Comparative multi-omics analyses reveal differential expression of key genes relevant for parasitism between non-encapsulated and encapsulated Trichinella

Genome assemblies provide a powerful basis of comparative multi-omics analyses that offer insight into parasite pathogenicity, host-parasite interactions, and invasion biology. As a unique intracellular nematode, Trichinella consists of two clades, encapsulated and non-encapsulated. Genomic correlation of the distinct differences between the two clades is still unclear. Here, we report an annotated draft reference genome of non-encapsulated Trichinella, T. pseudospiralis, and perform comparative multi-omics analyses with encapsulated T. spiralis. Genome and methylome analyses indicate that, during Trichinella evolution, the two clades of Trichinella exhibit differential expansion and methylation of parasitism-related multi-copy gene families, especially for the DNase II members of the phospholipase D superfamily and Glutathione S-transferases. Further, methylome and transcriptome analyses revealed divergent key excretory/secretory (E/S) genes between the two clades. Among these key E/S genes, TP12446 is significantly more expressed across three life stages in T. pseudospiralis. Overexpression of TP12446 in the mouse C2C12 skeletal muscle cell line could induce inhibition of myotube formation and differentiation, further indicating its key role in parasitism of T. pseudospiralis. This multi-omics study provides a foundation for further elucidation of the mechanism of nurse cell formation and immunoevasion, as well as the identification of pharmacological and diagnostic targets of trichinellosis.

Bivariate genomic prediction of phenotypes by selecting epistatic interactions across years

The importance of accurate genomic prediction of phenotypes in plant breeding is undeniable, as higher prediction accuracy can increase selection responses. In this study, we investigated the ability of three models to improve prediction accuracy by including phenotypic information from the last growing season. This was done by considering a single biological trait in two growing seasons (2017 and 2018) as separate traits in a multi-trait model. Thus, bivariate variants of the Genomic Best Linear Unbiased Prediction (GBLUP) as an additive model, Epistatic Random Regression BLUP (ERRBLUP) and selective Epistatic Random Regression BLUP (sERRBLUP) as epistasis models were compared with respect to their prediction accuracies for the second year. The results indicate that bivariate ERRBLUP is slightly superior to bivariate GBLUP in predication accuracy, while bivariate sERRBLUP has the highest prediction accuracy in most cases. The average relative increase in prediction accuracy from bivariate GBLUP to maximum bivariate sERRBLUP across eight phenotypic traits and studied dataset from 471/402 doubled haploid lines in the European maize landrace Kemater Landmais Gelb/Petkuser Ferdinand Rot, were 7.61 and 3.47 percent, respectively. We further investigated the genomic correlation, phenotypic correlation and trait heritability as the factors affecting the bivariate model’s predication accuracy, with genetic correlation between growing seasons being the most important one. For all three considered model architectures results were far worse when using a univariate version of the model, e.g. with an average reduction in prediction accuracy of 0.23/0.14 for Kemater/Petkuser when using univariate GBLUP.Key MassageBivariate models based on selected subsets of pairwise SNP interactions can increase the prediction accuracy by utilizing phenotypic data across years under the assumption of high genomic correlation across years.

Sleep-related traits and attention-deficit/hyperactivity disorder comorbidity: shared genetic risk factors, molecular mechanisms, and causal effects

Study Objectives: To evaluate the level of shared genetic components between attention-deficit/hyperactivity disorder (ADHD) and sleep phenotype, common pathways between them and a possible causal relationship between traits. Methods: We used summary statistics of the largest genome-wide association studies available for ADHD and sleep-related phenotypes including insomnia, napping, daytime dozing, snoring, ease getting up, daytime sleepiness, sleep duration and chronotype. We estimated the genomic correlation between ADHD and sleep-related traits using cross-trait LD-score regression and investigated potential common mechanisms using gene-based cross-trait metanalyses and functional enrichment analyses. The causal effect between the sleep related traits and ADHD was estimated with two sample Mendelian randomization (TSMR), using the Inverse Variance Weighted method as the main estimator. Results: Positive genomic correlation between insomnia, daytime napping, daytime dozing, snoring, daytime sleepiness, short and long sleep duration, and ADHD were observed. Insomnia, sleep duration, daytime sleepiness, and snoring shared genes with ADHD, which were involved in neurobiological functions and regulatory signaling pathways. The TSMR approach supported a causal effect of insomnia, daytime napping, and short sleep duration on ADHD, and of ADHD on long sleep duration and chronotype. Conclusion: Our findings suggest that the comorbidity between sleep phenotypes and ADHD may be mediated by common genetic factors with an important role on neuronal signaling pathways. In addition, it may also exist a causal effect of sleep disturbances and short sleep duration on ADHD, reinforcing the role of these sleep phenotypes as predictors or early markers of ADHD.

Integrating genomic correlation structure improves copy number variations detection

Motivation Copy number variation plays important roles in human complex diseases. The detection of copy number variants (CNVs) is identifying mean shift in genetic intensities to locate chromosomal breakpoints, the step of which is referred to as chromosomal segmentation. Many segmentation algorithms have been developed with a strong assumption of independent observations in the genetic loci, and they assume each locus has an equal chance to be a breakpoint (i.e. boundary of CNVs). However, this assumption is violated in the genetics perspective due to the existence of correlation among genomic positions, such as linkage disequilibrium (LD). Our study showed that the LD structure is related to the location distribution of CNVs, which indeed presents a non-random pattern on the genome. To generate more accurate CNVs, we proposed a novel algorithm, LDcnv, that models the CNV data with its biological characteristics relating to genetic dependence structure (i.e. LD). Results We theoretically demonstrated the correlation structure of CNV data in SNP array, which further supports the necessity of integrating biological structure in statistical methods for CNV detection. Therefore, we developed the LDcnv that integrated the genomic correlation structure with a local search strategy into statistical modeling of the CNV intensities. To evaluate the performance of LDcnv, we conducted extensive simulations and analyzed large-scale HapMap datasets. We showed that LDcnv presented high accuracy, stability and robustness in CNV detection and higher precision in detecting short CNVs compared to existing methods. This new segmentation algorithm has a wide scope of potential application with data from various high-throughput technology platforms. Availability and implementation https://github.com/FeifeiXiaoUSC/LDcnv. Supplementary information Supplementary data are available at Bioinformatics online.

Molecular basis of differential parasitism between non-encapsulated and encapsulated Trichinella revealed by a high-quality genome assembly

Understanding roles of repetitive sequences in genomes of parasites could offer insights into their evolution, speciation, and parasitism. As a unique intracellular nematode, Trichinella consists of two clades, encapsulated and non-encapsulated. Genomic correlation to the distinct differences between the two clades is still unclear. Here we report an annotated draft reference genome of non-encapsulated Trichinella, T. pseudospiralis, and performed comparative analyses with encapsulated T. spiralis. Genome analysis revealed that, during Trichinella evolution, repetitive sequence insertions played an important role in gene family expansion in synergy with DNA methylation, especially for the DNase II members of the phospholipase D superfamily and Glutathione S-transferases. We further identify the genomic and epigenomic regulation of excretory/secretory products in relation to differences in parasitism, pathology and immunology between the two clades Trichinella. The present study provided a foundation for further elucidation of mechanism of nurse cell formation and immunoevasion as well as identification of phamarcological and diagnostic targets of trichinellosis.

Microbiability of meat quality and carcass composition traits in swine

The impact of gut microbiome composition was investigated at different stages of production (Wean, Mid-test, and Off-test) on meat quality and carcass composition traits of 1,123 three-way-crossbred pigs. Data were analyzed using linear mixed models which included the fixed effects of dam line, contemporary group and gender as well as the random effects of pen, animal and microbiome information at different stages. The contribution of the microbiome to all traits was prominent although it varied over time, increasing from weaning to Off-test for most traits. Microbiability estimates of carcass composition traits were greater compared to meat quality traits. Adding microbiome information did not affect the estimates of genomic heritability of meat quality traits but affected the estimates of carcass composition traits. High microbial correlations were found among different traits, particularly with traits related to fat deposition with decrease in genomic correlation up to 20% for loin weight and belly weight. Decrease in genomic heritabilities and genomic correlations with the inclusion of microbiome information suggested that genomic correlation was partially contributed by genetic similarity of microbiome composition.

Dermatoskopie: Naevi besser verstehen

Melanocytic naevi are common melanocytic proliferations that may simulate the appearance of cutaneous melanoma. Naevi commonly harbour somatic mutations implicated in melanomagenesis but in most cases lack the necessary genomic alterations required for melanoma development. While the mitogen-activated protein kinase pathway and ultraviolet radiation strongly contribute to naevogenesis, the somatic mutational landscape of dermoscopic naevus subsets distinguishes some of the molecular hallmarks of naevi in relation to melanoma. We herein discuss the classification of naevi and theories of naevogenesis and review the current literature on the somatic alterations in naevi and melanoma. This review focusses on the clinical-dermoscopic-pathological and genomic correlation of naevi that shapes the current understanding of naevi.

Defining the Molecular Genetics of Dermoscopic Naevus Patterns

Melanocytic naevi are common melanocytic proliferations that may simulate the appearance of cutaneous melanoma. Naevi commonly harbour somatic mutations implicated in melanomagenesis but in most cases lack the necessary genomic alterations required for melanoma development. While the mitogen-activated protein kinase pathway and ultraviolet radiation strongly contribute to naevogenesis, the somatic mutational landscape of dermoscopic naevus subsets distinguishes some of the molecular hallmarks of naevi in relation to melanoma. We herein discuss the classification of naevi and theories of naevogenesis and review the current literature on the somatic alterations in naevi and melanoma. This review focusses on the clinical-dermoscopic-pathological and genomic correlation of naevi that shapes the current understanding of naevi.