Phylogenomic analysis of gene co-expression networks reveals the evolution of functional modules

Identification of tomato F-box proteins functioning in phenylpropanoid metabolism

Phenylpropanoids, a class of specialized metabolites, play crucial roles in plant growth and stress adaptation and include diverse phenolic compounds such as flavonoids. Phenylalanine ammonia-lyase (PAL) and chalcone synthase (CHS) are essential enzymes functioning at the entry points of general phenylpropanoid biosynthesis and flavonoid biosynthesis, respectively. In Arabidopsis, PAL and CHS are turned over through ubiquitination-dependent proteasomal degradation. Specific kelch domain-containing F-Box (KFB) proteins as components of ubiquitin E3 ligase directly interact with PAL or CHS, leading to polyubiquitinated PAL and CHS, which in turn influences phenylpropanoid and flavonoid production. Although phenylpropanoids are vital for tomato nutritional value and stress responses, the post-translational regulation of PAL and CHS in tomato remains unknown. We identified 31 putative KFB-encoding genes in the tomato genome. Our homology analysis and phylogenetic study predicted four PAL-interacting SlKFBs, while SlKFB18 was identified as the sole candidate for the CHS-interacting KFB. Consistent with their homolog function, the predicted four PAL-interacting SlKFBs function in PAL degradation. Surprisingly, SlKFB18 did not interact with tomato CHS and the overexpression or knocking out of SlKFB18 did not affect phenylpropanoid contents in tomato transgenic lines, suggesting its irreverence with flavonoid metabolism. Our study successfully discovered the post-translational regulatory machinery of PALs in tomato while highlighting the limitation of relying solely on a homology-based approach to predict interacting partners of F-box proteins.

Application of orthology and network biology to infer gene functions in non-model plants

Approximately 60% of the genes and gene products in the model species Arabidopsis thaliana have been functionally characterized. In non-model plant species, the functional annotation of the gene space is largely based on homology, with the assumption that genes with shared common ancestry have conserved functions. However, the wide variety in possible morphological, physiological, and ecological differences between plant species gives rise to many species- and clade-specific genes, for which this transfer of knowledge is not possible. Other complications, such as difficulties with genetic transformation, the absence of large-scale mutagenesis methods, and long generation times, further lead to the slow characterization of genes in non-model species. Here, we discuss different resources that integrate plant gene function information. Different approaches that support the functional annotation of gene products, based on orthology or network biology, are described. While sequence-based tools to characterize the functional landscape in non-model species are maturing and becoming more readily available, easy-to-use network-based methods inferring plant gene functions are not as prevalent and have limited functionality.

How to utilize comparative transcriptomics to dissect morphological diversity in plants

Comparative transcriptomics has emerged as a powerful approach that allows us to unravel the genetic basis of organ morphogenesis and its diversification processes during evolution. However, the application of comparative transcriptomics in studying plant morphological diversity addresses challenges such as identifying homologous gene pairs, selecting appropriate developmental stages for comparison, and extracting biologically meaningful networks. Methods such as phylostratigraphy, clustering, and gene co-expression networks are explored to identify functionally equivalent genes, align developmental stages, and uncover gene regulatory relationships. In the current review, we highlight the importance of these approaches in overcoming the complexity of plant genomes, the impact of heterochrony on stage alignment, and the integration of gene networks with additional data for a comprehensive understanding of morphological evolution.

oggmap: a Python package to extract gene ages per orthogroup and link them with single-cell RNA data

SUMMARY

For model species, single-cell RNA-based cell atlases are available. A good cell atlas includes all major stages in a species' ontogeny, and soon, they will be standard even for nonmodel species. Here, we propose a Python package called oggmap, which allows for the easy extraction of an orthomap (gene ages per orthogroup) for any given query species from OrthoFinder and other gene family data resources, like homologous groups from eggNOG or PLAZA. oggmap provides extracted gene ages for more than thousand eukaryotic species which can be further used to calculate gene age-weighted expression data from scRNA sequencing objects using the Python Scanpy toolkit. Not limited to one transcriptome evolutionary index, oggmap can visualize the individual gene category (e.g. age class, nucleotide diversity bin) and their corresponding expression profiles to investigate scRNA-based cell type assignments in an evolutionary context.

AVAILABILITY AND IMPLEMENTATION

oggmap source code is available at https://github.com/kullrich/oggmap, documentation is available at https://oggmap.readthedocs.io/en/latest/. oggmap can be installed via PyPi or directly used via a docker container.

Comparative Coexpression Analysis of Indole Synthase and Tryptophan Synthase A Reveals the Independent Production of Auxin via the Cytosolic Free Indole

Indole synthase (INS), a homologous cytosolic enzyme of the plastidal tryptophan synthase A (TSA), has been reported as the first enzyme in the tryptophan-independent pathway of auxin synthesis. This suggestion was challenged as INS or its free indole product may interact with tryptophan synthase B (TSB) and, therefore, with the tryptophan-dependent pathway. Thus, the main aim of this research was to find out whether INS is involved in the tryptophan-dependent or independent pathway. The gene coexpression approach is widely recognized as an efficient tool to uncover functionally related genes. Coexpression data presented here were supported by both RNAseq and microarray platforms and, hence, considered reliable. Coexpression meta-analyses of Arabidopsis genome was implemented to compare between the coexpression of TSA and INS with all genes involved in the production of tryptophan via the chorismate pathway. Tryptophan synthase A was found to be coexpressed strongly with TSB1/2, anthranilate synthase A1/B1, phosphoribosyl anthranilate transferase1, as well as indole-3-glycerol phosphate synthase1. However, INS was not found to be coexpressed with any target genes suggesting that it may exclusively and independently be involved in the tryptophan-independent pathway. Additionally, annotation of examined genes as ubiquitous or differentially expressed were described and subunits-encoded genes available for the assembly of tryptophan and anthranilate synthase complex were suggested. The most probable TSB subunits expected to interact with TSA is TSB1 then TSB2. Whereas TSB3 is only used under limited hormone conditions to assemble tryptophan synthase complex, putative TSB4 is not expected to be involved in the plastidial synthesis of tryptophan in Arabidopsis.

Toward kingdom-wide analyses of gene expression

Gene expression data for Archaeplastida are accumulating exponentially, with more than 300 000 RNA-sequencing (RNA-seq) experiments available for hundreds of species. The gene expression data stem from thousands of experiments that capture gene expression in various organs, tissues, cell types, (a)biotic perturbations, and genotypes. Advances in software tools make it possible to process all these data in a matter of weeks on modern office computers, giving us the possibility to study gene expression in a kingdom-wide manner for the first time. We discuss how the expression data can be accessed and processed and outline analyses that take advantage of cross-species analyses, allowing us to generate powerful and robust hypotheses about gene function and evolution.

Crossroads in the evolution of plant specialized metabolism

The monophyletic group of embryophytes (land plants) stands out among photosynthetic eukaryotes: they are the sole constituents of the macroscopic flora on land. In their entirety, embryophytes account for the majority of the biomass on land and constitute an astounding biodiversity. What allowed for the massive radiation of this particular lineage? One of the defining features of all land plants is the production of an array of specialized metabolites. The compounds that the specialized metabolic pathways of embryophytes produce have diverse functions, ranging from superabundant structural polymers and compounds that ward off abiotic and biotic challenges, to signaling molecules whose abundance is measured at the nanomolar scale. These specialized metabolites govern the growth, development, and physiology of land plants-including their response to the environment. Hence, specialized metabolites define the biology of land plants as we know it. And they were likely a foundation for their success. It is thus intriguing to find that the closest algal relatives of land plants, freshwater organisms from the grade of streptophyte algae, possess homologs for key enzymes of specialized metabolic pathways known from land plants. Indeed, some studies suggest that signature metabolites emerging from these pathways can be found in streptophyte algae. Here we synthesize the current understanding of which routes of the specialized metabolism of embryophytes can be traced to a time before plants had conquered land.

GeneFriends: gene co-expression databases and tools for humans and model organisms

Gene co-expression analysis has emerged as a powerful method to provide insights into gene function and regulation. The rapid growth of publicly available RNA-sequencing (RNA-seq) data has created opportunities for researchers to employ this abundant data to help decipher the complexity and biology of genomes. Co-expression networks have proven effective for inferring the relationship between the genes, for gene prioritization and for assigning function to poorly annotated genes based on their co-expressed partners. To facilitate such analyses we created previously an online co-expression tool for humans and mice entitled GeneFriends. To continue providing a valuable tool to the scientific community, we have now updated the GeneFriends database and website. Here, we present the new version of GeneFriends, which includes gene and transcript co-expression networks based on RNA-seq data from 46 475 human and 34 322 mouse samples. The new database also encompasses tissue-specific gene co-expression networks for 20 human and 21 mouse tissues, dataset-specific gene co-expression maps based on TCGA and GTEx projects and gene co-expression networks for additional seven model organisms (fruit fly, zebrafish, worm, rat, yeast, cow and chicken). GeneFriends is freely available at http://www.genefriends.org/.

Evolutionary analysis of the LORELEI gene family in plants reveals regulatory subfunctionalization

A signaling complex comprising members of the LORELEI (LRE)-LIKE GPI-anchored protein (LLG) and Catharanthus roseus RECEPTOR-LIKE KINASE 1-LIKE (CrRLK1L) families perceive RAPID ALKALINIZATION FACTOR (RALF) peptides and regulate growth, reproduction, immunity, and stress responses in Arabidopsis (Arabidopsis thaliana). Genes encoding these proteins are members of multigene families in most angiosperms and could generate thousands of signaling complex variants. However, the links between expansion of these gene families and the functional diversification of this critical signaling complex as well as the evolutionary factors underlying the maintenance of gene duplicates remain unknown. Here, we investigated LLG gene family evolution by sampling land plant genomes and explored the function and expression of angiosperm LLGs. We found that LLG diversity within major land plant lineages is primarily due to lineage-specific duplication events, and that these duplications occurred both early in the history of these lineages and more recently. Our complementation and expression analyses showed that expression divergence (i.e. regulatory subfunctionalization), rather than functional divergence, explains the retention of LLG paralogs. Interestingly, all but one monocot and all eudicot species examined had an LLG copy with preferential expression in male reproductive tissues, while the other duplicate copies showed highest levels of expression in female or vegetative tissues. The single LLG copy in Amborella trichopoda is expressed vastly higher in male compared to in female reproductive or vegetative tissues. We propose that expression divergence plays an important role in retention of LLG duplicates in angiosperms.

Genome Wide Analysis of Family-1 UDP Glycosyltransferases in Populus trichocarpa Specifies Abiotic Stress Responsive Glycosylation Mechanisms

Populus trichocarpa (Black cottonwood) is a dominant timber-yielding tree that has become a notable model plant for genome-level insights in forest trees. The efficient transport and solubility of various glycoside-associated compounds is linked to Family-1 UDP-glycosyltransferase (EC 2.4.1.x; UGTs) enzymes. These glycosyltransferase enzymes play a vital role in diverse plant functions, such as regulation of hormonal homeostasis, growth and development (seed, flower, fiber, root, etc.), xenobiotic detoxification, stress response (salt, drought, and oxidative), and biosynthesis of secondary metabolites. Here, we report a genome-wide analysis of the P. trichocarpa genome that identified 191 putative UGTs distributed across all chromosomes (with the exception of chromosome 20) based on 44 conserved plant secondary product glycosyltransferase (PSPG) motif amino acid sequences. Phylogenetic analysis of the 191 Populus UGTs together with 22 referenced UGTs from Arabidopsis and maize clustered the putative UGTs into 16 major groups (A–P). Whole-genome duplication events were the dominant pattern of duplication among UGTs in Populus. A well-conserved intron insertion was detected in most intron-containing UGTs across eight examined eudicots, including Populus. Most of the UGT genes were found preferentially expressed in leaf and root tissues in general. The regulation of putative UGT expression in response to drought, salt and heat stress was observed based on microarray and available RNA sequencing datasets. Up- and down-regulated UGT expression models were designed, based on transcripts per kilobase million values, confirmed their maximally varied expression under drought, salt and heat stresses. Co-expression networking of putative UGTs indicated their maximum co-expression with cytochrome P450 genes involved in triterpenoid biosynthesis. Our results provide an important resource for the identification of functional UGT genes to manipulate abiotic stress responsive glycosylation in Populus.

Coexpression reveals conserved gene programs that co-vary with cell type across kingdoms

What makes a mouse a mouse, and not a hamster? Differences in gene regulation between the two organisms play a critical role. Comparative analysis of gene coexpression networks provides a general framework for investigating the evolution of gene regulation across species. Here, we compare coexpression networks from 37 species and quantify the conservation of gene activity 1) as a function of evolutionary time, 2) across orthology prediction algorithms, and 3) with reference to cell- and tissue-specificity. We find that ancient genes are expressed in multiple cell types and have well conserved coexpression patterns, however they are expressed at different levels across cell types. Thus, differential regulation of ancient gene programs contributes to transcriptional cell identity. We propose that this differential regulation may play a role in cell diversification in both the animal and plant kingdoms.

Systems biology-based analysis indicates that PHO1;H10 positively modulates high light-induced anthocyanin biosynthesis in Arabidopsis leaves

Arabidopsis PHO1;H10 is a member of the PHO1 gene family with SPX and EXS domains, and its functions remain largely unknown. As shown in PCSD database, the upstream region of PHO1;H10 gene is in the active chromatin states, with high DHS accessibility and binding sites of multiple transcription factors, especially ABI5, SPCH and HY5. Co-expression network and data-mining analyses showed PHO1;H10 and co-expression genes were with activation under high light stress. We did wet-lab experiments, and found that the detached leaves of PHO1;H10 overexpression lines accumulated more anthocyanin than those of WT and mutant under high light treatment. RNA-seq results showed overexpression of PHO1;H10 up-regulated many anthocyanin biosynthetic genes. The GSEA analysis result showed that the functional module related to anthocyanin pathway was significantly enriched. In summary, we conducted systems biology approach, combining dry- and wet-lab analyses, and discovered that PHO1;H10 might play an essential role during modulating high light-induced anthocyanin accumulation in the Arabidopsis detached leaves.

Genetic and Genomic Resources for Soybean Breeding Research

Soybean (Glycine max) is a legume species of significant economic and nutritional value. The yield of soybean continues to increase with the breeding of improved varieties, and this is likely to continue with the application of advanced genetic and genomic approaches for breeding. Genome technologies continue to advance rapidly, with an increasing number of high-quality genome assemblies becoming available. With accumulating data from marker arrays and whole-genome resequencing, studying variations between individuals and populations is becoming increasingly accessible. Furthermore, the recent development of soybean pangenomes has highlighted the significant structural variation between individuals, together with knowledge of what has been selected for or lost during domestication and breeding, information that can be applied for the breeding of improved cultivars. Because of this, resources such as genome assemblies, SNP datasets, pangenomes and associated databases are becoming increasingly important for research underlying soybean crop improvement.

Addressing the mean-correlation relationship in co-expression analysis

Estimates of correlation between pairs of genes in co-expression analysis are commonly used to construct networks among genes using gene expression data. As previously noted, the distribution of such correlations depends on the observed expression level of the involved genes, which we refer to this as a mean-correlation relationship in RNA-seq data, both bulk and single-cell. This dependence introduces an unwanted technical bias in co-expression analysis whereby highly expressed genes are more likely to be highly correlated. Such a relationship is not observed in protein-protein interaction data, suggesting that it is not reflecting biology. Ignoring this bias can lead to missing potentially biologically relevant pairs of genes that are lowly expressed, such as transcription factors. To address this problem, we introduce spatial quantile normalization (SpQN), a method for normalizing local distributions in a correlation matrix. We show that spatial quantile normalization removes the mean-correlation relationship and corrects the expression bias in network reconstruction.

Comprehensive Time-Course Transcriptome and Co-expression Network Analyses Identify Salt Stress Responding Mechanisms in Chlamydomonas reinhardtii Strain GY-D55

It is highly necessary to understand the molecular mechanism underlying the salt stress response in green algae, which may contribute to finding the evolutionary cues of abiotic stress response in plants. Here, we reported a comprehensive temporal investigation of transcriptomes using data at eight different time points, from an early stage (2 h) to a late stage (up to 96 h) in Chlamydomonas reinhardtii GY-D55 cells. The principal component analysis (PCA) of transcriptome profiles showed that the samples of the early and late stages were well separated. A total of 12,445 genes were detected as differentially expressed genes. There were 1,861/2,270 common upregulated/downregulated genes for each time point compared with control samples. Samples treated with salt for 2, 8, and 24 h had a relatively large number of characteristic upregulated/downregulated genes. The functional enrichment analysis highlighted the timing of candidate regulatory mechanisms for salt stress responses in GY-D55 cells. Short time exposure to salt stress impaired oxidation-reduction, protein synthesis and modification, and photosynthesis. The algal cells promoted transcriptional regulation and protein folding to deal with protein synthesis/modification impairments and rapidly accumulated glycerol in the early stage (2-4 h) to cope with osmotic stress. At 12 and 24 h, GY-D55 cells showed increased expressions of signaling and photosynthetic genes to deal with the damage of photosynthesis. The co-expression module blue was predicted to regulate endoplasmic reticulum (ER) stress at early time points. In addition, we identified a total of 113 transcription factors (TFs) and predicted the potential roles of Alfin, C2C2, and the MYB family TFs in algal salt stress response.

Plant genome sequence assembly in the era of long reads: Progress, challenges and future directions

Third-generation long-read sequencing is transforming plant genomics. Oxford Nanopore Technologies and Pacific Biosciences are offering competing long-read sequencing technologies and enable plant scientists to investigate even large and complex plant genomes. Sequencing projects can be conducted by single research groups and sequences of smaller plant genomes can be completed within days. This also resulted in an increased investigation of genomes from multiple species in large scale to address fundamental questions associated with the origin and evolution of land plants. Increased accessibility of sequencing devices and user-friendly software allows more researchers to get involved in genomics. Current challenges are accurately resolving diploid or polyploid genome sequences and better accounting for the intra-specific diversity by switching from the use of single reference genome sequences to a pangenome graph.

Gene Co-expression Network Analysis

Gene co-expression analysis is a data analysis technique that helps identify groups of genes with similar expression patterns across several different conditions. By means of these techniques, different groups have been able to assign putative metabolic pathways and functions to understudied genes and to identify novel metabolic regulation networks for different metabolites. Some groups have even used network comparative studies to understand the evolution of these networks from green algae to land plants. In this chapter, we will go over the basic definitions required to understand network topology and gene module identification. Additionally, we offer the reader a walk-through a standard analysis pipeline as implemented in the package WGCNA that takes as input raw fastq files and obtains co-expressed gene clusters and representative gene expression patterns from each module for downstream applications.

Cross-Kingdom Comparative Transcriptomics Reveals Conserved Genetic Modules in Response to Cadmium Stress

It is known that organisms have developed various mechanisms to cope with cadmium (Cd) stress, while we still lack a system-level understanding of the functional isomorphy among them. In the present study, a cross-kingdom comparison was conducted among Escherichia coli, Saccharomyces cerevisiae, and Chlamydomonas reinhardtii, through toxicological tests, comparative transcriptomics, as well as conventional functional genomics. An equivalent level of Cd stress was determined via inhibition tests. Through transcriptome comparison, the three organisms exhibited differential gene expression under the same Cd stress relative to the corresponding no-treatment control. Results from functional enrichment analysis of differentially expressed genes (DEGs) showed that four metabolic pathways responsible for combating Cd stress were commonly regulated in the three organisms, including antioxidant reactions, sulfur metabolism, cell wall remodeling, and metal transport. In vivo expression patterns of 43 DEGs from the four pathways were further examined using quantitative PCR and resulted in a relatively comparable dynamic of gene expression patterns with transcriptome sequencing (RNA-seq). Cross-kingdom comparison of typical Cd stress-responding proteins resulted in the detection of 12 groups of homologous proteins in the three species. A class of potential metal transporters were subjected to cross-transformation to test their functional complementation. An ABC transporter gene in E. coli, possibly homologous to the yeast ycf1, was heterologously expressed in S. cerevisiae, resulting in enhanced Cd tolerance. Overall, our findings indicated that conserved genetic modules against Cd toxicity were commonly regulated among distantly related microbial species, which will be helpful for utilizing them in modifying microbial traits for bioremediation.

IMPORTANCE Research is establishing a systems biology view of biological response to Cd stress. It is meaningful to explore whether there is regulatory isomorphy among distantly related organisms. A transcriptomic comparison was done among model microbes, leading to the identification of a conserved cellular model pinpointing the generic strategies utilized by microbes for combating Cd stress. A novel E. coli transporter gene substantially increased yeast’s Cd tolerance. Knowledge on systems understanding of the cellular response to metals provides the basis for developing bioengineering remediation technology.

Comparative transcriptomic analysis reveals conserved programmes underpinning organogenesis and reproduction in land plants

The appearance of plant organs mediated the explosive radiation of land plants, which shaped the biosphere and allowed the establishment of terrestrial animal life. The evolution of organs and immobile gametes required the coordinated acquisition of novel gene functions, the co-option of existing genes and the development of novel regulatory programmes. However, no large-scale analyses of genomic and transcriptomic data have been performed for land plants. To remedy this, we generated gene expression atlases for various organs and gametes of ten plant species comprising bryophytes, vascular plants, gymnosperms and flowering plants. A comparative analysis of the atlases identified hundreds of organ- and gamete-specific orthogroups and revealed that most of the specific transcriptomes are significantly conserved. Interestingly, our results suggest that co-option of existing genes is the main mechanism for evolving new organs. In contrast to female gametes, male gametes showed a high number and conservation of specific genes, which indicates that male reproduction is highly specialized. The expression atlas capturing pollen development revealed numerous transcription factors and kinases essential for pollen biogenesis and function.

Comparative transcriptomic analysis reveals conserved programmes underpinning organogenesis and reproduction in land plants

The appearance of plant organs mediated the explosive radiation of land plants, which shaped the biosphere and allowed the establishment of terrestrial animal life. The evolution of organs and immobile gametes required the coordinated acquisition of novel gene functions, the co-option of existing genes and the development of novel regulatory programmes. However, no large-scale analyses of genomic and transcriptomic data have been performed for land plants. To remedy this, we generated gene expression atlases for various organs and gametes of ten plant species comprising bryophytes, vascular plants, gymnosperms and flowering plants. A comparative analysis of the atlases identified hundreds of organ- and gamete-specific orthogroups and revealed that most of the specific transcriptomes are significantly conserved. Interestingly, our results suggest that co-option of existing genes is the main mechanism for evolving new organs. In contrast to female gametes, male gametes showed a high number and conservation of specific genes, which indicates that male reproduction is highly specialized. The expression atlas capturing pollen development revealed numerous transcription factors and kinases essential for pollen biogenesis and function.

Comparative transcriptomics reveals hidden issues in the plant response to arthropod herbivores

Plants experience different abiotic/biotic stresses, which trigger their molecular machinery to cope with them. Besides general mechanisms prompted by many stresses, specific mechanisms have been introduced to optimize the response to individual threats. However, these key mechanisms are difficult to identify. Here, we introduce an in-depth species-specific transcriptomic analysis and conduct an extensive meta-analysis of the responses to related species to gain more knowledge about plant responses. The spider mite Tetranychus urticae was used as the individual species, several arthropod herbivores as the related species for meta-analysis, and Arabidopsis thaliana plants as the common host. The analysis of the transcriptomic data showed typical common responses to herbivory, such as jasmonate signaling or glucosinolate biosynthesis. Also, a specific set of genes likely involved in the particularities of the Arabidopsis-spider mite interaction was discovered. The new findings have determined a prominent role in this interaction of the jasmonate-induced pathways leading to the biosynthesis of anthocyanins and tocopherols. Therefore, tandem individual/general transcriptomic profiling has been revealed as an effective method to identify novel relevant processes and specificities in the plant response to environmental stresses.

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.

Evolutionary network genomics of wood formation in a phylogenetic survey of angiosperm forest trees

Wood formation was present in early angiosperms, but has been highly modified through evolution to generate the anatomical diversity seen in extant angiosperm lineages. In this project, we modeled changes in gene coexpression relationships associated with the evolution of wood formation in a phylogenetic survey of 13 angiosperm tree species. Gravitropic stimulation was used as an experimental treatment to alter wood formation and also perturb gene expression. Gene transcript abundances were determined using RNA sequencing of developing wood tissues from upright trees, and from the top (tension wood) and bottom (opposite wood) tissues of gravistimulated trees. A network-based approach was employed to align gene coexpression networks across species based on orthologous relationships. A large-scale, multilayer network was modeled that identified both lineage-specific gene coexpression modules and modules conserved across multiple species. Functional annotation and analysis of modules identified specific regulatory processes associated with conserved modules, including regulation of hormones, protein phosphorylation, meristem development and epigenetic processes. Our results provide novel insights into the evolution and development of wood formation, and demonstrate the ability to identify biological processes and genes important for the evolution of a foundational trait in nonmodel, undomesticated forest trees.

Fungi.guru: Comparative genomic and transcriptomic resource for the fungi kingdom

The fungi kingdom is composed of eukaryotic heterotrophs, which are responsible for balancing the ecosystem and play a major role as decomposers. They also produce a vast diversity of secondary metabolites, which have antibiotic or pharmacological properties. However, our lack of knowledge of gene function in fungi precludes us from tailoring them to our needs and tapping into their metabolic diversity. To help remedy this, we gathered genomic and gene expression data of 19 most widely-researched fungi to build an online tool, fungi.guru, which contains tools for cross-species identification of conserved pathways, functional gene modules, and gene families. We exemplify how our tool can elucidate the molecular function, biological process and cellular component of genes involved in various biological processes, by identifying a secondary metabolite pathway producing gliotoxin in Aspergillus fumigatus, the catabolic pathway of cellulose in Coprinopsis cinerea and the conserved DNA replication pathway in Fusarium graminearum and Pyricularia oryzae. The tool is available at www.fungi.guru.

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.

Coordination of five class III peroxidase-encoding genes for early germination events of Arabidopsis thaliana

The Class III peroxidases (CIII Prxs) belong to a plant-specific multigene family. Thanks to their double catalytic cycle they can oxidize compounds or release reactive oxygen species (ROS). They are either involved in different cell wall stiffening processes such as lignification and suberization, in cell wall loosening or defense mechanisms. Germination is an important developmental stage requiring specific peroxidase activity. However, little is known about which isoforms are involved. Five CIII Prx encoding genes: AtPrx04, AtPrx16, AtPrx62, AtPrx69, and AtPrx71 were identified from published microarray data mining. Delayed or induced testa and endosperm rupture were observed for the corresponding CIII Prx mutant lines indicating either a gene-specific inducing or repressing role during germination, respectively. Via in situ hybridization AtPrx16, AtPrx62, AtPrx69 and AtPrx71 transcripts were exclusively localized to the micropylar endosperm facing the radicle, and transcriptomic data analysis enabled positioning the five CIII Prxs in a co-expression network enriched in germination, cell wall, cell wall proteins and xyloglucan hits. Evidence were produced showing that the five CIII Prxs were cell wall-targeted proteins and that the micropylar endosperm displayed a complex cell wall domain topochemistry. Finally, we drew a spatio-temporal model highlighting the fine sequential gene expression and the possible involvement of micropylar endosperm cell wall domains to explain the non-redundant cell wall stiffening and loosening functions of the CIII Prxs in a single cell type. We also highlighted the necessity of a peroxidase homeostasis to accurately control the micropylar endosperm cell wall dynamics during Arabidopsis germination events.

Network aggregation improves gene function prediction of grapevine gene co-expression networks

Aggregation across multiple networks highlights robust co-expression interactions and improves the functional connectivity of grapevine gene co-expression networks. In recent years, the rapid accumulation of transcriptome datasets from diverse experimental conditions has enabled the widespread use of gene co-expression network (GCN) analysis in plants. In grapevine, GCN analysis has shown great promise for gene function prediction, however, measurable progress is currently lacking. Using accumulated microarray datasets from the grapevine whole-genome array (33 experiments, 1359 samples), we explored how meta-analysis through aggregation influences the functional connectivity (performance) of derived networks using guilt-by-association neighbor voting. Two annotation schemes, i.e. MapMan BIN and Pfam, at two sparsity thresholds, i.e. top 100 (stringent) and 300 (relaxed) ranked genes were evaluated. We observed that aggregating across multiple networks improves performance dramatically, with the aggregate outperforming the majority of functional terms across individual networks. Network sparsity and size (i.e. the number of samples and aggregates) were key factors influencing performance while the choice of annotation scheme had little. Systematic comparison with various state-of-the-art microarray and RNA-seq networks was also performed, however, none outperformed the aggregate microarray network despite having good predictive performance. Repeating these series of tests using a functional enrichment-based performance metric also showed remarkably consistent findings with guilt-by-association neighbor voting. To demonstrate its functionality, we explore the function and transcriptional regulation of grapevine EXPANSIN genes. We envisage that network aggregation will offer new and unique opportunities for gene function prediction in future grapevine functional genomics studies. To this end, we make the aggregate networks and associated metadata publicly available at VTC-Agg (https://sites.google.com/view/vtc-agg).

PEATmoss (Physcomitrella Expression Atlas Tool): a unified gene expression atlas for the model plant Physcomitrella patens

Physcomitrella patens is a bryophyte model plant that is often used to study plant evolution and development. Its resources are of great importance for comparative genomics and evo-devo approaches. However, expression data from Physcomitrella patens were so far generated using different gene annotation versions and three different platforms: CombiMatrix and NimbleGen expression microarrays and RNA sequencing. The currently available P. patens expression data are distributed across three tools with different visualization methods to access the data. Here, we introduce an interactive expression atlas, Physcomitrella Expression Atlas Tool (PEATmoss), that unifies publicly available expression data for P. patens and provides multiple visualization methods to query the data in a single web-based tool. Moreover, PEATmoss includes 35 expression experiments not previously available in any other expression atlas. To facilitate gene expression queries across different gene annotation versions, and to access P. patens annotations and related resources, a lookup database and web tool linked to PEATmoss was implemented. PEATmoss can be accessed at https://peatmoss.online.uni-marburg.de.

Expression Atlas of Selaginella moellendorffii Provides Insights into the Evolution of Vasculature, Secondary Metabolism, and Roots

Selaginella moellendorffii is a representative of the lycophyte lineage that is studied to understand the evolution of land plant traits such as the vasculature, leaves, stems, roots, and secondary metabolism. However, only a few studies have investigated the expression and transcriptional coordination of Selaginella genes, precluding us from understanding the evolution of the transcriptional programs behind these traits. We present a gene expression atlas comprising all major organs, tissue types, and the diurnal gene expression profiles for S. moellendorffii We show that the transcriptional gene module responsible for the biosynthesis of lignocellulose evolved in the ancestor of vascular plants and pinpoint the duplication and subfunctionalization events that generated multiple gene modules involved in the biosynthesis of various cell wall types. We demonstrate how secondary metabolism is transcriptionally coordinated and integrated with other cellular pathways. Finally, we identify root-specific genes and show that the evolution of roots did not coincide with an increased appearance of gene families, suggesting that the development of new organs does not coincide with increased fixation of new gene functions. Our updated database at conekt.plant.tools represents a valuable resource for studying the evolution of genes, gene families, transcriptomes, and functional gene modules in the Archaeplastida kingdom.

Competition of Parental Genomes in Plant Hybrids

Interspecific hybridization represents one of the main mechanisms of plant speciation. Merging of two genomes from different subspecies, species, or even genera is frequently accompanied by whole-genome duplication (WGD). Besides its evolutionary role, interspecific hybridization has also been successfully implemented in multiple breeding programs. Interspecific hybrids combine agronomic traits of two crop species or can be used to introgress specific loci of interests, such as those for resistance against abiotic or biotic stresses. The genomes of newly established interspecific hybrids (both allopolyploids and homoploids) undergo dramatic changes, including chromosome rearrangements, amplifications of tandem repeats, activation of mobile repetitive elements, and gene expression modifications. To ensure genome stability and proper transmission of chromosomes from both parental genomes into subsequent generations, allopolyploids often evolve mechanisms regulating chromosome pairing. Such regulatory systems allow only pairing of homologous chromosomes and hamper pairing of homoeologs. Despite such regulatory systems, several hybrid examples with frequent homoeologous chromosome pairing have been reported. These reports open a way for the replacement of one parental genome by the other. In this review, we provide an overview of the current knowledge of genomic changes in interspecific homoploid and allopolyploid hybrids, with strictly homologous pairing and with relaxed pairing of homoeologs.

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.

Diurnal.plant.tools: Comparative Transcriptomic and Co-expression Analyses of Diurnal Gene Expression of the Archaeplastida Kingdom

Almost all organisms coordinate some aspects of their biology through the diurnal cycle. Photosynthetic organisms, and plants especially, have established complex programs that coordinate physiological, metabolic and developmental processes with the changing light. The diurnal regulation of the underlying transcriptional processes is observed when groups of functionally related genes (gene modules) are expressed at a specific time of the day. However, studying the diurnal regulation of these gene modules in the plant kingdom was hampered by the large amount of data required for the analyses. To meet this need, we used gene expression data from 17 diurnal studies spanning the whole Archaeplastida kingdom (Plantae kingdom in the broad sense) to make an online diurnal database. We have equipped the database with tools that allow user-friendly cross-species comparisons of gene expression profiles, entire co-expression networks, co-expressed clusters (involved in specific biological processes), time-specific gene expression and others. We exemplify how these tools can be used by studying three important biological questions: (i) the evolution of cell division, (ii) the diurnal control of gene modules in algae and (iii) the conservation of diurnally controlled modules across species. The database is freely available at http://diurnal.plant.tools.

Networks Consolidate the Core Concepts of Evolution by Natural Selection

Microbiology has unraveled rich evidence of ongoing reticulate evolutionary processes and complex interactions both within and between cells. These phenomena feature real biological networks, which can logically be analyzed using network-based tools. It is thus not surprising that network sciences, a field independent from evolutionary biology and microbiology, have recently pervasively infused their methods into both fields. Importantly, network tools bring forward observations enhancing the understanding of three core evolutionary concepts: variation, fitness, and heredity. Consequently, our work shows how network sciences can enhance evolutionary theory by explaining the evolution by natural selection of a broad diversity of units of selection, while updating the popular figure of Darwin's tree of life with a comprehensive sketch of the networks of evolution.

Phylostratigraphic Analysis Shows the Earliest Origination of the Abiotic Stress Associated Genes in A. thaliana

Plants constantly fight with stressful factors as high or low temperature, drought, soil salinity and flooding. Plants have evolved a set of stress response mechanisms, which involve physiological and biochemical changes that result in adaptive or morphological changes. At a molecular level, stress response in plants is performed by genetic networks, which also undergo changes in the process of evolution. The study of the network structure and evolution may highlight mechanisms of plants adaptation to adverse conditions, as well as their response to stresses and help in discovery and functional characterization of the stress-related genes. We performed an analysis of Arabidopsis thaliana genes associated with several types of abiotic stresses (heat, cold, water-related, light, osmotic, salt, and oxidative) at the network level using a phylostratigraphic approach. Our results show that a substantial fraction of genes associated with various types of abiotic stress is of ancient origin and evolves under strong purifying selection. The interaction networks of genes associated with stress response have a modular structure with a regulatory component being one of the largest for five of seven stress types. We demonstrated a positive relationship between the number of interactions of gene in the stress gene network and its age. Moreover, genes of the same age tend to be connected in stress gene networks. We also demonstrated that old stress-related genes usually participate in the response for various types of stress and are involved in numerous biological processes unrelated to stress. Our results demonstrate that the stress response genes represent the ancient and one of the fundamental molecular systems in plants.

Co-expression networks for plant biology: why and how

Co-expression network analysis is one of the most powerful approaches for interpretation of large transcriptomic datasets. It enables characterization of modules of co-expressed genes that may share biological functional linkages. Such networks provide an initial way to explore functional associations from gene expression profiling and can be applied to various aspects of plant biology. This review presents the applications of co-expression network analysis in plant biology and addresses optimized strategies from the recent literature for performing co-expression analysis on plant biological systems. Additionally, we describe the combined interpretation of co-expression analysis with other genomic data to enhance the generation of biologically relevant information.

A global coexpression network of soybean genes gives insights into the evolution of nodulation in nonlegumes and legumes

A coexpression network is a powerful tool for revealing genes' relationship with many biological processes. Mass transcriptomic and genomic data from different plant species provide the foundation for understanding the evolution of nodulation across the Viridiplantae at a systematic level. We used weighted coexpression network analysis (WGCNA) to mine a nodule-related module (NRM) in Glycine max. Comparative genomic analysis of 78 green plant species revealed that NRM genes are recruited from different evolutionary nodes along with gene duplication events. A set of core coexpressed genes within legumes may play vital roles in regulating nodule environments essential for nitrogen fixation, including oxygen concentrations, sulfur transport, and iron homeostasis (such as GmCHY). The regulation of these genes occurred mainly at the transcription level, although some of them, such as sulfate transporters, may also undergo positive selection at protein level. We revealed that ancient orthologs and duplication events before the origin of legumes were preadapted for symbiosis. Conserved coregulated genes found within legumes paved the way for nodule formation and nitrogen fixation. These findings provide significant insights into the evolution of nodulation and indicate promising candidates for identifying other key components of legume nodulation and nitrogen fixation.

CoNekT: an open-source framework for comparative genomic and transcriptomic network analyses

The recent accumulation of gene expression data in the form of RNA sequencing creates unprecedented opportunities to study gene regulation and function. Furthermore, comparative analysis of the expression data from multiple species can elucidate which functional gene modules are conserved across species, allowing the study of the evolution of these modules. However, performing such comparative analyses on raw data is not feasible for many biologists. Here, we present CoNekT (Co-expression Network Toolkit), an open source web server, that contains user-friendly tools and interactive visualizations for comparative analyses of gene expression data and co-expression networks. These tools allow analysis and cross-species comparison of (i) gene expression profiles; (ii) co-expression networks; (iii) co-expressed clusters involved in specific biological processes; (iv) tissue-specific gene expression; and (v) expression profiles of gene families. To demonstrate these features, we constructed CoNekT-Plants for green alga, seed plants and flowering plants (Picea abies, Chlamydomonas reinhardtii, Vitis vinifera, Arabidopsis thaliana, Oryza sativa, Zea mays and Solanum lycopersicum) and thus provide a web-tool with the broadest available collection of plant phyla. CoNekT-Plants is freely available from http://conekt.plant.tools, while the CoNekT source code and documentation can be found at https://github.molgen.mpg.de/proost/CoNekT/.

PhytoNet: comparative co-expression network analyses across phytoplankton and land plants

Phytoplankton consists of autotrophic, photosynthesizing microorganisms that are a crucial component of freshwater and ocean ecosystems. However, despite being the major primary producers of organic compounds, accounting for half of the photosynthetic activity worldwide and serving as the entry point to the food chain, functions of most of the genes of the model phytoplankton organisms remain unknown. To remedy this, we have gathered publicly available expression data for one chlorophyte, one rhodophyte, one haptophyte, two heterokonts and four cyanobacteria and integrated it into our PlaNet (Plant Networks) database, which now allows mining gene expression profiles and identification of co-expressed genes of 19 species. We exemplify how the co-expressed gene networks can be used to reveal functionally related genes and how the comparative features of PhytoNet allow detection of conserved transcriptional programs between cyanobacteria, green algae, and land plants. Additionally, we illustrate how the database allows detection of duplicated transcriptional programs within an organism, as exemplified by two putative DNA repair programs within Chlamydomonas reinhardtii. PhytoNet is available from www.gene2function.de.

Function, dynamics and evolution of network motif modules in integrated gene regulatory networks of worm and plant

Gene regulatory networks (GRNs) consist of different molecular interactions that closely work together to establish proper gene expression in time and space. Especially in higher eukaryotes, many questions remain on how these interactions collectively coordinate gene regulation. We study high quality GRNs consisting of undirected protein–protein, genetic and homologous interactions, and directed protein–DNA, regulatory and miRNA–mRNA interactions in the worm Caenorhabditis elegans and the plant Arabidopsis thaliana. Our data-integration framework integrates interactions in composite network motifs, clusters these in biologically relevant, higher-order topological network motif modules, overlays these with gene expression profiles and discovers novel connections between modules and regulators. Similar modules exist in the integrated GRNs of worm and plant. We show how experimental or computational methodologies underlying a certain data type impact network topology. Through phylogenetic decomposition, we found that proteins of worm and plant tend to functionally interact with proteins of a similar age, while at the regulatory level TFs favor same age, but also older target genes. Despite some influence of the duplication mode difference, we also observe at the motif and module level for both species a preference for age homogeneity for undirected and age heterogeneity for directed interactions. This leads to a model where novel genes are added together to the GRNs in a specific biological functional context, regulated by one or more TFs that also target older genes in the GRNs. Overall, we detected topological, functional and evolutionary properties of GRNs that are potentially universal in all species.

TF2Network: predicting transcription factor regulators and gene regulatory networks in Arabidopsis using publicly available binding site information

A gene regulatory network (GRN) is a collection of regulatory interactions between transcription factors (TFs) and their target genes. GRNs control different biological processes and have been instrumental to understand the organization and complexity of gene regulation. Although various experimental methods have been used to map GRNs in Arabidopsis thaliana, their limited throughput combined with the large number of TFs makes that for many genes our knowledge about regulating TFs is incomplete. We introduce TF2Network, a tool that exploits the vast amount of TF binding site information and enables the delineation of GRNs by detecting potential regulators for a set of co-expressed or functionally related genes. Validation using two experimental benchmarks reveals that TF2Network predicts the correct regulator in 75–92% of the test sets. Furthermore, our tool is robust to noise in the input gene sets, has a low false discovery rate, and shows a better performance to recover correct regulators compared to other plant tools. TF2Network is accessible through a web interface where GRNs are interactively visualized and annotated with various types of experimental functional information. TF2Network was used to perform systematic functional and regulatory gene annotations, identifying new TFs involved in circadian rhythm and stress response.

Neofunctionalization of Mitochondrial Proteins and Incorporation into Signaling Networks in Plants

Because of their symbiotic origin, many mitochondrial proteins are well conserved across eukaryotic kingdoms. It is however less obvious how specific lineages have obtained novel nuclear-encoded mitochondrial proteins. Here, we report a case of mitochondrial neofunctionalization in plants. Phylogenetic analysis of genes containing the Domain of Unknown Function 295 (DUF295) revealed that the domain likely originated in Angiosperms. The C-terminal DUF295 domain is usually accompanied by an N-terminal F-box domain, involved in ubiquitin ligation via binding with ASK1/SKP1-type proteins. Due to gene duplication, the gene family has expanded rapidly, with 94 DUF295-related genes in Arabidopsis thaliana alone. Two DUF295 family subgroups have uniquely evolved and quickly expanded within Brassicaceae. One of these subgroups has completely lost the F-box, but instead obtained strongly predicted mitochondrial targeting peptides. We show that several representatives of this DUF295 Organellar group are effectively targeted to plant mitochondria and chloroplasts. Furthermore, many DUF295 Organellar genes are induced by mitochondrial dysfunction, whereas F-Box DUF295 genes are not. In agreement, several Brassicaceae-specific DUF295 Organellar genes were incorporated in the evolutionary much older ANAC017-dependent mitochondrial retrograde signaling pathway. Finally, a representative set of DUF295 T-DNA insertion mutants was created. No obvious aberrant phenotypes during normal growth and mitochondrial dysfunction were observed, most likely due to the large extent of gene duplication and redundancy. Overall, this study provides insight into how novel mitochondrial proteins can be created via “intercompartmental” gene duplication events. Moreover, our analysis shows that these newly evolved genes can then be specifically integrated into relevant, pre-existing coexpression networks.

Gene co-expression network analysis identifies trait-related modules in Arabidopsis thaliana

A comprehensive network of the Arabidopsis transcriptome was analyzed and may serve as a valuable resource for candidate gene function investigations. A web tool to explore module information was also provided. Arabidopsis thaliana is a widely studied model plant whose transcriptome has been substantially profiled in various tissues, development stages and other conditions. These data can be reused for research on gene function through a systematic analysis of gene co-expression relationships. We collected microarray data from National Center for Biotechnology Information Gene Expression Omnibus, identified modules of co-expressed genes and annotated module functions. These modules were associated with experiments/traits, which provided potential signature modules for phenotypes. Novel heat shock proteins were implicated according to guilt by association. A higher-order module networks analysis suggested that the Arabidopsis network can be further organized into 15 meta-modules and that a chloroplast meta-module has a distinct gene expression pattern from the other 14 meta-modules. A comparison with the rice transcriptome revealed preserved modules and KEGG pathways. All the module gene information was available from an online tool at http://bioinformatics.fafu.edu.cn/arabi/ . Our findings provide a new source for future gene discovery in Arabidopsis.

Genome-Wide Identification and Characterization of the PERK Gene Family in Gossypium hirsutum Reveals Gene Duplication and Functional Divergence

Proline-rich extensin-like receptor kinases (PERKs) are an important class of receptor kinases in plants. Receptor kinases comprise large gene families in many plant species, including the 15 PERK genes in Arabidopsis. At present, there is no comprehensive published study of PERK genes in G. hirsutum. Our study identified 33 PERK genes in G. hirsutum. Phylogenetic analysis of conserved PERK protein sequences from 15 plant species grouped them into four well defined clades. The GhPERK gene family is an evolutionarily advanced gene family that lost its introns over time. Several cis-elements were identified in the promoter regions of the GhPERK genes that are important in regulating growth, development, light responses and the response to several stresses. In addition, we found evidence for gene loss or addition through segmental or whole genome duplication in cotton. Gene duplication and synteny analysis identified 149 orthologous/paralogous gene pairs. Ka/Ks values show that most GhPERK genes experienced strong purifying selection during the rapid evolution of the gene family. GhPERK genes showed high expression levels in leaves and during ovule development. Furthermore, the expression of GhPERK genes can be regulated by abiotic stresses and phytohormone treatments. Additionally, PERK genes could be involved in several molecular, biological and physiological processes that might be the result of functional divergence.

Phylostratigraphic analysis of gene co-expression network reveals the evolution of functional modules for ovarian cancer

Ovarian cancer (OV) is an extremely lethal disease. However, the evolutionary machineries of OV are still largely unknown. Here, we used a method that combines phylostratigraphy information with gene co-expression networks to extensively study the evolutionary compositions of OV. The present co-expression network construction yielded 18,549 nodes and 114,985 edges based on 307 OV expression samples obtained from the Genome Data Analysis Centers database. A total of 20 modules were identified as OV related clusters. The human genome sequences were divided into 19 phylostrata (PS), the majority (67.45%) of OV genes was already present in the eukaryotic ancestor. There were two strong peaks of the emergence of OV genes screened by hypergeometric test: the evolution of the multicellular metazoan organisms (PS5 and PS6, P value = 0.002) and the emergence of bony fish (PS11 and PS12, P value = 0.009). Hence, the origin of OV is far earlier than its emergence. The integrated analysis of the topology of OV modules and the phylogenetic data revealed an evolutionary pattern of OV in human, namely, OV modules have arisen step by step during the evolution of the respective lineages. New genes have evolved and become locked into a pathway, where more and more biological pathways are fixed into OV modules by recruiting new genes during human evolution.

Kingdom-wide comparison reveals the evolution of diurnal gene expression in Archaeplastida

Plants have adapted to the diurnal light-dark cycle by establishing elaborate transcriptional programs that coordinate many metabolic, physiological, and developmental responses to the external environment. These transcriptional programs have been studied in only a few species, and their function and conservation across algae and plants is currently unknown. We performed a comparative transcriptome analysis of the diurnal cycle of nine members of Archaeplastida, and we observed that, despite large phylogenetic distances and dramatic differences in morphology and lifestyle, diurnal transcriptional programs of these organisms are similar. Expression of genes related to cell division and the majority of biological pathways depends on the time of day in unicellular algae but we did not observe such patterns at the tissue level in multicellular land plants. Hence, our study provides evidence for the universality of diurnal gene expression and elucidates its evolutionary history among different photosynthetic eukaryotes.

Recent advances in gene function prediction using context-specific coexpression networks in plants

Predicting gene functions from genome sequence alone has been difficult, and the functions of a large fraction of plant genes remain unknown. However, leveraging the vast amount of currently available gene expression data has the potential to facilitate our understanding of plant gene functions, especially in determining complex traits. Gene coexpression networks-created by integrating multiple expression datasets-connect genes with similar patterns of expression across multiple conditions. Dense gene communities in such networks, commonly referred to as modules, often indicate that the member genes are functionally related. As such, these modules serve as tools for generating new testable hypotheses, including the prediction of gene function and importance. Recently, we have seen a paradigm shift from the traditional "global" to more defined, context-specific coexpression networks. Such coexpression networks imply genetic correlations in specific biological contexts such as during development or in response to a stress. In this short review, we highlight a few recent studies that attempt to fill the large gaps in our knowledge about cellular functions of plant genes using context-specific coexpression networks.

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.