Multi-omics analysis reveals a macrophage-related marker gene signature for prognostic prediction, immune landscape, genomic heterogeneity, and drug choices in prostate cancer.

Introduction: Macrophages are components of the innate immune system and can play an anti-tumor or pro-tumor role in the tumor microenvironment owing to their high heterogeneity and plasticity. Meanwhile, prostate cancer (PCa) is an immune-sensitive tumor, making it essential to investigate the value of macrophage-associated networks in its prognosis and treatment. Methods: Macrophage-related marker genes (MRMGs) were identified through the comprehensive analysis of single-cell sequencing data from GSE141445 and the impact of macrophages on PCa was evaluated using consensus clustering of MRMGs in the TCGA database. Subsequently, a macrophage-related marker gene prognostic signature (MRMGPS) was constructed by LASSO-Cox regression analysis and grouped based on the median risk score. The predictive ability of MRMGPS was verified by experiments, survival analysis, and nomogram in the TCGA cohort and GEO-Merged cohort. Additionally, immune landscape, genomic heterogeneity, tumor stemness, drug sensitivity, and molecular docking were conducted to explore the relationship between MRMGPS and the tumor immune microenvironment, therapeutic response, and drug selection. Results: We identified 307 MRMGs and verified that macrophages had a strong influence on the development and progression of PCa. Furthermore, we showed that the MRMGPS constructed with 9 genes and the predictive nomogram had excellent predictive ability in both the TCGA and GEO-Merged cohorts. More importantly, we also found the close relationship between MRMGPS and the tumor immune microenvironment, therapeutic response, and drug selection by multi-omics analysis. Discussion: Our study reveals the application value of MRMGPS in predicting the prognosis of PCa patients. It also provides a novel perspective and theoretical basis for immune research and drug choices for PCa.

Morphological, biological, and genomic characterization of Klebsiella pneumoniae phage vB_Kpn_ZC2.

BACKGROUND: Bacteriophages (phages) are one of the most promising alternatives to traditional antibiotic therapies, especially against multidrug-resistant bacteria. Klebsiella pneumoniae is considered to be an opportunistic pathogen that can cause life-threatening infections. Thus, this study aims at the characterization of a novel isolated phage vB_Kpn_ZC2 (ZCKP2, for short). METHODS: The phage ZCKP2 was isolated from sewage water by using the clinical isolate KP/08 as a host strain. The isolated bacteriophage was purified and amplified, followed by testing of its molecular weight using Pulse-Field Gel Electrophoresis (PFGE), transmission electron microscopy, antibacterial activity against a panel of other Klebsiella pneumoniae hosts, stability studies, and whole genome sequencing. RESULTS: Phage ZCKP2 belongs morphologically to siphoviruses as indicated from the Transmission Electron Microscopy microgram. The Pulsed Field Gel Electrophoresis and the phage sequencing estimated the phage genome size of 48.2 kbp. Moreover, the absence of lysogeny-related genes, antibiotic resistance genes, and virulence genes in the annotated genome suggests that phage ZCKP2 is safe for therapeutic use. Genome-based taxonomic analysis indicates that phage ZCKP2 represents a new family that has not been formally rated yet. In addition, phage ZCKP2 preserved high stability at different temperatures and pH values (-20 - 70 °C and pH 4 - 9). For the antibacterial activity, phage ZCKP2 maintained consistent clear zones on KP/08 bacteria along with other hosts, in addition to effective bacterial killing over time at different MOIs (0.1, 1, and 10). Also, the genome annotation predicted antibacterial lytic enzymes. Furthermore, the topology of class II holins was predicted in some putative proteins with dual transmembrane domains that contribute significantly to antibacterial activity. Phage ZCKP2 characterization demonstrates safety and efficiency against multidrug-resistant K. pneumoniae, hence ZCKP2 is a good candidate for further in vivo and phage therapy clinical applications.

Genomic characterization of two new viruses infecting Ageratum conyzoides in China.

Two new RNA viruses were identified in Ageratum conyzoides in China using high-throughput sequencing, and their genome sequences were determined using PCR and rapid amplification of cDNA ends. The new viruses, which have positive-sense, single-stranded RNA genomes, were provisionally named "ageratum virus 1" (AgV1) and "ageratum virus 2" (AgV2). AgV1 has a genome of 3,526 nucleotides with three open reading frames (ORFs) and shares 49.9% nucleotide sequence identity with the complete genome of Ethiopian tobacco bushy top virus (genus Umbravirus, family Tombusviridae). The genome of AgV2 consists of 5,523 nucleotides and contains five ORFs that are commonly observed in members of the genus Enamovirus of the family Solemoviridae. Proteins encoded by AgV2 exhibited the highest amino acid sequence similarity (31.7-75.0% identity) to the corresponding proteins of pepper enamovirus R1 (an unclassified enamovirus) and citrus vein enation virus (genus Enamovirus). Based on their genome organization, sequence, and phylogenetic relationships, AgV1 is proposed to be a new umbra-like virus of the family Tombusviridae, and AgV2 is proposed to be a new member of the genus Enamovirus of the family Solemoviridae.

Semi-supervised learning improves regulatory sequence prediction with unlabeled sequences.

MOTIVATION: Genome-wide association studies have systematically identified thousands of single nucleotide polymorphisms (SNPs) associated with complex genetic diseases. However, the majority of those SNPs were found in non-coding genomic regions, preventing the understanding of the underlying causal mechanism. Predicting molecular processes based on the DNA sequence represents a promising approach to understand the role of those non-coding SNPs. Over the past years, deep learning was successfully applied to regulatory sequence prediction using supervised learning. Supervised learning required DNA sequences associated with functional data for training, whose amount is strongly limited by the finite size of the human genome. Conversely, the amount of mammalian DNA sequences is exponentially increasing due to ongoing large sequencing projects, but without functional data in most cases. RESULTS: To alleviate the limitations of supervised learning, we propose a paradigm shift with semi-supervised learning, which does not only exploit labeled sequences (e.g. human genome with ChIP-seq experiment), but also unlabeled sequences available in much larger amounts (e.g. from other species without ChIP-seq experiment, such as chimpanzee). Our approach is flexible and can be plugged into any neural architecture including shallow and deep networks, and shows strong predictive performance improvements compared to supervised learning in most cases (up to [Formula: see text]). AVAILABILITY AND IMPLEMENTATION: https://forgemia.inra.fr/raphael.mourad/deepgnn .

[Multi-omics approaches for revealing the etiology of cancer: from genomics, exposomics, metabolomics to system epidemiology].

Identifying risk factors of the disease are one of the main tasks of epidemiology. With the advancement of omics technologies (e.g., genome, transcriptome, proteome, metabolome, and exposome), cancer etiology research has entered the stage of systems epidemiology. Genomic research identifies cancer susceptibility loci and uncovers their biological mechanisms. Exposomic research investigates the impact of environmental factors on biological processes and disease risks. The metabolome is downstream of biological regulatory networks, reflecting the effects of the gene, environment, and their interactions, which can help elucidate the biological mechanisms of genetic and environmental risk factors and identify new biomarkers. Here, we reviewed the applications of genomic, exposomic, and metabolomic studies in the etiologic research on cancer. We summarized the importance of multi-omics approaches and systems epidemiology in cancer etiology research and outlined future perspectives.

Tracking Adaptive Pathways of Invasive Insects: Novel Insight from Genomics.

Despite the huge human and economic costs of invasive insects, which are the main group of invasive species, their environmental impacts through various mechanisms remain inadequately explained in databases and much of the invasion biology literature. High-throughput sequencing technology, especially whole-genome sequencing, has been used as a powerful method to study the mechanisms through which insects achieve invasion. In this study, we reviewed whole-genome sequencing-based advances in revealing several important invasion mechanisms of invasive insects, including (1) the rapid genetic variation and evolution of invasive populations, (2) invasion history and dispersal paths, (3) rapid adaptation to different host plant ranges, (4) strong environmental adaptation, (5) the development of insecticide resistance, and (6) the synergistic damage caused by invasive insects and endosymbiotic bacteria. We also discussed prevention and control technologies based on whole-genome sequencing and their prospects.

Complete Genomic Sequence of Xanthomonas oryzae pv. oryzae Strain, LA20, for Studying Resurgence of Rice Bacterial Blight in the Yangtze River Region, China.

Xanthomonas oryzae pv. oryzae (Xoo) is a causative agent of rice bacterial blight (BB). In 2020-2022, BB re-emerged, and there was a break out in the Yangtze River area, China. The pandemic Xoo strain, LA20, was isolated and identified from cultivar Quanyou1606 and demonstrated to be the Chinese R9 Xoo strain, which is able to override the widely adopted xa5-, Xa7- and xa13-mediated resistance in rice varieties in Yangtze River. Here, we report the complete genome of LA20 by PacBio and Illumina sequencing. The assembled genome consists of one circular chromosome of 4,960,087 bp, sharing 99.65% sequence identity with the traditional representative strain, YC11 (R5), in the Yangtze River. Comparative genome analysis of LA20 and YC11 revealed the obvious variability in Tal genes (the uppermost virulence determinants) in numbers and sequences. Particularly, six Tal genes were only found in LA20, but not in YC11, among which Tal1b (pthXo1)/Tal4 (pthXo6), along with the lost one, pthXo3 (avrXa7), might be the major factors for LA20 to overcome xa5-, Xa7- and xa13-mediated resistance, thus, leading to the resurgence of BB. This complete genome of the new pandemic Xoo strain will provide novel insights into pathogen evolution, the traits of pathogenicity on genomic level and the epidemic disease status in China.

Diverse Virulence Attributes of Pantoea alfalfae sp. nov. CQ10 Responsible for Bacterial Leaf Blight in Alfalfa Revealed by Genomic Analysis.

Alfalfa is widely grown worldwide for its excellent nutritional value. Pantoea species living in alfalfa seeds can easily spread over great distances with frequent trade. However, the pathogenic properties of this dangerous hitchhiker on alfalfa have not been evaluated. Here, we identified the taxonomic status of Pantoea strain CQ10 isolated from the interior of alfalfa seeds based on the whole genome sequence. The diverse virulence attributes of strain CQ10 during host infection were characterized through pathogenicity assays and functional and genomic analyses. We report that strain CQ10 belongs to a novel species in the genus Pantoea, which was phylogenetically close to Pantoea vagans and Pantoea agglomerans. Strain CQ10 caused bacterial leaf blight of alfalfa after inoculation from the roots. We found that strain CQ10 possesses a large number of pathogenic genes involved in shaping the virulence properties during bacteria-host interactions, including motility, biofilm, type VI secretion system, and nutrient acquisition. Compared with P. vagans and P. agglomerans, the unique virulence factors of strain CQ10 were mainly involved in motility and biofilm, which were confirmed by in vitro experiments. Taken together, our results suggest that strain CQ10 is the first Pantoea species to infect alfalfa, and it possesses diverse virulence attributes among which motility and biofilm may be the best weapons.

Zebrafish: A Powerful Model for Genetics and Genomics.

Our understanding of fundamental biological mechanisms and the pathogenesis of human diseases has been greatly improved by studying the genetics and genomics of zebrafish [...].

Genetic Insight into the Interaction of IBDV with Host-A Clue to the Development of Novel IBDV Vaccines.

Infectious bursal disease virus (IBDV) is an immunosuppressive pathogen causing enormous economic losses to the poultry industry across the globe. As a double-stranded RNA virus, IBDV undergoes genetic mutation or recombination in replication during circulation among flocks, leading to the generation and spread of variant or recombinant strains. In particular, the recent emergence of variant IBDV causes severe immunosuppression in chickens, affecting the efficacy of other vaccines. It seems that the genetic mutation of IBDV during the battle against host response is an effective strategy to help itself to survive. Therefore, a comprehensive understanding of the viral genome diversity will definitely help to develop effective measures for prevention and control of infectious bursal disease (IBD). In recent years, considerable progress has been made in understanding the relation of genetic mutation and genomic recombination of IBDV to its pathogenesis using the reverse genetic technique. Therefore, this review focuses on our current genetic insight into the IBDV's genetic typing and viral genomic variation.

Genomic and Transcriptional Profiles of Kelch-like (klhl) Gene Family in Polyploid Carassius Complex.

Genome duplication supplies raw genetic materials and has been thought to be essential for evolutionary innovation and ecological adaptation. Here, we select Kelch-like (klhl) genes to study the evolution of the duplicated genes in the polyploid Carassius complex, including amphidiploid C. auratus and amphitriploid C. gibelio. Phylogenetic, chromosomal location and read coverage analyses indicate that most of Carassius klhl genes exhibit a 2:1 relationship with zebrafish orthologs and confirm two rounds of polyploidy, an allotetraploidy followed by an autotriploidy, occurred during Carassius evolution. The lineage-specific expansion and biased retention/loss of klhl genes are also found in Carassius. Transcriptome analyses across eight adult tissues and seven embryogenesis stages reveal varied expression dominance and divergence between the two species. The expression of klhls in response to Carassius herpesvirus 2 infection shows different expression changes corresponding to distinct herpesvirus resistances in three C. gibelio gynogenetic clones. Finally, we find that most C. gibelio klhl genes possess three alleles except eight genes that have lost one or two alleles due to genome rearrangement. The allele expression bias is prosperous for Cgklhl genes and varies during embryogenesis owning to the sequential expression manner of the alleles. The current study provides global insights into the genomic and transcriptional evolution of duplicated genes in a given superfamily resulting from multiple rounds of polyploidization.

Deep Learning Framework for Complex Disease Risk Prediction Using Genomic Variations.

Genome-wide association studies have proven their ability to improve human health outcomes by identifying genotypes associated with phenotypes. Various works have attempted to predict the risk of diseases for individuals based on genotype data. This prediction can either be considered as an analysis model that can lead to a better understanding of gene functions that underlie human disease or as a black box in order to be used in decision support systems and in early disease detection. Deep learning techniques have gained more popularity recently. In this work, we propose a deep-learning framework for disease risk prediction. The proposed framework employs a multilayer perceptron (MLP) in order to predict individuals' disease status. The proposed framework was applied to the Wellcome Trust Case-Control Consortium (WTCCC), the UK National Blood Service (NBS) Control Group, and the 1958 British Birth Cohort (58C) datasets. The performance comparison of the proposed framework showed that the proposed approach outperformed the other methods in predicting disease risk, achieving an area under the curve (AUC) up to 0.94.

Restriction site-associated DNA sequencing technologies as an alternative to low-density SNP chips for genomic selection: a simulation study in layer chickens.

BACKGROUND: To reduce the cost of genomic selection, a low-density (LD) single nucleotide polymorphism (SNP) chip can be used in combination with imputation for genotyping selection candidates instead of using a high-density (HD) SNP chip. Next-generation sequencing (NGS) techniques have been increasingly used in livestock species but remain expensive for routine use for genomic selection. An alternative and cost-efficient solution is to use restriction site-associated DNA sequencing (RADseq) techniques to sequence only a fraction of the genome using restriction enzymes. From this perspective, use of RADseq techniques followed by an imputation step on HD chip as alternatives to LD chips for genomic selection was studied in a pure layer line. RESULTS: Genome reduction and sequencing fragments were identified on reference genome using four restriction enzymes (EcoRI, TaqI, AvaII and PstI) and a double-digest RADseq (ddRADseq) method (TaqI-PstI). The SNPs contained in these fragments were detected from the 20X sequence data of the individuals in our population. Imputation accuracy on HD chip with these genotypes was assessed as the mean correlation between true and imputed genotypes. Several production traits were evaluated using single-step GBLUP methodology. The impact of imputation errors on the ranking of the selection candidates was assessed by comparing a genomic evaluation based on ancestry using true HD or imputed HD genotyping. The relative accuracy of genomic estimated breeding values (GEBVs) was investigated by considering the GEBVs estimated on offspring as a reference. With AvaII or PstI and ddRADseq with TaqI and PstI, more than 10 K SNPs were detected in common with the HD SNP chip, resulting in an imputation accuracy greater than 0.97. The impact of imputation errors on genomic evaluation of the breeders was reduced, with a Spearman correlation greater than 0.99. Finally, the relative accuracy of GEBVs was equivalent. CONCLUSIONS: RADseq approaches can be interesting alternatives to low-density SNP chips for genomic selection. With more than 10 K SNPs in common with the SNPs of the HD SNP chip, good imputation and genomic evaluation results can be obtained. However, with real data, heterogeneity between individuals with missing data must be considered.

GraphSNP: an interactive distance viewer for investigating outbreaks and transmission networks using a graph approach.

BACKGROUND: Cluster and transmission analysis utilising pairwise SNP distance are increasingly used in genomic epidemiological studies. However, current methods are often challenging to install and use, and lack interactive functionalities for easy data exploration. RESULTS: GraphSNP is an interactive visualisation tool running in a web browser that allows users to rapidly generate pairwise SNP distance networks, investigate SNP distance distributions, identify clusters of related organisms, and reconstruct transmission routes. The functionality of GraphSNP is demonstrated using examples from recent multi-drug resistant bacterial outbreaks in healthcare settings. CONCLUSIONS: GraphSNP is freely available at https://github.com/nalarbp/graphsnp . An online version of GraphSNP, including demonstration datasets, input templates, and quick start guide is available for use at https://graphsnp.fordelab.com .

A first insight into the genomic background of Ilex pubescens (Aquifoliaceae) by flow cytometry and genome survey sequencing.

BACKGROUND: Ilex pubescens is an important traditional Chinese medicinal plant with many naturally occurring compounds and multiple pharmacological effects. However, the lack of reference genomic information has led to tardiness in molecular biology research and breeding programs of this plant. RESULTS: To obtain knowledge on the genomic information of I. pubescens, a genome survey was performed for the first time by next generation sequencing (NGS) together with genome size estimation using flow cytometry. The whole genome survey of I. pubescens generated 46.472 Gb of sequence data with approximately 82.2 × coverage. K-mer analysis indicated that I. pubescens has a small genome of approximately 553 Mb with 1.93% heterozygosity rate and 39.1% repeat rate. Meanwhile, the genome size was estimated to be 722 Mb using flow cytometry, which was possibly more precise for assessment of genome size than k-mer analysis. A total of 45.842 Gb clean reads were assembled into 808,938 scaffolds with a relatively short N50 of 760 bp. The average guanine and cytosine (GC) content was 37.52%. In total, 197,429 microsatellite motifs were detected with a frequency of 2.8 kb, among which mononucleotide motifs were the most abundant (up to 62.47% of the total microsatellite motifs), followed by dinucleotide and trinucleotide motifs. CONCLUSION: In summary, the genome of I. pubescens is small but complex with a high level of heterozygosity. Even though not successfully applied for estimation of genome size due to its complex genome, the survey sequences will help to design whole genome sequencing strategies and provide genetic information support for resource protection, genetic diversity analysis, genetic improvement and artificial breeding of I. pubescens.

Comparative developmental genomics of sex-biased gene expression in early embryogenesis across mammals.

BACKGROUND: Mammalian gonadal sex is determined by the presence or absence of a Y chromosome and the subsequent production of sex hormones contributes to secondary sexual differentiation. However, sex chromosome-linked genes encoding dosage-sensitive transcription and epigenetic factors are expressed well before gonad formation and have the potential to establish sex-biased expression that persists beyond the appearance of gonadal hormones. Here, we apply a comparative bioinformatics analysis on a pair of published single-cell datasets from mouse and human during very early embryogenesis-from two-cell to pre-implantation stages-to characterize sex-specific signals and to assess the degree of conservation among early acting sex-specific genes and pathways. RESULTS: Clustering and regression analyses of gene expression across samples reveal that sex initially plays a significant role in overall gene expression patterns at the earliest stages of embryogenesis which potentially may be the byproduct of signals from male and female gametes during fertilization. Although these transcriptional sex effects rapidly diminish, sex-biased genes appear to form sex-specific protein-protein interaction networks across pre-implantation stages in both mammals providing evidence that sex-biased expression of epigenetic enzymes may establish sex-specific patterns that persist beyond pre-implantation. Non-negative matrix factorization (NMF) on male and female transcriptomes generated clusters of genes with similar expression patterns across sex and developmental stages, including post-fertilization, epigenetic, and pre-implantation ontologies conserved between mouse and human. While the fraction of sex-differentially expressed genes (sexDEGs) in early embryonic stages is similar and functional ontologies are conserved, the genes involved are generally different in mouse and human. CONCLUSIONS: This comparative study uncovers much earlier than expected sex-specific signals in mouse and human embryos that pre-date hormonal signaling from the gonads. These early signals are diverged with respect to orthologs yet conserved in terms of function with important implications in the use of genetic models for sex-specific disease.

Sex differences are traditionally assumed to arise after the reproductive systems are set up in male and female embryos, and especially after these organs begin producing sex hormones. However, very early in embryo development, the sex chromosomes distinguish males (XY) and females (XX) and genes on these chromosomes are expressed differentially. In this study, we have analyzed gene expression data from mouse and human early embryos to determine whether there is sex-biased expression before implantation. Our results confirm that there is abundant sex-biased expression from the earliest stages of development, soon after fertilization. The comparison between human and mouse embryos shows comparable function of the sex-biased genes, although the specific genes involved differ between the two species.

Identification and genomic analysis of Pseudosulfitobacter koreense sp. nov. isolated from toxin-producing dinoflagellate Alexandrium pacificum.

The bacterial strain AP-MA-4T isolated from the marine dinoflagellate Alexandrium pacificum (KCTC AG60911), was subjected to a taxonomic analysis. Cells of strain AP-MA-4T were Gram-stain-negative, aerobic, rod-shaped, optimum growth at 20 °C, pH 7.0, in the presence of 5% (w/v) NaCl. Strain AP-MA-4T shared the highest 16S rRNA gene sequence similarity to Pseudosulfitobacter pseudonitzschiae DSM 26824T (98.5%), followed by Ascidiaceihabitans donghaensis RSS1-M3T (96.3%), Pseudoseohaeicola caenipelagi BS-W13T (95.7%), and Sulfitobacter pontiacus CHLG 10T (95.3%). Based on 16S rRNA phylogeny, strain AP-MA-4T is phylogenetically closely related to Pseudosulfitobacter pseudonitzschiae (type species of Pseudosulfitobacter) and could be distinguished from the type species based on their phenotypic properties. The genome length of strain AP-MA-4T was 3.48 Mbp with a 62.9% G + C content. The average nucleotide identity (ANI) and digital DNA-DNA hybridization (dDDH) values between strain AP-MA-4 T and its closely related type strains were 72.2-83.3 and 18.2-27.6%, respectively. Summed feature 8 (C18:1ω7c and/or C18:1ω6c) was identified the major fatty acids (> 10%). Phosphatidylglycerol (PG), phosphatidylethanolamine (PE), and phospholipid (PL) were demonstrated as the major polar lipids. The major respiratory quinone is ubiquinone-10 (Q-10). Based on genotypic and phenotypic features, strain AP-MA-4T (= KCTC 92289T = GDMCC 1.3585T) represents a new Pseudosulfitobacter species, in which the name Pseudosulfitobacter koreense sp. nov. is proposed.

Genomic benchmarks: a collection of datasets for genomic sequence classification.

BACKGROUND: Recently, deep neural networks have been successfully applied in many biological fields. In 2020, a deep learning model AlphaFold won the protein folding competition with predicted structures within the error tolerance of experimental methods. However, this solution to the most prominent bioinformatic challenge of the past 50 years has been possible only thanks to a carefully curated benchmark of experimentally predicted protein structures. In Genomics, we have similar challenges (annotation of genomes and identification of functional elements) but currently, we lack benchmarks similar to protein folding competition. RESULTS: Here we present a collection of curated and easily accessible sequence classification datasets in the field of genomics. The proposed collection is based on a combination of novel datasets constructed from the mining of publicly available databases and existing datasets obtained from published articles. The collection currently contains nine datasets that focus on regulatory elements (promoters, enhancers, open chromatin region) from three model organisms: human, mouse, and roundworm. A simple convolution neural network is also included in a repository and can be used as a baseline model. Benchmarks and the baseline model are distributed as the Python package 'genomic-benchmarks', and the code is available at https://github.com/ML-Bioinfo-CEITEC/genomic_benchmarks . CONCLUSIONS: Deep learning techniques revolutionized many biological fields but mainly thanks to the carefully curated benchmarks. For the field of Genomics, we propose a collection of benchmark datasets for the classification of genomic sequences with an interface for the most commonly used deep learning libraries, implementation of the simple neural network and a training framework that can be used as a starting point for future research. The main aim of this effort is to create a repository for shared datasets that will make machine learning for genomics more comparable and reproducible while reducing the overhead of researchers who want to enter the field, leading to healthy competition and new discoveries.

HostSeq: a Canadian whole genome sequencing and clinical data resource.

HostSeq was launched in April 2020 as a national initiative to integrate whole genome sequencing data from 10,000 Canadians infected with SARS-CoV-2 with clinical information related to their disease experience. The mandate of HostSeq is to support the Canadian and international research communities in their efforts to understand the risk factors for disease and associated health outcomes and support the development of interventions such as vaccines and therapeutics. HostSeq is a collaboration among 13 independent epidemiological studies of SARS-CoV-2 across five provinces in Canada. Aggregated data collected by HostSeq are made available to the public through two data portals: a phenotype portal showing summaries of major variables and their distributions, and a variant search portal enabling queries in a genomic region. Individual-level data is available to the global research community for health research through a Data Access Agreement and Data Access Compliance Office approval. Here we provide an overview of the collective project design along with summary level information for HostSeq. We highlight several statistical considerations for researchers using the HostSeq platform regarding data aggregation, sampling mechanism, covariate adjustment, and X chromosome analysis. In addition to serving as a rich data source, the diversity of study designs, sample sizes, and research objectives among the participating studies provides unique opportunities for the research community.