Integrated Analysis of Protein Abundance, Transcript Level, and Tissue Diversity To Reveal Developmental Regulation of Maize

Maize multi-omics reveal leaf water status controlling of differential transcriptomes, proteomes and hormones as mechanisms of age-dependent osmotic stress response in leaves

Drought-induced osmotic stress severely affects the growth and yield of maize. However, the mechanisms underlying the different responses of young and old maize leaves to osmotic stress remain unclear. To gain a systematic understanding of age-related stress responses, we compared osmotic-stress-induced changes in maize leaves of different ages using multi-omics approaches. After short-term osmotic stress, old leaves suffered more severe water deficits than young leaves. The adjustments of transcriptomes, proteomes, and hormones in response to osmotic stress were more dynamic in old leaves. Metabolic activities, stress signaling pathways, and hormones (especially abscisic acid) responded to osmotic stress in an age-dependent manner. We identified multiple functional clusters of genes and proteins with potential roles in stress adaptation. Old leaves significantly accumulated stress proteins such as dehydrin, aquaporin, and chaperones to cope with osmotic stress, accompanied by senescence-like cellular events, whereas young leaves exhibited an effective water conservation strategy mainly by hydrolyzing transitory starch and increasing proline production. The stress responses of individual leaves are primarily determined by their intracellular water status, resulting in differential transcriptomes, proteomes, and hormones. This study extends our understanding of the mechanisms underlying plant responses to osmotic stress.

Workability of mRNA Sequencing for Predicting Protein Abundance

Transcriptomics methods (RNA-Seq, PCR) today are more routine and reproducible than proteomics methods, i.e., both mass spectrometry and immunochemical analysis. For this reason, most scientific studies are limited to assessing the level of mRNA content. At the same time, protein content (and its post-translational status) largely determines the cell's state and behavior. Such a forced extrapolation of conclusions from the transcriptome to the proteome often seems unjustified. The ratios of "transcript-protein" pairs can vary by several orders of magnitude for different genes. As a rule, the correlation coefficient between transcriptome-proteome levels for different tissues does not exceed 0.3-0.5. Several characteristics determine the ratio between the content of mRNA and protein: among them, the rate of movement of the ribosome along the mRNA and the number of free ribosomes in the cell, the availability of tRNA, the secondary structure, and the localization of the transcript. The technical features of the experimental methods also significantly influence the levels of the transcript and protein of the corresponding gene on the outcome of the comparison. Given the above biological features and the performance of experimental and bioinformatic approaches, one may develop various models to predict proteomic profiles based on transcriptomic data. This review is devoted to the ability of RNA sequencing methods for protein abundance prediction.

Candida albicans Multilocus Sequence Typing Clade I Contributes to the Clinical Phenotype of Vulvovaginal Candidiasis Patients

Candida albicans is the most frequent fungal species responsible for vulvovaginal candidiasis (VVC), which exhibits distinct genetic diversity that is linked with the clinical phenotype. This study aimed to assess the genotypes and clinical characteristics of different C. albicans isolates from VVC patients. Based on multilocus sequence typing (MLST), clade 1 was identified as the largest C. albicans group, which appeared most frequently in recurrent VVC and treatment failure cases. Further study of antifungal susceptibility demonstrated that MLST clade 1 strains presented significantly higher drug resistance ability than non-clade 1 strains, which result from the overexpression of MDR1. The mRNA and protein expression levels of virulence-related genes were also significantly higher in clade 1 isolates than in non-clade 1 isolates. Proteomic analysis indicated that the protein stabilization pathway was significantly enriched in clade 1 strains and that RPS4 was a central regulator of proteins involved in stress resistance, adherence, and DNA repair, which all contribute to the resistance and virulence of MLST clade 1 strains. This study was the first attempt to compare the correlation mechanisms between C. albicans MLST clade 1 and non-clade 1 strains and the clinical phenotype, which is of great significance for VVC classification and treatment.

To the proteome and beyond: advances in single-cell omics profiling for plant systems

Recent advances in single-cell proteomics for animal systems could be adapted for plants to increase our understanding of plant development, response to stimuli, and cell-to-cell signaling.

qTeller: a tool for comparative multi-genomic gene expression analysis

MOTIVATION

Over the last decade, RNA-Seq whole-genome sequencing has become a widely used method for measuring and understanding transcriptome-level changes in gene expression. Since RNA-Seq is relatively inexpensive, it can be used on multiple genomes to evaluate gene expression across many different conditions, tissues and cell types. Although many tools exist to map and compare RNA-Seq at the genomics level, few web-based tools are dedicated to making data generated for individual genomic analysis accessible and reusable at a gene-level scale for comparative analysis between genes, across different genomes and meta-analyses.

RESULTS

To address this challenge, we revamped the comparative gene expression tool qTeller to take advantage of the growing number of public RNA-Seq datasets. qTeller allows users to evaluate gene expression data in a defined genomic interval and also perform two-gene comparisons across multiple user-chosen tissues. Though previously unpublished, qTeller has been cited extensively in the scientific literature, demonstrating its importance to researchers. Our new version of qTeller now supports multiple genomes for intergenomic comparisons, and includes capabilities for both mRNA and protein abundance datasets. Other new features include support for additional data formats, modernized interface and back-end database and an optimized framework for adoption by other organisms' databases.

AVAILABILITY AND IMPLEMENTATION

The source code for qTeller is open-source and available through GitHub (https://github.com/Maize-Genetics-and-Genomics-Database/qTeller). A maize instance of qTeller is available at the Maize Genetics and Genomics database (MaizeGDB) (https://qteller.maizegdb.org/), where we have mapped over 200 unique datasets from GenBank across 27 maize genomes.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Deciphering carbohydrate metabolism during wheat grain development via integrated transcriptome and proteome dynamics

Grain development of Triticum aestivum is being studied extensively using individual OMICS tools. However, integrated transcriptome and proteome studies are limited mainly due to complexity of genome. Current study focused to unravel the transcriptome-proteome coordination of key mechanisms underlying carbohydrate metabolism during whole wheat grain development. Wheat grains were manually dissected to obtain grain tissues for proteomics and transcriptomics analyses. Differentially expressed proteins and transcripts at the 11 stages of grain development were compared. Computational workflow for integration of two datasets related to carbohydrate metabolism was designed. For CM proteins, output peptide sequences of proteomic analyses (via LC-MS/MS) were used as source to search corresponding transcripts. The transcript that turned out with higher number of peptides was selected as bona fide ribonucleotide sequence for respective protein synthesis. More than 90% of hits resulted in successful identification of respective transcripts. Comparative analysis of protein and transcript expression profiles resulted in overall 32% concordance between these two series of data. However, during grain development correlation of two datasets gradually increased up to ~ tenfold from 152 to 655 °Cd and then dropped down. Proteins involved in carbohydrate metabolism were divided in five categories in accordance with their functions. Enzymes involved in starch and sucrose biosynthesis showed the highest correlations between proteome-transcriptome profiles. High percentage of identification and validation of protein-transcript hits highlighted the power of omics data integration approach over existing gene functional annotation tools. We found that correlation of two datasets is highly influenced by stage of grain development. Further, gene regulatory networks would be helpful in unraveling the mechanisms underlying the complex and significant traits such as grain weight and yield.

Characterization of Proteome Variation During Modern Maize Breeding

The success of modern maize breeding has been demonstrated by remarkable increases in productivity with tremendous modification of agricultural phenotypes over the last century. Although the underlying genetic changes of the maize adaptation from tropical to temperate regions have been extensively studied, our knowledge is limited regarding the accordance of protein and mRNA expression levels accompanying such adaptation. Here we conducted an integrative analysis of proteomic and transcriptomic changes in a maize association panel. The minimum extent of correlation between protein and RNA levels suggests that variation in mRNA expression is often not indicative of protein expression at a population scale. This is corroborated by the observation that mRNA- and protein-based coexpression networks are relatively independent of each other, and many pQTLs arise without the presence of corresponding eQTLs. Importantly, compared with transcriptome, the subtypes categorized by the proteome show a markedly high accuracy to resemble the genomic subpopulation. These findings suggest that proteome evolved under a greater evolutionary constraint than transcriptome during maize adaptation from tropical to temperate regions. Overall, the integrated multi-omics analysis provides a functional context to interpret gene expression variation during modern maize breeding.