss the probable causes for the discrepancy. A likely one is that G. sonneratii samples from parks or private collections are all recent hybrids, with roughly 10% of their autosomal genome originating from G. gallus. The removal of those regions is needed to provide reliable data, which was not done in previous studies. We took care of this and additionally included two wild G. sonneratii samples from India, showing no trace of introgression. This reinforces the importance of carefully selecting and validating samples and genomic components in phylogenomics.

Automatic de novo identification of the main regulons of a bacterium from genome and transcriptome data remains a challenge. To address this task, we propose a statistical model that can use information on exact positions of the transcription start sites and condition-dependent expression profiles. The central idea of this model is to improve the probabilistic representation of the promoter DNA sequences by incorporating covariates summarizing expression profiles (e.g. coordinates in projection spaces or hierarchical clustering trees). A dedicated trans-dimensional Markov chain Monte Carlo algorithm adjusts the width and palindromic properties of the corresponding position-weight matrices, the number of parameters to describe exact position relative to the transcription start site, and chooses the expression covariates relevant for each motif. All parameters are estimated simultaneously, for many motifs and many expression covariates. The method is applied to a dataset of transcription start sites and expression profiles available for Listeria monocytogenes . The results validate the approach and provide a new global view of the transcription regulatory network of this important pathogen. Remarkably, a previously unreported motif is found in promoter regions of ribosomal protein genes, suggesting a role in the regulation of growth.

Automatic de novo identification of the main regulons of a bacterium from genome and transcriptome data remains a challenge. To address this task, we propose a statistical model that can use information on exact positions of the transcription start sites and condition-dependent expression profiles. The central idea of this model is to improve the probabilistic representation of the promoter DNA sequences by incorporating covariates summarizing expression profiles (e.g. coordinates in projection spaces or hierarchical clustering trees). A dedicated trans-dimensional Markov chain Monte Carlo algorithm adjusts the width and palindromic properties of the corresponding position-weight matrices, the number of parameters to describe exact position relative to the transcription start site, and chooses the expression covariates relevant for each motif. All parameters are estimated simultaneously, for many motifs and many expression covariates. The method is applied to a dataset of transcription start sites and expression profiles available for Listeria monocytogenes . The results validate the approach and provide a new global view of the transcription regulatory network of this important pathogen. Remarkably, a previously unreported motif is found in promoter regions of ribosomal protein genes, suggesting a role in the regulation of growth.

A role of the gut microbiota in psychiatric disorders is supported by a growing body of literature. The effects of a probiotic mixture of four bacterial strains were studied in two models of anxiety and depression, naturally stress-sensitive Fischer rats and Long Evans rats subjected to maternal deprivation. Rats chronically received either the probiotic mixture (1.10(9) CFU/day) or the vehicle. Anxiety- and depressive-like behaviors were evaluated in several tests. Brain monoamine levels and gut RNA expression of tight junction proteins (Tjp) and inflammatory markers were quantified. The gut microbiota was analyzed in feces by 16S rRNA gene sequencing. Untargeted metabolite analysis reflecting primary metabolism was performed in the cecal content and in serum. Fischer rats treated with the probiotic mixture manifested a decrease in anxiety-like behaviors, in the immobility time in the forced swimming test, as well as in levels of dopamine and its major metabolites, and those of serotonin metabolites in the hippocampus and striatum. In maternally deprived Long Evans rats treated with the probiotic mixture, the number of entries into the central area in the open-field test was increased, reflecting an anxiolytic effect. The probiotic mixture increased Tjp1 and decreased Ifn gamma mRNA levels in the ileum of maternally deprived rats. In both models, probiotic supplementation changed the proportions of several Operational Taxonomic Units (OTU) in the gut microbiota, and the levels of certain cecal and serum metabolites were correlated with behavioral changes. Chronic administration of the tested probiotic mixture can therefore beneficially affect anxiety- and depressive-like behaviors in rats, possibly owing to changes in the levels of certain metabolites, such as 21-deoxycortisol, and changes in brain monoamines.

A role of the gut microbiota in psychiatric disorders is supported by a growing body of literature. The effects of a probiotic mixture of four bacterial strains were studied in two models of anxiety and depression, naturally stress-sensitive Fischer rats and Long Evans rats subjected to maternal deprivation. Rats chronically received either the probiotic mixture (1.10(9) CFU/day) or the vehicle. Anxiety- and depressive-like behaviors were evaluated in several tests. Brain monoamine levels and gut RNA expression of tight junction proteins (Tjp) and inflammatory markers were quantified. The gut microbiota was analyzed in feces by 16S rRNA gene sequencing. Untargeted metabolite analysis reflecting primary metabolism was performed in the cecal content and in serum. Fischer rats treated with the probiotic mixture manifested a decrease in anxiety-like behaviors, in the immobility time in the forced swimming test, as well as in levels of dopamine and its major metabolites, and those of serotonin metabolites in the hippocampus and striatum. In maternally deprived Long Evans rats treated with the probiotic mixture, the number of entries into the central area in the open-field test was increased, reflecting an anxiolytic effect. The probiotic mixture increased Tjp1 and decreased Ifn gamma mRNA levels in the ileum of maternally deprived rats. In both models, probiotic supplementation changed the proportions of several Operational Taxonomic Units (OTU) in the gut microbiota, and the levels of certain cecal and serum metabolites were correlated with behavioral changes. Chronic administration of the tested probiotic mixture can therefore beneficially affect anxiety- and depressive-like behaviors in rats, possibly owing to changes in the levels of certain metabolites, such as 21-deoxycortisol, and changes in brain monoamines.

A role of the gut microbiota in psychiatric disorders is supported by a growing body of literature. The effects of a probiotic mixture of four bacterial strains were studied in two models of anxiety and depression, naturally stress-sensitive Fischer rats and Long Evans rats subjected to maternal deprivation. Rats chronically received either the probiotic mixture (1.10(9) CFU/day) or the vehicle. Anxiety- and depressive-like behaviors were evaluated in several tests. Brain monoamine levels and gut RNA expression of tight junction proteins (Tjp) and inflammatory markers were quantified. The gut microbiota was analyzed in feces by 16S rRNA gene sequencing. Untargeted metabolite analysis reflecting primary metabolism was performed in the cecal content and in serum. Fischer rats treated with the probiotic mixture manifested a decrease in anxiety-like behaviors, in the immobility time in the forced swimming test, as well as in levels of dopamine and its major metabolites, and those of serotonin metabolites in the hippocampus and striatum. In maternally deprived Long Evans rats treated with the probiotic mixture, the number of entries into the central area in the open-field test was increased, reflecting an anxiolytic effect. The probiotic mixture increased Tjp1 and decreased Ifn gamma mRNA levels in the ileum of maternally deprived rats. In both models, probiotic supplementation changed the proportions of several Operational Taxonomic Units (OTU) in the gut microbiota, and the levels of certain cecal and serum metabolites were correlated with behavioral changes. Chronic administration of the tested probiotic mixture can therefore beneficially affect anxiety- and depressive-like behaviors in rats, possibly owing to changes in the levels of certain metabolites, such as 21-deoxycortisol, and changes in brain monoamines.

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