A minimal Fanconi Anemia complex in early diverging fungi.

Fanconi Anemia (FA) pathway resolves DNA interstrand cross links (ICL). The FA pathway was initially recognized in vertebrates, but was later confirmed in other animals and speculated in fungi. FA proteins FANCM, FANCL and FANCJ are present in Saccharomyces cerevisiae but, their mechanism of interaction to resolve ICL is still unclear. Unlike Dikarya, early diverging fungi (EDF) possess more traits shared with animals. We traced the evolutionary history of the FA pathway across Opisthokonta. We scanned complete proteomes for FA-related homologs to establish their taxonomic distribution and analyzed their phylogenetic trees. We checked transcription profiles of FA genes to test if they respond to environmental conditions and their genomic localizations for potential co-localization. We identified fungal homologs of the activation and ID complexes, 5 out of 8 core proteins, all of the endonucleases, and deubiquitination proteins. All fungi lack FANCC, FANCF and FANCG proteins responsible for post-replication repair and chromosome stability in animals. The observed taxonomic distribution can be attributed to a gradual degradation of the FA pathway from EDF to Dikarya. One of the key differences is that EDF have the ID complex recruiting endonucleases to the site of ICL. Moreover, 21 out of 32 identified FA genes are upregulated in response to different growth conditions. Several FA genes are co-localized in fungal genomes which also could facilitate co-expression. Our results indicate that a minimal FA pathway might still be functional in Mucoromycota with a gradual loss of components in Dikarya ancestors.

Fucose as a nutrient ligand for Dikarya and a building block of early diverging lineages.

Fucose is a deoxyhexose sugar present and studied in mammals. The process of fucosylation has been the primary focus in studies relating to fucose in animals due to the presence of fucose in Lewis antigens. Very few studies have reported its presence in Fungi, mostly in Mucoromycotina. The constitution of 25% and 12% of this sugar in the carbohydrates of cell wall in the respective Umbelopsis and Mucorales strains boosts the need to bridge the gap of knowledge on fucose metabolism across the fungal tree of life. In the absence of a network map involving fucose proteins, we carried out an in-silico approach to construct the fucose metabolic map in Fungi. We analyzed the taxonomic distribution of 85 protein families in Fungi including diverse early diverging fungal lineages. The expression of fucose-related protein-coding genes proteins was validated with the help of transcriptomic data originating from representatives of early diverging fungi. We found proteins involved in several metabolic activities apart from fucosylation such as synthesis, transport and binding. Most of the identified protein families are shared with Metazoa suggesting an ancestral origin in Opisthokonta. However, the overall complexity of fucose metabolism is greater in Metazoa than in Fungi. Massive gene loss has shaped the evolutionary history of these metabolic pathways, leading to a repeated reduction of these pathways in most yeast-forming lineages. Our results point to a distinctive mode of utilization of fucose among fungi belonging to Dikarya and the early diverging lineages. We speculate that, while Dikarya used fucose as a source of nutrients for metabolism, the early diverging group of fungi depended on fucose as a building block and signaling compound.

Identifying Signal-Crosstalk Mechanism in Maize Plants during Combined Salinity and Boron Stress Using Integrative Systems Biology Approaches.

Combined stress has been seen as a major threat to world agriculture production. Maize is one of the leading cereal crops of the world due to its wide spectrum of growth conditions and is moderately sensitive to salt stress. A saline soil environment is a major factor that hinders its growth and overall yield and causes an increase in the concentration of micronutrients like boron, leading to excess over the requirement of the plant. Boron toxicity combined with salinity has been reported to be a serious threat to the yield and quality of maize. The response signatures of the maize plants to the combined effect of salinity and boron stress have not been studied well. We carried out an integrative systems-level analysis of the publicly available transcriptomic data generated on tolerant maize (Lluteño maize from the Atacama Desert, Chile) landrace under combined salt and boron stress. We identified significant biological processes that are differentially regulated in combined salt and boron stress in the leaves and roots of maize, respectively. Protein-protein interaction network analysis identified important roles of aldehyde dehydrogenase (ALDH), galactinol synthase 2 (GOLS2) proteins of leaf and proteolipid membrane potential regulator (pmpm4), metallothionein lea protein group 3 (mlg3), and cold regulated 410 (COR410) proteins of root in salt tolerance and regulating boron toxicity in maize. Identification of transcription factors coupled with regulatory network analysis using machine learning approach identified a few heat shock factors (HSFs) and NAC (NAM (no apical meristem, Petunia), ATAF1-2 (Arabidopsis thaliana activating factor), and CUC2 (cup-shaped cotyledon, Arabidopsis)) family transcription factors (TFs) to play crucial roles in salt tolerance, maintaining reactive oxygen species (ROS) levels and minimizing oxidative damage to the cells. These findings will provide new ways to design targeted functional validation experiments for developing multistress-resistant maize crops.

RiPPMiner-Genome: A Web Resource for Automated Prediction of Crosslinked Chemical Structures of RiPPs by Genome Mining.

RiPPMiner-Genome is a unique bioinformatics resource for identifying Biosynthetic Gene Clusters (BGC) for RiPPs (Ribosomally Synthesized and Post-translationally Modified Peptides) and automated prediction of crosslinked chemical structures of RiPPs starting from genomic sequences. It is a major update of the RiPPMiner webserver, which used only peptide sequence of RiPP precursors as input for predicting RiPP class and crosslinked chemical structures. Other major improvements are, machine learning (ML) based identification of correct RiPP precursor peptide from among multiple small ORFs (Open Reading Frames) in a BGC, prediction of the cleavage site and cross-links in thiopeptides and identification of non-crosslinked modified residues in lanthipeptides. It has been benchmarked on a dataset of 204 experimentally characterized RiPP BGCs. RiPPMiner-Genome also facilitates visualization of the RiPP BGCs and depiction of the chemical structure of crosslinked RiPP. It also has an interface for searching characterized RiPPs, similar to the predicted core peptide sequence or crosslinked chemical structure.