Análise de associação quanto à produtividade e seus caracteres componentes em linhagens e cultivares de arroz de terras altas

The objective of this work was to identify, through analysis of associative mapping, the molecular markers related to upland rice yield and its component traits. One hundred thirteen lines and cultivars of upland rice, with reduced admixture, from the Rice Core Collection of Embrapa, were used. The following yield component traits were evaluated: number of panicles per meter, number of grains per panicle, and weight of 100 grains. Out of the 115 used markers, 25 (21.7%) were significantly associated with one or more traits. Among the 29 SSR (simple sequence repeats) co‑located in yield QTL (quantitative trait loci) in rice, 12 were associated with the evaluated traits and considered candidates for use in marker‑assisted selection. The markers NP914540, Q6ZGD1, and Q69JE3, associated with the number of grains per panicle, are not yet annotated in rice and should constitute the starting point for functional genomics studies. Among the markers derived from transcribed sequences, NP914526 and NP914533 stand out for belonging to metabolic pathways related to increased yield potential of rice.O objetivo deste trabalho foi identificar, por meio da análise de mapeamento associativo, os marcadores moleculares relacionados à produtividade do arroz de terras altas e aos seus caracteres componentes. Foram usadas 113 linhagens e cultivares de arroz de terras altas, da Coleção Nuclear de Arroz da Embrapa, com reduzido vínculo genético entre si. Os seguintes caracteres componentes da produtividade foram avaliados: número de panículas por metro, número de grãos por panícula e peso de 100 grãos. Dos 115 marcadores utilizados, 25 (21,7%) associaram-se significativamente a um ou mais caracteres. Entre os 29 SSR ("simple sequence repeats") colocalizados em QTL ("quantitative trait loci") de produtividade de arroz, 12 foram associados aos caracteres avaliados e considerados como candidatos para uso na seleção assistida por marcadores. Os marcadores NP914540, Q6ZGD1 e Q69JE3, associados ao número de grãos por panícula, ainda não foram anotados no arroz e podem constituir o ponto de partida para estudos de genômica funcional. Entre os marcadores derivados de sequências transcritas, NP914526 e NP914533 destacam-se por pertencer a rotas metabólicas relacionadas ao aumento do potencial produtivo de arroz

CTGF expression is indicative of better survival rates in patients with medulloblastoma

Medulloblastoma (MB) is the most frequent malignant brain tumor in children and it is subgrouped into 4 entities (SHH, WNT, Group 3, and Group 4). Molecular pathways involved in these different subgroups still are evolving and can be of clinical relevance to therapy. The YAP1-CTGF axis is known to regulate cell proliferation, differentiation, and cell death; however, its role in MB is poorly explored. We aimed to investigate the role of YAP1 gene in the MB SHH cell line DAOY and evaluate cell proliferation, doubling time and 3D spheroids invasion and its consequence on CTGF regulation. We assessed CTGF expression from 22 children with MB. Lastly, we validated our findings through in silico analysis in large cohorts dataset of patients. We observed an increased invasion rate of DAOY cells and CTGF downregulation under YAP1 knockdown (p < 0.0001). Additionally CTGF is overexpressed in MB with extensive nodularity subtype and an indicative of higher survival rates in pediatric MB (p < 0.05). Interestingly, no difference of CTGF expression was observed between molecular subgroups. These results provide new evidence of CTGF as a potential prognostic marker for MB, corroborating to the role of YAP1 in restricting MB cell

YAP1 Is a Potential Predictive Molecular Biomarker for Response to SMO Inhibitor in Medulloblastoma Cells

Advances in genomics have led to the identification of twelve relevant molecular subtypes within medulloblastoma (MB). The alpha subtype of Sonic hedgehog-driven MB is resistant to therapy (including smoothened inhibitors) due to activation of genes from the non-canonical SHH pathway, such as MYCN, YAP1, or TP53. Using retrospective cohort microarray data, we found that YAP1 is overexpressed in SHH alpha MB and patients profiled as resistant to SMO inhibitors compared to good responders. Here, we performed YAP1 depletion via CRISPR/Cas9 in two in vitro models of SHH-like MB cells and found that this protein is involved in responsiveness to the SMO inhibitor regarding proliferation, apoptosis, and colony formation. Further, considering the synergic combination of YAP1 depletion with SMO inhibition, we assessed single-cell RNA-seq data from five patients and found that SMO and YAP1 are enriched within cells of SHH MB. Importantly, our data suggest that YAP1 is not only a reliable biomarker for cellular response to SMOi but may indicate prospective testing of combination therapy using YAP1 and SMO inhibitors in preclinical models of SHH MB

A simplified approach using Taqman low-density array for medulloblastoma subgrouping

Abstract Next-generation sequencing platforms are routinely used for molecular assignment due to their high impact for risk stratification and prognosis in medulloblastomas. Yet, low and middle-income countries still lack an accurate cost-effective platform to perform this allocation. TaqMan Low Density array (TLDA) assay was performed using a set of 20 genes in 92 medulloblastoma samples. The same methodology was assessed in silico using microarray data for 763 medulloblastoma samples from the GSE85217 study, which performed MB classification by a robust integrative method (Transcriptional, Methylation and cytogenetic profile). Furthermore, we validated in 11 MBs samples our proposed method by Methylation Array 450 K to assess methylation profile along with 390 MB samples (GSE109381) and copy number variations. TLDA with only 20 genes accurately assigned MB samples into WNT, SHH, Group 3 and Group 4 using Pearson distance with the average-linkage algorithm and showed concordance with molecular assignment provided by Methylation Array 450 k. Similarly, we tested this simplified set of gene signatures in 763 MB samples and we were able to recapitulate molecular assignment with an accuracy of 99.1% (SHH), 94.29% (WNT), 92.36% (Group 3) and 95.40% (Group 4), against 97.31, 97.14, 88.89 and 97.24% (respectively) with the Ward.D2 algorithm. t-SNE analysis revealed a high level of concordance (k = 4) with minor overlapping features between Group 3 and Group 4. Finally, we condensed the number of genes to 6 without significantly losing accuracy in classifying samples into SHH, WNT and non-SHH/non-WNT subgroups. Additionally, we found a relatively high frequency of WNT subgroup in our cohort, which requires further epidemiological studies. TLDA is a rapid, simple and cost-effective assay for classifying MB in low/middle income countries. A simplified method using six genes and restricting the final stratification into SHH, WNT and non-SHH/non-WNT appears to be a very interesting approach for rapid clinical decision-making