Skin dysbiosis in the microbiome in atopic dermatitis is site-specific and involves bacteria, fungus and virus.

BACKGROUND: Microbial dysbiosis with increased Staphylococcus aureus (S. aureus) colonization on the skin is a hallmark of atopic dermatitis (AD), however most microbiome studies focus on bacteria in the flexures and the microbial composition at other body sites have not been studied systematically. OBJECTIVES: The aim of the study is to characterize the skin microbiome, including bacteria, fungi and virus, at different body sites in relation to AD, lesional state, and S. aureus colonization, and to test whether the nares could be a reservoir for S. aureus strain colonization. METHODS: Using shotgun metagenomics we characterized microbial compositions from 14 well defined skin sites from 10 patients with AD and 5 healthy controls. RESULTS: We found clear differences in microbial composition between AD and controls at multiple skin sites, most pronounced on the flexures and neck. The flexures exhibited lower alpha-diversity and were colonized by S. aureus, accompanied by S. epidermidis in lesions. Malassezia species were absent on the neck in AD. Virus mostly constituted Propionibacterium and Staphylococcus phages, with increased abundance of Propionibacterium phages PHL041 and PHL092 and Staphylococcus epidermidis phages CNPH82 and PH15 in AD. In lesional samples, both the genus Staphylococcus and Staphylococcus phages were more abundant. S. aureus abundance was higher across all skin sites except from the feet. In samples where S. aureus was highly abundant, lower abundances of S. hominis and Cutibacterium acnes were observed. M. osloensis and M. luteus were more abundant in AD. By single nucleotide variant analysis of S. aureus we found strains to be subject specific. On skin sites some S. aureus strains were similar and some dissimilar to the ones in the nares. CONCLUSIONS: Our data indicate a global and site-specific dysbiosis in AD, involving both bacteria, fungus and virus. When defining targeted treatment clinicians should both consider the individual and skin site and future research into potential crosstalk between microbiota in AD yields high potential.

DISEASES: text mining and data integration of disease-gene associations.

Text mining is a flexible technology that can be applied to numerous different tasks in biology and medicine. We present a system for extracting disease-gene associations from biomedical abstracts. The system consists of a highly efficient dictionary-based tagger for named entity recognition of human genes and diseases, which we combine with a scoring scheme that takes into account co-occurrences both within and between sentences. We show that this approach is able to extract half of all manually curated associations with a false positive rate of only 0.16%. Nonetheless, text mining should not stand alone, but be combined with other types of evidence. For this reason, we have developed the DISEASES resource, which integrates the results from text mining with manually curated disease-gene associations, cancer mutation data, and genome-wide association studies from existing databases. The DISEASES resource is accessible through a web interface at http://diseases.jensenlab.org/, where the text-mining software and all associations are also freely available for download.

DistiLD Database: diseases and traits in linkage disequilibrium blocks.

Genome-wide association studies (GWAS) have identified thousands of single nucleotide polymorphisms (SNPs) associated with the risk of hundreds of diseases. However, there is currently no database that enables non-specialists to answer the following simple questions: which SNPs associated with diseases are in linkage disequilibrium (LD) with a gene of interest? Which chromosomal regions have been associated with a given disease, and which are the potentially causal genes in each region? To answer these questions, we use data from the HapMap Project to partition each chromosome into so-called LD blocks, so that SNPs in LD with each other are preferentially in the same block, whereas SNPs not in LD are in different blocks. By projecting SNPs and genes onto LD blocks, the DistiLD database aims to increase usage of existing GWAS results by making it easy to query and visualize disease-associated SNPs and genes in their chromosomal context. The database is available at http://distild.jensenlab.org/.

Vaginal microbiome following orally administered probiotic.

Here, we present a longitudinal shotgun sequencing metagenomics study of 16 healthy, Danish women in the reproductive age. The aim of the study was to investigate whether lactobacilli, orally consumed, had any impact on the vaginal microbiome and its functional potential. The 16 women aged 19-45 years were recruited from Copenhagen, Denmark. One baseline vaginal sample (Day 0) and two study samples (Days 25-30 and Days 55-60, respectively), were sampled. The vaginal samples were analyzed by shotgun metagenomics. We detected 26 species in the vaginal microbiota of the 16 women, of which six belonged to the Lactobacillus genus. We observed three vaginal microbiome clusters mainly dominated by Gardnerella vaginalis, Lactobacillus iners, or Lactobacillus crispatus. The oral probiotic had no detectable effect on either the composition or the functional potential of the vaginal microbiota. Most of the study subjects (11 out of 16 women) exhibited only minor changes in the vaginal microbiome during the treatment with probiotics. Any compositional changes could not be associated to the probiotic treatment. Future studies may benefit from an increased number of participants, and administration of the probiotics during conditions with bacterial imbalance (e.g., during/after antibiotic treatment) or the use of different Lactobacillus spp. known to colonize the vagina.

Microbiome Compositions and Resistome Levels after Antibiotic Treatment of Critically Ill Patients: An Observational Cohort Study.

Hospitalization and treatment with antibiotics increase the risk of acquiring multidrug-resistant bacteria due to antibiotic-mediated changes in patient microbiota. This study aimed to investigate how broad- and narrow-spectrum antibiotics affect the gut microbiome and the resistome in antibiotic naïve patients during neurointensive care. Patients admitted to the neurointensive care unit were treated with broad-spectrum (meropenem or piperacillin/tazobactam) or narrow-spectrum antibiotic treatment (including ciprofloxacin, cefuroxime, vancomycin and dicloxacillin) according to clinical indications. A rectal swab was collected from each patient before and after 5-7 days of antibiotic therapy (N = 34), respectively. Shotgun metagenomic sequencing was performed and the composition of metagenomic species (MGS) was determined. The resistome was characterized with CARD RGI software and the CARD database. As a measure for selection pressure in the patient, we used the sum of the number of days with each antibiotic (antibiotic days). We observed a significant increase in richness and a tendency for an increase in the Shannon index after narrow-spectrum treatment. For broad-spectrum treatment the effect was more diverse, with some patients increasing and some decreasing in richness and Shannon index. This was studied further by comparison of patients who had gained or lost >10 MGS, respectively. Selection pressure was significantly higher in patients with decreased richness and a decreased Shannon index who received the broad treatment. A decrease in MGS richness was significantly correlated to the number of drugs administered and the selection pressure in the patient. Bray-Curtis dissimilarities were significant between the pre- and post-treatment of samples in the narrow group, indicating that the longer the narrow-spectrum treatment, the higher the differences between the pre- and the post-treatment microbial composition. We did not find significant differences between pre- and post-treatment for both antibiotic spectrum treatments; however, we observed that most of the antibiotic class resistance genes were higher in abundance in post-treatment after broad-spectrum treatment.

The Influence of FUT2 and FUT3 Polymorphisms and Nasopharyngeal Microbiome on Respiratory Infections in Breastfed Bangladeshi Infants from the Microbiota and Health Study.

Acute respiratory infections (ARIs) are one of the most common causes of morbidity and mortality in young children. The aim of our study was to examine whether variation in maternal FUT2 (α1,2-fucosyltransferase 2) and FUT3 (α1,3/4-fucosyltransferase 3) genes, which shape fucosylated human milk oligosaccharides (HMOs) in breast milk, are associated with the occurrence of ARIs in breastfed infants as well as the influence of the nasopharyngeal microbiome on ARI risk. Occurrences of ARIs were prospectively recorded in a cohort of 240 breastfed Bangladeshi infants from birth to 2 years. Secretor and Lewis status was established by sequencing of FUT2/3 genes. The nasopharyngeal microbiome was characterized by shotgun metagenomics, complemented by specific detection of respiratory pathogens; 88.6% of mothers and 91% of infants were identified as secretors. Maternal secretor status was associated with reduced ARI incidence among these infants in the period from birth to 6 months (incidence rate ratio [IRR], 0.66; 95% confidence interval [CI], 0.47 to 0.94; P = 0.020), but not at later time periods. The nasopharyngeal microbiome, despite precise characterization to the species level, was not predictive of subsequent ARIs. The observed risk reduction of ARIs among infants of secretor mothers during the predominant breastfeeding period is consistent with the hypothesis that fucosylated oligosaccharides in human milk contribute to protection against respiratory infections. However, we found no evidence that modulation of the nasopharyngeal microbiome influenced ARI risk. IMPORTANCE The observed risk reduction of acute respiratory infections (ARIs) among infants of secretor mothers during the predominant breastfeeding period is consistent with the hypothesis that fucosylated oligosaccharides in human milk contribute to protection against respiratory infections. Respiratory pathogens were only weak modulators of risk, and the nasopharyngeal microbiome did not influence ARI risk, suggesting that the associated protective effects of human milk oligosaccharides (HMOs) are not conveyed via changes in the nasopharyngeal microbiome. Our observations add to the evidence for a role of fucosylated HMOs in protection against respiratory infections in exclusively or predominantly breastfed infants in low-resource settings. There is no indication that the nasopharyngeal microbiome substantially modulates the risk of subsequent mild ARIs. Larger studies are needed to provide mechanistic insights on links between secretor status, HMOs, and risk of respiratory infections.

Adaptation of the short intergenic spacers between co-directional genes to the Shine-Dalgarno motif among prokaryote genomes.

BACKGROUND: In prokaryote genomes most of the co-directional genes are in close proximity. Even the coding sequence or the stop codon of a gene can overlap with the Shine-Dalgarno (SD) sequence of the downstream co-directional gene. In this paper we analyze how the presence of SD may influence the stop codon usage or the spacing lengths between co-directional genes. RESULTS: The SD sequences for 530 prokaryote genomes have been predicted using computer calculations of the base-pairing free energy between translation initiation regions and the 16S rRNA 3' tail. Genomes with a large number of genes with the SD sequence concentrate this regulatory motif from 4 to 11 bps before the start codon. However, not all genes seem to have the SD sequence. Genes separated from 1 to 4 bps from a co-directional upstream gene show a high SD presence, though this regulatory signal is located towards the 3' end of the coding sequence of the upstream gene. Genes separated from 9 to 15 bps show the highest SD presence as they accommodate the SD sequence within an intergenic region. However, genes separated from around 5 to 8 bps have a lower percentage of SD presence and when the SD is present, the stop codon usage of the upstream gene changes to accommodate the overlap between the SD sequence and the stop codon. CONCLUSION: The SD presence makes the intergenic lengths from 5 to 8 bps less frequent and causes an adaptation of the stop codon usage. Our results introduce new elements to the discussion of which factors affect the intergenic lengths, which cannot be totally explained by the pressure to compact the prokaryote genomes.

PairWise Neighbours database: overlaps and spacers among prokaryote genomes.

BACKGROUND: Although prokaryotes live in a variety of habitats and possess different metabolic and genomic complexity, they have several genomic architectural features in common. The overlapping genes are a common feature of the prokaryote genomes. The overlapping lengths tend to be short because as the overlaps become longer they have more risk of deleterious mutations. The spacers between genes tend to be short too because of the tendency to reduce the non coding DNA among prokaryotes. However they must be long enough to maintain essential regulatory signals such as the Shine-Dalgarno (SD) sequence, which is responsible of an efficient translation. DESCRIPTION: PairWise Neighbours is an interactive and intuitive database used for retrieving information about the spacers and overlapping genes among bacterial and archaeal genomes. It contains 1,956,294 gene pairs from 678 fully sequenced prokaryote genomes and is freely available at the URL http://genomes.urv.cat/pwneigh. This database provides information about the overlaps and their conservation across species. Furthermore, it allows the wide analysis of the intergenic regions providing useful information such as the location and strength of the SD sequence. CONCLUSION: There are experiments and bioinformatic analysis that rely on correct annotations of the initiation site. Therefore, a database that studies the overlaps and spacers among prokaryotes appears to be desirable. PairWise Neighbours database permits the reliability analysis of the overlapping structures and the study of the SD presence and location among the adjacent genes, which may help to check the annotation of the initiation sites.

Metabolic and gut microbiome changes following GLP-1 or dual GLP-1/GLP-2 receptor agonist treatment in diet-induced obese mice.

Enteroendocrine L-cell derived peptide hormones, notably glucagon-like peptide-1 (GLP-1) and glucagon-like peptide-2 (GLP-2), have become important targets in the treatment of type 2 diabetes, obesity and intestinal diseases. As gut microbial imbalances and maladaptive host responses have been implicated in the pathology of obesity and diabetes, this study aimed to determine the effects of pharmacologically stimulated GLP-1 and GLP-2 receptor function on the gut microbiome composition in diet-induced obese (DIO) mice. DIO mice received treatment with a selective GLP-1 receptor agonist (liraglutide, 0.2 mg/kg, BID) or dual GLP-1/GLP-2 receptor agonist (GUB09-145, 0.04 mg/kg, BID) for 4 weeks. Both compounds suppressed caloric intake, promoted a marked weight loss, improved glucose tolerance and reduced plasma cholesterol levels. 16S rDNA sequencing and deep-sequencing shotgun metagenomics was applied for comprehensive within-subject profiling of changes in gut microbiome signatures. Compared to baseline, DIO mice assumed phylogenetically similar gut bacterial compositional changes following liraglutide and GUB09-145 treatment, characterized by discrete shifts in low-abundant species and related bacterial metabolic pathways. The microbiome alterations may potentially associate to the converging biological actions of GLP-1 and GLP-2 receptor signaling on caloric intake, glucose metabolism and lipid handling.

Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer.

Association studies have linked microbiome alterations with many human diseases. However, they have not always reported consistent results, thereby necessitating cross-study comparisons. Here, a meta-analysis of eight geographically and technically diverse fecal shotgun metagenomic studies of colorectal cancer (CRC, n = 768), which was controlled for several confounders, identified a core set of 29 species significantly enriched in CRC metagenomes (false discovery rate (FDR) < 1 × 10-5). CRC signatures derived from single studies maintained their accuracy in other studies. By training on multiple studies, we improved detection accuracy and disease specificity for CRC. Functional analysis of CRC metagenomes revealed enriched protein and mucin catabolism genes and depleted carbohydrate degradation genes. Moreover, we inferred elevated production of secondary bile acids from CRC metagenomes, suggesting a metabolic link between cancer-associated gut microbes and a fat- and meat-rich diet. Through extensive validations, this meta-analysis firmly establishes globally generalizable, predictive taxonomic and functional microbiome CRC signatures as a basis for future diagnostics.

Large gene overlaps in prokaryotic genomes: result of functional constraints or mispredictions?

BACKGROUND: Across the fully sequenced microbial genomes there are thousands of examples of overlapping genes. Many of these are only a few nucleotides long and are thought to function by permitting the coordinated regulation of gene expression. However, there should also be selective pressure against long overlaps, as the existence of overlapping reading frames increases the risk of deleterious mutations. Here we examine the longest overlaps and assess whether they are the product of special functional constraints or of erroneous annotation. RESULTS: We analysed the genes that overlap by 60 bps or more among 338 fully-sequenced prokaryotic genomes. The likely functional significance of an overlap was determined by comparing each of the genes to its respective orthologs. If a gene showed a significantly different length from its orthologs it was considered unlikely to be functional and therefore the result of an error either in sequencing or gene prediction. Focusing on 715 co-directional overlaps longer than 60 bps, we classified the erroneous ones into five categories: i) 5'-end extension of the downstream gene due to either a mispredicted start codon or a frameshift at 5'-end of the gene (409 overlaps), ii) fragmentation of a gene caused by a frameshift (163), iii) 3'-end extension of the upstream gene due to either a frameshift at 3'-end of a gene or point mutation at the stop codon (68), iv) Redundant gene predictions (4), v) 5' & 3'-end extension which is a combination of i) and iii) (71). We also studied 75 divergent overlaps that could be classified as misannotations of group i). Nevertheless we found some convergent long overlaps (54) that might be true overlaps, although an important part of convergent overlaps could be classified as group iii) (124). CONCLUSION: Among the 968 overlaps larger than 60 bps which we analysed, we did not find a single real one among the co-directional and divergent orientations and concluded that there had been an excessive number of misannotations. Only convergent orientation seems to permit some long overlaps, although convergent overlaps are also hampered by misannotations. We propose a simple rule to flag these erroneous gene length predictions to facilitate automatic annotation.

Recovery of gut microbiota of healthy adults following antibiotic exposure.

To minimize the impact of antibiotics, gut microorganisms harbour and exchange antibiotics resistance genes, collectively called their resistome. Using shotgun sequencing-based metagenomics, we analysed the partial eradication and subsequent regrowth of the gut microbiota in 12 healthy men over a 6-month period following a 4-day intervention with a cocktail of 3 last-resort antibiotics: meropenem, gentamicin and vancomycin. Initial changes included blooms of enterobacteria and other pathobionts, such as Enterococcus faecalis and Fusobacterium nucleatum, and the depletion of Bifidobacterium species and butyrate producers. The gut microbiota of the subjects recovered to near-baseline composition within 1.5 months, although 9 common species, which were present in all subjects before the treatment, remained undetectable in most of the subjects after 180 days. Species that harbour ß-lactam resistance genes were positively selected for during and after the intervention. Harbouring glycopeptide or aminoglycoside resistance genes increased the odds of de novo colonization, however, the former also decreased the odds of survival. Compositional changes under antibiotic intervention in vivo matched results from in vitro susceptibility tests. Despite a mild yet long-lasting imprint following antibiotics exposure, the gut microbiota of healthy young adults are resilient to a short-term broad-spectrum antibiotics intervention and their antibiotics resistance gene carriage modulates their recovery processes.

Roux-en-Y gastric bypass surgery of morbidly obese patients induces swift and persistent changes of the individual gut microbiota.

BACKGROUND: Roux-en-Y gastric bypass (RYGB) is an effective means to achieve sustained weight loss for morbidly obese individuals. Besides rapid weight reduction, patients achieve major improvements of insulin sensitivity and glucose homeostasis. Dysbiosis of gut microbiota has been associated with obesity and some of its co-morbidities, like type 2 diabetes, and major changes of gut microbial communities have been hypothesized to mediate part of the beneficial metabolic effects observed after RYGB. Here we describe changes in gut microbial taxonomic composition and functional potential following RYGB. METHODS: We recruited 13 morbidly obese patients who underwent RYGB, carefully phenotyped them, and had their gut microbiomes quantified before (n = 13) and 3 months (n = 12) and 12 months (n = 8) after RYGB. Following shotgun metagenomic sequencing of the fecal microbial DNA purified from stools, we characterized the gut microbial composition at species and gene levels followed by functional annotation. RESULTS: In parallel with the weight loss and metabolic improvements, gut microbial diversity increased within the first 3 months after RYGB and remained high 1 year later. RYGB led to altered relative abundances of 31 species (P < 0.05, q < 0.15) within the first 3 months, including those of Escherichia coli, Klebsiella pneumoniae, Veillonella spp., Streptococcus spp., Alistipes spp., and Akkermansia muciniphila. Sixteen of these species maintained their altered relative abundances during the following 9 months. Interestingly, Faecalibacterium prausnitzii was the only species that decreased in relative abundance. Fifty-three microbial functional modules increased their relative abundance between baseline and 3 months (P < 0.05, q < 0.17). These functional changes included increased potential (i) to assimilate multiple energy sources using transporters and phosphotransferase systems, (ii) to use aerobic respiration, (iii) to shift from protein degradation to putrefaction, and (iv) to use amino acids and fatty acids as energy sources. CONCLUSIONS: Within 3 months after morbidly obese individuals had undergone RYGB, their gut microbiota featured an increased diversity, an altered composition, an increased potential for oxygen tolerance, and an increased potential for microbial utilization of macro- and micro-nutrients. These changes were maintained for the first year post-RYGB. TRIAL REGISTRATION: Current controlled trials (ID NCT00810823 , NCT01579981 , and NCT01993511 ).

HOODS: finding context-specific neighborhoods of proteins, chemicals and diseases.

Clustering algorithms are often used to find groups relevant in a specific context; however, they are not informed about this context. We present a simple algorithm, HOODS, which identifies context-specific neighborhoods of entities from a similarity matrix and a list of entities specifying the context. We illustrate its applicability by finding disease-specific neighborhoods of functionally associated proteins, kinase-specific neighborhoods of structurally similar inhibitors, and physiological-system-specific neighborhoods of interconnected diseases. HOODS can be used via a simple interface at http://hoods.jensenlab.org, from where the source code can also be downloaded.

Identification of novel type 1 diabetes candidate genes by integrating genome-wide association data, protein-protein interactions, and human pancreatic islet gene expression.

Genome-wide association studies (GWAS) have heralded a new era in susceptibility locus discovery in complex diseases. For type 1 diabetes, >40 susceptibility loci have been discovered. However, GWAS do not inevitably lead to identification of the gene or genes in a given locus associated with disease, and they do not typically inform the broader context in which the disease genes operate. Here, we integrated type 1 diabetes GWAS data with protein-protein interactions to construct biological networks of relevance for disease. A total of 17 networks were identified. To prioritize and substantiate these networks, we performed expressional profiling in human pancreatic islets exposed to proinflammatory cytokines. Three networks were significantly enriched for cytokine-regulated genes and, thus, likely to play an important role for type 1 diabetes in pancreatic islets. Eight of the regulated genes (CD83, IFNGR1, IL17RD, TRAF3IP2, IL27RA, PLCG2, MYO1B, and CXCR7) in these networks also harbored single nucleotide polymorphisms nominally associated with type 1 diabetes. Finally, the expression and cytokine regulation of these new candidate genes were confirmed in insulin-secreting INS-1 ß-cells. Our results provide novel insight to the mechanisms behind type 1 diabetes pathogenesis and, thus, may provide the basis for the design of novel treatment strategies.

In silico prediction of the origin of replication among bacteria: a case study of Bacteroides thetaiotaomicron.

The initiation of chromosomal replication occurs only once during the prokaryote cell cycle. Some origins of replication have been experimentally determined and have led to the development of in silico approaches to find the origin of replication among other prokaryotes. DNA base composition asymmetry is the basis of numerous in silico methods used to detect the origin and terminus of replication in prokaryotes. However, the composition asymmetry does not allow us to locate precisely the positions of the origin and terminus. Since DNA replication is a key step in the cell cycle it is important to determine properly the origin and terminus regions. Therefore, we have reviewed here the methods, tools, and databases for predicting the origins and terminuses of replication, and we have proposed some complementary analyses to reinforce these predictions. These analyses include finding the dnaA gene and its binding sites; making BLAST analyses of the intergenic sequences compared to related species; studying the gene order around the origin sequence; and studying the distribution of the genes encoded in the leading versus the lagging strand.