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Identifying gene function and module connections by the integration of multispecies expression compendia

The functions of many eukaryotic genes are still poorly understood. Here, we developed and validated a new method, termed GeneBridge, which is based on two linked approaches to impute gene function and bridge genes with biological processes. First, Gene-Module Association Determination (G-MAD) allows the annotation of gene function. Second, Module-Module Association Determination (M-MAD) allows predicting connectivity among modules. We applied the GeneBridge tools to large-scale multispecies expression compendia—1700 data sets with over 300,000 samples from human, mouse, rat, fly, worm, and yeast—collected in this study. G-MAD identifies novel functions of genes—for example, DDT in mitochondrial respiration and WDFY4 in T cell activation—and also suggests novel components for modules, such as for cholesterol biosynthesis. By applying G-MAD on data sets from respective tissues, tissue-specific functions of genes were identified—for instance, the roles of EHHADH in liver and kidney, as well as SLC6A1 in brain and liver. Using M-MAD, we identified a list of module-module associations, such as those between mitochondria and proteasome, mitochondria and histone demethylation, as well as ribosomes and lipid biosynthesis. The GeneBridge tools together with the expression compendia are available as an open resource, which will facilitate the identification of connections linking genes, modules, phenotypes, and diseases.


Introduction
The identification of gene function and the integrated understanding of their roles in physiology are core aims of many biological and biomedical research projects -an effort that is still far from being complete (Edwards et al. 2011;Pandey et al. 2014;Dolgin 2017;Stoeger et al. 2018). Traditionally, gene function has been elucidated through experimental approaches, including the evaluation of the phenotypic consequences of gain-or loss-of-function (G/LOF) mutations (Austin et al. 2004;Dickinson et al. 2016), or by genetic linkage or association studies (Williams and Auwerx 2015). A large number of bioinformatics tools have been developed to predict gene function based on sequence homology Radivojac et al. 2013;Jiang et al. 2016), protein structure (Roy et al. 2010;Radivojac et al. 2013;Jiang et al. 2016), phylogenetic profiles Tabach et al. 2013;Li et al. 2014), protein-protein interactions (Rolland et al. 2014;Hein et al. 2015;Huttlin et al. 2017), genetic interactions (Tong et al. 2004;Costanzo et al. 2010;Horlbeck et al. 2018), and co-expression (Langfelder and Horvath 2008;Warde-Farley et al. 2010;Greene et al. 2015;van Dam et al. 2015;Szklarczyk et al. 2016;Li et al. 2017;Obayashi et al. 2019).
With the development of transcriptome profiling technologies, thousands of high-throughput studies have generated a wealth of genomewide data that has become a valuable resource for systems genetics analyses. A few web resources, including GEO (Barrett et al. 2013), ArrayExpress (Kolesnikov et al. 2015), GeneNetwork (Chesler et al. 2004), and Bgee (Bastian et al. 2008) amongst others, have created repositories of such expression data for curation, reuse, and integration. Several tools, such as GeneMANIA (Warde-Farley et al. 2010), GIANT , SEEK (Zhu et al. 2015), GeneFriends (van Dam et al. 2015), WeGET (Szklarczyk et al. 2016), COXPRESdb (Obayashi et al. 2019), WGCNA (Langfelder and Horvath 2008), and CLIC , are able to assign putative new functions to genes by means of correlations or co-expression networks. At their core, these methods rely on the concept of guilt-byassociation -that transcripts or proteins exhibiting similar expression patterns tend to be functionally related (Eisen et al. 1998). By using overrepresentation analyses on sub-networks or modules, one can then deduce aspects of gene functions.
However, these approaches generally depend on discrete sets of genes whose expression correlations exceed either a hard or soft threshold, which would strongly influence the final results. In addition, such analyses typically focus on positive or absolute values of correlations among datasets.
The key polarity of interactions is often lost among gene products and linked modules (Warde-Farley et al. 2010;Greene et al. 2015;van Dam et al. 2015;Zhu et al. 2015;Li et al. 2017). Gene set analyses, such as gene set enrichment analysis (GSEA) (Subramanian et al. 2005), have been developed to identify processes or modules that are affected by certain genetic or environmental perturbations (Khatri et al. 2012). While GSEA removes the necessity of assigning a certain threshold, its application has mainly been limited to studying G/LOF models or environmental perturbations, where comparisons are inherently among discrete categories. This limits its applicability in most populations, in which variations among individuals are often subtle and continuous (Williams and Auwerx 2015).
perspectives, it is clear that most human genes are still poorly annotated. The pattern is similar in other model species. Specifically, over 48% (10,166 genes) in mouse, 60% (11,833 genes) in rat, 61% (8,514 genes) in fly, 29% (5,885 genes) in worm, and 26% (1,566 genes) in yeast, have fewer than 10 entries in GO (Supplemental Fig. S1). This is also true for gene annotations retrieved from other sources, such as GeneRIF (Gene Reference Into Function) (Mitchell et al. 2003) (Fig. 1D-F), as well as for publications archived in PubMed (Barrett et al. 2013;Dolgin 2017) (Fig. 1G-I, Supplemental Fig.   S1). The phenomenon that many genes are ignored in biological research has been pointed out before (Edwards et al. 2011;Pandey et al. 2014;Stoeger et al. 2018). Several possible reasons for this bias, such as prior knowledge, publication bias, and priorities of funding support have been raised (Edwards et al. 2011;Greene and Troyanskaya 2012;Stoeger et al. 2018). Therefore, an unbiased approach for gene function analysis would most likely provide many novel insights for future research.

Gene-Module Association Determination (G-MAD)
Owing to the fact that a large number of genes are still not well annotated or even uncharacterized, we propose here a new computational strategy, "Gene-Module Association Determination" (G-MAD), which uses expression data from large-scale cohorts to propose potential functions of genes. We use the term "modules" to refer the knowledge-based gene sets, ontology terms, and biological pathways from different resources for simplicity in the rest of the paper. The differences between gene sets or directed or undirected pathways are important in many contexts, but for our purpose they can be treated in the same manner as modules and will not be distinguished. The basic concept is similar to classic pathway/gene set analysis, i.e. genes that possess similar functions tend to have similar expression patterns (Subramanian et al. 2005).
However, instead of using binary group settings (e.g., control vs. treatment, or wild-type vs. knockout) as commonly used in gene set analysis, we consider the continuous expression levels of the gene-of-interest across a population and determine its possible functions based on its co-expression patterns against all genes. We applied a competitive gene set testing method -Correlation Adjusted MEan RAnk gene set test (CAMERA), which adjusts for inter-gene correlations (Wu and Smyth 2012) -to compute the enrichment between gene-of-interest and biological modules. Gene-module connections with enrichment p-values that survived multiple testing corrections of the gene or module numbers were allocated connection scores of 1 or -1, based on the enrichment direction, and 0 otherwise. The results were then meta-analyzed across datasets, and gene-module association scores (GMAS) were computed as the averages of the connection scores weighted by the sample sizes and inter-gene correlation coefficients within modules ( Fig. 2A).
We collected transcriptome datasets with over 80 samples from 6 species (human, mouse, rat, fly, worm and yeast), from GEO, ArrayExpress, dbGaP, GeneNetwork, and other data repository sources (Supplemental Table S1). For example, 1'337 datasets containing over 265'000 human samples with whole genome transcript levels were analyzed in this study (Supplemental Table S1). Genes annotated to some modules have higher coexpression in datasets from certain tissues than others (Supplemental Fig.   S2A), suggesting the tissue-specific activation of these modules. For instance, genes involved in pancreatic secretion have much higher co-expressions in datasets obtained from pancreas (Fig. 2B). Genes belonging to "collecting duct acid secretion" module are highly co-expressed in kidney , while genes in the "lamellar body" module are highly coexpressed in lung (Supplemental Fig. S2E-G).
One should be aware of the fact that modules can overlap partially or completely. For example, GO categories have a hierarchical structure. Each GO term has "parent" terms (related usually by "part of" or "is a" relations), and all genes annotated to the term will also be annotated to its parents.
Symmetrically, a GO term can be the parent term of other GO terms (Ashburner et al. 2000). In addition, modules from different sources can be very similar in composition. For example, oxidative phosphorylation (84 genes) from GO biological process, and respiratory chain (80 genes) from GO cellular component have 65 genes (66%) in common in humans. Therefore, we computed the similarities across all modules, and generated a global module similarity network. As expected, redundant modules formed clusters in the network, and we were able to extract 62 distinct module clusters in the human module similarity network (Fig. 2C, Supplemental Table S2).
We assessed the performance of G-MAD in prioritizing known genes for modules through cross validations. We then compared the area under the receiver operating characteristic (ROC) curve (AUC) with the ones obtained from WeGET, a method predicting novel genes for various modules based on weighted co-expression of around 1,000 expression datasets (Szklarczyk et al. 2016). G-MAD exhibits better predictive performance than WeGET (Supplemental Fig. S3). Furthermore, in order to determine the threshold of significance of gene-module associations, we computed the GMAS of all the known gene-module pairs. To be stringent in proposing novel gene-module associations, we consider only 10% of all the known gene-module pairs as significant, and picked a GMAS threshold of 0.268 (Fig. 2D). With this threshold, we saw only 0.24% of unknown gene-module pairs are significant, which is 40 times less than the known pairs.
The gene-module connections predicted by G-MAD provide a resource, which researchers can use as a reference when annotating gene functions.
We describe below some examples on how the G-MAD results can be used to facilitate the discovery of novel gene functions or the identification of new members of modules. WDFY4 was recently annotated as a crucial gene in activating immunological T cells in antiviral and antitumor immunity through a functional CRISPR screen (Theisen et al. 2018). Through G-MAD, we found that WDFY4 is indeed associated with antigen processing, T cell activation, and immune response in human, mouse, and rat ( G-MAD can also highlight tissue-specific gene-module associations using datasets from specific tissues. EHHADH is a peroxisomal protein highly expressed in liver and kidney ( Fig. 3A) (Uhlen et al. 2015). Although best known for its key role in the peroxisomal oxidation pathway, recent report demonstrated that EHHADH mutations cause renal Fanconi's syndrome (Klootwijk et al. 2014). G-MAD of EHHADH in liver and kidney identifies its conserved role in peroxisome and fatty acid oxidation, and also recovers its specific functions in liver (e.g. bile acid biosynthesis) and kidney (e.g. brush border membrane) ( Fig. 3B-E, Supplemental Table S3). SLC6A1 is one of the major gamma-aminobutyric acid (GABA) transporters in the neurotransmitter release cycle in brain (Carvill et al. 2015). However, SLC6A1 is also highly expressed in the liver (Supplemental Fig. S5A), and its function in liver remains poorly understood. G-MAD of SLC6A1 in all datasets and only datasets from brain confirms its function as neurotransmitter transporters in GABA release cycle (Supplemental Fig. S5B-C), while G-MAD using datasets from liver identifies its possible role in carboxylic acid transport and metabolism (Supplemental Fig. S5D-E, Supplemental Table S4).

G-MAD determines novel genes linked to mitochondria
Mitochondria are the main powerhouses of cells and harvest energy in the form of ATP through mitochondrial respiration. There are around 1,100 genes known to encode mitochondria-localized proteins (mito-proteins), depending on the source used (e.g. 1,158 mito-proteins in Mitocarta ), 1,074 in Human Protein Atlas (Uhlen et al. 2015)); however, many of these genes remain uncharacterized, and the list of mito-proteins is still incomplete (Williams et al. 2018).
By using the genes annotated to be involved in respiratory electron transport chain (ETC, Reactome: R-HSA-611105), we searched for genes potentially related to respiratory electron transport, by applying G-MAD to expression datasets in human, mouse, and rat. As expected, genes annotated in the ETC module are strongly enriched; moreover, other known ETC genes that were not included in the module were also positively enriched, providing proof that G-MAD can recover known gene functions (Fig. 4A, Supplemental   Fig. S6A-B). Based on G-MAD results from human, mouse and rat, there were 707 genes showing conserved associations with the ETC (Fig. 4B).

Module-Module Association Determination (M-MAD)
Biological processes and modules, such as metabolism, cellular signaling, biogenesis, and degradation are interconnected and coordinated (Barabasi et al. 2011). However, there are few reports exploring the connections between modules in a systematic fashion (Li et al. 2008). Here we extend G-MAD to develop Module-Module Association Determination (M-MAD) to investigate the connections between modules based on the expression compendia.
Results for individual modules against all genes, obtained from G-MAD, were used to compute their associations against all modules. The enrichment scores of all genes for the target module were used as the gene-level statistics to calculate the enrichment against all modules using CAMERA (Wu and Smyth 2012). The resulting enrichment p-values across modules were transformed to 1, 0, or -1 based on the Bonferroni threshold, and then meta-analyzed across all datasets to obtain the module-module association scores (MMAS) (Fig. 5A).
Module-module associations with an absolute MMAS of over 0.268, corresponding to 4% of the total number of module pairs, were considered significant and were used to construct a module association network (Fig. 5B).
Modules were represented as nodes with the same colors as the module clusters from Fig

Discussion
Significant efforts in biological research have been devoted to defining the molecular and physiological functions of genes. However, many genes are still not well annotated and even remain uncharacterized (Edwards et al. 2011;Dolgin 2017;Stoeger et al. 2018). Here we developed an approach, termed G-MAD, to facilitate the identification of novel gene functions and to establish robust connections between genes and modules. Using transcriptome datasets from cohorts ranging from human to mouse, rat, fly, worm, and yeast, we identified millions of gene-module connections, many of which are novel. Unlike usual co-expression analyses for predicting gene functions, G-MAD can identify not only positive gene-module connections, but also negative associations between genes and modules or processes. We illustrated the predictive power of G-MAD in revealing potential gene-module connections using the mitochondrial electron transport chain (ETC) module as an example. 707 genes were consistently associated with the ETC in human, mouse and rat, of which DDT and BOLA3 were validated through experiments. A negative connection between ARID1A, a member of the SWI/SNF family, and the ETC was also identified using G-MAD, which was consistent with a report that inactivation of SWI/SNF complex increased mitochondrial respiration (Lissanu Deribe et al. 2018). Meanwhile, tissuespecific functions of genes, for example EHHADH and SLC6A1, can also be identified using datasets derived from respective tissues.
In addition, we extended G-MAD to M-MAD, to uncover connections between modules. Association scores of one module against all genes from G-MAD were used to compute its associations with all modules. Similar to G-MAD, M-MAD can identify both positive and negative module associations.
For example, in humans we identified around 2,000,000 associations between all modules, over 170,000 of which negative. We constructed a module association network based on these connected modules, and compared it to the module similarity network. Interestingly, many of the associated module pairs have low or no similarities in gene compositions. By applying M-MAD on the ETC module, we discovered a conserved connection between mitochondria and the proteasome in various organisms (D'Amico et al. 2017).
In addition, we identified negative associations between histone lysine demethylation and mitochondrial modules, underscoring the inverse connection between epigenetic regulation and mitochondrial function (Schroeder et al. 2013;Merkwirth et al. 2016;Tian et al. 2016). Moreover, we discovered and validated a novel negative regulatory role of ribosomal proteins on lipid biosynthesis (Li et al. 2018).
In summary, we described here a set of approaches to identify gene function and module connectivity, that we collectively termed GeneBridge, to reflect their capacity to bridge genes to biological functions and phenotypes.
The GeneBridge toolset is accessible through our open web resource (systems-genetics.org) to the research community for hypothesis generation or validation. It should be noted that although only protein-coding genes were included in our analysis, the same approach can be applied to non-coding genes to reveal their potential functions. Similarly, GeneBridge can also be utilized to identify novel gene-disease associations based on known diseaseassociated genes from databases, such as the Human Disease Ontology (DO) (Schriml et al. 2019) or DisGeNET (Pinero et al. 2017). The GeneBridge toolkit could also be applied to large-scale proteomics datasets after correcting for the background of all measured proteins. Integration of GeneBridge with other well-established databases, such as BioGRID (Stark et al. 2006) and STRING (Szklarczyk et al. 2015), will facilitate the investigation of the connections between genes, modules, and diseases. Module data for all the species were retrieved from GO (Ashburner et al. 2000), Kyoto Encyclopedia of Genes and Genomes (KEGG) (Kanehisa et al. 2012), and Reactome (Croft et al. 2011). Annotations from GO with evidence codes of IEA (inferred from electronic annotation), ND (No biological data available), NR (Not recorded), NAS (Non-traceable author statement) were removed from the analysis. The parent-child hierarchical structure of GO was ignored. All modules, including the redundant modules (modules with similar gene components), as well as parent-child modules, were considered as independent in the analysis.

Methods
Modules with less than 15 genes or larger than 1,000 genes were excluded, resulting in 6,979, 7,489, 7,462, 3,811, 2,495, and 2,381 modules for human, mouse, rat, fly, worm, and yeast, respectively, for the analysis. The analysis uses the competitive gene set testing method CAMERA, which adjusts for inter-gene correlations (Wu and Smyth 2012). This adjustment is important, because left unadjusted too many significant results would emerge.
To perform CAMERA, we first regress all genes on according to the following relationship where weight for the !! dataset is ! = ! ! . Under the null hypothesis, if we consider the positive and negative associations separately, the random variables !|! ( ) follow a Bernoulli distribution with probability of success = !.!" # !"#$%&' . Therefore, statistic GMAS is the weighted sum of Bernoulli variables, whose theoretical distribution is hard to establish. The weight is proportional to the square root of the sample size in the !! dataset. Another important component of ! is the average correlation coefficients among genes in the module in the !! dataset, ! , which reflects the co-expression or "level of activation" of the module for this dataset.
For the final decision we use thresholding of GMAS. We selected a very stringent threshold for GMAS, so that only a small proportion of the known gene-module connections are recovered. We found that a threshold of 0.268 enables us to recover 10% of the known gene-module links.

Module-Module Association Determination (M-MAD). M-MAD takes the
association p-value matrix between a target module and all genes in all datasets ( Fig. 2A bottom- colors were obtained using the community detection algorithm (Vincent et al. 2008) embedded as the modularity tool in Gephi. Clusters with more than 20 nodes were colored to illustrate the module communities. The most frequent 10 biological terms (excluding biological meaningless words, such as "of", "in", or "and") were used to represent the modules of these communities. The statistical characteristics of the module networks were computed using Gephi.
For the network visualization of G-MAD results for one gene, modules were plotted according to their x and y coordinates of the module similarity network, and the gene-module association scores (GMAS) against all modules were used to color the modules using indicated color codes.
Gene correlation network analysis. Gene correlation networks were constructed based on the Pearson correlation among genes of indicated modules in respective datasets using the "layout_with_fr" function in the igraph R package. Edges with correlation p-values lower than the indicated cutoffs in the figure panels were plotted.

Cross validation.
In order to test the predictive performance of G-MAD and compare it with the available methods using co-expression, we performed a cross validation analysis by removing groups of genes from modules, re-computing the associations between the removed genes and the reduced module and testing if we can rediscover the removed genes (Szklarczyk et al. 2016). We applied leave-one-out cross validation for modules with no more than 50 genes, and 10-fold cross validation for larger modules. The area under the receiver operating characteristic (ROC) curve (AUC) is used to estimate the performance of prediction, with an AUC of 1 indicating perfect prediction and 0.5 indicating random guess.
Gene set enrichment analysis. Transcriptome data of uterus-specific Arid1a knockout mice (Kim et al. 2015) were downloaded from GEO under the accession number GSE72200. For enrichment analysis, genes were ranked based on their fold changes between Arid1a knockout and control samples, and gene set enrichment analysis (GSEA) was performed to identify the enriched gene sets using the R/fgsea package (Subramanian et al. 2005;Sergushichev 2016).

Transcript-phenotype correlation analysis in mouse cohorts.
Phenotype data, as well as transcriptome data of liver and white adipose tissue, from the BXD (Wu et al. 2014) and CTB6F2 (Schadt et al. 2008) (Li et al. 2018). The lipid droplets were quantified using Fiji (ImageJ) as previously described (Li et al. 2018). Statistical significance was determined by two-tailed Student's t-test.

Data access
Data Availability. All data included in the study is available from https://systems-genetics.org/. Code Availability. Code used in the study is available from https://github.com/lihaone/GeneBridge.

Figure 2. Gene-Module Association Determination (G-MAD).
A, G-MAD methodology. See text and Materials and Methods for detailed description.
B, Co-expressions among genes of pancreatic secretion module across tissues in human. The average correlation coefficient across all genes in the pancreatic secretion module in around 1,300 human expression datasets from 18 major tissues is used as to illustrate the co-expressions of this module across tissues. Genes in the pancreatic secretion module have higher coexpression in datasets from the pancreas compared to those from other tissues.
C, Module similarity network showing the composition similarities across all module pairs. Modules were detected using community detection algorithm embedded in Gephi and indicated in different colors. The 10 most frequent words of the module terms in each module were used to represent the module, and can be found at Supplemental Table S2.   Table S3. current annotations. The association results for all genes in human, mouse and rat can be found at Supplemental Table S4.   B, E, Distribution of the co-expression of genes in the "collecting duct acid secretion" (B) or "lamellar body" (E) module across tissues in human. The average correlation coefficient of the gene pairs of the module in 1,300 human expression datasets from 18 major tissues was used as to illustrate the coexpressions of this module across tissues. Genes in "collecting duct acid secretion" (B) and "lamellar body" (E) module have higher co-expression in datasets from kidney and lung, respectively, indicating the potential to assign tissue-specificity.
C-G, Pearson correlation network of genes in the "collecting duct acid secretion" (C, D) or "lamellar body" (F, G) module in representative datasets of lung (C, F) and kidney (D, G). The number of genes (nodes) and gene pairs (edges) that survive the indicated threshold of correlation significance are shown. Genes in the "collecting duct acid secretion" module have higher coexpression in datasets from the kidney (C) than the lung (D), while genes in the "lamellar body" module have higher co-expression in datasets from the lung (F) than the kidney (G). The predictive performance of G-MAD is compared to WeGET using crossvalidation. Cross validation evaluates the robustness of the methods by removing the genes from the query module and test the performance in redetecting them. Performance of the method is computed as the area under the receiver operating characteristic curve (AUC) for each module. A high AUC indicates that most of the genes in the module are rediscovered when they are removed from the module in the analysis.  E, Comparison of G-MAD results of SLC6A1 in brain and liver. Known modules connected to SLC6A1 are shown in red dots. The threshold of significant gene-module association is indicated by the red dashed line. Modules significantly associated with SLC6A1 only in one specific tissue are highlighted. The comparison of the association results of SLC6A1 in brain and liver can be found at Supplemental Table S4.  E, Silencing BOLA3 expression in HEK293 cells decreases expression levels of indicated genes involved in mitochondrial respiratory chain complexes. Error bars represent standard errors. *, p < 0.05; **, p < 0.01; ***, p < 0.001. n=9.
F, BOLA3 knockdown leads to the reduction of oxygen consumption rate (OCR) as a reflection of mitochondrial respiration in human HEK293 cells. Addition of specific mitochondrial inhibitors, including the oligomycin (ATPase inhibitor), FCCP (uncoupling agent), and rotenone/antimycin A (electron transport chain inhibitors) are indicated.  G, G-MAD results for electron transport chain from human and mouse are plotted in the x and y axes, respectively. Genes annotated to be involved in respective modules are indicated in different colors. C-D, Transcripts of genes encoding for ribosomal proteins in the white adipose tissue negatively correlate with metabolic traits in the BXD (C) and CTB6F2 (D) mouse cohorts. *, p < 0.05; **, p < 0.01; ***, p < 0.001. ScWAT, subcutaneous white adipose tissue.
E, Scheme of the experimental design. L1 worm larvae were grown on regular NGM plates at 20°C for 2 days and then transferred to RNAi plates with 1mM IPTG containing HT115 bacteria expressing RNAi clones for ribosomal genes or empty vector (ev). After 2 days, worms were collected and lipid droplets were stained using Oil Red O.
F, Representative images of worm lipid droplet staining after RNAi of ribosomal genes.

Supplemental Tables
Supplemental Table S1. Datasets used in this study.
Supplemental Table S2. Top 10 most frequent terms of the module clusters in Fig. 2C.
Supplemental Table S3. Tissue-specific association of EHHADH in kidney and liver tissues in humans.
Supplemental Table S4. Tissue-specific association of SLC6A1 in brain and liver tissues in humans. Table S5. G-MAD results on respiratory electron transport in human, mouse and rat.

Supplemental
Supplemental Table S6. qPCR primer sequences used in this study.