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The human skin is colonized by a wide array of microorganisms playing a role in skin disorders. Studying the skin microbiome provides unique obstacles such as low microbial biomass. The objective of this study was to establish methodology for skin microbiome analyses, focusing on sampling technique and DNA extraction. Skin swabs and scrapes were collected from 9 healthy adult subjects, and DNA extracted using 12 commercial kits. All 165 samples were sequenced using the 16S rRNA gene. Comparing the populations captured by eSwabs and scrapes, 99.3% of sequences overlapped. Using eSwabs yielded higher consistency. The success rate of library preparation applying different DNA extraction kits ranged from 39% to 100%. Some kits had higher Shannon alpha-diversity. Metagenomic shotgun analyses were performed on a subset of samples (N = 12). These data indicate that a reduction of human DNA from 90% to 57% is feasible without lowering the success of 16S rRNA library preparation and without introducing taxonomic bias. Using swabs is a reliable technique to investigate the skin microbiome. DNA extraction methodology is crucial for success of sequencing and adds a substantial amount of variation in microbiome analyses. Reduction of host DNA is recommended for interventional studies applying metagenomics.

Background Environmental and commensal bacteria maintain a diverse and largely unknown collection of antibiotic resistance genes (ARGs) that, over time, may be mobilized and transferred to pathogens. Metagenomics enables cultivation-independent characterization of bacterial communities but the resulting data is noisy and highly fragmented, severely hampering the identification of previously undescribed ARGs. We have therefore developed fARGene, a method for identification and reconstruction of ARGs directly from shotgun metagenomic data. Results fARGene uses optimized gene models and can therefore with high accuracy identify previously uncharacterized resistance genes, even if their sequence similarity to known ARGs is low. By performing the analysis directly on the metagenomic fragments, fARGene also circumvents the need for a high-quality assembly. To demonstrate the applicability of fARGene, we reconstructed β-lactamases from five billion metagenomic reads, resulting in 221 ARGs, of which 58 were previously not reported. Based on 38 ARGs reconstructed by fARGene, experimental verification showed that 81% provided a resistance phenotype in Escherichia coli . Compared to other methods for detecting ARGs in metagenomic data, fARGene has superior sensitivity and the ability to reconstruct previously unknown genes directly from the sequence reads. Conclusions We conclude that fARGene provides an efficient and reliable way to explore the unknown resistome in bacterial communities. The method is applicable to any type of ARGs and is freely available via GitHub under the MIT license.

Our study explores signatures for Crohn’s disease (CD) and Ulcerative Colitis (UC) reflecting an interplay between the intestinal microbiota, systemic inflammation, and plasma bile acid homeostasis. For this, 1,257 individuals scheduled for colonoscopy were included and completed a comprehensive questionnaire. Individuals with IBD (‘CD’ n = 64 and ‘UC’ n = 55), were age- and gender-matched to controls without findings during colonoscopy. Shotgun metagenomic profiles of the fecal microbiota and plasma profiles of inflammatory proteins and bile acids were used to build disease classifiers. Omics integration identified associations across datasets. B. hydrogenotrophica was associated with CD and C. eutactus, C. sp. CAG167, B. cellulosilyticus, C. mitsuokai with controls. Ten inflammation markers were increased in CD, and eleven bile acids and derivatives were decreased in CD, while 7a-Hydroxy-3-oxo-4-cholestenoate (7-HOCA) and chenodeoxycholic acid (CDCA) were increased compared to controls.In UC, commensals such as F. prausnitzii and A. muciniphila were depleted. CCL11, IL-17A, and TNF were increased in UC and associated to gut microbial changes. Correlations between taxa and bile acids were all positive. For both CD and UC, taxonomic differences were primarily characterized by a reduction in commensal gut microbes which exhibited positive correlations with secondary bile acids and negative correlations with inflammation markers.

Physiological hormonal fluctuations exert endogenous pressures on the structure and function of the human microbiome. As such, the menstrual cycle may selectively disrupt the homeostasis of the resident oral microbiome, thus compromising oral health. Hence, the aim of the present study was to structurally and functionally profile the salivary microbiome of 103 women in reproductive age with regular menstrual cycle, while evaluating the modifying influences of hormonal contraceptives, sex hormones, diet, and smoking. Whole saliva was sampled during the menstrual, follicular, and luteal phases (n = 309) of the cycle, and the participants reported questionnaire-based data concerning their life habits and oral or systemic health. No significant differences in alpha-diversity or phase-specific clustering of the overall microbiome were observed. Nevertheless, the salivary abundances of genera Campylobacter , Haemophilus , Prevotella , and Oribacterium varied throughout the cycle, and a higher species-richness was observed during the luteal phase. While the overall community structure maintained relatively intact, its functional properties were drastically affected. In particular, 11 functional modules were differentially abundant throughout the menstrual cycle, including pentose phosphate metabolism, and biosynthesis of cobalamin and neurotransmitter gamma-aminobutyric acid. The menstrual cycle phase, but not oral contraceptive usage, was accountable for greater variations in the metabolic pathways of the salivary microbiome. Further co-risk factor analysis demonstrated that Prevotella and Veillonella were increased in current smokers, whereas high dietary sugar consumption modified the richness and diversity of the microbiome during the cycle. This is the first large study to systematically address dysbiotic variations of the oral microbiome during the course of menstrual cycle, and document the additive effect of smoking and sugar consumption as environmental risk factors. It reveals the structural resilience and functional adaptability of the oral microbiome to the endogenous hormonal pressures of the menstrual cycle, while revealing its vulnerability to the exogenous exposures of diet and smoking.

A range of methodologies may be used for analyzing bacteria, depending on the purpose and the level of resolution needed. The capability for recognition of species distinctions within the complex spectrum of bacterial diversity is necessary for progress in microbiological research. In clinical settings, accurate, rapid and cost-effective methods are essential for early and efficient treatment of infections. Characterization and identification of microorganisms, using, bottom-up proteomics, or \"proteotyping\", relies on recognition of species-unique or associated peptides, by tandem mass spectrometry analyses, dependent upon an accurate and comprehensive foundation of genome sequence data, allowing for differentiation of species, at amino acid-level resolution. In this study, the high resolution and accuracy of MS/MS-based proteotyping was demonstrated, through analyses of the three phylogenetically and taxonomically most closely-related species of the Mitis Group of the genus Streptococcus: i.e., the pathogenic species, Streptococcus pneumoniae (pneumococcus), and the commensal species, Streptococcus pseudopneumoniae and Streptococcus mitis. To achieve high accuracy, a genome sequence database used for matching peptides was created and carefully curated. Here, MS-based, bottom-up proteotyping was observed and confirmed to attain the level of resolution necessary for differentiating and identifying the most-closely related bacterial species, as demonstrated by analyses of species of the Streptococcus Mitis Group, even when S. pneumoniae were mixed with S. pseudopneumoniae and S. mitis, by matching and identifying more than 200 unique peptides for each species.

PurposeThe Swedish Maternal Microbiome (SweMaMi) project was initiated to better understand the dynamics of the microbiome in pregnancy, with longitudinal microbiome sampling, shotgun metagenomics, extensive questionnaires and health registry linkage.ParticipantsPregnant women were recruited before the 20th gestational week during 2017–2021 in Sweden. In total, 5439 pregnancies (5193 unique women) were included. For 3973 pregnancies (73%), samples were provided at baseline, and for 3141 (58%) at all three timepoints (second and third trimester and postpartum). In total, 31 740 maternal microbiome samples (vaginal, faecal and saliva) and 3109 infant faecal samples were collected. Questionnaires were used to collect information on general, reproductive and mental health, diet and lifestyle, complemented by linkage to the nationwide health registries, also used to follow up the health of the offspring (up to age 10).Findings to dateThe cohort is fairly representative for the total Swedish pregnant population (data from 2019), with 41% first-time mothers. Women with university level education, born in Sweden, with normal body mass index, not using tobacco-products and aged 30–34 years were slightly over-represented.Future plansThe sample and data collection were finalised in November 2021. The next steps are the characterisation of the microbial DNA and linkage to the health and demographic information from the questionnaires and registries. The role of the microbiome on maternal and neonatal outcomes and early-childhood diseases will be explored (including preterm birth, miscarriage) and the role and interaction of other risk factors and confounders (including endometriosis, polycystic ovarian syndrome, diet, drug use). This is currently among the largest pregnancy cohorts in the world with longitudinal design and detailed and standardised microbiome sampling enabling follow-up of both mothers and children. The findings are expected to contribute greatly to the field of reproductive health focusing on pregnancy and neonatal outcomes.

Background Fluoroquinolones are broad-spectrum antibiotics used to prevent and treat a wide range of bacterial infections. Plasmid-mediated qnr genes provide resistance to fluoroquinolones in many bacterial species and are increasingly encountered in clinical settings. Over the last decade, several families of qnr genes have been discovered and characterized, but their true prevalence and diversity still remain unclear. In particular, environmental and host-associated bacterial communities have been hypothesized to maintain a large and unknown collection of qnr genes that could be mobilized into pathogens. Results In this study we used computational methods to screen genomes and metagenomes for novel qnr genes. In contrast to previous studies, we analyzed an almost 20-fold larger dataset comprising almost 13 terabases of sequence data. In total, 362,843 potential qnr gene fragments were identified, from which 611 putative qnr genes were reconstructed. These gene sequences included all previously described plasmid-mediated qnr gene families. Fifty-two of the 611 identified qnr genes were reconstructed from metagenomes, and 20 of these were previously undescribed. All of the novel qnr genes were assembled from metagenomes associated with aquatic environments. Nine of the novel genes were selected for validation, and six of the tested genes conferred consistently decreased susceptibility to ciprofloxacin when expressed in Escherichia coli . Conclusions The results presented in this study provide additional evidence for the ubiquitous presence of qnr genes in environmental microbial communities, expand the number of known qnr gene variants and further elucidate the diversity of this class of resistance genes. This study also strengthens the hypothesis that environmental bacterial communities act as sources of previously uncharacterized qnr genes.

Despite mounting evidence of gut-brain involvement in psychiatric conditions, functional data remain limited, and analyses of other microbial niches, such as the vaginal microbiota, are lacking in relation to mental health. This aim of this study was to investigate if the connections between the gut microbiome and mental health observed in populations with a clinical diagnosis of mental illness extend to healthy women experiencing stress and depressive symptoms. Additionally, this study examined the functional pathways of the gut microbiota according to the levels of psychological symptoms. Furthermore, the study aimed to explore potential correlations between the vaginal microbiome and mental health parameters in young women without psychiatric diagnoses. In this cross-sectional study, 160 healthy Danish women (aged 18-40 years) filled out questionnaires with validated scales measuring symptoms of stress and depression and frequency of dietary intake. Fecal and vaginal microbiota samples were collected at the beginning of the menstrual cycle and vaginal samples were also collected at cycle day 8-12 and 18-22. Shotgun metagenomic profiling of the gut and vaginal microbiome was performed. The Kyoto Encyclopedia of Genes and Genomes (KEGG) was used for functional profiling and 56 Gut Brain Modules were analyzed in the fecal samples. The relative abundance in the gut of the genera , , and was higher in women with elevated depressive symptoms. Women with high perceived stress showed a tendency of increased abundance of , , and . Amongst others, the potentially pathogenic genera, Escherichia and Shigella correlate with alterations in the neuroactive pathways such as the glutamatergic, GABAeric, dopaminergic, and Kynurenine pathways. Vaginosis symptoms were more prevalent in women reporting high levels of stress and depressive symptoms. The findings of this study support the concept of a microbiota-associated effect on the neuroactive pathways even in healthy young women. This suggest, that targeting the gut microbiome could be a promising approach for future psychiatric interventions.

Species composition of the healthy adult gut microbiota tends to be stable over time. Destabilization of the gut microbiome under the influence of different factors is the main driver of the microbial dysbiosis and subsequent impacts on host physiology. Here, we used metagenomics data from a Swedish longitudinal cohort, to determine the stability of the gut microbiome and uncovered two distinct microbial species groups; persistent colonizing species (PCS) and transient colonizing species (TCS). We validated the continuation of this grouping, generating gut metagenomics data for additional time points from the same Swedish cohort. We evaluated the existence of PCS/TCS across different geographical regions and observed they are globally conserved features. To characterize PCS/TCS phenotypes, we performed bioreactor fermentation with faecal samples and metabolic modeling. Finally, using chronic disease gut metagenome and other multi-omics data, we identified roles of TCS in microbial dysbiosis and link with abnormal changes to host physiology.

Background Broad-spectrum fluoroquinolone antibiotics are central in modern health care and are used to treat and prevent a wide range of bacterial infections. The recently discovered qnr genes provide a mechanism of resistance with the potential to rapidly spread between bacteria using horizontal gene transfer. As for many antibiotic resistance genes present in pathogens today, qnr genes are hypothesized to originate from environmental bacteria. The vast amount of data generated by shotgun metagenomics can therefore be used to explore the diversity of qnr genes in more detail. Results In this paper we describe a new method to identify qnr genes in nucleotide sequence data. We show, using cross-validation, that the method has a high statistical power of correctly classifying sequences from novel classes of qnr genes, even for fragments as short as 100 nucleotides. Based on sequences from public repositories, the method was able to identify all previously reported plasmid-mediated qnr genes. In addition, several fragments from novel putative qnr genes were identified in metagenomes. The method was also able to annotate 39 chromosomal variants of which 11 have previously not been reported in literature. Conclusions The method described in this paper significantly improves the sensitivity and specificity of identification and annotation of qnr genes in nucleotide sequence data. The predicted novel putative qnr genes in the metagenomic data support the hypothesis of a large and uncharacterized diversity within this family of resistance genes in environmental bacterial communities. An implementation of the method is freely available at http://bioinformatics.math.chalmers.se/qnr/ .