Curtis James Kapsak

In July 2021, the California Code of Regulations Title 17 required all laboratories performing SARS‑CoV‑2 whole genome sequencing (WGS) to report their sequencing results to the California Department of Public Health (CDPH). These viral genomic data and patient metadata were compiled into the Integrated Genomic Epidemiology Database (IGED). Linking anonymized viral sequences with patient‑level information enabled monitoring of infectiousness, pathogenicity, transmission dynamics, evolution, and vaccine evasion among emerging SARS‑CoV‑2 lineages. Laboratories performing SARS-CoV-2 WGS transmitted sequencing results to CDPH through Electronic Laboratory Reporting (ELR) and non-ELR pathways. CDPH applied uniform reporting requirements but allowed flexibility in specific data formats to accommodate diverse data systems. To preserve data quality and interoperability across heterogeneous sources, CDPH implemented standardization, validation, and deduplication protocols. Snowflake, a cloud‑based data storage and analytics platform, and Posit Connect, a cloud deployment and automation platform, supported the management, processing, and integration of data within the IGED. The IGED established links between SARS‑CoV‑2 WGS data and epidemiologic metadata for 801,418 sequences, representing 81.7% of all sequences reported in California. Lineages reported to the IGED showed strong concordance with lineage proportions in GISAID. Sequences reported to the IGED had average turnaround times longer than one month, and the majority of sequencing was performed in Southern California and Los Angeles. The IGED enhanced genomic surveillance through predictive modeling and monitoring concerning evolutionary trends such as recombination and saltations in persistent infections. Development of the IGED highlighted the need for standardized data requirements, sustained funding for sequencing, incentives for data submission, and interdisciplinary collaboration to build an effective genomic surveillance system. This framework for linking genomic and epidemiologic data has not only generated critical insights for SARS‑CoV‑2 but also provided the foundation for CDPH and other public health organizations to develop similar IGED‑like systems for other priority pathogens as genomic surveillance expands.

Shiga toxins in Shiga toxin-producing Escherichia coli (STEC) infections are responsible for bloody diarrhea and serious complications such as hemolytic uremic syndrome. Two types of toxins have been identified: Shiga toxin type 1 (Stx1) and the immunologically distinct Shiga toxin type 2 (Stx2). Numerous STEC that express toxin variants within those two major groups have been characterized, some of which confer unique biological properties. These variants are grouped within the Stx1 or Stx2 types and are often assigned subtypes to indicate they are not identical in sequence or phenotype. Because serious outcomes of infection are associated with certain Stx subtypes, there is a need to assign Stx sequences to the proper subtype. Here, we report a comprehensive analysis of known Stx subtypes and describe a scheme and algorithm to classify the Stx toxins and Stx operon sequences by phylogenetic sequence-based relatedness of the holotoxin conforming to historical type designations. We used this analysis to develop the free and open-source StxTyper software and database that implements this typing algorithm; StxTyper is also integrated into AMRFinderPlus 4.0 at the National Center for Biotechnology Information (NCBI). We validated and compared the results to PCR assays on a set of isolates and current state of the art surveillance methods used in the Danish public health system, and we summarize StxTyper results for over 111,000 publicly available E. coli genomes. We further propose a procedure to coordinate naming and identification for newly discovered and characterized Stx subtypes.

In July 2021, the California Code of Regulations Title 17 required all laboratories performing SARS-CoV-2 whole genome sequencing (WGS) to report their sequencing results to the California Department of Public Health (CDPH). These viral genomic data and patient metadata were compiled into the Integrated Genomic Epidemiology Database (IGED). Linking anonymized viral sequences with patient-level information enabled monitoring of infectiousness, pathogenicity, transmission dynamics, evolution, and vaccine evasion among emerging SARS-CoV-2 lineages. Laboratories performing SARS-CoV-2 WGS transmitted sequencing results to CDPH through Electronic Laboratory Reporting (ELR) and non-ELR pathways. CDPH applied uniform reporting requirements but allowed flexibility in specific data formats to accommodate diverse data systems. To preserve data quality and interoperability across heterogeneous sources, CDPH implemented standardization, validation, and deduplication protocols. Snowflake, a cloud-based data storage and analytics platform, and Posit Connect, a cloud deployment and automation platform, supported the management, processing, and integration of data within the IGED. The IGED established links between SARS-CoV-2 WGS data and epidemiologic metadata for 801,418 sequences, representing 81.7% of all sequences reported in California. Lineages reported to the IGED showed strong concordance with lineage proportions in GISAID. Sequences reported to the IGED had average turnaround times longer than one month, and the majority of sequencing was performed in Southern California and Los Angeles. The IGED enhanced genomic surveillance through predictive modeling and monitoring concerning evolutionary trends such as recombination and saltations in persistent infections. Development of the IGED highlighted the need for standardized data requirements, sustained funding for sequencing, incentives for data submission, and interdisciplinary collaboration to build an effective genomic surveillance system. This framework for linking genomic and epidemiologic data has not only generated critical insights for SARS-CoV-2 but also provided the foundation for CDPH and other public health organizations to develop similar IGED-like systems for other priority pathogens as genomic surveillance expands. Author Summary: In California, the COVID-19 pandemic generated an unprecedented volume of anonymized viral genomic data, creating a critical need to link sequencing results with patient information for genomic epidemiology. To meet this need, we developed the Integrated Genomic Epidemiology Database (IGED), a comprehensive resource that connects SARS-CoV-2 whole-genome sequencing (WGS) data with corresponding patient records. Using cloud-based computational infrastructure, we standardized and integrated submissions from numerous laboratories and jurisdictions, each with distinct technical requirements for providing data to CDPH. Of nearly one million records received, we successfully linked 801,418 WGS records to patient data. The IGED supported public reporting of circulating SARS-CoV-2 lineages, improved understanding of viral evolutionary dynamics, and served as the foundation for a genomic epidemiology tool used in outbreak investigations. By establishing a robust framework for linking WGS and patient-level data, we provide a model that can be adapted by other public health agencies for emerging pathogens of concern.

Case-based infectious disease surveillance is fundamental to public health, but is resource-intensive, logistically complex, and prone to sampling bias. Wastewater testing and sequencing have increasingly been used for population-scale monitoring of pathogen dynamics, including in low-resource settings. Broader adoption of wastewater genomic surveillance, however, is limited by a lack of flexibility across sequencing platforms and approaches, and adaptability to additional pathogens. Here, we describe “Freyja 2”, an integrated bioinformatics tool enabling robust real-time inference of pathogen lineage prevalence and growth dynamics from wastewater and other complex samples. In Freyja 2, we develop new methods for estimating lineage prevalence and growth rates, and demonstrate robustness across common sequencing platforms and to low genomic coverage. By incorporating global pathogen data streams, we extend Freyja 2 to support multi-pathogen surveillance. We demonstrate tracking of multiple recent or ongoing public health emergencies, including COVID-19, mpox, and H5N1 influenza, revealing unreported diversity and lineage co-circulation.

Background: Antimicrobial resistance (AMR) poses significant risks to human and animal health, while the environment can contribute to its spread. National AMR surveillance programs are pivotal for assessing AMR prevalence, trends, and intervention outcomes; however, integrating advanced surveillance tools can be difficult. This pilot study, conducted by FAO ECTAD Indonesia and DGLAHS, the Indonesian Ministry of Agriculture, evaluated the costs and benefits of integrating the Nanopore MinION, Illumina MiSeq, and Sensititre system into a culture-based slaughterhouse–river surveillance system. Methods: Water samples were collected from six chicken slaughterhouses and adjacent rivers (pre- and post-treatment effluent, upstream, and downstream). Culture-based ESBL and general E. coli concentrations were estimated via the WHO Tricycle Protocol, while isolates (n = 42) were sequenced (MinION, MiSeq) and antimicrobial susceptibility testing conducted (Sensititre). Results: The Tricycle Protocol results provided estimates of effluent and river concentrations of ESBL and general E. coli identifying ESBL-to-general E. coli ratios of 13.8% and 6.2%, respectively. Compared to hybrid sequencing assemblies, MinION had a higher concordance than MiSeq for ARG identification (98%), virulence genes (96%), and locations for both (predominately plasmids). Furthermore, MinION concordance with Sensititre AST was 91%. Conclusions: Cost–benefit comparisons suggest sequencing can complement culture-based methods but is dependent on the value placed on the additional information gained.

The sharing of genome sequences in online data repositories allows for large scale analyses of specific genes or gene families. This can result in the detection of novel gene subtypes as well as the development of improved detection methods. Here, we used publicly available WGS data to detect a novel Stx subtype, Stx2n in two clinical E. coli strains isolated in the USA. During this process, additional Stx2 subtypes were detected; six Stx2j, one Stx2m strain, and one Stx2o, were all analyzed for variability from the originally described subtypes. Complete genome sequences were assembled from short- or long-read sequencing and analyzed for serotype, and ST types. The WGS data from Stx2n- and Stx2o-producing STEC strains were further analyzed for virulence genes pro-phage analysis and phage insertion sites. Nucleotide and amino acid maximum parsimony trees showed expected clustering of the previously described subtypes and a clear separation of the novel Stx2n subtype. WGS data were used to design OMNI PCR primers for the detection of all known stx1 (283 bp amplicon), stx2 (400 bp amplicon), intimin encoded by eae (221 bp amplicon), and stx2f (438 bp amplicon) subtypes. These primers were tested in three different laboratories, using standard reference strains. An analysis of the complete genome sequence showed variability in serogroup, virulence genes, and ST type, and Stx2 pro-phages showed variability in size, gene composition, and phage insertion sites. The strains with Stx2j, Stx2m, Stx2n, and Stx2o showed toxicity to Vero cells. Stx2j carrying strain, 2012C-4221, was induced when grown with sub-inhibitory concentrations of ciprofloxacin, and toxicity was detected. Taken together, these data highlight the need to reinforce genomic surveillance to identify the emergence of potential new Stx2 or Stx1 variants. The importance of this surveillance has a paramount impact on public health. Per our description in this study, we suggest that 2017C-4317 be designated as the Stx2n type-strain.

The sharing of genome sequences in online data repositories, allows for large scale analyses of specific genes or gene families. This can result in the detection of novel gene subtypes as well as development of improved detection methods. Here we used publicly available WGS data to detect a novel Stx subtype, Stx2n in two clinical E. coli strains isolated in the USA. During this process, additional Stx2 subtypes were detected; six Stx2j one Stx2m strain and one Stx2o, all were analyzed for variability from the originally described subtypes [1,2]. Complete genome sequences were assembled from short or long read sequencing and analyzed for serotype, and ST types. The stx2n and Stx2o WGS were further analyzed for virulence genes pro-phage analysis and phage insertion sites. Nucleotide and amino acid maximum parsimony trees showed expected clustering of the previously described subtypes and a clear separation of the novel Stx2n subtype. WGS data was used to design OMNI PCR primers for the detection of all known Stx1 (283 bp amplicon), Stx2 (400 bp amplicon), eae (222bp amplicon) and Stx2f (438 bp amplicon) subtypes. These primers were tested in three different laboratories, using standard reference strains. Analysis of complete genome sequence showed variability in serogroup, virulence genes, ST type, and Stx2 pro-pages showed variability in size, gene composition, and phage insertion sites. The isolates with Stx2n and Stx2o in this study demonstrated toxicity for Vero cells. The Stx2n strain were induced when grown with sub-inhibitory concentrations of Cip, and toxicity was detected. Taken together, these data highlight the need to reinforce genomic surveillance to identify the emergence of potential new Stx2 or Stx1 variants. The importance of this surveillance has a paramount impact in public health. Per our description in this study, we suggest that 2017C-4317 be designated as the Stx2n type-strain.

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.

The capacity for pathogen genomics in public health expanded rapidly during the coronavirus disease 2019 (COVID-19) pandemic, but many public health laboratories did not have the infrastructure in place to handle the vast amount of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequence data generated. The California Department of Public Health, in partnership with Theiagen Genomics, was an early adopter of cloud-based resources for bioinformatics and genomic epidemiology, resulting in the creation of a SARS-CoV-2 genomic surveillance system that combined the efforts of more than 40 sequencing laboratories across government, academia and industry to form California COVIDNet, California’s SARS-CoV-2 Whole-Genome Sequencing Initiative. Open-source bioinformatics workflows, ongoing training sessions for the public health workforce, and automated data transfer to visualization tools all contributed to the success of California COVIDNet. While challenges remain for public health genomic surveillance worldwide, California COVIDNet serves as a framework for a scaled and successful bioinformatics infrastructure that has expanded beyond SARS-CoV-2 to other pathogens of public health importance,