Dysbiosis of host-associated commensal microbiota is emerging as an important factor in risk and phenotype of immunologic, metabolic, and behavioral diseases. Accurate analysis of microbial composition and functional state in humans or mice requires appropriate collection and pre-processing of biospecimens. Methods to sample luminal and mucosal microbiota from human or mouse intestines and to profile microbial phylogenetic composition using 16S rRNA sequencing are presented here. Data generated using the methods in this unit can be used for downstream quantitative analysis of microbial ecology.

The classic organization by Socransky and coworkers categorized the oral bacteria of the subgingival plaque into different complexes. Treponema denticola, Porphyromonas gingivalis and Tannerella forsythia are grouped into the red complex that is highly correlated with periodontal disease. Socransky's work closely associates red with orange complex species such as Fusobacterium nucleatum and Prevotella intermedia but not with members of the other complexes. While the relationship between species contained by these complexes is in part supported by their ability to physically attach to each other, the physiological consequences of these interactions and associations are less clear. In this study, we employed T. denticola as a model organism to analyze contact-dependent responses to interactions with species belonging to the same complex (P. gingivalis and T. forsythia), the closely associated orange complex (using F. nucleatum and P. intermedia as representatives) and the unconnected yellow complex (using Streptococcus sanguinis and S. gordonii as representatives). RNA was extracted from T. denticola alone as well as after pairwise co-incubation for 5 hrs with representatives of the different complexes, and the respective gene expression profiles were determined using microarrays. Numerous genes related to motility, metabolism, transport, outer membrane and hypothetical proteins were differentially regulated in T. denticola in the presence of the tested partner species. Further analysis revealed a significant overlap in the affected genes and we identified a general response to the presence of other species, those specific to two of the three complexes as well as individual complexes. Most interestingly, many predicted major antigens (e.g. flagella, Msp, CTLP) were suppressed in responses that included red complex species indicating that the presence of the most closely associated species induces immune-evasive strategies. In summary, the data presented here provide an in-depth understanding of the transcriptional responses triggered by contact-dependent interactions between microorganisms inhabiting the periodontal pocket.

Biological specimens for microbiological analysis are often collected in BD Vacutainers®, which are not specifically designed for microbial recovery. Bacterial and fungal recovery was analyzed for glass and plastic tubes with or without clot-activating silica. No significant impact was found for the recovery of most bacteria and yeasts tested, however, Haemophilus influenzae recovery from cerebrospinal fluid was significantly reduced in both glass and plastic clot activator tubes.

Abstract Background Regardless of infection route, the intestine is the primary site for HIV-1 infection establishment and results in significant mucosal CD4+ T lymphocyte depletion, induces an inflammatory state that propagates viral dissemination, facilitates microbial translocation, and fosters establishment of one of the largest HIV reservoirs. Here we test the prediction that HIV infection modifies the composition and function of the mucosal commensal microbiota. Results Rectal mucosal microbiota were collected from human subjects using a sponge-based sampling methodology. Samples were collected from 20 HIV-positive men not receiving combination anti-retroviral therapy (cART), 20 HIV-positive men on cART and 20 healthy, HIV-negative men. Microbial composition of samples was analyzed using barcoded 16S Illumina deep sequencing (85,900 reads per sample after processing). Microbial metagenomic information for the samples was imputed using the bioinformatic tools PICRUST and HUMAnN. Microbial composition and imputed function in HIV-positive individuals not receiving cART was significantly different from HIV-negative individuals. Genera including Roseburia, Coprococcus, Ruminococcus, Eubacterium, Alistipes and Lachnospira were depleted in HIV-infected subjects not receiving cART, while Fusobacteria, Anaerococcus, Peptostreptococcus and Porphyromonas were significantly enriched. HIV-positive subjects receiving cART exhibited similar depletion and enrichment for these genera, but were of intermediate magnitude and did not achieve statistical significance. Imputed metagenomic functions, including amino acid metabolism, vitamin biosynthesis, and siderophore biosynthesis differed significantly between healthy controls and HIV-infected subjects not receiving cART. Conclusions HIV infection was associated with rectal mucosal changes in microbiota composition and imputed function that cART failed to completely reverse. HIV infection was associated with depletion of some commensal species and enrichment of a few opportunistic pathogens. Many imputed metagenomic functions differed between samples from HIV-negative and HIV-positive subjects not receiving cART, possibly reflecting mucosal metabolic changes associated with HIV infection. Such functional pathways may represent novel interventional targets for HIV therapy if normalizing the microbial composition or functional activity of the microbiota proves therapeutically useful.

Coccidioidomycosis is associated with a broad spectrum of illness severity, ranging from asymptomatic or self-limited pulmonary infection to life-threatening manifestations of disseminated disease. Serologic studies before the widespread availability of antifungals established current understanding of serologic kinetics and dynamics. Chart histories and complement fixation (CF) titer trends were analyzed for 434 antifungal-treated coccidioidomycosis patients, who were classified by three infectious disease physicians as having either pulmonary uncomplicated coccidioidomycosis (PUC) (n = 248), pulmonary chronic coccidioidomycosis (PCC) (n = 64), disseminated coccidioidomycosis (DC) not including meningitis (n = 86), or coccidioidal meningitis (CM) (n = 36). The median maximal CF titers were 1:4 for PUC patients, 1:24 for PCC patients, 1:128 for DC patients, and 1:32 for CM patients. Approximately 25.4% of PUC patients, 6.2% of PCC patients, 2.3% of DC patients, and 8.3% of CM patients did not develop detectable titers during the study period. Maximal titers developed a mean of 31 days (95% confidence interval [CI], 13 to 50 days) after initial serologic positivity, with no significant differences between groups. Serologic recurrence occurred in 9% of PUC patients, 36% of PCC patients, 50% of DC patients, and 52% of CM patients. Median titer improvement rates were 91 days/dilution for PUC patients, 112 days/dilution for PCC patients, 136 days/dilution for DC patients, and 146 days/dilution for CM patients. Receiver operating characteristic (ROC) analysis revealed that CF testing retains moderate classification value for disseminated infections (area under the curve [AUC], 0.82 [95% CI, 0.78 to 0.87]) and complicated infections (AUC, 0.82 [95% CI, 0.77 to 0.86]). A suitable cutoff value for complicated infections is ≥1:32. Findings update serologic parameters that are relevant for clinical assessment of coccidioidomycosis patients in the triazole era.

Abstract Background Consistent compositional shifts in the gut microbiota are observed in IBD and other chronic intestinal disorders and may contribute to pathogenesis. The identities of microbial biomolecular mechanisms and metabolic products responsible for disease phenotypes remain to be determined, as do the means by which such microbial functions may be therapeutically modified. Results The composition of the microbiota and metabolites in gut microbiome samples in 47 subjects were determined. Samples were obtained by endoscopic mucosal lavage from the cecum and sigmoid colon regions, and each sample was sequenced using the 16S rRNA gene V4 region (Illumina-HiSeq 2000 platform) and assessed by UPLC mass spectroscopy. Spearman correlations were used to identify widespread, statistically significant microbial-metabolite relationships. Metagenomes for identified microbial OTUs were imputed using PICRUSt, and KEGG metabolic pathway modules for imputed genes were assigned using HUMAnN. The resulting metabolic pathway abundances were mostly concordant with metabolite data. Analysis of the metabolome-driven distribution of OTU phylogeny and function revealed clusters of clades that were both metabolically and metagenomically similar. Conclusions The results suggest that microbes are syntropic with mucosal metabolome composition and therefore may be the source of and/or dependent upon gut epithelial metabolites. The consistent relationship between inferred metagenomic function and assayed metabolites suggests that metagenomic composition is predictive to a reasonable degree of microbial community metabolite pools. The finding that certain metabolites strongly correlate with microbial community structure raises the possibility of targeting metabolites for monitoring and/or therapeutically manipulating microbial community function in IBD and other chronic diseases.

Background & aims

Microbes may increase susceptibility to inflammatory bowel disease (IBD) by producing bioactive metabolites that affect immune activity and epithelial function. We undertook a family based study to identify microbial and metabolic features of IBD that may represent a predisease risk state when found in healthy first-degree relatives.

Methods

Twenty-one families with pediatric IBD were recruited, comprising 26 Crohn's disease patients in clinical remission, 10 ulcerative colitis patients in clinical remission, and 54 healthy siblings/parents. Fecal samples were collected for 16S ribosomal RNA gene sequencing, untargeted liquid chromatography-mass spectrometry metabolomics, and calprotectin measurement. Individuals were grouped into microbial and metabolomics states using Dirichlet multinomial models. Multivariate models were used to identify microbes and metabolites associated with these states.

Results

Individuals were classified into 2 microbial community types. One was associated with IBD but irrespective of disease status, had lower microbial diversity, and characteristic shifts in microbial composition including increased Enterobacteriaceae, consistent with dysbiosis. This microbial community type was associated similarly with IBD and reduced microbial diversity in an independent pediatric cohort. Individuals also clustered bioinformatically into 2 subsets with shared fecal metabolomics signatures. One metabotype was associated with IBD and was characterized by increased bile acids, taurine, and tryptophan. The IBD-associated microbial and metabolomics states were highly correlated, suggesting that they represented an integrated ecosystem. Healthy relatives with the IBD-associated microbial community type had an increased incidence of elevated fecal calprotectin.

Conclusions

Healthy first-degree relatives can have dysbiosis associated with an altered intestinal metabolome that may signify a predisease microbial susceptibility state or subclinical inflammation. Longitudinal prospective studies are required to determine whether these individuals have a clinically significant increased risk for developing IBD.

As SARS-CoV-2 continues to spread and evolve, detecting emerging variants early is critical for public health interventions. Inferring lineage prevalence by clinical testing is infeasible at scale, especially in areas with limited resources, participation, or testing and/or sequencing capacity, which can also introduce biases^1-3. SARS-CoV-2 RNA concentration in wastewater successfully tracks regional infection dynamics and provides less biased abundance estimates than clinical testing^4,5. Tracking virus genomic sequences in wastewater would improve community prevalence estimates and detect emerging variants. However, two factors limit wastewater-based genomic surveillance: low-quality sequence data and inability to estimate relative lineage abundance in mixed samples. Here we resolve these critical issues to perform a high-resolution, 295-day wastewater and clinical sequencing effort, in the controlled environment of a large university campus and the broader context of the surrounding county. We developed and deployed improved virus concentration protocols and deconvolution software that fully resolve multiple virus strains from wastewater. We detected emerging variants of concern up to 14 days earlier in wastewater samples, and identified multiple instances of virus spread not captured by clinical genomic surveillance. Our study provides a scalable solution for wastewater genomic surveillance that allows early detection of SARS-CoV-2 variants and identification of cryptic transmission.

Summary: The maturation of genomic surveillance in the past decade has enabled tracking of the emergence and spread of epidemics at an unprecedented level. During the COVID-19 pandemic, for example, genomic data revealed that local epidemics varied considerably in the frequency of SARS-CoV-2 lineage importation and persistence, likely due to a combination of COVID-19 restrictions and changing connectivity. Here, we show that local COVID-19 epidemics are driven by regional transmission, including across international boundaries, but can become increasingly connected to distant locations following the relaxation of public health interventions. By integrating genomic, mobility, and epidemiological data, we find abundant transmission occurring between both adjacent and distant locations, supported by dynamic mobility patterns. We find that changing connectivity significantly influences local COVID-19 incidence. Our findings demonstrate a complex meaning of ‘local’ when investigating connected epidemics and emphasize the importance of collaborative interventions for pandemic prevention and mitigation.

Summary

As SARS-CoV-2 continues to spread and evolve, detecting emerging variants early is critical for public health interventions. Inferring lineage prevalence by clinical testing is infeasible at scale, especially in areas with limited resources, participation, or testing/sequencing capacity, which can also introduce biases. SARS-CoV-2 RNA concentration in wastewater successfully tracks regional infection dynamics and provides less biased abundance estimates than clinical testing. Tracking virus genomic sequences in wastewater would improve community prevalence estimates and detect emerging variants. However, two factors limit wastewater-based genomic surveillance: low-quality sequence data and inability to estimate relative lineage abundance in mixed samples. Here, we resolve these critical issues to perform a high-resolution, 295-day wastewater and clinical sequencing effort, in the controlled environment of a large university campus and the broader context of the surrounding county. We develop and deploy improved virus concentration protocols and deconvolution software that fully resolve multiple virus strains from wastewater. We detect emerging variants of concern up to 14 days earlier in wastewater samples, and identify multiple instances of virus spread not captured by clinical genomic surveillance. Our study provides a scalable solution for wastewater genomic surveillance that allows early detection of SARS-CoV-2 variants and identification of cryptic transmission.