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.

Insect Gut Bacteria Promoting the Growth of Tomato Plants (Solanum lycopersicum L.)

We investigated gut bacteria from three insect species for the presence of plant growth properties (PGP). Out of 146 bacterial strains obtained from 20 adult specimens of Scolytidae sp., 50 specimens of Oulema melanopus, and 150 specimens of Diabrotica virgifera, we selected 11 strains displaying the following: PGP, phosphate solubility, production of cellulase, siderophore, lipase, protease, and hydrogen cyanide. The strains were tested for growth promotion ability on tomato (Lycopersicon esculentum) plants. Each strain was tested individually, and all strains were tested together as a bacterial consortium. Tomato fruit yield was compared with the negative control. The plants treated with bacterial consortium showed a significant increase in fruit yield, in both number of fruits (+41%) and weight of fruits (+44%). The second highest yield was obtained for treatment with Serratia liquefaciens Dv032 strain, where the number and weight of yielded fruits increased by 35% and 30%, respectively. All selected 11 strains were obtained from Western Corn Rootworm (WCR), Diabrotica virgifera. The consortium comprised: Ewingella americana, Lactococcus garvieae, L. lactis, Pseudomonas putida, Serratia liquefaciens, and S. plymuthica. To our knowledge, this is the first successful application of D. virgifera gut bacteria for tomato plant growth stimulation that has been described.

The structure of the cereal leaf beetle (Oulema melanopus) microbiome depends on the insect's developmental stage, host plant, and origin

Cereal leaf beetle (CLB, Oulema melanopus, Coleoptera, Chrysomelidae) is a serious agricultural pest that causes considerable damages to agricultural production. The aim of this study was to characterize the bacterial communities associated with larvae and imagoes of CLB collected from various cereal host species and locations. The bacterial profile was characterized by 16S rRNA gene sequencing at the V3-V4 hypervariable region. Using taxonomy-based analysis, the bacterial community of CLB containing 16 phyla, 26 classes, 49 orders, 78 families, 94 genera, and 63 species of bacteria was identified. The abundance of Wolbachia, Rickettsia, and Lactococcus genus was significantly higher in CLB imagoes than in larvae. Statistical analysis confirmed that the bacterial community of the larvae is more diverse in comparison to imagoes and that insects collected from spring barley and wheat are characterized by a much higher biodiversity level of bacterial genera and species than insects collected from other cereals. Obtained results indicated that the developmental stage, the host plant, and the insect's sampling location affected the CLB's microbiome. Additionally, the CLB core microbiome was determined. It consists of 2 genera (Wolbachia and Rickettsia) shared by at least 90% tested CLB insects, regardless of the variables analysed.

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.

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.