In plant development, the flowering transition and inflorescence architecture are modulated by two homologous proteins, FLOWERING LOCUS T (FT) and TERMINAL FLOWER 1 (TFL1). The florigen FT promotes the transition to reproductive development and flowering, while TFL1 represses this transition. Despite their importance to plant adaptation and crop improvement and their extensive study by the plant community, the molecular mechanisms controlling the opposing actions of FT and TFL1 have remained mysterious. Recent studies in multiple species have unveiled diverse roles of the FT/TFL1 gene family in developmental processes other than flowering regulation. In addition, the striking evolution of FT homologs into flowering repressors has occurred independently in several species during the evolution of flowering plants. These reports indicate that the FT/TFL1 gene family is a major target of evolution in nature. Here, we comprehensively survey the conserved and diverse functions of the FT/TFL1 gene family throughout the plant kingdom, summarize new findings regarding the unique evolution of FT in multiple species, and highlight recent work elucidating the molecular mechanisms of these proteins.

BackgroundGenotyping-by-sequencing (GBS), a method to identify genetic variants and quickly genotype samples, reduces genome complexity by using restriction enzymes to divide the genome into fragments whose ends are sequenced on short-read sequencing platforms. While cost-effective, this method produces extensive missing data and requires complex bioinformatics analysis. GBS is most commonly used on crop plant genomes, and because crop plants have highly variable ploidy and repeat content, the performance of GBS analysis software can vary by target organism. Here we focus our analysis on soybean, a polyploid crop with a highly duplicated genome, relatively little public GBS data and few dedicated tools.ResultsWe compared the performance of five GBS pipelines using low-coverage Illumina sequence data from three soybean populations. To address issues identified with existing methods, we developed GB-eaSy, a GBS bioinformatics workflow that incorporates widely used genomics tools, parallelization and automation to increase the accuracy and accessibility of GBS data analysis. Compared to other GBS pipelines, GB-eaSy rapidly and accurately identified the greatest number of SNPs, with SNP calls closely concordant with whole-genome sequencing of selected lines. Across all five GBS analysis platforms, SNP calls showed unexpectedly low convergence but generally high accuracy, indicating that the workflows arrived at largely complementary sets of valid SNP calls on the low-coverage data analyzed.ConclusionsWe show that GB-eaSy is approximately as good as, or better than, other leading software solutions in the accuracy, yield and missing data fraction of variant calling, as tested on low-coverage genomic data from soybean. It also performs well relative to other solutions in terms of the run time and disk space required. In addition, GB-eaSy is built from existing open-source, modular software packages that are regularly updated and commonly used, making it straightforward to install and maintain. While GB-eaSy outperformed other individual methods on the datasets analyzed, our findings suggest that a comprehensive approach integrating the results from multiple GBS bioinformatics pipelines may be the optimal strategy to obtain the largest, most highly accurate SNP yield possible from low-coverage polyploid sequence data.

BackgroundSclerotinia Stem Rot (SSR), caused by the fungal pathogen Sclerotinia sclerotiorum, is ubiquitous in cooler climates where soybean crops are grown. Breeding for resistance to SSR remains challenging in crops like soybean, where no single gene provides strong resistance, but instead, multiple genes work together to provide partial resistance. In this study, a genome-wide association study (GWAS) was performed to dissect the complex genetic architecture of soybean quantitative resistance to SSR and to provide effective molecular markers that could be used in breeding programs. A collection of 420 soybean genotypes were selected based on either reports of resistance, or from one of three different breeding programs in Brazil, two commercial, one public. Plant genotype sensitivity to SSR was evaluated by the cut stem inoculation method, and lesion lengths were measured at 4 days post inoculation.ResultsGenotyping-by-sequencing was conducted to genotype the 420 soybean lines. The TASSEL 5 GBSv2 pipeline was used to call SNPs under optimized parameters, and with the extra step of trimming adapter sequences. After filtering missing data, heterozygosity, and minor allele frequency, a total of 11,811 SNPs and 275 soybean genotypes were obtained for association analyses. Using a threshold of FDR-adjusted p-values <0.1, the Compressed Mixed Linear Model (CMLM) with Genome Association and Prediction Integrated Tool (GAPIT), and the Fixed and Random Model Circulating Probability Unification (FarmCPU) methods, both approaches identified SNPs with significant association to disease response on chromosomes 1, 11, and 18. The CMLM also found significance on chromosome 19, whereas FarmCPU also identified significance on chromosomes 4, 9, and 16.ConclusionsThese similar and yet different results show that the computational methods used can impact SNP associations in soybean, a plant with a high degree of linkage disequilibrium, and in SSR resistance, a trait that has a complex genetic basis. A total of 125 genes were located within linkage disequilibrium of the three loci shared between the two models. Their annotations and gene expressions in previous studies of soybean infected with S. sclerotiorum were examined to narrow down the candidates.Electronic supplementary materialThe online version of this article (10.1186/s12864-017-4160-1) contains supplementary material, which is available to authorized users.

Tumors acquire numerous mutations during development and progression. When translated into proteins, these mutations give rise to neoantigens that can be recognized by T cells and generate antibodies, representing an exciting direction of cancer immunotherapy. While neoantigens have been reported in many cancer types, the profiling of neoantigens often focused on the class-I subtype that are presented to CD8 + T cells, and the relationship between neoantigen load and clinical outcomes was often inconsistent among cancer types. In this study, we described an informatics workflow, REAL-neo, for identification, quality control (QC), and prioritization of both class-I and class-II human leukocyte antigen (HLA) bound neoantigens that arise from somatic single nucleotide mutations (SNM), small insertions and deletions (INDEL), and gene fusions. We applied REAL-neo to 835 primary breast tumors in the Cancer Genome Atlas (TCGA) and performed comprehensive profiling and characterization of the detected neoantigens. We found recurrent HLA class-I and class-II restricted neoantigens across breast cancer cases, and uncovered associations between neoantigen load and clinical traits. Both class-I and class-II neoantigen loads from SNM and INDEL were found to predict overall survival independent of tumor mutational burden (TMB), breast cancer subtypes, tumor-infiltrating lymphocyte (TIL) levels, tumor stage, and age at diagnosis. Our study highlighted the importance of accurate and comprehensive neoantigen profiling and QC, and is the first to report the predictive value of neoantigen load for overall survival in breast cancer. ARTICLE HISTORY

The etiology of triple-negative breast cancers (TNBC) is poorly understood. As many TNBCs develop prior to the initiation of breast cancer screening or at younger ages when the sensitivity of mammography is comparatively low, understanding the etiology of TNBCs is critical for discovering novel prevention approaches for these tumors. Furthermore, the higher incidence rate of estrogen receptornegative breast cancers, and specifically, of TNBCs, among young African American women (AAW) versus white women is a source of racial disparities in breast cancer mortality. Whereas immune responses to TNBCs have received considerable attention in relation to prognosis and treatment, the concept that dysregulated immune responses may predispose to the development of TNBCs has received limited attention. We present evidence that dysregulated immune responses are critical in the pathogenesis of TNBCs, based on the molecular biology of the cancers and the mechanisms proposed to mediate TNBC risk factors. Furthermore, proposed risk factors for TNBC, especially childbearing without breastfeeding, high parity, and obesity, are more prevalent among AAW than white women. Limited data suggest genetic differences in immune responses by race, which favor a stronger Thr type 2 (Th2) immune response among AAW than white women. Th2 responses contribute to wound-healing processes, which are implicated in the pathogenesis of TNBCs. Accordingly, we review data on the link between immune responses and TNBC risk and consider whether the prevalence of risk factors that result in dysregulated immunity is higher among AAW than white women. Incidence of TNBCTNBCs are clinically aggressive breast cancers that do not express estrogen receptor (ER) or progesterone receptor, and do not overexpress ERBB2 (also known as HER2) (1, 2), and thus do not respond to hormonal or HER2-targeted therapeutics. The incidence rate of TNBC among African American women (AAW) is approximately double than among white women, with much of the disparity occurring at early ages (3,4). Data indicate that the relative percentage of TNBCs is highest in Sub-Saharan West African countries of Ghana and Nigeria than among AAW and lower in East African countries; however, further epidemiologic data are needed to confirm these results (5, 6). It has been proposed that genetic admixture between white Americans and Ghanaian/West Africans as a result of West Africa-based trans-Atlantic slave trade accounts for the intermediate prevalence of TNBC in AAW (5, 7). Improved registry infrastructure in Africa would enable estimation of age-adjusted rates, as would be needed to definitively compare rates with AAW and white women. If confirmed, the higher incidence of TNBCs among African and AAW may suggest a genetic predisposition to TNBC. TNBC and Immune Responses in TissuesTNBCs are diverse with regard to clinicopathologic features and prognosis (8), and immune factors figure prominently in the subclassification of TNBCs (particularly definition of the numerically predominant subtype of TNBC...