Development of late blight resistant potatoes by cisgenic stacking

Background Phytophthora infestans, causing late blight in potato, remains one of the most devastating pathogens in potato production and late blight resistance is a top priority in potato breeding. The introduction of multiple resistance (R) genes with different spectra from crossable species into potato varieties is required. Cisgenesis is a promising approach that introduces native genes from the crops own gene pool using GM technology, thereby retaining favourable characteristics of established varieties. Results We pursued a cisgenesis approach to introduce two broad spectrum potato late blight R genes, Rpi-sto1 and Rpi-vnt1.1 from the crossable species Solanum stoloniferum and Solanum venturii, respectively, into three different potato varieties. First, single R gene-containing transgenic plants were produced for all varieties to be used as references for the resistance levels and spectra to be expected in the respective genetic backgrounds. Next, a construct containing both cisgenic late blight R genes (Rpi-vnt1.1 and Rpi-sto1), but lacking the bacterial kanamycin resistance selection marker (NPTII) was transformed to the three selected potato varieties using Agrobacterium-mediated transformation. Gene transfer events were selected by PCR among regenerated shoots. Through further analyses involving morphological evaluations in the greenhouse, responsiveness to Avr genes and late blight resistance in detached leaf assays, the selection was narrowed down to eight independent events. These cisgenic events were selected because they showed broad spectrum late blight resistance due to the activity of both introduced R genes. The marker-free transformation was compared to kanamycin resistance assisted transformation in terms of T-DNA and vector backbone integration frequency. Also, differences in regeneration time and genotype dependency were evaluated. Conclusions We developed a marker-free transformation pipeline to select potato plants functionally expressing a stack of late blight R genes. Marker-free transformation is less genotype dependent and less prone to vector backbone integration as compared to marker-assisted transformation. Thereby, this study provides an important tool for the successful deployment of R genes in agriculture and contributes to the production of potentially durable late blight resistant potatoes

Targeted RNA-Sequencing Enables Detection of Relevant Translocations and Single Nucleotide Variants and Provides a Method for Classification of Hematological Malignancies-RANKING

BACKGROUND: Patients with hematological malignancies (HMs) carry a wide range of chromosomal and molecular abnormalities that impact their prognosis and treatment. Since no current technique can detect all relevant abnormalities, technique(s) are chosen depending on the reason for referral, and abnormalities can be missed. We tested targeted transcriptome sequencing as a single platform to detect all relevant abnormalities and compared it to current techniques. MATERIAL AND METHODS: We performed RNA-sequencing of 1385 genes (TruSight RNA Pan-Cancer, Illumina) in bone marrow from 136 patients with a primary diagnosis of HM. We then applied machine learning to expression profile data to perform leukemia classification, a method we named RANKING. Gene fusions for all the genes in the panel were detected, and overexpression of the genes EVI1, CCND1, and BCL2 was quantified. Single nucleotide variants/indels were analyzed in acute myeloid leukemia (AML), myelodysplastic syndrome and patients with acute lymphoblastic leukemia (ALL) using a virtual myeloid (54 genes) or lymphoid panel (72 genes). RESULTS: RANKING correctly predicted the leukemia classification of all AML and ALL samples and improved classification in 3 patients. Compared to current methods, only one variant was missed, c.2447A>T in KIT (RT-PCR at 10(-4)), and BCL2 overexpression was not seen due to a t(14; 18)(q32; q21) in 2% of the cells. Our RNA-sequencing method also identified 6 additional fusion genes and overexpression of CCND1 due to a t(11; 14)(q13; q32) in 2 samples. CONCLUSIONS: Our combination of targeted RNA-sequencing and data analysis workflow can improve the detection of relevant variants, and expression patterns can assist in establishing HM classification

Occupational exposure to gases/fumes and mineral dust affect DNA methylation levels of genes regulating expression

Many workers are daily exposed to occupational agents like gases/fumes, mineral dust or biological dust, which could induce adverse health effects. Epigenetic mechanisms, such as DNA methylation, have been suggested to play a role. We therefore aimed to identify differentially methylated regions (DMRs) upon occupational exposures in never-smokers and investigated if these DMRs associated with gene expression levels. To determine the effects of occupational exposures independent of smoking, 903 never-smokers of the LifeLines cohort study were included. We performed three genome-wide methylation analyses (Illumina 450 K), one per occupational exposure being gases/fumes, mineral dust and biological dust, using robust linear regression adjusted for appropriate confounders. DMRs were identified using comb-p in Python. Results were validated in the Rotterdam Study (233 never-smokers) and methylation-expression associations were assessed using Biobank-based Integrative Omics Study data (n = 2802). Of the total 21 significant DMRs, 14 DMRs were associated with gases/fumes and 7 with mineral dust. Three of these DMRs were associated with both exposures (RPLP1 and LINC02169 (2x)) and 11 DMRs were located within transcript start sites of gene expression regulating genes. We replicated two DMRs with gases/fumes (VTRNA2-1 and GNAS) and one with mineral dust (CCDC144NL). In addition, nine gases/fumes DMRs and six mineral dust DMRs significantly associated with gene expression levels. Our data suggest that occupational exposures may induce differential methylation of gene expression regulating genes and thereby may induce adverse health effects. Given the millions of workers that are exposed daily to occupational exposures, further studies on this epigenetic mechanism and health outcomes are warranted

Constraints on the χ_(c1) versus χ_(c2) polarizations in proton-proton collisions at √s = 8 TeV

The polarizations of promptly produced χ_(c1) and χ_(c2) mesons are studied using data collected by the CMS experiment at the LHC, in proton-proton collisions at √s=8  TeV. The χ_c states are reconstructed via their radiative decays χ_c → J/ψγ, with the photons being measured through conversions to e⁺e⁻, which allows the two states to be well resolved. The polarizations are measured in the helicity frame, through the analysis of the χ_(c2) to χ_(c1) yield ratio as a function of the polar or azimuthal angle of the positive muon emitted in the J/ψ → μ⁺μ⁻ decay, in three bins of J/ψ transverse momentum. While no differences are seen between the two states in terms of azimuthal decay angle distributions, they are observed to have significantly different polar anisotropies. The measurement favors a scenario where at least one of the two states is strongly polarized along the helicity quantization axis, in agreement with nonrelativistic quantum chromodynamics predictions. This is the first measurement of significantly polarized quarkonia produced at high transverse momentum