Putting everything in its place: using the INSDC compliant Pathogen Data Object Model to better structure genomic data submitted for public health applications.

Fast, efficient public health actions require well-organized and coordinated systems that can supply timely and accurate knowledge. Public databases of pathogen genomic data, such as the International Nucleotide Sequence Database Collaboration (INSDC), have become essential tools for efficient public health decisions. However, these international resources began primarily for academic purposes, rather than for surveillance or interventions. Now, queries need to access not only the whole genomes of multiple pathogens but also make connections using robust contextual metadata to identify issues of public health relevance. Databases that over time developed a patchwork of submission formats and requirements need to be consistently organized and coordinated internationally to allow effective searches.To help resolve these issues, we propose a common pathogen data structure called the Pathogen Data Object Model (DOM) that will formalize the minimum pieces of sequence data and contextual data necessary for general public health uses, while recognizing that submitters will likely withhold a wide range of non-public contextual data. Further, we propose contributors use the Pathogen DOM for all pathogen submissions (bacterial, viral, fungal, and parasites), which will simplify data submissions and provide a consistent and transparent data structure for downstream data analyses. We also highlight how improved submission tools can support the Pathogen DOM, offering users additional easy-to-use methods to ensure this structure is followed.

Accelerating bioinformatics implementation in public health.

We have adopted an open bioinformatics ecosystem to address the challenges of bioinformatics implementation in public health laboratories (PHLs). Bioinformatics implementation for public health requires practitioners to undertake standardized bioinformatic analyses and generate reproducible, validated and auditable results. It is essential that data storage and analysis are scalable, portable and secure, and that implementation of bioinformatics fits within the operational constraints of the laboratory. We address these requirements using Terra, a web-based data analysis platform with a graphical user interface connecting users to bioinformatics analyses without the use of code. We have developed bioinformatics workflows for use with Terra that specifically meet the needs of public health practitioners. These Theiagen workflows perform genome assembly, quality control, and characterization, as well as construction of phylogeny for insights into genomic epidemiology. Additonally, these workflows use open-source containerized software and the WDL workflow language to ensure standardization and interoperability with other bioinformatics solutions, whilst being adaptable by the user. They are all open source and publicly available in Dockstore with the version-controlled code available in public GitHub repositories. They have been written to generate outputs in standardized file formats to allow for further downstream analysis and visualization with separate genomic epidemiology software. Testament to this solution meeting the requirements for bioinformatic implementation in public health, Theiagen workflows have collectively been used for over 5 million sample analyses in the last 2 years by over 90 public health laboratories in at least 40 different countries. Continued adoption of technological innovations and development of further workflows will ensure that this ecosystem continues to benefit PHLs.

Unrecognized introductions of SARS-CoV-2 into the US state of Georgia shaped the early epidemic.

In early 2020, as diagnostic and surveillance responses for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ramped up, attention focused primarily on returning international travelers. Here, we build on existing studies characterizing early patterns of SARS-CoV-2 spread within the USA by analyzing detailed clinical, molecular, and viral genomic data from the state of Georgia through March 2020. We find evidence for multiple early introductions into Georgia, despite relatively sparse sampling. Most sampled sequences likely stemmed from a single or small number of introductions from Asia three weeks prior to the state's first detected infection. Our analysis of sequences from domestic travelers demonstrates widespread circulation of closely related viruses in multiple US states by the end of March 2020. Our findings indicate that the exclusive focus on identifying SARS-CoV-2 in returning international travelers early in the pandemic may have led to a failure to recognize locally circulating infections for several weeks and point toward a critical need for implementing rapid, broadly targeted surveillance efforts for future pandemics.

Shiftwork, functional bowel symptoms, and the microbiome.

BACKGROUND: There are about 15 million Americans working full-time on evening, night, or rotating shifts. Between 48% and 81.9% of those working rotating or night shifts report abdominal pain, constipation, diarrhea and other symptoms of functional bowel disorders. The basis for this high prevalence of functional bowel disorders, including irritable bowel syndrome (IBS), among shift workers is unknown. Animal studies, however, suggest that circadian disruption, similar to that in shift workers, may contribute to the development of GI complaints among shift workers by altering the composition and normal diurnal rhythmicity of the resident intestinal microbes. Therefore, the present study was designed to determine if there were differences in (1) composition and diversity of the microbiome of night shift workers compared to day shift workers; and (2) the composition and diversity of the microbiome among shift workers experiencing functional bowel symptoms compared to shift workers who did not experience functional bowel symptoms. METHODS: Fifty-one full time staff nurses who worked either 12-hour day or night shifts completed demographic information, and the Rome III IBS module. They also collected two samples of gut microbiota before the beginning and at the end of their last work shift on day 14, using validated field-tested methods consistent with the Human Microbiome Project. After DNA extraction, 16S rRNA sequencing and assignment to the genus level was completed, samples were then compared to determine if there were (1) differences in the diversity and profile of the microbiome by shift type; (2) if there were differences in the microbiome by time of day for collection; and (3) whether there were differences in the diversity and profile of the microbiome of nurses with IBS and those without IBS. RESULTS: There were no differences in alpha or beta diversity of gut microbiota when specimens from day and night shift nurses were compared. There were however marginal differences in beta diversity when specimens collected at the beginning and end of the shifts were compared, with seven OTUs being differentially abundant when collected from day shift workers in the evening. There were also three OTUs to be differentially abundant in participants reporting IBS symptoms.

Genomic analysis of variability in Delta-toxin levels between Staphylococcus aureus strains.

BACKGROUND: The delta-toxin (δ-toxin) of Staphylococcus aureus is the only hemolysin shown to cause mast cell degranulation and is linked to atopic dermatitis, a chronic inflammatory skin disease. We sought to characterize variation in δ-toxin production across S. aureus strains and identify genetic loci potentially associated with differences between strains. METHODS: A set of 124 S. aureus strains was genome-sequenced and δ-toxin levels in stationary phase supernatants determined by high performance liquid chromatography (HPLC). SNPs and kmers were associated with differences in toxin production using four genome-wide association study (GWAS) methods. Transposon mutations in candidate genes were tested for their δ-toxin levels. We constructed XGBoost models to predict toxin production based on genetic loci discovered to be potentially associated with the phenotype. RESULTS: The S. aureus strain set encompassed 40 sequence types (STs) in 23 clonal complexes (CCs). δ-toxin production ranged from barely detectable levels to >90,000 units, with a median of >8,000 units. CC30 had significantly lower levels of toxin production than average while CC45 and CC121 were higher. MSSA (methicillin sensitive) strains had higher δ-toxin production than MRSA (methicillin resistant) strains. Through multiple GWAS approaches, 45 genes were found to be potentially associated with toxicity. Machine learning models using loci discovered through GWAS as features were able to predict δ-toxin production (as a high/low binary phenotype) with a precision of .875 and specificity of .990 but recall of .333. We discovered that mutants in the carA gene, encoding the small chain of carbamoyl phosphate synthase, completely abolished toxin production and toxicity in Caenorhabditis elegans. CONCLUSIONS: The amount of stationary phase production of the toxin is a strain-specific phenotype likely affected by a complex interaction of number of genes with different levels of effect. We discovered new candidate genes that potentially play a role in modulating production. We report for the first time that the product of the carA gene is necessary for δ-toxin production in USA300. This work lays a foundation for future work on understanding toxin regulation in S. aureus and prediction of phenotypes from genomic sequences.

Fine-scale differentiation between Bacillus anthracis and Bacillus cereus group signatures in metagenome shotgun data.

BACKGROUND: It is possible to detect bacterial species in shotgun metagenome datasets through the presence of only a few sequence reads. However, false positive results can arise, as was the case in the initial findings of a recent New York City subway metagenome project. False positives are especially likely when two closely related are present in the same sample. Bacillus anthracis, the etiologic agent of anthrax, is a high-consequence pathogen that shares >99% average nucleotide identity with Bacillus cereus group (BCerG) genomes. Our goal was to create an analysis tool that used k-mers to detect B. anthracis, incorporating information about the coverage of BCerG in the metagenome sample. METHODS: Using public complete genome sequence datasets, we identified a set of 31-mer signatures that differentiated B. anthracis from other members of the B. cereus group (BCerG), and another set which differentiated BCerG genomes (including B. anthracis) from other Bacillus strains. We also created a set of 31-mers for detecting the lethal factor gene, the key genetic diagnostic of the presence of anthrax-causing bacteria. We created synthetic sequence datasets based on existing genomes to test the accuracy of a k-mer based detection model. RESULTS: We found 239,503 B. anthracis-specific 31-mers (the Ba31 set), 10,183 BCerG 31-mers (the BCerG31 set), and 2,617 lethal factor k-mers (the lef31 set). We showed that false positive B. anthracis k-mers-which arise from random sequencing errors-are observable at high genome coverages of B. cereus. We also showed that there is a "gray zone" below 0.184× coverage of the B. anthracis genome sequence, in which we cannot expect with high probability to identify lethal factor k-mers. We created a linear regression model to differentiate the presence of B. anthracis-like chromosomes from sequencing errors given the BCerG background coverage. We showed that while shotgun datasets from the New York City subway metagenome project had no matches to lef31 k-mers and hence were negative for B. anthracis, some samples showed evidence of strains very closely related to the pathogen. DISCUSSION: This work shows how extensive libraries of complete genomes can be used to create organism-specific signatures to help interpret metagenomes. We contrast "specialist" approaches to metagenome analysis such as this work to "generalist" software that seeks to classify all organisms present in the sample and note the more general utility of a k-mer filter approach when taxonomic boundaries lack clarity or high levels of precision are required.

The single-species metagenome: subtyping Staphylococcus aureus core genome sequences from shotgun metagenomic data.

In this study we developed a genome-based method for detecting Staphylococcus aureus subtypes from metagenome shotgun sequence data. We used a binomial mixture model and the coverage counts at >100,000 known S. aureus SNP (single nucleotide polymorphism) sites derived from prior comparative genomic analysis to estimate the proportion of 40 subtypes in metagenome samples. We were able to obtain >87% sensitivity and >94% specificity at 0.025X coverage for S. aureus. We found that 321 and 149 metagenome samples from the Human Microbiome Project and metaSUB analysis of the New York City subway, respectively, contained S. aureus at genome coverage >0.025. In both projects, CC8 and CC30 were the most common S. aureus clonal complexes encountered. We found evidence that the subtype composition at different body sites of the same individual were more similar than random sampling and more limited evidence that certain body sites were enriched for particular subtypes. One surprising finding was the apparent high frequency of CC398, a lineage often associated with livestock, in samples from the tongue dorsum. Epidemiologic analysis of the HMP subject population suggested that high BMI (body mass index) and health insurance are possibly associated with S. aureus carriage but there was limited power to identify factors linked to carriage of even the most common subtype. In the NYC subway data, we found a small signal of geographic distance affecting subtype clustering but other unknown factors influence taxonomic distribution of the species around the city.