Using a chat-based informed consent tool in large-scale genomic research.

OBJECTIVE: We implemented a chatbot consent tool to shift the time burden from study staff in support of a national genomics research study. MATERIALS AND METHODS: We created an Institutional Review Board-approved script for automated chat-based consent. We compared data from prospective participants who used the tool or had traditional consent conversations with study staff. RESULTS: Chat-based consent, completed on a user's schedule, was shorter than the traditional conversation. This did not lead to a significant change in affirmative consents. Within affirmative consents and declines, more prospective participants completed the chat-based process. A quiz to assess chat-based consent user understanding had a high pass rate with no reported negative experiences. CONCLUSION: Our report shows that a structured script can convey important information while realizing the benefits of automation and burden shifting. Analysis suggests that it may be advantageous to use chatbots to scale this rate-limiting step in large research projects.

Benchmarking long-read genome sequence alignment tools for human genomics applications.

Background: The utility of long-read genome sequencing platforms has been shown in many fields including whole genome assembly, metagenomics, and amplicon sequencing. Less clear is the applicability of long reads to reference-guided human genomics, which is the foundation of genomic medicine. Here, we benchmark available platform-agnostic alignment tools on datasets from nanopore and single-molecule real-time platforms to understand their suitability in producing a genome representation. Results: For this study, we leveraged publicly-available data from sample NA12878 generated on Oxford Nanopore and sample NA24385 on Pacific Biosciences platforms. We employed state of the art sequence alignment tools including GraphMap2, long-read aligner (LRA), Minimap2, CoNvex Gap-cost alignMents for Long Reads (NGMLR), and Winnowmap2. Minimap2 and Winnowmap2 were computationally lightweight enough for use at scale, while GraphMap2 was not. NGMLR took a long time and required many resources, but produced alignments each time. LRA was fast, but only worked on Pacific Biosciences data. Each tool widely disagreed on which reads to leave unaligned, affecting the end genome coverage and the number of discoverable breakpoints. No alignment tool independently resolved all large structural variants (1,001-100,000 base pairs) present in the Database of Genome Variants (DGV) for sample NA12878 or the truthset for NA24385. Conclusions: These results suggest a combined approach is needed for LRS alignments for human genomics. Specifically, leveraging alignments from three tools will be more effective in generating a complete picture of genomic variability. It should be best practice to use an analysis pipeline that generates alignments with both Minimap2 and Winnowmap2 as they are lightweight and yield different views of the genome. Depending on the question at hand, the data available, and the time constraints, NGMLR and LRA are good options for a third tool. If computational resources and time are not a factor for a given case or experiment, NGMLR will provide another view, and another chance to resolve a case. LRA, while fast, did not work on the nanopore data for our cluster, but PacBio results were promising in that those computations completed faster than Minimap2. Due to its significant burden on computational resources and slow run time, Graphmap2 is not an ideal tool for exploration of a whole human genome generated on a long-read sequencing platform.

Increased diagnostic yield from negative whole genome-slice panels using automated reanalysis.

We evaluated the diagnostic yield using genome-slice panel reanalysis in the clinical setting using an automated phenotype/gene ranking system. We analyzed whole genome sequencing (WGS) data produced from clinically ordered panels built as bioinformatic slices for 16 clinically diverse, undiagnosed cases referred to the Pediatric Mendelian Genomics Research Center, an NHGRI-funded GREGoR Consortium site. Genome-wide reanalysis was performed using Moon™, a machine-learning-based tool for variant prioritization. In five out of 16 cases, we discovered a potentially clinically significant variant. In four of these cases, the variant was found in a gene not included in the original panel due to phenotypic expansion of a disorder or incomplete initial phenotyping of the patient. In the fifth case, the gene containing the variant was included in the original panel, but being a complex structural rearrangement with intronic breakpoints outside the clinically analyzed regions, it was not initially identified. Automated genome-wide reanalysis of clinical WGS data generated during targeted panels testing yielded a 25% increase in diagnostic findings and a possibly clinically relevant finding in one additional case, underscoring the added value of analyses versus those routinely performed in the clinical setting.

"Development and Implementation of Novel Chatbot-based Genomic Research Consent".

Objective: To conduct a retrospective analysis comparing traditional human-based consenting to an automated chat-based consenting process. Materials and Methods: We developed a new chat-based consent using our IRB-approved consent forms. We leveraged a previously developed platform (GiaⓇ, or "Genetic Information Assistant") to deliver the chat content to candidate participants. The content included information about the study, educational information, and a quiz to assess understanding. We analyzed 144 families referred to our study during a 6-month time period. A total of 37 families completed consent using the traditional process, while 35 families completed consent using Gia. Results: Engagement rates were similar between both consenting methods. The median length of the consent conversation was shorter for Gia users compared to traditional (44 vs. 76 minutes). Additionally, the total time from referral to consent completion was faster with Gia (5 vs. 16 days). Within Gia, understanding was assessed with a 10-question quiz that most participants (96%) passed. Feedback about the chat consent indicated that 86% of participants had a positive experience. Discussion: Using Gia resulted in time savings for both the participant and study staff. The chatbot enables studies to reach more potential candidates. We identified five key features related to human-centered design for developing a consent chat. Conclusion: This analysis suggests that it is feasible to use an automated chatbot to scale obtaining informed consent for a genomics research study. We further identify a number of advantages when using a chatbot.

Multisystem Inflammatory Syndrome of Children: Subphenotypes, Risk Factors, Biomarkers, Cytokine Profiles, and Viral Sequencing.

OBJECTIVE: To assess demographic, clinical, and biomarker features distinguishing patients with multisystem inflammatory syndrome in children (MIS-C); compare MIS-C sub-phenotypes; identify cytokine biosignatures; and characterize viral genome sequences. STUDY DESIGN: We performed a prospective observational cohort study of 124 children hospitalized and treated under the institutional MIS-C Task Force protocol from March to September 2020 at Children's National, a quaternary freestanding children's hospital in Washington, DC. Of this cohort, 63 of the patients had the diagnosis of MIS-C (39 confirmed, 24 probable) and 61 were from the same cohort of admitted patients who subsequently had an alternative diagnosis (controls). RESULTS: Median age and sex were similar between MIS-C and controls. Black (46%) and Latino (35%) children were over-represented in the MIS-C cohort, with Black children at greatest risk (OR 4.62, 95% CI 1.151-14.10; P = .007). Cardiac complications were more frequent in critically ill patients with MIS-C (55% vs 28%; P = .04) including systolic myocardial dysfunction (39% vs 3%; P = .001) and valvular regurgitation (33% vs 7%; P = .01). Median cycle threshold was 31.8 (27.95-35.1 IQR) in MIS-C cases, significantly greater (indicating lower viral load) than in primary severe acute respiratory syndrome coronavirus 2 infection. Cytokines soluble interleukin 2 receptor, interleukin [IL]-10, and IL-6 were greater in patients with MIS-C compared with controls. Cytokine analysis revealed subphenotype differences between critically ill vs noncritically ill (IL-2, soluble interleukin 2 receptor, IL-10, IL-6); polymerase chain reaction positive vs negative (tumor necrosis factor-α, IL-10, IL-6); and presence vs absence of cardiac abnormalities (IL-17). Phylogenetic analysis of viral genome sequences revealed predominance of GH clade originating in Europe, with no differences comparing patients with MIS-C with patients with primary coronavirus disease 19. Treatment was well tolerated, and no children died. CONCLUSIONS: This study establishes a well-characterized large cohort of MIS-C evaluated and treated following a standardized protocol and identifies key clinical, biomarker, cytokine, viral load, and sequencing features. Long-term follow-up will provide opportunity for future insights into MIS-C and its sequelae.

Paucity and Disparity of Publicly Available Sex-Disaggregated Data for the COVID-19 Epidemic Hamper Evidence-Based Decision-Making.

COVID-19 has joined the long list of sexually dimorphic human disorders. Higher lethality in men, evident in the first reports from China, was confirmed in the subsequent Italian outbreak. Newspapers and scientific journals commented on this finding and the preexisting conditions, biological processes, and behavioral differences that may underlie it. However, little appeared to be released about sex differences in severity of disease, comorbidities, rate of recovery, length of hospital stay, or number of tests performed. Systematic analysis of official websites for 20 countries and 6 US states revealed a wide disparity in sex-disaggregated data made available to the public and scholars. Only a handful reported cases by sex. None of the other characteristics, including deaths, were stratified by sex at the time. Beyond suboptimal sex disaggregation, we found a paucity of usable raw data sets and a generalized lack of standardization of captured data, making comparisons difficult. A second round of data capture in April found more complete, but even more disparate, information. Our analysis revealed a wide range of sex ratios among confirmed cases. In countries where a male bias was initially reported, the proportion of women dramatically increased in 3 weeks. Analysis also revealed a complex pattern of sex ratio variation with age. Accurate, peer-reviewed, analysis of harmonized, sex-disaggregated data for characteristics of epidemics, such as availability of testing, suspected source of infection, or comorbidities, will be critical to understand where the observed disparities come from and to generate evidence-based recommendations for decision-making by governments.

Mitochondrial Enzymes of the Urea Cycle Cluster at the Inner Mitochondrial Membrane.

Mitochondrial enzymes involved in energy transformation are organized into multiprotein complexes that channel the reaction intermediates for efficient ATP production. Three of the mammalian urea cycle enzymes: N-acetylglutamate synthase (NAGS), carbamylphosphate synthetase 1 (CPS1), and ornithine transcarbamylase (OTC) reside in the mitochondria. Urea cycle is required to convert ammonia into urea and protect the brain from ammonia toxicity. Urea cycle intermediates are tightly channeled in and out of mitochondria, indicating that efficient activity of these enzymes relies upon their coordinated interaction with each other, perhaps in a cluster. This view is supported by mutations in surface residues of the urea cycle proteins that impair ureagenesis in the patients, but do not affect protein stability or catalytic activity. We find the NAGS, CPS1, and OTC proteins in liver mitochondria can associate with the inner mitochondrial membrane (IMM) and can be co-immunoprecipitated. Our in-silico analysis of vertebrate NAGS proteins, the least abundant of the urea cycle enzymes, identified a protein-protein interaction region present only in the mammalian NAGS protein-"variable segment," which mediates the interaction of NAGS with CPS1. Use of super resolution microscopy showed that NAGS, CPS1 and OTC are organized into clusters in the hepatocyte mitochondria. These results indicate that mitochondrial urea cycle proteins cluster, instead of functioning either independently or in a rigid multienzyme complex.

Baseline human gut microbiota profile in healthy people and standard reporting template.

A comprehensive knowledge of the types and ratios of microbes that inhabit the healthy human gut is necessary before any kind of pre-clinical or clinical study can be performed that attempts to alter the microbiome to treat a condition or improve therapy outcome. To address this need we present an innovative scalable comprehensive analysis workflow, a healthy human reference microbiome list and abundance profile (GutFeelingKB), and a novel Fecal Biome Population Report (FecalBiome) with clinical applicability. GutFeelingKB provides a list of 157 organisms (8 phyla, 18 classes, 23 orders, 38 families, 59 genera and 109 species) that forms the baseline biome and therefore can be used as healthy controls for studies related to dysbiosis. This list can be expanded to 863 organisms if closely related proteomes are considered. The incorporation of microbiome science into routine clinical practice necessitates a standard report for comparison of an individual's microbiome to the growing knowledgebase of "normal" microbiome data. The FecalBiome and the underlying technology of GutFeelingKB address this need. The knowledgebase can be useful to regulatory agencies for the assessment of fecal transplant and other microbiome products, as it contains a list of organisms from healthy individuals. In addition to the list of organisms and their abundances, this study also generated a collection of assembled contiguous sequences (contigs) of metagenomics dark matter. In this study, metagenomic dark matter represents sequences that cannot be mapped to any known sequence but can be assembled into contigs of 10,000 nucleotides or higher. These sequences can be used to create primers to study potential novel organisms. All data is freely available from https://hive.biochemistry.gwu.edu/gfkb and NCBI's Short Read Archive.

An assessment of US microbiome research.

Genome-enabled technologies have supported a dramatic increase in our ability to study microbial communities in environments and hosts. Taking stock of previously funded microbiome research can help to identify common themes, under-represented areas and research priorities to consider moving forward. To assess the status of US microbiome research, a team of government scientists conducted an analysis of federally funded microbiome research. Microbiomes were defined as host-, ecosystem- or habitat-associated communities of microorganisms, and microbiome research was defined as those studies that emphasize community-level analyses using 'omics technologies. Single pathogen, single strain and culture-based studies were not included, except symbiosis studies that served as models for more complex communities. Fourteen governmental organizations participated in the data call. The analysis examined three broad research themes, eight environments and eight microbial categories. Human microbiome research was larger than any other environment studied, and the basic biology research theme accounted for half of the total research activities. Computational biology and bioinformatics, reference databases and biorepositories, standardized protocols and high-throughput tools were commonly identified needs. Longitudinal and functional studies and interdisciplinary research were also identified as needs. This study has implications for the funding of future microbiome research, not only in the United States but beyond.