Co-localization of antibiotic resistance genes is widespread in the infant gut microbiome and associates with an immature gut microbial composition.

BACKGROUND: In environmental bacteria, the selective advantage of antibiotic resistance genes (ARGs) can be increased through co-localization with genes such as other ARGs, biocide resistance genes, metal resistance genes, and virulence genes (VGs). The gut microbiome of infants has been shown to contain numerous ARGs, however, co-localization related to ARGs is unknown during early life despite frequent exposures to biocides and metals from an early age. RESULTS: We conducted a comprehensive analysis of genetic co-localization of resistance genes in a cohort of 662 Danish children and examined the association between such co-localization and environmental factors as well as gut microbial maturation. Our study showed that co-localization of ARGs with other resistance and virulence genes is common in the early gut microbiome and is associated with gut bacteria that are indicative of low maturity. Statistical models showed that co-localization occurred mainly in the phylum Proteobacteria independent of high ARG content and contig length. We evaluated the stochasticity of co-localization occurrence using enrichment scores. The most common forms of co-localization involved tetracycline and fluoroquinolone resistance genes, and, on plasmids, co-localization predominantly occurred in the form of class 1 integrons. Antibiotic use caused a short-term increase in mobile ARGs, while non-mobile ARGs showed no significant change. Finally, we found that a high abundance of VGs was associated with low gut microbial maturity and that VGs showed even higher potential for mobility than ARGs. CONCLUSIONS: We found that the phenomenon of co-localization between ARGs and other resistance and VGs was prevalent in the gut at the beginning of life. It reveals the diversity that sustains antibiotic resistance and therefore indirectly emphasizes the need to apply caution in the use of antimicrobial agents in clinical practice, animal husbandry, and daily life to mitigate the escalation of resistance. Video Abstract.

Differential responses of the gut microbiome and resistome to antibiotic exposures in infants and adults.

Despite their crucial importance for human health, there is still relatively limited knowledge on how the gut resistome changes or responds to antibiotic treatment across ages, especially in the latter case. Here, we use fecal metagenomic data from 662 Danish infants and 217 young adults to fill this gap. The gut resistomes are characterized by a bimodal distribution driven by E. coli composition. The typical profile of the gut resistome differs significantly between adults and infants, with the latter distinguished by higher gene and plasmid abundances. However, the predominant antibiotic resistance genes (ARGs) are the same. Antibiotic treatment reduces bacterial diversity and increased ARG and plasmid abundances in both cohorts, especially core ARGs. The effects of antibiotic treatments on the gut microbiome last longer in adults than in infants, and different antibiotics are associated with distinct impacts. Overall, this study broadens our current understanding of gut resistome dynamics and the impact of antibiotic treatment across age groups.

Clostridium scindens secretome suppresses virulence gene expression of Clostridioides difficile in a bile acid-independent manner.

Clostridioides difficile infection (CDI) is a major health concern and one of the leading causes of hospital-acquired diarrhea in many countries. C. difficile infection is challenging to treat as C. difficile is resistant to multiple antibiotics. Alternative solutions are needed as conventional treatment with broad-spectrum antibiotics often leads to recurrent CDI. Recent studies have shown that specific microbiota-based therapeutics such as bile acids (BAs) are promising approaches to treat CDI. Clostridium scindens encodes the bile acid-induced (bai) operon that carries out 7-alpha-dehydroxylation of liver-derived primary BAs to secondary BAs. This biotransformation is thought to increase the antibacterial effects of BAs on C. difficile. Here, we used an automated multistage fermentor to study the antibacterial actions of C. scindens and BAs on C. difficile in the presence/absence of a gut microbial community derived from healthy human donor fecal microbiota. We observed that C. scindens inhibited C. difficile growth when the medium was supplemented with primary BAs. Transcriptomic analysis indicated upregulation of C. scindens bai operon and suppressed expression of C. difficile exotoxins that mediate CDI. We also observed BA-independent antibacterial activity of the secretome from C. scindens cultured overnight in a medium without supplementary primary BAs, which suppressed growth and exotoxin expression in C. difficile mono-culture. Further investigation of the molecular basis of our observation could lead to a more specific treatment for CDI than current approaches. IMPORTANCE There is an urgent need for new approaches to replace the available treatment options against Clostridioides difficile infection (CDI). Our novel work reports a bile acid-independent reduction of C. difficile growth and virulence gene expression by the secretome of Clostridium scindens. This potential treatment combined with other antimicrobial strategies could facilitate the development of alternative therapies in anticipation of CDI and in turn reduce the risk of antimicrobial resistance.

Oropharyngeal Microbiota Clusters in Children with Asthma or Wheeze Associate with Allergy, Blood Transcriptomic Immune Pathways, and Exacerbation Risk.

Rationale: Children with preschool wheezing or school-age asthma are reported to have airway microbial imbalances. Objectives: To identify clusters in children with asthma or wheezing using oropharyngeal microbiota profiles. Methods: Oropharyngeal swabs from the U-BIOPRED (Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes) pediatric asthma or wheezing cohort were characterized using 16S ribosomal RNA gene sequencing, and unsupervised hierarchical clustering was performed on the Bray-Curtis ß-diversity. Enrichment scores of the Molecular Signatures Database hallmark gene sets were computed from the blood transcriptome using gene set variation analysis. Children with severe asthma or severe wheezing were followed up for 12-18 months, with assessment of the frequency of exacerbations. Measurements and Main Results: Oropharyngeal samples from 241 children (age range, 1-17 years; 40% female) revealed four taxa-driven clusters dominated by Streptococcus, Veillonella, Rothia, and Haemophilus. The clusters showed significant differences in atopic dermatitis, grass pollen sensitization, FEV1% predicted after salbutamol, and annual asthma exacerbation frequency during follow-up. The Veillonella cluster was the most allergic and included the highest percentage of children with two or more exacerbations per year during follow-up. The oropharyngeal clusters were different in the enrichment scores of TGF-ß (transforming growth factor-ß) (highest in the Veillonella cluster) and Wnt/ß-catenin signaling (highest in the Haemophilus cluster) transcriptomic pathways in blood (all q values <0.05). Conclusions: Analysis of the oropharyngeal microbiota of children with asthma or wheezing identified four clusters with distinct clinical characteristics (phenotypes) that associate with risk for exacerbation and transcriptomic pathways involved in airway remodeling. This suggests that further exploration of the oropharyngeal microbiota may lead to novel pathophysiologic insights and potentially new treatment approaches.

Identification of representative species-specific genes for abundance measurements.

Motivation: Metagenomic binning facilitates the reconstruction of genomes and identification of Metagenomic Species Pan-genomes or Metagenomic Assembled Genomes. We propose a method for identifying a set of de novo representative genes, termed signature genes, which can be used to measure the relative abundance and used as markers of each metagenomic species with high accuracy. Results: An initial set of the 100 genes that correlate with the median gene abundance profile of the entity is selected. A variant of the coupon collector's problem was utilized to evaluate the probability of identifying a certain number of unique genes in a sample. This allows us to reject the abundance measurements of strains exhibiting a significantly skewed gene representation. A rank-based negative binomial model is employed to assess the performance of different gene sets across a large set of samples, facilitating identification of an optimal signature gene set for the entity. When benchmarked the method on a synthetic gene catalog, our optimized signature gene sets estimate relative abundance significantly closer to the true relative abundance compared to the starting gene sets extracted from the metagenomic species. The method was able to replicate results from a study with real data and identify around three times as many metagenomic entities. Availability and implementation: The code used for the analysis is available on GitHub: https://github.com/trinezac/SG_optimization. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Standardized multi-omics of Earth's microbiomes reveals microbial and metabolite diversity.

Despite advances in sequencing, lack of standardization makes comparisons across studies challenging and hampers insights into the structure and function of microbial communities across multiple habitats on a planetary scale. Here we present a multi-omics analysis of a diverse set of 880 microbial community samples collected for the Earth Microbiome Project. We include amplicon (16S, 18S, ITS) and shotgun metagenomic sequence data, and untargeted metabolomics data (liquid chromatography-tandem mass spectrometry and gas chromatography mass spectrometry). We used standardized protocols and analytical methods to characterize microbial communities, focusing on relationships and co-occurrences of microbially related metabolites and microbial taxa across environments, thus allowing us to explore diversity at extraordinary scale. In addition to a reference database for metagenomic and metabolomic data, we provide a framework for incorporating additional studies, enabling the expansion of existing knowledge in the form of an evolving community resource. We demonstrate the utility of this database by testing the hypothesis that every microbe and metabolite is everywhere but the environment selects. Our results show that metabolite diversity exhibits turnover and nestedness related to both microbial communities and the environment, whereas the relative abundances of microbially related metabolites vary and co-occur with specific microbial consortia in a habitat-specific manner. We additionally show the power of certain chemistry, in particular terpenoids, in distinguishing Earth's environments (for example, terrestrial plant surfaces and soils, freshwater and marine animal stool), as well as that of certain microbes including Conexibacter woesei (terrestrial soils), Haloquadratum walsbyi (marine deposits) and Pantoea dispersa (terrestrial plant detritus). This Resource provides insight into the taxa and metabolites within microbial communities from diverse habitats across Earth, informing both microbial and chemical ecology, and provides a foundation and methods for multi-omics microbiome studies of hosts and the environment.

Asthma and Wheeze Severity and the Oropharyngeal Microbiota in Children and Adolescents.

Rationale: There is a major unmet need for improving the care of children and adolescents with severe asthma and wheeze. Identifying factors contributing to disease severity may lead to improved diagnostics, biomarkers, or therapies. The airway microbiota may be such a key factor. Objectives: To compare the oropharyngeal airway microbiota of children and adolescents with severe and mild/moderate asthma/wheeze. Methods: Oropharyngeal swab samples from school-age and preschool children in the European U-BIOPRED (Unbiased BIOmarkers in the PREDiction of respiratory disease outcomes) multicenter study of severe asthma, all receiving severity-appropriate treatment, were examined using 16S ribosomal RNA gene sequencing. Bacterial taxa were defined as amplicon sequence variants. Results: We analyzed 241 samples from four cohorts: A) 86 school-age children with severe asthma; B) 39 school-age children with mild/moderate asthma; C) 65 preschool children with severe wheeze; and D) 51 preschool children with mild/moderate wheeze. The most common bacteria were Streptococcus (mean relative abundance, 33.5%), Veillonella (10.3%), Haemophilus (7.0%), Prevotella (5.9%), and Rothia (5.5%). Age group (school-age vs. preschool) was associated with the microbiota in ß-diversity analysis (F = 3.32, P = 0.011) and in a differential abundance analysis (28 significant amplicon sequence variants). Among all children, we found no significant difference in the microbiota between children with severe and mild/moderate asthma/wheeze in univariable ß-diversity analysis (F = 1.99, P = 0.08, N = 241), but a significant difference in a multivariable model (F = 2.66, P = 0.035), including the number of exacerbations in the previous year. Age was also significant when expressed as a microbial maturity score (Spearman Rho, 0.39; P = 4.6 × 10-10); however, this score was not associated with asthma/wheeze severity. Conclusions: There was a modest difference in the oropharyngeal airway microbiota between children with severe and mild/moderate asthma/wheeze across all children but not in individual age groups, and a strong association between the microbiota and age. This suggests the oropharyngeal airway microbiota as an interesting entity in studying asthma severity, but probably without the strength to serve as a biomarker for targeted intervention.

The molecular impact of life in an indoor environment.

The chemistry of indoor surfaces and the role of microbes in shaping and responding to that chemistry are largely unexplored. We found that, over 1 month, people's presence and activities profoundly reshaped the chemistry of a house. Molecules associated with eating/cooking, bathroom use, and personal care were found throughout the entire house, while molecules associated with medications, outdoor biocides, and microbially derived compounds were distributed in a location-dependent manner. The house and its microbial occupants, in turn, also introduced chemical transformations such as oxidation and transformations of foodborne molecules. The awareness of and the ability to observe the molecular changes introduced by people should influence future building designs.

Metagenomic evidence for co-occurrence of antibiotic, biocide and metal resistance genes in pigs.

Antibiotic-resistant pathogens constitute an escalating public health concern. Hence a better understanding of the underlying processes responsible for this expansion is urgently needed. Co-selection of heavy metal/biocide and antibiotic resistance genes (ARGs) has been suggested as one potential mechanism promoting the proliferation of antimicrobial resistance (AMR). This paper aims to elucidate this interplay and exploit differences in antibiotic usage to infer patterns of co-selection by the non-antibiotic factors metals and biocides in the context of pig farming. We examined 278 gut metagenomes from pigs with continuous antibiotic exposure, only at weaning and at no exposure. Metals as growth promoters and biocides as disinfectants are currently used with little restrictions in stock farming. The pigs under continuous antibiotic exposure displayed the highest co-occurrence of ARGs and other genetic elements while the pigs under limited use of antibiotics still showed abundant co-occurrences. Pathogens belonging to Enterobacteriaceae displayed increased co-occurrence phenomena, suggesting that this maintenance is not a random selection process from a mobilized pool but pertains to specific phylogenetic clades. These results suggest that metals and biocides displayed strong selective pressures on ARGs exerted by intensive farming, regardless of the current use of antibiotics.

Impact of intensive lifestyle intervention on gut microbiota composition in type 2 diabetes: a post-hoc analysis of a randomized clinical trial.

Type 2 diabetes (T2D) management is based on combined pharmacological and lifestyle intervention approaches. While their clinical benefits are well studied, less is known about their effects on the gut microbiota. We aimed to investigate if an intensive lifestyle intervention combined with conventional standard care leads to a different gut microbiota composition compared to standard care alone treatment in individuals with T2D, and if gut microbiota is associated with the clinical benefits of the treatments. Ninety-eight individuals with T2D were randomized to either an intensive lifestyle intervention combined with standard care group (N = 64), or standard care alone group (N = 34) for 12 months. All individuals received standardized, blinded, target-driven medical therapy, and individual counseling. The lifestyle intervention group moreover received intensified physical training and dietary plans. Clinical characteristics and fecal samples were collected at baseline, 3-, 6-, 9-, and 12-month follow-up. The gut microbiota was profiled with 16S rRNA gene amplicon sequencing. There were no statistical differences in the change of gut microbiota composition between treatments after 12 months, except minor and transient differences at month 3. The shift in gut microbiota alpha diversity at all time windows did not correlate with the change in clinical characteristics, and the gut microbiota did not mediate the treatment effect on clinical characteristics. The clinical benefits of intensive lifestyle and/or pharmacological interventions in T2D are unlikely to be explained by, or causally related to, changes in the gut microbiota composition.

NPOmix: A machine learning classifier to connect mass spectrometry fragmentation data to biosynthetic gene clusters.

Microbial specialized metabolites are an important source of and inspiration for many pharmaceuticals, biotechnological products and play key roles in ecological processes. Untargeted metabolomics using liquid chromatography coupled with tandem mass spectrometry is an efficient technique to access metabolites from fractions and even environmental crude extracts. Nevertheless, metabolomics is limited in predicting structures or bioactivities for cryptic metabolites. Efficiently linking the biosynthetic potential inferred from (meta)genomics to the specialized metabolome would accelerate drug discovery programs by allowing metabolomics to make use of genetic predictions. Here, we present a k-nearest neighbor classifier to systematically connect mass spectrometry fragmentation spectra to their corresponding biosynthetic gene clusters (independent of their chemical class). Our new pattern-based genome mining pipeline links biosynthetic genes to metabolites that they encode for, as detected via mass spectrometry from bacterial cultures or environmental microbiomes. Using paired datasets that include validated genes-mass spectral links from the Paired Omics Data Platform, we demonstrate this approach by automatically linking 18 previously known mass spectra (17 for which the biosynthesis gene clusters can be found at the MIBiG database plus palmyramide A) to their corresponding previously experimentally validated biosynthetic genes (e.g., via nuclear magnetic resonance or genetic engineering). We illustrated a computational example of how to use our Natural Products Mixed Omics (NPOmix) tool for siderophore mining that can be reproduced by the users. We conclude that NPOmix minimizes the need for culturing (it worked well on microbiomes) and facilitates specialized metabolite prioritization based on integrative omics mining.

The infant gut resistome associates with E. coli, environmental exposures, gut microbiome maturity, and asthma-associated bacterial composition.

Antimicrobial resistance (AMR) is an accelerating global threat, yet the nature of AMR in the gut microbiome and how AMR is acquired during early life remain largely unknown. In a cohort of 662 Danish children, we characterized the antibiotic resistance genes (ARGs) acquired during the first year of life and assessed the impacts of diverse environmental exposures on ARG load. Our study reveals a clear bimodal distribution of ARG richness that is driven by the composition of the gut microbiome, especially E. coli. ARG profiles were significantly affected by various environmental factors. Among these factors, the importance of antibiotics diminished with time since treatment. Finally, ARG load and ARG clusters were also associated with the maturity of the gut microbiome and a bacterial composition associated with increased risk of asthma. These findings broaden our understanding of AMR in early life and have critical implications for efforts to mitigate its spread.

The Airway Microbiota Modulates Effect of Azithromycin Treatment for Episodes of Recurrent Asthma-like Symptoms in Preschool Children: A Randomized Clinical Trial.

Rationale: Childhood asthma is often preceded by recurrent episodes of asthma-like symptoms, which can be triggered by both viral and bacterial agents. Recent randomized controlled trials have shown that azithromycin treatment reduces episode duration and severity through yet undefined mechanisms. Objectives: To study the influence of the airway microbiota on the effect of azithromycin treatment during acute episodes of asthma-like symptoms. Methods: Children from the COPSAC2010 (Copenhagen Prospective Studies on Asthma in Childhood 2010) cohort with recurrent asthma-like symptoms aged 12-36 months were randomized during acute episodes to azithromycin or placebo as previously reported. Before randomization, hypopharyngeal aspirates were collected and examined by 16S ribosomal RNA gene amplicon sequencing. Measurements and Main Results: In 139 airway samples from 68 children, episode duration after randomization was associated with microbiota richness (7.5% increased duration per 10 additional operational taxonomic units [OTUs]; 95% confidence interval, 1-14%; P = 0.025), with 15 individual OTUs (including several Neisseria and Veillonella), and with microbial pneumotypes defined from weighted UniFrac distances (longest durations in a Neisseria-dominated pneumotype). Microbiota richness before treatment increased the effect of azithromycin by 10% per 10 additional OTUs, and more OTUs were positively versus negatively associated with an increased azithromycin effect (82 vs. 58; P = 0.0032). Furthermore, effect modification of azithromycin was found for five individual OTUs (three OTUs increased and two OTUs decreased the effect; q < 0.05). Conclusions: The airway microbiota in acute episodes of asthma-like symptoms is associated with episode duration and modifies the effect of azithromycin treatment of the episodes in preschool children with recurrent asthma-like symptoms. Clinical trial registered with www.clinicaltrials.gov (NCT01233297).

Modeling transfer of vaginal microbiota from mother to infant in early life.

Early-life microbiota has been linked to the development of chronic inflammatory diseases. It has been hypothesized that maternal vaginal microbiota is an important initial seeding source and therefore might have lifelong effects on disease risk. To understand maternal vaginal microbiota's role in seeding the child's microbiota and the extent of delivery mode-dependent transmission, we studied 665 mother-child dyads from the COPSAC2010 cohort. The maternal vaginal microbiota was evaluated twice in the third trimester and compared with the children's fecal (at 1 week, 1 month, and 1 year of age) and airway microbiota (at 1 week, 1 month, and 3 months). Based on the concept of weighted transfer ratios (WTRs), we have identified bacterial orders for which the WTR displays patterns indicate persistent or transient transfer from the maternal vaginal microbiome, as well as orders that are shared at later time points independent of delivery mode, indicating a common reservoir.

Chemically informed analyses of metabolomics mass spectrometry data with Qemistree.

Untargeted mass spectrometry is employed to detect small molecules in complex biospecimens, generating data that are difficult to interpret. We developed Qemistree, a data exploration strategy based on the hierarchical organization of molecular fingerprints predicted from fragmentation spectra. Qemistree allows mass spectrometry data to be represented in the context of sample metadata and chemical ontologies. By expressing molecular relationships as a tree, we can apply ecological tools that are designed to analyze and visualize the relatedness of DNA sequences to metabolomics data. Here we demonstrate the use of tree-guided data exploration tools to compare metabolomics samples across different experimental conditions such as chromatographic shifts. Additionally, we leverage a tree representation to visualize chemical diversity in a heterogeneous collection of samples. The Qemistree software pipeline is freely available to the microbiome and metabolomics communities in the form of a QIIME2 plugin, and a global natural products social molecular networking workflow.

Co-selection of antibiotic resistance genes, and mobile genetic elements in the presence of heavy metals in poultry farm environments.

Environmental selection of antibiotic resistance genes (ARGs) is considered to be caused by antibiotic or metal residues, frequently used in livestock. In this study we examined three commercial poultry farms to correlate the co-occurrence patterns of antibiotic and metal residues to the presence of ARGs. We quantified 283 ARGs, 12 mobile genetic elements (MGEs), 49 targeted antibiotics, 7 heavy metals and sequenced 16S rRNA genes. The abundance and type of ARG were significantly enriched in manure while soil harbored the most diverse bacterial community. Procrustes analysis displayed significant correlations between ARGs/MGEs and the microbiome. Cadmium (Cd), arsenic (As), zinc (Zn), copper (Cu) and lead (Pb) were responsible for a majority of positive correlations to ARGs when compared to antibiotics. Integrons and transposons co-occurred with ARGs corresponding to 9 classes of antibiotics, especially Class1 integrase intI-1LC. Redundancy analysis (RDA) and Variance partitioning analysis (VPA) showed that antibiotics, metals, MGEs and bacteria explain solely 0.7%, 5.7%, 12.4%, and 21.9% of variances of ARGs in the microbial community, respectively. These results suggested that bacterial composition and horizontal gene transfer were the major factors shaping the composition of ARGs; Metals had a bigger effect on ARG profile than detected antibiotics in this study.

Delivery mode and gut microbial changes correlate with an increased risk of childhood asthma.

There have been reports of associations between cesarean section delivery and the risk of childhood asthma, potentially mediated through changes in the gut microbiota. We followed 700 children in the Copenhagen Prospective Studies on Asthma in Childhood2010 (COPSAC2010) cohort prospectively from birth. We examined the effects of cesarean section delivery on gut microbial composition by 16S rRNA gene amplicon sequencing during the first year of life. We then explored whether gut microbial perturbations due to delivery mode were associated with a risk of developing asthma in the first 6 years of life. Delivery by cesarean section was accompanied by marked changes in gut microbiota composition at one week and one month of age, but by one year of age only minor differences persisted compared to vaginal delivery. Increased asthma risk was found in children born by cesarean section only if their gut microbiota composition at 1 year of age still retained a cesarean section microbial signature, suggesting that appropriate maturation of the gut microbiota could mitigate against the increased asthma risk associated with gut microbial changes due to cesarean section delivery.

Response of the Human Milk Microbiota to A Maternal Prebiotic Intervention is Individual and Influenced by Maternal Age.

Maternal bacteria are shared with infants via breastfeeding. Prebiotics modulate the gut microbiota, promoting health benefits. We investigated whether the maternal diet supplementation with a prebiotic (fructooligosaccharides, FOS) could influence the milk microbiota. Twenty-eight lactating women received 4.5 g of fructooligosaccharides + 2 g of maltodextrin (FOS group) and twenty-five received 2 g of maltodextrin (placebo group) for 20 days. Breast-milk samples were taken before and after the intervention. The DNA from samples was used for 16S rRNA sequencing. No statistical differences between the groups were found for the bacterial genera after the intervention. However, the distances of the trajectories covered by paired samples from the beginning to the end of the supplementation were higher for the FOS group (p = 0.0007) indicating greater changes in milk microbiota compared to the control group. Linear regression models suggested that the maternal age influenced the response for FOS supplementation (p = 0.02). Interestingly, the pattern of changes to genus abundance upon supplementation was not shared between mothers. We demonstrated that manipulating the human milk microbiota through prebiotics is possible, and the maternal age can affect this response. .