Screening of olive germplasm for resistance to Xylella fastidiosa ST53: the state of the art

While different sources of natural resistance to X. fastidiosa have been described in grapevines and citrus, lack of solid information exists on possible sources of resistance/tolerance in the cultivars that characterize the wide olive germplasm. Preliminary field observations and laboratory analyses of a few cultivars, have shown that differential responses to X. fastidiosa infections exist. To confirm these preliminary findings, a large panel of olive cultivars is being specifically investigated. Currently, the screening procedure relies on field observations looking for symptomless subjects (trees of known cultivars/volunteer seedlings), mechanical inoculations, qualitative and quantitative diagnostic assays (ELISA & qPCR) and, in selected cases, comparative transcriptomic profiling. Field experiments include the planting of the target cultivars/selections in an infected area under high inoculum pressure. All the plots are located in the Apulia Region (Italy) in the demarcated infected area, surrounded by X. fastidiosa heavily affected olive groves. A first experimental plot was established in April 2015 with 10 different cultivars, which was extended in 2016 to 49 cultivars, and will be further enlarged in 2017 with the addition of 40 new accessions. Other plots, comprising newly planted or grafted cultivars (over 260 cvs) have been also established, bringing to over 300 the total number of accessions under evaluation. Cvs Leccino and FS-17®, both expressing interesting traits of resistance, have already been identified

Functional single nucleotide polymorphisms within the cyclin-dependent kinase inhibitor 2A/2B region affect pancreatic cancer risk

The CDKN2A (p16) gene plays a key role in pancreatic cancer etiology. It is one of the most commonly somatically mutated genes in pancreatic cancer, rare germline mutations have been found to be associated with increased risk of developing familiar pancreatic cancer and CDKN2A promoter hyper-methylation has been suggested to play a critical role both in pancreatic cancer onset and prognosis. In addition several unrelated SNPs in the 9p21.3 region, that includes the CDNK2A, CDNK2B and the CDNK2B-AS1 genes, are associated with the development of cancer in various organs. However, association between the common genetic variability in this region and pancreatic cancer risk is not clearly understood. We sought to fill this gap in a case-control study genotyping 13 single nucleotide polymorphisms (SNPs) in 2,857 pancreatic ductal adenocarcinoma (PDAC) patients and 6,111 controls in the context of the Pancreatic Disease Research (PANDoRA) consortium. We found that the A allele of the rs3217992 SNP was associated with an increased pancreatic cancer risk (ORhet=1.14, 95% CI 1.01-1.27, p=0.026, ORhom=1.30, 95% CI 1.12-1.51, p=0.00049). This pleiotropic variant is reported to be a mir-SNP that, by changing the binding site of one or more miRNAs, could influence the normal cell cycle progression and in turn increase PDAC risk. In conclusion, we observed a novel association in a pleiotropic region that has been found to be of key relevance in the susceptibility to various types of cancer and diabetes suggesting that the CDKN2A/B locus could represent a genetic link between diabetes and pancreatic cancer risk

Reconstruction of the Core and Extended Regulons of Global Transcription Factors

The processes underlying the evolution of regulatory networks are unclear. To address this question, we used a comparative genomics approach that takes advantage of the large number of sequenced bacterial genomes to predict conserved and variable members of transcriptional regulatory networks across phylogenetically related organisms. Specifically, we developed a computational method to predict the conserved regulons of transcription factors across α-proteobacteria. We focused on the CRP/FNR super-family of transcription factors because it contains several well-characterized members, such as FNR, FixK, and DNR. While FNR, FixK, and DNR are each proposed to regulate different aspects of anaerobic metabolism, they are predicted to recognize very similar DNA target sequences, and they occur in various combinations among individual α-proteobacterial species. In this study, the composition of the respective FNR, FixK, or DNR conserved regulons across 87 α-proteobacterial species was predicted by comparing the phylogenetic profiles of the regulators with the profiles of putative target genes. The utility of our predictions was evaluated by experimentally characterizing the FnrL regulon (a FNR-type regulator) in the α-proteobacterium Rhodobacter sphaeroides. Our results show that this approach correctly predicted many regulon members, provided new insights into the biological functions of the respective regulons for these regulators, and suggested models for the evolution of the corresponding transcriptional networks. Our findings also predict that, at least for the FNR-type regulators, there is a core set of target genes conserved across many species. In addition, the members of the so-called extended regulons for the FNR-type regulators vary even among closely related species, possibly reflecting species-specific adaptation to environmental and other factors. The comparative genomics approach we developed is readily applicable to other regulatory networks

Multidimensional signals and analytic flexibility: Estimating degrees of freedom in human speech analyses

Recent empirical studies have highlighted the large degree of analytic flexibility in data analysis which can lead to substantially different conclusions based on the same data set. Thus, researchers have expressed their concerns that these researcher degrees of freedom might facilitate bias and can lead to claims that do not stand the test of time. Even greater flexibility is to be expected in fields in which the primary data lend themselves to a variety of possible operationalizations. The multidimensional, temporally extended nature of speech constitutes an ideal testing ground for assessing the variability in analytic approaches, which derives not only from aspects of statistical modeling, but also from decisions regarding the quantification of the measured behavior. In the present study, we gave the same speech production data set to 46 teams of researchers and asked them to answer the same research question, resulting insubstantial variability in reported effect sizes and their interpretation. Using Bayesian meta-analytic tools, we further find little to no evidence that the observed variability can be explained by analysts’ prior beliefs, expertise or the perceived quality of their analyses. In light of this idiosyncratic variability, we recommend that researchers more transparently share details of their analysis, strengthen the link between theoretical construct and quantitative system and calibrate their (un)certainty in their conclusions