Patient-Related Metadata Reported in Sequencing Studies of SARS-CoV-2: Protocol for a Scoping Review and Bibliometric Analysis.

Background: There has been an unprecedented effort to sequence the SARS-CoV-2 virus and examine its molecular evolution. This has been facilitated by the availability of publicly accessible databases, the Global Initiative on Sharing All Influenza Data (GISAID) and GenBank, which collectively hold millions of SARS-CoV-2 sequence records. Genomic epidemiology, however, seeks to go beyond phylogenetic analysis by linking genetic information to patient characteristics and disease outcomes, enabling a comprehensive understanding of transmission dynamics and disease impact.While these repositories include fields reflecting patient-related metadata for a given sequence, inclusion of these demographic and clinical details is scarce. The extent to which patient-related metadata is reported in published sequencing studies and its quality remains largely unexplored. Methods: The NIH's LitCovid collection will be used for automated classification of articles reporting having deposited SARS-CoV-2 sequences in public repositories, while an independent search will be conducted in PubMed for validation. Data extraction will be conducted using Covidence. The extracted data will be synthesized and summarized to quantify the availability of patient metadata in the published literature of SARS-CoV-2 sequencing studies. For the bibliometric analysis, relevant data points, such as author affiliations and citation metrics will be extracted. Discussion: This scoping review will report on the extent and types of patient-related metadata reported in genomic viral sequencing studies of SARS-CoV-2, identify gaps in this reporting, and make recommendations for improving the quality and consistency of reporting in this area. The bibliometric analysis will uncover trends and patterns in the reporting of patient-related metadata, including differences in reporting based on study types or geographic regions. Co-occurrence networks of author keywords will also be presented. The insights gained from this study may help improve the quality and consistency of reporting patient metadata, enhancing the utility of sequence metadata and facilitating future research on infectious diseases.

Text mining biomedical literature to identify extremely unbalanced data for digital epidemiology and systematic reviews: dataset and methods for a SARS-CoV-2 genomic epidemiology study.

There are many studies that require researchers to extract specific information from the published literature, such as details about sequence records or about a randomized control trial. While manual extraction is cost efficient for small studies, larger studies such as systematic reviews are much more costly and time-consuming. To avoid exhaustive manual searches and extraction, and their related cost and effort, natural language processing (NLP) methods can be tailored for the more subtle extraction and decision tasks that typically only humans have performed. The need for such studies that use the published literature as a data source became even more evident as the COVID-19 pandemic raged through the world and millions of sequenced samples were deposited in public repositories such as GISAID and GenBank, promising large genomic epidemiology studies, but more often than not lacked many important details that prevented large-scale studies. Thus, granular geographic location or the most basic patient-relevant data such as demographic information, or clinical outcomes were not noted in the sequence record. However, some of these data was indeed published, but in the text, tables, or supplementary material of a corresponding published article. We present here methods to identify relevant journal articles that report having produced and made available in GenBank or GISAID, new SARS-CoV-2 sequences, as those that initially produced and made available the sequences are the most likely articles to include the high-level details about the patients from whom the sequences were obtained. Human annotators validated the approach, creating a gold standard set for training and validation of a machine learning classifier. Identifying these articles is a crucial step to enable future automated informatics pipelines that will apply Machine Learning and Natural Language Processing to identify patient characteristics such as co-morbidities, outcomes, age, gender, and race, enriching SARS-CoV-2 sequence databases with actionable information for defining large genomic epidemiology studies. Thus, enriched patient metadata can enable secondary data analysis, at scale, to uncover associations between the viral genome (including variants of concern and their sublineages), transmission risk, and health outcomes. However, for such enrichment to happen, the right papers need to be found and very detailed data needs to be extracted from them. Further, finding the very specific articles needed for inclusion is a task that also facilitates scoping and systematic reviews, greatly reducing the time needed for full-text analysis and extraction.

Surveillance of rhinovirus diversity among a university community identifies multiple types from all three species including an unassigned rhinovirus A genotype.

We determine the presence and diversity of rhinoviruses in nasopharyngeal swab samples from 248 individuals who presented with influenza-like illness (ILI) at a university clinic in the Southwest United States between October 1, 2020 and March 31, 2021. We identify at least 13 rhinovirus genotypes (A11, A22, A23, A25, A67, A101, B6, B79, C1, C17, C36, and C56, as well a new genotype [AZ88**]) and 16 variants that contributed to the burden of ILI in the community. We also describe the complete capsid protein gene of a member (AZ88**) of an unassigned rhinovirus A genotype.

Impact of sample clarification by size exclusion on virus detection and diversity in wastewater-based epidemiology.

The use of wastewater-based epidemiology (WBE) for early detection of virus circulation and response during the SARS-CoV-2 pandemic increased interest in and use of virus concentration protocols that are quick, scalable, and efficient. One such protocol involves sample clarification by size fractionation using either low-speed centrifugation to produce a clarified supernatant or membrane filtration to produce an initial filtrate depleted of solids, eukaryotes and bacterial present in wastewater (WW), followed by concentration of virus particles by ultrafiltration of the above. While this approach has been successful in identifying viruses from WW, it assumes that majority of the viruses of interest should be present in the fraction obtained by ultrafiltration of the initial filtrate, with negligible loss of viral particles and viral diversity. We used WW samples collected in a population of ~700,000 in southwest USA between October 2019 and March 2021, targeting three non-enveloped viruses (enteroviruses [EV], canine picornaviruses [CanPV], and human adenovirus 41 [Ad41]), to evaluate whether size fractionation of WW prior to ultrafiltration leads to appreciable differences in the virus presence and diversity determined. We showed that virus presence or absence in WW samples in both portions (filter trapped solids [FTS] and filtrate) are not consistent with each other. We also found that in cases where virus was detected in both fractions, virus diversity (or types) captured either in FTS or filtrate were not consistent with each other. Hence, preferring one fraction of WW over the other can undermine the capacity of WBE to function as an early warning system and negatively impact the accurate representation of virus presence and diversity in a population.

Genome Sequence of a Microvirus Recovered from Wastewater in Arizona, USA, in October 2020, Encodes a Previously Undescribed DNA-Binding Protein.

We describe the genome of Microvirus-AZ-2020, which was identified from wastewater in Arizona, USA, in October 2020. Microvirus-AZ-2020 belongs to subfamily Gokushovirinae and contains six (five known and one hypothetical) open reading frames (ORFs), each with >40 codons. HHPred analysis and Colabfold structure prediction suggest that the hypothetical ORF encodes a previously undescribed putative DNA-binding protein.

Canine picornaviruses detected in wastewater in Arizona, USA 2019 and 2021.

Virus surveillance by wastewater-based epidemiology (WBE) in two Arizona municipalities in Maricopa County, USA (~700,000 people), revealed the presence of six canine picornavirus (CanPV) variants: five in 2019 and one in 2021. Phylogenetic analysis suggests these viruses might be from domestic dog breeds living within or around the area. Phylogenetic and pairwise identity analyses suggest over 15 years of likely enzootic circulation of multiple lineages of CanPV in the USA and possibly globally. Considering <10 CanPV sequences are publicly available in GenBank as of June 2, 2022, the results provided here constitute an increase of current knowledge on CanPV diversity and highlight the need for increased surveillance.

HuD Regulates mRNA-circRNA-miRNA Networks in the Mouse Striatum Linked to Neuronal Development and Drug Addiction.

The RNA-binding protein HuD (a.k.a., ELAVL4) is involved in neuronal development and synaptic plasticity mechanisms, including addiction-related processes such as cocaine conditioned-place preference (CPP) and food reward. The most studied function of this protein is mRNA stabilization; however, we have recently shown that HuD also regulates the levels of circular RNAs (circRNAs) in neurons. To examine the role of HuD in the control of coding and non-coding RNA networks associated with substance use, we identified sets of differentially expressed mRNAs, circRNAs and miRNAs in the striatum of HuD knockout (KO) mice. Our findings indicate that significantly downregulated mRNAs are enriched in biological pathways related to cell morphology and behavior. Furthermore, deletion of HuD altered the levels of 15 miRNAs associated with drug seeking. Using these sets of data, we predicted that a large number of upregulated miRNAs form competing endogenous RNA (ceRNA) networks with circRNAs and mRNAs associated with the neuronal development and synaptic plasticity proteins LSAMP and MARK3. Additionally, several downregulated miRNAs form ceRNA networks with mRNAs and circRNAs from MEF2D, PIK3R3, PTRPM and other neuronal proteins. Together, our results indicate that HuD regulates ceRNA networks controlling the levels of mRNAs associated with neuronal differentiation and synaptic physiology.