Large-scale atlas of microarray data reveals the distinct expression landscape of different tissues in Arabidopsis

Specific Transcriptional Regulation Controls Plant Organ-Specific Infection by the Oomycete Pathogen Phytophthora sojae

The organs of a plant species vary in cell structure, metabolism and defence responses. However, the mechanisms that enable a single pathogen to colonise different plant organs remain unclear. Here we compared the transcriptome of the oomycete pathogen Phytophthora sojae during infection of roots versus leaves of soybeans. We found differences in the transcript levels of hundreds of pathogenicity-related genes, particularly genes encoding carbohydrate-active enzymes, secreted (effector) proteins, oxidoreductase-related proteins and transporters. To identify the key regulator for root-specific infection, we knocked out root-specific transcription factors (TFs) and found the mutants of PsBZPc29, which encodes a member of an oomycete-specific class of basic leucine zipper (bZIP) TFs, displayed reduced virulence on soybean roots but not on leaves. More than 60% of the root-specific genes showed reduced expression in the mutants during root infection. The results suggest that transcriptional regulation underlies the organ-specific infection by P. sojae, and that a bZIP TF plays a key role in root-specific transcriptional regulation.

ARSK1 activates TORC1 signaling to adjust growth to phosphate availability in Arabidopsis

Nutrient sensing and signaling are essential for adjusting growth and development to available resources. Deprivation of the essential mineral phosphorus (P) inhibits root growth.1 The molecular processes that sense P limitation to trigger early root growth inhibition are not known yet. Target of rapamycin (TOR) kinase is a central regulatory hub in eukaryotes to adapt growth to internal and external nutritional cues.2,3 How nutritional signals are transduced to TOR to control plant growth remains unclear. Here, we identify Arabidopsis-root-specific kinase 1 (ARSK1), which attenuates initial root growth inhibition in response to P limitation. We demonstrate that ARSK1 phosphorylates and stabilizes the regulatory-associated protein of TOR 1B (RAPTOR1B), a component of the TOR complex 1, to adjust root growth to P availability. These findings uncover signaling components acting upstream of TOR to balance growth to P availability.

A Network-Based Approach for Improving Annotation of Transcription Factor Functions and Binding Sites in Arabidopsis thaliana

Transcription factors are an integral component of the cellular machinery responsible for regulating many biological processes, and they recognize distinct DNA sequence patterns as well as internal/external signals to mediate target gene expression. The functional roles of an individual transcription factor can be traced back to the functions of its target genes. While such functional associations can be inferred through the use of binding evidence from high-throughput sequencing technologies available today, including chromatin immunoprecipitation sequencing, such experiments can be resource-consuming. On the other hand, exploratory analysis driven by computational techniques can alleviate this burden by narrowing the search scope, but the results are often deemed low-quality or non-specific by biologists. In this paper, we introduce a data-driven, statistics-based strategy to predict novel functional associations for transcription factors in the model plant Arabidopsis thaliana. To achieve this, we leverage one of the largest available gene expression compendia to build a genome-wide transcriptional regulatory network and infer regulatory relationships among transcription factors and their targets. We then use this network to build a pool of likely downstream targets for each transcription factor and query each target pool for functionally enriched gene ontology terms. The results exhibited sufficient statistical significance to annotate most of the transcription factors in Arabidopsis with highly specific biological processes. We also perform DNA binding motif discovery for transcription factors based on their target pool. We show that the predicted functions and motifs strongly agree with curated databases constructed from experimental evidence. In addition, statistical analysis of the network revealed interesting patterns and connections between network topology and system-level transcriptional regulation properties. We believe that the methods demonstrated in this work can be extended to other species to improve the annotation of transcription factors and understand transcriptional regulation on a system level.

METACLUSTER-an R package for context-specific expression analysis of metabolic gene clusters

SUMMARY

Plants and microbes produce numerous compounds to cope with their environments but the biosynthetic pathways for most of these compounds have yet to be elucidated. Some biosynthetic pathways are encoded by enzymes collocated in the chromosome. To facilitate a more comprehensive condition and tissue-specific expression analysis of metabolic gene clusters, we developed METACLUSTER, a probabilistic framework for characterizing metabolic gene clusters using context-specific gene expression information.

AVAILABILITY AND IMPLEMENTATION

METACLUSTER is freely available at https://github.com/mbanf/METACLUSTER.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Revealing Shared and Distinct Genes Responding to JA and SA Signaling in Arabidopsis by Meta-Analysis

Plant resistance against biotrophic and necrotrophic pathogens is mediated by mutually synergistic and antagonistic effects of salicylic acid (SA) and jasmonic acid (JA) signals. However, the unique and shared genes responding to the defense mediated by JA/SA signals were largely unclear. To reveal discrete, synergistic and antagonistic JA/SA responsive genes in Arabidopsis thaliana, Meta-Analysis was employed with 257 publicly available Arabidopsis thaliana RNA-Seq gene expression profiles following treatment of mock, JA or SA analogs. JA/SA signalings were found to co-induce broad-spectrum disease-response genes, co-repress the genes related to photosynthesis, auxin, and gibberellin, and reallocate resources of growth toward defense. JA might attenuate SA induced immune response by inhibiting the expression of resistance genes and receptor-like proteins/kinases. Strikingly, co-expression network analysis revealed that JA/SA uniquely regulated genes showing highly coordinated co-expression only in their respective treatment. Using principal component analysis, and hierarchical cluster analysis, JA/SA analogs were segregated into separate entities based on the global differential expression matrix rather than the expression matrix. To accurately classify JA/SA analogs with as few genes as possible, 87 genes, including the SA receptor NPR4, and JA biosynthesis gene AOC1 and JA response biomarkers VSP1/2, were identified by three feature selection algorithms as JA/SA markers. The results were confirmed by independent datasets and provided valuable resources for further functional analyses in JA- or SA- mediated plant defense. These methods would provide cues to build a promising approach for probing the mode of action of potential elicitors.

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

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

Identification of gene expression logical invariants in Arabidopsis

Numerous gene expression datasets from diverse tissue samples from the plant variety Arabidopsis thaliana have been already deposited in the public domain. There have been several attempts to do large scale meta-analyses of all of these datasets. Most of these analyses summarize pairwise gene expression relationships using correlation, or identify differentially expressed genes in two conditions. We propose here a new large scale meta-analysis of the publicly available Arabidopsis datasets to identify Boolean logical relationships between genes. Boolean logic is a branch of mathematics that deals with two possible values. In the context of gene expression datasets we use qualitative high and low expression values. A strong logical relationship between genes emerges if at least one of the quadrants is sparsely populated. We pointed out serious issues in the data normalization steps widely accepted and published recently in this context. We put together a web resource where gene expression relationships can be explored online which helps visualize the logical relationships between genes. We believe that this website will be useful in identifying important genes in different biological context. The web link is http://hegemon.ucsd.edu/plant/.

Shifting the limits in wheat research and breeding using a fully annotated reference genome

An annotated reference sequence representing the hexaploid bread wheat genome in 21 pseudomolecules has been analyzed to identify the distribution and genomic context of coding and noncoding elements across the A, B, and D subgenomes. With an estimated coverage of 94% of the genome and containing 107,891 high-confidence gene models, this assembly enabled the discovery of tissue- and developmental stage-related coexpression networks by providing a transcriptome atlas representing major stages of wheat development. Dynamics of complex gene families involved in environmental adaptation and end-use quality were revealed at subgenome resolution and contextualized to known agronomic single-gene or quantitative trait loci. This community resource establishes the foundation for accelerating wheat research and application through improved understanding of wheat biology and genomics-assisted breeding.

Combination of novel and public RNA-seq datasets to generate an mRNA expression atlas for the domestic chicken

Background

The domestic chicken (Gallus gallus) is widely used as a model in developmental biology and is also an important livestock species. We describe a novel approach to data integration to generate an mRNA expression atlas for the chicken spanning major tissue types and developmental stages, using a diverse range of publicly-archived RNA-seq datasets and new data derived from immune cells and tissues.

Results

Randomly down-sampling RNA-seq datasets to a common depth and quantifying expression against a reference transcriptome using the mRNA quantitation tool Kallisto ensured that disparate datasets explored comparable transcriptomic space. The network analysis tool Graphia was used to extract clusters of co-expressed genes from the resulting expression atlas, many of which were tissue or cell-type restricted, contained transcription factors that have previously been implicated in their regulation, or were otherwise associated with biological processes, such as the cell cycle. The atlas provides a resource for the functional annotation of genes that currently have only a locus ID. We cross-referenced the RNA-seq atlas to a publicly available embryonic Cap Analysis of Gene Expression (CAGE) dataset to infer the developmental time course of organ systems, and to identify a signature of the expansion of tissue macrophage populations during development.

Conclusion

Expression profiles obtained from public RNA-seq datasets – despite being generated by different laboratories using different methodologies – can be made comparable to each other. This meta-analytic approach to RNA-seq can be extended with new datasets from novel tissues, and is applicable to any species.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-4972-7) contains supplementary material, which is available to authorized users.

PlaD: A Transcriptomics Database for Plant Defense Responses to Pathogens, Providing New Insights into Plant Immune System

High-throughput transcriptomics technologies have been widely used to study plant transcriptional reprogramming during the process of plant defense responses, and a large quantity of gene expression data have been accumulated in public repositories. However, utilization of these data is often hampered by the lack of standard metadata annotation. In this study, we curated 2444 public pathogenesis-related gene expression samples from the model plant Arabidopsis and three major crops (maize, rice, and wheat). We organized the data into a user-friendly database termed as PlaD. Currently, PlaD contains three key features. First, it provides large-scale curated data related to plant defense responses, including gene expression and gene functional annotation data. Second, it provides the visualization of condition-specific expression profiles. Third, it allows users to search co-regulated genes under the infections of various pathogens. Using PlaD, we conducted a large-scale transcriptome analysis to explore the global landscape of gene expression in the curated data. We found that only a small fraction of genes were differentially expressed under multiple conditions, which might be explained by their tendency of having more network connections and shorter network distances in gene networks. Collectively, we hope that PlaD can serve as an important and comprehensive knowledgebase to the community of plant sciences, providing insightful clues to better understand the molecular mechanisms underlying plant immune responses. PlaD is freely available at http://systbio.cau.edu.cn/plad/index.php or http://zzdlab.com/plad/index.php.

Identification of regulatory modules in genome scale transcription regulatory networks

Background

In gene regulatory networks, transcription factors often function as co-regulators to synergistically induce or inhibit expression of their target genes. However, most existing module-finding algorithms can only identify densely connected genes but not co-regulators in regulatory networks.

Methods

We have developed a new computational method, CoReg, to identify transcription co-regulators in large-scale regulatory networks. CoReg calculates gene similarities based on number of common neighbors of any two genes. Using simulated and real networks, we compared the performance of different similarity indices and existing module-finding algorithms and we found CoReg outperforms other published methods in identifying co-regulatory genes. We applied CoReg to a large-scale network of Arabidopsis with more than 2.8 million edges and we analyzed more than 2,300 published gene expression profiles to charaterize co-expression patterns of gene moduled identified by CoReg.

Results

We identified three types of modules in the Arabidopsis network: regulator modules, target modules and intermediate modules. Regulator modules include genes with more than 90% edges as out-going edges; Target modules include genes with more than 90% edges as incoming edges. Other modules are classified as intermediate modules. We found that genes in target modules tend to be highly co-expressed under abiotic stress conditions, suggesting this network struture is robust against perturbation.

Conclusions

Our analysis shows that the CoReg is an accurate method in identifying co-regulatory genes in large-scale networks. We provide CoReg as an R package, which can be applied in finding co-regulators in any organisms with genome-scale regulatory network data.

Electronic supplementary material

The online version of this article (10.1186/s12918-017-0493-2) contains supplementary material, which is available to authorized users.

Extracting genotype information of Arabidopsis thaliana recombinant inbred lines from transcript profiles established with high-density oligonucleotide arrays

Polymorphic probes identified via a sequence-based approach are suitable to infer the genotypes of recombinant inbred lines from hybridisation intensities of GeneChip ^® transcript profiling experiments. The sequences of the probes of the ATH1 GeneChip^® exactly match transcript sequences of the Arabidopsis thaliana reference genome Col-0, whereas nucleotide differences and/or insertions/deletions may be observed for transcripts of other A. thaliana accessions. Individual probes of the GeneChip^® that show sequence polymorphisms between different A. thaliana accessions may serve as single-feature polymorphism (SFP) markers, provided that the sequence changes cause differences in hybridisation intensity for the accessions of interest. A sequence-based approach identified features on the high-density oligonucleotide array that showed sequence polymorphisms between A. thaliana accessions Col-0 and C24. Hybridisation intensities of polymorphic probes were extracted from genome-wide transcript profiles of Col-0/C24 and C24/Col-0 recombinant inbred lines and assessed after standardisation via sliding window analyses to identify SFP markers. The genotypes of the recombinant inbred lines were determined with the SFP markers and the resulting data were integrated with information, which had been established previously with single nucleotide polymorphism and insertion/deletion markers, to enrich the linkage map of the Col-0/C24 and C24/Col-0 recombinant inbred populations. Congruence between the molecular marker map and the sequence maps of the A. thaliana Col-0 chromosomes proved the reliability of the genotype information which was deduced from the transcript profiles of the Col-0/C24 and C24/Col-0 recombinant inbred lines.

The gene expression landscape of pine seedling tissues

Conifers dominate vast regions of the Northern hemisphere. They are the main source of raw materials for timber industry as well as a wide range of biomaterials. Despite their inherent difficulties as experimental models for classical plant biology research, the technological advances in genomics research are enabling fundamental studies on these plants. The use of laser capture microdissection followed by transcriptomic analysis is a powerful tool for unravelling the molecular and functional organization of conifer tissues and specialized cells. In the present work, 14 different tissues from 1-month-old maritime pine (Pinus pinaster) seedlings have been isolated and their transcriptomes analysed. The results increased the sequence information and number of full-length transcripts from a previous reference transcriptome and added 39 841 new transcripts. In total, 2376 transcripts were ubiquitously expressed in all of the examined tissues. These transcripts could be considered the core 'housekeeping genes' in pine. The genes have been clustered in function to their expression profiles. This analysis reduced the number of profiles to 38, most of these defined by their expression in a unique tissue that is much higher than in the other tissues. The expression and localization data are accessible at ConGenIE.org (http://v22.popgenie.org/microdisection/). This study presents an overview of the gene expression distribution in different pine tissues, specifically highlighting the relationships between tissue gene expression and function. This transcriptome atlas is a valuable resource for functional genomics research in conifers.

Tailoring high-density oligonucleotide arrays for transcript profiling of different Arabidopsis thaliana accessions using a sequence-based approach

Excluding polymorphic probes from GeneChip ^® transcript profiling experiments via a sequence-based approach results in improved detection of differentially expressed genes in developing seeds of Arabidopsis thaliana accessions Col-0 and C24. GeneChip^® arrays represent a powerful tool for transcript profiling experiments. The ATH1 GeneChip^® has been designed based on the sequence of the Arabidopsis thaliana reference genome Col-0, hence the features on the array exactly match the sequences of Col-0 transcripts. In contrast, transcripts of other A. thaliana accessions or related species may show nucleotide differences and/or insertions/deletions when compared to the corresponding Col-0 transcripts, therefore, comparisons of transcript abundance involving different A. thaliana accessions or related species may be compromised for a certain number of transcripts. To tackle this limitation, a sequence-based strategy was developed. Only features on the array that were identical in sequence for the specimen to be compared were considered for transcript profiling. The impact of the proposed strategy was evaluated for transcript profiles that were established for developing seeds of A. thaliana accessions Col-0 and C24.

CrY2H-seq: a massively multiplexed assay for deep-coverage interactome mapping

Broad-scale protein-protein interaction mapping is a major challenge given the cost, time, and sensitivity constraints of existing technologies. Here, we present a massively multiplexed yeast two-hybrid method, CrY2H-seq, which uses a Cre recombinase interaction reporter to intracellularly fuse the coding sequences of two interacting proteins and next-generation DNA sequencing to identify these interactions en masse. We applied CrY2H-seq to investigate sparsely annotated Arabidopsis thaliana transcription factors interactions. By performing ten independent screens testing a total of 36 million binary interaction combinations, and uncovering a network of 8,577 interactions among 1,453 transcription factors, we demonstrate CrY2H-seq's improved screening capacity, efficiency, and sensitivity over those of existing technologies. The deep-coverage network resource we call AtTFIN-1 recapitulates one-third of previously reported interactions derived from diverse methods, expands the number of known plant transcription factor interactions by three-fold, and reveals previously unknown family-specific interaction module associations with plant reproductive development, root architecture, and circadian coordination.

Bioinformatics analysis to identify the critical genes, microRNAs and long noncoding RNAs in melanoma

Melanoma, which is usually induced by ultraviolet light exposure and the following DNA damage, is the most dangerous skin cancer. The purpose of the present study was to screen key molecules involved in melanoma.Microarray data of E-MTAB-1862 were downloaded from the ArrayExpress database, which included 21 primary melanoma samples and 11 benign nevus samples. In addition, the RNASeq version 2 and microRNA (miRNA) sequencing data of cutaneous melanoma were downloaded from The Cancer Genome Atlas database. After identifying the differentially expressed genes (DEGs) using Limma package, enrichment analysis and protein-protein interaction (PPI) network analysis were performed separately for them using DAVID software and Cytoscape software. In addition, survival analysis and regulatory network analysis were further performed by log-rank test and Cytoscape software, respectively. Moreover, real-time reverse transcription polymerase chain reaction (RT-PCR) was performed to further verify the expression patterns of several selected DEGs.A total of 382 DEGs were identified in primary melanoma samples, including 206 upregulated genes and 176 downregulated genes. Functional enrichment analysis showed that COL17A1 was enriched in epidermis development. In the PPI network, CXCL8 (degree = 29) and STAT1 (degree = 28) had higher degrees and could interact with each other. Survival analysis showed that 21 DEGs, 55 long noncoding RNAs (lncRNAs) and 32 miRNAs were found to be associated with prognosis. Furthermore, several regulatory relationships were found in the lncRNA-gene regulatory network (such as RP11-361L15.4 targeting COL17A1) and the miRNA-gene regulatory network (such as hsa-miR-375 targeting CCL27 and hsa-miR-375 targeting insulin-like growth factor 1 receptor [IGF1R]). Real-time RT-PCR results showed that the overall direction of differential expression was consistent except COL17A1.CXCL8 interacted with STAT1, CCL27, and IGF1R targeted by hsa-miR-375, and COL17A1 targeted by RP11-361L15.4 might function in the development and progression of melanoma, which should be verified by more detailed experiments.

Large-scale transcriptome analysis reveals arabidopsis metabolic pathways are frequently influenced by different pathogens

Through large-scale transcriptional data analyses, we highlighted the importance of plant metabolism in plant immunity and identified 26 metabolic pathways that were frequently influenced by the infection of 14 different pathogens. Reprogramming of plant metabolism is a common phenomenon in plant defense responses. Currently, a large number of transcriptional profiles of infected tissues in Arabidopsis (Arabidopsis thaliana) have been deposited in public databases, which provides a great opportunity to understand the expression patterns of metabolic pathways during plant defense responses at the systems level. Here, we performed a large-scale transcriptome analysis based on 135 previously published expression samples, including 14 different pathogens, to explore the expression pattern of Arabidopsis metabolic pathways. Overall, metabolic genes are significantly changed in expression during plant defense responses. Upregulated metabolic genes are enriched on defense responses, and downregulated genes are enriched on photosynthesis, fatty acid and lipid metabolic processes. Gene set enrichment analysis (GSEA) identifies 26 frequently differentially expressed metabolic pathways (FreDE_Paths) that are differentially expressed in more than 60% of infected samples. These pathways are involved in the generation of energy, fatty acid and lipid metabolism as well as secondary metabolite biosynthesis. Clustering analysis based on the expression levels of these 26 metabolic pathways clearly distinguishes infected and control samples, further suggesting the importance of these metabolic pathways in plant defense responses. By comparing with FreDE_Paths from abiotic stresses, we find that the expression patterns of 26 FreDE_Paths from biotic stresses are more consistent across different infected samples. By investigating the expression correlation between transcriptional factors (TFs) and FreDE_Paths, we identify several notable relationships. Collectively, the current study will deepen our understanding of plant metabolism in plant immunity and provide new insights into disease-resistant crop improvement.

Pan- and core- network analysis of co-expression genes in a model plant

Genome-wide gene expression experiments have been performed using the model plant Arabidopsis during the last decade. Some studies involved construction of coexpression networks, a popular technique used to identify groups of co-regulated genes, to infer unknown gene functions. One approach is to construct a single coexpression network by combining multiple expression datasets generated in different labs. We advocate a complementary approach in which we construct a large collection of 134 coexpression networks based on expression datasets reported in individual publications. To this end we reanalyzed public expression data. To describe this collection of networks we introduced concepts of 'pan-network' and 'core-network' representing union and intersection between a sizeable fractions of individual networks, respectively. We showed that these two types of networks are different both in terms of their topology and biological function of interacting genes. For example, the modules of the pan-network are enriched in regulatory and signaling functions, while the modules of the core-network tend to include components of large macromolecular complexes such as ribosomes and photosynthetic machinery. Our analysis is aimed to help the plant research community to better explore the information contained within the existing vast collection of gene expression data in Arabidopsis.

Differential Coexpression Analysis Reveals Extensive Rewiring of Arabidopsis Gene Coexpression in Response to Pseudomonas syringae Infection

Plant defense responses to pathogens involve massive transcriptional reprogramming. Recently, differential coexpression analysis has been developed to study the rewiring of gene networks through microarray data, which is becoming an important complement to traditional differential expression analysis. Using time-series microarray data of Arabidopsis thaliana infected with Pseudomonas syringae, we analyzed Arabidopsis defense responses to P. syringae through differential coexpression analysis. Overall, we found that differential coexpression was a common phenomenon of plant immunity. Genes that were frequently involved in differential coexpression tend to be related to plant immune responses. Importantly, many of those genes have similar average expression levels between normal plant growth and pathogen infection but have different coexpression partners. By integrating the Arabidopsis regulatory network into our analysis, we identified several transcription factors that may be regulators of differential coexpression during plant immune responses. We also observed extensive differential coexpression between genes within the same metabolic pathways. Several metabolic pathways, such as photosynthesis light reactions, exhibited significant changes in expression correlation between normal growth and pathogen infection. Taken together, differential coexpression analysis provides a new strategy for analyzing transcriptional data related to plant defense responses and new insights into the understanding of plant-pathogen interactions.

Large-Scale Public Transcriptomic Data Mining Reveals a Tight Connection between the Transport of Nitrogen and Other Transport Processes in Arabidopsis

Movement of nitrogen to the plant tissues where it is needed for growth is an important contribution to nitrogen use efficiency. However, we have very limited knowledge about the mechanisms of nitrogen transport. Loading of nitrogen into the xylem and/or phloem by transporter proteins is likely important, but there are several families of genes that encode transporters of nitrogenous molecules (collectively referred to as N transporters here), each comprised of many gene members. In this study, we leveraged publicly available microarray data of Arabidopsis to investigate the gene networks of N transporters to elucidate their possible biological roles. First, we showed that tissue-specificity of nitrogen (N) transporters was well reflected among the public microarray data. Then, we built coexpression networks of N transporters, which showed relationships between N transporters and particular aspects of plant metabolism, such as phenylpropanoid biosynthesis and carbohydrate metabolism. Furthermore, genes associated with several biological pathways were found to be tightly coexpressed with N transporters in different tissues. Our coexpression networks provide information at the systems-level that will serve as a resource for future investigation of nitrogen transport systems in plants, including candidate gene clusters that may work together in related biological roles.