Independent mutations in the transcription factor MYB10 cause most of the anthocyanin variation observed in diploid woodland strawberry (Fragaria vesca) and oct

In recent years, high-throughput next-generation sequencing technology has allowed a rapid increase in diagnostic capacity and precision through different bioinformatics processing algorithms, tools and pipelines. The identification, annotation and classification of sequence variants within different target regions are now considered a gold standard in clinical genetic diagnosis. However, this procedure lacks the ability to link regulatory events such as differential splicing to diseases. RNA-seq is necessary in clinical routine in order to interpret and detect among others splicing events and splicing variants, as it would increase the diagnostic rate by up to 10-35%. The transcriptome has a very dynamic nature, varying according to tissue type, cellular conditions and environmental factors that may affect regulatory events such as splicing and the expression of genes or their isoforms. RNA-seq offers a robust technical analysis of this complexity, but it requires a profound knowledge of computational/statistical tools that may need to be adjusted depending on the disease under study. In this article we will cover RNA-seq analyses best practices applied to clinical routine, bioinformatics procedures and present challenges of this approach.

The use of biological control agents (BCAs) is of interest within an integrated management strategy of Verticillium wilt of olive (VWO) caused by the soil-borne fungus Verticillium dahliae Kleb. Previous studies have shown that the root/rhizosphere of healthy olive plants is an important reservoir of microorganisms displaying biocontrol activity against VWO (i.e., Pseudomonas strains PICF7 and PIC141). Moreover, these BCAs are already adapted to the ecological niche where they are deployed. Three novel bacteria (strains PIC28, PIC73 and PIC167) from nursery-produced olive plants were in-depth characterized using a previously implemented approach consisting of in situ isolation, in vitro antagonism tests, in planta bioassays, phenotypic and metabolic characterization, genome analyses and in silico identification of traits involved in plant-bacteria interactions, and multi-locus sequence analyses. All strains displayed in vitro growth inhibition of different olive pathogens and biocontrol effectiveness against Verticillium dahliae, with strain PIC73 being the most effective BCA. Strains PIC73 and PIC167 were identified as Paenibacillus polymyxa (Prazmowski) Ash et al. and Paenibacillus terrae Yoon et al., respectively. Strain PIC28 belongs to the Bacillus genus. Some of these Bacillales members showed in vitro compatibility with previously characterized BCAs (Pseudomonas spp. strains) also originating from the olive rhizosphere, paving the way for the future development of tailored bacterial consortia effective against VWO.

Molecular diagnosis of myeloid neoplasms (MN) is based on the detection of multiple genetic alterations using various techniques. Next-generation sequencing (NGS) has been proved as a useful method for analyzing many genes simultaneously. In this context, we analyzed diagnostic samples from 121 patients affected by MN and ten relapse samples from a subset of acute myeloid leukemia patients using two enrichment-capture NGS gene panels. Pathogenicity classification of variants was enhanced by the development and application of a custom onco-hematology score. A total of 278 pathogenic variants were detected in 84% of patients. For structural alterations, 82% of those identified by cytogenetics were detected by NGS, 25 of 31 copy number variants and three out of three translocations. The detection of variants using NGS changed the diagnosis of seven patients and the prognosis of 15 patients and enabled us to identify 44 suitable candidates for clinical trials. Regarding AML, six of the ten relapsed patients lost or gained variants, comparing with diagnostic samples. In conclusion, the use of NGS panels in MN improves genetic characterization of the disease compared with conventional methods, thus demonstrating its potential clinical utility in routine clinical testing. This approach leads to better-adjusted treatments for each patient.

It remains unclear whether disease course in multiple sclerosis (MS) is influenced by genetic polymorphisms. Here, we aimed to identify genetic variants associated with benign and aggressive disease courses in MS patients. MS patients were classified into benign and aggressive phenotypes according to clinical criteria. We performed exome sequencing in a discovery cohort, which included 20 MS patients, 10 with benign and 10 with aggressive disease course, and genotyping in 2 independent validation cohorts. The first validation cohort encompassed 194 MS patients, 107 with benign and 87 with aggressive phenotypes. The second validation cohort comprised 257 patients, of whom 224 patients had benign phenotypes and 33 aggressive disease courses. Brain immunohistochemistries were performed using disease course associated genes antibodies. By means of single-nucleotide polymorphism (SNP) detection and comparison of allele frequencies between patients with benign and aggressive phenotypes, a total of 16 SNPs were selected for validation from the exome sequencing data in the discovery cohort. Meta-analysis of genotyping results in two validation cohorts revealed two polymorphisms, rs28469012 and rs10894768, significantly associated with disease course. SNP rs28469012 is located in CPXM2 (carboxypeptidase X, M14 family, member 2) and was associated with aggressive disease course (uncorrected p value < 0.05). SNP rs10894768, which is positioned in IGSF9B (immunoglobulin superfamily member 9B) was associated with benign phenotype (uncorrected p value < 0.05). In addition, a trend for association with benign phenotype was observed for a third SNP, rs10423927, in NLRP9 (NLR family pyrin domain containing 9). Brain immunohistochemistries in chronic active lesions from MS patients revealed expression of IGSF9B in astrocytes and macrophages/microglial cells, and expression of CPXM2 and NLRP9 restricted to brain macrophages/microglia. Genetic variants located in CPXM2, IGSF9B, and NLRP9 have the potential to modulate disease course in MS patients and may be used as disease activity biomarkers to identify patients with divergent disease courses. Altogether, the reported results from this study support the influence of genetic factors in MS disease course and may help to better understand the complex molecular mechanisms underlying disease pathogenesis.

Some species of the genus Pseudo-nitzschia produce the toxin domoic acid, which causes amnesic shellfish poisoning (ASP). Given that bivalve mollusks are filter feeders, they can accumulate these toxins in their tissues. To elucidate the transcriptional response of the queen scallop Aequipecten opercularis after exposure to domoic acid-producing Pseudo-nitzschia, the digestive gland transcriptome was de novo assembled using an Illumina HiSeq 2000 platform. Then, a differential gene expression analysis was performed. After the assembly, 142,137 unigenes were obtained, and a total of 10,144 genes were differentially expressed in the groups exposed to the toxin. Functional enrichment analysis found that 374 Pfam (protein families database) domains were significantly enriched. The C1q domain, the C-type lectin, the major facilitator superfamily, the immunoglobulin domain, and the cytochrome P450 were among the most enriched Pfam domains. Protein network analysis showed a small number of highly connected nodes involved in specific functions: proteasome components, mitochondrial ribosomal proteins, protein translocases of mitochondrial membranes, cytochromes P450, and glutathione S-transferases. The results suggest that exposure to domoic acid-producing organisms causes oxidative stress and mitochondrial dysfunction. The transcriptional response counteracts these effects with the up-regulation of genes coding for some mitochondrial proteins, proteasome components, and antioxidant enzymes (glutathione S-transferases, thioredoxins, glutaredoxins, and copper/zinc superoxide dismutases).