Amee R. Manges

Background: Ethnic disparities in extrahepatic manifestations (EHMs) among individuals with chronic hepatitis C virus (HCV) infection are poorly understood, especially in diverse populations. We aimed to examine ethnic disparities in EHMs among individuals diagnosed with HCV in British Columbia (BC), Canada. Methods: Using linked administrative health data from the BC Hepatitis Testers Cohort (1990–2015), we assessed EHMs incidence and risk by ethnicity (East Asian, South Asian, and Other) across 4 groups: individuals who remained untreated, pre-HCV treatment completion, post-HCV treatment completion, and those who spontaneously cleared HCV. EHMs included chronic kidney diseases (CKDs) and end-stage renal diseases (ESRDs), type 2 diabetes (T2DM), stroke, major adverse cardiac events (MACEs), and neurocognitive disorders. To assess the risk of EHMs by ethnicity, we used multivariable cause-specific proportional hazards models. Results: Among 41 874 individuals, South and East Asians had higher incidence rates of CKD and ESRD, T2DM, stroke, and MACE compared with other ethnicities, particularly among untreated individuals. Adjusted analyses showed that untreated South Asians had significantly higher risk of CKD and ESRD (adjusted hazard ratio [aHR] 1.27, 95% confidence interval [CI] 1.02–2.16) and T2DM (aHR 2.12, 95% CI 1.53–2.94). Following HCV treatment, these disparities largely disappeared except for diabetes. Conclusions: This study highlights the disproportionate burden of EHMs among Asians in BC and underscores the potential of HCV treatment to reduce ethnic disparities. Public health programming is needed to increase linkage to HCV treatment among those who remain untreated. Further research should focus on intra-ethnic differences and mechanisms underlying the impact of HCV treatment on mitigating these disparities.

Current molecular diagnostics are limited in the number and type of detectable pathogens. Metagenomic next generation sequencing (mNGS) is an emerging, and increasingly feasible, pathogen-agnostic diagnostic approach. Translational barriers prohibit the widespread adoption of this technology in clinical laboratories. We validate an end-to-end mNGS assay for detection of respiratory viruses. Our assay is optimized to reduce turnaround time, lower cost-per-sample, increase throughput, and deploy secure and actionable bioinformatic results. We validated our assay using residual nasopharyngeal swab specimens from Vancouver General Hospital (n = 359), RT-PCR-positive, or negative for Influenza, SARS-CoV-2, and RSV. We quantified sample stability, assay precision, the effect of background nucleic acid levels, and analytical limits of detection. Diagnostic performance metrics were estimated. We report that our mNGS assay is highly precise, semi-quantitative, with analytical limits of detection ranging from 10³-10⁴ copies/mL. Our assay is highly specific (100%) and sensitive (61.9% Overall: 86.8%; RT-PCR C_t < 30). Multiplexing capabilities enable processing of up to 55-specimens simultaneously on an Oxford Nanopore GridION device, with results reported within 12-hours. This study outlines the diagnostic performance and feasibility of mNGS for respiratory viral diagnostics, infection control, and public health surveillance. We addressed translational barriers to widespread mNGS adoption.

Key Points SinoNasal Microbiota Transfer (SNMT) was safe with immediate benefit in all recipients, with sustained improvement in two of three recipients for up to 180 days. The addition of antimicrobial photodynamic therapy worsened chronic rhinosinusitis. These promising SNMT results warrant further study of safety and efficacy.

The advent of next-generation sequencing (NGS) technologies has expanded our ability to detect and analyze microbial genomes and has yielded novel molecular approaches for infectious disease diagnostics. While several targeted multiplex PCR and NGS-based assays have been widely used in public health settings in recent years, these targeted approaches are limited in that they still rely on a priori knowledge of a pathogen’s genome, and an untargeted or unknown pathogen will not be detected.

Stunting affects one-in-five children globally and is associated with greater infectious morbidity, mortality and neurodevelopmental deficits. Recent evidence suggests that the early-life gut microbiome affects child growth through immune, metabolic and endocrine pathways. Using whole metagenomic sequencing, we map the assembly of the gut microbiome in 335 children from rural Zimbabwe from 1–18 months of age who were enrolled in the Sanitation, Hygiene, Infant Nutrition Efficacy Trial (SHINE; NCT01824940), a randomized trial of improved water, sanitation and hygiene (WASH) and infant and young child feeding (IYCF). Here, we show that the early-life gut microbiome undergoes programmed assembly that is unresponsive to the randomized interventions intended to improve linear growth. However, maternal HIV infection is associated with over-diversification and over-maturity of the early-life gut microbiome in their uninfected children, in addition to reduced abundance of Bifidobacterium species. Using machine learning models (XGBoost), we show that taxonomic microbiome features are poorly predictive of child growth, however functional metagenomic features, particularly B-vitamin and nucleotide biosynthesis pathways, moderately predict both attained linear and ponderal growth and growth velocity. New approaches targeting the gut microbiome in early childhood may complement efforts to combat child undernutrition.

Objectives: The COVID-19 pandemic and ensuing public health emergency has emphasized the need to study SARS-CoV-2 pathogenesis. The human microbiome has been shown to regulate the host immune system and may influence host susceptibility to viral infection, as well as disease severity. Several studies have assessed whether compositional alterations in the nasopharyngeal microbiota are associated with SARS-CoV-2 infection. However, the results of these studies were varied, and many did not account for disease severity. This study aims to examine whether compositional differences in the nasopharyngeal microbiota are associated with SARS-CoV-2 infection status and disease severity. Methods: We performed Nanopore full-length 16S rRNA sequencing on 194 nasopharyngeal swab specimens from hospitalized and community-dwelling SARS-CoV-2-infected and uninfected individuals. Sequence data analysis was performed using the BugSeq 16S analysis pipeline. Results: We found significant beta (PERMANOVA p < 0.05), but not alpha (Kruskal-Wallis p > 0.05) diversity differences in the nasopharyngeal microbiota among our study groups. We identified several differentially abundant taxa associated with SARS-CoV-2 infection status and disease severity using ALDEx2. Finally, we observed a trend towards higher abundance of Enterobacteriaceae in specimens from hospitalized SARS-CoV-2-infected patients. Conclusions: This study identified several alterations in the nasopharyngeal microbiome associated with SARS-CoV-2 infection status and disease severity. Understanding the role of the microbiome in infection susceptibility and severity may open new avenues of research for disease prevention and treatment.

Objectives: The COVID-19 pandemic and ensuing public health emergency has emphasized the need to study SARS-CoV-2 pathogenesis. The human microbiome has been shown to regulate the host immune system and may influence host susceptibility to viral infection, as well as disease severity. Several studies have assessed whether compositional alterations in the nasopharyngeal microbiota are associated with SARS-CoV-2 infection. However, the results of these studies were varied, and many did not account for disease severity. This study aims to examine whether compositional differences in the nasopharyngeal microbiota are associated with SARS-CoV-2 infection status and disease severity. Methods: We performed Nanopore full-length 16S rRNA sequencing on 194 nasopharyngeal swab specimens from hospitalized and community-dwelling SARS-CoV-2-infected and uninfected individuals. Sequence data analysis was performed using the BugSeq 16S analysis pipeline. Results: We found significant beta (PERMANOVA p < 0.05), but not alpha (Kruskal-Wallis p > 0.05) diversity differences in the nasopharyngeal microbiota among our study groups. We identified several differentially abundant taxa associated with SARS-CoV-2 infection status and disease severity using ALDEx2. Finally, we observed a trend towards higher abundance of Enterobacteriaceae in specimens from hospitalized SARS-CoV-2-infected patients. Conclusions: This study identified several alterations in the nasopharyngeal microbiome associated with SARS-CoV-2 infection status and disease severity. Understanding the role of the microbiome in infection susceptibility and severity may open new avenues of research for disease prevention and treatment.

Stunting affects one-in-five children globally and is associated with greater infectious morbidity, mortality and neurodevelopmental deficits. Recent evidence suggests that the early-life gut microbiome affects child growth through immune, metabolic and endocrine pathways, and microbiome perturbations may contribute to undernutrition. We examined early-life fecal microbiome composition and function in 875 stool samples collected longitudinally in 335 children from 1-18 months of age in rural Zimbabwe, from a cluster-randomized trial of improved water, sanitation, and hygiene (WASH), and improved infant and young child feeding (IYCF). Using whole metagenome shotgun sequencing, we examined the effect of the interventions, in addition to environmental or host factors including maternal HIV infection, on the succession of the early-life gut microbiome, and employed extreme gradient boosting machines (XGBoost) to model microbiome maturation and to predict child growth. WASH and IYCF interventions had little impact on the fecal microbiome, however children who were HIV-exposed but uninfected exhibited over-diversification and over-maturity of the early-life gut microbiome in addition to reduced abundance of Bifidobacteria species. Taxonomic microbiome features were poorly predictive of linear and ponderal growth, however functional metagenomic features, particularly B-vitamin and nucleotide biosynthesis pathways, moderately predicted both attained linear and ponderal growth and growth velocity. We find that the succession of the gut microbiome in a population at risk of stunting is unresponsive to WASH and IYCF interventions, but is strongly associated with maternal HIV infection, which may contribute to deficits in growth. New approaches targeting the gut microbiome in early childhood may complement efforts to combat child undernutrition. One sentence summary: The gut microbiome of rural Zimbabwean infants undergoes programmed maturation that is unresponsive to sanitation and nutrition interventions but is comprehensively modified by maternal HIV infection and can moderately predict linear growth.

A large gap remains between sequencing a microbial community and characterizing all of the organisms inside of it. Here we develop a novel method to taxonomically bin metagenomic assemblies through alignment of contigs against a reference database. We show that this workflow, BugSplit, bins metagenome-assembled contigs to species with a 33% absolute improvement in F1-score when compared to alternative tools. We perform nanopore mNGS on patients with COVID-19, and using a reference database predating COVID-19, demonstrate that BugSplit’s taxonomic binning enables sensitive and specific detection of a novel coronavirus not possible with other approaches. When applied to nanopore mNGS data from cases ofKlebsiella pneumoniaeandNeisseria gonorrhoeaeinfection, BugSplit’s taxonomic binning accurately separates pathogen sequences from those of the host and microbiota, and unlocks the possibility of sequence typing, in silico serotyping, and antimicrobial resistance prediction of each organism within a sample. BugSplit is available athttps://bugseq.com/academic.