Use Of Whole-genome Sequencing Research Articles

Whole genome sequencing increases the diagnostic rate in Charcot-Marie-Tooth disease.

Charcot-Marie-Tooth disease (CMT) is one of the most common and genetically heterogeneous inherited neurological diseases, with more than 130 disease-causing genes. Whole genome sequencing (WGS) has improved diagnosis across genetic diseases, but the diagnostic impact in CMT is yet to be fully reported. We present the diagnostic results from a single specialist inherited neuropathy centre, including the impact of WGS diagnostic testing. Patients were assessed at our specialist inherited neuropathy centre from 2009 to 2023. Genetic testing was performed using single gene testing, next-generation sequencing targeted panels, research whole exome sequencing and WGS and, latterly, WGS through the UK National Health Service. Variants were assessed using the American College of Medical Genetics and Genomics and Association for Clinical Genomic Science criteria. Excluding patients with hereditary ATTR amyloidosis, 1515 patients with a clinical diagnosis of CMT and related disorders were recruited. In summary, 621 patients had CMT1 (41.0%), 294 CMT2 (19.4%), 205 intermediate CMT (CMTi, 13.5%), 139 hereditary motor neuropathy (HMN, 9.2%), 93 hereditary sensory neuropathy (HSN, 6.1%), 38 sensory ataxic neuropathy (2.5%), 72 hereditary neuropathy with liability to pressure palsies (HNPP, 4.8%) and 53 'complex' neuropathy (3.5%). Overall, a genetic diagnosis was reached in 76.9% (1165/1515). A diagnosis was most likely in CMT1 (96.8%, 601/621), followed by CMTi (81.0%, 166/205) and then HSN (69.9%, 65/93). Diagnostic rates remained less than 50% in CMT2, HMN and complex neuropathies. The most common genetic diagnosis was PMP22 duplication (CMT1A; 505/1165, 43.3%), then GJB1 (CMTX1; 151/1165, 13.0%), PMP22 deletion (HNPP; 72/1165, 6.2%) and MFN2 (CMT2A; 46/1165, 3.9%). We recruited 233 cases to the UK 100 000 Genomes Project (100KGP), of which 74 (31.8%) achieved a diagnosis; 28 had been otherwise diagnosed since recruitment, leaving a true diagnostic rate of WGS through the 100KGP of 19.7% (46/233). However, almost half of the solved cases (35/74) received a negative report from the study, and the diagnosis was made through our research access to the WGS data. The overall diagnostic uplift of WGS for the entire cohort was 3.5%. Our diagnostic rate is the highest reported from a single centre and has benefitted from the use of WGS, particularly access to the raw data. However, almost one-quarter of all cases remain unsolved, and a new reference genome and novel technologies will be important to narrow the 'diagnostic gap'.

Virus sequencing performance during the SARS-CoV-2 pandemic: a retrospective analysis of data from multiple rounds of external quality assessment in Austria.

Introduction: A notable feature of the 2019 coronavirus disease (COVID-19) pandemic was the widespread use of whole genome sequencing (WGS) to monitor severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. Countries around the world relied on sequencing and other forms of variant detection to perform contact tracing and monitor changes in the virus genome, in the hopes that epidemic waves caused by variants would be detected and managed earlier. As sequencing was encouraged and rewarded by the government in Austria, but represented a new technicque for many laboratories, we designed an external quality assessment (EQA) scheme to monitor the accuracy of WGS and assist laboratories in validating their methods. Methods: We implemented SARS-CoV-2 WGS EQAs in Austria and report the results from 7 participants over 5 rounds from February 2021 until June 2023. The participants received sample material, sequenced genomes with routine methods, and provided the sequences as well as information about mutations and lineages. Participants were evaluated on the completeness and accuracy of the submitted sequence and the ability to analyze and interpret sequencing data. Results: The results indicate that performance was excellent with few exceptions, and these exceptions showed improvement over time. We extend our findings to infer that most publicly available sequences are accurate within ≤1 nucleotide, somewhat randomly distributed through the genome. Conclusion: WGS continues to be used for SARS-CoV-2 surveillance, and will likely be instrumental in future outbreak scenarios. We identified hurdles in building next-generation sequencing capacity in diagnostic laboratories. EQAs will help individual laboratories maintain high quality next-generation sequencing output, and strengthen variant monitoring and molecular epidemiology efforts.

Genomic prediction based on preselected single-nucleotide polymorphisms from genome-wide association study and imputed whole-genome sequence data annotation for growth traits in Duroc pigs.

The use of whole-genome sequence (WGS) data is expected to improve genomic prediction (GP) power of complex traits because it may contain mutations that in strong linkage disequilibrium pattern with causal mutations. However, a few previous studies have shown no or small improvement in prediction accuracy using WGS data. Incorporating prior biological information into GP seems to be an attractive strategy that might improve prediction accuracy. In this study, a total of 6334 pigs were genotyped using 50K chips and subsequently imputed to the WGS level. This cohort includes two prior discovery populations that comprise 294 Landrace pigs and 186 Duroc pigs, as well as two validation populations that consist of 3770 American Duroc pigs and 2084 Canadian Duroc pigs. Then we used annotation information and genome-wide association study (GWAS) from the WGS data to make GP for six growth traits in two Duroc pig populations. Based on variant annotation, we partitioned different genomic classes, such as intron, intergenic, and untranslated regions, for imputed WGS data. Based on GWAS results of WGS data, we obtained trait-associated single-nucleotide polymorphisms (SNPs). We then applied the genomic feature best linear unbiased prediction (GFBLUP) and genomic best linear unbiased prediction (GBLUP) models to estimate the genomic estimated breeding values for growth traits with these different variant panels, including six genomic classes and trait-associated SNPs. Compared with 50K chip data, GBLUP with imputed WGS data had no increase in prediction accuracy. Using only annotations resulted in no increase in prediction accuracy compared to GBLUP with 50K, but adding annotation information into the GFBLUP model with imputed WGS data could improve the prediction accuracy with increases of 0.00%-2.82%. In conclusion, a GFBLUP model that incorporated prior biological information might increase the advantage of using imputed WGS data for GP.

Posibilities for use of whole genome sequencing (WGS) for the analysis of Streptococcus pneumoniae isolates.

Streptococcus pneumoniae (pneumococcus) is a Gram-positive coccus causing both non-invasive and invasive infectious diseases. Pneumococcal diseases are vaccine preventable. Invasive pneumococcal diseases (IPD) meeting the international case definition are reported nationally and internationally and are subject to surveillance programmes in many countries, including the Czech Republic. An important part of IPD surveillance is the monitoring of causative serotypes and their frequency over time and in relation to ongoing vaccination programmes. In the world and in the Czech Republic, whole genome sequencing (WGS) is increasingly used for pneumococci, which allows for serotyping from sequencing data, precise analysis of their genetic relationships, and the study of genes present in their genome. Whole-genome sequencing enables the generation of reliable and internationally comparable data that can be easily shared. Sequencing data are analysed using bioinformatics tools that require knowledge in the field of natural sciences with an emphasis on genetics and expertise in bioinformatics. This publication presents some options for pneumococcal analysis, i.e., serotyping, multilocus sequence typing (MLST), ribosomal MLST (rMLST), core genome MLST (cgMLST), whole genome MLST (wgMLST), single nucleotide polymorphism (SNP) analysis, assignment to Global Pneumococcal Sequence Cluster (GPSC), and identification of virulence genes and antibiotic resistance genes. The WGS strategies and applications for Europe and WGS implementation in practice are presented. WGS analysis of pneumococci allows for improved IPD surveillance, thanks to molecular serotyping, more detailed typing, generation of internationally comparable data, and improved evaluation of the effectiveness of vaccination programmes.

Use of whole genome sequencing for surveillance and control of foodborne diseases: status quo and quo vadis.

Improvements in sequencing quality, availability, speed and costs results in an increased presence of genomics in infectious disease applications. Nevertheless, there are still hurdles in regard to the optimal use of WGS for public health purposes. Here, we discuss the current state ("status quo") and future directions ("quo vadis") based on literature regarding the use of genomics in surveillance, hazard characterization and source attribution of foodborne pathogens. The future directions include the application of new techniques, such as machine learning and network approaches that may overcome the current shortcomings. These include the use of fixed genomic distances in cluster delineation, disentangling similarity or lack thereof in source attribution, and difficulties ascertaining function in hazard characterization. Although, the aforementioned methods can relatively easily be applied technically, an overarching challenge is the inference and biological/epidemiological interpretation of these large amounts of high-resolution data. Understanding the context in terms of bacterial isolate and host diversity allows to assess the level of representativeness in regard to sources and isolates in the dataset, which in turn defines the level of certainty associated with defining clusters, sources and risks. This also marks the importance of metadata (clinical, epidemiological, and biological) when using genomics for public health purposes.

Uncommon Protein-Coding Variants Associated With Suicide Attempt in a Diverse Sample of U.S. Army Soldiers

BackgroundSuicide is a societal and public health concern of global scale. Identifying genetic risk factors for suicide attempt can characterize underlying biology and enable early interventions to prevent deaths. Recent studies have described common genetic variants for suicide-related behaviors. Here, we advance this search for genetic risk by analyzing the association between suicide attempt and uncommon variation exome-wide in a large, ancestrally diverse sample. MethodsWe sequenced whole genomes of 13,584 soldiers from the Army STARRS (Army Study to Assess Risk and Resilience in Servicemembers), including 979 individuals with a history of suicide attempt. Uncommon, nonsilent protein–coding variants were analyzed exome-wide for association with suicide attempt using gene-collapsed and single-variant analyses. ResultsWe identified 19 genes with variants enriched in individuals with history of suicide attempt, either through gene-collapsed or single-variant analysis (Bonferroni padjusted < .05). These genes were CIB2, MLF1, HERC1, YWHAE, RCN2, VWA5B1, ATAD3A, NACA, EP400, ZNF585A, LYST, RC3H2, PSD3, STARD9, SGMS1, ACTR6, RGS7BP, DIRAS2, and KRTAP10-1. Most genes had variants across multiple genomic ancestry groups. Seventeen of these genes were expressed in healthy brain tissue, with 9 genes expressed at the highest levels in the brain versus other tissues. Brains from individuals deceased from suicide aberrantly expressed RGS7BP (padjusted = .035) in addition to nominally significant genes including YWHAE and ACTR6, all of which have reported associations with other mental disorders. ConclusionsThese results advance the molecular characterization of suicide attempt behavior and support the utility of whole-genome sequencing for complementing the findings of genome-wide association studies in suicide research.

Whole genome sequencing in the palm of your hand: how to implement a MinION Galaxy-based workflow in a food safety laboratory for rapid Salmonella spp. serotyping, virulence, and antimicrobial resistance gene identification.

Whole Genome Sequencing (WGS) implementation in food safety laboratories is a significant advancement in food pathogen control and outbreak tracking. However, the initial investment for acquiring next-generation sequencing platforms and the need for bioinformatic skills represented an obstacle for the widespread use of WGS. Long-reading technologies, such as the one developed by Oxford Nanopore Technologies, can be easily implemented with a minor initial investment and with simple protocols that can be performed with basic laboratory equipment. Herein, we report a simple MinION Galaxy-based workflow with analysis parameters that allow its implementation in food safety laboratories with limited computer resources and without previous knowledge in bioinformatics for rapid Salmonella serotyping, virulence, and identification of antimicrobial resistance genes. For that purpose, the single use Flongle flow cells, along with the MinION Mk1B for WGS, and the community-driven web-based analysis platform Galaxy for bioinformatic analysis was used. Three strains belonging to three different serotypes, monophasic S. Typhimurium, S. Grancanaria, and S. Senftenberg, were sequenced. After 24 h of sequencing, enough coverage was achieved in order to perform de novo assembly in all three strains. After evaluating different tools, Flye de novo assemblies with medaka polishing were shown to be optimal for in silico Salmonella spp. serotyping with SISRT tool followed by antimicrobial and virulence gene identification with ABRicate. The implementation of the present workflow in food safety laboratories with limited computer resources allows a rapid characterization of Salmonella spp. isolates.

Predicting Listeria monocytogenes virulence potential using whole genome sequencing and machine learning

Contamination with food-borne pathogens, such as Listeria monocytogenes, remains a big concern for food safety. Hence, rigorous and continuous microbial surveillance is a standard procedure. At this point, however, the food industry and authorities only focus on detection of Listeria monocytogenes without characterization of individual strains into groups of more or less concern. As whole genome sequencing (WGS) gains increasing interest in the industry, this methodology presents an opportunity to obtain finer resolution of microbial traits such as virulence. Within this study, we therefore aimed to explore the use of WGS in combination with Machine Learning (ML) to predict L. monocytogenes virulence potential on a sub-species level.The WGS datasets used in this study for ML model training consisted of i) national surveillance isolates (n = 169, covering 38 MLST types) and ii) publicly available isolates acquired through the GenomeTrakr network (n = 2880, spanning 80 MLST types). We used the clinical frequency, i.e., ratio of the number of clinical isolates to total amount of isolates, as estimate for virulence potential. The predictive performance of input features from three different genomic levels (i.e., virulence genes, pan-genome genes, and single nucleotide polymorphisms (SNPs)) and six machine learning algorithms (i.e., Support Vector Machine with a linear kernel, Support Vector Machine with a radial kernel, Random Forrest, Neural Networks, LogitBoost, and Majority Voting) were compared.Our machine learning models predicted sub-species virulence potential with nested cross-validation F1-scores up to 0.88 for the majority voting classifier trained on national surveillance data and using pan-genome genes as input features. The validation of the pre-trained ML models based on 101 previously in vivo studied isolates resulted in F1-scores up to 0.76. Furthermore, we found that the more rapid and less computationally intensive raw read alignment yields comparably accurate models as de novo assembly.The results of our study suggest that a majority voting classifier trained on pan-genome genes is the best and most robust choice for the prediction of clinical frequency. Our study contributes to more rapid and precise characterization of L. monocytogenes virulence and its variation on a sub-species level. We further demonstrated a possible application of WGS data in the context of microbial hazard characterization for food safety. In the future, predictive models may assist case-specific microbial risk management in the food industry. The python code, pre-trained models, and prediction pipeline are deposited at (https://github.com/agmei/LmonoVirulenceML).

Whole genome sequencing improves the discrimination between Mycobacterium bovis strains on the southern border of Kruger National Park, South Africa

BackgroundMycobacterium bovis forms part of the Mycobacterium tuberculosis complex and has an extensive host range and zoonotic potential. Various genotyping methods (e.g., spoligotyping) have been used to describe the molecular epidemiology of M. bovis. Advances in whole genome sequencing (WGS) have increased resolution to enable detection of genomic variants to the level of single nucleotide polymorphisms. This is especially relevant to One Health research on tuberculosis which benefits by being able to use WGS to identify epidemiologically linked cases, especially recent transmission. The use of WGS in molecular epidemiology has been extensively used in humans and cattle but is limited in wildlife. This approach appears to overcome the limitations of conventional genotyping methods due to lack of genetic diversity in M. bovis. MethodsThis pilot study investigated the spoligotype and WGS of M. bovis isolates (n = 7) from wildlife in Marloth Park (MP) and compared these with WGS data from other South African M. bovis isolates. In addition, the greater resolution of WGS was used to explore the phylogenetic relatedness of M. bovis isolates in neighbouring wildlife populations. ResultsThe phylogenetic analyses showed the closest relatives to the seven isolates from MP were isolates from wildlife in Kruger National Park (KNP), which shares a border with MP. However, WGS data indicated that the KNP and MP isolates formed two distinct clades, even though they had similar spoligotypes and identical in silico genetic regions of difference profiles. ConclusionsMycobacterium bovis isolates from MP were hypothesized to be directly linked to KNP wildlife, based on spoligotyping. However, WGS indicated more complex epidemiology. The presence of two distinct clades which were genetically distinct (SNP distance of 19–47) and suggested multiple transmission events. Therefore, WGS provided new insight into the molecular epidemiology of the M. bovis isolates from MP and their relationship to isolates from KNP. This approach will facilitate greater understanding of M. bovis transmission at wildlife-livestock-human interfaces and advances One Health research on tuberculosis, especially across different host species.

Genome-wide association study of heart failure in a multiethnic south-east asian cohort

Abstract NMRC Heart failure (HF) is a leading cause of morbidity and mortality worldwide (1). While existing genome-wide association studies (GWAS) have yielded many different loci and causal genes, most of such studies has been conducted in European populations (2). There has been little insight into the genetic architecture of complex diseases, especially HF among Asians, despite up to 60% of the world population consisting of Asians. Through this study, we aim to uncover the genomic basis of heart failure in a South-East Asian cohort, with deep phenotyping of cardiovascular, metabolic, immune, and inflammatory traits. We conducted the largest multi-ancestry South-east Asian all-cause HF GWAS (n=2310, 1305 cases and 1005 controls), utilizing whole genome sequencing (WGS) data. Eleven independent loci were found to be associated with HF (P-value = 5e10-8). In-silico and functional studies were interrogated, allowing us to map most of the loci (n=9) to various genes implicated in various metabolic diseases - hypertension (HTN), diabetes mellitus (DM), and coronary artery disease (CAD). We further conducted a phenome-wide association study (PWAS) of significant loci, which revealed that they augment signals related to CAD, atrial fibrillation, DM and HTN, suggesting a shared genetic etiology between these metabolic risk factors and Asian HF. Of note, this the first HF GWAS to implicate the PDE9A gene, which was previously discovered as a potential biomarker for HF with preserved ejection fraction (HFpEF) through its involvement in the cGMP signaling pathways, and is currently being explored as a therapeutic target in the treatment of HFpEF (3). Polygenic risk scores (PRS) were calculated for heart failure in this Asian cohort, which identified that the most optimized model (HF PRS), included 19,431 of the most significant genetic variants (p=0.073). The prediction power of the HF PRS on HF risk was tested in UKB Asian subjects (N=10,016; 413 cases and 9,603 controls). Our results showed that the highest 10% percentile HF PRS risk subjects in UKB Asian were at 1.56-fold risks (95% CI: 1.16-2.08) for HF. This study provides a comprehensive understanding of the genetic architecture, and sheds light on novel biological pathways underlying HF in a south-east Asian population, indicating a highly metabolic underpinnings of this disease. It provides a step towards developing a precision-medicine based approach for risk stratification, prevention, and management of HF among Asians. Furthermore, this study provides evidence for the use of WGS in GWAS, to expand the discovery of novel targets for downstream interrogation, biomarker and drug development for complex diseases.

Cost-effectiveness analysis of Next generation sequencing tests in critically ill pediatric patients

Abstract Rare genetic diseases in the pediatric population constitute an urgent global public health issue; overall, more than 300 million people are affected worldwide. Next Generation Sequencing techniques, as Whole genome sequencing (WGS) and Whole exome sequencing (WES), have proven to be significantly supportive in diagnosing these complex conditions. The aim is to evaluate the cost-effectiveness of WGS versus WES in pediatric patients with suspected genetic disorders. A Bayesian Markov model was calibrated among this target population, comparing WGS to WES. Model parameters were retrieved from the scientific literature. Costs and benefits were discounted at a rate of 3%. A lifetime time horizon and the National Health Service perspective was chosen. The Eurozone threshold, ranging from €30,000 to €50,000, was adopted. Markov Chain Monte Carlo was used as the simulation method for Bayesian inference. Uncertainty was explored through a probabilistic sensitivity analysis (PSA) and a value of information analysis (VOI), illustrated through Cost-Effectiveness Acceptability Curve (CEAC) and Expected Value of Perfect Information (EVPI). Results were reported as Incremental Cost-Effectiveness Ratio (ICER), expressed as euros per additional diagnosis. The base case findings highlighted that WGS was cost-effective with an ICER of €31,973. The CEAC showed that for all thresholds over the ICER, WGS had the highest probability of being cost-effective. The EVPI per patient was estimated to be €6,535 on a threshold of €50,000/diagnosis. In addition to being cost-effective, WGS could allow early genetic diagnosis shortening the diagnostic odyssey. The use of WGS in the diagnostic workup has the potential to revolutionise personalised medicine and to play a significant role in achieving SDG 3 by providing personalised healthcare, identifying genetic risk factors for diseases, and informing public health policies for a target population that represents the human capital of the future. Key messages • This analysis informs that WGS is cost-effective and possibly cost-saving in the long term. • WGS could be implemented in the diagnostic workup of children with suspected rare disorders.

Phenotypic and genotypic drug susceptibility patterns of Mycobacterium tuberculosis isolates from pulmonary tuberculosis patients in Central and Southern Ethiopia.

The persistence of tuberculosis (TB) infection in some patients after treatment has highlighted the importance of drug susceptibility testing (DST). This study aimed to determine the drug susceptibility patterns of Mycobacterium tuberculosis (M. tuberculosis) isolates from pulmonary TB (PTB) patients in Central and Southern Ethiopia. A health institution-based cross-sectional study was conducted between July 2021 and April 2022. Sputum samples were collected from newly diagnosed smear microscopy and/or Xpert MTB/RIF-positive PTB patients. The samples were processed and cultivated in Lowenstein-Jensen (LJ) pyruvate and glycerol medium. M. tuberculosis isolates were identified using polymerase chain reaction (PCR) based region of difference 9 (RD9) deletion typing. Phenotypic DST patterns of the isolates were characterized using the BACTEC MGIT™ 960 instrument with SIRE kit. Isoniazid (INH) and Rifampicin (RIF) resistant M. tuberculosis isolates were identified using the GenoType® MTBDRplus assay. Sputum samples were collected from 350 PTB patients, 315 (90%) of which were culture-positive, and phenotypic and genotypic DST were determined for 266 and 261 isolates, respectively. Due to invalid results and missing data, 6% (16/266) of the isolates were excluded, while 94% (250/266) were included in the paired analysis. According to the findings, 14.4% (36/250) of the isolates tested positive for resistance to at least one anti-TB drug. Gene mutations were observed only in the rpoB and katG gene loci, indicating RIF and high-level INH resistance. The GenoType® MTBDRplus assay has a sensitivity of 42% and a specificity of 100% in detecting INH-resistant M. tuberculosis isolates, with a kappa value of 0.56 (95%CI: 0.36-0.76) compared to the BACTEC MGIT™ DST. The overall discordance between the two methods was 5.6% (14/250) for INH alone and 0% for RIF resistance and MDR-TB (resistance to both INH and RIF) detection. This study reveals a higher prevalence of phenotypic and genotypic discordant INH-resistant M. tuberculosis isolates in the study area. The use of whole-genome sequencing (WGS) is essential for gaining a comprehensive understanding of these discrepancies within INH-resistant M. tuberculosis strains.

Unraveling virulence determinants in extended-spectrum beta-lactamase-producing Escherichia coli from East Africa using whole-genome sequencing

Escherichia coli significantly causes nosocomial infections and rampant spread of antimicrobial resistance (AMR). There is limited data on genomic characterization of extended-spectrum β-lactamase (ESBL)-producing E. coli from African clinical settings. This hospital-based longitudinal study unraveled the genetic resistance elements in ESBL E. coli isolates from Uganda and Tanzania using whole-genome sequencing (WGS). A total of 142 ESBL multi-drug resistant E. coli bacterial isolates from both Tanzania and Uganda were sequenced and out of these, 36/57 (63.1%) and 67/85 (78.8%) originated from Uganda and Tanzania respectively. Mutations in RarD, yaaA and ybgl conferring resistances to chloramphenicol, peroxidase and quinolones were observed from Ugandan and Tanzanian isolates. We reported very high frequencies for blaCTX−M−15 with 11/18(61.1%), and blaCTX−M−27 with 12/23 (52.1%), blaTEM−1B with 13/23 (56.5%) of isolates originating from Uganda and Tanzania respectively all conferring resistance to Beta-lactam-penicillin inhibitors. We observed chloramphenicol resistance-conferring gene mdfA in 21/23 (91.3%) of Tanzanian isolates. Extraintestinal E. coli sequence type (ST) 131 accounted for 5/59 (8.4%) of Tanzanian isolates while enterotoxigenic E. coli ST656 was reported in 9/34 (26.4%) of Ugandan isolates. Virulence factors originating from Shigella dysenteriae Sd197 (gspC, gspD, gspE, gspF, gspG, gspF, gspH, gspI), Yersinia pestis CO92 (irp1, ybtU, ybtX, iucA), Salmonella enterica subsp. enterica serovar Typhimurium str. LT2 (csgF and csgG), and Pseudomonas aeruginosa PAO1 (flhA, fliG, fliM) were identified in these isolates. Overall, this study highlights a concerning prevalence and diversity of AMR-conferring elements shaping the genomic structure of multi-drug resistant E. coli in clinical settings in East Africa. It underscores the urgent need to strengthen infection-prevention controls and advocate for the routine use of WGS in national AMR surveillance and monitoring programs.Availability of WGS analysis pipeline: the rMAP source codes, installation, and implementation manual can free be accessed via https://github.com/GunzIvan28/rMAP.

Use of whole genome sequencing to identify low-frequency mutations in SARS-CoV-2 patients treated with remdesivir.

Remdesivir (RDV) has been shown to reduce hospitalization and mortality in COVID-19 patients. Resistance mutations caused by RDV are rare and have been predominantly reported in patients who are on prolonged therapy and immunocompromised. We investigate the effects of RDV treatment on intra-host SARS-CoV-2 diversity and low-frequency mutations in moderately ill hospitalized COVID-19 patients and compare them to patients without RDV treatment. From March 2020 to April 2022, sequential collections of nasopharyngeal and mid-turbinate swabs were obtained from 14 patients with and 30 patients without RDV treatment. Demographic and clinical data on all patients were reviewed. A total of 109 samples were sequenced and mutation analyses were performed. Previously reported drug resistant mutations in nsp12 were not identified during short courses of RDV therapy. In genes encoding and surrounding the replication complex (nsp6-nsp14), low-frequency minority variants were detected in 7/14 (50%) and 18/30 (60%) patients with and without RDV treatment, respectively. We did not detect significant differences in within-host diversity and positive selection between the RDV-treated and untreated groups. Minimal intra-host variability and stochastic low-frequency variants detected in moderately ill patients suggests little selective pressure in patients receiving short courses of RDV. The barrier to RDV resistance is high in patients with moderate disease. Patients undergoing short regimens of RDV therapy should continue to be monitored.

An Overview of Antimicrobial Resistance Profiles of Publicly Available Salmonella Genomes with Sufficient Quality and Metadata.

Salmonella enterica (S. enterica) is a commensal organism or pathogen causing diseases in animals and humans, as well as widespread in the environment. Antimicrobial resistance (AMR) has increasingly affected both animal and human health and continues to raise public health concerns. A decade ago, it was estimated that the increased use of whole genome sequencing (WGS) combined with sharing of public data would drastically change and improve the surveillance and understanding of Salmonella epidemiology and AMR. This study aimed to evaluate the current usefulness of public WGS data for Salmonella surveillance and to investigate the associations between serovars, antibiotic resistance genes (ARGs), and metadata. Out of 191,306 Salmonella genomes deposited in European Nucleotide Archive and NCBI databases, 47,452 WGS with sufficient minimum metadata (country, year, and source) of S. enterica were retrieved from 116 countries and isolated between 1905 and 2020. For in silico analysis of the WGS data, KmerFinder, SISTR, and ResFinder were used for species, serovars, and AMR identification, respectively. The results showed that the five common isolation sources of S. enterica are human (29.10%), avian (22.50%), environment (11.89%), water (9.33%), and swine (6.62%). The most common ARG profiles for each class of antimicrobials are β-lactam (blaTEM-1B; 6.78%), fluoroquinolone [(parC[T57S], qnrB19); 0.87%], folate pathway antagonist (sul2; 8.35%), macrolide [mph(A); 0.39%], phenicol (floR; 5.94%), polymyxin B (mcr-1.1; 0.09%), and tetracycline [tet(A); 12.95%]. Our study reports the first overview of ARG profiles in publicly available Salmonella genomes from online databases. All data sets from this study can be searched at Microreact.

Results of the 2020 Genomic Proficiency Test for the network of European Union Reference Laboratory for Antimicrobial Resistance assessing whole-genome-sequencing capacities.

The global surveillance and outbreak investigation of antimicrobial resistance (AMR) is amidst a paradigm shift from traditional biology to bioinformatics. This is due to developments in whole-genome-sequencing (WGS) technologies, bioinformatics tools, and reduced costs. The increased use of WGS is accompanied by challenges such as standardization, quality control (QC), and data sharing. Thus, there is global need for inter-laboratory WGS proficiency test (PT) schemes to evaluate laboratories' capacity to produce reliable genomic data. Here, we present the results of the first iteration of the Genomic PT (GPT) organized by the Global Capacity Building Group at the Technical University of Denmark in 2020. Participating laboratories sequenced two isolates and corresponding DNA of Salmonella enterica, Escherichia coli and Campylobacter coli, using WGS methodologies routinely employed at their laboratories. The participants' ability to obtain consistently good-quality WGS data was assessed based on several QC WGS metrics. A total of 21 laboratories from 21 European countries submitted WGS and meta-data. Most delivered high-quality sequence data with only two laboratories identified as overall underperforming. The QC metrics, N50 and number of contigs, were identified as good indicators for high-sequencing quality. We propose QC thresholds for N50 greater than 20 000 and 25 000 for Campylobacter coli and Escherichia coli, respectively, and number of contigs >200 bp greater than 225, 265 and 100 for Salmonella enterica, Escherichia coli and Campylobacter coli, respectively. The GPT2020 results confirm the importance of systematic QC procedures, ensuring the submission of reliable WGS data for surveillance and outbreak investigation to meet the requirements of the paradigm shift in methodology.

Genomic newborn screening: Are we entering a new era of screening?

Population newborn screening (NBS) for phenylketonuria began in the United States in 1963. In the 1990s electrospray ionization mass spectrometry permitted an array of pathognomonic metabolites to be identified simultaneously, enabling up to 60 disorders to be recognized with a single test. In response, differing approaches to the assessment of the harms and benefits of screening have resulted in variable screening panels worldwide. Thirty years on and another screening revolution has emerged with the potential for first line genomic testing extending the range of screening conditions recognized after birth to many hundreds. At the annual SSIEM conference in 2022 in Freiburg, Germany, an interactive plenary discussion on genomic screening strategies and their challenges and opportunities was conducted. The Genomics England Research project proposes the use of Whole Genome Sequencing to offer extended NBS to 100 000 babies for defined conditions with a clear benefit for the child. The European Organization for Rare Diseases seeks to include "actionable" conditions considering also other types of benefits. Hopkins Van Mil, a private UK research institute, determined the views of citizens and revealed as a precondition that families are provided with adequate information, qualified support, and that autonomy and data are protected. From an ethical standpoint, the benefits ascribed to screening and early treatment need to be considered in relation to asymptomatic, phenotypically mild or late-onset presentations, where presymptomatic treatment may not be required. The different perspectives and arguments demonstrate the unique burden of responsibility on those proposing new and far-reaching developments in NBS programs and the need to carefully consider both harms and benefits.

The Relative Positioning of Genotyping and Phenotyping for Tuberculosis Resistance Screening in Two EU National Reference Laboratories in 2023.

The routine use of whole genome sequencing (WGS) as a reference typing technique for Mycobacterium tuberculosis epidemiology combined with the catalogued and extensive knowledge base of resistance-associated mutations means an initial susceptibility prediction can be derived from all cultured isolates in our laboratories based on WGS data alone. Preliminary work has confirmed, in our low-burden settings, these predictions are for first-line drugs, reproducible, robust with an accuracy similar to phenotypic drug susceptibility testing (pDST) and in many cases able to also predict the level of resistance (MIC). Routine screening for drug resistance by WGS results in approximately 80% of the isolates received being predicted as fully susceptible to the first-line drugs. Parallel testing with both WGS and pDST has demonstrated that routine pDST of genotypically fully susceptible isolates yields minimal additional information. Thus, rather than re-confirming all fully sensitive WGS-based predictions, we suggest that a more efficient use of available mycobacterial culture capacity in our setting is the development of a more extensive and detailed pDST targeted at any mono or multi-drug-resistant isolates identified by WGS screening. Phenotypic susceptibility retains a key role in the determination of an extended susceptibility profile for mono/multi-drugresistant isolates identified by WGS screening. The pDST information collected is also needed to support the development of future catalogues of resistance-associated mutations.

The use of whole-genome sequencing and development of bioinformatics to monitor overlapping outbreaks of Candida auris in southern Nevada.

A Candida auris outbreak has been ongoing in Southern Nevada since August 2021. In this manuscript we describe the sequencing of over 200 C. auris isolates from patients at several facilities. Genetically distinct subgroups of C. auris were detected from Clade I (3 distinct lineages) and III (1 lineage). Open-source bioinformatic tools were developed and implemented to aid in the epidemiological investigation. The work herein compares three methods for C. auris whole genome analysis: Nullarbor, MycoSNP and a new pipeline TheiaEuk. We also describe a novel analysis method focused on elucidating phylogenetic linkages between isolates within an ongoing outbreak. Moreover, this study places the ongoing outbreaks in a global context utilizing existing sequences provided worldwide. Lastly, we describe how the generated results were communicated to the epidemiologists and infection control to generate public health interventions.

Risk-conferring HLA variants in an epilepsy cohort: benefits of multifaceted use of whole genome sequencing in clinical practice

BackgroundWhole genome sequencing is increasingly used in healthcare, particularly for diagnostics. However, its clinically multifaceted potential for individually customised diagnostic and therapeutic care remains largely unexploited. We used existing whole...