There is considerable interest in the occurrence and molecular mechanisms of phenotypic plasticity and genotype-by-environment interactions (G×E) in plant populations. The emergence of genomic tools, including quantitative trait locus (QTL) mapping and transcriptome studies, provides opportunities to identify G×E patterns and mechanisms across a diversity of phenotypes, species, and environments. We review progress in evaluating the presence and characterizing the mechanisms of G×E using genomic studies of abiotic responses in plants. Our review reveals that G×E is common, often caused by changes in the magnitude of genetic effects in response to the environment, and associated with diverse genetic factors and molecular variants. We illustrate this diversity with an examination of transcriptome studies and discussion of cloned genes underlying G×E. We discuss the caveats associated with existing studies and outline future directions for better understanding G×E and its impact on local adaptation and plant improvement.

The goal of the National Cancer Institute’s (NCI’s) Genomic Data Commons (GDC) is to provide the cancer research community with a data repository of uniformly processed genomic and associated clinical data that enables data sharing and collaborative analysis in the support of precision medicine. The initial GDC dataset include genomic, epigenomic, proteomic, clinical and other data from the NCI TCGA and TARGET programs. Data production for the GDC started in June, 2015 using an OpenStack-based private cloud. By June of 2016, the GDC had analyzed more than 50,000 raw sequencing data inputs, as well as multiple other data types. Using the latest human genome reference build GRCh38, the GDC generated a variety of data types from aligned reads to somatic mutations, gene expression, miRNA expression, DNA methylation status, and copy number variation. In this paper, we describe the pipelines and workflows used to process and harmonize the data in the GDC. The generated data, as well as the original input files from TCGA and TARGET, are available for download and exploratory analysis at the GDC Data Portal and Legacy Archive (https://gdc.cancer.gov/).

Non-ER nuclear receptor activity can alter estrogen receptor (ER) chromatin association and resultant ER-mediated transcription. Consistent with GR modulation of ER activity, high tumor glucocorticoid receptor (GR) expression correlates with improved relapse-free survival in ER+ breast cancer (BC) patients. In vitro cell proliferation assays were used to assess ER-mediated BC cell proliferation following GR modulation. ER chromatin association following ER/GR co-liganding was measured using global ChIP sequencing and directed ChIP analysis of proliferative gene enhancers. We found that GR liganding with either a pure agonist or a selective GR modulator (SGRM) slowed estradiol (E2)-mediated proliferation in ER+ BC models. SGRMs that antagonized transcription of GR-unique genes both promoted GR chromatin association and inhibited ER chromatin localization at common DNA enhancer sites. Gene expression analysis revealed that ER and GR co-activation decreased proliferative gene activation (compared to ER activation alone), specifically reducing CCND1, CDK2, and CDK6 gene expression. We also found that ligand-dependent GR occupancy of common ER-bound enhancer regions suppressed both wild-type and mutant ER chromatin association and decreased corresponding gene expression. In vivo, treatment with structurally diverse SGRMs also reduced MCF-7 Y537S ER-expressing BC xenograft growth. These studies demonstrate that liganded GR can suppress ER chromatin occupancy at shared ER-regulated enhancers, including CCND1 (Cyclin D1), regardless of whether the ligand is a classic GR agonist or antagonist. Resulting GR-mediated suppression of ER+ BC proliferative gene expression and cell division suggests that SGRMs could decrease ER-driven gene expression.

The COVID-19 pandemic has sparked an urgent need to uncover the underlying biology of this devastating disease. Though RNA viruses mutate more rapidly than DNA viruses, there are a relatively small number of single nucleotide polymorphisms (SNPs) that differentiate the main SARS-CoV-2 lineages that have spread throughout the world. In this study, we investigated 129 RNA-seq data sets and 6928 consensus genomes to contrast the intra-host and inter-host diversity of SARS-CoV-2. Our analyses yielded three major observations. First, the mutational profile of SARS-CoV-2 highlights intra-host single nucleotide variant (iSNV) and SNP similarity, albeit with differences in C > U changes. Second, iSNV and SNP patterns in SARS-CoV-2 are more similar to MERS-CoV than SARS-CoV-1. Third, a significant fraction of insertions and deletions contribute to the genetic diversity of SARS-CoV-2. Altogether, our findings provide insight into SARS-CoV-2 genomic diversity, inform the design of detection tests, and highlight the potential of iSNVs for tracking the transmission of SARS-CoV-2.

In contrast to most solid tumors, cancers of the central nervous system (CNS) pose a unique challenge for effective detection and tracking via plasma because of the blood-brain barrier. Informed diagnosis of primary and metastatic CNS tumors can be facilitated using a liquid biopsy assay that evaluates tumor-derived DNA from the cerebrospinal fluid (CSF), potentially increasing the efficacy of diagnosis and reducing the uncertainty and morbidities associated with the current standard of care that involves neurosurgical procedures. The Belay Summit assay involves tumor-derived DNA-based genomic profiling of CSF to inform diagnosis of CNS tumors. The analytical sensitivity of Summit for single-nucleotide/multinucleotide variants and insertions/deletions is 96% at a 95% limit of detection of 0.30% variant allele fraction. Analytical sensitivity for chromosomal arm-level aneuploidy is 91% at abs(log2r) of 0.09 limit of detection. Clinical sensitivity across a cohort of 124 specimens, including primary and metastatic CNS tumors, was demonstrated to be 90% with a specificity of 95%, supporting the potential for positive clinical utility. These results demonstrate that the Belay Summit assay can accurately and reproducibly be used to inform the diagnosis of primary and metastatic CNS tumors using CSF.

The COVID-19 pandemic has sparked an urgent need to uncover the underlying biology of this devastating disease. Though RNA viruses mutate more rapidly than DNA viruses, there are a relatively small number of single nucleotide polymorphisms (SNPs) that differentiate the main SARS-CoV-2 clades that have spread throughout the world. In this study, we investigated over 7,000 SARS-CoV-2 datasets to unveil both intrahost and interhost diversity. Our intrahost and interhost diversity analyses yielded three major observations. First, the mutational profile of SARS-CoV-2 highlights iSNV and SNP similarity, albeit with high variability in C>T changes. Second, iSNV and SNP patterns in SARS-CoV-2 are more similar to MERS-CoV than SARS-CoV-1. Third, a significant fraction of small indels fuel the genetic diversity of SARS-CoV-2. Altogether, our findings provide insight into SARS-CoV-2 genomic diversity, inform the design of detection tests, and highlight the potential of iSNVs for tracking the transmission of SARS-CoV-2.