The Immune Landscape of Cancer.

We performed an extensive immunogenomic analysis of more than 10,000 tumors comprising 33 diverse cancer types by utilizing data compiled by TCGA. Across cancer types, we identified six immune subtypes-wound healing, IFN-γ dominant, inflammatory, lymphocyte depleted, immunologically quiet, and TGF-ß dominant-characterized by differences in macrophage or lymphocyte signatures, Th1:Th2 cell ratio, extent of intratumoral heterogeneity, aneuploidy, extent of neoantigen load, overall cell proliferation, expression of immunomodulatory genes, and prognosis. Specific driver mutations correlated with lower (CTNNB1, NRAS, or IDH1) or higher (BRAF, TP53, or CASP8) leukocyte levels across all cancers. Multiple control modalities of the intracellular and extracellular networks (transcription, microRNAs, copy number, and epigenetic processes) were involved in tumor-immune cell interactions, both across and within immune subtypes. Our immunogenomics pipeline to characterize these heterogeneous tumors and the resulting data are intended to serve as a resource for future targeted studies to further advance the field.

An aged immune system drives senescence and ageing of solid organs.

Ageing of the immune system, or immunosenescence, contributes to the morbidity and mortality of the elderly1,2. To define the contribution of immune system ageing to organism ageing, here we selectively deleted Ercc1, which encodes a crucial DNA repair protein3,4, in mouse haematopoietic cells to increase the burden of endogenous DNA damage and thereby senescence5-7 in the immune system only. We show that Vav-iCre+/-;Ercc1-/fl mice were healthy into adulthood, then displayed premature onset of immunosenescence characterized by attrition and senescence of specific immune cell populations and impaired immune function, similar to changes that occur during ageing in wild-type mice8-10. Notably, non-lymphoid organs also showed increased senescence and damage, which suggests that senescent, aged immune cells can promote systemic ageing. The transplantation of splenocytes from Vav-iCre+/-;Ercc1-/fl or aged wild-type mice into young mice induced senescence in trans, whereas the transplantation of young immune cells attenuated senescence. The treatment of Vav-iCre+/-;Ercc1-/fl mice with rapamycin reduced markers of senescence in immune cells and improved immune function11,12. These data demonstrate that an aged, senescent immune system has a causal role in driving systemic ageing and therefore represents a key therapeutic target to extend healthy ageing.

Pasa: leveraging population pangenome graph to scaffold prokaryote genome assemblies.

Whole genome sequencing has increasingly become the essential method for studying the genetic mechanisms of antimicrobial resistance and for surveillance of drug-resistant bacterial pathogens. The majority of bacterial genomes sequenced to date have been sequenced with Illumina sequencing technology, owing to its high-throughput, excellent sequence accuracy, and low cost. However, because of the short-read nature of the technology, these assemblies are fragmented into large numbers of contigs, hindering the obtaining of full information of the genome. We develop Pasa, a graph-based algorithm that utilizes the pangenome graph and the assembly graph information to improve scaffolding quality. By leveraging the population information of the bacteria species, Pasa is able to utilize the linkage information of the gene families of the species to resolve the contig graph of the assembly. We show that our method outperforms the current state of the arts in terms of accuracy, and at the same time, is computationally efficient to be applied to a large number of existing draft assemblies.

AMRViz enables seamless genomics analysis and visualization of antimicrobial resistance.

We have developed AMRViz, a toolkit for analyzing, visualizing, and managing bacterial genomics samples. The toolkit is bundled with the current best practice analysis pipeline allowing researchers to perform comprehensive analysis of a collection of samples directly from raw sequencing data with a single command line. The analysis results in a report showing the genome structure, genome annotations, antibiotic resistance and virulence profile for each sample. The pan-genome of all samples of the collection is analyzed to identify core- and accessory-genes. Phylogenies of the whole genome as well as all gene clusters are also generated. The toolkit provides a web-based visualization dashboard allowing researchers to interactively examine various aspects of the analysis results. Availability: AMRViz is implemented in Python and NodeJS, and is publicly available under open source MIT license at https://github.com/amromics/amrviz .

AMRomics: a scalable workflow to analyze large microbial genome collections.

Whole genome analysis for microbial genomics is critical to studying and monitoring antimicrobial resistance strains. The exponential growth of microbial sequencing data necessitates a fast and scalable computational pipeline to generate the desired outputs in a timely and cost-effective manner. Recent methods have been implemented to integrate individual genomes into large collections of specific bacterial populations and are widely employed for systematic genomic surveillance. However, they do not scale well when the population expands and turnaround time remains the main issue for this type of analysis. Here, we introduce AMRomics, an optimized microbial genomics pipeline that can work efficiently with big datasets. We use different bacterial data collections to compare AMRomics against competitive tools and show that our pipeline can generate similar results of interest but with better performance. The software is open source and is publicly available at https://github.com/amromics/amromics under an MIT license.

A study of genetic variants associated with skin traits in the Vietnamese population.

BACKGROUND: Most skin-related traits have been studied in Caucasian genetic backgrounds. A comprehensive study on skin-associated genetic effects on underrepresented populations such as Vietnam is needed to fill the gaps in the field. OBJECTIVES: We aimed to develop a computational pipeline to predict the effect of genetic factors on skin traits using public data (GWAS catalogs and whole-genome sequencing (WGS) data from the 1000 Genomes Project-1KGP) and in-house Vietnamese data (WGS and genotyping by SNP array). Also, we compared the genetic predispositions of 25 skin-related traits of Vietnamese population to others to acquire population-specific insights regarding skin health. METHODS: Vietnamese cohorts of whole-genome sequencing (WGS) of 1008 healthy individuals for the reference and 96 genotyping samples (which do not have any skin cutaneous issues) by Infinium Asian Screening Array-24 v1.0 BeadChip were employed to predict skin-associated genetic variants of 25 skin-related and micronutrient requirement traits in population analysis and correlation analysis. Simultaneously, we compared the landscape of cutaneous issues of Vietnamese people with other populations by assessing their genetic profiles. RESULTS: The skin-related genetic profile of Vietnamese cohorts was similar at most to East Asian cohorts (JPT: Fst = 0.036, CHB: Fst = 0.031, CHS: Fst = 0.027, CDX: Fst = 0.025) in the population study. In addition, we identified pairs of skin traits at high risk of frequent co-occurrence (such as skin aging and wrinkles (r = 0.45, p = 1.50e-5) or collagen degradation and moisturizing (r = 0.35, p = 1.1e-3)). CONCLUSION: This is the first investigation in Vietnam to explore genetic variants of facial skin. These findings could improve inadequate skin-related genetic diversity in the currently published database.

LmTag: functional-enrichment and imputation-aware tag SNP selection for population-specific genotyping arrays.

Despite the rapid development of sequencing technology, single-nucleotide polymorphism (SNP) arrays are still the most cost-effective genotyping solutions for large-scale genomic research and applications. Recent years have witnessed the rapid development of numerous genotyping platforms of different sizes and designs, but population-specific platforms are still lacking, especially for those in developing countries. SNP arrays designed for these countries should be cost-effective (small size), yet incorporate key information needed to associate genotypes with traits. A key design principle for most current platforms is to improve genome-wide imputation so that more SNPs not included in the array (imputed SNPs) can be predicted. However, current tag SNP selection methods mostly focus on imputation accuracy and coverage, but not the functional content of the array. It is those functional SNPs that are most likely associated with traits. Here, we propose LmTag, a novel method for tag SNP selection that not only improves imputation performance but also prioritizes highly functional SNP markers. We apply LmTag on a wide range of populations using both public and in-house whole-genome sequencing databases. Our results show that LmTag improved both functional marker prioritization and genome-wide imputation accuracy compared to existing methods. This novel approach could contribute to the next generation genotyping arrays that provide excellent imputation capability as well as facilitate array-based functional genetic studies. Such arrays are particularly suitable for under-represented populations in developing countries or non-model species, where little genomics data are available while investment in genome sequencing or high-density SNP arrays is limited. $\textrm{LmTag}$ is available at: https://github.com/datngu/LmTag.

Assessing polygenic risk score models for applications in populations with under-represented genomics data: an example of Vietnam.

Most polygenic risk score (PRS)models have been based on data from populations of European origins (accounting for the majority of the large genomics datasets, e.g. >78% in the UK Biobank and >85% in the GTEx project). Although several large-scale Asian biobanks were initiated (e.g. Japanese, Korean, Han Chinese biobanks), most other Asian countries have little or near-zero genomics data. To implement PRS models for under-represented populations, we explored transfer learning approaches, assuming that information from existing large datasets can compensate for the small sample size that can be feasibly obtained in developing countries, like Vietnam. Here, we benchmark 13 common PRS methods in meta-population strategy (combining individual genotype data from multiple populations) and multi-population strategy (combining summary statistics from multiple populations). Our results highlight the complementarity of different populations and the choice of methods should depend on the target population. Based on these results, we discussed a set of guidelines to help users select the best method for their datasets. We developed a robust and comprehensive software to allow for benchmarking comparisons between methods and proposed a computational framework for improving PRS performance in a dataset with a small sample size. This work is expected to inform the development of genomics applications in under-represented populations. PRSUP framework is available at: https://github.com/BiomedicalMachineLearning/VGP.

The incidence of dermatitis following application of foam tape in healthy volunteers-A prospective trial.

BACKGROUND: Foam tape is commonly used in the emergency department as a dressing over chest tubes owing to its occlusive and compressible properties. There is a paucity of data regarding the incidence of significant cutaneous reactions to this material. We conducted a prospective trial to evaluate the incidence of dermatitis following application of foam tape to the upper arm of a cohort of healthy volunteers. METHODS: This was a prospective, interventional trial. We enrolled a cohort of consenting, healthy physicians, nurses, and ancillary staff at a teaching facility who did not have known hypersensitivity to foam tape. Study investigators applied a 2 × 2 inch piece of 3 M microfoam adhesive to the medial aspect of each subject's upper arm. The contralateral arm served as a reference for comparison. The adhesive remained in place for 48 h and the study authors assessed patients utilizing the previously validated Cutaneous Irritancy Scoring System (CISS). Categorical variables analyzed by chi-square, continuous variables with t-tests. RESULTS: There were 40 subjects in the study group; 52% female, mean age 40±7 years, 55% non-White race. 10/40 (25%; 95%CI[14%, 41%]) of subjects had erythema; 9/40 (22%) had an erythema score of 1 and 1/40 (2.5%) had a score of 2. With respect to edema, 2/40 (5%; 95% CI[1%,18%]); 1/40(2.5%) had an edema score of 1, and 1/40(2.5%) had a score of 2. There were 9/40 subjects with an irritancy score > 0; (22%; 95%CI[12%,38%]); 7/40(18%) had an irritancy score of 1, and 2/4(5.0%) had a score of 2. In terms of the severity score, 10/40 (25%; 95%CI[14%, 41%]) had a score > 0; 9/40(22%) had a score of 1, and 1/40(2.5%) had a score of 2. Overall, 10/40 (25%; 95%CI[14%, 41%]) of subjects had at least one positive measure of a reaction of any kind. Subjects' age, gender and race were not found to be statistically significantly associated with the incidence of erythema, edema, or irritancy. In addition, these characteristics were not statistically significantly associated with severity score > 0. The p values for all the above bivariate analyses were > 0.05. CONCLUSIONS: Cutaneous reactions occurred in 25% of healthy volunteers after the application of foam tape to the arm. Patient characteristics were not associated with risk of a skin reaction. CLINICAL TRIALS REGISTRATION: #NCT06059417.

Pain-related single nucleotide polymorphisms: association with lumbar spinal stenosis patient experience and non-surgical treatment outcomes.

PURPOSE: Lumbar spinal stenosis (LSS) is common in our aging population resulting in pain and functional impairment. Recent advances in pain research have identified several single nucleotide polymorphisms (SNP) associated with inter-individual symptom and treatment response. The goal of the current study was to investigate the association of SNPs in Neuropeptide Y (NPY) and Catechol-O-methyltransferase (COMT) with pain, function, and treatment outcomes in Lumbar spinal stenosis (LSS) patients receiving non-surgical treatments. METHODS: An exploratory observational biomarker study was performed ancillary to a previously published clinical trial evaluating three different non-surgical treatments for LSS. Saliva samples were obtained for single nucleotide polymorphism genotyping and blood samples were collected for NPY protein. Data on pain and function collected as part of the clinical trial at baseline, 2 and 6 months were examined for association with known polymorphisms in NPY and COMT. RESULTS: Subjects with the NPY rs16147 TT genotype exhibited higher baseline symptom severity but also a higher likelihood of responding to non-surgical treatments. Subjects with the COMT rs4680 GG genotype also exhibited higher baseline symptom severity but did not demonstrate greater response to treatment. CONCLUSIONS: NPY rs16147 and COMT rs4680 are important potential biomarkers associated with pain and function. NPY genotype may be useful in predicting response to non-surgical treatments in older adults with LSS.

MicroRNA-29a: a novel target for non-operative management of symptomatic lumbar spinal stenosis.

PURPOSE: Lumbar spinal stenosis (LSS) is the most common reason for spinal surgery in patients over the age of 65, and there are few effective non-surgical treatments. Therefore, the development of novel treatment or preventative modalities to decrease overall cost and morbidity associated with LSS is an urgent matter. The cause of LSS is multifactorial; however, a significant contributor is ligamentum flavum hypertrophy (LFH) which causes mechanical compression of the cauda equina or nerve roots. We assessed the role of a novel target, microRNA-29a (miR-29a), in LFH and investigated the potential for using miR-29a as a therapeutic means to combat LSS. METHODS: Ligamentum flavum (LF) tissue was collected from patients undergoing decompressive surgery for LSS and assessed for levels of miR-29a and pro-fibrotic protein expression. LF cell cultures were then transfected with either miR-29a over-expressor (agonist) or inhibitor (antagonist). The effects of over-expression and under-expression of miR-29a on expression of pro-fibrotic proteins was assessed. RESULTS: We demonstrated that LF at stenotic levels had a loss of miR-29a expression. This was associated with greater LF tissue thickness and higher mRNA levels of collagen I and III. We also demonstrated that miR29-a plays a direct role in the regulation of collagen gene expression in ligamentum flavum. Specifically, agents that increase miR-29a may attenuate LFH, while those that decrease miR-29a promote fibrosis and LFH. CONCLUSION: This study demonstrates that miR-29a may potentially be used to treat LFH and provides groundwork to initiate the development of a therapeutic product for LSS.

Protocol for Biospecimen Collection and Analysis Within the BACPAC Research Program.

The Biospecimen Collection and Processing Working Group of the National Institutes of Health (NIH) HEAL Initiative BACPAC Research Program was charged with identifying molecular biomarkers of interest to chronic low back pain (cLBP). Having identified biomarkers of interest, the Working Group worked with the New York University Grossman School of Medicine, Center for Biospecimen Research and Development-funded by the Early Phase Pain Investigation Clinical Network Data Coordinating Center-to harmonize consortium-wide and site-specific efforts for biospecimen collection and analysis. Biospecimen collected are saliva, blood (whole, plasma, serum), urine, stool, and spine tissue (paraspinal muscle, ligamentum flavum, vertebral bone, facet cartilage, disc endplate, annulus fibrosus, or nucleus pulposus). The omics data acquisition and analyses derived from the biospecimen include genomics and epigenetics from DNA, proteomics from protein, transcriptomics from RNA, and microbiomics from 16S rRNA. These analyses contribute to the overarching goal of BACPAC to phenotype cLBP and will guide future efforts for precision medicine treatment.

Toward the Identification of Distinct Phenotypes: Research Protocol for the Low Back Pain Biological, Biomechanical, and Behavioral (LB3P) Cohort Study and the BACPAC Mechanistic Research Center at the University of Pittsburgh.

As a member of the Back Pain Consortium (BACPAC), the University of Pittsburgh Mechanistic Research Center's research goal is to phenotype chronic low back pain using biological, biomechanical, and behavioral domains using a prospective, observational cohort study. Data will be collected from 1,000 participants with chronic low back pain according to BACPAC-wide harmonized and study-specific protocols. Participation lasts 12 months with one required in person baseline visit, an optional second in person visit for advanced biomechanical assessment, and electronic follow ups at months 1, 2, 3, 4, 5, 6, 9, and 12 to assess low back pain status and response to prescribed treatments. Behavioral data analysis includes a battery of patient-reported outcomes, social determinants of health, quantitative sensory testing, and physical activity. Biological data analysis includes omics generated from blood, saliva, and spine tissue. Biomechanical data analysis includes a physical examination, lumbopelvic kinematics, and intervertebral kinematics. The statistical analysis includes traditional unsupervised machine learning approaches to categorize participants into groups and determine the variables that differentiate patients. Additional analysis includes the creation of a series of decision rules based on baseline measures and treatment pathways as inputs to predict clinical outcomes. The characteristics identified will contribute to future studies to assist clinicians in designing a personalized, optimal treatment approach for each patient.

The Back Pain Consortium (BACPAC) Research Program Data Harmonization: Rationale for Data Elements and Standards.

OBJECTIVE: One aim of the Back Pain Consortium (BACPAC) Research Program is to develop an integrated model of chronic low back pain that is informed by combined data from translational research and clinical trials. We describe efforts to maximize data harmonization and accessibility to facilitate Consortium-wide analyses. METHODS: Consortium-wide working groups established harmonized data elements to be collected in all studies and developed standards for tabular and nontabular data (eg, imaging and omics). The BACPAC Data Portal was developed to facilitate research collaboration across the Consortium. RESULTS: Clinical experts developed the BACPAC Minimum Dataset with required domains and outcome measures to be collected by use of questionnaires across projects. Other nonrequired domain-specific measures are collected by multiple studies. To optimize cross-study analyses, a modified data standard was developed on the basis of the Clinical Data Interchange Standards Consortium Study Data Tabulation Model to harmonize data structures and facilitate integration of baseline characteristics, participant-reported outcomes, chronic low back pain treatments, clinical exam, functional performance, psychosocial characteristics, quantitative sensory testing, imaging, and biomechanical data. Standards to accommodate the unique features of chronic low back pain data were adopted. Research units submit standardized study data to the BACPAC Data Portal, developed as a secure cloud-based central data repository and computing infrastructure for researchers to access and conduct analyses on data collected by or acquired for BACPAC. CONCLUSIONS: BACPAC harmonization efforts and data standards serve as an innovative model for data integration that could be used as a framework for other consortia with multiple, decentralized research programs.

The Back Pain Consortium (BACPAC) Research Program: Structure, Research Priorities, and Methods.

In 2019, the National Health Interview survey found that nearly 59% of adults reported pain some, most, or every day in the past 3 months, with 39% reporting back pain, making back pain the most prevalent source of pain, and a significant issue among adults. Often, identifying a direct, treatable cause for back pain is challenging, especially as it is often attributed to complex, multifaceted issues involving biological, psychological, and social components. Due to the difficulty in treating the true cause of chronic low back pain (cLBP), an over-reliance on opioid pain medications among cLBP patients has developed, which is associated with increased prevalence of opioid use disorder and increased risk of death. To combat the rise of opioid-related deaths, the National Institutes of Health (NIH) initiated the Helping to End Addiction Long-TermSM (HEAL) initiative, whose goal is to address the causes and treatment of opioid use disorder while also seeking to better understand, diagnose, and treat chronic pain. The NIH Back Pain Consortium (BACPAC) Research Program, a network of 14 funded entities, was launched as a part of the HEAL initiative to help address limitations surrounding the diagnosis and treatment of cLBP. This paper provides an overview of the BACPAC research program's goals and overall structure, and describes the harmonization efforts across the consortium, define its research agenda, and develop a collaborative project which utilizes the strengths of the network. The purpose of this paper is to serve as a blueprint for other consortia tasked with the advancement of pain related science.

Biological Characterization and Genomic Analysis of Novel Phages DLDT_So2 and BHDT_So9 Against Pseudomonas solanacearum, an Infectious Agent in Tomato in Vietnam.

Tomato (Solanum lycopersicum L.) is an important grown vegetable in Vietnam. Bacterial wilt caused by Pseudomonas solanacearum has been considered to be an important disease resulting in a harvest loss up to 90% and significant economic loss to farmers. In this study, two bacteriophages DLDT_So2 and BHDT_So9 specific to P. solanacearum were isolated. Morphological analysis indicated that DLDT_So2 and BHDT_So9 had podovirus morphology and were classified into Autographiviridae family. The latent period and burst size of DLDT_So2 was found to be approximately 120 min and 20.0 ± 2.4 virions per infected cell. Meanwhile, the latent period of BHDT_So9 was 140 min with a burst size of 11.5 ± 2.8 virions per infected cell. Of the 23 bacterial strains tested, the phages infected 7/11 strains of P. solanacearum and none of the other bacteria tested were susceptible to the phages. Stability of the phages at different temperatures, pHs, solvents was also investigated. The genomes of DLDT_So2 and BHDT_So9 are 41,341 bp and 41,296 bp and long with a total GC content of 63%, contains 48 and 46 predicted protein-encoding CDSs. No virulence or antibiotic resistance genes were found in the genomes, suggesting they would be useful biocontrol agents against P. solanacearum. Classification of the phage using average nucleotide identity, phylogenetic analysis was also carried out. The two phages represented new species when they had overall average nucleotide identity of < 95%. This is first report of the isolation and characterization of P. solanacearum-specific phages from tomato farms in Vietnam. Supplementary Information: The online version contains supplementary material available at 10.1007/s12088-023-01090-9.

Extract antibody and antigen names from biomedical literature.

BACKGROUND: The roles of antibody and antigen are indispensable in targeted diagnosis, therapy, and biomedical discovery. On top of that, massive numbers of new scientific articles about antibodies and/or antigens are published each year, which is a precious knowledge resource but has yet been exploited to its full potential. We, therefore, aim to develop a biomedical natural language processing tool that can automatically identify antibody and antigen entities from articles. RESULTS: We first annotated an antibody-antigen corpus including 3210 relevant PubMed abstracts using a semi-automatic approach. The Inter-Annotator Agreement score of 3 annotators ranges from 91.46 to 94.31%, indicating that the annotations are consistent and the corpus is reliable. We then used the corpus to develop and optimize BiLSTM-CRF-based and BioBERT-based models. The models achieved overall F1 scores of 62.49% and 81.44%, respectively, which showed potential for newly studied entities. The two models served as foundation for development of a named entity recognition (NER) tool that automatically recognizes antibody and antigen names from biomedical literature. CONCLUSIONS: Our antibody-antigen NER models enable users to automatically extract antibody and antigen names from scientific articles without manually scanning through vast amounts of data and information in the literature. The output of NER can be used to automatically populate antibody-antigen databases, support antibody validation, and facilitate researchers with the most appropriate antibodies of interest. The packaged NER model is available at https://github.com/TrangDinh44/ABAG_BioBERT.git .

Role of autophagy in intervertebral disc degeneration.

Intervertebral disc degeneration (IDD) is a leading contributor to low back pain. The intervertebral disc (IVD) is composed of three tissue types: the central gelatinous nucleus pulposus (NP) tissue, the surrounding annulus fibrosus (AF) tissue, and the inferior and superior cartilage endplates. The IVD microenvironment is hypoxic, acidic, hyperosmotic, and low in nutrients because it is mostly avascular. The cellular processes that underlie IDD initiation and progression are still poorly understood. Specifically, a lack of understanding regarding NP cell metabolism and physiology hinders the development of effective therapeutics to treat IDD patients. Autophagy is a vital intracellular degradation process that removes damaged organelles, misfolded proteins, and intracellular pathogens and recycles the degraded components for cellular energy and function. NP cells have adapted to survive within their harsh tissue microenvironment using processes that are largely unknown, and we postulate autophagy is one of these undiscovered mechanisms. In this review, we describe unique features of the IVD tissue, review how physiological stressors impact autophagy in NP cells in vitro, survey the current understanding of autophagy regulation in the IVD, and assess the relationship between autophagy and IDD. Published studies confirm autophagy markers are present in IVD tissue, and IVD cells can regulate autophagy in response to cellular stressors in vitro. However, data are still lacking to determine the exact mechanisms regulating autophagy in IVD cells. More in-depth research is needed to establish whether autophagy is necessary to maintain IVD cell health and validate autophagy as a relevant therapeutic target for treating IDD.

Investigation of target sequencing of SARS-CoV-2 and immunogenic GWAS profiling in host cells of COVID-19 in Vietnam.

BACKGROUND: A global pandemic has been declared for coronavirus disease 2019 (COVID-19), which has serious impacts on human health and healthcare systems in the affected areas, including Vietnam. None of the previous studies have a framework to provide summary statistics of the virus variants and assess the severity associated with virus proteins and host cells in COVID-19 patients in Vietnam. METHOD: In this paper, we comprehensively investigated SARS-CoV-2 variants and immune responses in COVID-19 patients. We provided summary statistics of target sequences of SARS-CoV-2 in Vietnam and other countries for data scientists to use in downstream analysis for therapeutic targets. For host cells, we proposed a predictive model of the severity of COVID-19 based on public datasets of hospitalization status in Vietnam, incorporating a polygenic risk score. This score uses immunogenic SNP biomarkers as indicators of COVID-19 severity. RESULT: We identified that the Delta variant of SARS-CoV-2 is most prevalent in southern areas of Vietnam and it is different from other areas in the world using various data sources. Our predictive models of COVID-19 severity had high accuracy (Random Forest AUC = 0.81, Elastic Net AUC = 0.7, and SVM AUC = 0.69) and showed that the use of polygenic risk scores increased the models' predictive capabilities. CONCLUSION: We provided a comprehensive analysis for COVID-19 severity in Vietnam. This investigation is not only helpful for COVID-19 treatment in therapeutic target studies, but also could influence further research on the disease progression and personalized clinical outcomes.