We used established databases in standard ways to systematically characterize gene ontologies, pathways and functional linkages in the large set of genes now associated with autism spectrum disorders (ASDs). These conditions are particularly challenging—they lack clear pathognomonic biological markers, they involve great heterogeneity across multiple levels (genes, systemic biological and brain characteristics, and nuances of behavioral manifestations)—and yet everyone with this diagnosis meets the same defining behavioral criteria. Using the human gene list from Simons Foundation Autism Research Initiative (SFARI) we performed gene set enrichment analysis with the Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway Database, and then derived a pathway network from pathway-pathway functional interactions again in reference to KEGG. Through identifying the GO (Gene Ontology) groups in which SFARI genes were enriched, mapping the coherence between pathways and GO groups, and ranking the relative strengths of representation of pathway network components, we 1) identified 10 disease-associated and 30 function-associated pathways 2) revealed calcium signaling pathway and neuroactive ligand-receptor interaction as the most enriched, statistically significant pathways from the enrichment analysis, 3) showed calcium signaling pathways and MAPK signaling pathway to be interactive hubs with other pathways and also to be involved with pervasively present biological processes, 4) found convergent indications that the process “calcium-PRC (protein kinase C)-Ras-Raf-MAPK/ERK” is likely a major contributor to ASD pathophysiology, and 5) noted that perturbations associated with KEGG’s category of environmental information processing were common. These findings support the idea that ASD-associated genes may contribute not only to core features of ASD themselves but also to vulnerability to other chronic and systemic problems potentially including cancer, metabolic conditions and heart diseases. ASDs may thus arise, or emerge, from underlying vulnerabilities related to pleiotropic genes associated with pervasively important molecular mechanisms, vulnerability to environmental input and multiple systemic co-morbidities.

In this study we developed an ASD pathway network and performed a systematic analysis of the network. With this approach we aimed to expand our understanding to include not only genes but also the function of gene products, as well as the networking of reactions and interactions. In thus generating a coordinated presentation whose depictions take account of gene-pathway coherence, pathway-pathway interactions, and disease-disease relationships, our objective was to provide a framework and clues for further investigations.

The networks in which cellular processes often function, which involve sets of gene products acting in concert, can be mapped as pathways. Since there are many overlaps among pathways, identifying convergent molecular pathways in which multiple candidate genes are involved has been discussed for years [ 1 ], as this may be an effective way to gain insight into the underlying molecular basis of ASDs. There are a number of studies that aim to discern potential important pathways to ASDs, whether by developing protein interaction networks [ 2 , 3 ] or gene co-expression networks [ 4 ], or by identifying specific affected pathways from ASD genetic databases [ 5 ]. However, there are very few studies that have systematically investigated pathways or pathway networks that may link to ASDs, and to our knowledge there are no prior studies looking at pathway-pathway interactions at the functional level in ASDs.

In this study we chose to consider the accumulated set of genes as a whole, and to utilize established databases and analytical processes to seek patterns and connections within this large aggregate. To do this we expanded the framework from just genes to also include the contexts, reactions and processes in which they participate. We used enrichment analysis to identify 1) pathways in which ASD-associated genes were over-represented, 2) biological features for which ASD-associated genes were particularly present, and 3) overlaps and convergences amongst the several types of analyses that we performed. Future studies may add more nuances to the analyses we report, including addressing biases that may be present at various stages of the process ranging at least from gene discovery to database construction. For the present study we did not introduce any biases from favoring certain types of biological processes or domains over others, and we did not utilize any weighting systems, as we could not locate any established or fully developed weighting methods that could do justice to all the different varieties of ASDs, considering the marked heterogeneity this diagnosis encompasses. We aimed simply to let the data speak through the patterns and relationships that emerged from performing standard analyses, regardless of whether the resultant findings were consistent with areas of predominant focus in the existing literature. To our knowledge the aggregate of genes associated with ASD, along with their functions, contexts and pathway networks, have not previously been analyzed in this manner.

As of December 2014, the SFARI Gene-Human Gene Module recorded 667 human genes implicated as relevant to ASDs. These genes are widely expressed and are involved in a variety of biological activities. To make full biological sense of this large number and diversity of genes, it is important both to consider the full range of their biological activities, and to identify processes with higher likelihood of substantial relevance to ASDs.

While there remains controversy regarding the relative contributions of artifact and true increase to the rising prevalence of ASDs, there is no question that these spectrum conditions have major human, public health and economic impacts. They also pose major challenges to research, since while ASDs are defined by social impairments, communication difficulties, and restricted, repetitive, and stereotyped patterns of behavior, there are no pathognomonic biological features universally present in people meeting the behavior-level diagnostic criteria, and many other biological features have been identified in subgroups both in the brain and systemically whose contributions to the development and ongoing characteristics of ASDs are not clearly understood.

We then examined the pathway network for patterns of more significant or common interactions and functional impacts, aiming to highlight ways that these pathways and processes might particularly contribute to ASD pathology at the function level. We examined the pathway network to identify any “hub” (most interactive) pathways, most involved genes, possible links between the pathway network and GO groups. Then we looked for any convergences between the predominant pathways, genes and GO groups thereby identified. Out of this examination we identified and depicted convergent mechanisms.

In cells, pathways interact with each other rather like street-meets-street in a city map. In order to obtain a systems overview of how these pathways function together, it is of great value to generate a pathway network, which can be done by taking account of the connections/interactions between pathways. One pathway may interact with another for multiple reasons: shared genes or compounds, interactions or reactions between genes or compounds, signal transductions, etc. We chose to work with KEGG Pathway Database, which documents these many types of influences and interactions, to help us generate this pathway network overview, because it is a comprehensive and frequently updated database that represents our present knowledge of pathways and the known interactions amongst pathways based on accumulated research findings. Pathway-pathway interactions are represented in KEGG in each pathway map by displaying the name of an interacting pathway in that map. For example, if one pathway appears at the certain location in the map of another pathway, this suggests that these two pathways were interacting in relation to the activities or functions represented in that part of the map, or that one was involved in a certain reaction or process of another. On this basis, we determined the pathway-pathway interactions by tabulating these instances. For the merged collections, if one pathway not in the collection interacted with at least one of the pathways in the collection, this pathway was considered to be interacting with the collection. For the pathway “pathways in cancer” ( http://www.genome.jp/kegg-bin/show_pathway?map05200 , or go to KEGG, search for “pathways in cancer”, and find the map05200), other cancer pathways (such as “glioma”, “thyroid cancer” etc.) which were listed on the bottom of the KEGG pathway map, did not count as interacting with the “pathways in cancer”, as they are not actually interacting with each other but only being listed in the map.

Highly overlapped pathways may cause redundancy, and this might bias the results of analysis. To minimize the impact of such redundancy among our gene sets, Redundancy Control in Pathway Databases (ReCiPa) [ 7 ] was applied to the top 50 pathways resulting from the enrichment analysis. The ReCiPa package was downloaded from the url described in the original paper ( http://cran.r-project.org/web/packages/ReCiPa/ ) and installed and loaded in R ( http://www.r-project.org/ ). The KEGG pathway database file was downloaded from MSigDB Downloads ( http://www.broadinstitute.org/gsea/downloads.jsp ). We used the enriched 50 KEGG pathways as our input database. Highly overlapped pathways were merged into collections (setting Max = 0.85, Min = 0.10). For each merged collection, the p-value from the dominant pathway (pathways with lowest p-values) was used as the p-value for the particular collection.

The overlaps between SFARI genes and KEGG gene sets (derived from the KEGG pathway database and each of the sub-collections of GO gene sets, (i.e., the Biological Processes (BP), cellular component (CC) and molecular function (MF) ontologies), were computed individually. Data obtained from this procedure can be found in S2 Table ; the top 50 enriched gene sets with false discovery rate (FDR) q-value below 0.05 were listed, ranked by p-values (low to high). GSEA allows us to choose the enriched groups from top 10, 20, 50, and 100. We made the judgment to choose 50 because it is a moderate number for pathway network analysis–large enough to build an interaction network and small enough to avoid overly diluting the ASD specificity of the genes. Further information regarding the “Compute Overlaps” tool can be found on the “Help” page under Molecular Signatures Database, under “Help with Investigating Gene Sets” here: http://software.broadinstitute.org/gsea/msigdb/help_annotations.jsp —overlap (one needs to register and login to use this link).

To identify a variety of pathways and biological signatures, we investigated enrichment within the Human Gene Module of SFARI Gene. ASD genes were downloaded from SFARI Gene–Human Gene ( https://gene.sfari.org/autdb/HG_Home.do , S1 Table ), and investigated in the freely available Molecular Signatures Database, MSigDB [ 6 ] v4.0 ( http://www.broadinstitute.org/gsea/msigdb/index.jsp ). We applied the “Compute Overlaps” tool from MSigDB website, under “Investigate gene sets” category, which uses the hypergeometric distribution to examine how SFARI genes and other gene sets may overlap. Gene symbol was used as gene identifier to import the gene set. MSigDB is a collection of annotated gene sets for use with Gene Set Enrichment Analysis (GSEA) software [ 6 ]. Here we used the MSigDB collections derived from the KEGG Pathway Database ( http://www.genome.jp/kegg/pathway.html ) and the GO Consortium ( http://geneontology.org/ ). The GO dataset provides a central collection of the attributes already known and assigned to specific genes.

This contextual information allows us to discern that the overlapping ASD genes in the MAPK and calcium signaling pathways, together with most involved ASD pathway network genes, function in the following sequential process whose pertinent highlights include: calcium channel mediates calcium influx, and increased calcium level activates PKC, leading consequently to the Ras-Raf-MEK-MAPK/ERK cascade.

Looking at the function of these overlapping genes can aid in understanding the potential integrated functions of MAPK and calcium signaling, CACNA1s genes encode a group of proteins in the family of voltage-gated calcium channel alpha 1 subunits. The PRKCB gene encodes protein kinase C (PKC), beta. PKC is a family of serine- and threonine-specific protein kinases that can be activated by calcium and diacylglycerol (DAG) [ 8 ]. Based on the common ASD genes in the MAPK and calcium signaling pathways (CACNA1s and PRKCB), we narrowed down the overlapping function of these two pathways in ASDs to voltage-gated calcium channels and calcium activated PKC. Calcium channels in many cell types mediate calcium influx in response to action potentials. An increase in concentration of calcium activates PKC, which regulates Ras and Raf1 proteins by phosphorylation [ 9 ]. Once activated, Ras proteins bind to and activate the protein kinase activity of Raf kinases, which phosphorylates and activates MEK (Mitogen-activated protein kinase, MEK1 and MEK2). MEK Phosphorylates and activates MAPK. [ 10 ] ( Fig 3B ). In our ASD pathway network, the top 5 highly involved genes—MAPK1 (ERK2), MAPK3 (ERK1), HRAS, PRKCB and BRAF—encode the proteins MAP kinase 1, MAP kinase 3, small GTPase H-Ras, PKC, and serine/threonine protein kinase B-Raf; together they function in the PKC-Ras-Raf-MAPK/ERK process.

a. Overlapping genes in calcium and MAPK signaling pathways. b. The voltage-gated calcium channel mediates the influx of Ca 2+ , which modulates PKC and Ras. Growth factors bind to the extracellular domain of receptor tyrosine kinases (RTKs), and signal molecules Grb2 (growth factor receptor-bound protein 2) and Sos (son of sevenless) are consequently recruited to the internal docking site, resulting in Ras activation. Ras triggers a phosphorylation cascade including Raf and MEK. This leads to ERK activation and translocation to the nucleus, where ERK then activates transcription factors that mediate gene expression. The ASD genes are listed in light blue frames close to the corresponding gene products that are shown in orange.

In addition to finding that the MAPK pathway has the highest number of interactions with other pathways in our pathway-pathway interaction map, we also noted that the above-listed genes we separately identified using MSigDB as involved in the most enriched pathways are all according to KEGG Pathway Database specifically involved in the MAPK signaling pathway. We interpreted this coherence across genes, pathways and methodologies as suggesting a very high level of involvement of the MAPK signaling pathway, together with MAPK/ERK genes, in the ASD pathway network.

To investigate the involvement of each ASD gene in the pathway network, we counted the number of pathways in which each gene participates ( Table 3 ). We found that MAPK1 (ERK2), MAPK3 (ERK1), HRAS, PRKCB and BRAF are the most involved genes, in that they participate in 14–23 out of 40 enriched pathways/collections.

Calcium signaling pathway is not only the most enriched pathway but also very interactive in the ASD pathway network: it is the second most interactive pathway, interacting with 12 out of 40 pathways ( Table 1 ). ASD genes in the calcium signaling pathway (PRKCB, PLCB1, CACNA1C, CACNA1D, and CACNA1F) are also highly involved in the pathway network, participating in 7–17 out of 40 pathways ( Table 3 ). Additionally, Ca 2+ (calcium ion) participates in 23 out of the 40 enriched pathways in reference to KEGG ( http://www.genome.jp/dbget-bin/www_bget?C00076 , or go to KEGG and search for C00076 –the compound number for calcium ion in KEGG).

The MAPK (mitogen-activated protein kinases, also known as ERK, extracellular signal-regulated kinases) signaling pathway interacts with half of the pathways in the network (20/40, Table 1 ), making this pathway the most inter-connected pathway in our map; within the terms of our analytical framework this identifies it as the main hub among the enriched pathways.

Pathways are grouped vertically under two classes, Disease and Function. The color of each node within the groupings represents the p-value of that pathway. The size of each node represents the number of ASD genes in that pathway. Interactions between pairs of pathways are indicated by edges. The pathway-pathway interaction matrix is in S3 Table .

We used the number of interactions a pathway has with other pathways in an overall network as an indicator of its level of involvement in the pathway network; this of course involves making a working assumption about the relevance of number of interactions to overall involvement; we proceeded in this way in order to generate hypotheses that will need to be tested by future investigations such as biological experiments and clinical studies. To display the results of the present analyses aimed at generating a provisional metric of extent of involvement of each pathway in the overall ASD pathway network we presented the counts of how many interactions each pathway in this network had with other pathways in the network, and evaluated the level of involvement of each pathway by the number of interactions it has ( Table 1 and S3 Table ).

We noted that the three top enriched GO categories together contain one of the two above-mentioned most significant pathways, “neuroactive ligand-receptor interaction,” which is indeed involved in the “nervous system”, functions on “plasma membrane” and relates to “receptor activities”; that is, the most enriched pathway coincides with the most enriched GO groups in BP, CC and MF.

Pathways were grouped into subsets of two classes. Pathways in the Disease class are surrounded by a black border; the remainder of the pie chart sections are in the Functional class. Labels indicate pathway groupings (number of pathways in grouping/percentage of total number of pathways).

Fig 1 displays the proportional contribution of pathway groups and subgroups to the overall ASD pathway network, and shows that the largest proportions of pathways were contributed by the cell signaling subgroup in the function-related pathways group and the collection cancer in the disease-related pathways group.

The KEGG Pathway Database contains diversely named pathways that overlap with one another to varying degrees. In our data, among our 50 pathways, we identified 13 enriched cancer pathways that might overlap amongst themselves at multiple levels. The application of ReCiPa [ 7 ] merged highly overlapped pathways and generated two collections. By merging 2 cardiac disease pathways a single collection cardiac disease was generated, and by merging 10 cancer pathways a single collection cancer was generated. The final yield from 50 pathways was a list of 40 pathways/collections, which we used for further analysis ( Table 1 ).

659 out of 667 ASD genes from SFARI Genes were identified in MSigDB v4.0. We did not include those genes that were not identified in MSigDB in our analysis. The top 50 enriched pathways (from the 659 genes) were chosen from the computation of overlaps with KEGG gene sets ( S2 Table ). These top 50 pathways were sorted by p-values regarding significance of overlaps; the “calcium signaling pathway” (p-value 2.84E-29) and “neuroactive ligand-receptor interaction” (p-value 2.87E-29) stand out with their markedly stronger statistical significance ( S2 Table ).

Discussion

In this study we performed a descriptive investigation of the relationships between a large set of ASD-associated genes, the pathways and functions in which these genes participate, and the interactions amongst the pathways. We made every effort to eliminate bias, by using only established and freely available databases and established analytics for utilizing them, and by letting the data generated by these analyses speak for themselves rather than weighting the data on the basis of any a priori assumptions or as yet incomplete weighting systems. We present our findings as a kind of “demographics” of ASD-associated genes with the hope of generating hypotheses for further investigation, some of which might not otherwise have emerged from the diverse range of ASD genetic data and the analytic approaches currently in use.

The pathway map we generated shows marked interactions amongst the genes and pathways, and participation of pathways with a broad range of functions and diseases by no means limited to those (such as neurological diseases or development) most commonly considered as centrally relevant to ASD. Although our findings were generated through several separate analyses, we were struck by the convergent findings that emerged from these different approaches. Two pathways stood out most strongly amongst the pathways in which the SFARI ASD-associated genes participated: the MAPK signaling pathway which was the most highly interactive, and the calcium signaling pathway, which was the most highly enriched pathway. Of further interest, based on our pathway-gene interaction analyses, the 8 genes that overlap between the MAPK and calcium signaling pathways are involved in the process “calcium-PKC-Ras-Raf-MAPK/ERK”. What might these findings suggest about possible underlying mechanisms and vulnerabilities in ASDs?

Diversity of pathway groupings Amongst the functional pathway groupings, it was interesting to note that there was greater proportional involvement of pathways associated with cell signaling and with metabolism than of pathways associated with nervous system or immune function. Even so, we did find a nervous system pathway, “neuroactive ligand-receptor interaction” as well as a nervous system GO category, “nervous system development”, to be among the most enriched pathways and GO groups, respectively, which is consistent with the predominant emphasis on nervous system development in ASD research and clinical practice. Nevertheless, the diversity of pathway groupings and patterns of proportional involvement suggest that the clinically strongly apparent neuropsychiatric phenotypic features in ASDs may be supplemented and potentially significantly driven by underlying more pervasive processes that are not specific to ASD brain or behavior features–nor for that matter to the increasingly appreciated immune system features of ASDs. Amongst the disease pathways enriched for ASD genes, we found overlaps with several categories of chronic illness. This observation raises intriguing questions about shared underlying vulnerabilities for these various types of diseases some of which also show at least apparent increases, and may shed some light on the mechanisms underlying that portion of increases in prevalence of ASDs that cannot be explained away by increased awareness, changes in diagnostic criteria, earlier age at diagnosis, diagnostic substitution or other confounds [11,12].

Diversity and overlaps of Gene Ontology groups The range of involved GO Gene Sets within each of GO groups is also striking, and again suggests that a broad range of processes may contribute to ASD pathogenesis, though clearly not all at the same time in every single person carrying the ASD diagnostic label. The convergence of the most enriched GO categories in each of the three GO groups with the “neuroactive ligand-receptor interaction” pathway is one of several pointers to potential common underlying mechanisms that our investigation uncovered. Notably, many ASD genes that participate in this pathway encode receptors for neurotransmitters such as serotonin, dopamine, gamma-aminobutyric acid (GABA), glutamate and oxytocin, all of which have documented relationships to ASDs (Table 4) [13]. This suggests that the abnormalities in ASDs related to such neurotransmitters may arise at least in part from dysfunction of their receptor activities. This convergence also suggests that the neuroactive ligand-receptor activities on the plasma membrane, perhaps particularly during nervous system development, might be critical for promoting both the development of ASDs and the ongoing brain dysfunctions associated with the condition. PPT PowerPoint slide

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Pathway-pathway interactions, network hubs and cancer Although it is intriguing and surprising that the most represented disease pathway / collection in our ASD pathway network is cancer pathways, other methodologies have identified multifunctional genes associated with both cancer and nervous system development or disease [14–16]. The significance of MAPK signaling in our findings may contribute a bit more to understand how ASD and cancer might be associated. The single most interactive pathway in the ASD Pathway Network, MAPK signaling pathway (also known as Ras-Raf-MEK-ERK pathway), has a diverse range of relationships to functions and diseases. It is involved in a variety of fundamental cellular processes such as proliferation, differentiation, migration, stress response, and apoptosis [10,17]. It is extensively researched in cancer studies, as abnormalities in MAPK signaling play a critical role in the development of cancer [18]. Lately, MAPK signaling has been discussed in ASDs [1] and several recent studies have supported a link between MAPK signaling and ASD traits [19–22]. Also of note is that the important role of MAPK signaling in cancer also coincides with our findings of significantly enriched and interactive cancer pathways—collection cancer, which participates in the ASD pathway network (Fig 2). It is worth considering what the biological overlap might be between ASD and cancer, specifically given the involvement of MAPK signaling. We would suggest that metabolism might bridge this apparently large gap. Our chain of reasoning here includes the following considerations: 1) MAPK signaling plays an important role in metabolism: it responds to changes in the extracellular or intracellular milieu that often affect the metabolism of the cell; in this regard, altered MAPK signaling has been associated with metabolic syndrome [23]. 2) In our ASD pathway network, there are 8 functional metabolism pathways and 1 metabolic disease pathway, which together constitute 22.5% of the network (Table 1, Fig 1). 3) Moreover, metabolic problems do occur in both cancer and ASDs. It is well known that tumor cells have abnormal glucose metabolism [24]. Regarding ASDs, studies have shown that many people with ASDs are at risk of developing or having metabolic problems as comorbidities [25–27], and maternal metabolic conditions have been recognized as contributing susceptibility to ASDs [28–30]. These documented linkages lend enough plausibility to the idea that ASDs and cancer may be either related at a metabolic level—or at least have marked parallels in contributory mechanisms—including potentially through involvement in both of MAPK signaling regulated metabolic processes, for us to suggest that further investigation to more systematically assess this possible linkage via metabolism would be warranted.

Calcium signaling dysregulation in ASDs The enrichment analysis of SFARI genes showed the calcium signaling pathway to be the most enriched, statistically significant pathway. This is consistent with the results from recent ASD studies that used genome-wide association studies (GWAS) data for enrichment analysis [5,31]. GWAS examines genetic variants (typically focusing on SNPs (single-nucleotide polymorphisms)) in different individuals to see if any variant is associated with a trait. In contrast, SFARI Gene integrates different kinds of genetic data that are being generated by research studies. The fact that enrichment analyses using two different kinds of data sources both revealed the calcium signaling pathway as highly significant strongly suggests that it is highly involved in the molecular basis of ASDs. It was not surprising to see that the calcium signaling pathway emerged from these analyses as important for ASDs. Ever since mutation of the CACNA1C gene was found to be associated with Timothy syndrome (a syndromic autism) in 2004 [32], neurogenic calcium dysregulation has been studied in ASD research. Another study, which used GWAS data, also supported the association of calcium channel genes with ASD [33]. The importance of the calcium signaling pathway stands out in our data in four major ways: 1) it is the most enriched pathway; 2) it is the second most interactive pathway in the network; 3) Ca2+ (calcium ion) participates in the majority of the enriched pathways/collections, and 4) ASD-associated genes in the calcium signaling pathway are highly involved in the network. It was unexpected to derive a number of cardiomyopathy pathways from ASD genes (Table 1). After literature searching, we found that people with ASDs indeed appeared to be at increased risk for developing heart disease [26], and reduced cardiac parasympathetic activity was found in children with ASDs [34]. A study that analyzed rare coding variation in ASD cases also found that ASD genes were related to congenital heart disease as well as metabolic disorders [35]. How might the neural and cardiac phenomena mechanistically overlap in ASDs? Common vulnerability to calcium signaling abnormalities are a potential link. Calcium signaling has a wide range of important physiological roles including learning and memory, cellular motility, muscle contraction, etc. [36]. In the heart, calcium is crucial for the regulation of contraction, which is vital to heart functioning [37]. The relationship between calcium and cardiomyopathy has been studied for years [38]. In the nervous system, calcium signaling is important in neuronal synaptic transmission and plasticity [36]. Therefore, if something went wrong with calcium signaling systemically, both cardiac and neural problems might ensue. Although identifying an overlap between neural and cardiac genes ought to make us more attentive to potential comorbidities, the overlap may simply highlight how core molecular functions play roles across diverse systems of the body and brain.

Integrated MAPK and calcium signaling The common ASD genes in the MAPK and calcium signaling pathways are CACNA1s and PRKCB. CACNA1s genes encode a group of proteins functioning in voltage-sensitive calcium channels, which mediate the entry of calcium ions into excitable cells (e.g. muscle, glial cells, neurons, etc.), and thereafter initiate a variety of calcium-dependent processes, including muscle contraction, hormone or neurotransmitter release, gene expression, cell motility, cell division, and cell death [39]. The protein encoded by PRKCB has been reported to be involved in many different cellular functions, which associate with tumorigenesis [40], insulin resistance [41] and diabetic problems such as hyperglycemia-evoked blood-brain barrier damage [42], as well as diabetic nephropathy [43] and retinopathy [44]. We thus note a theme already observed here, in that in addition to their involvement in ASDs, both CACNA1s and PRKCB are reported to be involved in both cancer development and metabolism-related issues. That our integrated findings point to the process “calcium-PKC-Ras-Raf-MAPK/ERK” suggests the possibility that disturbances of this process involving the overlapping genes may contribute to ASDs. Notably, the mutation of RAF1, which is also an ASD-associated gene, is additionally associated with dilated cardiomyopathy [45] and hypertrophic cardiomyopathy [46], whose two corresponding pathways were found to be enriched and merged into collection cardiac diseases from the pathway network. PKC and Ras-MAPK/ERK signaling are both known to be associated with cancer [18,47] and involved in regulation of metabolism [23,48]. Taken together, we see that the process “calcium-PKC-Ras-Raf-MAPK/ERK” which our findings suggest may play a key role in ASDs, is also involved in cancer, heart diseases and metabolic problems, some of which are already known to co-occur with or lead to increased familial risk for ASDs [25–27].

A systematic biological framework Based upon the above analyses of pathway-gene relationships, we developed a visualization linking across the different categorical groups using the results as well as the methods from each analysis (Fig 4A and 4B). Based on the GO groups and pathways that we found to be most enriched, we would suggest that it might well be fruitful to allocate serious attention to neuroactive ligand-receptor activities on plasma membrane especially during nervous system development in ASDs. In addition, the MAPK signaling pathway and the calcium signaling pathway, which we found to be most involved in the ASD pathway network, integrate at the process of calcium-PKC-Ras-Raf-MAPK/ERK, and do so via ASD genes that encode proteins functioning in multiple steps. Alterations in the activity of this process can significantly affect its output to multiple systems and may result in serious problems throughout the whole body. Given the increasing appreciation of ASD’s multi-scale, multi-system complexity including its various comorbidities, such a widespread systems impact might be expected. The enriched pathways in cancer, cardiac diseases, and metabolic diseases in the ASD Pathway Network we derived, as well as their interactions with those functional pathways in the ASD pathway network, may also at some level be associated with ASD’s heterogeneous comorbidities. All of this suggests the need for further studies that focus on clarifying how ASDs and other complex diseases are linked, guided by relationships illuminated by interactions among pertinent pathways and reactions. PPT PowerPoint slide

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larger image TIFF original image Download: Fig 4. A systematic biological framework. The flow chart in a, summarizes the analyses and related results. The conclusions are visualized into a cellular model in b. The relationship between genes, gene products, environmental information processing and phenotypes is schematized in c. https://doi.org/10.1371/journal.pone.0153329.g004 Based on the whole picture of the conclusions we have derived from our analyses (Fig 4B), we further inferred that “environmental information processing (EIP)” might be important in ASDs. EIP is a category of KEGG pathways (http://www.genome.jp/kegg/pathway.html#environmental, or go to KEGG, click on “KEGG PATHWAY”, and find the category “Environmental Information Processing” listed as number 3 under “Pathway Maps”) to which calcium and MAPK signaling pathways and many of our other enriched pathways belong. EIP means the procedures that cells utilize to react to environmental information and conduct consequent signal transductions. For example, the calcium signaling, MAPK signaling and receptor activities we have been discussing constantly react to signal molecules and changes in the environment outside of cells, with processing of that information leading to a series of reactions including activities inside the nucleus such as gene transcription (Fig 4C). On this basis, environmental factors may trigger intracellular changes and eventually affect gene expression. Environmental risk factors have been discussed in the ASD literature and various studies have suggested their importance [49–51]. Our findings here suggested that EIP might be specifically involved in the underlying mechanism of ASDs as well as gene or gene-function relationship to environmental vulnerability.