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Additional file 1: Figures S1-S3.

Additional file 4. Review history.

VCU Theses and Dissertations

VCU Theses and Dissertations

Antimicrobial resistant (AMR) pathogens represent urgent threats to human health, and their surveillance is of paramount importance. Metagenomic next generation sequencing (mNGS) has revolutionized such efforts, but remains challenging due to the lack of open-access bioinformatics tools capable of simultaneously analyzing both microbial and AMR gene sequences. To address this need, we developed the CZ ID AMR module, an open-access, cloud-based workflow designed to integrate detection of both microbes and AMR genes in mNGS and whole-genome sequencing (WGS) data. It leverages the Comprehensive Antibiotic Resistance Database and associated Resistance Gene Identifier software, and works synergistically with the CZ ID short-read mNGS module to enable broad detection of both microbes and AMR genes. We highlight diverse applications of the AMR module through analysis of both publicly available and newly generated mNGS and WGS data from four clinical cohort studies and an environmental surveillance project. Through genomic investigations of bacterial sepsis and pneumonia cases, hospital outbreaks, and wastewater surveillance data, we gain a deeper understanding of infectious agents and their resistomes, highlighting the value of integrating microbial identification and AMR profiling for both research and public health. We leverage additional functionalities of the CZ ID mNGS platform to couple resistome profiling with the assessment of phylogenetic relationships between nosocomial pathogens, and further demonstrate the potential to capture the longitudinal dynamics of pathogen and AMR genes in hospital acquired bacterial infections. In sum, the new AMR module advances the capabilities of the open-access CZ ID microbial bioinformatics platform by integrating pathogen detection and AMR profiling from mNGS and WGS data. Its development represents a critical step toward democratizing pathogen genomic analysis and supporting collaborative efforts to combat the growing threat of AMR., Competing Interests: Declarations Competing interests The authors declare that they have no competing interests.

Antimicrobial resistant (AMR) pathogens represent urgent threats to human health, and their surveillance is of paramount importance. Metagenomic next generation sequencing (mNGS) has revolutionized such efforts, but remains challenging due to the lack of open-access bioinformatics tools capable of simultaneously analyzing both microbial and AMR gene sequences. To address this need, we developed the Chan Zuckerberg ID (CZ ID) AMR module, an open-access, cloud-based workflow designed to integrate detection of both microbes and AMR genes in mNGS and whole-genome sequencing (WGS) data. It leverages the Comprehensive Antibiotic Resistance Database and associated Resistance Gene Identifier software, and works synergistically with the CZ ID short-read mNGS module to enable broad detection of both microbes and AMR genes. We highlight diverse applications of the AMR module through analysis of both publicly available and newly generated mNGS and WGS data from four clinical cohort studies and an environmental surveillance project. Through genomic investigations of bacterial sepsis and pneumonia cases, hospital outbreaks, and wastewater surveillance data, we gain a deeper understanding of infectious agents and their resistomes, highlighting the value of integrating microbial identification and AMR profiling for both research and public health. We leverage additional functionalities of the CZ ID mNGS platform to couple resistome profiling with the assessment of phylogenetic relationships between nosocomial pathogens, and further demonstrate the potential to capture the longitudinal dynamics of pathogen and AMR genes in hospital acquired bacterial infections. In sum, the new AMR module advances the capabilities of the open-access CZ ID microbial bioinformatics platform by integrating pathogen detection and AMR profiling from mNGS and WGS data. Its development represents a critical step toward democratizing pathogen genomic analysis and supporting collaborative efforts to combat the growing threat of AMR., Competing Interests: Competing interests The authors declare that they have no competing interests.

Doxycycline post-exposure prophylaxis (doxy-PEP) reduces bacterial sexually transmitted infections (STIs) among men who have sex with men and transgender women. While poised for widespread clinical implementation, the impact of doxy-PEP on antimicrobial resistance remains a primary concern as its effects on the gut microbiome and resistome, or the antimicrobial resistance genes (ARGs) present in the gut microbiome, are unknown. To investigate these effects, we studied participants from a randomized clinical trial who either received doxy-PEP as a one-time doxycycline 200 mg taken after condomless sex (DP arm, n = 100) or standard of care treatment (SOC arm, n = 50). From self-collected rectal swabs at enrollment (day-0) and after 6 months (month-6), we performed metagenomic DNA sequencing (DNA-seq) or metatranscriptomic RNA sequencing (RNA-seq). DNA-seq data was analyzable from 127 samples derived from 89 participants, and RNA-seq data from 86 samples derived from 70 participants. We compared the bacterial microbiome and resistome between the two study arms and over time. Tetracycline ARGs were detected in all day-0 DNA-seq samples and 85% of day-0 RNA-seq samples. The proportional mass of tetracycline ARGs in the resistome increased between day-0 and month-6 in DP participants from 46-51% in the metagenome (p = 0.02) and 4-15% in the metatranscriptome (p < 0.01), but no changes in other ARG classes were observed. Exposure to a higher number of doxycycline doses correlated with proportional enrichment of tetracycline ARGs in the metagenome (Spearman's ρ = 0.23, p < 0.01) and metatranscriptome (Spearman's ρ = 0.55, p < 0.01). Bacterial microbiome alpha diversity, beta diversity, and total bacterial mass did not differ between day-0 and month-6 samples from DP participants when assessed by either DNA-seq or RNA-seq. In an abundance-based correlation analysis, we observed an increase over time in the strength of the correlation between tetracycline ARGs and specific bacterial taxa, including some common human pathogens. In sum, doxy-PEP use over a 6-month period was associated with an increase in the proportion of tetracycline ARGs comprising the gut resistome, and an increase in the expression of tetracycline ARGs. Notably, doxy-PEP did not significantly alter alpha diversity or taxonomic composition of the gut microbiome, and did not demonstrate significant increases in non-tetracycline ARG classes. Further studies and population level surveillance are needed to understand the implications of these findings as doxy-PEP is implemented as a public health strategy., Competing Interests: Disclosures Mayne Pharmaceuticals donated doxycycline hyclate to the study and Cepheid and Hologic provided reagents and STI diagnostic assays. Several authors have served as a scientific adviser to Merck (CC, DD), Gilead Sciences (CC), GSK (CC) and Vir (AL). All other authors have no conflict of interests to declare.

Antimicrobial resistant lower respiratory tract infections (LRTI) are an increasing public health threat, and an important cause of global mortality. The lung microbiome influences LRTI susceptibility and represents an important reservoir for exchange of antimicrobial resistance genes (ARGs). Studies of the gut microbiome have found an association between age and increasing antimicrobial resistance gene (ARG) burden, however corollary studies in the lung microbiome remain absent, despite the respiratory tract representing one of the most clinically significant sites for drug resistant infections. We performed a prospective, multicenter observational study of 261 children and 88 adults with acute respiratory failure, ranging in age from 31 days to ≥ 89 years, admitted to intensive care units in the United States. We performed RNA sequencing on tracheal aspirates collected within 72 hours of intubation, and evaluated age-related differences in detectable ARG expression in the lung microbiome as a primary outcome. Secondary outcomes included number and classes of ARGs detected, proportion of patients with an ARG class, and composition of the lung microbiome. Multivariable logistic regression models (adults vs children) or continuous age (years) were adjusted for sex, race/ethnicity, LRTI status, and days from intubation to specimen collection. Detection of ARGs was significantly higher in adults compared with children after adjusting for sex, race/ethnicity, LRTI diagnosis, and days from intubation to specimen collection (adjusted odds ratio (aOR): 2.16, 95% confidence interval (CI): 1.10-4.22). A greater proportion of adults compared with children had beta-lactam ARGs (31% (CI: 21-41%) vs 13% (CI: 10-18%)), aminoglycoside ARGs (20% (CI: 13-30%) vs 2% (CI: 0.6-4%)), and tetracycline ARGs (14% (CI: 7-23%) vs 3% (CI: 1-5%)). Adults ≥70 years old had the highest proportion of these three ARG classes. The total bacterial abundance of the lung microbiome increased with age, and microbiome alpha diversity varied with age. Taxonomic composition of the lung microbiome, measured by Bray Curtis dissimilarity index, differed between adults and children (p = 0.003). The association between age and increased ARG detection remained significant after additionally including lung microbiome total bacterial abundance and alpha diversity in the multivariable logistic regression model (aOR: 2.38, (CI: 1.25-4.54)). Furthermore, this association remained robust when modeling age as a continuous variable (aOR: 1.02, (CI: 1.01-1.03) per year of age). Taken together, our results demonstrate that age is an independent risk factor for ARG detection in the lower respiratory tract microbiome. These data shape our understanding of the lung resistome in critically ill patients across the lifespan, which may have implications for clinical management and global public health.

The severe acute respiratory coronavirus-2 (SARS-CoV-2) is the cause of the global outbreak of COVID-19. Evidence suggests that the virus is evolving to allow efficient spread through the human population, including vaccinated individuals. Here we report a study of viral variants from surveillance of the Delaware Valley, including the city of Philadelphia, and variants infecting vaccinated subjects. We sequenced and analyzed complete viral genomes from 2621 surveillance samples from March 2020 to September 2021 and compared them to genome sequences from 159 vaccine breakthroughs. In the early spring of 2020, all detected variants were of the B.1 and closely related lineages. A mixture of lineages followed, notably including B.1.243 followed by B.1.1.7 (alpha), with other lineages present at lower levels. Later isolations were dominated by B.1.617.2 (delta) and other delta lineages; delta was the exclusive variant present by the last time sampled. To investigate whether any variants appeared preferentially in vaccine breakthroughs, we devised a model based on Bayesian autoregressive moving average logistic multinomial regression to allow rigorous comparison. This revealed that B.1.617.2 (delta) showed three-fold enrichment in vaccine breakthrough cases (odds ratio of 3; 95% credible interval 0.89-11). Viral point substitutions could also be associated with vaccine breakthroughs, notably the N501Y substitution found in the alpha, beta and gamma variants (odds ratio 2.04; 95% credible interval of 1.25-3.18). This study thus provides a detailed picture of viral evolution in the Delaware Valley and a geographically matched analysis of vaccine breakthroughs; it also introduces a rigorous statistical approach to interrogating enrichment of viral variants., Competing Interests: Declaration of interest RGC reports support to his lab for COVID-19 work unrelated to the current manuscript from OraSure, Inc, and ongoing collaborations with Resilient Biotics, Inc. All other authors have no competing interests.

Background: Little is known about the dynamics of SARS-CoV-2 antigen burden in respiratory samples in different patient populations at different stages of infection. Current rapid antigen tests cannot quantitate and track antigen dynamics with high sensitivity and specificity in respiratory samples., Methods: We developed and validated an ultra-sensitive SARS-CoV-2 antigen assay with smartphone readout using the Microbubbling Digital Assay previously developed by our group, which is a platform that enables highly sensitive detection and quantitation of protein biomarkers. A computer vision-based algorithm was developed for microbubble smartphone image recognition and quantitation. A machine learning-based classifier was developed to classify the smartphone images based on detected microbubbles. Using this assay, we tracked antigen dynamics in serial swab samples from COVID patients hospitalized in ICU and immunocompromised COVID patients., Results: The limit of detection (LOD) of the Microbubbling SARS-CoV-2 Antigen Assay was 0.5 pg/mL (10.6 fM) recombinant nucleocapsid (N) antigen or 4000 copies/mL inactivated SARS-CoV-2 virus in nasopharyngeal (NP) swabs, comparable to many rRT-PCR methods. The assay had high analytical specificity towards SARS-CoV-2. Compared to EUA-approved rRT-PCR methods, the Microbubbling Antigen Assay demonstrated a positive percent agreement (PPA) of 97% (95% confidence interval (CI), 92-99%) in symptomatic individuals within 7 days of symptom onset and positive SARS-CoV-2 nucleic acid results, and a negative percent agreement (NPA) of 97% (95% CI, 94-100%) in symptomatic and asymptomatic individuals with negative nucleic acid results. Antigen positivity rate in NP swabs gradually decreased as days-after-symptom-onset increased, despite persistent nucleic acid positivity of the same samples. The computer vision and machine learning-based automatic microbubble image classifier could accurately identify positives and negatives, based on microbubble counts and sizes. Total microbubble volume, a potential marker of antigen burden, correlated inversely with Ct values and days-after-symptom-onset. Antigen was detected for longer periods of time in immunocompromised patients with hematologic malignancies, compared to immunocompetent individuals. Simultaneous detectable antigens and nucleic acids may indicate the presence of replicating viruses in patients with persistent infections., Conclusions: The Microbubbling SARS-CoV-2 Antigen Assay enables sensitive and specific detection of acute infections, and quantitation and tracking of antigen dynamics in different patient populations at various stages of infection. With smartphone compatibility and automated image processing, the assay is well-positioned to be adapted for point-of-care diagnosis and to explore the clinical implications of antigen dynamics in future studies.