ARACNe-based inference, using curated microarray data, of Arabidopsis thaliana root transcriptional regulatory networks

Enhancing Gene Co-Expression Network Inference for the Malaria Parasite Plasmodium falciparum

BACKGROUND

Malaria results in more than 550,000 deaths each year due to drug resistance in the most lethal Plasmodium (P.) species P. falciparum. A full P. falciparum genome was published in 2002, yet 44.6% of its genes have unknown functions. Improving the functional annotation of genes is important for identifying drug targets and understanding the evolution of drug resistance.

RESULTS

Genes function by interacting with one another. So, analyzing gene co-expression networks can enhance functional annotations and prioritize genes for wet lab validation. Earlier efforts to build gene co-expression networks in P. falciparum have been limited to a single network inference method or gaining biological understanding for only a single gene and its interacting partners. Here, we explore multiple inference methods and aim to systematically predict functional annotations for all P. falciparum genes. We evaluate each inferred network based on how well it predicts existing gene-Gene Ontology (GO) term annotations using network clustering and leave-one-out crossvalidation. We assess overlaps of the different networks' edges (gene co-expression relationships), as well as predicted functional knowledge. The networks' edges are overall complementary: 47-85% of all edges are unique to each network. In terms of the accuracy of predicting gene functional annotations, all networks yielded relatively high precision (as high as 87% for the network inferred using mutual information), but the highest recall reached was below 15%. All networks having low recall means that none of them capture a large amount of all existing gene-GO term annotations. In fact, their annotation predictions are highly complementary, with the largest pairwise overlap of only 27%. We provide ranked lists of inferred gene-gene interactions and predicted gene-GO term annotations for future use and wet lab validation by the malaria community.

CONCLUSIONS

The different networks seem to capture different aspects of the P. falciparum biology in terms of both inferred interactions and predicted gene functional annotations. Thus, relying on a single network inference method should be avoided when possible.

SUPPLEMENTARY DATA

Attached.

A Common Molecular Signature Indicates the Pre-Meristematic State of Plant Calli

In response to different degrees of mechanical injury, certain plant cells re-enter the division cycle to provide cells for tissue replenishment, tissue rejoining, de novo organ formation, and/or wound healing. The intermediate tissue formed by the dividing cells is called a callus. Callus formation can also be induced artificially in vitro by wounding and/or hormone (auxin and cytokinin) treatments. The callus tissue can be maintained in culture, providing starting material for de novo organ or embryo regeneration and thus serving as the basis for many plant biotechnology applications. Due to the biotechnological importance of callus cultures and the scientific interest in the developmental flexibility of somatic plant cells, the initial molecular steps of callus formation have been studied in detail. It was revealed that callus initiation can follow various ways, depending on the organ from which it develops and the inducer, but they converge on a seemingly identical tissue. It is not known, however, if callus is indeed a special tissue with a defined gene expression signature, whether it is a malformed meristem, or a mass of so-called "undifferentiated" cells, as is mostly believed. In this paper, I review the various mechanisms of plant regeneration that may converge on callus initiation. I discuss the role of plant hormones in the detour of callus formation from normal development. Finally, I compare various Arabidopsis gene expression datasets obtained a few days, two weeks, or several years after callus induction and identify 21 genes, including genes of key transcription factors controlling cell division and differentiation in meristematic regions, which were upregulated in all investigated callus samples. I summarize the information available on all 21 genes that point to the pre-meristematic nature of callus tissues underlying their wide regeneration potential.

Dissection of canopy layer-specific genetic control of leaf angle in Sorghum bicolor by RNA sequencing

BACKGROUND

Leaf angle is an important plant architecture trait, affecting plant density, light interception efficiency, photosynthetic rate, and yield. The "smart canopy" model proposes more vertical leaves in the top plant layers and more horizontal leaves in the lower canopy, maximizing conversion efficiency and photosynthesis. Sorghum leaf arrangement is opposite to that proposed in the "smart canopy" model, indicating the need for improvement. Although leaf angle quantitative trait loci (QTL) have been previously reported, only the Dwarf3 (Dw3) auxin transporter gene, colocalizing with a major-effect QTL on chromosome 7, has been validated. Additionally, the genetic architecture of leaf angle across canopy layers remains to be elucidated.

RESULTS

This study characterized the canopy-layer specific transcriptome of five sorghum genotypes using RNA sequencing. A set of 284 differentially expressed genes for at least one layer comparison (FDR < 0.05) co-localized with 69 leaf angle QTL and were consistently identified across genotypes. These genes are involved in transmembrane transport, hormone regulation, oxidation-reduction process, response to stimuli, lipid metabolism, and photosynthesis. The most relevant eleven candidate genes for layer-specific angle modification include those homologous to genes controlling leaf angle in rice and maize or genes associated with cell size/expansion, shape, and cell number.

CONCLUSIONS

Considering the predicted functions of candidate genes, their potential undesirable pleiotropic effects should be further investigated across tissues and developmental stages. Future validation of proposed candidates and exploitation through genetic engineering or gene editing strategies targeted to collar cells will bring researchers closer to the realization of a "smart canopy" sorghum.

Defective cytokinin signaling reprograms lipid and flavonoid gene-to-metabolite networks to mitigate high salinity in Arabidopsis

Cytokinin (CK) in plants regulates both developmental processes and adaptation to environmental stresses. Arabidopsis histidine phosphotransfer ahp2,3,5 and type-B Arabidopsis response regulator arr1,10,12 triple mutants are almost completely defective in CK signaling, and the ahp2,3,5 mutant was reported to be salt tolerant. Here, we demonstrate that the arr1,10,12 mutant is also more tolerant to salt stress than wild-type (WT) plants. A comprehensive metabolite profiling coupled with transcriptome analysis of the ahp2,3,5 and arr1,10,12 mutants was conducted to elucidate the salt tolerance mechanisms mediated by CK signaling. Numerous primary (e.g., sugars, amino acids, and lipids) and secondary (e.g., flavonoids and sterols) metabolites accumulated in these mutants under nonsaline and saline conditions, suggesting that both prestress and poststress accumulations of stress-related metabolites contribute to improved salt tolerance in CK-signaling mutants. Specifically, the levels of sugars (e.g., trehalose and galactinol), amino acids (e.g., branched-chain amino acids and γ-aminobutyric acid), anthocyanins, sterols, and unsaturated triacylglycerols were higher in the mutant plants than in WT plants. Notably, the reprograming of flavonoid and lipid pools was highly coordinated and concomitant with the changes in transcriptional levels, indicating that these metabolic pathways are transcriptionally regulated by CK signaling. The discovery of the regulatory role of CK signaling on membrane lipid reprogramming provides a greater understanding of CK-mediated salt tolerance in plants. This knowledge will contribute to the development of salt-tolerant crops with the ability to withstand salinity as a key driver to ensure global food security in the era of climate crisis.

Gene Regulatory Network Inference: Connecting Plant Biology and Mathematical Modeling

Plant responses to environmental and intrinsic signals are tightly controlled by multiple transcription factors (TFs). These TFs and their regulatory connections form gene regulatory networks (GRNs), which provide a blueprint of the transcriptional regulations underlying plant development and environmental responses. This review provides examples of experimental methodologies commonly used to identify regulatory interactions and generate GRNs. Additionally, this review describes network inference techniques that leverage gene expression data to predict regulatory interactions. These computational and experimental methodologies yield complex networks that can identify new regulatory interactions, driving novel hypotheses. Biological properties that contribute to the complexity of GRNs are also described in this review. These include network topology, network size, transient binding of TFs to DNA, and competition between multiple upstream regulators. Finally, this review highlights the potential of machine learning approaches to leverage gene expression data to predict phenotypic outputs.

A transcriptional regulatory network of Rsv3-mediated extreme resistance against Soybean mosaic virus

Resistance genes are an effective means for disease control in plants. They predominantly function by inducing a hypersensitive reaction, which results in localized cell death restricting pathogen spread. Some resistance genes elicit an atypical response, termed extreme resistance, where resistance is not associated with a hypersensitive reaction and its standard defense responses. Unlike hypersensitive reaction, the molecular regulatory mechanism(s) underlying extreme resistance is largely unexplored. One of the few known, naturally occurring, instances of extreme resistance is resistance derived from the soybean Rsv3 gene, which confers resistance against the most virulent Soybean mosaic virus strains. To discern the regulatory mechanism underlying Rsv3-mediated extreme resistance, we generated a gene regulatory network using transcriptomic data from time course comparisons of Soybean mosaic virus-G7-inoculated resistant (L29, Rsv3-genotype) and susceptible (Williams82, rsv3-genotype) soybean cultivars. Our results show Rsv3 begins mounting a defense by 6 hpi via a complex phytohormone network, where abscisic acid, cytokinin, jasmonic acid, and salicylic acid pathways are suppressed. We identified putative regulatory interactions between transcription factors and genes in phytohormone regulatory pathways, which is consistent with the demonstrated involvement of these pathways in Rsv3-mediated resistance. One such transcription factor identified as a putative transcriptional regulator was MYC2 encoded by Glyma.07G051500. Known as a master regulator of abscisic acid and jasmonic acid signaling, MYC2 specifically recognizes the G-box motif ("CACGTG"), which was significantly enriched in our data among differentially expressed genes implicated in abscisic acid- and jasmonic acid-related activities. This suggests an important role for Glyma.07G051500 in abscisic acid- and jasmonic acid-derived defense signaling in Rsv3. Resultantly, the findings from our network offer insights into genes and biological pathways underlying the molecular defense mechanism of Rsv3-mediated extreme resistance against Soybean mosaic virus. The computational pipeline used to reconstruct the gene regulatory network in this study is freely available at https://github.com/LiLabAtVT/rsv3-network.

A transcriptional regulatory network of Rsv3-mediated extreme resistance against Soybean mosaic virus

Resistance genes are an effective means for disease control in plants. They predominantly function by inducing a hypersensitive reaction, which results in localized cell death restricting pathogen spread. Some resistance genes elicit an atypical response, termed extreme resistance, where resistance is not associated with a hypersensitive reaction and its standard defense responses. Unlike hypersensitive reaction, the molecular regulatory mechanism(s) underlying extreme resistance is largely unexplored. One of the few known, naturally occurring, instances of extreme resistance is resistance derived from the soybean Rsv3 gene, which confers resistance against the most virulent Soybean mosaic virus strains. To discern the regulatory mechanism underlying Rsv3-mediated extreme resistance, we generated a gene regulatory network using transcriptomic data from time course comparisons of Soybean mosaic virus-G7-inoculated resistant (L29, Rsv3-genotype) and susceptible (Williams82, rsv3-genotype) soybean cultivars. Our results show Rsv3 begins mounting a defense by 6 hpi via a complex phytohormone network, where abscisic acid, cytokinin, jasmonic acid, and salicylic acid pathways are suppressed. We identified putative regulatory interactions between transcription factors and genes in phytohormone regulatory pathways, which is consistent with the demonstrated involvement of these pathways in Rsv3-mediated resistance. One such transcription factor identified as a putative transcriptional regulator was MYC2 encoded by Glyma.07G051500. Known as a master regulator of abscisic acid and jasmonic acid signaling, MYC2 specifically recognizes the G-box motif ("CACGTG"), which was significantly enriched in our data among differentially expressed genes implicated in abscisic acid- and jasmonic acid-related activities. This suggests an important role for Glyma.07G051500 in abscisic acid- and jasmonic acid-derived defense signaling in Rsv3. Resultantly, the findings from our network offer insights into genes and biological pathways underlying the molecular defense mechanism of Rsv3-mediated extreme resistance against Soybean mosaic virus. The computational pipeline used to reconstruct the gene regulatory network in this study is freely available at https://github.com/LiLabAtVT/rsv3-network.

Overexpression of MADS-box Gene AGAMOUS-LIKE 12 Activates Root Development in Juglans sp. and Arabidopsis thaliana

Until recently, the roles of plant MADS-box genes have mainly been characterized during inflorescence and flower differentiation. In order to precise the roles of AGAMOUS-LIKE 12, one of the few MADS-box genes preferentially expressed in roots, we placed its cDNA under the control of the double 35S CaMV promoter to produce transgenic walnut tree and Arabidopsis plants. In Juglans sp., transgenic somatic embryos showed significantly higher germination rates but abnormal development of their shoot apex prevented their conversion into plants. In addition, a wide range of developmental abnormalities corresponding to ectopic root-like structures affected the transgenic lines suggesting partial reorientations of the embryonic program toward root differentiation. In Arabidopsis, AtAGL12 overexpression lead to the production of faster growing plants presenting dramatically wider and shorter root phenotypes linked to increased meristematic cell numbers within the root apex. In the upper part of the roots, abnormal cell divisions patterns within the pericycle layer generated large ectopic cell masses that did not prevent plants to grow. Taken together, our results confirm in both species that AGL12 positively regulates root meristem cell division and promotes overall root vascular tissue formation. Genetic engineering of AGL12 expression levels could be useful to modulate root architecture and development.

Shannon entropy approach reveals relevant genes in Alzheimer's disease

Alzheimer’s disease (AD) is the most common type of dementia and affects millions of people worldwide. Since complex diseases are often the result of combinations of gene interactions, microarray data and gene co-expression analysis can provide tools for addressing complexity. Our study aimed to find groups of interacting genes that are relevant in the development of AD. In this perspective, we implemented a method proposed in a previous work to detect gene communities linked to AD. Our strategy combined co-expression network analysis with the study of Shannon entropy of the betweenness. We analyzed the publicly available GSE1297 dataset, achieved from the GEO database in NCBI, containing hippocampal gene expression of 9 control and 22 AD human subjects. Co-expressed genes were clustered into different communities. Two communities of interest (composed by 72 and 39 genes) were found by calculating the correlation coefficient between communities and clinical features. The detected communities resulted stable, replicated on two independent datasets and mostly enriched in pathways closely associated with neuro-degenative diseases. A comparison between our findings and other module detection techniques showed that the detected communities were more related to AD phenotype. Lastly, the hub genes within the two communities of interest were identified by means of a centrality analysis and a bootstrap procedure. The communities of the hub genes presented even stronger correlation with clinical features. These findings and further explorations on the detected genes could shed light on the genetic aspects related with physiological aspects of Alzheimer’s disease.

Genome-scale mining of root-preferential genes from maize and characterization of their promoter activity

BACKGROUND

Modification of root architecture and improvement of root resistance to stresses can increase crop productivity. Functional analyses of root-specific genes are necessary for root system improvement, and root-specific promoters enable research into the regulation of root development and genetic manipulation of root traits. Maize is an important crop species; however, little systematic mining of root-specific genes and promoters has been performed to date.

RESULTS

Genomic-scale mining based on microarray data sets followed by transcript detection resulted in the identification of 222 root-specific genes. Gene Ontology enrichment analyses revealed that these 222 root-specific genes were mainly involved in responses to chemical, biotic, and abiotic stresses. Of the 222 genes, 33 were verified by quantitative reverse transcription polymerase chain reaction, and 31 showed root-preferential activity. About 2 kb upstream 5 of the 31 identified root-preferential genes were cloned from the maize genome as putative promoters and named p8463, p5023, p1534, p8531 and p6629. GUS staining of transgenic maize-derived promoter-GUS constructs revealed that the five promoters drove GUS expression in a root-preferential manner.

CONCLUSIONS

We mined root-preferential genes and their promoters in maize and verified p8463, p5023, p1534, p8531 and p6629 as root-preferential promoters. Our research enables the identification of other tissue-specific genes and promoters in maize and other species. In addition, the five promoters may enable enhancement of target gene(s) of maize in a root-preferential manner to generate novel maize cultivars with resistance to water, fertilizer constraints, or biotic stresses.

Gene regulatory network inference resources: A practical overview

Transcriptional regulation is a fundamental molecular mechanism involved in almost every aspect of life, from homeostasis to development, from metabolism to behavior, from reaction to stimuli to disease progression. In recent years, the concept of Gene Regulatory Networks (GRNs) has grown popular as an effective applied biology approach for describing the complex and highly dynamic set of transcriptional interactions, due to its easy-to-interpret features. Since cataloguing, predicting and understanding every GRN connection in all species and cellular contexts remains a great challenge for biology, researchers have developed numerous tools and methods to infer regulatory processes. In this review, we catalogue these methods in six major areas, based on the dominant underlying information leveraged to infer GRNs: Coexpression, Sequence Motifs, Chromatin Immunoprecipitation (ChIP), Orthology, Literature and Protein-Protein Interaction (PPI) specifically focused on transcriptional complexes. The methods described here cover a wide range of user-friendliness: from web tools that require no prior computational expertise to command line programs and algorithms for large scale GRN inferences. Each method for GRN inference described herein effectively illustrates a type of transcriptional relationship, with many methods being complementary to others. While a truly holistic approach for inferring and displaying GRNs remains one of the greatest challenges in the field of systems biology, we believe that the integration of multiple methods described herein provides an effective means with which experimental and computational biologists alike may obtain the most complete pictures of transcriptional relationships. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.

Gene networks in cancer are biased by aneuploidies and sample impurities

Gene regulatory network inference is a standard technique for obtaining structured regulatory information from, for instance, gene expression measurements. Methods performing this task have been extensively evaluated on synthetic, and to a lesser extent real data sets. In contrast to these test evaluations, applications to gene expression data of human cancers are often limited by fewer samples and more potential regulatory links, and are biased by copy number aberrations as well as cell mixtures and sample impurities. Here, we take networks inferred from TCGA cohorts as an example to show that (1) transcription factor annotations are essential to obtain reliable networks, and (2) even for state of the art methods, we expect that between 20 and 80% of edges are caused by copy number changes and cell mixtures rather than transcription factor regulation.

MADS-box genes underground becoming mainstream: plant root developmental mechanisms

Plant growth is largely post-embryonic and depends on meristems that are active throughout the lifespan of an individual. Developmental patterns rely on the coordinated spatio-temporal expression of different genes, and the activity of transcription factors is particularly important during most morphogenetic processes. MADS-box genes constitute a transcription factor family in eukaryotes. In Arabidopsis, their proteins participate in all major aspects of shoot development, but their role in root development is still not well characterized. In this review we synthetize current knowledge pertaining to the function of MADS-box genes highly expressed in roots: XAL1, XAL2, ANR1 and AGL21, as well as available data for other MADS-box genes expressed in this organ. The role of Trithorax group and Polycomb group complexes on MADS-box genes' epigenetic regulation is also discussed. We argue that understanding the role of MADS-box genes in root development of species with contrasting architectures is still a challenge. Finally, we propose that MADS-box genes are key components of the gene regulatory networks that underlie various gene expression patterns, each one associated with the distinct developmental fates observed in the root. In the case of XAL1 and XAL2, their role within these networks could be mediated by regulatory feedbacks with auxin.

Transcription factors involved in abiotic stress responses in Maize (Zea mays L.) and their roles in enhanced productivity in the post genomics era

BACKGROUND

Maize (Zea mays L.) is a principal cereal crop cultivated worldwide for human food, animal feed, and more recently as a source of biofuel. However, as a direct consequence of water insufficiency and climate change, frequent occurrences of both biotic and abiotic stresses have been reported in various regions around the world, and recently, this has become a constant threat in increasing global maize yields. Plants respond to abiotic stresses by utilizing the activities of transcription factors (TFs), which are families of genes coding for specific TF proteins. TF target genes form a regulon that is involved in the repression/activation of genes associated with abiotic stress responses. Therefore, it is of utmost importance to have a systematic study on each TF family, the downstream target genes they regulate, and the specific TF genes involved in multiple abiotic stress responses in maize and other staple crops.

METHOD

In this review, the main TF families, the specific TF genes and their regulons that are involved in abiotic stress regulation will be briefly discussed. Great emphasis will be given on maize abiotic stress improvement throughout this review, although other examples from different plants like rice, Arabidopsis, wheat, and barley will be used.

RESULTS

We have described in detail the main TF families in maize that take part in abiotic stress responses together with their regulons. Furthermore, we have also briefly described the utilization of high-efficiency technologies in the study and characterization of TFs involved in the abiotic stress regulatory networks in plants with an emphasis on increasing maize production. Examples of these technologies include next-generation sequencing, microarray analysis, machine learning, and RNA-Seq.

CONCLUSION

In conclusion, it is expected that all the information provided in this review will in time contribute to the use of TF genes in the research, breeding, and development of new abiotic stress tolerant maize cultivars.

Computational prediction of gene regulatory networks in plant growth and development

Plants integrate a wide range of cellular, developmental, and environmental signals to regulate complex patterns of gene expression. Recent advances in genomic technologies enable differential gene expression analysis at a systems level, allowing for improved inference of the network of regulatory interactions between genes. These gene regulatory networks, or GRNs, are used to visualize the causal regulatory relationships between regulators and their downstream target genes. Accordingly, these GRNs can represent spatial, temporal, and/or environmental regulations and can identify functional genes. This review summarizes recent computational approaches applied to different types of gene expression data to infer GRNs in the context of plant growth and development. Three stages of GRN inference are described: first, data collection and analysis based on the dataset type; second, network inference application based on data availability and proposed hypotheses; and third, validation based on in silico, in vivo, and in planta methods. In addition, this review relates data collection strategies to biological questions, organizes inference algorithms based on statistical methods and data types, discusses experimental design considerations, and provides guidelines for GRN inference with an emphasis on the benefits of integrative approaches, especially when a priori information is limited. Finally, this review concludes that computational frameworks integrating large-scale heterogeneous datasets are needed for a more accurate (e.g. fewer false interactions), detailed (e.g. discrimination between direct versus indirect interactions), and comprehensive (e.g. genetic regulation under various conditions and spatial locations) inference of GRNs.

New roles of NO TRANSMITTING TRACT and SEEDSTICK during medial domain development in Arabidopsis fruits

The gynoecium, the female reproductive part of the flower, is key for plant sexual reproduction. During its development, inner tissues such as the septum and the transmitting tract tissue, important for pollen germination and guidance, are formed. In Arabidopsis, several transcription factors are known to be involved in the development of these tissues. One of them is NO TRANSMITTING TRACT (NTT), essential for transmitting tract formation. We found that the NTT protein can interact with several gynoecium-related transcription factors, including several MADS-box proteins, such as SEEDSTICK (STK), known to specify ovule identity. Evidence suggests that NTT and STK control enzyme and transporter-encoding genes involved in cell wall polysaccharide and lipid distribution in gynoecial medial domain cells. The results indicate that the simultaneous loss of NTT and STK activity affects polysaccharide and lipid deposition and septum fusion, and delays entry of septum cells to their normal degradation program. Furthermore, we identified KAWAK, a direct target of NTT and STK, which is required for the correct formation of fruits in Arabidopsis These findings position NTT and STK as important factors in determining reproductive competence.

Transcriptome-wide analysis of MADS-box family genes involved in aluminum and fluoride assimilation in Camellia sinensis

MADS-box transcription factors (TFs) are involved in a variety of processes in flowering plants ranging from root growth to flower and fruit development. However, studies of the tolerance-related functions of MADS-box genes are very limited, and to date no such studies have been conducted on Camellia sinensis. To gain insight into the functions of genes of this family and to elucidate the role they may play in tissue development and Al and F response, we identified 45 MADS-box genes through transcriptomic analysis of C. sinensis. Phylogenetic analysis of these CsMADS-box genes, along with their homologues in Arabidopsis thaliana, enabled us to classify them into distinct groups, including: M-type (Mα), MIKC* and MIKC^c (which contains the SOC1, AGL12, AGL32, SEP, ANR1, SVP, and FLC subgroups). Conserved motif analysis of the CsMADS-box proteins revealed diverse motif compositions indicating a complex evolutionary relationship. Finally, we examined the expression patterns of CsMADS-box genes in various tissues and under different Al and F concentration treatments. Our qPCR results showed that these CsMADS-box genes were involved in Al and F accumulation and root growth in C. sinensis. These findings lay the foundation for future research on the function of CsMADS-box genes and their role in response to Al and F accumulation in root tissues.

Gene co-expression analysis for functional classification and gene-disease predictions

Gene co-expression networks can be used to associate genes of unknown function with biological processes, to prioritize candidate disease genes or to discern transcriptional regulatory programmes. With recent advances in transcriptomics and next-generation sequencing, co-expression networks constructed from RNA sequencing data also enable the inference of functions and disease associations for non-coding genes and splice variants. Although gene co-expression networks typically do not provide information about causality, emerging methods for differential co-expression analysis are enabling the identification of regulatory genes underlying various phenotypes. Here, we introduce and guide researchers through a (differential) co-expression analysis. We provide an overview of methods and tools used to create and analyse co-expression networks constructed from gene expression data, and we explain how these can be used to identify genes with a regulatory role in disease. Furthermore, we discuss the integration of other data types with co-expression networks and offer future perspectives of co-expression analysis.

Allele specific expression analysis identifies regulatory variation associated with stress-related genes in the Mexican highland maize landrace Palomero Toluqueño

BACKGROUND

Gene regulatory variation has been proposed to play an important role in the adaptation of plants to environmental stress. In the central highlands of Mexico, farmer selection has generated a unique group of maize landraces adapted to the challenges of the highland niche. In this study, gene expression in Mexican highland maize and a reference maize breeding line were compared to identify evidence of regulatory variation in stress-related genes. It was hypothesised that local adaptation in Mexican highland maize would be associated with a transcriptional signature observable even under benign conditions.

METHODS

Allele specific expression analysis was performed using the seedling-leaf transcriptome of an F1 individual generated from the cross between the highland adapted Mexican landrace Palomero Toluqueño and the reference line B73, grown under benign conditions. Results were compared with a published dataset describing the transcriptional response of B73 seedlings to cold, heat, salt and UV treatments.

RESULTS

A total of 2,386 genes were identified to show allele specific expression. Of these, 277 showed an expression difference between Palomero Toluqueño and B73 alleles under benign conditions that anticipated the response of B73 cold, heat, salt and/or UV treatments, and, as such, were considered to display a prior stress response. Prior stress response candidates included genes associated with plant hormone signaling and a number of transcription factors. Construction of a gene co-expression network revealed further signaling and stress-related genes to be among the potential targets of the transcription factors candidates.

DISCUSSION

Prior activation of responses may represent the best strategy when stresses are severe but predictable. Expression differences observed here between Palomero Toluqueño and B73 alleles indicate the presence of cis-acting regulatory variation linked to stress-related genes in Palomero Toluqueño. Considered alongside gene annotation and population data, allele specific expression analysis of plants grown under benign conditions provides an attractive strategy to identify functional variation potentially linked to local adaptation.

Change of Fate and Staminodial Laminarity as Potential Agents of Floral Diversification in the Zingiberales

The evolution of floral morphology in the monocot order Zingiberales shows a trend in which androecial whorl organs are progressively modified into variously conspicuous "petaloid" structures with differing degrees of fertility. Petaloidy of androecial members results from extensive laminarization of an otherwise radially symmetric structure. The genetic basis of the laminarization of androecial members has been addressed through recent candidate gene studies focused on understanding the spatiotemporal expression patterns of genes known to be necessary to floral organ formation. Here, we explore the correlation between gene duplication events and floral and inflorescence morphological diversification across the Zingiberales by inferring ancestral character states and gene copy number using the most widely accepted phylogenetic hypotheses. Our results suggest that the duplication and differential loss of GLOBOSA (GLO) copies is correlated with a change in the degree of the laminarization of androecial members. We also find an association with increased diversification in most families. We hypothesize that retention of paralogs in flower development genes could have led to a developmental shift affecting androecial organs with potential adaptive consequences, thus favoring diversification in some lineages but not others.

The MADS-box XAANTAL1 increases proliferation at the Arabidopsis root stem-cell niche and participates in transition to differentiation by regulating cell-cycle components

Background Morphogenesis depends on the concerted modulation of cell proliferation and differentiation. Such modulation is dynamically adjusted in response to various external and internal signals via complex transcriptional regulatory networks that mediate between such signals and regulation of cell-cycle and cellular responses (proliferation, growth, differentiation). In plants, which are sessile, the proliferation/differentiation balance is plastically adjusted during their life cycle and transcriptional networks are important in this process. MADS-box genes are key developmental regulators in eukaryotes, but their role in cell proliferation and differentiation modulation in plants remains poorly studied. Methods We characterize the XAL1 loss-of-function xal1-2 allele and overexpression lines using quantitative cellular and cytometry analyses to explore its role in cell cycle, proliferation, stem-cell patterning and transition to differentiation. We used quantitative PCR and cellular markers to explore if XAL1 regulates cell-cycle components and PLETHORA1 (PLT1) gene expression, as well as confocal microscopy to analyse stem-cell niche organization. Key Results We previously showed that XAANTAL1 (XAL1/AGL12) is necessary for Arabidopsis root development as a promoter of cell proliferation in the root apical meristem. Here, we demonstrate that XAL1 positively regulates the expression of PLT1 and important components of the cell cycle: CYCD3;1, CYCA2;3, CYCB1;1, CDKB1;1 and CDT1a. In addition, we show that xal1-2 mutant plants have a premature transition to differentiation with root hairs appearing closer to the root tip, while endoreplication in these plants is partially compromised. Coincidently, the final size of cortex cells in the mutant is shorter than wild-type cells. Finally, XAL1 overexpression-lines corroborate that this transcription factor is able to promote cell proliferation at the stem-cell niche. Conclusion XAL1 seems to be an important component of the networks that modulate cell proliferation/differentiation transition and stem-cell proliferation during Arabidopsis root development; it also regulates several cell-cycle components.

Computational inference of gene regulatory networks: Approaches, limitations and opportunities

Gene regulatory networks lie at the core of cell function control. In E. coli and S. cerevisiae, the study of gene regulatory networks has led to the discovery of regulatory mechanisms responsible for the control of cell growth, differentiation and responses to environmental stimuli. In plants, computational rendering of gene regulatory networks is gaining momentum, thanks to the recent availability of high-quality genomes and transcriptomes and development of computational network inference approaches. Here, we review current techniques, challenges and trends in gene regulatory network inference and highlight challenges and opportunities for plant science. We provide plant-specific application examples to guide researchers in selecting methodologies that suit their particular research questions. Given the interdisciplinary nature of gene regulatory network inference, we tried to cater to both biologists and computer scientists to help them engage in a dialogue about concepts and caveats in network inference. Specifically, we discuss problems and opportunities in heterogeneous data integration for eukaryotic organisms and common caveats to be considered during network model evaluation. This article is part of a Special Issue entitled: Plant Gene Regulatory Mechanisms and Networks, edited by Dr. Erich Grotewold and Dr. Nathan Springer.

Regulatory network analysis reveals novel regulators of seed desiccation tolerance in Arabidopsis thaliana

Desiccation tolerance (DT) is a remarkable process that allows seeds in the dry state to remain viable for long periods of time that in some instances exceed 1,000 y. It has been postulated that seed DT evolved by rewiring the regulatory and signaling networks that controlled vegetative DT, which itself emerged as a crucial adaptive trait of early land plants. Understanding the networks that regulate seed desiccation tolerance in model plant systems would provide the tools to understand an evolutionary process that played a crucial role in the diversification of flowering plants. In this work, we used an integrated approach that included genomics, bioinformatics, metabolomics, and molecular genetics to identify and validate molecular networks that control the acquisition of DT in Arabidopsis seeds. Two DT-specific transcriptional subnetworks were identified related to storage of reserve compounds and cellular protection mechanisms that act downstream of the embryo development master regulators LEAFY COTYLEDON 1 and 2, FUSCA 3, and ABSCICIC ACID INSENSITIVE 3. Among the transcription factors identified as major nodes in the DT regulatory subnetworks, PLATZ1, PLATZ2, and AGL67 were confirmed by knockout mutants and overexpression in a desiccation-intolerant mutant background to play an important role in seed DT. Additionally, we found that constitutive expression of PLATZ1 in WT plants confers partial DT in vegetative tissues.

Differential Regulatory Analysis Based on Coexpression Network in Cancer Research

With rapid development of high-throughput techniques and accumulation of big transcriptomic data, plenty of computational methods and algorithms such as differential analysis and network analysis have been proposed to explore genome-wide gene expression characteristics. These efforts are aiming to transform underlying genomic information into valuable knowledges in biological and medical research fields. Recently, tremendous integrative research methods are dedicated to interpret the development and progress of neoplastic diseases, whereas differential regulatory analysis (DRA) based on gene coexpression network (GCN) increasingly plays a robust complement to regular differential expression analysis in revealing regulatory functions of cancer related genes such as evading growth suppressors and resisting cell death. Differential regulatory analysis based on GCN is prospective and shows its essential role in discovering the system properties of carcinogenesis features. Here we briefly review the paradigm of differential regulatory analysis based on GCN. We also focus on the applications of differential regulatory analysis based on GCN in cancer research and point out that DRA is necessary and extraordinary to reveal underlying molecular mechanism in large-scale carcinogenesis studies.

OsMADS26 Negatively Regulates Resistance to Pathogens and Drought Tolerance in Rice

Functional analyses of MADS-box transcription factors in plants have unraveled their role in major developmental programs (e.g. flowering and floral organ identity) as well as stress-related developmental processes, such as abscission, fruit ripening, and senescence. Overexpression of the rice (Oryza sativa) MADS26 gene in rice has revealed a possible function related to stress response. Here, we show that OsMADS26-down-regulated plants exhibit enhanced resistance against two major rice pathogens: Magnaporthe oryzae and Xanthomonas oryzae. Despite this enhanced resistance to biotic stresses, OsMADS26-down-regulated plants also displayed enhanced tolerance to water deficit. These phenotypes were observed in both controlled and field conditions. Interestingly, alteration of OsMADS26 expression does not have a strong impact on plant development. Gene expression profiling revealed that a majority of genes misregulated in overexpresser and down-regulated OsMADS26 lines compared with control plants are associated to biotic or abiotic stress response. Altogether, our data indicate that OsMADS26 acts as an upstream regulator of stress-associated genes and thereby, a hub to modulate the response to various stresses in the rice plant.

Descriptive vs. mechanistic network models in plant development in the post-genomic era

Network modeling is now a widespread practice in systems biology, as well as in integrative genomics, and it constitutes a rich and diverse scientific research field. A conceptually clear understanding of the reasoning behind the main existing modeling approaches, and their associated technical terminologies, is required to avoid confusions and accelerate the transition towards an undeniable necessary more quantitative, multidisciplinary approach to biology. Herein, we focus on two main network-based modeling approaches that are commonly used depending on the information available and the intended goals: inference-based methods and system dynamics approaches. As far as data-based network inference methods are concerned, they enable the discovery of potential functional influences among molecular components. On the other hand, experimentally grounded network dynamical models have been shown to be perfectly suited for the mechanistic study of developmental processes. How do these two perspectives relate to each other? In this chapter, we describe and compare both approaches and then apply them to a given specific developmental module. Along with the step-by-step practical implementation of each approach, we also focus on discussing their respective goals, utility, assumptions, and associated limitations. We use the gene regulatory network (GRN) involved in Arabidopsis thaliana Root Stem Cell Niche patterning as our illustrative example. We show that descriptive models based on functional genomics data can provide important background information consistent with experimentally supported functional relationships integrated in mechanistic GRN models. The rationale of analysis and modeling can be applied to any other well-characterized functional developmental module in multicellular organisms, like plants and animals.

Arabidopsis ensemble reverse-engineered gene regulatory network discloses interconnected transcription factors in oxidative stress

The abiotic stress response in plants is complex and tightly controlled by gene regulation. We present an abiotic stress gene regulatory network of 200,014 interactions for 11,938 target genes by integrating four complementary reverse-engineering solutions through average rank aggregation on an Arabidopsis thaliana microarray expression compendium. This ensemble performed the most robustly in benchmarking and greatly expands upon the availability of interactions currently reported. Besides recovering 1182 known regulatory interactions, cis-regulatory motifs and coherent functionalities of target genes corresponded with the predicted transcription factors. We provide a valuable resource of 572 abiotic stress modules of coregulated genes with functional and regulatory information, from which we deduced functional relationships for 1966 uncharacterized genes and many regulators. Using gain- and loss-of-function mutants of seven transcription factors grown under control and salt stress conditions, we experimentally validated 141 out of 271 predictions (52% precision) for 102 selected genes and mapped 148 additional transcription factor-gene regulatory interactions (49% recall). We identified an intricate core oxidative stress regulatory network where NAC13, NAC053, ERF6, WRKY6, and NAC032 transcription factors interconnect and function in detoxification. Our work shows that ensemble reverse-engineering can generate robust biological hypotheses of gene regulation in a multicellular eukaryote that can be tested by medium-throughput experimental validation.