Genomic Variants Research Articles

Genomic variants and molecular epidemiological characteristics of dengue virus in China revealed by genome-wide analysis

Abstract Since its first academic record in 1978, dengue epidemics have occurred in all provinces of China, except Xizang. The epidemiological and molecular features of the whole genome of dengue virus (DENV) have not yet been completely elucidated, interfering with prevention and control strategies for dengue fever in China. Here, we obtained 553 complete genomes of the four serotypes of DENV (DENV1–4) isolated in China from the GenBank database to analyze the phylogeny, recombination, genomic variants, and selection pressure, and to estimate the substitution rates of DENV genomes. Phylogenetic analyses indicated that DENV sequences from China did not cluster together and were genetically closer to those from Southeast Asian countries in the maximum likelihood (ML) trees, indicating that DENV was not endemic in China. Thirty intra-serotype recombinant sequences were identified for DENV1–4, with the highest frequency in DENV-4. Selection pressure analyses revealed that thirteen codons under positive selection were located in the C, NS1, NS2A, NS3, and NS5 proteins. For DENV-1 to DENV-3, the substitution rates evaluated in this study were 9.23 × 10-4, 7.59 × 10-4, and 7.06 × 10-4 substitutions per site per year, respectively. These findings improve our understanding of the evolution of DENV in China.

The regulatory role of ACP5 in the diesel exhaust particle-induced AHR inflammatory signaling pathway in a human bronchial epithelial cell line

Exposure to diesel exhaust particles (DEPs), which are major constituents of urban air pollution, is associated with adverse health outcomes. Previous studies have shown that DEPs enhance the expression of pro-inflammatory cytokines and immune responses. However, few studies have focused on genomic variants that regulate DEP-induced signaling. Here, we identify a frequently found genomic variant, ACP5, in allergic diseases, and establish an ACP5 knock-out (KO) human bronchial epithelial cell line (BEAS-2B) using CRISPR/Cas9 editing to mimic the ACP5 mutation. DEP-induced apoptosis and intracellular reactive oxygen species (ROS) were significantly increased in the ACP5 KO cells compared with controls, suggesting that ACP5 KO cells were at increased risk from DEP exposure. A gene expression profile revealed an activated aryl hydrocarbon receptor (AHR)-CYP1A1 axis followed by upregulated pro-inflammatory signaling. Treatment of a DEP-exposed ACP5 KO BEAS-2B conditioned medium (CM) supernatant induced an inflammatory response and tissue damage in mice, and AHR inhibition effectively prevented inflammation-induced damage, suggesting that AHR-CYP1A1-inflammatory signaling is a prominent mechanism responsible for detrimental effects. Collectively, our findings reveal a novel link between ACP5 KO and the AHR-CYP1A1 inflammatory signaling pathway in DEP-exposed cells, and identify the AHR-CYP1A1 axis as a potential therapeutic target in individuals suffering from DEP-induced toxicity, particularly those with ACP5 mutations.

Benchmarking long-read structural variant calling tools and combinations for detecting somatic variants in cancer genomes

Cancer genomes have a complicated landscape of mutations, including large-scale rearrangements known as structural variants (SVs). These SVs can disrupt genes or regulatory elements, playing a critical role in cancer development and progression. Despite their importance, accurate identification of somatic structural variants (SVs) remains a significant bottleneck in cancer genomics. Long-read sequencing technologies hold great promise in SV discovery, and there is an increasing number of efforts to develop new tools to detect them. In this study, we employ eight widely used SV callers on paired tumor and matched normal samples from both the NCI-H2009 lung cancer cell line and the COLO829 melanoma cell line, the latter of which has a well-established somatic SV truth set. Following separate variation detection in both tumor and normal DNA, the VCF merging procedure and a subtraction method were used to identify candidate somatic SVs. Additionally, we explored different combinations of the tools to enhance the accuracy of true somatic SV detection. Our analysis adopts a comprehensive approach, evaluating the performance of each SV caller across a spectrum of variant types and numbers in finding cancer-related somatic SVs. This study, by comparing eight different tools and their combinations, not only reveals the benefits and limitations of various techniques but also establishes a framework for developing more robust SV calling pipelines. Our findings highlight the strengths and weaknesses of current SV calling tools and suggest that combining multiple tools and testing different combinations can significantly enhance the validation of somatic alterations.

Revisiting CDKN2A dysregulation in Ewing sarcoma.

Ewing sarcoma (EwS) is a rare and aggressive malignancy, which frequently affects children. One of the few recurrent genomic variants in EwS is genomic copy number deletion of CDKN2A; however, the clinical consequences of dysregulation of CDKN2A in EwS are unclear. In this study, we revisit CDKN2A to investigate its role as a potential prognostic biomarker in EwS using data from EwS pre-clinical models as well as clinical samples from patients with EwS. We demonstrate the potential essentiality of CDKN2A dysregulation and sustained downstream CDK4/CCND1 activity. Finally, we present evidence that high expression of CDKN2A is a negative prognostic biomarker at diagnosis in EwS in three independent datasets. Our data may suggest that the role of CDKN2A may change across the clinical context of EwS, however, further study is necessary to validate the function of CDKN2A expression in EwS.

Disruption and adaptation: infant gut microbiota’s dynamic response to SARS-CoV-2 infection

BackgroundThe responses of the infant gut microbiota to infection significantly disrupt the natural intrahost evolutionary processes of the microbiome. Here, we collected a 16-month longitudinal cohort of infant gut microbiomes affected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Then, we developed a multicriteria approach to identify core interaction network driving community dynamics under environmental disturbances, which we termed the Conserved Variated Interaction Group (CVIgroup).ResultsThe CVIgroup showed significant advantages on pinpointing a sparse set associated with the disturbances, as validated both our own and publicly available datasets. Leveraging the Oxford Nanopore Technology, we found this group facilitates the ecosystem's adaptation to environmental disruptions by enhancing the mobility of mobile genetic elements, including the reinforcement of the twin-arginine translocation pathway in response to increased virulence factors. Furthermore, the CVIgroup serves as an effective indicator of ecosystem health. The timescale for the gut microbiota's adaptation extends beyond 10 months. Members of the CVIgroup, such as Bacteroides thetaiotaomicron and Faecalibacterium, exhibit varying degrees of genomic structural variants, which contribute to guiding the community toward a new stable state rather than returning to its original configuration.ConclusionsCollectively, the CVIgroup offers a snapshot of the gut microbiota's adaptive response to environmental disturbances. The disruption and subsequent adaptation of the gut microbiota in infants after COVID-19 infection underscores the necessity of re-evaluating reference standards in the context of the post-pandemic era.At4D4VkGNCpfimFwDjBvXSVideo

Experiences from dual genome next-generation sequencing panel testing for mitochondrial disorders: a comprehensive molecular diagnosis.

The molecular diagnosis of mitochondrial disorders is complicated by phenotypic variability, genetic heterogeneity, and the complexity of mitochondrial heteroplasmy. Next-generation sequencing (NGS) of the mitochondrial genome in combination with a targeted panel of nuclear genes associated with mitochondrial disease provides the highest likelihood of obtaining a comprehensive molecular diagnosis. To assess the clinical utility of this approach, we describe the results from a retrospective review of patients having dual genome panel testing for mitochondrial disease. Dual genome panel testing by NGS was performed on a cohort of 1,509 unrelated affected individuals with suspected mitochondrial disorders. This test included 163 nuclear genes associated with mitochondrial diseases and the entire mitochondrial genome. A retrospective review was performed to evaluate diagnostic yield, disease-gene contributions, and heteroplasmy levels of pathogenic/likely pathogenic (P/LP) mitochondrial DNA (mtDNA) variants. The overall diagnostic yield was 14.6%, with 7.7% from the nuclear genome and 6.9% from the mtDNA genome. P/LP variants in nuclear genes were enriched in both well-established genes (e.g., POLG) and more recently described genes (e.g., FBXL4), highlighting the importance of keeping the panel design updated. Variants in nuclear and mitochondrial genomes equally contributed to a 14.6% diagnostic yield in this patient cohort. Dual genome NGS testing provides a comprehensive framework for diagnosing mitochondrial disorders, offering clinical utility that can be considered as first-tier approach compared to single genome testing. Characterizing disease-causing genes, variants, and mtDNA heteroplasmy enhances understanding of mitochondrial disorders. Testing alternative tissues can further increase diagnostic yield.

A sequence context-based approach for classifying tumor structural variants without paired normal samples.

A sequence context-based approach for classifying tumor structural variants without paired normal samples.

Genome-wide functional annotation of variants: a systematic review of state-of-the-art tools, techniques and resources.

The recent advancement of sequencing technologies marks a significant shift in the character and complexity of the digital genomic data universe, encompassing diverse types of molecular data, screened through manifold technological platforms. As a result, a plethora of fully assembled genomes are generated that span vertically the evolutionary scale. Notwithstanding the tsunami of thriving innovations that accomplish unprecedented, nucleotide-level, structural and functional annotation, an exhaustive, systemic, massive genome-wide functional annotation remains elusive, particularly when the criterion is automation and efficiency in data-agnostic interpretation. The latter is of paramount importance for the elaboration of strategies for sophisticated, data-driven genome-wide annotation, which aim to impart a sustainable and comprehensive systemic approach to addressing whole genome variation. Therefore, it is essential to develop methods and tools that promote systematic functional genomic annotation, with emphasis on mechanistic information exceeding the limits of coding regions, and exploiting the chunks of pertinent information residing in non-coding regions, including promoter and enhancer sequences, non-coding RNAs, DNA methylation sites, transcription factor binding sites, transposable elements and more. This review provides an overview of the current state-of-the-art in genome-wide functional annotation of genetic variation, including existing bioinformatic tools, resources, databases and platforms currently available or reported in the literature. Particular emphasis is placed on the functional annotation of variants that lie outside protein-coding genomic regions (intronic or intergenic), their potential co-localization with regulatory element areas, such as putative non-coding RNA regions, and the assessment of their functional impact on the investigated phenotype. In addition, state-of-the-art tools that leverage data obtained from WGS and GWAS-based analyses are discussed, along with future bioinformatics directions and developments. These future directions emphasize efficient, comprehensive, and largely automated functional annotation of both coding and non-coding genomic variants, as well as their optimal evaluation.

Monkeys at Rigged Typewriters: A Population and Network View of Plant Immune System Incompatibility.

Immune system incompatibilities between naturally occurring genomic variants underlie many hybrid defects in plants and present a barrier for crop improvement. In this review, we approach immune system incompatibilities from pan-genomic and network perspectives. Pan-genomes offer insights into how natural variation shapes the evolutionary landscape of immune system incompatibilities, and through it, selection, polymorphisms, and recombination resistance emerge as common features that synergistically drive these incompatibilities. By contextualizing incompatibilities within the immune network, immune receptor promiscuity, complex dysregulation, and single-point failure appear to be recurrent themes of immune system defects. As geneticists break genes to investigate their function, so can we investigate broken immune systems to enrich our understanding of plant immune systems and work toward improving them.

Genetic suppression interactions are highly conserved across genetically diverse yeast isolates.

Genetic suppression occurs when the phenotypic defects caused by a deleterious mutation are rescued by another mutation. Suppression interactions are of particular interest for genetic diseases, as they identify ways to reduce disease severity, thereby potentially highlighting avenues for therapeutic intervention. To what extent suppression interactions are influenced by the genetic background in which they operate remains largely unknown. However, a high degree of suppression conservation would be crucial for developing therapeutic strategies that target suppressors. To gain an understanding of the effect of the genetic context on suppression, we isolated spontaneous suppressor mutations of temperature sensitive alleles of SEC17, TAO3, and GLN1 in three genetically diverse natural isolates of the budding yeast Saccharomyces cerevisiae. After identifying and validating the genomic variants responsible for suppression, we introduced the suppressors in all three genetic backgrounds, as well as in a laboratory strain, to assess their specificity. Ten out of eleven tested suppression interactions were conserved in the four yeast strains, although the extent to which a suppressor could rescue the temperature sensitive mutant varied across genetic backgrounds. These results suggest that suppression mechanisms are highly conserved across genetic contexts, a finding that is potentially reassuring for the development of therapeutics that mimic genetic suppressors.

Prospective Genomic Profiling Study in Sarcoma Patients

Abstract Introduction Sarcoma is a heterogenous group of malignancy with diverse pathology and clinical behavior. Survival rates differ among histological subtypes, but overall prognosis remains poor due to the scarcity of effective systemic therapies. An insight into the genomic characteristics of different sarcoma histological subtypes enhances our understanding of the disease and highlights potential targeted therapies. Objective We aim to enhance our understanding on the genomic profile of sarcomas and identify actionable genetic variants with the associated targeted therapies. Materials and Methods A prospective tumor genomic profiling study was conducted via next-generation sequencing, involving 30 patients with a diagnosis of soft tissue or bone sarcoma at the University of Malaya Medical Centre. We evaluated the frequency and types of genomic aberrations and identified genomic variants with a therapeutic target. Results A total of 70 genetic mutations were identified. The most frequently involved genes were TP53 (30.0%), followed by RB1 (20.0%), PIK3CA (10.0%), KIT (10.0%), PDGFR-α (10.0%), CKS1B (10.0%), KDR (10.0%), and MCL1 (10.0%). Genomic alteration involving the ALK gene was the only actionable variant identified. The DCTN1–ALK fusion was found to be targetable using entrectinib. Conclusion Although the number of actionable variants identified was limited, such data are crucial for the selection of patients into clinical trials on novel therapies in the future and for establishing prognostic biomarkers.

Occurrence and allele frequencies of genetic variants associated with Varroa drone brood resistance (DBR) in African Apis mellifera subspecies.

Occurrence and allele frequencies of genetic variants associated with Varroa drone brood resistance (DBR) in African Apis mellifera subspecies.

Deciphering the coordinated roles of the host genome, duodenal mucosal genes, and microbiota in regulating complex traits in chickens

BackgroundThe complex interactions between host genetics and the gut microbiome are well documented. However, the specific impacts of gene expression patterns and microbial composition on each other remain to be further explored.ResultsHere, we investigated this complex interplay in a sizable population of 705 hens, employing integrative analyses to examine the relationships among the host genome, mucosal gene expression, and gut microbiota. Specific microbial taxa, such as the cecal family Christensenellaceae, which showed a heritability of 0.365, were strongly correlated with host genomic variants. We proposed a novel concept of regulatability (rb2), which was derived from h2, to quantify the cumulative effects of gene expression on the given phenotypes. The duodenal mucosal transcriptome emerged as a potent influencer of duodenal microbial taxa, with much higher rb2 values (0.17 ± 0.01, mean ± SE) than h2 values (0.02 ± 0.00). A comparative analysis of chickens and humans revealed similar average microbiability values of genes (0.18 vs. 0.20) and significant differences in average rb2 values of microbes (0.17 vs. 0.04). Besides, cis (hcis2) and trans heritability (htrans2) were estimated to assess the effects of genetic variations inside and outside the cis window of the gene on its expression. Higher htrans2 values than hcis2 values and a greater prevalence of trans-regulated genes than cis-regulated genes underscored the significant role of loci outside the cis window in shaping gene expression levels. Furthermore, our exploration of the regulatory effects of duodenal mucosal genes and the microbiota on 18 complex traits enhanced our understanding of the regulatory mechanisms, in which the CHST14 gene and its regulatory relationships with Lactobacillus salivarius jointly facilitated the deposition of abdominal fat by modulating the concentration of bile salt hydrolase, and further triglycerides, total cholesterol, and free fatty acids absorption and metabolism.ConclusionsOur findings highlighted a novel concept of rb2 to quantify the phenotypic variance attributed to gene expression and emphasize the superior role of intestinal mucosal gene expressions over host genomic variations in elucidating host‒microbe interactions for complex traits. This understanding could assist in devising strategies to modulate host–microbe interactions, ultimately improving economic traits in chickens.Graphical The host genome, hepatic and duodenal transcriptomes, and gut microbiome were integrated to elucidate the regulatory mechanisms underlying the complex phenotypes of chickens. Heritability estimation and GWASs from the genome, regulatability estimation from the transcriptome, and microbiability estimation from the microbiome were collectively calculated for the host phenotypes.By integrating genome and transcriptome data, the cis- and trans-heritability of genes were estimated, and methods such as eQTL mapping and SMR were used to elucidate the genetic regulatory mechanisms of phenotypes. By integrating genome and microbiome data, we conducted microbial heritability estimation and GWASs to explore the extent to which the host genome shapes the microbiota. By integrating transcriptome and microbiome data, we estimated the microbiability of genes and the regulatability of the microbiota, exploring the degree of interaction between host gene expression and the gut microbiota.By combining all the analytical methods mentioned above, a more advanced and comprehensive understanding of the extent and manner of host and microbiota interactions that regulate host phenotypes can be achieved.FJkP-WwXS51f6sFHx5DLbMVideo

GWAS and eQTL analyses reveal genetic components influencing the key fiber yield trait lint percentage in upland cotton.

Lint percentage is an important component of cotton yield traits and an important economic indicator of cotton production. The initial stage of fiber development is a critical developmental period that affects the lint percentage trait, but the genetic regulation of the initial stage of fiber development needs to be resolved. In this study, we used a genomewide association study (GWAS) to identify 11 quantitative trait loci (QTLs) related to lint percentage and identified a total of 13 859 expression QTL (eQTLs) through transcriptome sequencing of 312 upland cotton accessions. Candidate genes for improving the lint percentage trait were identified through transcriptome-wide association study (TWAS), colocalization analysis, and differentially expressed gene analysis. We located nine candidate genes through the TWAS, and prioritized two key candidate genes (Ghir_A12G025980 and Ghir_A12G025990) related to lint percentage through colocalization and differential expression analysis. We showed that two eQTL hotspots (Hot26 and Hot28) synergistically participate in regulating the biological pathways of fiber initiation and development. Additionally, we unlocked the potential of genomic variants in improving the lint percentage by aggregating favorable alleles in accessions. New accessions suitable for improving lint percentage were excavated.

Proteome-wide assessment of differential missense variant clustering in neurodevelopmental disorders and cancer.

Proteome-wide assessment of differential missense variant clustering in neurodevelopmental disorders and cancer.

Untargeted metabolomics analysis as a potential screening tool for 3-methylglutaconic aciduria syndromes.

Untargeted metabolomics analysis as a potential screening tool for 3-methylglutaconic aciduria syndromes.

Genetic Variability, Virulence and Epidemiology of Dengue Virus in Africa

Dengue Fever is one of the neglected tropical diseases that results in case of approximately 100 million per year with over 5 billion individuals at risk of infection and re-infection. It is now one of the most prominent vector-borne diseases of humans in subtropical and tropical parts of the globe with four dengue virus strains (DEN-1, 2, 3 and 4) being reported based on their genomic variants. Transmission to humans is mainly through theinfective female Aedes mosquito bite which can be enhanced due to the climate of the tropic and subtropics.Thevirulence of DENV is function of its genetic variability andthe elevation many cytokines level and chemical mediators, which responsible for capillary leakage and shock. This situation enhances the progression of DF to DHF and DSS in the infected patients. TNF- α and cytokines (IL-1b, IL-6 and IL-8)that are secreted downstream of TNF- α as the inflammatory cascade, contribute to the disease severity. Marked IL-2 and interferon (IFN)-γ are then produced by T cells due to the immune activation that occurs during DENV-infections in the patients with DHF and DSS than the patients with DF. Genetic diversities in the DENV is important in the understanding of viral virulence and mechanisms of its pathogenicity. This will help in the control of the virus especially in vaccine development and preparedness towards any emerging or re-emergence of the virus.

PIGOME: An Integrated and Comprehensive Multi-omics Database for Pig Functional Genomics Studies.

PIGOME: An Integrated and Comprehensive Multi-omics Database for Pig Functional Genomics Studies.

Novel Genomic Variants Sars-Cov-2 and blood glucose in the post-pandemic period: Retrospective epidemiology study

Coronavirus disease 2019 (COVID-19) is a highly infectious illness caused by the acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which started in China in December 2019 and rapidly spread across the world became the pandemic disease of the 21st century. Diabetes is a known risk factor for mortality in Coronavirus disease 2019 (COVID-19) patients. SARS-CoV-2 infects peripheral blood monocytes and enhances the expression of angiotensin-converting enzyme 2 (ACE2). We conducted this study to evaluate the impact of glucose morbidities on several events in COVID 19. Thus, the main of this study is to detect the presence of glucose levels in the post-pandemic period, as diabetic people have a higher risk of developing severe illness from COVID-19. This is a retrospective epidemiology study involving patients categorized into three groups to admission Blood Glucose (BG) levels: < 70 mg/dL; 77 – 90 mg/dL; < 99 mg/dL according to standard glycaemic targets in the post pandemic period among 2023 and 2023 y. It was carried out the tracking of 19 genomic variants by accessible via GISAID EpiCoV. About 110 countries shared 25,321 GRA (BA.2.86+BA.2.86.*) genome sequences from sample collection to making these data publicly. Therefore, further studies are needed to better be understanding of measurement in diabetic patients at high risk for COVID-19. New insulin therapy or increased dosing from baseline had not been considered. Genomic tools as Next Generation Sequencing (NGS) for the characterization of viral samples and in genetic engineering for the development of vaccines had been advanced molecular studies during SARS-CoV-2 pandemic.

Machine Learning Methods for Classifying Multiple Sclerosis and Alzheimer's Disease Using Genomic Data.

Complex diseases pose challenges in prediction due to their multifactorial and polygenic nature. This study employed machine learning (ML) to analyze genomic data from the UK Biobank, aiming to predict the genomic predisposition to complex diseases like multiple sclerosis (MS) and Alzheimer's disease (AD). We tested logistic regression (LR), ensemble tree methods, and deep learning models for this purpose. LR displayed remarkable stability across various subsets of data, outshining deep learning approaches, which showed greater variability in performance. Additionally, ML methods demonstrated an ability to maintain optimal performance despite correlated genomic features due to linkage disequilibrium. When comparing the performance of polygenic risk score (PRS) with ML methods, PRS consistently performed at an average level. By employing explainability tools in the ML models of MS, we found that the results confirmed the polygenicity of this disease. The highest-prioritized genomic variants in MS were identified as expression or splicing quantitative trait loci located in non-coding regions within or near genes associated with the immune response, with a prevalence of human leukocyte antigen (HLA) gene annotations. Our findings shed light on both the potential and the challenges of employing ML to capture complex genomic patterns, paving the way for improved predictive models.