Local-scale phylodynamics reveal differential community impact of SARS-CoV-2 in a metropolitan US county.

SARS-CoV-2 transmission is largely driven by heterogeneous dynamics at a local scale, leaving local health departments to design interventions with limited information. We analyzed SARS-CoV-2 genomes sampled between February 2020 and March 2022 jointly with epidemiological and cell phone mobility data to investigate fine scale spatiotemporal SARS-CoV-2 transmission dynamics in King County, Washington, a diverse, metropolitan US county. We applied an approximate structured coalescent approach to model transmission within and between North King County and South King County alongside the rate of outside introductions into the county. Our phylodynamic analyses reveal that following stay-at-home orders, the epidemic trajectories of North and South King County began to diverge. We find that South King County consistently had more reported and estimated cases, COVID-19 hospitalizations, and longer persistence of local viral transmission when compared to North King County, where viral importations from outside drove a larger proportion of new cases. Using mobility and demographic data, we also find that South King County experienced a more modest and less sustained reduction in mobility following stay-at-home orders than North King County, while also bearing more socioeconomic inequities that might contribute to a disproportionate burden of SARS-CoV-2 transmission. Overall, our findings suggest a role for local-scale phylodynamics in understanding the heterogeneous transmission landscape.

Sentinel Surveillance System Implementation and Evaluation for SARS-CoV-2 Genomic Data, Washington, USA, 2020-2021.

Genomic data provides useful information for public health practice, particularly when combined with epidemiologic data. However, sampling bias is a concern because inferences from nonrandom data can be misleading. In March 2021, the Washington State Department of Health, USA, partnered with submitting and sequencing laboratories to establish sentinel surveillance for SARS-CoV-2 genomic data. We analyzed available genomic and epidemiologic data during presentinel and sentinel periods to assess representativeness and timeliness of availability. Genomic data during the presentinel period was largely unrepresentative of all COVID-19 cases. Data available during the sentinel period improved representativeness for age, death from COVID-19, outbreak association, long-term care facility-affiliated status, and geographic coverage; timeliness of data availability and captured viral diversity also improved. Hospitalized cases were underrepresented, indicating a need to increase inpatient sampling. Our analysis emphasizes the need to understand and quantify sampling bias in phylogenetic studies and continue evaluation and improvement of public health surveillance systems.

Local-Scale phylodynamics reveal differential community impact of SARS-CoV-2 in metropolitan US county.

SARS-CoV-2 transmission is largely driven by heterogeneous dynamics at a local scale, leaving local health departments to design interventions with limited information. We analyzed SARS-CoV-2 genomes sampled between February 2020 and March 2022 jointly with epidemiological and cell phone mobility data to investigate fine scale spatiotemporal SARS-CoV-2 transmission dynamics in King County, Washington, a diverse, metropolitan US county. We applied an approximate structured coalescent approach to model transmission within and between North King County and South King County alongside the rate of outside introductions into the county. Our phylodynamic analyses reveal that following stay-at-home orders, the epidemic trajectories of North and South King County began to diverge. We find that South King County consistently had more reported and estimated cases, COVID-19 hospitalizations, and longer persistence of local viral transmission when compared to North King County, where viral importations from outside drove a larger proportion of new cases. Using mobility and demographic data, we also find that South King County experienced a more modest and less sustained reduction in mobility following stay-at-home orders than North King County, while also bearing more socioeconomic inequities that might contribute to a disproportionate burden of SARS-CoV-2 transmission. Overall, our findings suggest a role for local-scale phylodynamics in understanding the heterogeneous transmission landscape.

Associations Between Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Variants and Risk of Coronavirus Disease 2019 (COVID-19) Hospitalization Among Confirmed Cases in Washington State: A Retrospective Cohort Study.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic is dominated by variant viruses; the resulting impact on disease severity remains unclear. Using a retrospective cohort study, we assessed the hospitalization risk following infection with 7 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants. METHODS: Our study includes individuals with positive SARS-CoV-2 reverse transcription polymerase chain reaction (RT-PCR) in the Washington Disease Reporting System with available viral genome data, from 1 December 2020 to 14 January 2022. The analysis was restricted to cases with specimens collected through sentinel surveillance. Using a Cox proportional hazards model with mixed effects, we estimated hazard ratios (HR) for hospitalization risk following infection with a variant, adjusting for age, sex, calendar week, and vaccination. RESULTS: In total, 58 848 cases were sequenced through sentinel surveillance, of which 1705 (2.9%) were hospitalized due to COVID-19. Higher hospitalization risk was found for infections with Gamma (HR 3.20, 95% confidence interval [CI] 2.40-4.26), Beta (HR 2.85, 95% CI 1.56-5.23), Delta (HR 2.28 95% CI 1.56-3.34), or Alpha (HR 1.64, 95% CI 1.29-2.07) compared to infections with ancestral lineages; Omicron (HR 0.92, 95% CI .56-1.52) showed no significant difference in risk. Following Alpha, Gamma, or Delta infection, unvaccinated patients show higher hospitalization risk, while vaccinated patients show no significant difference in risk, both compared to unvaccinated, ancestral lineage cases. Hospitalization risk following Omicron infection is lower with vaccination. CONCLUSIONS: Infection with Alpha, Gamma, or Delta results in a higher hospitalization risk, with vaccination attenuating that risk. Our findings support hospital preparedness, vaccination, and genomic surveillance.

Associations between SARS-CoV-2 variants and risk of COVID-19 hospitalization among confirmed cases in Washington State: a retrospective cohort study.

BACKGROUND: The COVID-19 pandemic is dominated by variant viruses; the resulting impact on disease severity remains unclear. Using a retrospective cohort study, we assessed the hospitalization risk following infection with seven SARS-CoV-2 variants. METHODS: Our study includes individuals with positive SARS-CoV-2 RT-PCR in the Washington Disease Reporting System with available viral genome data, from December 1, 2020 to January 14, 2022. The analysis was restricted to cases with specimens collected through sentinel surveillance. Using a Cox proportional hazards model with mixed effects, we estimated hazard ratios (HR) for hospitalization risk following infection with a variant, adjusting for age, sex, calendar week, and vaccination. FINDINGS: 58,848 cases were sequenced through sentinel surveillance, of which 1705 (2.9%) were hospitalized due to COVID-19. Higher hospitalization risk was found for infections with Gamma (HR 3.20, 95%CI 2.40-4.26), Beta (HR 2.85, 95%CI 1.56-5.23), Delta (HR 2.28 95%CI 1.56-3.34) or Alpha (HR 1.64, 95%CI 1.29-2.07) compared to infections with ancestral lineages; Omicron (HR 0.92, 95%CI 0.56-1.52) showed no significant difference in risk. Following Alpha, Gamma, or Delta infection, unvaccinated patients show higher hospitalization risk, while vaccinated patients show no significant difference in risk, both compared to unvaccinated, ancestral lineage cases. Hospitalization risk following Omicron infection is lower with vaccination. CONCLUSION: Infection with Alpha, Gamma, or Delta results in a higher hospitalization risk, with vaccination attenuating that risk. Our findings support hospital preparedness, vaccination, and genomic surveillance. SUMMARY: Hospitalization risk following infection with SARS-CoV-2 variant remains unclear. We find a higher hospitalization risk in cases infected with Alpha, Beta, Gamma, and Delta, but not Omicron, with vaccination lowering risk. Our findings support hospital preparedness, vaccination, and genomic surveillance.

Viral genomes reveal patterns of the SARS-CoV-2 outbreak in Washington State.

The rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has gravely affected societies around the world. Outbreaks in different parts of the globe have been shaped by repeated introductions of new viral lineages and subsequent local transmission of those lineages. Here, we sequenced 3940 SARS-CoV-2 viral genomes from Washington State (USA) to characterize how the spread of SARS-CoV-2 in Washington State in early 2020 was shaped by differences in timing of mitigation strategies across counties and by repeated introductions of viral lineages into the state. In addition, we show that the increase in frequency of a potentially more transmissible viral variant (614G) over time can potentially be explained by regional mobility differences and multiple introductions of 614G but not the other variant (614D) into the state. At an individual level, we observed evidence of higher viral loads in patients infected with the 614G variant. However, using clinical records data, we did not find any evidence that the 614G variant affects clinical severity or patient outcomes. Overall, this suggests that with regard to D614G, the behavior of individuals has been more important in shaping the course of the pandemic in Washington State than this variant of the virus.

Viral genomes reveal patterns of the SARS-CoV-2 outbreak in Washington State.

The rapid spread of SARS-CoV-2 has gravely impacted societies around the world. Outbreaks in different parts of the globe are shaped by repeated introductions of new lineages and subsequent local transmission of those lineages. Here, we sequenced 3940 SARS-CoV-2 viral genomes from Washington State to characterize how the spread of SARS-CoV-2 in Washington State (USA) was shaped by differences in timing of mitigation strategies across counties, as well as by repeated introductions of viral lineages into the state. Additionally, we show that the increase in frequency of a potentially more transmissible viral variant (614G) over time can potentially be explained by regional mobility differences and multiple introductions of 614G, but not the other variant (614D) into the state. At an individual level, we see evidence of higher viral loads in patients infected with the 614G variant. However, using clinical records data, we do not find any evidence that the 614G variant impacts clinical severity or patient outcomes. Overall, this suggests that at least to date, the behavior of individuals has been more important in shaping the course of the pandemic than changes in the virus.

Cryptic transmission of SARS-CoV-2 in Washington state.

After its emergence in Wuhan, China, in late November or early December 2019, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus rapidly spread globally. Genome sequencing of SARS-CoV-2 allows the reconstruction of its transmission history, although this is contingent on sampling. We analyzed 453 SARS-CoV-2 genomes collected between 20 February and 15 March 2020 from infected patients in Washington state in the United States. We find that most SARS-CoV-2 infections sampled during this time derive from a single introduction in late January or early February 2020, which subsequently spread locally before active community surveillance was implemented.

Cryptic transmission of SARS-CoV-2 in Washington State.

Following its emergence in Wuhan, China, in late November or early December 2019, the SARS-CoV-2 virus has rapidly spread throughout the world. On March 11, 2020, the World Health Organization declared Coronavirus Disease 2019 (COVID-19) a pandemic. Genome sequencing of SARS-CoV-2 strains allows for the reconstruction of transmission history connecting these infections. Here, we analyze 346 SARS-CoV-2 genomes from samples collected between 20 February and 15 March 2020 from infected patients in Washington State, USA. We found that the large majority of SARS-CoV-2 infections sampled during this time frame appeared to have derived from a single introduction event into the state in late January or early February 2020 and subsequent local spread, strongly suggesting cryptic spread of COVID-19 during the months of January and February 2020, before active community surveillance was implemented. We estimate a common ancestor of this outbreak clade as occurring between 18 January and 9 February 2020. From genomic data, we estimate an exponential doubling between 2.4 and 5.1 days. These results highlight the need for large-scale community surveillance for SARS-CoV-2 introductions and spread and the power of pathogen genomics to inform epidemiological understanding.

Genomic characterization of the RH locus detects complex and novel structural variation in multi-ethnic cohorts.

PURPOSE: Rh antigens can provoke severe alloimmune reactions, particularly in high-risk transfusion contexts, such as sickle cell disease. Rh antigens are encoded by the paralogs, RHD and RHCE, located in one of the most complex genetic loci. Our goal was to characterize RH genetic variation in multi-ethnic cohorts, with the focus on detecting RH structural variation (SV). METHODS: We customized analytical methods to estimate paralog-specific copy number from next-generation sequencing (NGS) data. We applied these methods to clinically characterized samples, including four World Health Organization (WHO) genotyping references and 1135 Asian and Native American blood donors. Subsequently, we surveyed 1715 African American samples from the Jackson Heart Study. RESULTS: Most samples in each dataset exhibited SV. SV detection enabled prediction of the immunogenic RhD and RhC antigens in concordance (>99%) with serological phenotyping. RhC antigen expression was associated with exon 2 hybrid alleles (RHCE*CE-D(2)-CE). Clinically relevant exon 4-7 hybrid alleles (RHD*D-CE(4-7)-D) and exon 9 hybrid alleles (RHCE*CE-D(9)-CE) were prevalent in African Americans. CONCLUSION: This study shows custom NGS methods can accurately detect RH SV, and that SV is important to inform prediction of relevant RH alleles. Additionally, this study provides the first large NGS survey of RH alleles in African Americans.

Single suture craniosynostosis: Identification of rare variants in genes associated with syndromic forms.

We report RNA-Sequencing results on a cohort of patients with single suture craniosynostosis and demonstrate significant enrichment of heterozygous, rare, and damaging variants among key craniosynostosis-related genes. Genetic burden analysis identified a significant increase in damaging variants in ATR, EFNA4, ERF, MEGF8, SCARF2, and TGFBR2. Of 391 participants, 15% were found to have damaging and potentially causal variants in 29 genes. We observed transmission in 96% of the affected individuals, and thus penetrance, epigenetics, and oligogenic factors need to be considered when recommending genetic testing in patients with nonsyndromic craniosynostosis.

Analysis of exome sequencing data sets reveals structural variation in the coding region of ABO in individuals of African ancestry.

BACKGROUND: ABO is a blood group system of high clinical significance due to the prevalence of ABO variation that can cause major, potentially life-threatening, transfusion reactions. STUDY DESIGN AND METHODS: Using multiple large-scale next-generation sequence data sets, we demonstrate the application of read-depth approaches to discover previously unsuspected structural variation (SV) in the ABO gene in individuals of African ancestry. RESULTS: Our analysis of SV in the ABO gene across 6432 exomes reveals a partial deletion in the ABO gene in 32 individuals of African ancestry that predicts a novel O allele. CONCLUSION: Our study demonstrates the power that analyses of large-scale sequencing data, particularly data sets containing underrepresented populations, can provide in identifying novel SVs.

Hyporheic microbial community development is a sensitive indicator of metal contamination.

Accurate natural resource damage assessment necessitates monitoring organisms or communities that respond most sensitively to contaminants. Observational studies have demonstrated a correlation between fluvial heavy metal deposition and hyporheic microbial community structure. To establish a causal relationship between sediment metal content and the structure of colonizing bacterial communities, we performed a controlled field experiment River sediments of 1.75-2.36 mm in diameter with five different contaminant concentrations were collected from an environmental metal contamination gradient. Sediments were sterilized and then recolonized by incubation in the hyporheic zone of an uncontaminated river (i.e., a common garden experiment was performed). A significant correlation between hyporheic microbial community structure and heavy metal contamination (R2 = 0.81) was observed. The abundance of two phylogenetic groups was highly correlated with the level of heavy metal contamination (Group I, R2 = 0.96; Group III, R2 = 0.96, most closely affiliated with the alpha- and gamma-proteobacteria, respectively). Microbial community structural responses were detected at metal concentrations an order of magnitude lower than those previously reported to impact benthic macroinvertebrate communities. We conclude that hyporheic microbial communities could offer the most sensitive method for assessing natural resource damage in lotic ecosystems in response to fluvial heavy metal deposition.

Seasonal dynamics of shallow-hyporheic-zone microbial community structure along a heavy-metal contamination gradient.

Heavy metals contaminate numerous freshwater streams and rivers worldwide. Previous work by this group demonstrated a relationship between the structure of hyporheic microbial communities and the fluvial deposition of heavy metals along a contamination gradient during the fall season. Seasonal variation has been documented in microbial communities in numerous terrestrial and aquatic environments, including the hyporheic zone. The current study was designed to assess whether relationships between hyporheic microbial community structure and heavy-metal contamination vary seasonally by monitoring community structure along a heavy-metal contamination gradient for more than a year. No relationship between total bacterial abundance and heavy metals was observed (R(2) = 0.02, P = 0.83). However, denaturing gradient gel electrophoresis pattern analysis indicated a strong and consistent linear relationship between the difference in microbial community composition (populations present) and the difference in the heavy metal content of hyporheic sediments throughout the year (R(2) = 0.58, P < 0.001). Correlations between heavy-metal contamination and the abundance of four specific phylogenetic groups (most closely related to the alpha, beta, and gamma-proteobacteria and cyanobacteria) were apparent only during the fall and early winter, when the majority of organic matter is deposited into regional streams. These seasonal data suggest that the abundance of susceptible populations responds to heavy metals primarily during seasons when the potential for growth is highest.

Differences in hyporheic-zone microbial community structure along a heavy-metal contamination gradient.

The hyporheic zone of a river is nonphotic, has steep chemical and redox gradients, and has a heterotrophic food web based on the consumption of organic carbon entrained from downwelling surface water or from upwelling groundwater. The microbial communities in the hyporheic zone are an important component of these heterotrophic food webs and perform essential functions in lotic ecosystems. Using a suite of methods (denaturing gradient gel electrophoresis, 16S rRNA phylogeny, phospholipid fatty acid analysis, direct microscopic enumeration, and quantitative PCR), we compared the microbial communities inhabiting the hyporheic zone of six different river sites that encompass a wide range of sediment metal loads resulting from large base-metal mining activity in the region. There was no correlation between sediment metal content and the total hyporheic microbial biomass present within each site. However, microbial community structure showed a significant linear relationship with the sediment metal loads. The abundances of four phylogenetic groups (groups I, II, III, and IV) most closely related to alpha-, beta-, and gamma-proteobacteria and the cyanobacteria, respectively, were determined. The sediment metal content gradient was positively correlated with group III abundance and negatively correlated with group II abundance. No correlation was apparent with regard to group I or IV abundance. This is the first documentation of a relationship between fluvially deposited heavy-metal contamination and hyporheic microbial community structure. The information presented here may be useful in predicting long-term effects of heavy-metal contamination in streams and provides a basis for further studies of metal effects on hyporheic microbial communities.