Abstract In less than nine months, the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) killed over a million people, including >25,000 in New York City (NYC) alone. The COVID-19 pandemic caused by SARS-CoV-2 highlights clinical needs to detect infection, track strain evolution, and identify biomarkers of disease course. To address these challenges, we designed a fast (30-minute) colorimetric test (LAMP) for SARS-CoV-2 infection from naso/oropharyngeal swabs and a large-scale shotgun metatranscriptomics platform (total-RNA-seq) for host, viral, and microbial profiling. We applied these methods to clinical specimens gathered from 669 patients in New York City during the first two months of the outbreak, yielding a broad molecular portrait of the emerging COVID-19 disease. We find significant enrichment of a NYC-distinctive clade of the virus (20C), as well as host responses in interferon, ACE, hematological, and olfaction pathways. In addition, we use 50,821 patient records to find that renin–angiotensin–aldosterone system inhibitors have a protective effect for severe COVID-19 outcomes, unlike similar drugs. Finally, spatial transcriptomic data from COVID-19 patient autopsy tissues reveal distinct ACE2 expression loci, with macrophage and neutrophil infiltration in the lungs. These findings can inform public health and may help develop and drive SARS-CoV-2 diagnostic, prevention, and treatment strategies.
Abstract Dengue fever is a major tropical disease transmitted by Aedes mosquitoes. Dengue affects more than 120 countries with highly variable year to year infection rates. Despite high variability, dengue has a clear relationship to climate factors and human demography. Global trends to higher temperatures and greater disorderly urban development are increasing the scale and scope of dengue risk. Dengue has complex human immunity with 4 known serotypes that make multiple infections possible. Accurate forecasting of dengue fever would allow for appropriate interventions and improved public health outcomes. We demonstrate, GeoSeeq Dengue , a forecasting model for dengue fever in Brazil. GeoSeeq Dengue predicts dengue outbreaks monthly in 5,570 Brazilian municipalities at 1, 3, and 6 months ahead of the outbreak. Model accuracy compares favorably to a historical baseline model, making it a promising model for informing public health response. We evaluate how different types of input variables effect model accuracy and explore how this model could be adapted to other countries. This model could inform public health responses to dengue including targeting vector control programs, public health education messaging, and the newly launched dengue vaccine rollout.
Introduction: Cystic fibrosis liver disease (CFLD) is the 3rd leading cause of death in cystic fibrosis (CF) patients, contributing to 2.5-5% of overall mortality. Onset of CFLD has been mostly described in pediatric populations, but with improving life expectancy, adult onset CFLD is increasingly recognized with an estimated prevalence of 2-37%. Criteria for the diagnosis of adult CFLD are lacking and inconsistent; with few studies evaluating noninvasive biomarkers and radiographic data for classification systems. Aims: To characterize adult CFLD and develop a set of novel criteria for the diagnosis of adult CFLD utilizing biochemical, clinical and radiographic markers in an adult CF cohort followed at the NIH Clinical Center for up to 37 years (yrs). Methods: Patients with CF were evaluated with hepatic biomarkers of inflammation, synthetic function, portal hypertension, radiologic imaging, and transient elastography (TE). Charts were extracted for clinical data. Utilizing these biomarkers along with TE, APRI, and FIB-4, criteria were defined for the diagnosis CFLD. Patients who met CFLD criteria were compared to CF patients without evidence of liver disease. Results: 36 patients with CF (33% F508 homozygous, 23% F508 heterozygous) were studied (65% males, 97% white). The median age of CF diagnosis was 11 yrs, and the median follow-up duration was 23 yrs (range 2 to 37). At the time of last follow-up (mean age=46 yrs), 11 (31%) had died (respiratory failure=3, infection=3, complications of transplantation=2, or other causes=3). 17 of 36 (47%) patients met criteria for CFLD (mean age of diagnosis=31 yrs). Patients with CFLD had significantly higher mean ALT (44.3 vs 28.1, p=0.01), direct bilirubin (0.2 vs 0.1, p =0.04), PT (14.4 vs 12.8, p=0.01), and APRI (0.6 vs 0.2, p=0.04) over the last year of follow-up. In the CFLD group, 4 patients had radiographic evidence of advanced liver disease and 1 patient had nodular regenerative hyperplasia and experienced hepatic decompensation. On longitudinal comparison, platelet counts significantly declined in the CFLD group (312 to 237 U/L, p=0.007) as compared to patients without CFLD (328 to 297 U/L, p=0.11). Conclusions: By evaluating non-invasive markers of liver disease, a novel criteria can be employed to identify adult CFLD. These biomarkers not only suggest that a second wave of liver disease exists in adult CF patients, but also that it may be more prevalent than previously described. Further evaluation of this diagnostic criteria in other CF cohorts should be performed to evaluate its utility in adult CFLD.
Abstract Background As the SARS-CoV-2 (SCV-2) virus evolves, diagnostics and vaccines against novel strains rely on viral genome sequencing. Researchers have gravitated towards the cost-effective and highly sensitive amplicon-based (e.g. ARTIC) and hybrid capture sequencing (e.g. SARS-CoV-2 NGS Assay) to selectively target the SCV-2 genome. We provide an in silico model to compare these 2 technologies and present data on the high scalability of the Research Use Only (RUO) workflow of the SARS-CoV-2 NGS Assay. Methods In silico work included alignments of 383,656 high-quality genome sequences belonging to variant of concern (VOC) or variant of interest (VOI) isolates (GISAID). We profiled mismatches and sequencing dropouts using the ARTIC V3 primers, SARS-CoV-2 NGS Assay probes (Twist Bioscience) and 11 synthesized viral sequences containing mutations and compared the performance of these assays using clinical samples. Further, the miniaturized hybrid capture workflow was optimized and evaluated to support high-throughput (384-plex). The sequencing data was processed by COVID-DX software. Results We detected 101,432 viruses (27%) with > = 1 mismatch in the last 6 base pairs of the 3’ end of ARTIC primers; of these, 413 had > = 2 mismatches in one primer. In contrast, only 38 viruses (0.01%) had enough mutations ( > = 10) in a hybrid capture probe to have a similar effect on coverage. We observed that mutations in ARTIC primers led to complete dropout of the amplicon for 4/11 isolates and diminished coverage in additional 4. Twist probes showed uniform coverage throughout with little to no dropouts. Both assays detected a wide range of variants (~99.9% coverage at 5X depth) in clinical samples (CT value < 30) collected in NY (Spring 2020-Spring 2021). The distribution of the number of reads and on target rates were more uniform among specimens within amplicon-based sequencing. However, uneven genome coverage and primer dropouts, some in the spike protein, were observed on VOC/VOI and other isolates highlighting limitations of an amplicon-based approach. Conclusion The RUO workflow of the SARS-CoV-2 NGS Assay is a comprehensive and scalable sequencing tool for variant profiling, yields more consistent coverage and smaller dropout rate compared to ARTIC (0.05% vs. 7.7%). Disclosures Danny Antaki, PhD, Twist Bioscience (Employee, Shareholder) Mara Couto-Rodriguez, MS, Biotia (Employee) Kristin Butcher, MS, Twist Bioscience (Employee, Shareholder) Esteban Toro, PhD, Twist Bioscience (Employee) Bryan Höglund, BS, Twist Bioscience (Employee, Shareholder) Xavier O. Jirau Serrano, B.S., Biotia (Employee) Joseph Barrows, MS, Biotia (Employee) Christopher Mason, PhD, Biotia (Board Member, Advisor or Review Panel member, Shareholder) Niamh B. O’Hara, PhD, Biotia (Board Member, Employee, Shareholder) Dorottya Nagy-Szakal, MD PhD, Biotia Inc (Employee, Shareholder)
Abstract Background The quantitative level of pathogens present in a host is a major driver of infectious disease (ID) state and outcome. However, the majority of ID diagnostics are qualitative. Next-generation sequencing (NGS) is an emerging ID diagnostics and research tool to provide insights, including tracking transmission, evolution, and identifying novel strains. Methods We built a novel likelihood-based computational method to leverage pathogen-specific genome-wide NGS data to detect SARS-CoV-2, profile genetic variants, and furthermore quantify levels of these pathogens. We used de-identified clinical specimens tested for SARS-CoV-2 using RT-PCR, SARS-CoV-2 NGS Assay (hybrid capture, Twist Bioscience), or ARTIC (amplicon-based) platform, and COVID-DX software. A training (n=87) and validation (n=22) set was selected to establish the strength of our quantification model. We fit non-uniform probabilistic error profiles to a deterministic sigmoidal equation that more realistically represents observed data and used likelihood maximized over several different read depths to improve accuracy over a wide range of values of viral load. Given the proportion of the genome covered at varying depths for a single sample as input data, our model estimated the Ct of that sample as the value that produces the maximum likelihood of generating the observed genome coverage data. Results The model fit on 87 SARS-CoV-2 NGS Assay training samples produced a good fit to the 22 validation samples, with a coefficient of correlation (r2) of ~0.8. The accuracy of the model was high (mean absolute % error of ~10%, meaning our model is able to predict the Ct value of each sample within a margin of ±10% on average). Because of the nature of the commonly used ARTIC protocol, we found that all quantitative signals in this data were lost during PCR amplification and the model is not applicable for quantification of samples captured this way. The ability to model quantification is a major advantage of the SARS-CoV-2 NGS assay protocol. The likelihood-based model to estimate SARS-CoV-2 viral titer Left Observed genome coverage (y-axis) plotted against Ct value (x-axis). The best-fitting logistic curve is demonstrated with a red line with shaded areas above and below representing the fitted error profile. RIGHT: Model-estimated Ct values (y-axis) compared to laboratory Ct values (x-axis) with grey bars representing estimated confidence intervals. The 1:1 diagonal is shown as a dotted line. Conclusion To our knowledge, this is the first model to incorporate sequence data mapped across the genome of a pathogen to quantify the level of that pathogen in a clinical specimen. This has implications in ID diagnostics, research, and metagenomics. Disclosures Heather L. Wells, MPH, Biotia, Inc. (Consultant) Joseph Barrows, MS, Biotia (Employee) Mara Couto-Rodriguez, MS, Biotia (Employee) Xavier O. Jirau Serrano, B.S., Biotia (Employee) Marilyne Debieu, PhD, Biotia (Employee) Karen Wessel, PhD, Labor Zotz/Klimas (Employee) Christopher Mason, PhD, Biotia (Board Member, Advisor or Review Panel member, Shareholder) Dorottya Nagy-Szakal, MD PhD, Biotia Inc (Employee, Shareholder) Niamh B. O’Hara, PhD, Biotia (Board Member, Employee, Shareholder)
