AI-based AlphaFold2 significantly expands the structural space of the autophagy pathway

ABBREVIATIONS

AF2: AlphaFold2; AF2-Mult: AlphaFold2 multimer; ATG: autophagy-related; CTD: C-terminal domain; ECTD: extreme C-terminal domain; FR: flexible region; MD: molecular dynamics; NTD: N-terminal domain; pLDDT: predicted local distance difference test; UBL: ubiquitin-like.

Meta-Analysis of Microarray Data and Their Utility in Dissecting the Mapped QTLs for Heat Acclimation in Rice

In the current global warming scenario, it is imperative to develop crops with improved heat tolerance or acclimation, for which knowledge of major heat stress-tolerant genes or genomic regions is a prerequisite. Though several quantitative trait loci (QTLs) for heat tolerance have been mapped in rice, candidate genes from these QTLs have not been reported yet. The meta-analysis of microarray datasets for heat stress in rice can give us a better genomic resource for the dissection of QTLs and the identification of major candidate genes for heat stress tolerance. In the present study, a database, RiceMetaSys-H, comprising 4227 heat stress-responsive genes (HRGs), was created using seven publicly available microarray datasets. This included in-house-generated microarray datasets of Nagina 22 (N22) and IR64 subjected to 8 days of heat stress. The database has provisions for searching the HRGs through genotypes, growth stages, tissues, and physical intervals in the genome, as well as Locus IDs, which provide complete information on the HRGs with their annotations and fold changes, along with the experimental material used for the analysis. The up-regulation of genes involved in hormone biosynthesis and signalling, sugar metabolism, carbon fixation, and the ROS pathway were found to be the key mechanisms of enhanced heat tolerance. Integrating variant and expression analysis, the database was used for the dissection of the major effect of QTLs on chromosomes 4, 5, and 9 from the IR64/N22 mapping population. Out of the 18, 54, and 62 genes in these three QTLs, 5, 15, and 12 genes harboured non-synonymous substitutions. Fifty-seven interacting genes of the selected QTLs were identified by a network analysis of the HRGs in the QTL regions. Variant analysis revealed that the proportion of unique amino acid substitutions (between N22/IR64) in the QTL-specific genes was much higher than the common substitutions, i.e., 2.58:0.88 (2.93-fold), compared to the network genes at a 0.88:0.67 (1.313-fold) ratio. An expression analysis of these 89 genes showed 43 DEGs between IR64/N22. By integrating the expression profiles, allelic variations, and the database, four robust candidates (LOC_Os05g43870, LOC_Os09g27830, LOC_Os09g27650, andLOC_Os09g28000) for enhanced heat stress tolerance were identified. The database thus developed in rice can be used in breeding to combat high-temperature stress.

Pf-Phospho: a machine learning-based phosphorylation sites prediction tool for Plasmodium proteins

Even though several in silico tools are available for prediction of the phosphorylation sites for mammalian, yeast or plant proteins, currently no software is available for predicting phosphosites for Plasmodium proteins. However, the availability of significant amount of phospho-proteomics data during the last decade and advances in machine learning (ML) algorithms have opened up the opportunities for deciphering phosphorylation patterns of plasmodial system and developing ML-based phosphosite prediction tools for Plasmodium. We have developed Pf-Phospho, an ML-based method for prediction of phosphosites by training Random Forest classifiers using a large data set of 12 096 phosphosites of Plasmodium falciparum and Plasmodium bergei. Of the 12 096 known phosphosites, 75% of sites have been used for training/validation of the classifier, while remaining 25% have been used as completely unseen test data for blind testing. It is encouraging to note that Pf-Phospho can predict the kinase-independent phosphosites with 84% sensitivity, 75% specificity and 78% precision. In addition, it can also predict kinase-specific phosphosites for five plasmodial kinases-PfPKG, Plasmodium falciparum, PfPKA, PfPK7 and PbCDPK4 with high accuracy. Pf-Phospho (http://www.nii.ac.in/pfphospho.html) outperforms other widely used phosphosite prediction tools, which have been trained using mammalian phosphoproteome data. It also has been integrated with other widely used resources such as PlasmoDB, MPMP, Pfam and recently available ML-based predicted structures by AlphaFold2. Currently, Pf-phospho is the only bioinformatics resource available for ML-based prediction of phospho-signaling networks of Plasmodium and is a user-friendly platform for integrative analysis of phospho-signaling along with metabolic and protein-protein interaction networks.

Transcriptome Analysis Reveals Important Candidate Genes Related to Nutrient Reservoir, Carbohydrate Metabolism, and Defence Proteins during Grain Development of Hexaploid Bread Wheat and Its Diploid Progenitors

Wheat grain development after anthesis is an important biological process, in which major components of seeds are synthesised, and these components are further required for germination and seed vigour. We have made a comparative RNA-Seq analysis between hexaploid wheat and its individual diploid progenitors to know the major differentially expressed genes (DEGs) involved during grain development. Two libraries from each species were generated with an average of 55.63, 55.23, 68.13, and 103.81 million reads, resulting in 79.3K, 113.7K, 90.6K, and 121.3K numbers of transcripts in AA, BB, DD, and AABBDD genome species respectively. Number of expressed genes in hexaploid wheat was not proportional to its genome size, but marginally higher than that of its diploid progenitors. However, to capture all the transcripts in hexaploid wheat, sufficiently higher number of reads was required. Functional analysis of DEGs, in all the three comparisons, showed their predominance in three major classes of genes during grain development, i.e., nutrient reservoirs, carbohydrate metabolism, and defence proteins; some of them were subsequently validated through real time quantitative Reverse Transcription Polymerase Chain Reaction (qRT-PCR). Further, developmental stage-specific gene expression showed most of the defence protein genes expressed during initial developmental stages in hexaploid contrary to the diploids at later stages. Genes related to carbohydrates anabolism expressed during early stages, whereas catabolism genes expressed at later stages in all the species. However, no trend was observed in case of different nutrient reservoirs gene expression. This data could be used to study the comparative gene expression among the three diploid species and homeologue-specific expression in hexaploid.