Gllac7 Is Induced by Agricultural and Forestry Residues and Exhibits Allelic Expression Bias in Ganoderma lucidum

Ganoderma lucidum has a wide carbon spectrum, while the expression profile of key genes relevant to carbon metabolism on different carbon sources has been seldom studied. Here, the transcriptomes of G. lucidum mycelia cultured on each of 19 carbon sources were conducted. In comparison with glucose, 16 to 1,006 genes were upregulated and 7 to 1,865 genes were downregulated. Significant gene expression dynamics and induced activity were observed in laccase genes when using agricultural and forestry residues (AFRs) as solo carbon sources. Furthermore, study of laccase gene family in two haploids of G. lucidum GL0102 was conducted. Totally, 15 and 16 laccase genes were identified in GL0102_53 and GL0102_8, respectively, among which 15 pairs were allelic genes. Gene structures were conserved between allelic laccase genes, while sequence variations (most were SNPs) existed. Nine laccase genes rarely expressed on all the tested carbon sources, while the other seven genes showed high expression level on AFRs, especially Gllac2 and Gllac7, which showed 5- to 1,149-fold and 4- to 94-fold upregulation in mycelia cultured for 5 days, respectively. The expression of H53lac7 was consistently higher than that of H8lac7_1 on all the carbon sources except XM, exhibiting a case of allelic expression bias. A total of 47 SNPs and 3 insertions/deletions were observed between promoters of H53lac7 and H8lac7_1, which lead to differences in predicted binding sites of zinc fingers. These results provide scientific data for understanding the gene expression profile and regulatory role on different carbon sources and may support further functional research of laccase.

The axonemal dynein heavy chain 10 gene is essential for monocilia motility and spine alignment in zebrafish

Adolescent idiopathic scoliosis (AIS) is a common pediatric musculoskeletal disorder worldwide, characterized by atypical spine curvatures in otherwise healthy children. Human genetic studies have identified candidate genes associated with AIS, however, only a few of these have been shown to recapitulate adult-viable scoliosis in animal models. Using an F0 CRISPR screening approach in zebrafish, we demonstrate that disruption of the dynein axonemal heavy chain 10 (dnah10) gene results in recessive adult-viable scoliosis in zebrafish. Using a stably segregating dnah10 mutant zebrafish, we showed that the ependymal monocilia lining the hindbrain and spinal canal displayed reduced beat frequency, which was correlated with the disassembly of the Reissner fiber and the onset of body curvatures. Taken together, these results suggest that monocilia function in larval zebrafish contributes to the polymerization of the Reissner fiber and straightening of the body axis.

Feasibility of predicting allele specific expression from DNA sequencing using machine learning

Allele specific expression (ASE) concerns divergent expression quantity of alternative alleles and is measured by RNA sequencing. Multiple studies show that ASE plays a role in hereditary diseases by modulating penetrance or phenotype severity. However, genome diagnostics is based on DNA sequencing and therefore neglects gene expression regulation such as ASE. To take advantage of ASE in absence of RNA sequencing, it must be predicted using only DNA variation. We have constructed ASE models from BIOS (n = 3432) and GTEx (n = 369) that predict ASE using DNA features. These models are highly reproducible and comprise many different feature types, highlighting the complex regulation that underlies ASE. We applied the BIOS-trained model to population variants in three genes in which ASE plays a clinically relevant role: BRCA2, RET and NF1. This resulted in predicted ASE effects for 27 variants, of which 10 were known pathogenic variants. We demonstrated that ASE can be predicted from DNA features using machine learning. Future efforts may improve sensitivity and translate these models into a new type of genome diagnostic tool that prioritizes candidate pathogenic variants or regulators thereof for follow-up validation by RNA sequencing. All used code and machine learning models are available at GitHub and Zenodo.

aScan: A Novel Method for the Study of Allele Specific Expression in Single Individuals

In diploid organisms, two copies of each allele are normally inherited from parents. Paternal and maternal alleles can be regulated and expressed unequally, which is referred to as allele-specific expression (ASE). In this work, we present aScan, a novel method for the identification of ASE from the analysis of matched individual genomic and RNA sequencing data. By performing extensive analyses of both real and simulated data, we demonstrate that aScan can correctly identify ASE with high accuracy and sensitivity in different experimental settings. Additionally, by applying our method to a small cohort of individuals that are not included in publicly available databases of human genetic variation, we outline the value of possible applications of ASE analysis in single individuals for deriving a more accurate annotation of "private" low-frequency genetic variants associated with regulatory effects on transcription. All in all, we believe that aScan will represent a beneficial addition to the set of bioinformatics tools for the analysis of ASE. Finally, while our method was initially conceived for the analysis of RNA-seq data, it can in principle be applied to any quantitative NGS assay for which matched genotypic and expression data are available. AVAILABILITY: aScan is currently available in the form of an open source standalone software package at: https://github.com/Federico77z/aScan/. aScan version 1.0.3, available at https://github.com/Federico77z/aScan/releases/tag/1.0.3, has been used for all the analyses included in this manuscript. A Docker image of the tool has also been made available at https://github.com/pmandreoli/aScanDocker.

Investigating population-scale allelic differential expression in wild populations of Oithona similis (Cyclopoida, Claus, 1866)

Acclimation allowed by variation in gene or allele expression in natural populations is increasingly understood as a decisive mechanism, as much as adaptation, for species evolution. However, for small eukaryotic organisms, as species from zooplankton, classical methods face numerous challenges. Here, we propose the concept of allelic differential expression at the population-scale (psADE) to investigate the variation in allele expression in natural populations. We developed a novel approach to detect psADE based on metagenomic and metatranscriptomic data from environmental samples. This approach was applied on the widespread marine copepod, Oithona similis, by combining samples collected during the Tara Oceans expedition (2009-2013) and de novo transcriptome assemblies. Among a total of 25,768 single nucleotide variants (SNVs) of O. similis, 572 (2.2%) were affected by psADE in at least one population (FDR < 0.05). The distribution of SNVs under psADE in different populations is significantly shaped by population genomic differentiation (Pearson r = 0.87, p = 5.6 × 10-30), supporting a partial genetic control of psADE. Moreover, a significant amount of SNVs (0.6%) were under both selection and psADE (p < .05), supporting the hypothesis that natural selection and psADE tends to impact common loci. Population-scale allelic differential expression offers new insights into the gene regulation control in populations and its link with natural selection.

Evolution of cis- and trans-regulatory divergence in the chicken genome between two contrasting breeds analyzed using three tissue types at one-day-old

Background

Gene expression variation is a key underlying factor influencing phenotypic variation, and can occur via cis- or trans-regulation. To understand the role of cis- and trans-regulatory variation on population divergence in chicken, we developed reciprocal crosses of two chicken breeds, White Leghorn and Cornish Game, which exhibit major differences in body size and reproductive traits, and used them to determine the degree of cis versus trans variation in the brain, liver, and muscle tissue of male and female 1-day-old specimens.

Results

We provided an overview of how transcriptomes are regulated in hybrid progenies of two contrasting breeds based on allele specific expression analysis. Compared with cis-regulatory divergence, trans-acting genes were more extensive in the chicken genome. In addition, considerable compensatory cis- and trans-regulatory changes exist in the chicken genome. Most importantly, stronger purifying selection was observed on genes regulated by trans-variations than in genes regulated by the cis elements.

Conclusions

We present a pipeline to explore allele-specific expression in hybrid progenies of inbred lines without a specific reference genome. Our research is the first study to describe the regulatory divergence between two contrasting breeds. The results suggest that artificial selection associated with domestication in chicken could have acted more on trans-regulatory divergence than on cis-regulatory divergence.

Genomics of disease risk in globally diverse populations

Risk of disease is multifactorial and can be shaped by socio-economic, demographic, cultural, environmental and genetic factors. Our understanding of the genetic determinants of disease risk has greatly advanced with the advent of genome-wide association studies (GWAS), which detect associations between genetic variants and complex traits or diseases by comparing populations of cases and controls. However, much of this discovery has occurred through GWAS of individuals of European ancestry, with limited representation of other populations, including from Africa, The Americas, Asia and Oceania. Population demography, genetic drift and adaptation to environments over thousands of years have led globally to the diversification of populations. This global genomic diversity can provide new opportunities for discovery and translation into therapies, as well as a better understanding of population disease risk. Large-scale multi-ethnic and representative biobanks and population health resources provide unprecedented opportunities to understand the genetic determinants of disease on a global scale.

The Effect of Genetic Variation on the Placental Transcriptome in Humans

The knowledge of genetic variants shaping human placental transcriptome is limited and they are not cataloged in the Genotype-Tissue Expression project. So far, only one whole genome analysis of placental expression quantitative trait loci (eQTLs) has been published by Peng et al. (2017) with no external independent validation. We report the second study on the landscape of placental eQTLs. The study aimed to generate a high-confidence list of placental cis-eQTLs and to investigate their potential functional implications. Analysis of cis-eQTLs (±100 kbp from the gene) utilized 40 placental RNA sequencing and respective whole genome genotyping datasets. The identified 199 placental cis-eSNPs represented 88 independent eQTL signals (FDR < 5%). The most significant placental eQTLs (FDR < 10-5) modulated the expression of ribosomal protein RPL9, transcription factor ZSCAN9 and aminopeptidase ERAP2. The analysis confirmed 50 eSNP-eGenes pairs reported by Peng et al. (2017) and thus, can be claimed as robust placental eQTL signals. The study identified also 13 novel placental eGenes. Among these, ZSCAN9 is modulated by several eSNPs (experimentally validated: rs1150707) that have been also shown to affect the methylation level of genes variably escaping X-chromosomal inactivation. The identified 63 placental eGenes exhibited mostly mixed or ubiquitous expression. Functional enrichment analysis highlighted 35 Gene Ontology categories with the top ranking pathways "ruffle membrane" (FDR = 1.81 × 10-2) contributing to the formation of motile cell surface and "ATPase activity, coupled" (FDR = 2.88 × 10-2), critical for the membrane transport. Placental eGenes were also significantly enriched in pathways implicated in development, signaling and immune function. However, this study was not able to confirm a significant overrepresentation of genome-wide association studies top hits among the placental eSNP and eGenes, reported by Peng et al. (2017). The identified eSNPs were further analyzed in association with newborn and pregnancy traits. In the discovery step, a suggestive association was detected between the eQTL of ALPG (rs11678251) and reduced placental, newborn's and infant's weight. Meta-analysis across REPROMETA, HAPPY PREGNANCY, ALSPAC cohorts (n = 6830) did not replicate these findings. In summary, the study emphasizes the role of genetic variation in driving the transcriptome profile of the human placenta and the importance to explore further its functional implications.