Single Nucleotide Polymorphism Counts Research Articles

Assessing the discriminability of PCR-based open reading frame typing versus single-nucleotide polymorphism analysis via draft whole-genome sequencing of methicillin-resistant Staphylococcus aureus in nosocomial transmission analysis

Phylogenetic analysis based on single-nucleotide polymorphism (SNP)-based through whole-genome sequencing is recognized as the standard method for probing nosocomial transmission. However, the application of WGS is constrained by the high cost of equipment and the need for diverse analysis tools, which limits its widespread use in clinical laboratory settings. In Japan, the prevalent use of PCR-based open reading frame typing (POT) for tracing methicillin-resistant Staphylococcus aureus (MRSA) transmission routes is attributed to its simplicity and ease of use. Although POT's discriminatory power is considered insufficient for nosocomial transmission analysis, conclusive data supporting this notion is lacking. This study assessed the discriminatory capabilities of SNP analysis and POT across 64 clinical MRSA strains. All 21 MRSA strains of ST5/SCCmec IIa, having more than 16 SNPs, demonstrated distinct clones. Conversely, two strains shared the same POT number and were identified as group A. Among the 12 MRSA strains of ST8/SCCmec IVl with over nine SNPs, five fell into POT group B, and five into POT group C. All four MRSA strains of ST8/SCCmec IVa were classified into POT group D, although they included strains with more than 30 SNPs. Among the 27 MRSA strains of ST1/SCCmec IVa, 14 were classified into POT group E. However, except for two clusters (each comprising two or three strains), all had SNP counts >10 (Fig. 1-D). SNP analysis of MRSA in CC1/SCCmec IV showed that several strains had the same number of SNPs in POT number (106-183-37), even among bacteria with >100 SNPs, indicating POT's limited use in detailed nosocomial transmission analysis.

Are Current Aspergillus sojae Strains Originated from a Native Aflatoxigenic Aspergillus Species Population Also Present in California?

Aspergillus sojae has long been considered a domesticated strain of Aspergillus parasiticus. This study delineated relationships among the two species and an Aspergillus PWE36 isolate. Of 25 examined clustered aflatoxin genes of PWE36, 20 gene sequences were identical to those of A. sojae, but all had variations to those of A. parasiticus. Additionally, PWE36 developmental genes of conidiation and sclerotial formation, overall, shared higher degrees of nucleotide sequence identity with A. sojae genes than with A. parasiticus genes. Examination of defective cyclopiazonic acid gene clusters revealed that the PWE36 deletion pattern was identical only to those of A. sojae. Using A. sojae SMF134 genome sequence as a reference, visualization of locally collinear blocks indicated that PWE36 shared higher genome sequence homologies with A. sojae than with A. parasiticus. Phylogenetic inference based on genome-wide single nucleotide polymorphisms (SNPs) and total SNP counts showed that A. sojae strains formed a monophyletic clade and were clonal. Two (Argentinian and Ugandan) A. parasiticus isolates but not including an Ethiopian isolate formed a monophyletic clade, which showed that A. parasiticus population is genetically diverse and distant to A. sojae. PWE36 and A. sojae shared a most recent common ancestor (MRCA). The estimated divergence time for PWE36 and A. sojae was about 0.4 mya. Unlike Aspergillus oryzae, another koji mold that includes genetically diverse populations, the findings that current A. sojae strains formed a monophyletic group and shared the MRCA with PWE36 allow A. sojae to be continuously treated as a species for food safety reasons.

Genetic risk score for risk stratification of patients positive for SARS-CoV-2 virus. The CARGENCORS study

Funding AcknowledgementsType of funding sources: Other. Main funding source(s): Carlos III Health Institute and the European Regional Development Fund Government of Catalonia through the Agency for Management of University and Research Grants Crue-CSIC-Santander FONDO SUPERA COVID-19BackgroundThe disease presentation of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ranges from asymptomatic to fatal. COVID-19 patients with pre-existing coronary artery disease (CAD) risk factors or overt cardiovascular disease are at particular risk of severe disease. We hypothesised that a specific genetic risk score (GRS) based on single nucleotide polymorphisms (SNPs) allele count to score COVID-19 severity might include SNPs counts related to CAD incidence and to thrombosis, inflammation, and viral infectivity determinants involved in the severity of SARS-CoV-2. Such GRS could improve the early risk stratification of COVID-19 patients and optimize treatment strategies.PurposeTo evaluate the capacity of a genetic risk score (GRS) with candidate genes to predict COVID-19 severity.MethodsWe conducted an age- and sex-matched case-control study with 1:2 ratio recruitment involving 2454 patients from Catalan hospitals and primary care. Cases were hospitalized severe (requiring at least oxygen treatment) or fatal COVID-19 patients; and controls were moderate-symptom and asymptomatic patients treated at home. Standard parametric and non-parametric methods, as required, were used to compare patient characteristics by severity. Individual genotypes for 33 CAD, 14 thrombosis, 22 inflammation, 15 viral infectivity SNPs and 2 COVID-19 SNPs already published were tested for association with severity with Cochran-Armitage statistics and p-values corrected for multiple comparisons. GRS was computed as the unweighted count of adverse alleles (0, 1 or 2). The odds ratio of severe COVID-19 was analysed for GRS (and its component SNPs) with logistic regression models adjusted for potential confounding factors. Area under the curve (AUC) improvement and net reclassification index (NRI) for GRS was estimated from a basic model including CAD and COVID-19 severity risk factors. Models’ performance was measured with the Akaike information criterion.ResultsSNPs identifications are not shown to prevent patent conflict. Cases and control characteristics are compared in Table 1. Cases had a more adverse cardiovascular and anthropometric risk profile. After correcting for multiple testing by Benjamini-Hochberg criteria, we observed 13 SNPs to be significantly associated with severity. After excluding the close SNPs in linkage disequilibrium, 7 were retained in the GRS model, which yielded the discrimination and reclassification characteristics described in Table 2.ConclusionA GRS with 7 SNPs related to CAD, thrombosis and inflammation significantly improves the severe COVID-19 risk assessment done with age, sex, comorbidity, and anthropometry alone.CV and anthropometric risk profileOR of COVID-19 severity for a 7-SNP GRS

The coalescent with replication-independent mutations.

We develop the mathematical structure of the neutral coalescent with both replication-dependent and replication-independent mutations. This allows us to explain and quantify empirical results that explore differences in genetic diversity in bacterial cultures with different growth rates. We also derive an unbiased and consistent estimator for the replication-independent mutation rate that is based on a comparison of total single nucleotide polymorphism counts for two independent well-mixed cultures with different growth rates. In addition to explaining differences in genetic diversity between well-mixed cultures with different (but constant) growth rates, our coalescent also quantifies the effects of fluctuating growth rates—a situation that can be common in natural populations.

Implementing Privacy-Preserving Genotype Analysis with Consideration for Population Stratification

In bioinformatics, genome-wide association studies (GWAS) are used to detect associations between single-nucleotide polymorphisms (SNPs) and phenotypic traits such as diseases. Significant differences in SNP counts between case and control groups can signal association between variants and phenotypic traits. Most traits are affected by multiple genetic locations. To detect these subtle associations, bioinformaticians need access to more heterogeneous data. Regulatory restrictions in cross-border health data exchange have created a surge in research on privacy-preserving solutions, including secure computing techniques. However, in studies of such scale, one must account for population stratification, as under- and over-representation of sub-populations can lead to spurious associations. We improve on the state of the art of privacy-preserving GWAS methods by showing how to adapt principal component analysis (PCA) with stratification control (EIGENSTRAT), FastPCA, EMMAX and the genomic control algorithm for secure computing. We implement these methods using secure computing techniques—secure multi-party computation (MPC) and trusted execution environments (TEE). Our algorithms are the most complex ones at this scale implemented with MPC. We present performance benchmarks and a security and feasibility trade-off discussion for both techniques.

Exome-wide association study reveals largely distinct gene sets underlying specific resistance to dengue virus types 1 and 3 in Aedes aegypti.

Although specific interactions between host and pathogen genotypes have been well documented in invertebrates, the identification of host genes involved in discriminating pathogen genotypes remains a challenge. In the mosquito Aedes aegypti, the main dengue virus (DENV) vector worldwide, statistical associations between host genetic markers and DENV types or strains were previously detected, but the host genes underlying this genetic specificity have not been identified. In particular, it is unknown whether DENV type- or strain-specific resistance relies on allelic variants of the same genes or on distinct gene sets. Here, we investigated the genetic architecture of DENV resistance in a population of Ae. aegypti from Bakoumba, Gabon, which displays a stronger resistance phenotype to DENV type 1 (DENV-1) than to DENV type 3 (DENV-3) infection. Following experimental exposure to either DENV-1 or DENV-3, we sequenced the exomes of large phenotypic pools of mosquitoes that are either resistant or susceptible to each DENV type. Using variation in single-nucleotide polymorphism (SNP) frequencies among the pools, we computed empirical p values based on average gene scores adjusted for the differences in SNP counts, to identify genes associated with infection in a DENV type-specific manner. Among the top 5% most significant genes, 263 genes were significantly associated with resistance to both DENV-1 and DENV-3, 287 genes were only associated with DENV-1 resistance and 290 were only associated with DENV-3 resistance. The shared significant genes were enriched in genes with ATP binding activity and sulfur compound transmembrane transporter activity, whereas the genes uniquely associated with DENV-3 resistance were enriched in genes with zinc ion binding activity. Together, these results indicate that specific resistance to different DENV types relies on largely non-overlapping sets of genes in this Ae. aegypti population and pave the way for further mechanistic studies.

Genomic Evolution of Staphylococcus aureus During Artificial and Natural Colonization of the Human Nose

Staphylococcus aureus can colonize the human vestibulum nasi for many years. It is unknown whether and, how S. aureus adapts to this ecological niche during colonization. We determined the short (1 and 3 months) and mid-term (36 months) genomic evolution of S. aureus in natural carriers and artificially colonized volunteers. Eighty-five S. aureus strains were collected from 6 natural carriers during 3 years and 6 artificially colonized volunteers during 1 month. Multi-locus sequence typing (MLST) and single nucleotide polymorphism (SNP) analysis based on whole-genome sequencing (WGS) were carried out. Mutation frequencies within resident bacterial populations over time were quantified using core genome SNP counts (comparing groups of genomes) and pairwise SNP divergence assessment (comparing two genomes from strains originating from one host and sharing identical MLST). SNP counts (within 1–3 months) in all naturally colonizing strains varied from 0 to 757 (median 4). These strains showed random and independent patterns of pairwise SNP divergence (0 to 44 SNPs, median 7). When the different core genome SNP counts over a period of 3 years were considered, the median SNP count was 4 (range 0–26). Host-specific pairwise SNP divergence for the same period ranged from 9 to 57 SNPs (median 20). During short term artificial colonization the mutation frequency was even lower (0–7 SNPs, median 2) and the pairwise SNP distances were 0 to 5 SNPs (median 2). Quantifying mutation frequencies is important for the longitudinal follow-up of epidemics of infections and outbreak management. Random pattern of pairwise SNP divergence between the strains isolated from single carriers suggested that the WGS of multiple colonies is necessary in this context. Over periods up to 3 years, maximum median core genome SNP counts and SNP divergence for the strains studied were 4 and 20 SNPs or lower. During artificial colonization, where median core genome SNP and pairwise SNP distance scores were 2, there is no early stage selection of different genotypes. Therefore, we suggest an epidemiological cut off value of 20 SNPs as a marker of S. aureus strain identity during studies on nasal colonization and also outbreaks of infection.

Identification of divergent Leishmania (Viannia) braziliensis ecotypes derived from a geographically restricted area through whole genome analysis.

Leishmania braziliensis, the main etiological agent of cutaneous leishmaniasis (CL) in Latin America, is characterized by major differences in basic biology in comparison with better-known Leishmania species. It is also associated with a high phenotypic and possibly genetic diversity that need to be more adequately defined. Here we used whole genome sequences to evaluate the genetic diversity of ten L. braziliensis isolates from a CL endemic area from Northeastern Brazil, previously classified by Multi Locus Enzyme Electrophoresis (MLEE) into ten distinct zymodemes. These sequences were first mapped using the L. braziliensis M2904 reference genome followed by identification of Single Nucleotide Polymorphisms (SNPs). A substantial level of diversity was observed when compared with the reference genome, with SNP counts ranging from ~95,000 to ~131,000 for the different isolates. When the genome data was used to infer relationship between isolates, those belonging to zymodemes Z72/Z75, recovered from forested environments, were found to cluster separately from the others, generally associated with more urban environments. Among the remaining isolates, those from zymodemes Z74/Z106 were also found to form a separate group. Phylogenetic analyses were also performed using Multi-Locus Sequence Analysis from genes coding for four metabolic enzymes used for MLEE as well as the gene sequence coding for the Hsp70 heat shock protein. All 10 isolates were firmly identified as L. braziliensis, including the zymodeme Z26 isolate previously classified as Leishmania shawi, with the clustering into three groups confirmed. Aneuploidy was also investigated but found in general restricted to chromosome 31, with a single isolate, from zymodeme Z27, characterized by extra copies for other chromosomes. Noteworthy, both Z72 and Z75 isolates are characterized by a much reduced heterozygosity. Our data is consistent with the existence of distinct evolutionary groups in the restricted area sampled and a substantial genetic diversity within L. braziliensis.

Interpreting Whole-Genome Sequence Analyses of Foodborne Bacteria for Regulatory Applications and Outbreak Investigations.

Whole-genome sequence (WGS) analysis has revolutionized the food safety industry by enabling high-resolution typing of foodborne bacteria. Higher resolving power allows investigators to identify origins of contamination during illness outbreaks and regulatory activities quickly and accurately. Government agencies and industry stakeholders worldwide are now analyzing WGS data routinely. Although researchers have published many studies that assess the efficacy of WGS data analysis for source attribution, guidance for interpreting WGS analyses is lacking. Here, we provide the framework for interpreting WGS analyses used by the Food and Drug Administration’s Center for Food Safety and Applied Nutrition (CFSAN). We based this framework on the experiences of CFSAN investigators, collaborations and interactions with government and industry partners, and evaluation of the published literature. A fundamental question for investigators is whether two or more bacteria arose from the same source of contamination. Analysts often count the numbers of nucleotide differences [single-nucleotide polymorphisms (SNPs)] between two or more genome sequences to measure genetic distances. However, using SNP thresholds alone to assess whether bacteria originated from the same source can be misleading. Bacteria that are isolated from food, environmental, or clinical samples are representatives of bacterial populations. These populations are subject to evolutionary forces that can change genome sequences. Therefore, interpreting WGS analyses of foodborne bacteria requires a more sophisticated approach. Here, we present a framework for interpreting WGS analyses that combines SNP counts with phylogenetic tree topologies and bootstrap support. We also clarify the roles of WGS, epidemiological, traceback, and other evidence in forming the conclusions of investigations. Finally, we present examples that illustrate the application of this framework to real-world situations.

ASElux: an ultra-fast and accurate allelic reads counter.

Mapping bias causes preferential alignment to the reference allele, forming a major obstacle in allele-specific expression (ASE) analysis. The existing methods, such as simulation and SNP-aware alignment, are either inaccurate or relatively slow. To fast and accurately count allelic reads for ASE analysis, we developed a novel approach, ASElux, which utilizes the personal SNP information and counts allelic reads directly from unmapped RNA-sequence (RNA-seq) data. ASElux significantly reduces runtime by disregarding reads outside single nucleotide polymorphisms (SNPs) during the alignment. When compared to other tools on simulated and experimental data, ASElux achieves a higher accuracy on ASE estimation than non-SNP-aware aligners and requires a much shorter time than the benchmark SNP-aware aligner, GSNAP with just a slight loss in performance. ASElux can process 40 million read-pairs from an RNA-sequence (RNA-seq) sample and count allelic reads within 10 min, which is comparable to directly counting the allelic reads from alignments based on other tools. Furthermore, processing an RNA-seq sample using ASElux in conjunction with a general aligner, such as STAR, is more accurate and still ∼4× faster than STAR + WASP, and ∼33× faster than the lead SNP-aware aligner, GSNAP, making ASElux ideal for ASE analysis of large-scale transcriptomic studies. We applied ASElux to 273 lung RNA-seq samples from GTEx and identified a splice-QTL rs11078928 in lung which explains the mechanism underlying an asthma GWAS SNP rs11078927. Thus, our analysis demonstrated ASE as a highly powerful complementary tool to cis-expression quantitative trait locus (eQTL) analysis. The software can be downloaded from https://github.com/abl0719/ASElux. zmiao@ucla.edu or a5ko@ucla.edu. Supplementary data are available at Bioinformatics online.

Whole-Genome Sequencing Allows for Improved Identification of Persistent Listeria monocytogenes in Food-Associated Environments.

While the food-borne pathogen Listeria monocytogenes can persist in food associated environments, there are no whole-genome sequence (WGS) based methods to differentiate persistent from sporadic strains. Whole-genome sequencing of 188 isolates from a longitudinal study of L. monocytogenes in retail delis was used to (i) apply single-nucleotide polymorphism (SNP)-based phylogenetics for subtyping of L. monocytogenes, (ii) use SNP counts to differentiate persistent from repeatedly reintroduced strains, and (iii) identify genetic determinants of L. monocytogenes persistence. WGS analysis revealed three prophage regions that explained differences between three pairs of phylogenetically similar populations with pulsed-field gel electrophoresis types that differed by ≤3 bands. WGS-SNP-based phylogenetics found that putatively persistent L. monocytogenes represent SNP patterns (i) unique to a single retail deli, supporting persistence within the deli (11 clades), (ii) unique to a single state, supporting clonal spread within a state (7 clades), or (iii) spanning multiple states (5 clades). Isolates that formed one of 11 deli-specific clades differed by a median of 10 SNPs or fewer. Isolates from 12 putative persistence events had significantly fewer SNPs (median, 2 to 22 SNPs) than between isolates of the same subtype from other delis (median up to 77 SNPs), supporting persistence of the strain. In 13 events, nearly indistinguishable isolates (0 to 1 SNP) were found across multiple delis. No individual genes were enriched among persistent isolates compared to sporadic isolates. Our data show that WGS analysis improves food-borne pathogen subtyping and identification of persistent bacterial pathogens in food associated environments.