Microbiome Population Dynamics of Cold-Smoked Sockeye Salmon during Refrigerated Storage and after Culture Enrichment.

ABSTRACT: Cold-smoked salmon is a ready-to-eat seafood product of high commercial importance. The processing and storage steps facilitate the introduction, growth, and persistence of foodborne pathogens and spoilage bacteria. The growth of commensal bacteria during storage and once the product is opened also influence the quality and safety of cold-smoked salmon. Here we investigated the microbial community through targeted 16S rRNA gene and shotgun metagenomic sequencing as means to better understand the interactions among bacteria in cold-smoked salmon. Cold-smoked salmon samples were tested over 30 days of aerobic storage at 4°C and cultured at each time point in a buffered Listeria enrichment broth (BLEB) commonly used to detect Listeria in foods. The microbiomes were composed of Firmicutes and Proteobacteria, namely, Carnobacterium, Brochothrix, Pseudomonas, Serratia, and Psychrobacter. Pseudomonas species were the most diverse species, with 181 taxa identified. In addition, we identified potential homologs to 10 classes of bacteriocins in microbiomes of cold-smoked salmon stored at 4°C and corresponding BLEB culture enrichments. The findings presented here contribute to our understanding of microbiome population dynamics in cold-smoked salmon, including changes in bacterial taxa during aerobic cold storage and after culture enrichment. This may facilitate improvements to pathogen detection and quality preservation of this food.

Microbiome signatures of progression toward celiac disease onset in at-risk children in a longitudinal prospective cohort study.

Other than exposure to gluten and genetic compatibility, the gut microbiome has been suggested to be involved in celiac disease (CD) pathogenesis by mediating interactions between gluten/environmental factors and the host immune system. However, to establish disease progression markers, it is essential to assess alterations in the gut microbiota before disease onset. Here, a prospective metagenomic analysis of the gut microbiota of infants at risk of CD was done to track shifts in the microbiota before CD development. We performed cross-sectional and longitudinal analyses of gut microbiota, functional pathways, and metabolites, starting from 18 mo before CD onset, in 10 infants who developed CD and 10 matched nonaffected infants. Cross-sectional analysis at CD onset identified altered abundance of six microbial strains and several metabolites between cases and controls but no change in microbial species or pathway abundance. Conversely, results of longitudinal analysis revealed several microbial species/strains/pathways/metabolites occurring in increased abundance and detected before CD onset. These had previously been linked to autoimmune and inflammatory conditions (e.g., Dialister invisus, Parabacteroides sp., Lachnospiraceae, tryptophan metabolism, and metabolites serine and threonine). Others occurred in decreased abundance before CD onset and are known to have anti-inflammatory effects (e.g., Streptococcus thermophilus, Faecalibacterium prausnitzii, and Clostridium clostridioforme). Additionally, we uncovered previously unreported microbes/pathways/metabolites (e.g., Porphyromonas sp., high mannose-type N-glycan biosynthesis, and serine) that point to CD-specific biomarkers. Our study establishes a road map for prospective longitudinal study designs to better understand the role of gut microbiota in disease pathogenesis and therapeutic targets to reestablish tolerance and/or prevent autoimmunity.

Diversity of Plasmids and Genes Encoding Resistance to Extended-Spectrum ß-Lactamase in Escherichia coli from Different Animal Sources.

Antimicrobial resistance associated with the spread of plasmid-encoded extended-spectrum ß-lactamase (ESBL) genes conferring resistance to third generation cephalosporins is increasing worldwide. However, data on the population of ESBL producing E. coli in different animal sources and their antimicrobial characteristics are limited. The purpose of this study was to investigate potential reservoirs of ESBL-encoded genes in E. coli isolated from swine, beef, dairy, and poultry collected from different regions of the United States using whole-genome sequencing (WGS). Three hundred isolates were typed into different phylogroups, characterized by BOX AIR-1 PCR and tested for resistance to antimicrobials. Of the 300 isolates, 59.7% were resistant to sulfisoxazole, 49.3% to tetracycline, 32.3% to cephalothin, 22.3% to ampicillin, 20% to streptomycin, 16% to ticarcillin; resistance to the remaining 12 antimicrobials was less than 10%. Phylogroups A and B1 were most prevalent with A (n = 92, 30%) and B1 (87 = 29%). A total of nine E. coli isolates were confirmed as ESBL producers by double-disk synergy testing and multidrug resistant (MDR) to at least three antimicrobial drug classes. Using WGS, significantly higher numbers of ESBL-E. coli were detected in swine and dairy manure than from any other animal sources, suggesting that these may be the primary animal sources for ESBL producing E. coli. These isolates carry plasmids, such as IncFIA(B), IncFII, IncX1, IncX4, IncQ1, CollRNAI, Col440I, and acquired ARGs aph(6)-Id, aph(3″)-Ib, aadA5, aph(3')-Ia, blaCTX-M-15, blaTEM-1B, mphA, ermB, catA1, sul1, sul2, tetB, dfrA17. One of the E. coli isolates from swine with ST 410 was resistant to nine antibiotics and carried more than 28 virulence factors, and this ST has been shown to belong to an international high-risk clone. Our data suggests that ESBL producing E. coli are widely distributed in different animal sources, but swine and dairy cattle may be their main reservoir.

Multi-omics analysis reveals the influence of genetic and environmental risk factors on developing gut microbiota in infants at risk of celiac disease.

BACKGROUND: Celiac disease (CD) is an autoimmune digestive disorder that occurs in genetically susceptible individuals in response to ingesting gluten, a protein found in wheat, rye, and barley. Research shows that genetic predisposition and exposure to gluten are necessary but not sufficient to trigger the development of CD. This suggests that exposure to other environmental stimuli early in life, e.g., cesarean section delivery and exposure to antibiotics or formula feeding, may also play a key role in CD pathogenesis through yet unknown mechanisms. Here, we use multi-omics analysis to investigate how genetic and early environmental risk factors alter the development of the gut microbiota in infants at risk of CD. RESULTS: Toward this end, we selected 31 infants from a large-scale prospective birth cohort study of infants with a first-degree relative with CD. We then performed rigorous multivariate association, cross-sectional, and longitudinal analyses using metagenomic and metabolomic data collected at birth, 3 months and 6 months of age to explore the impact of genetic predisposition and environmental risk factors on the gut microbiota composition, function, and metabolome prior to the introduction of trigger (gluten). These analyses revealed several microbial species, functional pathways, and metabolites that are associated with each genetic and environmental risk factor or that are differentially abundant between environmentally exposed and non-exposed infants or between time points. Among our significant findings, we found that cesarean section delivery is associated with a decreased abundance of Bacteroides vulgatus and Bacteroides dorei and of folate biosynthesis pathway and with an increased abundance of hydroxyphenylacetic acid, alterations that are implicated in immune system dysfunction and inflammatory conditions. Additionally, longitudinal analysis revealed that, in infants not exposed to any environmental risk factor, the abundances of Bacteroides uniformis and of metabolite 3-3-hydroxyphenylproprionic acid increase over time, while those for lipoic acid and methane metabolism pathways decrease, patterns that are linked to beneficial immunomodulatory and anti-inflammatory effects. CONCLUSIONS: Overall, our study provides unprecedented insights into major taxonomic and functional shifts in the developing gut microbiota of infants at risk of CD linking genetic and environmental risk factors to detrimental immunomodulatory and inflammatory effects. Video Abstract.

Multiple NDM-5-Expressing Escherichia Coli Isolates From an Immunocompromised Pediatric Host.

BACKGROUND: Genes conferring carbapenem resistance have disseminated worldwide among Gram-negative bacteria. Here we present longitudinal changes in clinically obtained Escherichia coli isolates from 1 immunocompromised pediatric patient. This report demonstrates potential for antibiotic resistance genes and plasmids to emerge over time in clinical isolates from patients receiving intensive anticancer chemotherapy and broad-spectrum antibiotics. METHODS: Thirty-three isolates obtained over 7 months from 1 patient were included. Clinical data were abstracted from the medical record. For each isolate, studies included phenotypic antibacterial resistance patterns, sequence typing, bacterial isolate sequencing, plasmid identification, and antibiotic resistance gene identification. RESULTS: Sites of isolation included blood, wound culture, and culture for surveillance purposes from the perianal area. Isolates were of 5 sequence types (STs). All were resistant to multiple classes of antibiotics; 23 (69.6%) were phenotypically resistant to all carbapenems. The blaNDM-5 gene was identified in 22 (67%) isolates, all of ST-167 and ST-940, and appeared to coincide with the presence of the IncFII and IncX3 plasmid. CONCLUSIONS: We present unique microbiologic data from 33 multidrug-resistant E. coli isolates obtained over the course of 7 months from an individual patient in the United States. Two E. coli sequence types causing invasive infection in the same patient and harboring the blaNDM-5 gene, encoded on the IncX3 plasmid and the IncFII plasmid, were identified. This study highlights the emergence of multidrug-resistant bacteria on antibiotic therapy and the necessity of adequate neutrophil number and function in the clearance of bacteremia.

The GENDULF algorithm: mining transcriptomics to uncover modifier genes for monogenic diseases.

Modifier genes are believed to account for the clinical variability observed in many Mendelian disorders, but their identification remains challenging due to the limited availability of genomics data from large patient cohorts. Here, we present GENDULF (GENetic moDULators identiFication), one of the first methods to facilitate prediction of disease modifiers using healthy and diseased tissue gene expression data. GENDULF is designed for monogenic diseases in which the mechanism is loss of function leading to reduced expression of the mutated gene. When applied to cystic fibrosis, GENDULF successfully identifies multiple, previously established disease modifiers, including EHF, SLC6A14, and CLCA1. It is then utilized in spinal muscular atrophy (SMA) and predicts U2AF1 as a modifier whose low expression correlates with higher SMN2 pre-mRNA exon 7 retention. Indeed, knockdown of U2AF1 in SMA patient-derived cells leads to increased full-length SMN2 transcript and SMN protein expression. Taking advantage of the increasing availability of transcriptomic data, GENDULF is a novel addition to existing strategies for prediction of genetic disease modifiers, providing insights into disease pathogenesis and uncovering novel therapeutic targets.

UVB-Induced Tumor Heterogeneity Diminishes Immune Response in Melanoma.

Although clonal neo-antigen burden is associated with improved response to immune therapy, the functional basis for this remains unclear. Here we study this question in a novel controlled mouse melanoma model that enables us to explore the effects of intra-tumor heterogeneity (ITH) on tumor aggressiveness and immunity independent of tumor mutational burden. Induction of UVB-derived mutations yields highly aggressive tumors with decreased anti-tumor activity. However, single-cell-derived tumors with reduced ITH are swiftly rejected. Their rejection is accompanied by increased T cell reactivity and a less suppressive microenvironment. Using phylogenetic analyses and mixing experiments of single-cell clones, we dissect two characteristics of ITH: the number of clones forming the tumor and their clonal diversity. Our analysis of melanoma patient tumor data recapitulates our results in terms of overall survival and response to immune checkpoint therapy. These findings highlight the importance of clonal mutations in robust immune surveillance and the need to quantify patient ITH to determine the response to checkpoint blockade.

Prosthetic joint infections present diverse and unique microbial communities using combined whole-genome shotgun sequencing and culturing methods.

Introduction. Prosthetic joint infections (PJIs) are challenging to treat therapeutically because the infectious agents often are resistant to antibiotics and capable of abundant growth in surface-attached biofilms. Though infection rates are low, ca. 1-2 %, the overall increase in the sheer number of joint replacement surgeries results in an increase in patients at risk.Aims. This study investigates the consensus of microbial species comprising PJI ecology, which is currently lacking.Methodology. In this study, PJI populations from seven patients were analysed using combined culturing and whole-genome shotgun sequencing (WGSS) to establish population profiles and compare WGSS and culture methods for detection and identification of the PJI microbiome.Results. WGSS detected strains when culture did not, notably dormant, culture-resistant and rare microbes. The CosmosID algorithm was used to predict micro-organisms present in the PJI and discriminate contaminants. However, culturing indicated the presence of microbes falling below the WGSS algorithm threshold. In these instances, microbes cultured are believed to be minor species. The two strategies were combined to build a population profile.Conclusions. Variability between and among PJIs showed that most infections were distinct and unique. Comparative analysis of populations revealed PJIs to form clusters that were related to, but separate from, vaginal, skin and gut microbiomes. Fungi and protists were detected by WGSS, but the role of fungi is just beginning to be understood and for protists it is unknown. These micro-organisms and their novel and strain-specific microbial interactions remain to be determined in current clinical tests.

Urea Cycle Dysregulation Generates Clinically Relevant Genomic and Biochemical Signatures.

The urea cycle (UC) is the main pathway by which mammals dispose of waste nitrogen. We find that specific alterations in the expression of most UC enzymes occur in many tumors, leading to a general metabolic hallmark termed "UC dysregulation" (UCD). UCD elicits nitrogen diversion toward carbamoyl-phosphate synthetase2, aspartate transcarbamylase, and dihydrooratase (CAD) activation and enhances pyrimidine synthesis, resulting in detectable changes in nitrogen metabolites in both patient tumors and their bio-fluids. The accompanying excess of pyrimidine versus purine nucleotides results in a genomic signature consisting of transversion mutations at the DNA, RNA, and protein levels. This mutational bias is associated with increased numbers of hydrophobic tumor antigens and a better response to immune checkpoint inhibitors independent of mutational load. Taken together, our findings demonstrate that UCD is a common feature of tumors that profoundly affects carcinogenesis, mutagenesis, and immunotherapy response.

A pathway-centric view of spatial proximity in the 3D nucleome across cell lines.

In various contexts, spatially proximal genes have been shown to be functionally related. However, the extent to which spatial proximity of genes in a pathway contributes to the pathway's context-specific activity is not known. Leveraging Hi-C data in six human cell-lines, we show that spatial proximity of genes in a pathway is highly correlated with the pathway's context-specific expression and function. Furthermore, spatial proximity of pathway genes correlates with interactions of their protein products, and the specific pathway genes that are proximal to one another tend to occupy higher levels in the regulatory hierarchy. In addition to intra-pathway proximity, related pathways are spatially proximal to one another and housekeeping-genes tend to be proximal to several other pathways suggesting their coordinating role. Substantially extending previous works, our study reveals a pathway-centric organization of 3D-nucleome, whereby, functionally related interacting driver genes tend to be in spatial-proximity in a context-specific manner.

Database constraints applied to metabolic pathway reconstruction tools.

Our group developed two biological applications, Biblio-MetReS and Homol-MetReS, accessing the same database of organisms with annotated genes. Biblio-MetReS is a data-mining application that facilitates the reconstruction of molecular networks based on automated text-mining analysis of published scientific literature. Homol-MetReS allows functional (re)annotation of proteomes, to properly identify both the individual proteins involved in the process(es) of interest and their function. It also enables the sets of proteins involved in the process(es) in different organisms to be compared directly. The efficiency of these biological applications is directly related to the design of the shared database. We classified and analyzed the different kinds of access to the database. Based on this study, we tried to adjust and tune the configurable parameters of the database server to reach the best performance of the communication data link to/from the database system. Different database technologies were analyzed. We started the study with a public relational SQL database, MySQL. Then, the same database was implemented by a MapReduce-based database named HBase. The results indicated that the standard configuration of MySQL gives an acceptable performance for low or medium size databases. Nevertheless, tuning database parameters can greatly improve the performance and lead to very competitive runtimes.

Biblio-MetReS for user-friendly mining of genes and biological processes in scientific documents.

UNLABELLED: One way to initiate the reconstruction of molecular circuits is by using automated text-mining techniques. Developing more efficient methods for such reconstruction is a topic of active research, and those methods are typically included by bioinformaticians in pipelines used to mine and curate large literature datasets. Nevertheless, experimental biologists have a limited number of available user-friendly tools that use text-mining for network reconstruction and require no programming skills to use. One of these tools is Biblio-MetReS. Originally, this tool permitted an on-the-fly analysis of documents contained in a number of web-based literature databases to identify co-occurrence of proteins/genes. This approach ensured results that were always up-to-date with the latest live version of the databases. However, this 'up-to-dateness' came at the cost of large execution times. Here we report an evolution of the application Biblio-MetReS that permits constructing co-occurrence networks for genes, GO processes, Pathways, or any combination of the three types of entities and graphically represent those entities. We show that the performance of Biblio-MetReS in identifying gene co-occurrence is as least as good as that of other comparable applications (STRING and iHOP). In addition, we also show that the identification of GO processes is on par to that reported in the latest BioCreAtIvE challenge. Finally, we also report the implementation of a new strategy that combines on-the-fly analysis of new documents with preprocessed information from documents that were encountered in previous analyses. This combination simultaneously decreases program run time and maintains 'up-to-dateness' of the results. AVAILABILITY: http://metres.udl.cat/index.php/downloads, CONTACT: metres.cmb@gmail.com.

Two component systems: physiological effect of a third component.

Signal transduction systems mediate the response and adaptation of organisms to environmental changes. In prokaryotes, this signal transduction is often done through Two Component Systems (TCS). These TCS are phosphotransfer protein cascades, and in their prototypical form they are composed by a kinase that senses the environmental signals (SK) and by a response regulator (RR) that regulates the cellular response. This basic motif can be modified by the addition of a third protein that interacts either with the SK or the RR in a way that could change the dynamic response of the TCS module. In this work we aim at understanding the effect of such an additional protein (which we call "third component") on the functional properties of a prototypical TCS. To do so we build mathematical models of TCS with alternative designs for their interaction with that third component. These mathematical models are analyzed in order to identify the differences in dynamic behavior inherent to each design, with respect to functionally relevant properties such as sensitivity to changes in either the parameter values or the molecular concentrations, temporal responsiveness, possibility of multiple steady states, or stochastic fluctuations in the system. The differences are then correlated to the physiological requirements that impinge on the functioning of the TCS. This analysis sheds light on both, the dynamic behavior of synthetically designed TCS, and the conditions under which natural selection might favor each of the designs. We find that a third component that modulates SK activity increases the parameter space where a bistable response of the TCS module to signals is possible, if SK is monofunctional, but decreases it when the SK is bifunctional. The presence of a third component that modulates RR activity decreases the parameter space where a bistable response of the TCS module to signals is possible.

Biblio-MetReS: a bibliometric network reconstruction application and server.

BACKGROUND: Reconstruction of genes and/or protein networks from automated analysis of the literature is one of the current targets of text mining in biomedical research. Some user-friendly tools already perform this analysis on precompiled databases of abstracts of scientific papers. Other tools allow expert users to elaborate and analyze the full content of a corpus of scientific documents. However, to our knowledge, no user friendly tool that simultaneously analyzes the latest set of scientific documents available on line and reconstructs the set of genes referenced in those documents is available. RESULTS: This article presents such a tool, Biblio-MetReS, and compares its functioning and results to those of other user-friendly applications (iHOP, STRING) that are widely used. Under similar conditions, Biblio-MetReS creates networks that are comparable to those of other user friendly tools. Furthermore, analysis of full text documents provides more complete reconstructions than those that result from using only the abstract of the document. CONCLUSIONS: Literature-based automated network reconstruction is still far from providing complete reconstructions of molecular networks. However, its value as an auxiliary tool is high and it will increase as standards for reporting biological entities and relationships become more widely accepted and enforced. Biblio-MetReS is an application that can be downloaded from http://metres.udl.cat/. It provides an easy to use environment for researchers to reconstruct their networks of interest from an always up to date set of scientific documents.

Saccharomyces cerevisiae as a model organism: a comparative study.

BACKGROUND: Model organisms are used for research because they provide a framework on which to develop and optimize methods that facilitate and standardize analysis. Such organisms should be representative of the living beings for which they are to serve as proxy. However, in practice, a model organism is often selected ad hoc, and without considering its representativeness, because a systematic and rational method to include this consideration in the selection process is still lacking. METHODOLOGY/PRINCIPAL FINDINGS: In this work we propose such a method and apply it in a pilot study of strengths and limitations of Saccharomyces cerevisiae as a model organism. The method relies on the functional classification of proteins into different biological pathways and processes and on full proteome comparisons between the putative model organism and other organisms for which we would like to extrapolate results. Here we compare S. cerevisiae to 704 other organisms from various phyla. For each organism, our results identify the pathways and processes for which S. cerevisiae is predicted to be a good model to extrapolate from. We find that animals in general and Homo sapiens in particular are some of the non-fungal organisms for which S. cerevisiae is likely to be a good model in which to study a significant fraction of common biological processes. We validate our approach by correctly predicting which organisms are phenotypically more distant from S. cerevisiae with respect to several different biological processes. CONCLUSIONS/SIGNIFICANCE: The method we propose could be used to choose appropriate substitute model organisms for the study of biological processes in other species that are harder to study. For example, one could identify appropriate models to study either pathologies in humans or specific biological processes in species with a long development time, such as plants.

Methods for and results from the study of design principles in molecular systems.

Most aspects of molecular biology can be understood in terms of biological design principles. These principles can be loosely defined as qualitative and quantitative features that emerge in evolution and recur more frequently than one would expect by chance alone in biological systems that perform a given type of process or function. Furthermore, such recurrence can be rationalized in terms of the functional advantage that the design provides to the system when compared with possible alternatives. This paper focuses on those design features that can be related to improved functional effectiveness of molecular and regulatory networks. We begin by reviewing assumptions and methods that underlie the study of such principles in molecular networks. We follow by discussing many of the design principles that have been found in genetic, metabolic, and signal transduction circuits. We concentrate mainly on results in the context of Biochemical Systems Theory, although we also briefly discuss other work. We conclude by discussing the importance of these principles for both, understanding the natural evolution of complex networks at the molecular level and for creating artificial biological systems with specific features.

Human protein reference database--2006 update.

Human Protein Reference Database (HPRD) (http://www.hprd.org) was developed to serve as a comprehensive collection of protein features, post-translational modifications (PTMs) and protein-protein interactions. Since the original report, this database has increased to >20 000 proteins entries and has become the largest database for literature-derived protein-protein interactions (>30 000) and PTMs (>8000) for human proteins. We have also introduced several new features in HPRD including: (i) protein isoforms, (ii) enhanced search options, (iii) linking of pathway annotations and (iv) integration of a novel browser, GenProt Viewer (http://www.genprot.org), developed by us that allows integration of genomic and proteomic information. With the continued support and active participation by the biomedical community, we expect HPRD to become a unique source of curated information for the human proteome and spur biomedical discoveries based on integration of genomic, transcriptomic and proteomic data.