Cluster Of Single Nucleotide Polymorphisms Research Articles

The HDAC9-associated risk locus promotes coronary artery disease by governing TWIST1.

Genome wide association studies (GWAS) have identified thousands of single nucleotide polymorphisms (SNPs) associated with the risk of common disorders. However, since the large majority of these risk SNPs reside outside gene-coding regions, GWAS generally provide no information about causal mechanisms regarding the specific gene(s) that are affected or the tissue(s) in which these candidate gene(s) exert their effect. The 'gold standard' method for understanding causal genes and their mechanisms of action are laborious basic science studies often involving sophisticated knockin or knockout mouse lines, however, these types of studies are impractical as a high-throughput means to understand the many risk variants that cause complex diseases like coronary artery disease (CAD). As a solution, we developed a streamlined, data-driven informatics pipeline to gain mechanistic insights on complex genetic loci. The pipeline begins by understanding the SNPs in a given locus in terms of their relative location and linkage disequilibrium relationships, and then identifies nearby expression quantitative trait loci (eQTLs) to determine their relative independence and the likely tissues that mediate their disease-causal effects. The pipeline then seeks to understand associations with other disease-relevant genes, disease sub-phenotypes, potential causality (Mendelian randomization), and the regulatory and functional involvement of these genes in gene regulatory co-expression networks (GRNs). Here, we applied this pipeline to understand a cluster of SNPs associated with CAD within and immediately adjacent to the gene encoding HDAC9. Our pipeline demonstrated, and validated, that this locus is causal for CAD by modulation of TWIST1 expression levels in the arterial wall, and by also governing a GRN related to metabolic function in skeletal muscle. Our results reconciled numerous prior studies, and also provided clear evidence that this locus does not govern HDAC9 expression, structure or function. This pipeline should be considered as a powerful and efficient way to understand GWAS risk loci in a manner that better reflects the highly complex nature of genetic risk associated with common disorders.

Genome-wide association analysis discovered new loci and candidate genes associated with low-phosphorus tolerance based on shoot mineral elements concentrations in soybean.

Phosphorus (P) deficiency is one of the major limitations for soybean production. Moreover, it has been well reported P and other mineral elements function interdependently or antagonistically to control nutrients homeostasis in plants. Thus, it is urgently needed to understand the genetic mechanism of the accumulation of mineral elements in response to low-P stress. In this study, to identify single nucleotide polymorphisms (SNPs) and candidate genes controlling the accumulation of mineral elements suffering low-P stress in seedling stage of soybean plants, we measured concentrations of mineral elements, including P, Zn, Fe, Mn, Mg and Ca, in shoots of 211 soybean accessions under normal phosphorus (+P) and low phosphorus (-P) conditions in two hydroponic experiments. And genome-wide association study (GWAS) using high density NJAU 355K SoySNP array and concentrations of five of these mineral elements except P was performed. A total of 36 SNPs distributed on 13 chromosomes were identified to be significantly associated with low-P tolerance, and nine SNPs on chromosome 10 formed a SNP cluster. Meanwhile, the candidate gene GmFeB1 was found to serve as a negative regulator element involved in soybean P metabolism and the haplotype1 (Hap1) of GmFeB1 showed significantly higher shoot Fe concentration under -P condition than that of Hap2. In summary, we uncover 36 SNPs significantly associated with shoot mineral elements concentrations under different P conditions and a soybean low-P related gene GmFeB1, which will provide additional genetic information for soybean low-P tolerance and new gene resources for P-efficient soybean varieties breeding.

Mycobacterium tuberculosis transmission in Birmingham, UK, 2009-19: An observational study.

SummaryBackgroundOver 10-years of whole-genome sequencing (WGS) of Mycobacterium tuberculosis in Birmingham presents an opportunity to explore epidemiological trends and risk factors for transmission in new detail.MethodsBetween 1st January 2009 and 15th June 2019, we obtained the first WGS isolate from every patient resident in a postcode district covered by Birmingham's centralised tuberculosis service. Data on patients’ sex, country of birth, social risk-factors, anatomical locus of disease, and strain lineage were collected. Poisson harmonic regression was used to assess seasonal variation in case load and a mixed-effects multivariable Cox proportionate hazards model was used to assess risk factors for a future case arising in clusters defined by a 5 single nucleotide polymorphism (SNP) threshold, and by 12 SNPs in a sensitivity analysis.Findings511/1653 (31%) patients were genomically clustered with another. A seasonal variation in diagnoses was observed, peaking in spring, but only among clustered cases. Risk-factors for a future clustered case included UK-birth (aHR=2·03 (95%CI 1·35–3·04), p < 0·001), infectious (pulmonary/laryngeal/miliary) tuberculosis (aHR=3·08 (95%CI 1·98-4·78), p < 0·001), and M. tuberculosis lineage 3 (aHR=1·91 (95%CI 1·03–3·56), p = 0·041) and 4 (aHR=2·27 (95%CI 1·21–4·26), p = 0·011), vs. lineage 1. Similar results pertained to 12 SNP clusters, for which social risk-factors were also significant (aHR 1·72 (95%CI 1·02–2·93), p = 0·044). There was marked heterogeneity in transmission patterns between postcode districts.InterpretationThere is seasonal variation in the diagnosis of genomically clustered, but not non-clustered, cases. Risk factors for clustering include UK-birth, infectious forms of tuberculosis, and infection with lineage 3 or 4.FundingWellcome Trust, MRC, UKHSA

Genome-wide networks reveal emergence of epidemic strains of Salmonella Enteritidis

ObjectivesTo enhance monitoring of high-burden foodborne pathogens, there is opportunity to combine pangenome data with network analysis. MethodsSalmonella enterica subspecies Enterica serovar Enteritidis isolates were referred to the New South Wales (NSW) Enteric Reference Laboratory between August 2015 and December 2019 (1033 isolates in total), inclusive of a confirmed outbreak. All isolates underwent whole genome sequencing. Distances between genomes were quantified by in silico multiple-locus variable-number tandem repeat analysis (MLVA) as well as core single nucleotide polymorphisms (SNPs), which informed the construction of undirected networks. Centrality-prevalence spaces were generated from the undirected networks. Components on the undirected SNP network were considered alongside a phylogenetic tree representation. ResultsOutbreak isolates were identified as distinct components on the MLVA and SNP networks. The MLVA network-based centrality-prevalence space did not delineate the outbreak, whereas the outbreak was delineated in the SNP network-based centrality-prevalence space. Components on the undirected SNP network showed a high concordance to the SNP clusters based on phylogenetic analysis. ConclusionsBacterial whole-genome data in network-based analysis can improve the resolution of population analysis. High concordance of network components and SNP clusters is promising for rapid population analyses of foodborne Salmonella spp. owing to the low overhead of network analysis.

Genome-wide association and linkage mapping strategies reveal genetic loci and candidate genes of phosphorus utilization in soybean

Insufficient available phosphorus in soil has become an important limiting factor for the improvement of yield and quality in soybean. The mining of QTLs and candidate genes controlling soybean phosphorus utilization related traits is a necessary strategy to solve this problem. In this study, 11 phosphorus utilization related traits of a natural population of 281 typical soybean germplasms and a recombinant inbred line (RIL) population of 270 lines were evaluated under different phosphorus conditions at two critical stages: the four-leaf stage as the seedling critical stage was designated as the T1 stage, and the six-leaf stage as the flowering critical stage was designated as the T2 stage. In total, 200 single nucleotide polymorphism (SNP) loci associated with phosphorus utilization related traits were identified in the natural population, including 91 detected at the T1 stage, and 109 detected at the T2 stage. Among these SNP loci, one SNP cluster (s715611375, ss715611377, ss715611379 and ss715611380) on Gm12 was shown to be significantly associated with plant height under the low phosphorus condition at the T1 stage, and the elite haplotype showed significantly greater plant height than the others. Meanwhile, one pleiotropic SNP cluster (ss715606501, ss715606506 and ss715606543) on Gm10 was found to be significantly associated with the ratio of root/shoot, root and total dry weights under the low phosphorus condition at the T2 stage, and the elite haplotype also presented significantly higher values for related characteristics under the phosphorus starvation condition. Furthermore, four co-associated SNP loci (ss715597964, ss715607012, ss715622173 and ss715602331) were identified under the low phosphorus condition at both the T1 and T2 stages, and 12 QTLs were found to be consistent with these genetic loci in the RIL population. More importantly, 14 candidate genes, including MYB transcription factor, purple acid phosphatase, sugar transporter and HSP20-like chaperones superfamily genes, etc., showed differential expression levels after low phosphorus treatment, and three of them were further verified by qRT-PCR. Thus, these genetic loci and candidate genes could be applied in marker-assisted selection or map-based gene cloning for the genetic improvement of soybean phosphorus utilization.

Gene flow influences the genomic architecture of local adaptation in six riverine fish species.

Understanding how gene flow influences adaptive divergence is important for predicting adaptive responses. Theoretical studies suggest that when gene flow is high, clustering of adaptive genes in fewer genomic regions would protect adaptive alleles from recombination and thus be selected for, but few studies have tested it with empirical data. Here, we used restriction site-associated sequencing to generate genomic data for six fish species with contrasting life histories from six reaches of the Upper Mississippi River System, USA. We used four differentiation-based outlier tests and three genotype-environment association analyses to define neutral single nucleotide polymorphisms (SNPs) and outlier SNPs that were putatively under selection. We then examined the distribution of outlier SNPs along the genome and investigated whether these SNPs were found in genomic islands of differentiation and inversions. We found that gene flow varied among species, and outlier SNPs were clustered more tightly in species with higher gene flow. The two species with the highest overall FST (0.0303-0.0720) and therefore lowest gene flow showed little evidence of clusters of outlier SNPs, with outlier SNPs in these species spreading uniformly across the genome. In contrast, nearly all outlier SNPs in the species with the lowest FST (0.0003) were found in a single large putative inversion. Two other species with intermediate gene flow (FST ~0.0025-0.0050) also showed clustered genomic architectures, with most islands of differentiation clustered on a few chromosomes. Our results provide important empirical evidence to support the hypothesis that increasingly clustered architecture of local adaptation is associated with high gene flow.

Genome-wide association study and population structure analysis of seed-bound amino acids and total protein in watermelon.

BackgroundWatermelon seeds are a powerhouse of value-added traits such as proteins, free amino acids, vitamins, and essential minerals, offering a paleo-friendly dietary option. Despite the availability of substantial genetic variation, there is no sufficient information on the natural variation in seed-bound amino acids or proteins across the watermelon germplasm. This study aimed to analyze the natural variation in watermelon seed amino acids and total protein and explore underpinning genetic loci by genome-wide association study (GWAS).MethodsThe study evaluated the distribution of seed-bound free amino acids and total protein in 211 watermelon accessions of Citrullus spp, including 154 of Citrullus lanatus, 54 of Citrullus mucosospermus (egusi) and three of Citrullus amarus. We used the GWAS approach to associate seed phenotypes with 11,456 single nucleotide polymorphisms (SNPs) generated by genotyping-by-sequencing (GBS).ResultsOur results demonstrate a significant natural variation in different free amino acids and total protein content across accessions and geographic regions. The accessions with high protein content and proportion of essential amino acids warrant its use for value-added benefits in the food and feed industries via biofortification. The GWAS analysis identified 188 SNPs coinciding with 167 candidate genes associated with watermelon seed-bound amino acids and total protein. Clustering of SNPs associated with individual amino acids found by principal component analysis was independent of the speciation or cultivar groups and was not selected during the domestication of sweet watermelon. The identified candidate genes were involved in metabolic pathways associated with amino acid metabolism, such as Argininosuccinate synthase, explaining 7% of the variation in arginine content, which validate their functional relevance and potential for marker-assisted analysis selection. This study provides a platform for exploring potential gene loci involved in seed-bound amino acids metabolism, useful in genetic analysis and development of watermelon varieties with superior seed nutritional values.

Linking genotype to phenotype in multi-omics data of small sample

BackgroundGenome-wide association studies (GWAS) that link genotype to phenotype represent an effective means to associate an individual genetic background with a disease or trait. However, single-omics data only provide limited information on biological mechanisms, and it is necessary to improve the accuracy for predicting the biological association between genotype and phenotype by integrating multi-omics data. Typically, gene expression data are integrated to analyze the effect of single nucleotide polymorphisms (SNPs) on phenotype. Such multi-omics data integration mainly follows two approaches: multi-staged analysis and meta-dimensional analysis, which respectively ignore intra-omics and inter-omics associations. Moreover, both approaches require omics data from a single sample set, and the large feature set of SNPs necessitates a large sample size for model establishment, but it is difficult to obtain multi-omics data from a single, large sample set.ResultsTo address this problem, we propose a method of genotype-phenotype association based on multi-omics data from small samples. The workflow of this method includes clustering genes using a protein-protein interaction network and gene expression data, screening gene clusters with group lasso, obtaining SNP clusters corresponding to the selected gene clusters through expression quantitative trait locus data, integrating SNP clusters and corresponding gene clusters and phenotypes into three-layer network blocks, analyzing and predicting based on each block, and obtaining the final prediction by taking the average.ConclusionsWe compare this method to others using two datasets and find that our method shows better results in both cases. Our method can effectively solve the prediction problem in multi-omics data of small sample, and provide valuable resources for further studies on the fusion of more omics data.

Identification of Closely Related Listeria monocytogenes Isolates with No Apparent Evidence for a Common Source or Location: A Retrospective Whole Genome Sequencing Analysis

Public health and regulatory agencies worldwide sequence all Listeria monocytogenes isolates obtained as part of routine surveillance and outbreak investigations. Many of these entities submit the sequences to the National Center for Biotechnology Information Pathogen Detection (NCBI PD) database, which groups the L. monocytogenes isolates into single nucleotide polymorphism (SNP) clusters based on a pairwise SNP difference threshold of 50 SNPs. Our goal was to assess whether isolates with metadata that suggest different sources or locations could show evidence for close genetic relatedness indicating a recent common ancestor and a possible unknown common source. We compared the whole genome sequencing (WGS) data of 249 L. monocytogenes isolates sequenced here, which have detailed metadata, with WGS data of nonclinical isolates on NCBI PD. The 249 L. monocytogenes isolates originated from natural environments (n = 91) as well as from smoked fish (n = 62), dairy (n = 56), and deli meat (n = 40) operations in the United States. Using a combination of subtyping by core genome multilocus sequence typing and high-quality SNP, we observed five SNP clusters in which study isolates and SNP cluster isolates seemed to be closely related and either (i) shared the same geolocation but showed different source types (one SNP cluster); (ii) shared the same source type but showed different geolocations (two SNP clusters); or (iii) shared neither source type nor geolocation (two SNP clusters). For one of the two clusters under (iii), there was, however, no strong bootstrap support for a common ancestor shared between the study isolates and SNP cluster isolates, indicating the value of in-depth evolutionary analyses when WGS data are used for traceback and epidemiological investigations. Overall, our results demonstrate that some L. monocytogenes subtypes may be associated with specific locations or commodities; these associations can help in investigations involving multi-ingredient foods such as sandwiches. However, at least some L. monocytogenes subtypes can be widespread geographically and can be associated with different sources, which may present a challenge to traceback investigations involving these subtypes.

Polyploid QTL-seq towards rapid development of tightly linked DNA markers for potato and sweetpotato breeding through whole-genome resequencing.

Potato (Solanum tuberosum L.) and sweetpotato (Ipomoea batatas L.), which are nutritionally and commercially important tuberous crops, possess a perplexing heredity because of their autopolyploid genomes. To reduce cross‐breeding efforts for selecting superior cultivars from progenies with innumerable combinations of traits, DNA markers tightly linked to agronomical traits are required. To develop DNA markers, we developed a method for quantitative trait loci (QTL) mapping using whole‐genome next‐generation sequencing (NGS) in autopolyploid crops. To apply the NGS‐based bulked segregant method, QTL‐seq was modified. (1) Single parent‐specific simplex (unique for one homologous chromosome) single‐nucleotide polymorphisms (SNPs), which present a simple segregation ratio in the progenies, were exploited by filtering SNPs by SNP index (allele frequency). (2) Clusters of SNPs, which were inherited unevenly between bulked progenies with opposite phenotypes, especially those with an SNP index of 0 for the bulk that did not display the phenotypes of interest, were explored. These modifications allowed for separate tracking of alleles located on each of the multiple homologous chromosomes. By applying this method, clusters of SNPs linked to the potato cyst nematode resistance H1 gene and storage root anthocyanin (AN) content were identified in tetraploid potato and hexaploid sweetpotato, respectively, and completely linked DNA markers were developed at the site of the presented SNPs. Thus, polyploid QTL‐seq is a versatile method that is free from specialized manipulation for sequencing and construction of elaborate linkage maps and facilitates rapid development of tightly linked DNA markers in autopolyploid crops, such as potato and sweetpotato.

Rapid Adaptation to Temperature via a Potential Genomic Island of Divergence in the Invasive Green Crab, Carcinus maenas

Widespread species often adapt easily to novel conditions – both those found in new habitats and those generated by climate change. However, rapid adaptation may be hindered in the marine realm, where long-distance dispersal and consequently high gene flow are predicted to limit potential for local adaptation. Here, we use a highly dispersive invasive marine crab to test the nature and speed of adaptation to temperature in the sea. Using single nucleotide polymorphisms (SNPs) generated from cardiac transcriptome sequencing, we characterized six populations of the European green crab (Carcinus maenas) located across parallel thermal gradients in their native and invasive ranges. We compared SNP frequencies with local temperatures and previously generated data on cardiac heat and cold tolerance to identify candidate markers associated with population-level differences in thermal physiology. Of 10,790 SNPs, 104 were identified as frequency outliers, a signal that was strongly driven by association with temperature and/or cold tolerance. Seventy-two of these outlier markers, representing 28 different genes, were in a cluster of SNPs identified as a potential inversion polymorphism using linkage disequilibrium network analysis. This SNP cluster was unique in the data set, which was otherwise characterized by low levels of linkage disequilibrium, and markers in this cluster showed a significant enrichment of coding substitutions relative to the full SNP set. These 72 outlier SNPs appear to be transmitted as a unit, and represent a putative genomic island of divergence which varied in frequency with organismal cold tolerance. This relationship was strikingly similar across both native and invasive populations, all of which showed a very strong correlation with cold tolerance (R2 = 0.96 over all six populations). Notably, three of these populations have diverged recently (&amp;lt;100 years) and show little to no neutral divergence, suggesting that this genomic region may be responding to temperature on a relatively short time scale. This relationship indicates adaptation to temperature based on the action of a putative genomic island of divergence, perhaps partially explaining the extraordinary invasive ability of this species.

Identification of loci and candidate gene GmSPX-RING1 responsible for phosphorus efficiency in soybean via genome-wide association analysis

BackgroundPhosphorus (P) is an essential element in maintaining high biomass and yield in crops. Soybean [Glycine max (L.) Merr.] requires a large amount of P during growth and development. Improvement of P efficiency and identification of P efficiency genes are important strategies for increasing soybean yield.ResultsGenome-wide association analysis (GWAS) with NJAU 355 K SoySNP array was performed to identify single nucleotide polymorphisms (SNPs) significantly associated with three shoot P efficiency-related traits of a natural population of 211 cultivated soybeans and relative values of these traits under normal P (+P) condition and P deficiency (−P) condition. A total of 155 SNPs were identified significantly associated with P efficiency-related traits. SNPs that were significantly associated with shoot dry weight formed a SNP cluster on chromosome 11, while SNPs that were significantly associated with shoot P concentration formed a SNP cluster on chromosome 10. Thirteen haplotypes were identified based on 12 SNPs, and Hap9 was considered as the optimal haplotype. Four SNPs (AX-93636685, AX-93636692, AX-93932863, and AX-93932874) located on chromosome 10 were identified to be significantly associated with shoot P concentration under +P condition in two hydroponic experiments. Among these four SNPs, two of them (AX-93636685 and AX-93932874) were also significantly associated with the relative values of shoot P concentration under two P conditions. One SNP AX-93932874 was detected within 5′-untranslated region of Glyma.10 g018800, which contained SPX and RING domains and was named as GmSPX-RING1. Furthermore, the function research of GmSPX-RING1 was carried out in soybean hairy root transformation. Compared with their respective controls, P concentration in GmSPX-RING1 overexpressing transgenic hairy roots was significantly reduced by 32.75% under +P condition; In contrast, P concentration in RNA interference of GmSPX-RING1 transgenic hairy roots was increased by 38.90 and 14.51% under +P and -P conditions, respectively.ConclusionsThis study shows that the candidate gene GmSPX-RING1 affects soybean phosphorus efficiency by negatively regulating soybean phosphorus concentration in soybean hairy roots. The SNPs and candidate genes identified should be potential for improvement of P efficiency in future soybean breeding programs.

Multiple independent mechanisms link gene polymorphisms in the region of ZEB2 with risk of coronary artery disease

Background and aimsCoronary artery disease (CAD) arises from the interaction of genetic and environmental factors. Although genome-wide association studies (GWAS) have identified multiple risk loci and single nucleotide polymorphisms (SNPs) associated with risk of CAD, they are predominantly located in non-coding or intergenic regions and their mechanisms of effect are largely unknown. Accordingly, our objective was to develop a data-driven informatics pipeline to understand complex CAD risk loci, and to apply this to a poorly understood cluster of SNPs in the vicinity of ZEB2. MethodsWe developed a unique informatics pipeline leveraging a multi-tissue CAD genetics-of-gene-expression dataset, GWAS datasets, and other resources. The pipeline first dissected SNP locations and their linkage disequilibrium relationships, and progressed through analyses of tissue-specific expression quantitative trait loci, and then gene-gene, gene-phenotype, SNP-phenotype relationships. The pipeline concluded by exploring CAD-relevant gene regulatory networks (GRNs). ResultsWe identified three independent CAD risk SNPs in close proximity to the ZEB2 coding region (rs6740731, rs17678683 and rs2252641/rs1830321). Our pipeline determined that these SNPs likely act in concert via the atherosclerotic arterial wall and adipose tissues, by governing metabolic and lipid functions. In addition, ZEB2 is the top key driver of a liver-specific GRN that is related to lipid levels, metabolic and anthropometric measures, and CAD severity. ConclusionsUsing a novel informatics pipeline, we disclosed the multi-faceted mechanisms of action of the ZEB2-associated CAD risk SNPs. This pipeline can serve as a roadmap to dissect complex SNP-gene-tissue-phenotype relationships and to reveal targets for tissue- and gene-specific therapeutic interventions.

Defining the QTL associated with chill requirement during endodormancy in Malus × domestica Borkh

Current weather pattern predictions show the increase in winter temperatures globally. This could have a detrimental effect on fruit grown in temperate environments like the southern part of Africa. A rise in winter temperatures would lead to the non-fulfilment of chill requirements of fruit crops and subsequent losses. A need therefore arises to breed for good quality fruit varieties with a lower chill requirement. Identifying genomic regions that are associated with early flowering is the first step in breeding for such varieties. This study focuses on defining a quantitative trait locus (QTL) that is associated with budbreak in Malus × domestica Borkh. The International Rosaceae SNP consortium apple 8 K single nucleotide polymorphism (SNP) bead array v1 was used with recombination mapping and QTL analysis to identify this QTL on Linkage Group 9. A QTL was verified with association mapping and shown to be in the first 10 Mb of chromosome 9, and consisting of six clusters of SNPs, grouped together. These clusters were shown to segregate together with identical allele specification, as well as having a high linkage disequilibrium within the clusters. The haplotype blocks representing the clusters show a high correlation between them, explaining the substantial size of the QTL found with recombination mapping. Approximately 750 genes were represented by the clusters, most notably a high number of transcription factors and regulators.

Genomic Analysis of Vavilov's Historic Chickpea Landraces Reveals Footprints of Environmental and Human Selection.

A defining challenge of the 21st century is meeting the nutritional demands of the growing human population, under a scenario of limited land and water resources and under the specter of climate change. The Vavilov seed bank contains numerous landraces collected nearly a hundred years ago, and thus may contain ‘genetic gems’ with the potential to enhance modern breeding efforts. Here, we analyze 407 landraces, sampled from major historic centers of chickpea cultivation and secondary diversification. Genome-Wide Association Studies (GWAS) conducted on both phenotypic traits and bioclimatic variables at landraces sampling sites as extended phenotypes resulted in 84 GWAS hits associated to various regions. The novel haploblock-based test identified haploblocks enriched for single nucleotide polymorphisms (SNPs) associated with phenotypes and bioclimatic variables. Subsequent bi-clustering of traits sharing enriched haploblocks underscored both non-random distribution of SNPs among several haploblocks and their association with multiple traits. We hypothesize that these clusters of pleiotropic SNPs represent co-adapted genetic complexes to a range of environmental conditions that chickpea experienced during domestication and subsequent geographic radiation. Linking genetic variation to phenotypic data and a wealth of historic information preserved in historic seed banks are the keys for genome-based and environment-informed breeding intensification.

A nomogram based on genetic and clinicopathologic features to predict the nonsentinel lymph node metastases in Chinese women breast cancer patients.

e12577 Background: Sentinel lymph node biopsy (SLNB) is the standard treatment for breast cancer patients with clinically negative axilla. However, axillary lymph node dissection (ALND) is still the standard care for sentinel lymph node (SLN) positive patients. Clinical data reveals 20–60 % of patients without non-sentinel lymph node metastasis (NSLNM) after ALND. Unnecessary ALND increases the risk of lymphedema and detracts from quality of life. In this study, we expect to develop a nomogram based on genetic and clinicopathologic features to predict the risk of NSLN metastasis in SLN-positive Chinese women breast cancer patients. Methods: This retrospective study collected data from 310 women breast cancer patients who underwent SLNB followed by ALND and without any neo-adjuvant therapy in Chinese PLA General Hospital, China, between 2016 and 2017. Genetic features contain 96 single nucleotide polymorphisms (SNPs) associated with breast cancer susceptibility and prognosis based on the GWAS studies and clinical information. SNP genotyping was identified by the quantitative PCR detection platform. The genetic features were divided into two clusters by the gene functions and signaling pathways. The polygenic risk score (PRS) was used to evaluate the combined effect of each SNP cluster. Least absolute shrinkage and selection operator (LASSO) regression model was adopted for feature selection and nomogram construction. Internal validation was performed and the area under ROC curve (AUC) was assessed. Results: 81 patients of 310 patients (26%) had a positive axillary NSLNM. The LASSO regression analysis identified the clinicopathologic characteristics including molecular subtype, cN-stage, number of positive SLNs and number of negative SLNs as significant predictors of NSLNM. Furthermore, two SNP clusters were also showed statistically significant in the prediction of NSLNM. In internal validation, the average AUC of the nomogram was 0.795 and the model was well calibrated. Conclusions: We present a new nomogram by combining genetic and clinicopathologic factors to achieve higher sensitivity and specificity comparing with traditional clinicopathologic factors to predict NSLNM in Chinese women breast cancer. It is recommended that more validations are required in prospective studies among different patient populations.

Characterization of Salmonella enterica Isolates from Selected U.S. Swine Feed Mills by Whole-Genome Sequencing.

Every year salmonellosis is responsible for $2.3 billion in costs to the U.S. food industry, with nearly 6% of the reported cases associated with pork and/or pork products. Several studies have demonstrated the role of pigs as Salmonella reservoirs. Furthermore, this pathogen has been identified as a potential biological hazard in many livestock feeds. The overall objective of this research was to characterize Salmonella enterica isolates in selected U.S. swine feed mills by whole-genome sequencing (WGS) and evaluate isolates in association with the season and feed production stages. Salmonella isolates were collected from 11 facilities during a previous study. Samples were analyzed for Salmonella prevalence following the U.S. Department of Agriculture guidelines and confirmed by PCR. WGS was carried out on either the MiSeq or NextSeq sequencer. De novo genome assemblies were obtained with the Shovill pipeline, version 0.9. ResFinder and SPIFinder were used to identify antibiotic resistance genes and pathogenicity islands. Finally, their phylogenetic relationship and diversity were determined by core genome multilocus sequence typing. Overall, our analysis showed the presence of S. enterica in the feed mill environment. Isolates belonged to 16 different serotypes. Salmonella Agona, Salmonella Mbandaka, Salmonella Senfenberg, and Salmonella Scharzengrund were the most frequently found, and 18 single-nucleotide polymorphism clusters were identified. In silico analysis showed that 40% of the strains carried at least one antimicrobial resistance gene. All isolates in this study could be considered of public health concern and pathogenic potential. Our findings underscore the potential role of the feed mill environment as the pathogen entry route into the human food value chain.

Surfaceome analysis of Australian epidemic Bordetella pertussis reveals potential vaccine antigens

Since acellular vaccines (ACV) were introduced in Australia, epidemic Bordetella pertussis strains changed from single nucleotide polymorphism (SNP) cluster II to SNP cluster I. Our previous proteomic analysis identified potential proteomic adaptations in the whole cell and secretome of SNP cluster I. Additionally, current ACVs were shown to be less efficacious against cluster I in mice models and there is a pressing need to discover new antigens to improve the ACV. One important source of novel antigens is the surfaceome. Therefore, in this study we established surface shaving in B. pertussis to compare the surfaceome of SNP cluster I (L1423) and II (L1191), and identify novel surface antigens for vaccine development. Surface shaving using 1 μg of trypsin for 5 min identified 126 proteins with the most abundant being virulence-associated and known outer membrane proteins. Cell viability counts showed minimal lysis from shaving. The proportion of immunogenic proteins was higher in the surfaceome than in the whole cell and secretome. Key differences in the surfaceome were identified between SNP cluster I and II, consistent with those identified in the whole cell proteome and secretome. These differences include unique transport proteins and decreased immunogenic proteins in L1423, and provides further evidence of proteomic adaptation in SNP cluster I. Finally, a comparison of proteins in each sub-proteome identified 22 common proteins. These included 11 virulence proteins (Prn, PtxA, FhaB, CyaA, TcfA, SphB1, Vag8, BrkA, BopD, Bsp22 and BipA) and 11 housekeeping proteins (TuF, CtpA, TsF, OmpH, GltA, SucC, SucD, FusA, GroEL, BP3330 and BP3561) which were immunogenic, essential and consistently expressed thus demonstrating their potential as future targets. This study established surface shaving in B. pertussis, confirmed key expression differences and identified unknown surface proteins which may be potential vaccine antigens.

Association genetics of bunch weight and its component traits in East African highland banana (Musa spp. AAA group)

Key messageThe major quantitative trait loci associated with bunch weight and its component traits in the East African highland banana-breeding population are located on chromosome 3.Bunch weight increase is one of the major objectives of banana improvement programs, but little is known about the loci controlling bunch weight and its component traits. Here we report for the first time some genomic loci associated with bunch weight and its component traits in banana as revealed through a genome-wide association study. A banana-breeding population of 307 genotypes varying in ploidy was phenotyped in three locations under different environmental conditions, and data were collected on bunch weight, number of hands and fruits; fruit length and circumference; and diameter of both fruit and pulp for three crop cycles. The population was genotyped with genotyping by sequencing and 27,178 single nucleotide polymorphisms (SNPs) were generated. The association between SNPs and the best linear unbiased predictors of traits was performed with TASSEL v5 using a mixed linear model accounting for population structure and kinship. Using Bonferroni correction, false discovery rate, and long-range linkage disequilibrium (LD), 25 genomic loci were identified with significant SNPs and most were localized on chromosome 3. Most SNPs were located in genes encoding uncharacterized and hypothetical proteins, but some mapped to transcription factors and genes involved in cell cycle regulation. Inter-chromosomal LD of SNPs was present in the population, but none of the SNPs were significantly associated with the traits. The clustering of significant SNPs on chromosome 3 supported our hypothesis that fruit filling in this population was under control of a few quantitative trait loci with major effects.

Downstream TRPM4 Polymorphisms Are Associated with Intracranial Hypertension and Statistically Interact with ABCC8 Polymorphisms in a Prospective Cohort of Severe Traumatic Brain Injury.

Sulfonylurea-receptor-1(SUR1) and its associated transient-receptor-potential cation channel subfamily-M (TRPM4) channel are key contributors to cerebral edema and intracranial hypertension in traumatic brain injury (TBI) and other neurological disorders. Channel inhibition by glyburide is clinically promising. ABCC8 (encoding SUR1) single-nucleotide polymorphisms (SNPs) are reported as predictors of raised intracranial pressure (ICP). This project evaluated whether TRPM4 SNPs predicted ICP and TBI outcome. DNA was extracted from 435 consecutively enrolled severe TBI patients. Without a priori selection, all 11 TRPM4 SNPs available on the multiplex platform (Illumina:Human-Core-Exome v1.0) were genotyped spanning the 25 exon gene. A total of 385 patients were analyzed after quality control. Outcomes included ICP and 6 month Glasgow Outcome Scale (GOS) score. Proxy SNPs, spatial modeling, and functional predictions were determined using established software programs. rs8104571 (intron-20) and rs150391806 (exon-24) were predictors of ICP. rs8104571 heterozygotes predicted higher average ICP (β = 10.3 mm Hg, p = 0.00000029), peak ICP (β = 19.6 mm Hg, p = 0.0007), and proportion ICP >25 mm Hg (β = 0.16 p = 0.004). rs150391806 heterozygotes had higher mean (β = 7.2 mm Hg, p = 0.042) and peak (β = 28.9 mm Hg, p = 0.0015) ICPs. rs8104571, rs150391806, and 34 associated proxy SNPs in linkage-disequilibrium clustered downstream. This region encodes TRPM4's channel pore and a region postulated to juxtapose SUR1 sequences encoded by an ABCC8 DNA segment containing previously identified relevant SNPs. There was an interaction effect on ICP between rs8104571 and a cluster of predictive ABCC8 SNPs (rs2237982, rs2283261, rs11024286). Although not significant in univariable or a basic multivariable model, in an expanded model additionally accounting for injury pattern, computed tomographic (CT) appearance, and intracranial hypertension, heterozygous rs8104571 was associated with favorable 6 month GOS (odds ratio [OR] = 16.7, p = 0.007951). This trend persisted in a survivor-only subcohort (OR = 20.67, p = 0.0168). In this cohort, two TRPM4 SNPs predicted increased ICP with large effect sizes. Both clustered downstream, spanning a region encoding the channel pore and interacting with SUR1. If validated, this may guide risk stratification and eventually inform treatment-responder classification for SUR1-TRPM4 inhibition in TBI. Larger studies are warranted.