Proteome sequence features carry signatures of the environmental niche of prokaryotes

<p>Abstract</p> <p>Background</p> <p>Prokaryotic environmental adaptations occur at different levels within cells to ensure the preservation of genome integrity, proper protein folding and function as well as membrane fluidity. Although specific composition and structure of cellular components suitable for the variety of extreme conditions has already been postulated, a systematic study describing such adaptations has not yet been performed. We therefore explored whether the environmental niche of a prokaryote could be deduced from the sequence of its proteome. Finally, we aimed at finding the precise differences between proteome sequences of prokaryotes from different environments.</p> <p>Results</p> <p>We analyzed the proteomes of 192 prokaryotes from different habitats. We collected detailed information about the optimal growth conditions of each microorganism. Furthermore, we selected 42 physico-chemical properties of amino acids and computed their values for each proteome. Further, on the same set of features we applied two fundamentally different machine learning methods, Support Vector Machines and Random Forests, to successfully classify between bacteria and archaea, halophiles and non-halophiles, as well as mesophiles, thermophiles and mesothermophiles. Finally, we performed feature selection by using Random Forests.</p> <p>Conclusions</p> <p>To our knowledge, this is the first time that three different classification cases (domain of life, halophilicity and thermophilicity) of proteome adaptation are successfully performed with the same set of 42 features. The characteristic features of a specific adaptation constitute a signature that may help understanding the mechanisms of adaptation to extreme environments.</p

Multi-Trait and Multi-Environment QTL Analyses for Resistance to Wheat Diseases

BACKGROUND: Stripe rust, leaf rust, tan spot, and Karnal bunt are economically significant diseases impacting wheat production. The objectives of this study were to identify quantitative trait loci for resistance to these diseases in a recombinant inbred line (RIL) from a cross HD29/WH542, and to evaluate the evidence for the presence loci on chromosome region conferring multiple disease resistance. METHODOLOGY/PRINCIPAL FINDINGS: The RIL population was evaluated for four diseases and genotyped with DNA markers. Multi-trait (MT) analysis revealed thirteen QTLs on nine chromosomes, significantly associated with resistance. Phenotypic variation explained by all significant QTLs for KB, TS, Yr, Lr diseases were 57%, 55%, 38% and 22%, respectively. Marginal trait analysis identified the most significant QTLs for resistance to KB on chromosomes 1BS, 2DS, 3BS, 4BL, 5BL, and 5DL. Chromosomes 3AS and 4BL showed significant association with TS resistance. Significant QTLs for Yr resistance were identified on chromosomes 2AS, 4BL and 5BL, while Lr was significant on 6DS. MT analysis revealed that all the QTLs except 3BL significantly reduce KB and was contributed from parent HD29 while all resistant QTLs for TS except on chromosomes 2DS.1, 2DS.2 and 3BL came from WH542. Five resistant QTLs for Yr and six for Lr were contributed from parents WH542 and HD29 respectively. Chromosome region on 4BL showed significant association to KB, TS, and Yr in the population. The multi environment analysis for KB identified three putative QTLs of which two new QTLs, mapped on chromosomes 3BS and 5DL explained 10 and 20% of the phenotypic variation, respectively. CONCLUSIONS/SIGNIFICANCE: This study revealed that MT analysis is an effective tool for detection of multi-trait QTLs for disease resistance. This approach is a more effective and practical than individual QTL mapping analyses. MT analysis identified RILs that combine resistance to multiple diseases from parents WH542 and/or HD29

The Plant Pathogen Phytophthora andina Emerged via Hybridization of an Unknown Phytophthora Species and the Irish Potato Famine Pathogen, P. infestans

Emerging plant pathogens have largely been a consequence of the movement of pathogens to new geographic regions. Another documented mechanism for the emergence of plant pathogens is hybridization between individuals of different species or subspecies, which may allow rapid evolution and adaptation to new hosts or environments. Hybrid plant pathogens have traditionally been difficult to detect or confirm, but the increasing ease of cloning and sequencing PCR products now makes the identification of species that consistently have genes or alleles with phylogenetically divergent origins relatively straightforward. We investigated the genetic origin of Phytophthora andina, an increasingly common pathogen of Andean crops Solanum betaceum, S. muricatum, S. quitoense, and several wild Solanum spp. It has been hypothesized that P. andina is a hybrid between the potato late blight pathogen P. infestans and another Phytophthora species. We tested this hypothesis by cloning four nuclear loci to obtain haplotypes and using these loci to infer the phylogenetic relationships of P. andina to P. infestans and other related species. Sequencing of cloned PCR products in every case revealed two distinct haplotypes for each locus in P. andina, such that each isolate had one allele derived from a P. infestans parent and a second divergent allele derived from an unknown species that is closely related but distinct from P. infestans, P. mirabilis, and P. ipomoeae. To the best of our knowledge, the unknown parent has not yet been collected. We also observed sequence polymorphism among P. andina isolates at three of the four loci, many of which segregate between previously described P. andina clonal lineages. These results provide strong support that P. andina emerged via hybridization between P. infestans and another unknown Phytophthora species also belonging to Phytophthora clade 1c