A massive alteration of gene expression in undescended testicles of dogs and the association of KAT6A variants with cryptorchidism

Cryptorchidism is the most common form of disorder of sex development in male dogs, but its hereditary predisposition is poorly elucidated. The gonadal transcriptome of nine unilaterally cryptorchid dogs and seven control dogs was analyzed using RNA-seq. Comparison between the scrotal and inguinal gonads of unilateral cryptorchid dogs revealed 8,028 differentially expressed genes (DEGs) (3,377 up-regulated and 4,651 down-regulated). A similar number of DEGs (7,619) was found by comparing the undescended testicles with the descended testicles of the control dogs. The methylation status of the selected DEGs was also analyzed, with three out of nine studied DEGs showing altered patterns. Bioinformatic analysis of the cDNA sequences revealed 20,366 SNP variants, six of which showed significant differences in allelic counts between cryptorchid and control dogs. Validation studies in larger cohorts of cryptorchid (n = 122) and control (n = 173) dogs showed that the TT genotype (rs850666472, p.Ala1230Val) and the AA genotype in 3'UTR (16:23716202G>A) in KATA6, responsible for acetylation of lysine 9 in histone H3, are associated with cryptorchidism (P = 0.0383). Both the transcript level of KAT6A and H3K9 acetylation were lower in undescended testes, and additionally, the acetylation depended on the genotypes in exon 17 and the 3'UTR. Our study showed that the massive alteration of the transcriptome in undescended testicles is not caused by germinal DNA variants in DEG regulatory sequences but is partly associated with an aberrant DNA methylation and H3K9 acetylation patterns. Moreover, variants of KAT6A can be considered markers associated with the risk of this disorder.

Ambiguous genes due to aligners and their impact on RNA-seq data analysis

The main scope of the study is ambiguous genes, i.e. genes whose expression is difficult to estimate from the data produced by next-generation sequencing technologies. We focused on the RNA sequencing (RNA-Seq) type of experiment performed on the Illumina platform. It is crucial to identify such genes and understand the cause of their difficulty, as these genes may be involved in some diseases. By giving misleading results, they could contribute to a misunderstanding of the cause of certain diseases, which could lead to inappropriate treatment. We thought that the ambiguous genes would be difficult to map because of their complex structure. So we looked at RNA-seq analysis using different mappers to find genes that would have different measurements from the aligners. We were able to identify such genes using a generalized linear model with two factors: mappers and groups introduced by the experiment. A large proportion of ambiguous genes are pseudogenes. High sequence similarity of pseudogenes to functional genes may indicate problems in alignment procedures. In addition, predictive analysis verified the performance of difficult genes in classification. The effectiveness of classifying samples into specific groups was compared, including the expression of difficult and not difficult genes as covariates. In almost all cases considered, ambiguous genes have less predictive power.

Herbicide resistance status impacts the profile of non-anthocyanin polyphenolics and some phytomedical properties of edible cornflower (Centaurea cyanus L.) flowers

To ensure sufficient food supply worldwide, plants are treated with pesticides to provide protection against pathogens and pests. Herbicides are the most commonly utilised pesticides, used to reduce the growth of weeds. However, their long-term use has resulted in the emergence of herbicide-resistant biotypes in many weed species. Cornflower (Centaurea cyanus L., Asteraceae) is one of these plants, whose biotypes resistant to herbicides from the group of acetolactate synthase (ALS) inhibitors have begun to emerge in recent years. Some plants, although undesirable in crops and considered as weeds, are of great importance in phytomedicine and food production, and characterised by a high content of health-promoting substances, including antioxidants. Our study aimed to investigate how the acquisition of herbicide resistance affects the health-promoting properties of plants on the example of cornflower, as well as how they are affected by herbicide treatment. To this end, we analysed non-anthocyanin polyphenols and antioxidant capacity in flowers of C. cyanus from herbicide-resistant and susceptible biotypes. Our results indicated significant compositional changes associated with an increase in the content of substances and activities that have health-promoting properties. High antioxidant activity and higher total phenolic and flavonoid compounds as well as reducing power were observed in resistant biotypes. The latter one increased additionally after herbicide treatment which might also suggest their role in the resistance acquisition mechanism. Overall, these results show that the herbicide resistance development, although unfavourable to crop production, may paradoxically have very positive effects for medicinal plants such as cornflower.

The Epidermal Transcriptome Analysis of a Novel c.639_642dup LORICRIN Variant-Delineation of the Loricrin Keratoderma Pathology

Loricrin keratoderma (LK) is a rare autosomal dominant genodermatosis caused by LORICRIN gene mutations. The pathogenesis of the disease is not yet fully understood. So far, only 10 pathogenic variants in LORICRIN have been described, with all of them but one being deletions or insertions. The significance of rare nonsense variants remains unclear. Furthermore, no data regarding the RNA expression in affected patients are available. The aim of this study is to describe the two variants in the LORICRIN gene found in two distinct families: the novel pathogenic variant c.639_642dup and a rare c.10C > T (p.Gln4Ter) of unknown significance. We also present the results of the transcriptome analysis of the lesional loricrin keratoderma epidermis of a patient with c.639_642dup. We show that in the LK lesion, the genes associated with epidermis development and keratocyte differentiation are upregulated, while genes engaged in cell adhesion, differentiation developmental processes, ion homeostasis and transport, signaling and cell communication are downregulated. In the context of the p.Gln4Ter clinical significance evaluation, we provide data indicating that LORICRIN haploinsufficiency has no skin consequences. Our results give further insight into the pathogenesis of LK, which may have therapeutic implications in the future and important significance in the context of genetic counseling.

Whole genome sequencing identifies a missense polymorphism in PADI6 associated with testicular/ovotesticular XX disorder of sex development in dogs

Disorders of sex development (DSDs) are congenital malformations defined as discrepancies between sex chromosomes and phenotypical sex. Testicular or ovotesticular XX DSDs are frequently observed in female dogs, while monogenic XY DSDs are less frequent. Here, we applied whole genome sequencing (WGS) to search for causative mutations in XX DSD females in French Bulldogs (FB) and American Staffordshire Terries (AST) and in XY DSD Yorkshire Terries (YT). The WGS results were validated by Sanger sequencing and ddPCR. It was shown that a missense SNP of the PADI6 gene, is significantly associated with the XX DSD (SRY-negative) phenotype in AST (P = 0.0051) and FB (P = 0.0306). On the contrary, we did not find any associated variant with XY DSD in YTs. Our study suggests that the genetic background of the XX DSD may be more complex and breed-specific.

CellMixS: quantifying and visualizing batch effects in single-cell RNA-seq data

A key challenge in single-cell RNA-sequencing (scRNA-seq) data analysis is batch effects that can obscure the biological signal of interest. Although there are various tools and methods to correct for batch effects, their performance can vary. Therefore, it is important to understand how batch effects manifest to adjust for them. Here, we systematically explore batch effects across various scRNA-seq datasets according to magnitude, cell type specificity, and complexity. We developed a cell-specific mixing score (cms) that quantifies mixing of cells from multiple batches. By considering distance distributions, the score is able to detect local batch bias as well as differentiate between unbalanced batches and systematic differences between cells of the same cell type. We compare metrics in scRNA-seq data using real and synthetic datasets and whereas these metrics target the same question and are used interchangeably, we find differences in scalability, sensitivity, and ability to handle differentially abundant cell types. We find that cell-specific metrics outperform cell type-specific and global metrics and recommend them for both method benchmarks and batch exploration.

Transcriptome Remodeling in Gradual Development of Inverse Resistance between Paclitaxel and Cisplatin in Ovarian Cancer Cells

Resistance to anti-cancer drugs is the main challenge in oncology. In pre-clinical studies, established cancer cell lines are primary tools in deciphering molecular mechanisms of this phenomenon. In this study, we proposed a new, transcriptome-focused approach, utilizing a model of isogenic cancer cell lines with gradually changing resistance. We analyzed trends in gene expression in the aim to find out a scaffold of resistance development process. The ovarian cancer cell line A2780 was treated with stepwise increased concentrations of paclitaxel (PTX) to generate a series of drug resistant sublines. To monitor transcriptome changes we submitted them to mRNA-sequencing, followed by the identification of differentially expressed genes (DEGs), principal component analysis (PCA), and hierarchical clustering. Functional interactions of proteins, encoded by DEGs, were analyzed by building protein-protein interaction (PPI) networks. We obtained human ovarian cancer cell lines with gradually developed resistance to PTX and collateral sensitivity to cisplatin (CDDP) (inverse resistance). In their transcriptomes, we identified two groups of DEGs: (1) With fluctuations in expression in the course of resistance acquiring; and (2) with a consistently changed expression at each stage of resistance development, constituting a scaffold of the process. In the scaffold PPI network, the cell cycle regulator-polo-like kinase 2 (PLK2); proteins belonging to the tumor necrosis factor (TNF) ligand and receptor family, as well as to the ephrin receptor family were found, and moreover, proteins linked to osteo- and chondrogenesis and the nervous system development. Our cellular model of drug resistance allowed for keeping track of trends in gene expression and studying this phenomenon as a process of evolution, reflected by global transcriptome remodeling. This approach enabled us to explore novel candidate genes and surmise that abrogation of the osteomimic phenotype in ovarian cancer cells might occur during the development of inverse resistance between PTX and CDDP.

Effect of Zearalenone and Hormone Regulators on Microspore Embryogenesis in Anther Culture of Wheat

The purpose of this work was to assess the impact of zearalenone (ZEN) and selected hormone regulators on the effectiveness of microspore embryogenesis in anther culture of wheat. The plant material comprised F₁ hybrids of winter and spring wheat. Six combinations of media inducing microspore proliferation and formation of embryogenic structures were investigated: two combinations of growth regulators (D - 2,4-D + dicamba, K - 2,4-D + kinetin), each with three ZEN concentrations (0 mL/L, 0.1 mL/L, 0.2 mL/L). A significant increase in microspore embryogenesis effectiveness on media with the addition of ZEN was observed both at the stages of its induction and the formation of green plants in some genotypes. In case of both combinations of growth regulators, an increased concentration of ZEN resulted in more effective induction of microspore embryogenesis. The most effective induction medium was the D medium supplemented with 0.2 mL/L ZEN. As a result of the use of zearalenone together with two combinations of growth regulators, all genotypes tested produced androgenic structures, which indicates the breakdown of genotypic recalcitrant in the analysed hybrids. In addition, green plants were obtained from 18 out of 19 tested hybrids. The addition of ZEN to the medium did not affect the number of regenerated albino plants nor the number of spontaneous genome doublings proportion.

The Defense Response of Nicotiana benthamiana to Peanut Stunt Virus Infection in the Presence of Symptom Exacerbating Satellite RNA

Peanut stunt virus (PSV) is a widespread disease infecting legumes. The PSV strains are classified into four subgroups and some are defined by the association of satellite RNAs (satRNAs). In the case of PSV, the presence of satRNAs alters the symptoms of disease in infected plants. In this study, we elucidated the plant response to PSV-G strain, which occurs in natural conditions without satRNA. However, it was found that it might easily acquire satRNA, which exacerbated pathogenesis in Nicotiana benthamiana. To explain the mechanisms underlying PSV infection and symptoms exacerbation caused by satRNA, we carried out transcriptome profiling of N. benthamiana challenged by PSV-G and satRNA using species-specific microarrays. Co-infection of plants with PSV-G + satRNA increased the number of identified differentially expressed genes (DEGs) compared with the number identified in PSV-G-infected plants. In both treatments, the majority of up-regulated DEGs were engaged in translation, ribosome biogenesis, RNA metabolism, and response to stimuli, while the down-regulated DEGs were required for photosynthesis. The presence of satRNA in PSV-G-infected plants caused different trends in expression of DEGs associated with phosphorylation, ATP binding, and plasma membrane.

Genes sharing the protein family domain decrease the performance of classification with RNA-seq genomic signatures

BACKGROUND

The experience with running various types of classification on the CAMDA neuroblastoma dataset have led us to the conclusion that the results are not always obvious and may differ depending on type of analysis and selection of genes used for classification. This paper aims in pointing out several factors that may influence the downstream machine learning analysis. In particular those factors are: type of the primary analysis, type of the classifier and increased correlation between the genes sharing a protein domain. They influence the analysis directly, but also interplay between them may be important. We have compiled the gene-domain database and used it for analysis to see the differences between the genes that share a domain versus the rest of the genes in the datasets.

RESULTS

The major findings are: pairs of genes that share a domain have an increased Spearman's correlation coefficients of counts; genes sharing a domain are expected to have a lower predictive power due to increased correlation. For most of the cases it can be seen with the higher number of misclassified samples; classifiers performance may vary depending on a method, still in most cases using genes sharing a domain in the training set results in a higher misclassification rate; increased correlation in genes sharing a domain results most often in worse performance of the classifiers regardless of the primary analysis tools used, even if the primary analysis alignment yield varies.

CONCLUSIONS

The effect of sharing a domain is likely more a results of real biological co-expression than just sequence similarity and artifacts of mapping and counting. Still, this is more difficult to conclude and needs further research. The effect is interesting itself, but we also point out some practical aspects in which it may influence the RNA sequencing analysis and RNA biomarker use. In particular it means that a gene signature biomarker set build out of RNA-sequencing results should be depleted for genes sharing common domains. It may cause to perform better when applying classification.

REVIEWERS

This article was reviewed by Dimitar Vassiliev and Susmita Datta.

The effect of population size of paternal groups and herds on optimal estimation of the heritability index for gestation length in cattle

Estimation of genetic parameters is a crucial element in the process of population improvement. In the case of farm animals this process is based on a sample which is a subset of the whole population. For this reason it is important to know the effect of the size of paternal groups and of the herd on the accuracy of estimations of these parameters, particularly heritability. The aim of this study was to show the effect of the population size of paternal groups and herds on the accuracy of estimation of the heritability index (h2). The material for the analyses comprised data of Holstein-Friesian cattle born in 2005-2010 and subject to use value assessment in Poland. The trait analysed was gestation length. Calculations using a linear mixed model were performed using the R 3.1.3 platform. The analyses showed that calculations concerning daughters in paternal groups are more accurate when the daughters are in a smaller number of herds, but of greater size. An increase in the size of paternal groups at the expense of their number does not have such a negative effect on the accuracy of the estimate as in the case of a reduced number of small herds. Limiting the estimate to only the largest herds reduces its accuracy.

Analysis of boutique arrays: a universal method for the selection of the optimal data normalization procedure

DNA microarrays, which are among the most popular genomic tools, are widely applied in biology and medicine. Boutique arrays, which are small, spotted, dedicated microarrays, constitute an inexpensive alternative to whole-genome screening methods. The data extracted from each microarray-based experiment must be transformed and processed prior to further analysis to eliminate any technical bias. The normalization of the data is the most crucial step of microarray data pre-processing and this process must be carefully considered as it has a profound effect on the results of the analysis. Several normalization algorithms have been developed and implemented in data analysis software packages. However, most of these methods were designed for whole-genome analysis. In this study, we tested 13 normalization strategies (ten for double-channel data and three for single-channel data) available on R Bioconductor and compared their effectiveness in the normalization of four boutique array datasets. The results revealed that boutique arrays can be successfully normalized using standard methods, but not every method is suitable for each dataset. We also suggest a universal seven-step workflow that can be applied for the selection of the optimal normalization procedure for any boutique array dataset. The described workflow enables the evaluation of the investigated normalization methods based on the bias and variance values for the control probes, a differential expression analysis and a receiver operating characteristic curve analysis. The analysis of each component results in a separate ranking of the normalization methods. A combination of the ranks obtained from all the normalization procedures facilitates the selection of the most appropriate normalization method for the studied dataset and determines which methods can be used interchangeably.

Preferred analysis methods for single genomic regions in RNA sequencing revealed by processing the shape of coverage

The informational content of RNA sequencing is currently far from being completely explored. Most of the analyses focus on processing tables of counts or finding isoform deconvolution via exon junctions. This article presents a comparison of several techniques that can be used to estimate differential expression of exons or small genomic regions of expression, based on their coverage function shapes. The problem is defined as finding the differentially expressed exons between two samples using local expression profile normalization and statistical measures to spot the differences between two profile shapes. Initial experiments have been done using synthetic data, and real data modified with synthetically created differential patterns. Then, 160 pipelines (5 types of generator × 4 normalizations × 8 difference measures) are compared. As a result, the best analysis pipelines are selected based on linearity of the differential expression estimation and the area under the ROC curve. These platform-independent techniques have been implemented in the Bioconductor package rnaSeqMap. They point out the exons with differential expression or internal splicing, even if the counts of reads may not show this. The areas of application include significant difference searches, splicing identification algorithms and finding suitable regions for QPCR primers.