Reoccurring Escherichia coli O157:H7 Strain Linked to Leafy Greens-Associated Outbreaks, 2016-2019.

Genomic characterization of an Escherichia coli O157:H7 strain linked to leafy greens-associated outbreaks dates its emergence to late 2015. One clade has notable accessory genomic content and a previously described mutation putatively associated with increased arsenic tolerance. This strain is a reoccurring, emerging, or persistent strain causing illness over an extended period.

Cronobacter sakazakii Infections in Two Infants Linked to Powdered Infant Formula and Breast Pump Equipment - United States, 2021 and 2022.

Cronobacter sakazakii, a species of gram-negative bacteria belonging to the Enterobacteriaceae family, is known to cause severe and often fatal meningitis and sepsis in young infants. C. sakazakii is ubiquitous in the environment, and most reported infant cases have been attributed to contaminated powdered infant formula (powdered formula) or breast milk that was expressed using contaminated breast pump equipment (1-3). Previous investigations of cases and outbreaks have identified C. sakazakii in opened powdered formula, breast pump parts, environmental surfaces in the home, and, rarely, in unopened powdered formula and formula manufacturing facilities (2,4-6). This report describes two infants with C. sakazakii meningitis reported to CDC in September 2021 and February 2022. CDC used whole genome sequencing (WGS) analysis to link one case to contaminated opened powdered formula from the patient's home and the other to contaminated breast pump equipment. These cases highlight the importance of expanding awareness about C. sakazakii infections in infants, safe preparation and storage of powdered formula, proper cleaning and sanitizing of breast pump equipment, and using WGS as a tool for C. sakazakii investigations.

Predicting Food Sources of Listeria monocytogenes Based on Genomic Profiling Using Random Forest Model.

Listeria monocytogenes can cause severe foodborne illness, including miscarriage during pregnancy or death in newborn infants. When outbreaks of L. monocytogenes illness occur, it may be possible to determine the food source of the outbreak. However, most reported L. monocytogenes illnesses do not occur as part of a recognized outbreak and most of the time the food source of sporadic L. monocytogenes illness in people cannot be determined. In the United States, L. monocytogenes isolates from patients, foods, and environments are routinely sequenced and analyzed by whole genome multilocus sequence typing (wgMLST) for outbreak detection by PulseNet, the national molecular surveillance system for foodborne illnesses. We investigated whether machine learning approaches applied to wgMLST allele call data could assist in attribution analysis of food source of L. monocytogenes isolates. We compiled isolates with a known source from five food categories (dairy, fruit, meat, seafood, and vegetable) using the metadata of L. monocytogenes isolates in PulseNet, deduplicated closely genetically related isolates, and developed random forest models to predict the food sources of isolates. Prediction accuracy of the final model varied across the food categories; it was highest for meat (65%), followed by fruit (45%), vegetable (45%), dairy (44%), and seafood (37%); overall accuracy was 49%, compared with the naive prediction accuracy of 28%. Our results show that random forest can be used to capture genetically complex features of high-resolution wgMLST for attribution of isolates to their sources.

Clinical and Laboratory Findings in Patients With Potential Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Reinfection, May-July 2020.

BACKGROUND: We investigated patients with potential severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reinfection in the United States during May-July 2020. METHODS: We conducted case finding for patients with potential SARS-CoV-2 reinfection through the Emerging Infections Network. Cases reported were screened for laboratory and clinical findings of potential reinfection followed by requests for medical records and laboratory specimens. Available medical records were abstracted to characterize patient demographics, comorbidities, clinical course, and laboratory test results. Submitted specimens underwent further testing, including reverse transcription polymerase chain reaction (RT-PCR), viral culture, whole genome sequencing, subgenomic RNA PCR, and testing for anti-SARS-CoV-2 total antibody. RESULTS: Among 73 potential reinfection patients with available records, 30 patients had recurrent coronavirus disease 2019 (COVID-19) symptoms explained by alternative diagnoses with concurrent SARS-CoV-2 positive RT-PCR, 24 patients remained asymptomatic after recovery but had recurrent or persistent RT-PCR, and 19 patients had recurrent COVID-19 symptoms with concurrent SARS-CoV-2 positive RT-PCR but no alternative diagnoses. These 19 patients had symptom recurrence a median of 57 days after initial symptom onset (interquartile range: 47-76). Six of these patients had paired specimens available for further testing, but none had laboratory findings confirming reinfections. Testing of an additional 3 patients with recurrent symptoms and alternative diagnoses also did not confirm reinfection. CONCLUSIONS: We did not confirm SARS-CoV-2 reinfection within 90 days of the initial infection based on the clinical and laboratory characteristics of cases in this investigation. Our findings support current Centers for Disease Control and Prevention (CDC) guidance around quarantine and testing for patients who have recovered from COVID-19.

Implications of Mobile Genetic Elements for Salmonella enterica Single-Nucleotide Polymorphism Subtyping and Source Tracking Investigations.

Single-nucleotide polymorphisms (SNPs) are widely used for whole-genome sequencing (WGS)-based subtyping of foodborne pathogens in outbreak and source tracking investigations. Mobile genetic elements (MGEs) are commonly present in bacterial genomes and may affect SNP subtyping results if their evolutionary history and dynamics differ from that of the bacterial chromosomes. Using Salmonella enterica as a model organism, we surveyed major categories of MGEs, including plasmids, phages, insertion sequences, integrons, and integrative and conjugative elements (ICEs), in 990 genomes representing 21 major serotypes of S. enterica We evaluated whether plasmids and chromosomal MGEs affect SNP subtyping with 9 outbreak clusters of different serotypes found in the United States in 2018. The median total length of chromosomal MGEs accounted for 2.5% of a typical S. enterica chromosome. Of the 990 analyzed S. enterica isolates, 68.9% contained at least one assembled plasmid sequence. The median total length of assembled plasmids in these isolates was 93,671 bp. Plasmids that carry high densities of SNPs were found to substantially affect both SNP phylogenies and SNP distances among closely related isolates if they were present in the reference genome for SNP subtyping. In comparison, chromosomal MGEs were found to have limited impact on SNP subtyping. We recommend the identification of plasmid sequences in the reference genome and the exclusion of plasmid-borne SNPs from SNP subtyping analysis.IMPORTANCE Despite increasingly routine use of WGS and SNP subtyping in outbreak and source tracking investigations, whether and how MGEs affect SNP subtyping has not been thoroughly investigated. Besides chromosomal MGEs, plasmids are frequently entangled in draft genome assemblies and yet to be assessed for their impact on SNP subtyping. This study provides evidence-based guidance on the treatment of MGEs in SNP analysis for Salmonella to infer phylogenetic relationship and SNP distance between isolates.

Whole Genome and Core Genome Multilocus Sequence Typing and Single Nucleotide Polymorphism Analyses of Listeria monocytogenes Isolates Associated with an Outbreak Linked to Cheese, United States, 2013.

Epidemiological findings of a listeriosis outbreak in 2013 implicated Hispanic-style cheese produced by company A, and pulsed-field gel electrophoresis (PFGE) and whole genome sequencing (WGS) were performed on clinical isolates and representative isolates collected from company A cheese and environmental samples during the investigation. The results strengthened the evidence for cheese as the vehicle. Surveillance sampling and WGS 3 months later revealed that the equipment purchased by company B from company A yielded an environmental isolate highly similar to all outbreak isolates. The whole genome and core genome multilocus sequence typing and single nucleotide polymorphism (SNP) analyses results were compared to demonstrate the maximum discriminatory power obtained by using multiple analyses, which were needed to differentiate outbreak-associated isolates from a PFGE-indistinguishable isolate collected in a nonimplicated food source in 2012. This unrelated isolate differed from the outbreak isolates by only 7 to 14 SNPs, and as a result, the minimum spanning tree from the whole genome analyses and certain variant calling approach and phylogenetic algorithm for core genome-based analyses could not provide differentiation between unrelated isolates. Our data also suggest that SNP/allele counts should always be combined with WGS clustering analysis generated by phylogenetically meaningful algorithms on a sufficient number of isolates, and the SNP/allele threshold alone does not provide sufficient evidence to delineate an outbreak. The putative prophages were conserved across all the outbreak isolates. All outbreak isolates belonged to clonal complex 5 and serotype 1/2b and had an identical inlA sequence which did not have premature stop codons.IMPORTANCE In this outbreak, multiple analytical approaches were used for maximum discriminatory power. A PFGE-matched, epidemiologically unrelated isolate had high genetic similarity to the outbreak-associated isolates, with as few as 7 SNP differences. Therefore, the SNP/allele threshold should not be used as the only evidence to define the scope of an outbreak. It is critical that the SNP/allele counts be complemented by WGS clustering analysis generated by phylogenetically meaningful algorithms to distinguish outbreak-associated isolates from epidemiologically unrelated isolates. Careful selection of a variant calling approach and phylogenetic algorithm is critical for core-genome-based analyses. The whole-genome-based analyses were able to construct the highly resolved phylogeny needed to support the findings of the outbreak investigation. Ultimately, epidemiologic evidence and multiple WGS analyses should be combined to increase confidence levels during outbreak investigations.

Implementation of Nationwide Real-time Whole-genome Sequencing to Enhance Listeriosis Outbreak Detection and Investigation.

Listeria monocytogenes (Lm) causes severe foodborne illness (listeriosis). Previous molecular subtyping methods, such as pulsed-field gel electrophoresis (PFGE), were critical in detecting outbreaks that led to food safety improvements and declining incidence, but PFGE provides limited genetic resolution. A multiagency collaboration began performing real-time, whole-genome sequencing (WGS) on all US Lm isolates from patients, food, and the environment in September 2013, posting sequencing data into a public repository. Compared with the year before the project began, WGS, combined with epidemiologic and product trace-back data, detected more listeriosis clusters and solved more outbreaks (2 outbreaks in pre-WGS year, 5 in WGS year 1, and 9 in year 2). Whole-genome multilocus sequence typing and single nucleotide polymorphism analyses provided equivalent phylogenetic relationships relevant to investigations; results were most useful when interpreted in context of epidemiological data. WGS has transformed listeriosis outbreak surveillance and is being implemented for other foodborne pathogens.

Two Listeria monocytogenes Pseudo-outbreaks Caused by Contaminated Laboratory Culture Media.

Listeriosis is a serious foodborne infection that disproportionately affects elderly adults, pregnant women, newborns, and immunocompromised individuals. Diagnosis is made by culturing Listeria monocytogenes from sterile body fluids or from products of conception. This report describes the investigations of two listeriosis pseudo-outbreaks caused by contaminated laboratory media made from sheep blood.

Determination of Evolutionary Relationships of Outbreak-Associated Listeria monocytogenes Strains of Serotypes 1/2a and 1/2b by Whole-Genome Sequencing.

We used whole-genome sequencing to determine evolutionary relationships among 20 outbreak-associated clinical isolates of Listeria monocytogenes serotypes 1/2a and 1/2b. Isolates from 6 of 11 outbreaks fell outside the clonal groups or "epidemic clones" that have been previously associated with outbreaks, suggesting that epidemic potential may be widespread in L. monocytogenes and is not limited to the recognized epidemic clones. Pairwise comparisons between epidemiologically related isolates within clonal complexes showed that genome-level variation differed by 2 orders of magnitude between different comparisons, and the distribution of point mutations (core versus accessory genome) also varied. In addition, genetic divergence between one closely related pair of isolates from a single outbreak was driven primarily by changes in phage regions. The evolutionary analysis showed that the changes could be attributed to horizontal gene transfer; members of the diverse bacterial community found in the production facility could have served as the source of novel genetic material at some point in the production chain. The results raise the question of how to best utilize information contained within the accessory genome in outbreak investigations. The full magnitude and complexity of genetic changes revealed by genome sequencing could not be discerned from traditional subtyping methods, and the results demonstrate the challenges of interpreting genetic variation among isolates recovered from a single outbreak. Epidemiological information remains critical for proper interpretation of nucleotide and structural diversity among isolates recovered during outbreaks and will remain so until we understand more about how various population histories influence genetic variation.

Characterization of a Nonagglutinating Toxigenic Vibrio cholerae Isolate.

Toxigenic Vibrio cholerae serogroup O1 is the etiologic agent of the disease cholera, and strains of this serogroup are responsible for pandemics. A few other serogroups have been found to carry cholera toxin genes-most notably, O139, O75, and O141-and public health surveillance in the United States is focused on these four serogroups. A toxigenic isolate was recovered from a case of vibriosis from Texas in 2008. This isolate did not agglutinate with any of the four different serogroups' antisera (O1, O139, O75, or O141) routinely used in phenotypic testing and did not display a rough phenotype. We investigated several hypotheses that might explain the recovery of this potential nonagglutinating (NAG) strain using whole-genome sequencing analysis and phylogenetic methods. The NAG strain formed a monophyletic cluster with O141 strains in a whole-genome phylogeny. Furthermore, a phylogeny of ctxAB and tcpA sequences revealed that the sequences from the NAG strain also formed a monophyletic cluster with toxigenic U.S. Gulf Coast (USGC) strains (O1, O75, and O141) that were recovered from vibriosis cases associated with exposures to Gulf Coast waters. A comparison of the NAG whole-genome sequence showed that the O-antigen-determining region of the NAG strain was closely related to those of O141 strains, and specific mutations were likely responsible for the inability to agglutinate. This work shows the utility of whole-genome sequence analysis tools for characterization of an atypical clinical isolate of V. cholerae originating from a USGC state. IMPORTANCE Clinical cases of vibriosis are on the rise due to climate events and ocean warming (1, 2), and increased surveillance of toxigenic Vibrio cholerae strains is now more crucial than ever. While traditional phenotyping using antisera against O1 and O139 is useful for monitoring currently circulating strains with pandemic or epidemic potential, reagents are limited for non-O1/non-O139 strains. With the increased use of next-generation sequencing technologies, analysis of less well-characterized strains and O-antigen regions is possible. The framework for advanced molecular analysis of O-antigen-determining regions presented herein will be useful in the absence of reagents for serotyping. Furthermore, molecular analyses based on whole-genome sequence data and using phylogenetic methods will help characterize both historical and novel strains of clinical importance. Closely monitoring emerging mutations and trends will improve our understanding of the epidemic potential of Vibrio cholerae to anticipate and rapidly respond to future public health emergencies.

Rapid identification of enteric bacteria from whole genome sequences using average nucleotide identity metrics.

Identification of enteric bacteria species by whole genome sequence (WGS) analysis requires a rapid and an easily standardized approach. We leveraged the principles of average nucleotide identity using MUMmer (ANIm) software, which calculates the percent bases aligned between two bacterial genomes and their corresponding ANI values, to set threshold values for determining species consistent with the conventional identification methods of known species. The performance of species identification was evaluated using two datasets: the Reference Genome Dataset v2 (RGDv2), consisting of 43 enteric genome assemblies representing 32 species, and the Test Genome Dataset (TGDv1), comprising 454 genome assemblies which is designed to represent all species needed to query for identification, as well as rare and closely related species. The RGDv2 contains six Campylobacter spp., three Escherichia/Shigella spp., one Grimontia hollisae, six Listeria spp., one Photobacterium damselae, two Salmonella spp., and thirteen Vibrio spp., while the TGDv1 contains 454 enteric bacterial genomes representing 42 different species. The analysis showed that, when a standard minimum of 70% genome bases alignment existed, the ANI threshold values determined for these species were ≥95 for Escherichia/Shigella and Vibrio species, ≥93% for Salmonella species, and ≥92% for Campylobacter and Listeria species. Using these metrics, the RGDv2 accurately classified all validation strains in TGDv1 at the species level, which is consistent with the classification based on previous gold standard methods.

Genome sequences for six Rhodanobacter strains, isolated from soils and the terrestrial subsurface, with variable denitrification capabilities.

We report the first genome sequences for six strains of Rhodanobacter species isolated from a variety of soil and subsurface environments. Three of these strains are capable of complete denitrification and three others are not. However, all six strains contain most of the genes required for the respiration of nitrate to gaseous nitrogen. The nondenitrifying members of the genus lack only the gene for nitrate reduction, the first step in the full denitrification pathway. The data suggest that the environmental role of bacteria from the genus Rhodanobacter should be reevaluated.

Genomic basis of a polyagglutinating isolate of Neisseria meningitidis.

Containment strategies for outbreaks of invasive Neisseria meningitidis disease are informed by serogroup assays that characterize the polysaccharide capsule. We sought to uncover the genomic basis of conflicting serogroup assay results for an isolate (M16917) from a patient with acute meningococcal disease. To this end, we characterized the complete genome sequence of the M16917 isolate and performed a variety of comparative sequence analyses against N. meningitidis reference genome sequences of known serogroups. Multilocus sequence typing and whole-genome sequence comparison revealed that M16917 is a member of the ST-11 sequence group, which is most often associated with serogroup C. However, sequence similarity comparisons and phylogenetic analysis showed that the serogroup diagnostic capsule polymerase gene (synD) of M16917 belongs to serogroup B. These results suggest that a capsule-switching event occurred based on homologous recombination at or around the capsule locus of M16917. Detailed analysis of this locus uncovered the locations of recombination breakpoints in the M16917 genome sequence, which led to the introduction of an ∼2-kb serogroup B sequence cassette into the serogroup C genomic background. Since there is no currently available vaccine for serogroup B strains of N. meningitidis, this kind capsule-switching event could have public health relevance as a vaccine escape mutant.

Haemophilus haemolyticus isolates causing clinical disease.

We report seven cases of Haemophilus haemolyticus invasive disease detected in the United States, which were previously misidentified as nontypeable Haemophilus influenzae. All cases had different symptoms and presentations. Our study suggests that a testing scheme that includes reliable PCR assays and standard microbiological methods should be used in order to improve H. haemolyticus identification.

Evaluation of new biomarker genes for differentiating Haemophilus influenzae from Haemophilus haemolyticus.

PCR detecting the protein D (hpd) and fuculose kinase (fucK) genes showed high sensitivity and specificity for identifying Haemophilus influenzae and differentiating it from H. haemolyticus. Phylogenetic analysis using the 16S rRNA gene demonstrated two distinct groups for H. influenzae and H. haemolyticus.

Genome Sequences of Hemolytic and Nonhemolytic Listeria innocua Strains from Human, Food, and Environmental Sources.

This report describes genome sequences for nine Listeria innocua strains that varied in hemolytic phenotypes on sheep blood agar. All strains were sequenced using Pacific Biosciences (PacBio) single-molecule real-time (SMRT) chemistry; overall, the average read length of these sequences was 2,869,880 bp, with an average GC content of 37%.

Software testing in microbial bioinformatics: a call to action.

Computational algorithms have become an essential component of research, with great efforts by the scientific community to raise standards on development and distribution of code. Despite these efforts, sustainability and reproducibility are major issues since continued validation through software testing is still not a widely adopted practice. Here, we report seven recommendations that help researchers implement software testing in microbial bioinformatics. We have developed these recommendations based on our experience from a collaborative hackathon organised prior to the American Society for Microbiology Next Generation Sequencing (ASM NGS) 2020 conference. We also present a repository hosting examples and guidelines for testing, available from https://github.com/microbinfie-hackathon2020/CSIS.

Molecular characterization of circulating Salmonella Typhi strains in an urban informal settlement in Kenya.

A high burden of Salmonella enterica subspecies enterica serovar Typhi (S. Typhi) bacteremia has been reported from urban informal settlements in sub-Saharan Africa, yet little is known about the introduction of these strains to the region. Understanding regional differences in the predominant strains of S. Typhi can provide insight into the genomic epidemiology. We genetically characterized 310 S. Typhi isolates from typhoid fever surveillance conducted over a 12-year period (2007-2019) in Kibera, an urban informal settlement in Nairobi, Kenya, to assess the circulating strains, their antimicrobial resistance attributes, and how they relate to global S. Typhi isolates. Whole genome multi-locus sequence typing (wgMLST) identified 4 clades, with up to 303 pairwise allelic differences. The identified genotypes correlated with wgMLST clades. The predominant clade contained 290 (93.5%) isolates with a median of 14 allele differences (range 0-52) and consisted entirely of genotypes 4.3.1.1 and 4.3.1.2. Resistance determinants were identified exclusively in the predominant clade. Determinants associated with resistance to aminoglycosides were observed in 245 isolates (79.0%), sulphonamide in 243 isolates (78.4%), trimethoprim in 247 isolates (79.7%), tetracycline in 224 isolates (72.3%), chloramphenicol in 247 isolates (79.6%), ß-lactams in 239 isolates (77.1%) and quinolones in 62 isolates (20.0%). Multidrug resistance (MDR) determinants (defined as determinants conferring resistance to ampicillin, chloramphenicol and cotrimoxazole) were found in 235 (75.8%) isolates. The prevalence of MDR associated genes was similar throughout the study period (2007-2012: 203, 76.3% vs 2013-2019: 32, 72.7%; Fisher's Exact Test: P = 0.5478, while the proportion of isolates harboring quinolone resistance determinants increased (2007-2012: 42, 15.8% and 2013-2019: 20, 45.5%; Fisher's Exact Test: P<0.0001) following a decline in S. Typhi in Kibera. Some isolates (49, 15.8%) harbored both MDR and quinolone resistance determinants. There were no determinants associated with resistance to cephalosporins or azithromycin detected among the isolates sequenced in this study. Plasmid markers were only identified in the main clade including IncHI1A and IncHI1B(R27) in 226 (72.9%) isolates, and IncQ1 in 238 (76.8%) isolates. Molecular clock analysis of global typhoid isolates and isolates from Kibera suggests that genotype 4.3.1 has been introduced multiple times in Kibera. Several genomes from Kibera formed a clade with genomes from Kenya, Malawi, South Africa, and Tanzania. The most recent common ancestor (MRCA) for these isolates was from around 1997. Another isolate from Kibera grouped with several isolates from Uganda, sharing a common ancestor from around 2009. In summary, S. Typhi in Kibera belong to four wgMLST clades one of which is frequently associated with MDR genes and this poses a challenge in treatment and control.

Benchmark datasets for SARS-CoV-2 surveillance bioinformatics.

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of coronavirus disease 2019 (COVID-19), has spread globally and is being surveilled with an international genome sequencing effort. Surveillance consists of sample acquisition, library preparation, and whole genome sequencing. This has necessitated a classification scheme detailing Variants of Concern (VOC) and Variants of Interest (VOI), and the rapid expansion of bioinformatics tools for sequence analysis. These bioinformatic tools are means for major actionable results: maintaining quality assurance and checks, defining population structure, performing genomic epidemiology, and inferring lineage to allow reliable and actionable identification and classification. Additionally, the pandemic has required public health laboratories to reach high throughput proficiency in sequencing library preparation and downstream data analysis rapidly. However, both processes can be limited by a lack of a standardized sequence dataset. Methods: We identified six SARS-CoV-2 sequence datasets from recent publications, public databases and internal resources. In addition, we created a method to mine public databases to identify representative genomes for these datasets. Using this novel method, we identified several genomes as either VOI/VOC representatives or non-VOI/VOC representatives. To describe each dataset, we utilized a previously published datasets format, which describes accession information and whole dataset information. Additionally, a script from the same publication has been enhanced to download and verify all data from this study. Results: The benchmark datasets focus on the two most widely used sequencing platforms: long read sequencing data from the Oxford Nanopore Technologies platform and short read sequencing data from the Illumina platform. There are six datasets: three were derived from recent publications; two were derived from data mining public databases to answer common questions not covered by published datasets; one unique dataset representing common sequence failures was obtained by rigorously scrutinizing data that did not pass quality checks. The dataset summary table, data mining script and quality control (QC) values for all sequence data are publicly available on GitHub: https://github.com/CDCgov/datasets-sars-cov-2. Discussion: The datasets presented here were generated to help public health laboratories build sequencing and bioinformatics capacity, benchmark different workflows and pipelines, and calibrate QC thresholds to ensure sequencing quality. Together, improvements in these areas support accurate and timely outbreak investigation and surveillance, providing actionable data for pandemic management. Furthermore, these publicly available and standardized benchmark data will facilitate the development and adjudication of new pipelines.