Learning from Co-expression Networks: Possibilities and Challenges

Kiwifruit spatiotemporal multiomics networks uncover key tissue-specific regulatory processes throughout the life cycle

Kiwifruit (Actinidia chinensis), a recently commercialized horticultural crop, is rich in various nutrient compounds. However, the regulatory networks controlling the dynamic changes in key metabolites among different tissues remain largely unknown. Here, high-resolution spatiotemporal datasets obtained by ultraperformance liquid chromatography-tandem mass spectrometry methodology and RNA-seq were employed to investigate the dynamic changes in the metabolic and transcriptional landscape of major kiwifruit tissues across different developmental stages, including from fruit skin, outer pericarp, inner pericarp, and fruit core. Kiwifruit spatiotemporal regulatory networks (KSRN) were constructed by integrating the 1,243 identified metabolites and co-expressed genes into 10 different clusters and 11 modules based on their biological functions. These networks allowed the generation of a global map for the major metabolic and transcriptional changes occurring throughout the life cycle of different kiwifruit tissues and discovery of the underlying regulatory networks. KSRN predictions confirmed previously established regulatory networks, including the spatiotemporal accumulation of anthocyanin and ascorbic acid (AsA). More importantly, the networks led to the functional characterization of three transcription factors: an A. chinensis ethylene response factor 1, which negatively controls sugar accumulation and ethylene production by perceiving the ripening signal, a basic-leucine zipper 60 (AcbZIP60) transcription factor, which is involved in the biosynthesis of AsA as part of the L-galactose pathway, and a transcription factor related to apetala 2.4 (RAP2.4), which directly activates the expression of the kiwi fruit aroma terpene synthase gene AcTPS1b. Our findings provide insights into spatiotemporal changes in kiwifruit metabolism and generate a valuable resource for the study of metabolic regulatory processes in kiwifruit as well as other fruits.

Multi-omics analysis reveals genetic architecture and local adaptation of coumarins metabolites in Populus

BACKGROUND

Accumulation of coumarins plays key roles in response to immune and abiotic stress in plants, but the genetic adaptation basis of controlling coumarins in perennial woody plants remain unclear.

RESULTS

We detected 792 SNPs within 334 genes that were significantly associated with the phenotypic variations of 15 single-metabolic traits and multiple comprehensive index, such as principal components (PCs) of coumarins metabolites. Expression quantitative trait locus mapping uncovered that 337 eQTLs associated with the expression levels of 132 associated genes. Selective sweep revealed 55 candidate genes have potential selective signature among three geographical populations, highlighting that the coumarins biosynthesis have been encountered forceful local adaptation. Furthermore, we constructed a genetic network of seven candidate genes that coordinately regulate coumarins biosynthesis, revealing the multiple regulatory patterns affecting coumarins accumulation in Populus tomentosa. Validation of candidate gene variations in a drought-tolerated population and DUF538 heterologous transformation experiments verified the function of candidate genes and their roles in adapting to the different geographical conditions in poplar.

CONCLUSIONS

Our study uncovered the genetic regulation of the coumarins metabolic biosynthesis of Populus, and offered potential clues for drought-tolerance evaluation and regional improvement in woody plants.

MiRNA target enrichment analysis of co-expression network modules reveals important miRNAs and their roles in breast cancer progression

Breast cancer has the highest incidence and is the fifth cause of death in cancers. Progression is one of the important features of breast cancer which makes it a life-threatening cancer. MicroRNAs are small RNA molecules that have pivotal roles in the regulation of gene expression and they control different properties in breast cancer such as progression. Recently, systems biology offers novel approaches to study complicated biological systems like miRNAs to find their regulatory roles. One of these approaches is analysis of weighted co-expression network in which genes with similar expression patterns are considered as a single module. Because the genes in one module have similar expression, it is rational to think the same regulatory elements such as miRNAs control their expression. Herein, we use WGCNA to find important modules related to breast cancer progression and use hypergeometric test to perform miRNA target enrichment analysis and find important miRNAs. Also, we use negative correlation between miRNA expression and modules as the second filter to ensure choosing the right candidate miRNAs regarding to important modules. We found hsa-mir-23b, hsa-let-7b and hsa-mir-30a are important miRNAs in breast cancer and also investigated their roles in breast cancer progression.

Approaches for Benchmarking Single-Cell Gene Regulatory Network Methods

Gene regulatory networks are powerful tools for modeling genetic interactions that control the expression of genes driving cell differentiation, and single-cell sequencing offers a unique opportunity to build these networks with high-resolution genomic data. There are many proposed computational methods to build these networks using single-cell data, and different approaches are used to benchmark these methods. However, a comprehensive discussion specifically focusing on benchmarking approaches is missing. In this article, we lay the GRN terminology, present an overview of common gold-standard studies and data sets, and define the performance metrics for benchmarking network construction methodologies. We also point out the advantages and limitations of different benchmarking approaches, suggest alternative ground truth data sets that can be used for benchmarking, and specify additional considerations in this context.

k- Strong Inference Algorithm: A Hybrid Information Theory Based Gene Network Inference Algorithm

Gene networks allow researchers to understand the underlying mechanisms between diseases and genes while reducing the need for wet lab experiments. Numerous gene network inference (GNI) algorithms have been presented in the literature to infer accurate gene networks. We proposed a hybrid GNI algorithm, k-Strong Inference Algorithm (ksia), to infer more reliable and robust gene networks from omics datasets. To increase reliability, ksia integrates Pearson correlation coefficient (PCC) and Spearman rank correlation coefficient (SCC) scores to determine mutual information scores between molecules to increase diversity of relation predictions. To infer a more robust gene network, ksia applies three different elimination steps to remove redundant and spurious relations between genes. The performance of ksia was evaluated on microbe microarrays database in the overlap analysis with other GNI algorithms, namely ARACNE, C3NET, CLR, and MRNET. Ksia inferred less number of relations due to its strict elimination steps. However, ksia generally performed better on Escherichia coli (E.coli) and Saccharomyces cerevisiae (yeast) gene expression datasets due to F- measure and precision values. The integration of association estimator scores and three elimination stages slightly increases the performance of ksia based gene networks. Users can access ksia R package and user manual of package via https://github.com/ozgurcingiz/ksia .

Comparative transcriptomic analysis unveils candidate genes associated with sugarcane growth rate

Sugarcane (Saccharum spp.) growth is regulated by intricate gene networks and hormone secretions, positively correlating with sugarcane yield. There is a rising interest in exploring how the candidate genes found in sugarcane respond to plant growth. In this study, we simulated a typical growth environment to obtain accurate phenotypic data and screened for potential genes associated with plant growth through transcriptomics. Compared to Saccharum GuiTang 42, the other variety Saccharum GuiTang 44 exhibited earlier germination, a higher emergence rate, thicker pseudostems, taller plants, and a more extensive root system. The middle buds formed the greatest number of roots, followed by the lower and upper buds. Indole-3-acetic acid (IAA) and jasmonic acid effectively promoted bud development, while abscisic acid and trans-zeatin exhibited negative correlations with sugarcane bud growth. Transcriptome data from the upper, middle, and lower buds revealed 24,158 differentially expressed genes in all three comparisons, with MAPK signaling emerging as a critical pathway. The photosynthesis-antenna protein pathway is vital for middle and lower bud development during root germination. Lastly, key gene modules related to differences in hormone content between the two varieties were defined through weighted correlation network analysis and identified. The module significantly associated with IAA was enriched in pathways such as Proteasome and Protein processing in the endoplasmic reticulum, and the upregulation of key genes involved in this gene module had a highly significant positive correlation with bud outgrowth combined with IAA secretion. In conclusion, we have elucidated the pathways of hormones during sugarcane growth and the interactions between IAA and critical genes. These in-depth findings may guide modern sugarcane breeding.

Gene co-expression analysis reveals conserved and distinct gene networks between resistant and susceptible Lens ervoides challenged by hemibiotrophic and necrotrophic pathogens

As field crops are likely to be challenged by multiple pathogens during their development, the investigation of broad-spectrum resistance in the host is of great interest for crop genetic enhancement. In this study, we attempted to address this question by adopting a weighed gene co-expression approach to study the temporal transcriptome dynamics of resistant and susceptible recombinant inbred lines (RILs) derived from an intraspecific Len ervoides cross during the infection process with either the necrotrophic pathogens Ascochyta lentis or Stemphylium botryosum, or the hemibiotrophic pathogen Colletotrichum lentis. By comparing networks of resistant and susceptible RILs, seven network module pairs were found to possess high correlation coefficients (R > 0.70) and large number of overlapping genes (n > 100). The conserved co-regulation of genes in these network module pairs were involved in plant cell wall synthesis, cell division, cytoskeleton organization, and protein ubiquitin related processes and appeared to be common disease responses against these pathogens. On the other hand, we also identified eight modules with low correlation between resistance and susceptibility networks. Among those, a stronger gene co-expression in R-genes and small RNA processes in the resistant hosts may be enhancing L. ervoides resistance against A. lentis, C. lentis, and S. botryosum, whereas the higher level of synergistic regulation in the synthesis of arginine and glutamine and phospholipid and glycerophospholipids in the susceptible hosts may contribute to increased susceptibility in L. ervoides.

Integrating Molecular Perspectives: Strategies for Comprehensive Multi-Omics Integrative Data Analysis and Machine Learning Applications in Transcriptomics, Proteomics, and Metabolomics

With the advent of high-throughput technologies, the field of omics has made significant strides in characterizing biological systems at various levels of complexity. Transcriptomics, proteomics, and metabolomics are the three most widely used omics technologies, each providing unique insights into different layers of a biological system. However, analyzing each omics data set separately may not provide a comprehensive understanding of the subject under study. Therefore, integrating multi-omics data has become increasingly important in bioinformatics research. In this article, we review strategies for integrating transcriptomics, proteomics, and metabolomics data, including co-expression analysis, metabolite-gene networks, constraint-based models, pathway enrichment analysis, and interactome analysis. We discuss combined omics integration approaches, correlation-based strategies, and machine learning techniques that utilize one or more types of omics data. By presenting these methods, we aim to provide researchers with a better understanding of how to integrate omics data to gain a more comprehensive view of a biological system, facilitating the identification of complex patterns and interactions that might be missed by single-omics analyses.

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.

EpMYB2 positively regulates chicoric acid biosynthesis by activating both primary and specialized metabolic genes in purple coneflower

Chicoric acid is the major active ingredient of the world-popular medicinal plant purple coneflower (Echinacea purpurea (L.) Menoch). It is recognized as the quality index of commercial hot-selling Echinacea products. While the biosynthetic pathway of chicoric acid in purple coneflower has been elucidated recently, its regulatory network remains elusive. Through co-expression and phylogenetic analysis, we found EpMYB2, a typical R2R3-type MYB transcription factor (TF) responsive to methyl jasmonate (MeJA) simulation, is a positive regulator of chicoric acid biosynthesis. In addition to directly regulating chicoric acid biosynthetic genes, EpMYB2 positively regulates genes of the upstream shikimate pathway. We also found that EpMYC2 could activate the expression of EpMYB2 by binding to its G-box site, and the EpMYC2-EpMYB2 module is involved in the MeJA-induced chicoric acid biosynthesis. Overall, we identified an MYB TF that positively regulates the biosynthesis of chicoric acid by activating both primary and specialized metabolic genes. EpMYB2 links the gap between the JA signaling pathway and chicoric acid biosynthesis. This work opens a new direction toward engineering purple coneflower with higher medicinal qualities.

Supervised latent factor modeling isolates cell-type-specific transcriptomic modules that underlie Alzheimer's disease progression

Late onset Alzheimer's disease (AD) is a progressive neurodegenerative disease, with brain changes beginning years before symptoms surface. AD is characterized by neuronal loss, the classic feature of the disease that underlies brain atrophy. However, GWAS reports and recent single-nucleus RNA sequencing (snRNA-seq) efforts have highlighted that glial cells, particularly microglia, claim a central role in AD pathophysiology. Here, we tailor pattern-learning algorithms to explore distinct gene programs by integrating the entire transcriptome, yielding distributed AD-predictive modules within the brain's major cell-types. We show that these learned modules are biologically meaningful through the identification of new and relevant enriched signaling cascades. The predictive nature of our modules, especially in microglia, allows us to infer each subject's progression along a disease pseudo-trajectory, confirmed by post-mortem pathological brain tissue markers. Additionally, we quantify the interplay between pairs of cell-type modules in the AD brain, and localized known AD risk genes to enriched module gene programs. Our collective findings advocate for a transition from cell-type-specificity to gene modules specificity to unlock the potential of unique gene programs, recasting the roles of recently reported genome-wide AD risk loci.

StCoExpNet: a global co-expression network analysis facilitates identifying genes underlying agronomic traits in potatoes

KEY MESSAGE

We constructed a gene expression atlas and co-expression network for potatoes and identified several novel genes associated with various agronomic traits. This resource will accelerate potato genetics and genomics research. Potato (Solanum tuberosum L.) is the world's most crucial non-cereal food crop and ranks third in food production after wheat and rice. Despite the availability of several potato transcriptome datasets at public databases like NCBI SRA, an effort has yet to be put into developing a global transcriptome atlas and a co-expression network for potatoes. The objectives of our study were to construct a global expression atlas for potatoes using publicly available transcriptome datasets, identify housekeeping and tissue-specific genes, construct a global co-expression network and identify co-expression clusters, investigate the transcriptional complexity of genes involved in various essential biological processes related to agronomic traits, and provide a web server (StCoExpNet) to easily access the newly constructed expression atlas and co-expression network to investigate the expression and co-expression of genes of interest. In this study, we used data from 2299 publicly available potato transcriptome samples obtained from 15 different tissues to construct a global transcriptome atlas. We found that roughly 87% of the annotated genes exhibited detectable expression in at least one sample. Among these, we identified 281 genes with consistent and stable expression levels, indicating their role as housekeeping genes. Conversely, 308 genes exhibited marked tissue-specific expression patterns. We exemplarily linked some co-expression clusters to important agronomic traits of potatoes, such as self-incompatibility, anthocyanin biosynthesis, tuberization, and defense responses against multiple pathogens. The dataset compiled here constitutes a new resource (StCoExpNet), which can be accessed at https://stcoexpnet.julius-kuehn.de . This transcriptome atlas and the co-expression network will accelerate potato genetics and genomics research.

Prioritization of genes for translation: a computational approach

INTRODUCTION

Gene identification for genetic diseases is critical for the development of new diagnostic approaches and personalized treatment options. Prioritization of gene translation is an important consideration in the molecular biology field, allowing researchers to focus on the most promising candidates for further investigation.

AREAS COVERED

In this paper, we discussed different approaches to prioritize genes for translation, including the use of computational tools and machine learning algorithms, as well as experimental techniques such as knockdown and overexpression studies. We also explored the potential biases and limitations of these approaches and proposed strategies to improve the accuracy and reliability of gene prioritization methods. Although numerous computational methods have been developed for this purpose, there is a need for computational methods that incorporate tissue-specific information to enable more accurate prioritization of candidate genes. Such methods should provide tissue-specific predictions, insights into underlying disease mechanisms, and more accurate prioritization of genes.

EXPERT OPINION

Using advanced computational tools and machine learning algorithms to prioritize genes, we can identify potential targets for therapeutic intervention of complex diseases. This represents an up-and-coming method for drug development and personalized medicine.

Allelic variations of WAK106-E2Fa-DPb1-UGT74E2 module regulate fibre properties in Populus tomentosa

Wood formation, intricately linked to the carbohydrate metabolism pathway, underpins the capacity of trees to produce renewable resources and offer vital ecosystem services. Despite their importance, the genetic regulatory mechanisms governing wood fibre properties in woody plants remain enigmatic. In this study, we identified a pivotal module comprising 158 high-priority core genes implicated in wood formation, drawing upon tissue-specific gene expression profiles from 22 Populus samples. Initially, we conducted a module-based association study in a natural population of 435 Populus tomentosa, pinpointing PtoDPb1 as the key gene contributing to wood formation through the carbohydrate metabolic pathway. Overexpressing PtoDPb1 led to a 52.91% surge in cellulose content, a reduction of 14.34% in fibre length, and an increment of 38.21% in fibre width in transgenic poplar. Moreover, by integrating co-expression patterns, RNA-sequencing analysis, and expression quantitative trait nucleotide (eQTN) mapping, we identified a PtoDPb1-mediated genetic module of PtoWAK106-PtoDPb1-PtoE2Fa-PtoUGT74E2 responsible for fibre properties in Populus. Additionally, we discovered the two PtoDPb1 haplotypes that influenced protein interaction efficiency between PtoE2Fa-PtoDPb1 and PtoDPb1-PtoWAK106, respectively. The transcriptional activation activity of the PtoE2Fa-PtoDPb1 haplotype-1 complex on the promoter of PtoUGT74E2 surpassed that of the PtoE2Fa-PtoDPb1 haplotype-2 complex. Taken together, our findings provide novel insights into the regulatory mechanisms of fibre properties in Populus, orchestrated by PtoDPb1, and offer a practical module for expediting genetic breeding in woody plants via molecular design.

FAVA: high-quality functional association networks inferred from scRNA-seq and proteomics data

MOTIVATION

Protein networks are commonly used for understanding how proteins interact. However, they are typically biased by data availability, favoring well-studied proteins with more interactions. To uncover functions of understudied proteins, we must use data that are not affected by this literature bias, such as single-cell RNA-seq and proteomics. Due to data sparseness and redundancy, functional association analysis becomes complex.

RESULTS

To address this, we have developed FAVA (Functional Associations using Variational Autoencoders), which compresses high-dimensional data into a low-dimensional space. FAVA infers networks from high-dimensional omics data with much higher accuracy than existing methods, across a diverse collection of real as well as simulated datasets. FAVA can process large datasets with over 0.5 million conditions and has predicted 4210 interactions between 1039 understudied proteins. Our findings showcase FAVA's capability to offer novel perspectives on protein interactions. FAVA functions within the scverse ecosystem, employing AnnData as its input source.

AVAILABILITY AND IMPLEMENTATION

Source code, documentation, and tutorials for FAVA are accessible on GitHub at https://github.com/mikelkou/fava. FAVA can also be installed and used via pip/PyPI as well as via the scverse ecosystem https://github.com/scverse/ecosystem-packages/tree/main/packages/favapy.

Inferring co-expression networks of Arabidopsis thaliana genes during their interaction with Trichoderma spp

Fungi of the Trichoderma genus are called "biostimulants" because they promote plant growth and development and induce disease resistance. We used conventional transcriptome and gene co-expression analyses to understand the molecular response of the plant Arabidopsis thaliana to inoculation with Trichoderma atroviride or Trichoderma virens. The transcriptional landscape of the plant during the interaction with these fungi showed a reduction in functions such as reactive oxygen species production, defense mechanisms against pathogens, and hormone signaling. T. virens, as opposed to T. atroviride, was more effective at downregulating genes related to terpenoid metabolism, root development, and chemical homeostasis. Through gene co-expression analysis, we found functional gene modules that closely link plant defense with hypoxia. Notably, we found a transcription factor (locus AT2G47520) with two functional domains of interest: a DNA-binding domain and an N-terminal cysteine needed for protein stability under hypoxia. We hypothesize that the transcription factor can bind to the promoter sequence of the GCC-box that is connected to pathogenesis by positioned weight matrix analysis.

Superior haplotypes of key drought-responsive genes reveal opportunities for the development of climate-resilient rice varieties

Haplotype-based breeding is an emerging and innovative concept that enables the development of designer crop varieties by exploiting and exploring superior alleles/haplotypes among target genes to create new traits in breeding programs. In this regard, whole-genome re-sequencing of 399 genotypes (landraces and breeding lines) from the 3000 rice genomes panel (3K-RG) is mined to identify the superior haplotypes for 95 drought-responsive candidate genes. Candidate gene-based association analysis reveals 69 marker-trait associations (MTAs) in 16 genes for single plant yield (SPY) under drought stress. Haplo-pheno analysis of these 16 genes identifies superior haplotypes for seven genes associated with the higher SPY under drought stress. Our study reveals that the performance of lines possessing superior haplotypes is significantly higher (p ≤ 0.05) as measured by single plant yield (SPY), for the OsGSK1-H4, OsDSR2-H3, OsDIL1-H22, OsDREB1C-H3, ASR3-H88, DSM3-H4 and ZFP182-H4 genes as compared to lines without the superior haplotypes. The validation results indicate that a superior haplotype for the DREB transcription factor (OsDREB1C) is present in all the drought-tolerant rice varieties, while it was notably absent in all susceptible varieties. These lines carrying the superior haplotypes can be used as potential donors in haplotype-based breeding to develop high-yielding drought-tolerant rice varieties.

Multi-omic approaches for host-microbiome data integration

The gut microbiome interacts with the host through complex networks that affect physiology and health outcomes. It is becoming clear that these interactions can be measured across many different omics layers, including the genome, transcriptome, epigenome, metabolome, and proteome, among others. Multi-omic studies of the microbiome can provide insight into the mechanisms underlying host-microbe interactions. As more omics layers are considered, increasingly sophisticated statistical methods are required to integrate them. In this review, we provide an overview of approaches currently used to characterize multi-omic interactions between host and microbiome data. While a large number of studies have generated a deeper understanding of host-microbiome interactions, there is still a need for standardization across approaches. Furthermore, microbiome studies would also benefit from the collection and curation of large, publicly available multi-omics datasets.

Network Analysis of Publicly Available RNA-seq Provides Insights into the Molecular Mechanisms of Plant Defense against Multiple Fungal Pathogens in Arabidopsis thaliana

Fungal pathogens can have devastating effects on global crop production, leading to annual economic losses ranging from 10% to 23%. In light of climate change-related challenges, researchers anticipate an increase in fungal infections as a result of shifting environmental conditions. However, plants have developed intricate molecular mechanisms for effective defense against fungal attacks. Understanding these mechanisms is essential to the development of new strategies for protecting crops from multiple fungi threats. Public omics databases provide valuable resources for research on plant-pathogen interactions; however, integrating data from different studies can be challenging due to experimental variation. In this study, we aimed to identify the core genes that defend against the pathogenic fungi Colletotrichum higginsianum and Botrytis cinerea in Arabidopsis thaliana. Using a custom framework to control batch effects and construct Gene Co-expression Networks in publicly available RNA-seq dataset from infected A. thaliana plants, we successfully identified a gene module that was responsive to both pathogens. We also performed gene annotation to reveal the roles of previously unknown protein-coding genes in plant defenses against fungal infections. This research demonstrates the potential of publicly available RNA-seq data for identifying the core genes involved in defending against multiple fungal pathogens.

The Role of Transcription Factors in the Loss of Inter-Chromosomal Co-Expression for Breast Cancer Subtypes

Breast cancer encompasses a diverse array of subtypes, each exhibiting distinct clinical characteristics and treatment responses. Unraveling the underlying regulatory mechanisms that govern gene expression patterns in these subtypes is essential for advancing our understanding of breast cancer biology. Gene co-expression networks (GCNs) help us identify groups of genes that work in coordination. Previous research has revealed a marked reduction in the interaction of genes located on different chromosomes within GCNs for breast cancer, as well as for lung, kidney, and hematopoietic cancers. However, the reasons behind why genes on the same chromosome often co-express remain unclear. In this study, we investigate the role of transcription factors in shaping gene co-expression networks within the four main breast cancer subtypes: Luminal A, Luminal B, HER2+, and Basal, along with normal breast tissue. We identify communities within each GCN and calculate the transcription factors that may regulate these communities, comparing the results across different phenotypes. Our findings indicate that, in general, regulatory behavior is to a large extent similar among breast cancer molecular subtypes and even in healthy networks. This suggests that transcription factor motif usage does not fully determine long-range co-expression patterns. Specific transcription factor motifs, such as CCGGAAG, appear frequently across all phenotypes, even involving multiple highly connected transcription factors. Additionally, certain transcription factors exhibit unique actions in specific subtypes but with limited influence. Our research demonstrates that the loss of inter-chromosomal co-expression is not solely attributable to transcription factor regulation. Although the exact mechanism responsible for this phenomenon remains elusive, this work contributes to a better understanding of gene expression regulatory programs in breast cancer.

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.

Jacalin-related lectin 45 (OsJRL45) isolated from 'sea rice 86' enhances rice salt tolerance at the seedling and reproductive stages

BACKGROUND

Rice (Oryza sativa L.) is one of the most widely cultivated grain crops in the world that meets the caloric needs of more than half the world's population. Salt stress seriously affects rice production and threatens food security. Therefore, mining salt tolerance genes in salt-tolerant germplasm and elucidating their molecular mechanisms in rice are necessary for the breeding of salt tolerant cultivars.

RESULTS

In this study, a salt stress-responsive jacalin-related lectin (JRL) family gene, OsJRL45, was identified in the salt-tolerant rice variety 'sea rice 86' (SR86). OsJRL45 showed high expression level in leaves, and the corresponding protein mainly localized to the endoplasmic reticulum. The knockout mutant and overexpression lines of OsJRL45 revealed that OsJRL45 positively regulates the salt tolerance of rice plants at all growth stages. Compared with the wild type (WT), the OsJRL45 overexpression lines showed greater salt tolerance at the reproductive stage, and significantly higher seed setting rate and 1,000-grain weight. Moreover, OsJRL45 expression significantly improved the salt-resistant ability and yield of a salt-sensitive indica cultivar, L6-23. Furthermore, OsJRL45 enhanced the antioxidant capacity of rice plants and facilitated the maintenance of Na+-K+ homeostasis under salt stress conditions. Five proteins associated with OsJRL45 were screened by transcriptome and interaction network analysis, of which one, the transmembrane transporter Os10g0210500 affects the salt tolerance of rice by regulating ion transport-, salt stress-, and hormone-responsive proteins.

CONCLUSIONS

The OsJRL45 gene isolated from SR86 positively regulated the salt tolerance of rice plants at all growth stages, and significantly increased the yield of salt-sensitive rice cultivar under NaCl treatment. OsJRL45 increased the activity of antioxidant enzyme of rice and regulated Na+/K+ dynamic equilibrium under salinity conditions. Our data suggest that OsJRL45 may improve the salt tolerance of rice by mediating the expression of ion transport-, salt stress response-, and hormone response-related genes.

Integrated Systems Biology Pipeline to Compare Co-Expression Networks in Plants and Elucidate Differential Regulators

To identify sets of genes that exhibit similar expression characteristics, co-expression networks were constructed from transcriptome datasets that were obtained from plant samples at various stages of growth and development or treated with diverse biotic, abiotic, and other environmental stresses. In addition, co-expression network analysis can provide deeper insights into gene regulation when combined with transcriptomics. The coordination and integration of all these complex networks to deduce gene regulation are major challenges for plant biologists. Python and R have emerged as major tools for managing complex scientific data over the past decade. In this study, we describe a reproducible protocol POTFUL (pant co-expression transcription factor regulators), implemented in Python 3, for integrating co-expression and transcription factor target protein networks to infer gene regulation.

Expression Profile and Gene Regulation Network of NUSAP1 in Pan Cancers Based on Integrated Bioinformatics Analysis

Background

Nucleolar and spindle-associated protein 1 (NUSAP1) plays key roles in microtubules and chromosomes in normal cells both structurally and functionally. In malignancies, NUSAP1 is frequently dysregulated and mutated. However, the expression profiles and biological functions of NUSAP1 in tumors remain unclear.

Methods

NUSAP1 expression in BALB/c mice and human normal or tumor tissues was examined using immunohistochemistry. Kaplan-Meier survival analysis was utilized to assess the prognostic significance of NUSAP1 in tumors, and principal component analysis and co-expression analysis were performed to explore the unique roles of NUSAP1. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed with DAVID. The relevance between NUSAP1 and tumor-infiltrating immune cells was investigated using TIMER. A transcriptional regulation network was constructed using data from The Cancer Genome Atlas.

Results

NUSAP1 expression levels in various mice tissues were different. Compared with normal tissues, NUSAP1 was strongly expressed in several human tumor tissues. We believe that NUSAP1 distinctly impacts the prognosis of several cancers and plays various roles in thymoma and testicular germ cell tumors. Further, NUSAP1 expression levels were significantly positively associated with diverse infiltrating levels of immune cells, including B cells, CD4+ and CD8+ T cells, dendritic cells, and macrophages, in thymoma. The expression level of NUSAP1 demonstrated strong relevance with various immune markers in thymoma. Finally, the miR-1236-5p-NUSAP1 and TCF3-NUSAP1 network revealed the tumor-promoting role of NUSAP1 and pertinent underlying mechanisms in human liver hepatocellular carcinoma.

Conclusion

NUSAP1 may be regarded as a therapeutic target or potential prognostic biomarker for various cancer types.

Design, execution, and interpretation of plant RNA-seq analyses

Genomics has transformed our understanding of the genetic architecture of traits and the genetic variation present in plants. Here, we present a review of how RNA-seq can be performed to tackle research challenges addressed by plant sciences. We discuss the importance of experimental design in RNA-seq, including considerations for sampling and replication, to avoid pitfalls and wasted resources. Approaches for processing RNA-seq data include quality control and counting features, and we describe common approaches and variations. Though differential gene expression analysis is the most common analysis of RNA-seq data, we review multiple methods for assessing gene expression, including detecting allele-specific gene expression and building co-expression networks. With the production of more RNA-seq data, strategies for integrating these data into genetic mapping pipelines is of increased interest. Finally, special considerations for RNA-seq analysis and interpretation in plants are needed, due to the high genome complexity common across plants. By incorporating informed decisions throughout an RNA-seq experiment, we can increase the knowledge gained.

Debiased personalized gene coexpression networks for population-scale scRNA-seq data

Population-scale single-cell RNA-seq (scRNA-seq) data sets create unique opportunities for quantifying expression variation across individuals at the gene coexpression network level. Estimation of coexpression networks is well established for bulk RNA-seq; however, single-cell measurements pose novel challenges owing to technical limitations and noise levels of this technology. Gene-gene correlation estimates from scRNA-seq tend to be severely biased toward zero for genes with low and sparse expression. Here, we present Dozer to debias gene-gene correlation estimates from scRNA-seq data sets and accurately quantify network-level variation across individuals. Dozer corrects correlation estimates in the general Poisson measurement model and provides a metric to quantify genes measured with high noise. Computational experiments establish that Dozer estimates are robust to mean expression levels of the genes and the sequencing depths of the data sets. Compared with alternatives, Dozer results in fewer false-positive edges in the coexpression networks, yields more accurate estimates of network centrality measures and modules, and improves the faithfulness of networks estimated from separate batches of the data sets. We showcase unique analyses enabled by Dozer in two population-scale scRNA-seq applications. Coexpression network-based centrality analysis of multiple differentiating human induced pluripotent stem cell (iPSC) lines yields biologically coherent gene groups that are associated with iPSC differentiation efficiency. Application with population-scale scRNA-seq of oligodendrocytes from postmortem human tissues of Alzheimer's disease and controls uniquely reveals coexpression modules of innate immune response with distinct coexpression levels between the diagnoses. Dozer represents an important advance in estimating personalized coexpression networks from scRNA-seq data.

A comprehensive metabolic map reveals major quality regulations in red-flesh kiwifruit (Actinidia chinensis)

Kiwifruit (Actinidia chinensis) is one of the popular fruits world-wide, and its quality is mainly determined by key metabolites (sugars, flavonoids, and vitamins). Previous works on kiwifruit are mostly done via a single omics approach or involve only limited metabolites. Consequently, the dynamic metabolomes during kiwifruit development and ripening and the underlying regulatory mechanisms are poorly understood. In this study, using high-resolution metabolomic and transcriptomic analyses, we investigated kiwifruit metabolic landscapes at 11 different developmental and ripening stages and revealed a parallel classification of 515 metabolites and their co-expressed genes into 10 distinct metabolic vs gene modules (MM vs GM). Through integrative bioinformatics coupled with functional genomic assays, we constructed a global map and uncovered essential transcriptomic and transcriptional regulatory networks for all major metabolic changes that occurred throughout the kiwifruit growth cycle. Apart from known MM vs GM for metabolites such as soluble sugars, we identified novel transcription factors that regulate the accumulation of procyanidins, vitamin C, and other important metabolites. Our findings thus shed light on the kiwifruit metabolic regulatory network and provide a valuable resource for the designed improvement of kiwifruit quality.

Relationships between some transcription factors and concordantly expressed drought stress-related genes in bread wheat

The challenge of climate change makes it mandatory to improve tolerance to drought stress in bread wheat (Triticum aestivum) via biotechnological approaches. Drought stress experiment was conducted followed by RNA-Seq analysis for leaves of two wheat cultivars namely Giza 168 and Gemmiza 10 with contrasting genotypes. Expression patterns of the regulated stress-related genes and concordantly expressed TFs were detected, then, validated via qPCR for two loss-of-function mutants in Arabidopsis background harboring mutated genes analogue to those in wheat. Drought-stress related genes were searched for concordantly expressed TFs and a total of eight TFs were shown to coexpress with 14 stress-related genes. Among these genes, one TF belongs to the zinc finger protein CONSTANS family and proved via qPCR to drive expression of a gene encoding a speculative TF namely zinc transporter 3-like and two other stress related genes encoding tryptophan synthase alpha chain and asparagine synthetase. Known functions of the two TFs under drought stress complement those of the two concordantly expressed stress-related genes, thus, it is likely that they are related. This study highlights the possibility to utilize metabolic engineering approaches to decipher and incorporate existing regulatory frameworks under drought stress in future breeding programs of bread wheat.

Transcriptome meta-analysis of abiotic stresses-responsive genes and identification of candidate transcription factors for broad stress tolerance in wheat

Under field conditions, wheat is subjected to single or multiple stress conditions. The elucidation of the molecular mechanism of stress response is a key step to identify candidate genes for stress resistance in plants. In this study, RNA-seq data analysis identified 17.324, 10.562, 5.510, and 8.653 differentially expressed genes (DEGs) under salt, drought, heat, and cold stress, respectively. Moreover, the comparison of DEGs from each stress revealed 2374 shared genes from which 40% showed highly conserved expression patterns. Moreover, co-expression network analysis and GO enrichment revealed co-expression modules enriched with genes involved in transcription regulation, protein kinase pathway, and genes responding to phytohormones or modulating hormone levels. The expression of 15 selected transcription factor encoding genes was analyzed under abiotic stresses and ABA treatment in durum wheat. The identified transcription factor genes are excellent candidates for genetic engineering of stress tolerance in wheat.

A Survey of the Transcriptomic Resources in Durum Wheat: Stress Responses, Data Integration and Exploitation

Durum wheat (Triticum turgidum subsp. durum (Desf.) Husn.) is an allotetraploid cereal crop of worldwide importance, given its use for making pasta, couscous, and bulgur. Under climate change scenarios, abiotic (e.g., high and low temperatures, salinity, drought) and biotic (mainly exemplified by fungal pathogens) stresses represent a significant limit for durum cultivation because they can severely affect yield and grain quality. The advent of next-generation sequencing technologies has brought a huge development in transcriptomic resources with many relevant datasets now available for durum wheat, at various anatomical levels, also focusing on phenological phases and environmental conditions. In this review, we cover all the transcriptomic resources generated on durum wheat to date and focus on the corresponding scientific insights gained into abiotic and biotic stress responses. We describe relevant databases, tools and approaches, including connections with other "omics" that could assist data integration for candidate gene discovery for bio-agronomical traits. The biological knowledge summarized here will ultimately help in accelerating durum wheat breeding.

A combinatorial TRM-OFP module bilaterally fine-tunes tomato fruit shape

The mechanisms that regulate the vast diversity of plant organ shapes such as the fruit remain to be fully elucidated. TONNEAU1 Recruiting Motif proteins (TRMs) have been implicated in the control of organ shapes in a number of plant species, including tomato. However, the role of many of them is unknown. TRMs interact with Ovate Family Proteins (OFPs) via the M8 domain. However, the in planta function of the TRM-OFP interaction in regulating shape is unknown. We used CRISPR/Cas9 to generate knockout mutants in TRM proteins from different subclades and in-frame mutants within the M8 domain to investigate their roles in organ shape and interactions with OFPs. Our findings indicate that TRMs impact organ shape along both the mediolateral and proximo-distal axes of growth. Mutations in Sltrm3/4 and Sltrm5 act additively to rescue the elongated fruit phenotype of ovate/Slofp20 (o/s) to a round shape. Contrary, mutations in Sltrm19 and Sltrm17/20a result in fruit elongation and further enhance the obovoid phenotype in the o/s mutant. This study supports a combinatorial role of the TRM-OFP regulon where OFPs and TRMs expressed throughout development have both redundant and opposing roles in regulating organ shape.

Charting plant gene functions in the multi-omics and single-cell era

Despite the increased access to high-quality plant genome sequences, the set of genes with a known function remains far from complete. With the advent of novel bulk and single-cell omics profiling methods, we are entering a new era where advanced and highly integrative functional annotation strategies are being developed to elucidate the functions of all plant genes. Here, we review different multi-omics approaches to improve functional and regulatory gene characterization and highlight the power of machine learning and network biology to fully exploit the complementary information embedded in different omics layers. Finally, we discuss the potential of emerging single-cell methods and algorithms to further increase the resolution, allowing generation of functional insights about plant biology.

HGCA2.0: An RNA-Seq Based Webtool for Gene Coexpression Analysis in Homo sapiens

Genes with similar expression patterns in a set of diverse samples may be considered coexpressed. Human Gene Coexpression Analysis 2.0 (HGCA2.0) is a webtool which studies the global coexpression landscape of human genes. The website is based on the hierarchical clustering of 55,431 Homo sapiens genes based on a large-scale coexpression analysis of 3500 GTEx bulk RNA-Seq samples of healthy individuals, which were selected as the best representative samples of each tissue type. HGCA2.0 presents subclades of coexpressed genes to a gene of interest, and performs various built-in gene term enrichment analyses on the coexpressed genes, including gene ontologies, biological pathways, protein families, and diseases, while also being unique in revealing enriched transcription factors driving coexpression. HGCA2.0 has been successful in identifying not only genes with ubiquitous expression patterns, but also tissue-specific genes. Benchmarking showed that HGCA2.0 belongs to the top performing coexpression webtools, as shown by STRING analysis. HGCA2.0 creates working hypotheses for the discovery of gene partners or common biological processes that can be experimentally validated. It offers a simple and intuitive website design and user interface, as well as an API endpoint.

Deciphering Macromolecular Interactions Involved in Abiotic Stress Signaling: A Review of Bioinformatics Analysis

Plant functioning and responses to abiotic stresses largely involve regulations at the transcriptomic level via complex interactions of signal molecules, signaling cascades, and regulators. Nevertheless, all the signaling networks involved in responses to abiotic stresses have not yet been fully established. The in-depth analysis of transcriptomes in stressed plants has become a relevant state-of-the-art methodology to study these regulations and signaling pathways that allow plants to cope with or attempt to survive abiotic stresses. The plant science and molecular biology community has developed databases about genes, proteins, protein-protein interactions, protein-DNA interactions and ontologies, which are valuable sources of knowledge for deciphering such regulatory and signaling networks. The use of these data and the development of bioinformatics tools help to make sense of transcriptomic data in specific contexts, such as that of abiotic stress signaling, using functional biological approaches. The aim of this chapter is to present and assess some of the essential online tools and resources that will allow novices in bioinformatics to decipher transcriptomic data in order to characterize the cellular processes and functions involved in abiotic stress responses and signaling. The analysis of case studies further describes how these tools can be used to conceive signaling networks on the basis of transcriptomic data. In these case studies, particular attention was paid to the characterization of abiotic stress responses and signaling related to chemical and xenobiotic stressors.

Leaf physiology variations are modulated by natural variations that underlie stomatal morphology in Populus

Stomata are essential for photosynthesis and abiotic stress tolerance. Here, we used multiomics approaches to dissect the genetic architecture and adaptive mechanisms that underlie stomatal morphology in Populus tomentosa juvenile natural population (303 accessions). We detected 46 candidate genes and 15 epistatic gene-pairs, associated with 5 stomatal morphologies and 18 leaf development and photosynthesis traits, through genome-wide association studies. Expression quantitative trait locus mapping revealed that stomata-associated gene loci were significantly associated with the expression of leaf-related genes; selective sweep analysis uncovered significant differentiation in the allele frequencies of genes that underlie stomatal variations. An allelic regulatory network operating under drought stress and adequate precipitation conditions, with three key regulators (DUF538, TRA2 and AbFH2) and eight interacting genes, was identified that might regulate leaf physiology via modulation of stomatal shape and density. Validation of candidate gene variations in drought-tolerant and F₁ hybrid populations of P. tomentosa showed that the DUF538, TRA2 and AbFH2 loci cause functional stabilisation of spatiotemporal regulatory, whose favourable alleles can be faithfully transmitted to offspring. This study provides insights concerning leaf physiology and stress tolerance via the regulation of stomatal determination in perennial plants.

Network-based approaches for modeling disease regulation and progression

Molecular interaction networks lay the foundation for studying how biological functions are controlled by the complex interplay of genes and proteins. Investigating perturbed processes using biological networks has been instrumental in uncovering mechanisms that underlie complex disease phenotypes. Rapid advances in omics technologies have prompted the generation of high-throughput datasets, enabling large-scale, network-based analyses. Consequently, various modeling techniques, including network enrichment, differential network extraction, and network inference, have proven to be useful for gaining new mechanistic insights. We provide an overview of recent network-based methods and their core ideas to facilitate the discovery of disease modules or candidate mechanisms. Knowledge generated from these computational efforts will benefit biomedical research, especially drug development and precision medicine. We further discuss current challenges and provide perspectives in the field, highlighting the need for more integrative and dynamic network approaches to model disease development and progression.

Central resources of variant discovery and annotation and its role in precision medicine

Rapid technological advancement in high-throughput genomics, microarray, and deep sequencing technologies has accelerated the possibility of more complex precision medicine research using large amounts of heterogeneous health-related data from patients, including genomic variants. Genomic variants can be identified and annotated based on the reference human genome either within the sequence as a whole or in a putative functional genomic element. The American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) mutually created standards and guidelines for the appraisal of proof to expand consistency and straightforwardness in clinical variation interpretations. Various efforts toward precision medicine have been facilitated by many national and international public databases that classify and annotate genomic variation. In the present study, several resources are highlighted with recognition and data spreading of clinically important genetic variations.

Identification of growth regulators using cross-species network analysis in plants

With the need to increase plant productivity, one of the challenges plant scientists are facing is to identify genes that play a role in beneficial plant traits. Moreover, even when such genes are found, it is generally not trivial to transfer this knowledge about gene function across species to identify functional orthologs. Here, we focused on the leaf to study plant growth. First, we built leaf growth transcriptional networks in Arabidopsis (Arabidopsis thaliana), maize (Zea mays), and aspen (Populus tremula). Next, known growth regulators, here defined as genes that when mutated or ectopically expressed alter plant growth, together with cross-species conserved networks, were used as guides to predict novel Arabidopsis growth regulators. Using an in-depth literature screening, 34 out of 100 top predicted growth regulators were confirmed to affect leaf phenotype when mutated or overexpressed and thus represent novel potential growth regulators. Globally, these growth regulators were involved in cell cycle, plant defense responses, gibberellin, auxin, and brassinosteroid signaling. Phenotypic characterization of loss-of-function lines confirmed two predicted growth regulators to be involved in leaf growth (NPF6.4 and LATE MERISTEM IDENTITY2). In conclusion, the presented network approach offers an integrative cross-species strategy to identify genes involved in plant growth and development.

An integrated transcriptome mapping the regulatory network of coding and long non-coding RNAs provides a genomics resource in chickpea

Large-scale transcriptome analysis can provide a systems-level understanding of biological processes. To accelerate functional genomic studies in chickpea, we perform a comprehensive transcriptome analysis to generate full-length transcriptome and expression atlas of protein-coding genes (PCGs) and long non-coding RNAs (lncRNAs) from 32 different tissues/organs via deep sequencing. The high-depth RNA-seq dataset reveal expression dynamics and tissue-specificity along with associated biological functions of PCGs and lncRNAs during development. The coexpression network analysis reveal modules associated with a particular tissue or a set of related tissues. The components of transcriptional regulatory networks (TRNs), including transcription factors, their cognate cis-regulatory motifs, and target PCGs/lncRNAs that determine developmental programs of different tissues/organs, are identified. Several candidate tissue-specific and abiotic stress-responsive transcripts associated with quantitative trait loci that determine important agronomic traits are also identified. These results provide an important resource to advance functional/translational genomic and genetic studies during chickpea development and environmental conditions.

A full-length transcriptome and expression atlas of protein-coding genes and long non-coding RNAs is generated in chickpea. Components of transcriptional regulatory networks and candidate tissue-specific transcripts associated with quantitative trait loci are identified.

Regulatory mechanisms of fatty acids biosynthesis in Armeniaca sibirica seed kernel oil at different developmental stages

Background

Armeniaca sibirica seed kernel oil is rich in oleic acid and linoleic acid, thus holding potential value as a source of high-quality edible oils. However, some regulatory factors involved in fatty acids accumulation in A. sibirica seed kernels remain largely elusive. Thus, the aim of this study was to elucidate the regulatory mechanisms underlying fatty acids biosynthesis in A. sibirica developing seed kernels.

Methods

Seed kernels from six plants from a single A. sibirica clone were taken at five different developmental stages (days 30, 41, 52, 63, and 73 after anthesis). Fatty acid composition in seed kernel oil was determined by gas chromatography-mass spectrometry (GC-MS). In addition, transcriptome analysis was conducted using second-generation sequencing (SGS) and single-molecule real-time sequencing (SMRT).

Results

Rapid accumulation of fatty acids occurred throughout the different stages of seed kernels development, with oleic acid and linoleic acid as the main fatty acids. A total of 10,024, 9,803, 6,004, 6,719 and 9,688 unigenes were matched in the Nt, Nr, KOG, GO and KEGG databases, respectively. In the category lipid metabolism, 228 differentially expressed genes (DEGs) were annotated into 13 KEGG pathways. Specific unigenes encoding 12 key enzymes related to fatty acids biosynthesis were determined. Co-expression network analysis identified 11 transcription factors (TFs) and 13 long non-coding RNAs (lncRNAs) which putatively participate in the regulation of fatty acid biosynthesis. This study provides insights into the molecular regulatory mechanisms of fatty acids biosynthesis in A. sibirica developing seed kernels, and enabled the identification of novel candidate factors for future improvement of the production and quality of seed kernel oil by breeding.

A review of causal discovery methods for molecular network analysis

BACKGROUND

With the increasing availability and size of multi-omics datasets, investigating the casual relationships between molecular phenotypes has become an important aspect of exploring underlying biology andgenetics. There are an increasing number of methodlogies that have been developed and applied to moleular networks to investigate these causal interactions.

METHODS

We have introduced and reviewed the available methods for building large-scale causal molecular networks that have been developed and applied in the past decade.

RESULTS

In this review we have identified and summarized the existing methods for infering causality in large-scale causal molecular networks, and discussed important factors that will need to be considered in future research in this area.

CONCLUSION

Existing methods to infering causal molecular networks have their own strengths and limitations so there is no one best approach, and it is instead down to the discretion of the researcher. This review also to discusses some of the current limitations to biological interpretation of these networks, and important factors to consider for future studies on molecular networks.

Comparative transcriptome and metabolome analyses identified the mode of sucrose degradation as a metabolic marker for early vegetative propagation in bulbs of Lycoris

Vegetative propagation (VP) is an important practice for production in many horticultural plants. Sugar supply constitutes the basis of VP in bulb flowers, but the underlying molecular basis remains elusive. By performing a combined sequencing technologies coupled with ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry approach for metabolic analyses, we compared two Lycoris species with contrasting regeneration rates: high-regeneration Lycoris sprengeri and low-regeneration Lycoris aurea. A comprehensive multi-omics analyses identified both expected processes involving carbohydrate metabolism and transcription factor networks, as well as the metabolic characteristics for each developmental stage. A higher abundance of the differentially expressed genes including those encoding ethylene responsive factors was detected at bulblet initiation stage compared to the late stage of bulblet development. High hexose-to-sucrose ratio correlated to bulblet formation across all the species examined, indicating its role in the VP process in Lycoris bulb. Importantly, a clear difference between cell wall invertase (CWIN)-catalyzed sucrose unloading in high-regeneration species and the sucrose synthase-catalyzed pathway in low-regeneration species was observed at the bulblet initiation stage, which was supported by findings from carboxyfluorescein tracing and quantitative real-time PCR analyses. Collectively, the findings indicate a sugar-mediated model of the regulation of VP in which high CWIN expression or activity may promote bulblet initiation via enhancing apoplasmic unloading of sucrose or sugar signals, whereas the subsequent high ratio of hexose-to-sucrose likely supports cell division characterized in the next phase of bulblet formation.

Gene Differential Co-Expression Networks Based on RNA-Seq: Construction and Its Applications

Gene co-expression network (GCN) becomes an increasingly important tool in omics data analysis. A great challenge for GCN construction is that the sample size is far lower than the number of genes. Traditional methods rely on considerable samples. Moreover, association signals are likely weak, nonlinear and stochastic, which are difficult to be identified among thousands of candidates. In this paper, the gray correlation coefficient (GCC) is introduced, and a novel method to construct gene differential co-expression networks (GDCNs) is proposed. Based on the GDCNs, three measures are proposed to explore informative genes. The proposed method can make full use of the information provided by a handful of samples and overcome the shortages of GCNs, which can evaluate the changes of co-expression relationships that are possibly triggered by treatments. Based on RNA-seq data of Brassica napus, GDCNs under multiple experimental conditions are constructed and investigated. It is found that the GCC-based method is very robust to data processing. The GDCNs facilitate the inference of gene functions and the identification of informative genes that are responsible for stress responsiveness. The GDCN-based approaches integrate the 'guilt by association' and the 'guilt by rewiring' rules, which provide alternative tools for omics data analysis.

Tight gene co-expression in BCB positive cattle oocytes and their surrounding cumulus cells

BACKGROUND

Cytoplasmic and nuclear maturation of oocytes, as well as interaction with the surrounding cumulus cells, are important features relevant to the acquisition of developmental competence.

METHODS

Here, we utilized Brilliant cresyl blue (BCB) to distinguish cattle oocytes with low activity of the enzyme Glucose-6-Phosphate Dehydrogenase, and thus separated fully grown (BCB positive) oocytes from those in the growing phase (BCB negative). We then analyzed the developmental potential of these oocytes, mitochondrial DNA (mtDNA) copy number in single oocytes, and investigated the transcriptome of single oocytes and their surrounding cumulus cells of BCB positive versus BCB negative oocytes.

RESULTS

The BCB positive oocytes were twice as likely to produce a blastocyst in vitro compared to BCB- oocytes (P < 0.01). We determined that BCB negative oocytes have 1.3-fold more mtDNA copies than BCB positive oocytes (P = 0.004). There was no differential transcript abundance of genes expressed in oocytes, however, 172 genes were identified in cumulus cells with differential transcript abundance (FDR < 0.05) based on the BCB staining of their oocyte. Co-expression analysis between oocytes and their surrounding cumulus cells revealed a subset of genes whose co-expression in BCB positive oocytes (n = 75) and their surrounding cumulus cells (n = 108) compose a unique profile of the cumulus-oocyte complex.

CONCLUSIONS

If oocytes transition from BCB negative to BCB positive, there is a greater likelihood of producing a blastocyst, and a reduction of mtDNA copies, but there is no systematic variation of transcript abundance. Cumulus cells present changes in transcript abundance, which reflects in a dynamic co-expression between the oocyte and cumulus cells.

NetREx: Network-based Rice Expression Analysis Server for abiotic stress conditions

Recent focus on transcriptomic studies in food crops like rice, wheat and maize provide new opportunities to address issues related to agriculture and climate change. Re-analysis of such data available in public domain supplemented with annotations across molecular hierarchy can be of immense help to the plant research community, particularly co-expression networks representing transcriptionally coordinated genes that are often part of the same biological process. With this objective, we have developed NetREx, a Network-based Rice Expression Analysis Server, that hosts ranked co-expression networks of Oryza sativa using publicly available messenger RNA sequencing data across uniform experimental conditions. It provides a range of interactable data viewers and modules for analysing user-queried genes across different stress conditions (drought, flood, cold and osmosis) and hormonal treatments (abscisic and jasmonic acid) and tissues (root and shoot). Subnetworks of user-defined genes can be queried in pre-constructed tissue-specific networks, allowing users to view the fold change, module memberships, gene annotations and analysis of their neighbourhood genes and associated pathways. The web server also allows querying of orthologous genes from Arabidopsis, wheat, maize, barley and sorghum. Here, we demonstrate that NetREx can be used to identify novel candidate genes and tissue-specific interactions under stress conditions and can aid in the analysis and understanding of complex phenotypes linked to stress response in rice. Database URL: https://bioinf.iiit.ac.in/netrex/index.html.

Transcriptome and association mapping revealed functional genes respond to drought stress in Populus

Drought frequency and severity are exacerbated by global climate change, which could compromise forest ecosystems. However, there have been minimal efforts to systematically investigate the genetic basis of the response to drought stress in perennial trees. Here, we implemented a systems genetics approach that combines co-expression analysis, association genetics, and expression quantitative trait nucleotide (eQTN) mapping to construct an allelic genetic regulatory network comprising four key regulators (PtoeIF-2B, PtoABF3, PtoPSB33, and PtoLHCA4) under drought stress conditions. Furthermore, Hap_01PtoeIF-2B, a superior haplotype associated with the net photosynthesis, was revealed through allelic frequency and haplotype analysis. In total, 75 candidate genes related to drought stress were identified through transcriptome analyses of five Populus cultivars (P. tremula × P. alba, P. nigra, P. simonii, P. trichocarpa, and P. tomentosa). Through association mapping, we detected 92 unique SNPs from 38 genes and 104 epistatic gene pairs that were associated with six drought-related traits by association mapping. eQTN mapping unravels drought stress-related gene loci that were significantly associated with the expression levels of candidate genes for drought stress. In summary, we have developed an integrated strategy for dissecting a complex genetic network, which facilitates an integrated population genomics approach that can assess the effects of environmental threats.

Exploiting plant transcriptomic databases: Resources, tools, and approaches

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

Approaches in Gene Coexpression Analysis in Eukaryotes

Simple Summary

Genes whose expression levels rise and fall similarly in a large set of samples, may be considered coexpressed. Gene coexpression analysis refers to the en masse discovery of coexpressed genes from a large variety of transcriptomic experiments. The type of biological networks that studies gene coexpression, known as Gene Coexpression Networks, consist of an undirected graph depicting genes and their coexpression relationships. Coexpressed genes are clustered in smaller subnetworks, the predominant biological roles of which can be determined through enrichment analysis. By studying well-annotated gene partners, the attribution of new roles to genes of unknown function or assumption for participation in common metabolic pathways can be achieved, through a guilt-by-association approach. In this review, we present key issues in gene coexpression analysis, as well as the most popular tools that perform it.

Abstract

Gene coexpression analysis constitutes a widely used practice for gene partner identification and gene function prediction, consisting of many intricate procedures. The analysis begins with the collection of primary transcriptomic data and their preprocessing, continues with the calculation of the similarity between genes based on their expression values in the selected sample dataset and results in the construction and visualisation of a gene coexpression network (GCN) and its evaluation using biological term enrichment analysis. As gene coexpression analysis has been studied extensively, we present most parts of the methodology in a clear manner and the reasoning behind the selection of some of the techniques. In this review, we offer a comprehensive and comprehensible account of the steps required for performing a complete gene coexpression analysis in eukaryotic organisms. We comment on the use of RNA-Seq vs. microarrays, as well as the best practices for GCN construction. Furthermore, we recount the most popular webtools and standalone applications performing gene coexpression analysis, with details on their methods, features and outputs.

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

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