Ensemble gene function prediction database reveals genes important for complex I formation in Arabidopsis thaliana

PEO: Plant Expression Omnibus - a comparative transcriptomic database for 103 Archaeplastida

The Plant Expression Omnibus (PEO) is a web application that provides biologists with access to gene expression insights across over 100 plant species, ~60 000 manually annotated RNA-seq samples, and more than 4 million genes. The tool allows users to explore the expression patterns of genes across different organs, identify organ-specific genes, and discover top co-expressed genes for any gene of interest. PEO also provides functional annotations for each gene, allowing for the identification of genetic modules and pathways. PEO is designed to facilitate comparative kingdom-wide gene expression analysis and provide a valuable resource for plant biology research. We provide two case studies to demonstrate the utility of PEO in identifying candidate genes in pollen coat biosynthesis in Arabidopsis and investigating the biosynthetic pathway components of capsaicin in Capsicum annuum. The database is freely available at https://expression.plant.tools/.

Exploiting plant transcriptomic databases: Resources, tools, and approaches

There are now more than 300 000 RNA sequencing samples available, stemming from thousands of experiments capturing gene expression in organs, tissues, developmental stages, and experimental treatments for hundreds of plant species. The expression data have great value, as they can be re-analyzed by others to ask and answer questions that go beyond the aims of the study that generated the data. Because gene expression provides essential clues to where and when a gene is active, the data provide powerful tools for predicting gene function, and comparative analyses allow us to study plant evolution from a new perspective. This review describes how we can gain new knowledge from gene expression profiles, expression specificities, co-expression networks, differential gene expression, and experiment correlation. We also introduce and demonstrate databases that provide user-friendly access to these tools.

ATTED-II v11: A Plant Gene Coexpression Database Using a Sample Balancing Technique by Subagging of Principal Components

ATTED-II (https://atted.jp) is a gene coexpression database for nine plant species based on publicly available RNAseq and microarray data. One of the challenges in constructing condition-independent coexpression data based on publicly available gene expression data is managing the inherent sampling bias. Here, we report ATTED-II version 11, wherein we adopted a coexpression calculation methodology to balance the samples using principal component analysis and ensemble calculation. This approach has two advantages. First, omitting principal components with low contribution rates reduces the main contributors of noise. Second, balancing large differences in contribution rates enables considering various sample conditions entirely. In addition, based on RNAseq- and microarray-based coexpression data, we provide species-representative, integrated coexpression information to enhance the efficiency of interspecies comparison of the coexpression data. These coexpression data are provided as a standardized z-score to facilitate integrated analysis with different data sources. We believe that with these improvements, ATTED-II is more valuable and powerful for supporting interspecies comparative studies and integrated analyses using heterogeneous data.

Multi-omics network-based functional annotation of unknown Arabidopsis genes

Unraveling gene function is pivotal to understanding the signaling cascades that control plant development and stress responses. As experimental profiling is costly and labor intensive, there is a clear need for high-confidence computational annotation. In contrast to detailed gene-specific functional information, transcriptomics data are widely available for both model and crop species. Here, we describe a novel automated function prediction method, which leverages complementary information from multiple expression datasets by analyzing study-specific gene co-expression networks. First, we benchmarked the prediction performance on recently characterized Arabidopsis thaliana genes, and showed that our method outperforms state-of-the-art expression-based approaches. Next, we predicted biological process annotations for known (n = 15 790) and unknown (n = 11 865) genes in A. thaliana and validated our predictions using experimental protein-DNA and protein-protein interaction data (covering >220 000 interactions in total), obtaining a set of high-confidence functional annotations. Our method assigned at least one validated annotation to 5054 (42.6%) unknown genes, and at least one novel validated function to 3408 (53.0%) genes with computational annotations only. These omics-supported functional annotations shed light on a variety of developmental processes and molecular responses, such as flower and root development, defense responses to fungi and bacteria, and phytohormone signaling, and help fill the information gap on biological process annotations in Arabidopsis. An in-depth analysis of two context-specific networks, modeling seed development and response to water deprivation, shows how previously uncharacterized genes function within the respective networks. Moreover, our automated function prediction approach can be applied in future studies to facilitate gene discovery for crop improvement.

Transcription Factor-Based Genetic Engineering in Microalgae

Sequence-specific DNA-binding transcription factors (TFs) are key components of gene regulatory networks. Advances in high-throughput sequencing have facilitated the rapid acquisition of whole genome assembly and TF repertoires in microalgal species. In this review, we summarize recent advances in gene discovery and functional analyses, especially for transcription factors in microalgal species. Specifically, we provide examples of the genome-scale identification of transcription factors in genome-sequenced microalgal species and showcase their application in the discovery of regulators involved in various cellular functions. Herein, we highlight TF-based genetic engineering as a promising framework for designing microalgal strains for microalgal-based bioproduction.

A ferredoxin bridge connects the two arms of plant mitochondrial complex I

Mitochondrial complex I is the main site for electron transfer to the respiratory chain and generates much of the proton gradient across the inner mitochondrial membrane. Complex I is composed of two arms, which form a conserved L-shape. We report the structures of the intact, 47-subunit mitochondrial complex I from Arabidopsis thaliana and the 51-subunit complex I from the green alga Polytomella sp., both at around 2.9 Å resolution. In both complexes, a heterotrimeric γ-carbonic anhydrase domain is attached to the membrane arm on the matrix side. Two states are resolved in A. thaliana complex I, with different angles between the two arms and different conformations of the ND1 (NADH dehydrogenase subunit 1) loop near the quinol binding site. The angle appears to depend on a bridge domain, which links the peripheral arm to the membrane arm and includes an unusual ferredoxin. We propose that the bridge domain participates in regulating the activity of plant complex I.

Iron-sulfur proteins in plant mitochondria: roles and maturation

Iron-sulfur (Fe-S) clusters are prosthetic groups ensuring electron transfer reactions, activating substrates for catalytic reactions, providing sulfur atoms for the biosynthesis of vitamins or other cofactors, or having protein-stabilizing effects. Hence, metalloproteins containing these cofactors are essential for numerous and diverse metabolic pathways and cellular processes occurring in the cytoplasm. Mitochondria are organelles where the Fe-S cluster demand is high, notably because the activity of the respiratory chain complexes I, II, and III relies on the correct assembly and functioning of Fe-S proteins. Several other proteins or complexes present in the matrix require Fe-S clusters as well, or depend either on Fe-S proteins such as ferredoxins or on cofactors such as lipoic acid or biotin whose synthesis relies on Fe-S proteins. In this review, we have listed and discussed the Fe-S-dependent enzymes or pathways in plant mitochondria including some potentially novel Fe-S proteins identified based on in silico analysis or on recent evidence obtained in non-plant organisms. We also provide information about recent developments concerning the molecular mechanisms involved in Fe-S cluster synthesis and trafficking steps of these cofactors from maturation factors to client apoproteins.

Using Network-Based Machine Learning to Predict Transcription Factors Involved in Drought Resistance

Gene regulatory networks underpin stress response pathways in plants. However, parsing these networks to prioritize key genes underlying a particular trait is challenging. Here, we have built the Gene Regulation and Association Network (GRAiN) of rice (Oryza sativa). GRAiN is an interactive query-based web-platform that allows users to study functional relationships between transcription factors (TFs) and genetic modules underlying abiotic-stress responses. We built GRAiN by applying a combination of different network inference algorithms to publicly available gene expression data. We propose a supervised machine learning framework that complements GRAiN in prioritizing genes that regulate stress signal transduction and modulate gene expression under drought conditions. Our framework converts intricate network connectivity patterns of 2160 TFs into a single drought score. We observed that TFs with the highest drought scores define the functional, structural, and evolutionary characteristics of drought resistance in rice. Our approach accurately predicted the function of OsbHLH148 TF, which we validated using in vitro protein-DNA binding assays and mRNA sequencing loss-of-function mutants grown under control and drought stress conditions. Our network and the complementary machine learning strategy lends itself to predicting key regulatory genes underlying other agricultural traits and will assist in the genetic engineering of desirable rice varieties.

LSTrAP-Crowd: prediction of novel components of bacterial ribosomes with crowd-sourced analysis of RNA sequencing data

BACKGROUND

Bacterial resistance to antibiotics is a growing health problem that is projected to cause more deaths than cancer by 2050. Consequently, novel antibiotics are urgently needed. Since more than half of the available antibiotics target the structurally conserved bacterial ribosomes, factors involved in protein synthesis are thus prime targets for the development of novel antibiotics. However, experimental identification of these potential antibiotic target proteins can be labor-intensive and challenging, as these proteins are likely to be poorly characterized and specific to few bacteria. Here, we use a bioinformatics approach to identify novel components of protein synthesis.

RESULTS

In order to identify these novel proteins, we established a Large-Scale Transcriptomic Analysis Pipeline in Crowd (LSTrAP-Crowd), where 285 individuals processed 26 terabytes of RNA-sequencing data of the 17 most notorious bacterial pathogens. In total, the crowd processed 26,269 RNA-seq experiments and used the data to construct gene co-expression networks, which were used to identify more than a hundred uncharacterized genes that were transcriptionally associated with protein synthesis. We provide the identity of these genes together with the processed gene expression data.

CONCLUSIONS

We identified genes related to protein synthesis in common bacterial pathogens and thus provide a resource of potential antibiotic development targets for experimental validation. The data can be used to explore additional vulnerabilities of bacteria, while our approach demonstrates how the processing of gene expression data can be easily crowd-sourced.

Computational approaches to unravel the pathways and evolution of specialized metabolism

Specialized metabolites serve as a chemical arsenal that protects plants from abiotic stress, pathogens, and herbivores, and they are an essential component of our nutrition and medicine. Despite their importance, we are still at the beginning of unravelling biosynthetic pathways that produce these compounds, which is needed to produce more resilient and nutritious crops, expand our inventory of useful biomolecules, and give valuable insights into plant evolution. This review focuses on the evolution of specialized metabolism in the plant kingdom and the state-of-the-art approaches used to identify the biosynthetic pathways of these useful compounds.

LSTrAP-Cloud: A User-Friendly Cloud Computing Pipeline to Infer Coexpression Networks

As genomes become more and more available, gene function prediction presents itself as one of the major hurdles in our quest to extract meaningful information on the biological processes genes participate in. In order to facilitate gene function prediction, we show how our user-friendly pipeline, the Large-Scale Transcriptomic Analysis Pipeline in Cloud (LSTrAP-Cloud), can be useful in helping biologists make a shortlist of genes involved in a biological process that they might be interested in, by using a single gene of interest as bait. The LSTrAP-Cloud is based on Google Colaboratory, and provides user-friendly tools that process quality-control RNA sequencing data streamed from the European Nucleotide Archive. The LSTRAP-Cloud outputs a gene coexpression network that can be used to identify functionally related genes for any organism with a sequenced genome and publicly available RNA sequencing data. Here, we used the biosynthesis pathway of Nicotiana tabacum as a case study to demonstrate how enzymes, transporters, and transcription factors involved in the synthesis, transport, and regulation of nicotine can be identified using our pipeline.

Gene expression analysis of Cyanophora paradoxa reveals conserved abiotic stress responses between basal algae and flowering plants

The glaucophyte Cyanophora paradoxa represents the most basal member of the kingdom Archaeplastida, but the function and expression of most of its genes are unknown. This information is needed to uncover how functional gene modules, that is groups of genes performing a given function, evolved in the plant kingdom. We have generated a gene expression atlas capturing responses of Cyanophora to various abiotic stresses. The data were included in the CoNekT-Plants database, enabling comparative transcriptomic analyses across two algae and six land plants. We demonstrate how the database can be used to study gene expression, co-expression networks and gene function in Cyanophora, and how conserved transcriptional programs can be identified. We identified gene modules involved in phycobilisome biosynthesis, response to high light and cell division. While we observed no correlation between the number of differentially expressed genes and the impact on growth of Cyanophora, we found that the response to stress involves a conserved, kingdom-wide transcriptional reprogramming, which is activated upon most stresses in algae and land plants. The Cyanophora stress gene expression atlas and the tools found in the https://conekt.plant.tools/ database thus provide a useful resource to reveal functionally related genes and stress responses in the plant kingdom.

Inferring biosynthetic and gene regulatory networks from Artemisia annua RNA sequencing data on a credit card-sized ARM computer

Prediction of gene function and gene regulatory networks is one of the most active topics in bioinformatics. The accumulation of publicly available gene expression data for hundreds of plant species, together with advances in bioinformatical methods and affordable computing, sets ingenuity as one of the major bottlenecks in understanding gene function and regulation. Here, we show how a credit card-sized computer retailing for <50 USD can be used to rapidly predict gene function and infer regulatory networks from RNA sequencing data. To achieve this, we constructed a bioinformatical pipeline that downloads and allows quality-control of RNA sequencing data; and generates a gene co-expression network that can reveal enzymes and transcription factors participating and controlling a given biosynthetic pathway. We exemplify this by first identifying genes and transcription factors involved in the biosynthesis of secondary cell wall in the plant Artemisia annua, the main natural source of the anti-malarial drug artemisinin. Networks were then used to dissect the artemisinin biosynthesis pathway, which suggest potential transcription factors regulating artemisinin biosynthesis. We provide the source code of our pipeline (https://github.com/mutwil/LSTrAP-Lite) and envision that the ubiquity of affordable computing, availability of biological data and increased bioinformatical training of biologists will transform the field of bioinformatics. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.

Maize Empty Pericarp602 Encodes a P-Type PPR Protein That Is Essential for Seed Development

Pentatricopeptide repeat (PPR) proteins play crucial roles in intron splicing, which is important for RNA maturation. Identification of novel PPR protein with the function of intron splicing would help to understand the RNA splicing mechanism. In this study, we identified the maize empty pericarp602 (emp602) mutants, the mature kernels of which showed empty pericarp phenotype. We cloned the Emp602 gene from emp602 mutants and revealed that Emp602 encodes a mitochondrial-localized P-type PPR protein. We further revealed that Emp602 is specific for the cis-splicing of mitochondrial Nad4 intron 1 and intron 3, and mutation of Emp602 led to the loss of mature Nad4 transcripts. The loss of function of Emp602 nearly damaged the assembly and accumulation of complex I and arrested mitochondria formation, which arrested the seed development. The failed assembly of complex I triggers significant upregulation of Aox expression in emp602 mutants. Transcriptome analysis showed that the expression of mitochondrial-related genes, e.g. the genes associated with mitochondrial inner membrane presequence translocase complex and electron carrier activity, were extensively upregulated in emp602 mutant. These results demonstrate that EMP602 functions in the splicing of Nad4 intron 1 and intron 3, and the loss of function of Emp602 arrested maize seed development by disrupting the mitochondria complex I assembly.

Bottlenecks for genome-edited crops on the road from lab to farm

Gene discovery and government regulation are bottlenecks for the widespread adoption of genome-edited crops. We propose a culture of sharing and integrating crop data to accelerate the discovery and prioritization of candidate genes, as well as a strong engagement with governments and the public to address environmental and health concerns and to achieve appropriate regulatory standards.