Comparative Genomic Analysis and Rapid Molecular Detection of Xanthomonas euvesicatoria Using Unique ATP-Dependent DNA Helicase recQ, hrpB1, and hrpB2 Genes Isolated from Physalis pubescens in China.

Ground cherry (Physalis pubescens) is the most prominent species in the Solanaceae family due to its nutritional content, and prospective health advantages. It is grown all over the world, but notably in northern China. In 2019 firstly bacterial leaf spot (BLS) disease was identified on P. pubescens in China that caused by both BLS pathogens Xanthomonas euvesicatoria pv. euvesicatoria resulted in substantial monetary losses. Here, we compared whole genome sequences of X. euvesicatoria to other Xanthomonas species that caused BLS diseases for high similarities and dissimilarities in genomic sequences through average nucleotide identity (ANI) and BLAST comparison. Molecular techniques and phylogenetic trees were adopted to detect X. euvesicatoria on P. pubescens using recQ, hrpB1, and hrpB2 genes for efficient and precise identification. For rapid molecular detection of X. euvesicatoria, loop-mediated isothermal amplification, polymerase chain reaction (PCR), and real-time PCR techniques were used. Whole genome comparison results showed that the genome of X. euvesicatoria was more closely relative to X. perforans than X. vesicatoria, and X. gardneri with 98%, 84%, and 86% ANI, respectively. All infected leaves of P. pubescens found positive amplification, and negative controls did not show amplification. The findings of evolutionary history revealed that isolated strains XeC10RQ, XeH9RQ, XeA10RQ, and XeB10RQ that originated from China were closely relative and highly homologous to the X. euvesicatoria. This research provides information to researchers on genomic variation in BLS pathogens, and further molecular evolution and identification of X. euvesicatoria using the unique target recQ gene through advance molecular approaches.

A New Sequential Forward Feature Selection (SFFS) Algorithm for Mining Best Topological and Biological Features to Predict Protein Complexes from Protein-Protein Interaction Networks (PPINs).

Protein-protein interaction plays an important role in the understanding of biological processes in the body. A network of dynamic protein complexes within a cell that regulates most biological processes is known as a protein-protein interaction network (PPIN). Complex prediction from PPINs is a challenging task. Most of the previous computation approaches mine cliques, stars, linear and hybrid structures as complexes from PPINs by considering topological features and fewer of them focus on important biological information contained within protein amino acid sequence. In this study, we have computed a wide variety of topological features and integrate them with biological features computed from protein amino acid sequence such as bag of words, physicochemical and spectral domain features. We propose a new Sequential Forward Feature Selection (SFFS) algorithm, i.e., random forest-based Boruta feature selection for selecting the best features from computed large feature set. Decision tree, linear discriminant analysis and gradient boosting classifiers are used as learners. We have conducted experiments by considering two reference protein complex datasets of yeast, i.e., CYC2008 and MIPS. Human and mouse complex information is taken from CORUM 3.0 dataset. Protein interaction information is extracted from the database of interacting proteins (DIP). Our proposed SFFS, i.e., random forest-based Brouta feature selection in combination with decision trees, linear discriminant analysis and Gradient Boosting Classifiers outperforms other state of art algorithms by achieving precision, recall and F-measure rates, i.e. 94.58%, 94.92% and 94.45% for MIPS, 96.31%, 93.55% and 96.02% for CYC2008, 98.84%, 98.00%, 98.87 % for CORUM humans and 96.60%, 96.70%, 96.32% for CORUM mouse dataset complexes, respectively.

Quantification of Climate Warming and Crop Management Impacts on Cotton Phenology.

Understanding the impact of the warming trend on phenological stages and phases of cotton (Gossypium hirsutum L.) in central and lower Punjab, Pakistan, may assist in optimizing crop management practices to enhance production. This study determined the influence of the thermal trend on cotton phenology from 1980-2015 in 15 selected locations. The results demonstrated that observed phenological stages including sowing (S), emergence (E), anthesis (A) and physiological maturity (M) occurred earlier by, on average, 5.35, 5.08, 2.87 and 1.12 days decade-1, respectively. Phenological phases, sowing anthesis (S-A), anthesis to maturity (A-M) and sowing to maturity (S-M) were reduced by, on average, 2.45, 1.76 and 4.23 days decade-1, respectively. Observed sowing, emergence, anthesis and maturity were negatively correlated with air temperature by, on average, -2.03, -1.93, -1.09 and -0.42 days °C-1, respectively. Observed sowing-anthesis, anthesis to maturity and sowing-maturity were also negatively correlated with temperature by, on average, -0.94, -0.67 and -1.61 days °C-1, respectively. Applying the cropping system model CSM-CROPGRO-Cotton model using a standard variety in all locations indicated that the model-predicted phenology accelerated more due to warming trends than field-observed phenology. However, 30.21% of the harmful influence of the thermal trend was compensated as a result of introducing new cotton cultivars with higher growing degree day (thermal time) requirements. Therefore, new cotton cultivars which have higher thermal times and are high temperature tolerant should be evolved.