Flooding is a devastating abiotic stress that endangers crop production in the twenty-first century. Because of the severe susceptibility of common bean (Phaseolus vulgaris L.) to flooding, an understanding of the genetic architecture and physiological responses of this crop will set the stage for further improvement. However, challenging phenotyping methods hinder a large-scale genetic study of flooding tolerance in common bean and other economically important crops. A greenhouse phenotyping protocol was developed to evaluate the flooding conditions at early stages. The Middle-American diversity panel (n = 272) of common bean was developed to capture most of the diversity exits in North American germplasm. This panel was evaluated for seven traits under both flooded and non-flooded conditions at two early developmental stages. A subset of contrasting genotypes was further evaluated in the field to assess the relationship between greenhouse and field data under flooding condition. A genome-wide association study using ~150 K SNPs was performed to discover genomic regions associated with multiple physiological responses. The results indicate a significant strong correlation (r > 0.77) between greenhouse and field data, highlighting the reliability of greenhouse phenotyping method. Black and small red beans were the least affected by excess water at germination stage. At the seedling stage, pinto and great northern genotypes were the most tolerant. Root weight reduction due to flooding was greatest in pink and small red cultivars. Flooding reduced the chlorophyll content to the greatest extent in the navy bean cultivars compared with other market classes. Races of Durango/Jalisco and Mesoamerica were separated by both genotypic and phenotypic data indicating the potential effect of eco-geographical variations. Furthermore, several loci were identified that potentially represent the antagonistic pleiotropy. The GWAS analysis revealed peaks at Pv08/1.6 Mb and Pv02/41 Mb that are associated with root weight and germination rate, respectively. These regions are syntenic with two QTL reported in soybean (Glycine max L.) that contribute to flooding tolerance, suggesting a conserved evolutionary pathway involved in flooding tolerance for these related legumes.

The mycobacterial cell envelope has been implicated in the pathogenicity of tuberculosis and therefore has been a prime target for the identification and characterization of surface proteins with potential application in drug and vaccine development. In this study, the genome of Mycobacterium tuberculosis H37Rv was screened using Machine Learning tools that included feature-based predictors, general localizers and transmembrane topology predictors to identify proteins that are potentially secreted to the surface of M. tuberculosis, or to the extracellular milieu through different secretory pathways. The subcellular localization of a set of 8 hypothetically secreted/surface candidate proteins was experimentally assessed by cellular fractionation and immunoelectron microscopy (IEM) to determine the reliability of the computational methodology proposed here, using 4 secreted/surface proteins with experimental confirmation as positive controls and 2 cytoplasmic proteins as negative controls. Subcellular fractionation and IEM studies provided evidence that the candidate proteins Rv0403c, Rv3630, Rv1022, Rv0835, Rv0361 and Rv0178 are secreted either to the mycobacterial surface or to the extracellular milieu. Surface localization was also confirmed for the positive controls, whereas negative controls were located on the cytoplasm. Based on statistical learning methods, we obtained computational subcellular localization predictions that were experimentally assessed and allowed us to construct a computational protocol with experimental support that allowed us to identify a new set of secreted/surface proteins as potential vaccine candidates.

Background In plants, the plasma membrane is enclosed by the cell wall and anchors RLK and RLP proteins, which play a fundamental role in perception of developmental and environmental cues and are crucial in plant development and immunity. These plasma membrane receptors belong to large gene/protein families that are not easily classified computationally. This detailed analysis of these plasma membrane proteins brings a new source of information to the legume genetic, physiology and breeding research communities. Results A computational approach to identify and classify RLK and RLP proteins is presented. The strategy was evaluated using experimentally-validated RLK and RLP proteins and was determined to have a sensitivity of over 0.85, a specificity of 1.00, and a Matthews correlation coefficient of 0.91. The computational approach can be used to develop a detailed catalog of plasma membrane receptors (by type and domains) in several legume/crop species. The exclusive domains identified in legumes for RLKs are WaaY, APH Pkinase_C, LRR_2, and EGF, and for RLP are L-lectin LPRY and PAN_4. The RLK-nonRD and RLCK subclasses are also discovered by the methodology. In both classes, less than 20% of the total RLK predicted for each species belong to this class. Among the 10-species evaluated ~ 40% of the proteins in the kinome are RLKs. The exclusive legume domain combinations identified are B-Lectin/PR5K domains in G. max, M. truncatula, V. angularis, and V. unguiculata and a three-domain combination B-lectin/S-locus/WAK in C. cajan, M. truncatula, P. vulgaris, V. angularis. and V. unguiculata. Conclusions The analysis suggests that about 2% of the proteins of each genome belong to the RLK family and less than 1% belong to RLP family. Domain diversity combinations are greater for RLKs compared with the RLP proteins and LRR domains, and the dual domain combination LRR/Malectin were the most frequent domain for both groups of plasma membrane receptors among legume and non-legume species. Legumes exclusively show Pkinase extracellular domains, and atypical domain combinations in RLK and RLP compared with the non-legumes evaluated. The computational logic approach is statistically well supported and can be used with the proteomes of other plant species.