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Summary: In maize, kernel traits strongly impact overall grain yields, and it is known that sophisticated spatiotemporal programs of gene expression coordinate kernel development, so advancing our knowledge of kernel development can help efforts to improve grain yields. Here, using phenotype, genotype and transcriptomics data of maize kernels at 5 and 15 days after pollination (DAP) for a large association mapping panel, we employed multiple quantitative genetics approaches—genome‐wide association studies (GWAS) as well as expression quantitative trait loci (eQTL) and quantitative trait transcript (QTT) analyses—to gain insights about molecular genetic basis of kernel development in maize. This resulted in the identification of 137 putative kernel length‐related genes at 5 DAP, of which 43 are located in previously reported QTL regions. Strikingly, we identified an eQTL that overlaps the locus encoding a maize homolog of the recently described m6A methylation reader protein ECT2 from Arabidopsis; this putative epieQTL is associated with 53 genes and may represent a master epi‐transcriptomic regulator of kernel development. Notably, among the genes associated with this epieQTL, 10 are for the main storage proteins in the maize endosperm (zeins) and two are known regulators of zein expression or endosperm development (Opaque2 and ZmICE1). Collectively, beyond cataloging and characterizing genomic attributes of a large number of eQTL associated with kernel development in maize, our study highlights how an eQTL approach can bolster the impact of both GWAS and QTT studies and can drive insights about the basic biology of plants. Significance Statement: Quantitative genetics analysis of genomic, transcriptomic and phenotypic data of a large association panel of maize inbred lines allowed us to identify and characterize trends among eQTL regions associated with kernel traits and the complex spatiotemporal programs of gene expression that impact maize kernel development. Fascinatingly, we found an eQTL region overlapping the locus encoding the maize ortholog of the very recently described m6A reader protein ECT2 from Arabidopsis. [ABSTRACT FROM AUTHOR]

BackgroundPrecision oncology has the potential to leverage clinical and genomic data in advancing disease prevention, diagnosis, and treatment. A key research area focuses on the early detection of primary cancers and potential prediction of cancers of unknown primary in order to facilitate optimal treatment decisions. ObjectiveThis study presents a methodology to harmonize phenotypic and genetic data features to classify primary cancer types and predict cancers of unknown primaries. MethodsWe extracted genetic data elements from oncology genetic reports of 1011 patients with cancer and their corresponding phenotypical data from Mayo Clinic’s electronic health records. We modeled both genetic and electronic health record data with HL7 Fast Healthcare Interoperability Resources. The semantic web Resource Description Framework was employed to generate the network-based data representation (ie, patient-phenotypic-genetic network). Based on the Resource Description Framework data graph, Node2vec graph-embedding algorithm was applied to generate features. Multiple machine learning and deep learning backbone models were compared for cancer prediction performance. ResultsWith 6 machine learning tasks designed in the experiment, we demonstrated the proposed method achieved favorable results in classifying primary cancer types (area under the receiver operating characteristic curve [AUROC] 96.56% for all 9 cancer predictions on average based on the cross-validation) and predicting unknown primaries (AUROC 80.77% for all 8 cancer predictions on average for real-patient validation). To demonstrate the interpretability, 17 phenotypic and genetic features that contributed the most to the prediction of each cancer were identified and validated based on a literature review. ConclusionsAccurate prediction of cancer types can be achieved with existing electronic health record data with satisfactory precision. The integration of genetic reports improves prediction, illustrating the translational values of incorporating genetic tests early at the diagnosis stage for patients with cancer.

Plant protoplast transient transformation is a versatile tool for transient gene expression analysis. To study the effects of plant growth conditions on maize protoplast transformation, maize inbred line Q319 was used as material to discuss the effect of maize growth condition (light intensity, photoperiod, growth temperature and seedlings age) on maize protoplast transformation by orthogonal experiment L9 (34) design. The results showed that the 4 growth conditions all affected maize protoplast transformation, among which light intensity played the most important role on protoplast viability and transformation efficiency. Further study showed that there was a notable negative correlation between light intensity and protoplast viability and transformation efficiency. The maize etiolated seedlings were the best material for maize protoplast transformation. The protoplast viability and transformation efficiency of etiolated seedlings could reach 100% and 85%, respectively. In addition, the highest transformation efficiency was obtained using the protoplasts from etiolated seedlings 13 d after sowing. [ABSTRACT FROM AUTHOR]

BackgroundImmune checkpoint inhibitors are associated with unique immune-related adverse events (irAEs). As most of the immune checkpoint inhibitors are new to the market, it is important to conduct studies using real-world data sources to investigate their safety profiles. ObjectiveThe aim of the study was to develop a framework for signal detection and filtration of novel irAEs for 6 Food and Drug Administration–approved immune checkpoint inhibitors. MethodsIn our framework, we first used the Food and Drug Administration’s Adverse Event Reporting System (FAERS) standardized in an Observational Health Data Sciences and Informatics (OHDSI) common data model (CDM) to collect immune checkpoint inhibitor-related event data and conducted irAE signal detection. OHDSI CDM is a standard-driven data model that focuses on transforming different databases into a common format and standardizing medical terms to a common representation. We then filtered those already known irAEs from drug labels and literature by using a customized text-mining pipeline based on clinical text analysis and knowledge extraction system with Medical Dictionary for Regulatory Activities (MedDRA) as a dictionary. Finally, we classified the irAE detection results into three different categories to discover potentially new irAE signals. ResultsBy our text-mining pipeline, 490 irAE terms were identified from drug labels, and 918 terms were identified from the literature. In addition, of the 94 positive signals detected using CDM-based FAERS, 53 signals (56%) were labeled signals, 10 (11%) were unlabeled published signals, and 31 (33%) were potentially new signals. ConclusionsWe demonstrated that our approach is effective for irAE signal detection and filtration. Moreover, our CDM-based framework could facilitate adverse drug events detection and filtration toward the goal of next-generation pharmacovigilance that seamlessly integrates electronic health record data for improved signal detection.

Bacterial effectors are double-edged swords that enhance bacterial virulence in susceptible plants while trigger resistance in plants carrying cognate resistance proteins. A well-known example of this is Pseudomonas syringae protein AvrPto that is delivered into plant cells through the type III secretion system. AvrPto inhibits immune responses in Arabidopsis plants but triggers resistance in some tomato plants carrying cognate resistance proteins Pto, a serine/threonine kinase, and Prf, a nucleotide-binding leucine-rich repeat protein. In a recent structural study we showed that AvrPto is an inhibitor of the Pto protein kinase. Because Pto closely resemble the kinase domain of receptor kinases, which include pattern recognition receptors (PRRs) crucial for plants to detect invading pathogens, we tested the possibility that PRRs such as FLS2 and EFR are targeted by AvrPto in susceptible plants. Indeed, AvrPto is capable of binding the FLS2 and EFR kinases to block plant immune responses when expressed in protoplasts. In Arabidopsis plants containing FLS2, the P. syringae strain lacking avrPto is compromised in its ability to multiply. However, the defect of the avrPto-deletion strain was alleviated in fls2 plants, indicating a role of AvrPto in overcoming FLS2-mediated resistance. Interestingly, the FLS2-AvrPto and Pto-AvrPto interactions share significant similarity, raising the tantalizing possibility that Pto has evolved as a molecular decoy of the intended targets of AvrPto.