Genome Sequence Data Research Articles

Key variants via the Alzheimer's Disease Sequencing Project whole genome sequence data.

Genome-wide association studies (GWAS) have identified loci associated with Alzheimer's disease (AD) but did not identify specific causal genes or variants within those loci. Analysis of whole genome sequence (WGS) data, which interrogates the entire genome and captures rare variations, may identify causal variants within GWAS loci. We performed single common variant association analysis and rare variant aggregate analyses in the pooled population (N cases=2184, N controls=2383) and targeted analyses in subpopulations using WGS data from the Alzheimer's Disease Sequencing Project (ADSP). The analyses were restricted to variants within 100kb of 83 previously identified GWAS lead variants. Seventeen variants were significantly associated with AD within five genomic regions implicating the genes OARD1/NFYA/TREML1, JAZF1, FERMT2, and SLC24A4. KAT8 was implicated by both single variant and rare variant aggregate analyses. This study demonstrates the utility of leveraging WGS to gain insights into AD loci identified via GWAS.

Update on the proposed minimal standards for the use of genome data for the taxonomy of prokaryotes.

The field of microbial taxonomy is dynamic, aiming to provide a stable and contemporary classification system for prokaryotes. Traditionally, reliance on phenotypic characteristics limited the comprehensive understanding of microbial diversity and evolution. The introduction of molecular techniques, particularly DNA sequencing and genomics, has transformed our perception of prokaryotic diversity. In the past two decades, advancements in genome sequencing have transitioned from traditional methods to a genome-based taxonomic framework, not only to define species, but also higher taxonomic ranks. As technology and databases rapidly expand, maintaining updated standards is crucial. This work seeks to revise the 2018 guidelines for applying genome sequencing data in microbial taxonomy, adapting minimal standards and recommendations to reflect technological progress during this period.

Letter to the editor: Suspected discrepancies due to inadequate quality of an in vitro analysis revealed by an in silico analysis

The author's in vitro analysis results were compared with those of an in silico structure analysis of the relevant sequences obtained from the author's entire genome sequence data. It was speculated that the in silico results together with author's phylogenetic results demonstrate problems in the authors' published in vitro data, which were presumably due to an inadequate accuracy of an in vitro assay. It was fortuitously suggested that alignment images to an excess repeat structure of the same locus provide a improved speculation of a number of repeats and that accurate in vitro assays are expected to complementarily provide reliable insights in the era of whole genome sequencing.

Strong positive selection biases identity-by-descent-based inferences of recent demography and population structure in Plasmodium falciparum

Malaria genomic surveillance often estimates parasite genetic relatedness using metrics such as Identity-By-Decent (IBD), yet strong positive selection stemming from antimalarial drug resistance or other interventions may bias IBD-based estimates. In this study, we use simulations, a true IBD inference algorithm, and empirical data sets from different malaria transmission settings to investigate the extent of this bias and explore potential correction strategies. We analyze whole genome sequence data generated from 640 new and 3089 publicly available Plasmodium falciparum clinical isolates. We demonstrate that positive selection distorts IBD distributions, leading to underestimated effective population size and blurred population structure. Additionally, we discover that the removal of IBD peak regions partially restores the accuracy of IBD-based inferences, with this effect contingent on the population’s background genetic relatedness and extent of inbreeding. Consequently, we advocate for selection correction for parasite populations undergoing strong, recent positive selection, particularly in high malaria transmission settings.

Multiplex PCR method for MinION sequencing of Bagaza virus isolated from wild caught mosquitoes in South Africa

Bagaza virus (BAGV) is a mosquito-borne orthoflavivirus known to occur in regions of southern Europe, Africa, India and the Middle East. The virus has been associated with neurological disease and fatalities in various wild bird species. Association with human disease is not confirmed although limited serological evidence has suggested human infection. Surveillance programs for screening mosquitoes for evidence of arbovirus infection play an important role in providing information regarding the circulation and spread of viruses in specific regions. BAGV was detected in a mosquito pool during surveillance of mosquitoes collected in central South Africa between November 2019 and March 2023. Homogenized mosquito pools were screened for flaviviral RNA using conventional RT-PCR and virus isolation was attempted on positive samples. BAGV was detected and subsequently isolated using cell culture. A multiplex tiling PCR method for targeted enrichment using a PCR based or amplicon sequencing approach of the complete genome of BAGV was developed and optimized. Primers were designed using alignment of complete genome sequence data retrieved from GenBank to identify suitable primer sites that would generate overlapping fragments spanning the complete genome. Six forward primers and eight reverse primers were identified that target the complete genome and amplified nine overlapping fragments, that ranged in length from 1954 to 2039 with an overlap ranging from 71 to 711 base pairs. The design strategy included multiple forward and reverse primer pairs for the 5’ and 3’ ends. Phylogenetic analysis with other isolates was performed and BAGV isolate VBD 74/23/3 was shown to share high similarity with previous BAGV isolates from all regions, with genetic distance ranging from 0.026 to 0.083. VBD 74/23/3 was most closely related to previous isolates from southern Africa, ZRU96/16/2 isolated from a post-mortem sample from a pheasant in 2016 and MP-314-NA-2018 isolated from mosquitoes in northwestern Namibia with genetic distance 0.0085 and 0.016 respectively. Currently there is limited complete genome sequence data available for many of the arboviruses circulating in Africa. The multiplex tiling method provided a simple and cost-effective method for obtaining complete genome sequence. This method can be readily applied to other viruses using sequence data from publicly available databases and would have important application facilitating genomic surveillance of arboviruses in low resource countries.

Rifampicin resistant Mycobacterium tuberculosis in Vietnam, 2020–2022

ObjectiveWe conducted a descriptive analysis of multi-drug resistant tuberculosis (MDR-TB) in Vietnam’s two largest cities, Hanoi and Ho Chi Minh city. MethodsAll patients with rifampicin resistant tuberculosis were recruited from Hanoi and surrounding provinces between 2020 and 2022. Additional patients were recruited from Ho Chi Minh city over the same time period. Demographic data were recorded from all patients, and samples collected, cultured, whole genome sequenced and analysed for drug resistance mutations. Genomic susceptibility predictions were made on the basis of the World Health Organization’s catalogue of mutations in Mycobacterium tuberculosis associated with drug resistance, version 2. Comparisons were made against phenotypic drug susceptibility test results where these were available. Multivariable logistic regression was used to assess risk factors for previous episodes of tuberculosis. Results233/265 sequenced isolates were of sufficient quality for analysis, 146 (63 %) from Ho Chi Minh City and 87 (37 %) from Hanoi. 198 (85 %) were lineage 2, 20 (9 %) were lineage 4, and 15 (6 %) were lineage 1. 17/211 (8 %) for whom HIV status was known were infected, and 109/214 (51 %) patients had had a previous episode of tuberculosis. The main risk factor for a previous episode was HIV infection (odds ratio 5.1 (95 % confidence interval 1.3–20.0); p = 0.021). Sensitivity for predicting first-line drug resistance from whole genome sequencing data was over 90 %, with the exception of pyrazinamide (85 %). For moxifloxacin and amikacin it was 50 % or less. Among rifampicin-resistant isolates, prevalence of resistance to each non-first-line drug was < 20 %. ConclusionsDrug resistance among most MDR-TB strains in Vietnam’s two largest cities is confined largely to first-line drugs. Living with HIV is the main risk factor among patients with MDR-TB for having had a previous episode of tuberculosis.

Single-cell sequencing shows mosaic aneuploidy in most human embryos.

Mammalian preimplantation embryos often contain chromosomal defects that arose in the first divisions after fertilization and affect a subpopulation of cells - an event known as mosaic aneuploidy. In this issue of the JCI, Chavli et al. report single-cell genomic sequencing data for rigorous evaluation of the incidence and degree of mosaic aneuploidy in healthy human in vitro fertilization (IVF) embryos. Remarkably, mosaic aneuploidy occurred in at least 80% of human blastocyst-stage embryos, with often less than 20% of cells showing defects. These findings confirm that mosaic aneuploidy is prevalent in human embryos, indicating that the process is a widespread event that rarely has clinical consequences. There are major implications for preimplantation genetic testing of aneuploidy (PGT-A), a test commonly used to screen and select IVF embryos for transfer. The application and benefit of this technology is controversial, and the findings provide more cause for caution on its use.

Patterns of structural variants within TP53 introns and relocation of the TP53 promoter: a commentary†.

Gene disruption from double-strand DNA breaks within introns is a mechanism of inactivating the tumor suppressor TP53. This occurs more frequently in osteosarcoma and biliary adenocarcinoma compared with other cancer types. The patterns of intron breakpoints within TP53 do not correlate with prevalence, intron length, or overall genome-wide levels of rearrangements. Therefore, these breakpoints appear to be selected for reasons other than to disrupt TP53. A recent article published by Saba et al in The Journal of Pathology illustrates a benefit to having breakpoints within intron 1 using high-quality matched genomic and transcriptomic osteosarcoma sequencing data as well as in vitro validation. The authors describe how the rearrangement results in relocation of the TP53 promoter region to regions upstream of genes that encode members of cartilage, growth plate development, osteoclast formation, and other TP53-related pathways. The upregulation of these genes by the TP53 promoter are gain-of-function events that are likely to promote tumor development and growth. Therefore, this article presents a potential new paradigm in which a single mutation would result in both the loss of a tumor suppressor and the gain of an oncogenic program. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Chromosome-level genome assemblies of Musa ornata and M. velutina provide insights into pericarp dehiscence and anthocyanin biosynthesis in banana

Abstract Musa ornata and M. velutina are members of the Musaceae family and are indigenous to the South and Southeast Asia. They are very popular in the horticultural market, but the lack of genomic sequencing data and genetic studies has hampered efforts to improve their ornamental value. In this study, we generated the first chromosome-level genome assemblies for both species by utilizing Oxford Nanopore long reads and Hi-C reads. The genomes of M. ornata and M. velutina were assembled into 11 pseudochromosomes with genome sizes of 427.85 Mb and 478.10 Mb, respectively. Repetitive sequences comprised 46.70% and 50.91% of the total genomes for M. ornata and M. velutina, respectively. Differentially expressed gene (DEG) and Gene Ontology (GO) enrichment analyses indicated that upregulated genes in the mature pericarps of M. velutina were mainly associated with the saccharide metabolic processes, particularly at the cell wall and extracellular region. Furthermore, we identified polygalacturonase (PG) genes that exhibited higher expression level in mature pericarps of M. velutina compared to other tissues, potentially being accountable for pericarp dehiscence. This study also identified genes associated with anthocyanin biosynthesis pathway. Taken together, the chromosomal-level genome assemblies of M. ornata and M. velutina provide valuable insights into the mechanism of pericarp dehiscence and anthocyanin biosynthesis in banana, which will significantly contribute to future genetic and molecular breeding efforts.

Cytoplasmic genome contributions to domestication and improvement of modern maize

BackgroundStudies on maize evolution and domestication are largely limited to the nuclear genomes, and the contribution of cytoplasmic genomes to selection and domestication of modern maize remains elusive. Maize cytoplasmic genomes have been classified into fertile (NA and NB) and cytoplasmic-nuclear male-sterility (CMS-S, CMS-C, and CMS-T) groups, but their contributions to modern maize breeding have not been systematically investigated.ResultsHere we report co-selection and convergent evolution between nuclear and cytoplasmic genomes by analyzing whole genome sequencing data of 630 maize accessions modern maize and its relatives, including 24 fully assembled mitochondrial and chloroplast genomes. We show that the NB cytotype is associated with the expansion of modern maize to North America, gradually replaces the fertile NA cytotype probably through unequal division, and predominates in over 90% of modern elite inbred lines. The mode of cytoplasmic evolution is increased nucleotypic diversity among the genes involved in photosynthesis and energy metabolism, which are driven by selection and domestication. Furthermore, genome-wide association study reveals correlation of cytoplasmic nucleotypic variation with key agronomic and reproductive traits accompanied with the diversification of the nuclear genomes.ConclusionsOur results indicate convergent evolution between cytoplasmic and nuclear genomes during maize domestication and breeding. These new insights into the important roles of mitochondrial and chloroplast genomes in maize domestication and improvement should help select elite inbred lines to improve yield stability and crop resilience of maize hybrids.

Development and validation of a random forest algorithm for source attribution of animal and human Salmonella Typhimurium and monophasic variants of S. Typhimurium isolates in England and Wales utilising whole genome sequencing data.

Source attribution has traditionally involved combining epidemiological data with different pathogen characterisation methods, including 7-gene multi locus sequence typing (MLST) or serotyping, however, these approaches have limited resolution. In contrast, whole genome sequencing data provide an overview of the whole genome that can be used by attribution algorithms. Here, we applied a random forest (RF) algorithm to predict the primary sources of human clinical Salmonella Typhimurium (S. Typhimurium) and monophasic variants (monophasic S. Typhimurium) isolates. To this end, we utilised single nucleotide polymorphism diversity in the core genome MLST alleles obtained from 1,061 laboratory-confirmed human and animal S. Typhimurium and monophasic S. Typhimurium isolates as inputs into a RF model. The algorithm was used for supervised learning to classify 399 animal S. Typhimurium and monophasic S. Typhimurium isolates into one of eight distinct primary source classes comprising common livestock and pet animal species: cattle, pigs, sheep, other mammals (pets: mostly dogs and horses), broilers, layers, turkeys, and game birds (pheasants, quail, and pigeons). When applied to the training set animal isolates, model accuracy was 0.929 and kappa 0.905, whereas for the test set animal isolates, for which the primary source class information was withheld from the model, the accuracy was 0.779 and kappa 0.700. Subsequently, the model was applied to assign 662 human clinical cases to the eight primary source classes. In the dataset, 60/399 (15.0%) of the animal and 141/662 (21.3%) of the human isolates were associated with a known outbreak of S. Typhimurium definitive type (DT) 104. All but two of the 141 DT104 outbreak linked human isolates were correctly attributed by the model to the primary source classes identified as the origin of the DT104 outbreak. A model that was run without the clonal DT104 animal isolates produced largely congruent outputs (training set accuracy 0.989 and kappa 0.985; test set accuracy 0.781 and kappa 0.663). Overall, our results show that RF offers considerable promise as a suitable methodology for epidemiological tracking and source attribution for foodborne pathogens.

A New Cloud-Native Tool for Pharmacogenetic Analysis.

The advancement of next-generation sequencing (NGS) technologies provides opportunities for large-scale Pharmacogenetic (PGx) studies and pre-emptive PGx testing to cover a wide range of genotypes present in diverse populations. However, NGS-based PGx testing is limited by the lack of comprehensive computational tools to support genetic data analysis and clinical decisions. Bioinformatics utilities specialized for human genomics and the latest cloud-based technologies were used to develop a bioinformatics pipeline for analyzing the genomic sequence data and reporting PGx genotypes. A database was created and integrated in the pipeline for filtering the actionable PGx variants and clinical interpretations. Strict quality verification procedures were conducted on variant calls with the whole genome sequencing (WGS) dataset of the 1000 Genomes Project (G1K). The accuracy of PGx allele identification was validated using the WGS dataset of the Pharmacogenetics Reference Materials from the Centers for Disease Control and Prevention (CDC). The newly created bioinformatics pipeline, Pgxtools, can analyze genomic sequence data, identify actionable variants in 13 PGx relevant genes, and generate reports annotated with specific interpretations and recommendations based on clinical practice guidelines. Verified with two independent methods, we have found that Pgxtools consistently identifies variants more accurately than the results in the G1K dataset on GRCh37 and GRCh38. Pgxtools provides an integrated workflow for large-scale genomic data analysis and PGx clinical decision support. Implemented with cloud-native technologies, it is highly portable in a wide variety of environments from a single laptop to High-Performance Computing (HPC) clusters and cloud platforms for different production scales and requirements.

The complete mitochondrial genome and phylogenetic position of Schizothorax argentatus (Cyprinomorpha: Crypriniformes: Cyprinidae)

We investigated the vulnerable fish species Schizothorax argentatus Kessler, 1874, using low-coverage whole genome sequencing data. The assembled 16,587 bp mitochondrial genome has a nucleotide composition of A = 29.8%, T = 25.3%, G = 17.9%, and C = 27.0%, containing 13 protein-coding genes, two ribosomal RNA genes, and 22 transfer RNA genes with a gene arrangement identical to other cofamilial species. Phylogenetic analyses of 71 schizothoracine fishes suggest a close relationship between S. argentatus and Schizothorax eurystomus, sharing a common ancestor with Schizothorax pseudoaksaiensis. Our study supports dividing extant schizothoracine fishes into two tribes, Schizothoracini and Schizopygopsini. The estimated time to most recent common ancestor (tMRCA ) and their distribution imply geological and climatic events during the Miocene around the Qinghai-Tibet Plateau as significant evolutionary drivers explaining the diversification of main clades in schizothoracine fishes.

The National Wastewater Surveillance System (NWSS): From inception to widespread coverage, 2020–2022, United States

Wastewater surveillance is a valuable tool that can be used to track infectious diseases in a community. In September 2020, the Centers for Disease Control and Prevention (CDC) established the National Wastewater Surveillance System (NWSS) to coordinate and build the nation's capacity to detect and quantify concentrations of SARS-CoV-2 RNA in U.S. wastewater. This is the first surveillance summary of NWSS, covering September 1, 2020 to December 31, 2022. Through partnerships with state, tribal, local, and territorial health departments, NWSS became a national surveillance platform that can be readily expanded and adapted to meet changing public health needs. Beginning with 209 sampling sites in September 2020, NWSS rapidly expanded to >1500 sites by December 2022, covering ≈47 % of the U.S. population. As of December 2022, >152,000 unique wastewater samples have been collected by NWSS partners, primarily from wastewater treatment plants (WWTPs). WWTPs participating in NWSS tend to be larger than the average U.S. WWTP and serve more populated communities. In December 2022, ≈8 % of the nearly 16,000 U.S. WWTPs were participating in NWSS. NWSS partners used a variety of methods for sampling and testing wastewater samples; however, progress is being made to standardize these methods. In July 2021, NWSS partners started submitting SARS-CoV-2 genome sequencing data to NWSS. In October 2022, NWSS expanded to monkeypox virus testing, with plans to include additional infectious disease targets in the future. Through the rapid implementation and expansion of NWSS, important lessons have been learned. Wastewater surveillance programs should consider both surge and long-term capacities when developing an implementation plan, and early standardization of sampling and testing methods is important to facilitate data comparisons across sites. NWSS has proven to be a flexible and sustainable surveillance system that will continue to be a useful complement to case-based surveillance for guiding public health action.

Strengthening Bordetella pertussis genomic surveillance by direct sequencing of residual positive specimens.

Whole-genome sequencing (WGS) of microbial pathogens recovered from patients with infectious disease facilitates high-resolution strain characterization and molecular epidemiology. However, increasing reliance on culture-independent methods to diagnose infectious diseases has resulted in few isolates available for WGS. Here, we report a novel culture-independent approach to genome characterization of Bordetella pertussis, the causative agent of pertussis and a paradigm for insufficient genomic surveillance due to limited culture of clinical isolates. Sequencing libraries constructed directly from residual pertussis-positive diagnostic nasopharyngeal specimens were hybridized with biotinylated RNA "baits" targeting B. pertussis fragments within complex mixtures that contained high concentrations of host and microbial background DNA. Recovery of B. pertussis genome sequence data was evaluated with mock and pooled negative clinical specimens spiked with reducing concentrations of either purified DNA or inactivated cells. Targeted enrichment increased the yield of B. pertussis sequencing reads up to 90% while simultaneously decreasing host reads to less than 10%. Filtered sequencing reads provided sufficient genome coverage to perform characterization via whole-genome single nucleotide polymorphisms and whole-genome multilocus sequencing typing. Moreover, these data were concordant with sequenced isolates recovered from the same specimens such that phylogenetic reconstructions from either consistently clustered the same putatively linked cases. The optimized protocol is suitable for nasopharyngeal specimens with diagnostic IS481 Ct < 35 and >10 ng DNA. Routine implementation of these methods could strengthen surveillance and study of pertussis resurgence by capturing additional cases with genomic characterization.

A metagenomic investigation of phytoplasma diversity in Australian vegetable growing regions.

In this study, metagenomic sequence data was used to investigate the phytoplasma taxonomic diversity in vegetable-growing regions across Australia. Metagenomic sequencing was performed on 195 phytoplasma-positive samples, originating either from historic collections (n=46) or during collection efforts between January 2015 and June 2022 (n=149). The sampled hosts were classified as crop (n=155), weed (n=24), ornamental (n=7), native plant (n=6), and insect (n=3) species. Most samples came from Queensland (n=78), followed by Western Australia (n=46), the Northern Territory (n=32), New South Wales (n=17), and Victoria (n=10). Of the 195 draft phytoplasma genomes, 178 met our genome criteria for comparison using an average nucleotide identity approach. Ten distinct phytoplasma species were identified and could be classified within the 16SrII, 16SrXII (PCR only), 16SrXXV, and 16SrXXXVIII phytoplasma groups, which have all previously been recorded in Australia. The most commonly detected phytoplasma taxa in this study were species and subspecies classified within the 16SrII group (n=153), followed by strains within the 16SrXXXVIII group ('Ca. Phytoplasma stylosanthis'; n=6). Several geographic- and host-range expansions were reported, as well as mixed phytoplasma infections of 16SrII taxa and 'Ca. Phytoplasma stylosanthis'. Additionally, six previously unrecorded 16SrII taxa were identified, including five putative subspecies of 'Ca. Phytoplasma australasiaticum' and a new putative 16SrII species. PCR and sequencing of the 16S rRNA gene was a suitable triage tool for preliminary phytoplasma detection. Metagenomic sequencing, however, allowed for higher-resolution identification of the phytoplasmas, including mixed infections, than was afforded by only direct Sanger sequencing of the 16S rRNA gene. Since the metagenomic approach theoretically obtains sequences of all organisms in a sample, this approach was useful to confirm the host family, genus, and/or species. In addition to improving our understanding of the phytoplasma species that affect crop production in Australia, the study also significantly expands the genomic sequence data available in public sequence repositories to contribute to phytoplasma molecular epidemiology studies, revision of taxonomy, and improved diagnostics.

De novo assembly and annotation of the Patagonian toothfish (Dissostichus eleginoides) genome

BackgroundPatagonian toothfish (Dissostichus eleginoides) is an economically and ecologically important fish species in the family Nototheniidae. Juveniles occupy progressively deeper waters as they mature and grow, and adults have been caught as deep as 2500 m, living on or in just above the southern shelves and slopes around the sub-Antarctic islands of the Southern Ocean. As apex predators, they are a key part of the food web, feeding on a variety of prey, including krill, squid, and other fish. Despite its importance, genomic sequence data, which could be used for more accurate dating of the divergence between Patagonian and Antarctic toothfish, or establish whether it shares adaptations to temperature with fish living in more polar or equatorial climes, has so far been limited.ResultsA high-quality D. eleginoides genome was generated using a combination of Illumina, PacBio and Omni-C sequencing technologies. To aid the genome annotation, the transcriptome derived from a variety of toothfish tissues was also generated using both short and long read sequencing methods. The final genome assembly was 797.8 Mb with a N50 scaffold length of 3.5 Mb. Approximately 31.7% of the genome consisted of repetitive elements. A total of 35,543 putative protein-coding regions were identified, of which 50% have been functionally annotated. Transcriptomics analysis showed that approximately 64% of the predicted genes (22,617 genes) were found to be expressed in the tissues sampled. Comparative genomics analysis revealed that the anti-freeze glycoprotein (AFGP) locus of D. eleginoides does not contain any AFGP proteins compared to the same locus in the Antarctic toothfish (Dissostichus mawsoni). This is in agreement with previously published results looking at hybridization signals and confirms that Patagonian toothfish do not possess AFGP coding sequences in their genome.ConclusionsWe have assembled and annotated the Patagonian toothfish genome, which will provide a valuable genetic resource for ecological and evolutionary studies on this and other closely related species.

NPSV-deep: a deep learning method for genotyping structural variants in short read genome sequencing data.

Structural variants (SVs) play a causal role in numerous diseases but can be difficult to detect and accurately genotype (determine zygosity) with short-read genome sequencing data (SRS). Improving SV genotyping accuracy in SRS data, particularly for the many SVs first detected with long-read sequencing, will improve our understanding of genetic variation. NPSV-deep is a deep learning-based approach for genotyping previously reported insertion and deletion SVs that recasts this task as an image similarity problem. NPSV-deep predicts the SV genotype based on the similarity between pileup images generated from the actual SRS data and matching SRS simulations. We show that NPSV-deep consistently matches or improves upon the state-of-the-art for SV genotyping accuracy across different SV call sets, samples and variant types, including a 25% reduction in genotyping errors for the Genome-in-a-Bottle (GIAB) high-confidence SVs. NPSV-deep is not limited to the SVs as described; it improves deletion genotyping concordance a further 1.5 percentage points for GIAB SVs (92%) by automatically correcting imprecise/incorrectly described SVs. Python/C++ source code and pre-trained models freely available at https://github.com/mlinderm/npsv2.

Longitudinal antimicrobial susceptibility trends of canine Staphylococcus pseudintermedius

Antimicrobial resistance within Staphylococcus pseudintermedius poses a significant risk for the treatment of canine pyoderma and as a reservoir for resistance and potential zoonoses, but few studies examine long-term temporal trends of resistance. This study assesses the antimicrobial resistance prevalence and minimum inhibitory concentration (MIC) trends in S. pseudintermedius (n=1804) isolated from canine skin samples at the Cornell University Animal Health Diagnostic Center (AHDC) between 2007 and 2020. Not susceptible (NS) prevalence, Cochran-Armitage tests, logrank tests, MIC50 and MIC90 quantiles, and survival analysis models were used to evaluate resistance prevalence and temporal trends to 23 antimicrobials. We use splines as predictors in accelerated failure time (AFT) models to model non-linear temporal trends in MICs. Multidrug resistance was common among isolates (47%), and isolates had moderate to high NS prevalence to the beta-lactams, chloramphenicol, the fluoroquinolones, gentamicin, the macrolides/lincosamides, the tetracyclines, and trimethoprim-sulfamethoxazole. However, low levels of NS to amikacin, rifampin, and vancomycin were observed. Around one third of isolates (38%) were found to be methicillin resistant S. pseudintermedius (MRSP), and these isolates had a higher prevalence of NS to all tested antimicrobials than methicillin susceptible isolates. Amongst the MRSP isolates, one phenotypically vancomycin resistant isolate (MIC >16 µg/mL) was identified, but genomic sequence data was unavailable. AFT models showed increasing MICs across time to the beta-lactams, chloramphenicol, the fluoroquinolones, gentamicin, and the macrolides/lincosamides, and decreasing temporal resistance (decreasing MICs) to doxycycline was observed amongst isolates. Notably, ATF modeling showed changes in MIC distributions that were not identified using Cochran-Armitage tests on prevalence, MIC quantiles, and logrank tests. Increasing resistance amongst these S. pseudintermedius isolates highlights the need for rational, empirical prescribing practices and increased antimicrobial resistance (AMR) surveillance to maintain the efficacy of current therapeutic agents. AFT models with non-linear predictors may be a useful, breakpoint-independent, surveillance tool alongside other modeling methods and antibiograms.

Recombination and repeat-induced point mutation landscapes reveal trade-offs between the sexual and asexual cycles of Magnaporthe oryzae

The fungal disease caused by Magnaporthe oryzae is one of the most devastating diseases that endanger many crops worldwide. Evidence shows that sexual reproduction can be advantageous for fungal diseases as hybridization facilitates host-jumping. However, the pervasive clonal lineages of M. oryzae observed in natural fields contradict this expectation. A better understanding of the roles of recombination and the fungi-specific repeat-induced point mutation (RIP) in shaping its evolutionary trajectory is essential to bridge this knowledge gap. Here we systematically investigate the RIP and recombination landscapes in M. oryzae using a whole genome sequencing data from 252 population samples and 92 cross progenies. Our data reveal that the RIP can robustly capture the population history of M. oryzae, and we provide accurate estimations of the recombination and RIP rates across different M. oryzae clades. Significantly, our results highlight a parent-of-origin bias in both recombination and RIP rates, tightly associating with their sexual potential and variations of effector proteins. This bias suggests a critical trade-off between generating novel allelic combinations in the sexual cycle to facilitate host-jumping and stimulating transposon-associated diversification of effectors in the asexual cycle to facilitate host coevolution. These findings provide unique insights into understanding the evolution of blast fungus.