Whole-genome Sequencing Data Research Articles

HSCGD: a comprehensive database of single-cell whole-genome data and metadata.

Single-cell whole-genome sequencing is a powerful tool for uncovering mutations in individual cells. In recent times, the generation of vast amounts of data has significantly advanced our understanding of key biological processes, including cell development and tumor progression. This rapid accumulation of data underscores the urgent need for a comprehensive resource platform to manage and utilize this information effectively. To address this need, we introduce HSCGD, the first open-access and comprehensive database dedicated to the collection, integration, analysis, and visualization of single-cell whole-genome data and metadata. The current release of HSCGD includes processed single-cell whole-genome sequencing data and curated metadata of 74 154 human cells derived from 63 public single-cell datasets, involving 23 cell types and 17 major single-cell whole-genome amplification methods. HSCGD is designed to help researchers interested in cellular heterogeneity explore and utilize whole genome data at the single-cell level by providing browsing, searching, visualization, downloadingand online tools. The database can be accessed from the following URL: http://www.hscgd.com.

A genome-based survey of invasive pneumococci in Norway over four decades reveals lineage-specific responses to vaccination

BackgroundStreptococcus pneumoniae is a major cause of mortality globally. The introduction of pneumococcal conjugate vaccines (PCVs) has reduced the incidence of the targeted serotypes significantly, but expansion of non-targeted serotypes, serotype replacement, and incomplete vaccine-targeting contribute to pneumococcal disease in the vaccine era. Here, we characterize the changing population genetic landscape of S. pneumoniae in Norway over a 41-year period (1982–2022).MethodsSince 2018, all cases of invasive pneumococcal disease have undergone whole-genome sequencing (WGS) at the Norwegian Institute of Public Health. In order to characterize the changing population over time, historical isolates were re-cultured and sequenced, resulting in a historical WGS dataset. Isolates were assigned to global pneumococcal sequence clusters (GPSCs) using PathogenWatch and assigned to serotypes using in silico (SeroBA) and in vitro methods (Quellung reaction). Temporal phylogenetic analyses were performed on GPSCs of particular interest.ResultsThe availability of WGS data allowed us to study capsular variation at the level of individual lineages. We detect highly divergent fates for different GPSCs following the introduction of PCVs. For two out of eight major GPSCs, we identified multiple instances of serotype switching from vaccine types to non-vaccine types. Dating analyses suggest that most instances of serotype switching predated the introduction of PCVs, but expansion occurred after their introduction. Furthermore, selection for penicillin non-susceptibility was not a driving force for the changing serotype distribution within the GPSCs over time.ConclusionsPCVs have been major shapers of the Norwegian disease-causing pneumococcal population, both at the level of serotype distributions and the underlying lineage dynamics. Overall, the introduction of PCVs has reduced the incidence of invasive disease. However, some GPSCs initially dominated by vaccine types escaped the effect of vaccination through expansion of non-vaccine serotypes. Close monitoring of circulating lineages and serotypes will be key for ensuring optimal vaccination coverage going forward.

Salmonella enterica virulence databases and bioinformatic analysis tools development

Salmonella enterica, a prominent foodborne pathogen, contributes significantly to global foodborne illnesses annually. This species exhibits significant genetic diversity, potentially impacting its infectivity, disease severity, and antimicrobial resistance. Whole genome sequencing (WGS) offers comprehensive genetic insights that can be utilized for virulence assessment. However, existing bioinformatic tools for studying Salmonella virulence have notable limitations. To address this gap, a Salmonella Virulence Database with a non-redundant, comprehensive list of putative virulence factors was constructed. Two bioinformatic analysis tools, Virulence Factor Profile Assessment and Virulence Factor Profile Comparison tools, were developed. The former provides data on similarity to the reference genes, e-value, and bite score, while the latter assesses the presence/absence of virulence genes in Salmonella isolates and facilitates comparison of virulence profiles across multiple sequences. To validate the database and associated bioinformatic tools, WGS data from 43,853 Salmonella isolates spanning 14 serovars was extracted from GenBank, and WGS data previously generated in our lab was used. Overall, the Salmonella Virulence database and our bioinformatic tools effectively facilitated virulence assessment, enhancing our understanding of virulence profiles among Salmonella isolates and serovars. The public availability of these resources will empower researchers to assess Salmonella virulence comprehensively, which could inform strategies for pathogen control and risk evaluations associated with human illnesses.

Local emergence and global evolution of Neisseria gonorrhoeae with high-level resistance to azithromycin.

Antimicrobial resistance in Neisseria gonorrhoeae (Ng) has severely reduced treatment options, including azithromycin (AZM), which had previously been recommended as dual therapy with ceftriaxone. This study characterizes the emergence of high-level resistance to AZM (HLR-AZM) Ng in Baltimore, Maryland, USA, and describes the global evolution of HLR-AZM Ng. Whole genome sequencing (WGS) of 30 Ng isolates with and without HLR-AZM from Baltimore was used to identify clonality and resistance determinants. Publicly available WGS data from global HLR-AZM Ng (n = 286) and the Baltimore HLR-AZM Ng (n = 3) were used to assess the distribution, clonality, and diversity of HLR-AZM Ng. The HLR-AZM Ng isolates from Baltimore identified as multi-locus sequencing typing sequence type (ST) 9363 and likely emerged from circulating strains. ST9363 was the most widely disseminated ST globally represented in eight countries and was associated with sustained transmission events. The number of global HLR-AZM Ng, countries reporting these isolates, and strain diversity increased in the last decade. The majority (89.9%) of global HLR-AZM Ng harbored the A2059G mutation in all four alleles of the 23S rRNA gene, but isolates with two or three A2059G alleles, and alternative HLR-AZM mechanisms were also identified. In conclusion, HLR-AZM in Ng has increased in the last few years, with ST9363 emerging as an important gonococcal lineage globally. The 23S rRNA A2059G mutation is the most common resistance mechanism, but alternative mechanisms are emerging. Continued surveillance of HLR-AZM Ng, especially ST9363, and extensively drug-resistant Ng is warranted.

Blood-derived mitochondrial DNA copy number is associated with Alzheimer disease, Alzheimer-related biomarkers and serum metabolites

BackgroundBlood-derived mitochondrial DNA copy number (mtDNA-CN) is a proxy measurement of mitochondrial function in the peripheral and central systems. Abnormal mtDNA-CN not only indicates impaired mtDNA replication and transcription machinery but also dysregulated biological processes such as energy and lipid metabolism. However, the relationship between mtDNA-CN and Alzheimer disease (AD) is unclear.MethodsWe performed two-sample Mendelian randomization (MR) using publicly available summary statistics from GWAS for mtDNA-CN and AD to investigate the causal relationship between mtDNA-CN and AD. We estimated mtDNA-CN using whole-genome sequence data from blood and brain samples of 13,799 individuals from the Alzheimer’s Disease Sequencing Project. Linear and Cox proportional hazards models adjusting for age, sex, and study phase were used to assess the association of mtDNA-CN with AD. The association of AD biomarkers and serum metabolites with mtDNA-CN in blood was evaluated in Alzheimer’s Disease Neuroimaging Initiative using linear regression. We conducted a causal mediation analysis to test the natural indirect effects of mtDNA-CN change on AD risk through the significantly associated biomarkers and metabolites.ResultsMR analysis suggested a causal relationship between decreased blood-derived mtDNA-CN and increased risk of AD (OR = 0.68; P = 0.013). Survival analysis showed that decreased mtDNA-CN was significantly associated with higher risk of conversion from mild cognitive impairment to AD (HR = 0.80; P = 0.002). We also identified significant associations of mtDNA-CN with brain FDG-PET (β = 0.103; P = 0.022), amyloid-PET (β = 0.117; P = 0.034), CSF amyloid-β (Aβ) 42/40 (β=-0.124; P = 0.017), CSF t-Tau (β = 0.128; P = 0.015), p-Tau (β = 0.140; P = 0.008), and plasma NFL (β=-0.124; P = 0.004) in females. Several lipid species, amino acids, biogenic amines in serum were also significantly associated with mtDNA-CN. Causal mediation analyses showed that about a third of the effect of mtDNA-CN on AD risk was mediated by plasma NFL (P = 0.009), and this effect was more significant in females (P < 0.005).ConclusionsOur study indicates that mtDNA-CN measured in blood is predictive of AD and is associated with AD biomarkers including plasma NFL particularly in females. Further, we illustrate that decreased mtDNA-CN possibly increases AD risk through dysregulation of mitochondrial lipid metabolism and inflammation.

Genetic Adaptations of the Tibetan Pig to High-Altitude Hypoxia on the Qinghai-Tibet Plateau.

The Tibetan Plateau's distinctive high-altitude environment, marked by extreme cold and reduced oxygen levels, presents considerable survival challenges for both humans and mammals. Natural selection has led to the accumulation of adaptive mutations in Tibetan pigs, enabling them to develop distinctive adaptive phenotypes. Here, we aim to uncover the genetic mechanisms underlying the adaptation of Tibetan pigs to high-altitude hypoxia. Therefore, we conducted a systematic analysis of 140 whole-genome sequencing (WGS) data points from different representing pig populations. Our analysis identified a total of 27,614,561 mutations, including 22,386,319 single-nucleotide variants (SNVs) and 5,228,242 insertions/deletions (INDELs, size < 50 bp). A total of 11% (2,678,569) of the SNVs were newly identified in our project, significantly expanding the dataset of genetic variants in Tibetan pigs. Compared to other pig breeds, Tibetan pigs are uniquely adapted to high-altitude environments, exhibiting the highest genetic diversity and the lowest inbreeding coefficient. Employing the composite of multiple signals (CMS) method, we scanned the genome-wide Darwinian positive selection signals and identified 32,499 Tibetan pig positively selected SNVs (TBPSSs) and 129 selected genes (TBPSGs), including 213 newly discovered genes. Notably, we identified eight genes (PHACTR1, SFI1, EPM2A, SLC30A7, NKAIN2, TNNI3K, and PLIN2) with strong nature selection signals. They are likely to improve cardiorespiratory function and fat metabolism to help Tibetan pigs become adapted to the high-altitude environment. These findings provide new insights into the genetic mechanisms of high-altitude adaptation and the adaptive phenotypes of Tibetan pigs.

Whole-genome sequencing study in Koreans identifies novel loci for Alzheimer's disease.

The genetic basis of Alzheimer's disease (AD) in Koreans is poorly understood. We performed an AD genome-wide association study using whole-genome sequence data from 3540 Koreans (1583 AD cases, 1957 controls) and single-nucleotide polymorphism array data from 2978 Japanese (1336 AD cases, 1642 controls). Significant findings were evaluated by pathway enrichment and differential gene expression analysis in brain tissue from controls and AD cases with and without dementia prior to death. We identified genome-wide significant associations with APOE in the total sample and ROCK2 (rs76484417, p=2.71×10-8) among APOE ε4 non-carriers. A study-wide significant association was found with aggregated rare variants in MICALL1 (MICAL like 1) (p=9.04×10-7). Several novel AD-associated genes, including ROCK2 and MICALL1, were differentially expressed in AD cases compared to controls (p<3.33×10-3). ROCK2 was also differentially expressed between AD cases with and without dementia (p=1.34×10-4). Our results provide insight into genetic mechanisms leading to AD and cognitive resilience in East Asians. Novel genome-wide significant associations for AD identified with ROCK2 and MICALL1. ROCK2 and MICALL1 are differentially expressed between AD cases and controls in the brain. This is the largest whole-genome-sequence study of AD in an East Asian population.

Insights into trait-association of selection signatures and adaptive eQTL in indigenous African cattle

BackgroundAfrican cattle represent a unique resource of genetic diversity in response to adaptation to numerous environmental challenges. Characterising the genetic landscape of indigenous African cattle and identifying genomic regions and genes of functional importance can contribute to targeted breeding and tackle the loss of genetic diversity. However, pinpointing the adaptive variant and determining underlying functional mechanisms of adaptation remains challenging.ResultsIn this study, we use selection signatures from whole-genome sequence data of eight indigenous African cattle breeds in combination with gene expression and quantitative trait loci (QTL) databases to characterise genomic targets of artificial selection and environmental adaptation and to identify the underlying functional candidate genes. In general, the trait-association analyses of selection signatures suggest the innate and adaptive immune system and production traits as important selection targets. For example, a large genomic region, with selection signatures identified for all breeds except N’Dama, was located on BTA27, including multiple defensin DEFB coding-genes. Out of 22 analysed tissues, genes under putative selection were significantly enriched for those overexpressed in adipose tissue, blood, lung, testis and uterus. Our results further suggest that cis-eQTL are themselves selection targets; for most tissues, we found a positive correlation between allele frequency differences and cis-eQTL effect size, suggesting that positive selection acts directly on regulatory variants.ConclusionsBy combining selection signatures with information on gene expression and QTL, we were able to reveal compelling candidate selection targets that did not stand out from selection signature results alone (e.g. GIMAP8 for tick resistance and NDUFS3 for heat adaptation). Insights from this study will help to inform breeding and maintain diversity of locally adapted, and hence important, breeds.

Chromatin interaction maps identify oncogenic targets of enhancer duplications in cancer.

As a major type of structural variants, tandem duplication plays a critical role in tumorigenesis by increasing oncogene dosage. Recent work has revealed that noncoding enhancers are also affected by duplications leading to the activation of oncogenes that are inside or outside of the duplicated regions. However, the prevalence of enhancer duplication and the identity of their target genes remains largely unknown in the cancer genome. Here, by analyzing whole-genome sequencing data in a non-gene-centric manner, we identify 881 duplication hotspots in 13 major cancer types, most of which do not contain protein-coding genes. We show that the hotspots are enriched with distal enhancer elements and are highly lineage-specific. We develop a HiChIP-based methodology that navigates enhancer-promoter contact maps to prioritize the target genes for the duplication hotspots harboring enhancer elements. The methodology identifies many novel enhancer duplication events activating oncogenes such as ESR1, FOXA1, GATA3, GATA6, TP63, and VEGFA, as well as potentially novel oncogenes such as GRHL2, IRF2BP2, and CREB3L1 In particular, we identify a duplication hotspot on Chromosome 10p15 harboring a cluster of enhancers, which skips over two genes, through a long-range chromatin interaction, to activate an oncogenic isoform of the NET1 gene to promote migration of gastric cancer cells. Focusing on tandem duplications, our study substantially extends the catalog of noncoding driver alterations in multiple cancer types, revealing attractive targets for functional characterization and therapeutic intervention.

Hypothesis-based investigation of known AD risk variants reveals the genetic underpinnings of neuropathological lesions observed in Alzheimer’s-type dementia

Alzheimer’s disease (AD) is the leading cause of dementia worldwide. Besides neurofibrillary tangles and amyloid beta (Aβ) plaques, a wide range of co-morbid neuropathological features can be observed in AD brains. Since AD has a very strong genetic background and displays a wide phenotypic heterogeneity, this study aims at investigating the genetic underpinnings of co-morbid and hallmark neuropathological lesions. This was realized by obtaining the genotypes for 75 AD risk variants from low-coverage whole-genome sequencing data for 325 individuals from the Leuven Brain Collection. Association testing with deeply characterized neuropathological lesions revealed a strong and likely direct effect of rs117618017, a SNP in exon 1 of APH1B, with tau-related pathology. Second, a relation between APOE and granulovacuolar degeneration, a proxy for necroptosis, was also discovered in addition to replication of the well-known association of APOE with AD hallmark neuropathological lesions. Additionally, several nominal associations with AD risk genes were detected for pTDP pathology, α-synuclein lesions and pTau-related pathology. These findings were confirmed in a meta-analysis with three independent cohorts. For example, we replicated a prior association between TPCN1 (rs6489896) and LATE-NC risk. Furthermore, we identified new putative LATE-NC-linked SNPs, including rs7068231, located upstream of ANK3. We found association between BIN1 (rs6733839) and α-synuclein pathology, and replicated a prior association between USP6NL (rs7912495) and Lewy body pathology. Additionally, we also found that UMAD1 (rs6943429) was nominally associated with Lewy body pathology. Overall, these results contribute to a broader general understanding of how AD risk variants discovered in large-scale clinical genome-wide association studies are involved in the pathological mechanisms of AD and indicate the importance of downstream elimination of phenotypic heterogeneity introduced in these studies.

Mycobacterium tuberculosis evolution from monoresistance to pre-extensive drug resistance during a prolonged household outbreak

Whole genome sequencing (WGS) is sensitive tool for the analysis of tuberculosis transmission and drug-resistance. We used WGS to analyze the Mycobacterium tuberculosis evolution from isoniazid monoresistance to MDR/preXDR during a prolonged household outbreak. The outbreak started with a isoniazid resistant strain (katG S315T mutation) and evolve in two cases to pre-XDR phenotype (with mutations in katG, rpoB, embB, pncA and gyrA genes). Based on WGS data and epidemiological interview we proposed a possible chain of transmission an evolution of the strains.Similar intra-patient and inter-patient acquisition of variability was observed, making difficult to distinguish reinfection or reactivation. Analysis of WGS data together with epidemiological clinical history are discussed in order to distinguish between prolonged infections or transition from latency to reactivation. Classical interview and clinical history taking should be consider to fully understanding WGS data. With a still low incidence of TB cases, Uruguay could use universal WGS of all isolates to reduce time of diagnosis, detect outbreaks and perform public actions to reduce TB incidence.

SNP-Based and Kmer-Based eQTL Analysis Using Transcriptome Data.

Traditional expression quantitative trait locus (eQTL) mapping associates single nucleotide polymorphisms (SNPs) with gene expression, where the SNPs are derived from large-scale whole-genome sequencing (WGS) data or transcriptome data. While WGS provides a high SNP density, it also incurs substantial sequencing costs. In contrast, RNA-seq data, which are more accessible and less expensive, can simultaneously yield gene expressions and SNPs. Thus, eQTL analysis based on RNA-seq offers significant potential applications. Two primary strategies were employed for eQTL in this study. The first involved analyzing expression levels in relation to variant sites detected between populations from RNA-seq data. The second approach utilized kmers, which are sequences of length k derived from RNA-seq reads, to represent variant sites and associated these kmer genotypes with gene expression. We discovered 87 significant association signals involving eGene on the basis of the SNP-based eQTL analysis. These genes include DYNLT1, NMNAT1, and MRLC2, which are closely related to neurological functions such as motor coordination and homeostasis, play a role in cellular energy metabolism, and function in regulating calcium-dependent signaling in muscle contraction, respectively. This study compared the results obtained from eQTL mapping using RNA-seq identified SNPs and gene expression with those derived from kmers. We found that the vast majority (23/30) of the association signals overlapping the two methods could be verified by haplotype block analysis. This comparison elucidates the strengths and limitations of each method, providing insights into their relative efficacy for eQTL identification.

Overexpression of β-lactamase genes (blaKPC, blaSHV) and novel CirA deficiencies contribute to decreased cefiderocol susceptibility in carbapenem-resistant Klebsiella pneumoniae before its approval in China.

Cefiderocol (FDC) is an effective antibiotic that is used to treat severe infections caused by carbapenem-resistant Klebsiella pneumoniae (CRKP). The mechanisms underlying FDC resistance and molecular epidemiology in China remain unclear. We collected 477 non-duplicate CRKP clinical isolates in central China and characterized their susceptibility to FDC, virulence genes, and sequence typing. The overall FDC susceptibility rate of CRKP was 99.2% in central China, which was higher than that in North America and Europe (96.1%), with MIC50/90 values of 1/2 mg/L. The decrease in FDC susceptibility in central China was concentrated in the ST11 CRKP-carrying virulence plasmids. Whole-genome sequencing (WGS) and quantitative reverse transcription PCR (qRT-PCR) experiments showed that serine β-lactamases, especially highly expressed KPC and SHV, substantially decreased FDC susceptibility in four FDC non-susceptible isolates (two resistant and two intermediate isolates). Notably, different CirA deficiencies, p.E450GfsTer16 and p.E133Ter, were found in both of the resistant isolates. In contrast, global WGS data indicate that the resistance mechanisms in North America and Europe were primarily associated with NDM and KPC variants, predominantly found in ST307 and ST147. Overall, FDC exhibits excellent activity against CRKP in central China, with resistance mechanisms primarily related to high KPC and SHV expression, along with deficiencies in CirA, frequently observed in ST11. This is remarkably different from the situation in North America and Europe and will directly impact the choice of clinical interventions. Additionally, the surveillance of FDC resistance in China is imperative.

Integrating whole genome sequencing and machine learning for predicting antimicrobial resistance in critical pathogens: a systematic review of antimicrobial susceptibility tests.

Infections caused by antibiotic-resistant bacteria pose a major challenge to modern healthcare. This systematic review evaluates the efficacy of machine learning (ML) approaches in predicting antimicrobial resistance (AMR) in critical pathogens (CP), considering Whole Genome Sequencing (WGS) and antimicrobial susceptibility testing (AST). The search covered databases including PubMed/MEDLINE, EMBASE, Web of Science, SCOPUS, and SCIELO, from their inception until June 2024. The review protocol was officially registered on PROSPERO (CRD42024543099). The review included 26 papers, analyzing data from 104,141 microbial samples. Random Forest (RF), XGBoost, and logistic regression (LR) emerged as the top-performing models, with mean Area Under the Receiver Operating Characteristic (AUC) values of 0.89, 0.87, and 0.87, respectively. RF showed superior performance with AUC values ranging from 0.66 to 0.97, while XGBoost and LR showed similar performance with AUC values ranging from 0.83 to 0.91 and 0.76 to 0.96, respectively. Most studies indicate that integrating WGS and AST data into ML models enhances predictive performance, improves antibiotic stewardship, and provides valuable clinical decision support. ML shows significant promise for predicting AMR by integrating WGS and AST data in CP. Standardized guidelines are needed to ensure consistency in future research.

The utilization of N-acetylgalactosamine and its effect on the metabolism of amino acids in Erysipelotrichaceae strain

BackgroundThe metabolism of gut microbiota produces bioactive metabolites that modulate host physiology and promote self-growth. Erysipelotrichaceae is one of the most common anaerobic microorganism families in the gut, which has been discovered to play a vital role in host metabolic disorders and inflammatory diseases. Our previous study found that N-acetylgalactosamine (GalNAc) in caecal content of pigs significantly affected the abundance of Erysipelotrichaceae strains. However, it remains unknown how GalNAc feeding in vitro culture affects the expression levels of genes in the GalNAc metabolic pathway and the concentrations of intermediate metabolites in the Erysipelotrichaceae strain. Whether GalNAc feeding should influence the metabolism of other nutrients, such as amino acids, remains unrevealed.ResultsIn this study, whole-genome sequence, transcriptome, and metabolome data were analyzed to assess the utilization of a Erysipelotrichaceae strain on GalNAc. The results showed the presence of a complete GalNAc catabolism pathway in the genome of this Erysipelotrichaceae strain. GalNAc feeding to this Erysipelotrichaceae strain significantly changed the expression levels of genes involved in glycolysis and tricarboxylic acid (TCA) cycle. Meanwhile, the concentrations of lactate, pyruvate, citrate, succinate and malate from the glycolysis and TCA cycle were significantly increased. In addition, transcriptome analysis indicated that the genes involved in the metabolism of amino acids were affected by GalNAc, including lysA (a gene involved in lysine biosynthesis) that was significantly down-regulated. The intracellular concentrations of 14 amino acids in the Erysipelotrichaceae strain were significantly increased after feeding GalNAc.ConclusionsOur findings comfirmed and extended our previous works that demonstrated the utilization of GalNAc by Erysipelotrichaceae strain, and explained the possible mechanism of GalNAc affecting the abundance of Erysipelotrichaceae strain in vitro.

Rare variant analyses in 51,256 type 2 diabetes cases and 370,487 controls reveal the pathogenicity spectrum of monogenic diabetes genes.

Type 2 diabetes (T2D) genome-wide association studies (GWASs) often overlook rare variants as a result of previous imputation panels' limitations and scarce whole-genome sequencing (WGS) data. We used TOPMed imputation and WGS to conduct the largest T2D GWAS meta-analysis involving 51,256 cases of T2D and 370,487 controls, targeting variants with a minor allele frequency as low as 5 × 10-5. We identified 12 new variants, including a rare African/African American-enriched enhancer variant near the LEP gene (rs147287548), associated with fourfold increased T2D risk. We also identified a rare missense variant in HNF4A (p.Arg114Trp), associated with eightfold increased T2D risk, previously reported in maturity-onset diabetes of the young with reduced penetrance, but observed here in a T2D GWAS. We further leveraged these data to analyze 1,634 ClinVar variants in 22 genes related to monogenic diabetes, identifying two additional rare variants in HNF1A and GCK associated with fivefold and eightfold increased T2D risk, respectively, the effects of which were modified by the individual's polygenic risk score. For 21% of the variants with conflicting interpretations or uncertain significance in ClinVar, we provided support of being benign based on their lack of association with T2D. Our work provides a framework for using rare variant GWASs to identify large-effect variants and assess variant pathogenicity in monogenic diabetes genes.

Melanoma-specific mutation hotspots in distal, non-coding, promoter-interacting regions implicate novel candidate driver genes.

To develop targeted treatments, it is crucial to identify the full spectrum of genetic drivers in melanoma, including those in non-coding regions. However, recent efforts to explore non-coding regions have primarily focused on gene-adjacent elements such as promoters and non-coding RNAs, leaving intergenic distal regulatory elements largely unexplored. We used Hi-C chromatin contact data from melanoma cells to map distal, non-coding, promoter-interacting regulatory elements genome-wide in melanoma. Using this "promoter-interaction network", alongside whole-genome sequence and gene expression data from the Pan Cancer Analysis of Whole Genomes, we developed multivariate linear regression models to identify distal somatic mutation hotspots that affect promoter activity. We identified eight recurrently mutated hotspots that are novel, melanoma-specific, located in promoter-interacting distal regulatory elements, alter transcription factor binding motifs, and affect the expression of genes (e.g., HSPB7, CLDN1, ADCY9 and FDXR) previously implicated as tumour suppressors/oncogenes in various cancers. Our study suggests additional non-coding drivers beyond the well-characterised TERT promoter in melanoma, offering new insights into the disruption of complex regulatory networks by non-coding mutations that may contribute to melanoma development. Furthermore, our study provides a framework for integrating multiple levels of biological data to uncover cancer-specific non-coding drivers.

Linking intraspecies variability of Salmonella enterica isolates under acidic conditions to genotype.

There is a lack of information about Salmonella enterica strains under acidic conditions and their association with their genome. This study characterized intraspecies variability in the growth of 167 S. enterica isolates under two acid conditions (pH 4 and 5) and linked to the whole genome sequencing (WGS) data. A total of 1002 curves for each condition were obtained using turbidimetry measurements, and Baranyi and Roberts model was used to estimate the maximum rate of change (rcmax; OD600 nm h-1). Strains were categorized into slow, intermediate, and fast; and associations with their WGS data were performed. Huge variability in was observed at both conditions (pH 5=0.016-0.066 OD600nm h-1 and pH 4=0.003-0.028 OD600nm h-1). The majority of isolates was classified as intermediate (59.5% at pH 5 and 46.1% at pH 4). Strains classified as fast had a low frequency of allABCD genes at both pHs, and any of them having the presence of pefABCD, spvBCR, aadA2, dfrA12, and gyrA_D87G genes were linked to virulence or antimicrobial resistance. This study suggests that strains with fast capacity for growth under acidic conditions could have a fitness cost in their virulence or resistance potential. PRACTICAL APPLICATION: Data presented in this study could be used to select representative strains to evaluate the exposure assessment in different food items, mainly the growth and survival in acidic foods.

Whole-genome sequencing resolves biochemical misidentification of Neisseria species from urogenital specimens.

Neisseria meningitidis (Nm) and Neisseria gonorrhoeae (Ng) are human pathogens that sometimes occupy the same anatomical niche. Ng, the causative agent of gonorrhea, infects 87 million individuals annually worldwide and is an urgent threat due to increasing drug resistance. Ng is a pathogen of the urogenital tract and may infect the oropharyngeal or rectal site, often asymptomatically. Conversely, Nm is an opportunistic pathogen. While often a commensal in the oropharyngeal tract, it is also the leading cause of bacterial meningitis with 1.2 million cases globally, causing significant morbidity and mortality. Horizontal gene transfer (HGT) is likely to occur between Ng and Nm due to their shared anatomical niches and genetic similarity, which poses challenges for accurate detection and treatment. Routine surveillance through the Gonococcal Isolate Surveillance Project and Strengthening the U.S. Response to Resistant Gonorrhea detected six concerning urogenital Neisseria isolates with contradicting species identification in Milwaukee (MIL). While all six isolates were positive for Ng using nucleic acid amplification testing (NAAT) and matrix-assisted laser desorption/ionization time of flight identified the isolates as Ng, two biochemical tests, Gonochek-II and API NH, classified them as Nm. To address this discrepancy, we performed whole-genome sequencing (WGS) using Illumina MiSeq on all isolates and employed various bioinformatics tools. Species detection analysis using BMScan, which uses WGS data, identified all isolates as Ng. Furthermore, Kraken revealed over 98% of WGS reads mapped to the Ng genome and <1% to Nm. Recombination analysis identified putative HGT in all MIL isolates within the γ-glutamyl transpeptidase (ggt) gene, a key component in the biochemical tests used to differentiate between Nm and Ng. Further analysis identified Nm as the source of HGT event. Specifically, the active Nm ggt gene replaced the Ng pseudogenes, ggt1 and ggt2. Together, this study demonstrates that closely related Neisseria species sharing a niche underwent HGT, which led to the misidentification of species following biochemical testing. Importantly, NAAT accurately detected Ng. The misidentification highlights the importance of using WGS to continually evaluate diagnostic or bacterial identification tests.

Whole-genome sequencing in 333,100 individuals reveals rare non-coding single variant and aggregate associations with height

The role of rare non-coding variation in complex human phenotypes is still largely unknown. To elucidate the impact of rare variants in regulatory elements, we performed a whole-genome sequencing association analysis for height using 333,100 individuals from three datasets: UK Biobank (N = 200,003), TOPMed (N = 87,652) and All of Us (N = 45,445). We performed rare ( < 0.1% minor-allele-frequency) single-variant and aggregate testing of non-coding variants in regulatory regions based on proximal-regulatory, intergenic-regulatory and deep-intronic annotation. We observed 29 independent variants associated with height at P < 6×10−10\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$6\ imes {10}^{-10}$$\\end{document} after conditioning on previously reported variants, with effect sizes ranging from −7cm to +4.7 cm. We also identified and replicated non-coding aggregate-based associations proximal to HMGA1 containing variants associated with a 5 cm taller height and of highly-conserved variants in MIR497HG on chromosome 17. We have developed an approach for identifying non-coding rare variants in regulatory regions with large effects from whole-genome sequencing data associated with complex traits.