Search

Background: Drought stress restricts the growth, distribution and productivity of alfalfa (Medicago sativa L.). In order to study the response differences of alfalfa cultivars to drought stress, we previously carried out physiological and molecular comparative analysis on two alfalfa varieties with contrasting drought resistance (relatively drought-tolerant Longdong and drought-sensitive Algonquin). However, the differences in proteomic factors of the two varieties in response to drought stress still need to be further studied. Therefore, TMT-based quantitative proteomic analysis was performed using leaf tissues of the two alfalfa cultivars to identify and uncover differentially abundant proteins (DAPs). Results: In total, 677 DAPs were identified in Algonquin and 277 in Longdong under drought stress. Subsequently, we conducted various bioinformatics analysis on these DAPs, including subcellular location, functional classification and biological pathway enrichment. The first two main COG functional categories of DAPs in both alfalfa varieties after drought stress were ‘Translation, ribosomal structure and biogenesis’ and ‘Posttranslational modification, protein turnover, chaperones’. According to KEGG database, the DAPs of the two alfalfa cultivars after drought treatment were differentially enriched in different biological pathways. The DAPs from Algonquin were enriched in ‘photosynthesis’ and ‘ribosome’. The pathways of ‘linoleic acid metabolism’, ‘protein processing in endoplasmic reticulum’ and ‘RNA transport’ in Longdong were significantly enriched. Finally, we found significant differences in DAP enrichment and expression patterns between Longdong and Algonquin in glycolysis/glycogenesis, TCA cycle, photosynthesis, protein biosynthesis, flavonoid and isoflavonoid biosynthesis, and plant-pathogen interaction pathway after drought treatment. Conclusions: The differences of DAPs involved in various metabolic pathways may explain the differences in the resistance of the two varieties to drought stress. These DAPs can be used as candidate proteins for molecular breeding of alfalfa to cultivate new germplasm with more drought tolerance to adapt to unfavorable environments. [ABSTRACT FROM AUTHOR]

Background: In recent years, there has been a rapid increase in the number of microbial genomes reconstructed through shotgun sequencing, and obtained by newly developed approaches including metagenomic binning and single-cell sequencing. However, our ability to functionally characterize these genomes by experimental assays is orders of magnitude less efficient. Consequently, there is a pressing need for the development of swift and automated strategies for the functional classification of microbial genomes. Results: The present work leverages a suite of supervised machine learning algorithms to establish a range of 86 metabolic and other ecological functions, such as methanotrophy and plastic degradation, starting from widely obtainable microbial genome annotations. Tests performed on independent datasets demonstrated robust performance across complete, fragmented, and incomplete genomes above a 70% completeness level for most of the considered functions. Application of the algorithms to the Biogas Microbiome database yielded predictions broadly consistent with current biological knowledge and correctly detecting functionally-related nuances of archaeal genomes. Finally, a case study focused on acetoclastic methanogenesis demonstrated how the developed machine learning models can be refined or expanded with models describing novel functions of interest. Conclusions: The resulting tool, MICROPHERRET, incorporates a total of 86 models, one for each tested functional class, and can be applied to high-quality microbial genomes as well as to low-quality genomes derived from metagenomics and single-cell sequencing. MICROPHERRET can thus aid in understanding the functional role of newly generated genomes within their micro-ecological context. [ABSTRACT FROM AUTHOR]

Background: The almond tree (Prunus amygdalus Batsch) is an important nut tree grown in subtropical regions that produces nutrient-rich nuts. However, a paucity of genomic information and DNA markers has restricted the development of modern breeding technologies for almond trees. Results: In this study, almonds were sequenced with Illumina paired-end sequencing technology to obtain transcriptome data and develop simple sequence repeats (SSR) markers. We generated approximately 64 million clean reads from the various tissues of mixed almonds, and a total of 42,135 unigenes with an average length of 988 bp were obtained in the present study. A total of 27,586 unigenes (57.7% of all unigenes generated) were annotated using several databases. A total of 112,812 unigenes were annotated with the Gene Ontology (GO) database and assigned to 82 functional sub-groups, and 29,075 unigenes were assigned to the KOG database and classified into 25 function classifications. There were 9470 unigenes assigned to 129 Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathways from five categories in the KEGG pathway database. We further identified 8641 SSR markers from 48,012 unigenes. A total of 100 SSR markers were randomly selected to validate quality, and 82 markers could amplify the specific products of A. communis L., whereas 70 markers were successfully transferable to five species (A. ledebouriana, A. mongolica, A. pedunculata, A. tangutica, and A. triloba). Conclusions: Our study was the first to produce public transcriptome data from almonds. The development of SSR markers will promote genetics research and breeding programmes for almonds. [ABSTRACT FROM AUTHOR]

Background Membrane transporters catalyze the transport of small solute molecules across biological barriers such as lipid bilayer membranes. Experimental identification of the transported substrates is very tedious. Once a particular transport mechanism has been identified in one organism, it is thus highly desirable to transfer this information to related transporter sequences in different organisms based on bioinformatics evidence. Results We present a thorough benchmark at which level of sequence identity membrane transporters from Escherichia coli, Saccharomyces cerevisiae, and Arabidopsis thaliana belong to the same families of the Transporter Classification (TC) system, and at what level these membrane transporters mediate the transport of the same substrate. We found that two membrane transporter sequences from different organisms that are aligned with normalized BLAST expectation value better than E-value 1e-8 are highly likely to belong to the same TC family (F-measure around 90%). Enriched sequence motifs identified by MEME at thresholds below 1e-12 support accurate classification into TC families for about two thirds of the sequences (F-measure 80% and higher). For the comparison of transported substrates, we focused on the four largest substrate classes of amino acids, sugars, metal ions, and phosphate. At similar identity thresholds, the nature of the transported substrates was more divergent (F-measure 40 - 75% at the same thresholds) than the TC family membership. Conclusions We suggest an acceptable threshold of 1e-8 for BLAST and HMMER where at least three quarters of the sequences are classified according to the TC system with a reasonably high accuracy. Researchers who wish to apply these thresholds in their studies should multiply these thresholds by the size of the database they search against. Our findings should be useful to those who wish to transfer transporter functional annotations across species. [ABSTRACT FROM AUTHOR]