Insulin-like peptides (ILPs) play highly conserved roles in development and physiology. Most animal genomes encode multiple ILPs. Here we identify mechanisms for how the forty Caenorhabditis elegans ILPs coordinate diverse processes, including development, reproduction, longevity and several specific stress responses. Our systematic studies identify an ILP-based combinatorial code for these phenotypes characterized by substantial functional specificity and diversity rather than global redundancy. Notably, we show that ILPs regulate each other transcriptionally, uncovering an ILP-to-ILP regulatory network that underlies the combinatorial phenotypic coding by the ILP family. Extensive analyses of genetic interactions among ILPs reveal how their signals are integrated. A combined analysis of these functional and regulatory ILP interactions identifies local genetic circuits that act in parallel and interact by crosstalk, feedback and compensation. This organization provides emergent mechanisms for phenotypic specificity and graded regulation for the combinatorial phenotypic coding we observe. Our findings also provide insights into how large hormonal networks regulate diverse traits.
The organization and integration of multiple signals endow intercellular regulatory networks with information processing capabilities. For example, hormones modulate physiology and maintain homeostasis in variable environments [reviewed in 1], and morphogens give rise to intricate patterns during development [reviewed in 2]. Nevertheless, how simple circuits are organized into complex networks that perform sophisticated functions is not fully understood. The ILPs are a superfamily of hormones that regulate many processes, including development, cell proliferation, energy metabolism, neuronal function, reproduction stress resistance, and longevity [3]–[15]. Canonical ILP signaling is mediated by a receptor tyrosine kinase pathway that culminates in the regulation of FOXO transcription factors and other regulatory molecules [16]. In Caenorhabditis elegans, this occurs via the DAF-2 ILP receptor tyrosine kinase, which signals through DAF-16 FOXO [6], [17]–[19]. The importance of ILP signaling is underscored by the conservation of both the signal transduction pathway and the processes they regulate. Indeed, a C. elegans ILP (INS-6) resembles human insulin structurally and can bind and activate the human insulin receptor [20]. Most animal genomes encode multiple ILPs: humans have 10 [21]; Drosophila melanogaster has 8 [22]–[24]; and C. elegans has 40 [25]–[27]. Small-scale studies have shown that certain ILPs can regulate other ILPs [4], [24], [28]–[31], and that ILPs can act as either agonists or antagonists of their receptor to differentially affect multiple processes [5], [24], [27]. How do such simple interactions between these hormones generate complex functionality? Here, we address this question by an integrated analysis of the C. elegans ILPs during larval development, stress resistance, reproduction and lifespan. We systematically tested the function of C. elegans ILPs in the control of diverse phenotypes. In contrast to the common notion of broad redundancy among ILPs [32], we now provide evidence supporting a combinatorial code of action that maps the ILPs to multiple phenotypes. We also uncover the existence of a C. elegans ILP-to-ILP regulatory network that reveals the mechanisms through which multiple functionally diversified ILPs interact to regulate complex developmental and physiological traits. Thus, our analysis of the ILP-to-ILP network provides organizational principles for multiple-gene families and signaling networks.
As in many animals, the C. elegans daf-2 insulin/ILP receptor pathway affects multiple physiological processes, including development, aging, pathogen resistance, thermotolerance and reproduction [6], [8], [9], [17], [33]–[37]. The C. elegans ILP pathway also regulates entry into a specialized form of larval arrest known as dauer that forms preferentially under adverse conditions, such as high temperature, high population density, and low dietary sterols and food levels [reviewed in 6]. Under favorable conditions, animals exit the dauer stage to resume reproductive growth. Dauer exit is also regulated by the ILP pathway [34], [38], which suggests that ILPs function to regulate developmental plasticity in response to complex environmental cues [5], [31]. Previous studies, which focused on a few ILPs, suggest that different phenotypes are modulated by distinct ILPs [4], [5], [11], [26], [27]. These ILPs can exhibit complex functional interactions in the regulation of certain phenotypes [4], [5]. Together, these observations have raised the possible existence of an ILP combinatorial code in regulating physiology, in contrast to the prevailing notion of widespread redundancy as a feature of the ILPs and other gene families [32]. We tested this possibility by mapping the relationships between the 40 C. elegans ILPs, ins-1 to ins-39 and daf-28 [25]–[27], and their developmental and physiological outputs. We systematically tested mutants in 35 ILPs for 8 distinct developmental and physiological phenotypes (Figures 1 and S1). Thirty-four of these mutations delete part or all of the coding sequence of an ILP and are predicted to be null mutations. One mutation, ins-10 (tm3498), contained a deletion in the genomic sequence and a duplication that overexpressed the intact coding sequence, which represents a gain-of-function allele (Table S5, Figure S2 and Materials and Methods). To minimize genetic background effects, all mutants were outcrossed 6 times to wild type. We used well-established procedures to score the ILP mutants and applied several statistical criteria to classify the phenotypes as high or low confidence based on statistical significance and reproducibility (see Materials and Methods). We also confirmed the roles of many ILPs that showed new phenotypes or represented key conclusions in this study by rescuing their phenotypes with a transgene bearing the wild-type copy of the corresponding gene, as described in the following sections and in Table S3. We included our previously published work in the analysis for comparison (Figures 1 and S1, Table S2) [4], [5]. Importantly, we implicated distinct combinations of ILPs in every process tested and ascribed new functions to more than half of the C. elegans ILPs (Figure 1): 66% (23/35) of those tested showed at least one high-confidence phenotype, and 89% (31/35) showed high- or low-confidence phenotypes. We focused our analysis on the high-confidence hits. ILP-to-ILP signaling regulates several physiological processes [4], [24], [30]. To investigate its global nature, we used quantitative real-time PCR (qPCR) to identify changes in the mRNA levels of all 40 ILPs in each of 35 ILP mutants (Figure 2A, S2, and Table S5). Surprisingly, we found that ILP-to-ILP signaling extends to many members of this family, demonstrating the presence of an ILP-to-ILP regulatory network (Figure 2A). Out of a possible 1190, we observed only 101 ILP interactions (Figure 2A), which suggests that the inter-ILP regulation is sparse. These regulatory relationships also appear specific and diverse: each ILP is wired to a unique combination of regulators and targets, and regulation could be either negative (52%) or positive (48%) in a target-specific manner. These relationships showed an intermediate modularity of 0. 49, reflecting a mix of cross-regulation and compartmentalization in ILP gene expression. Thus, like the phenotypic screens, the qPCR data show that the diversification of C. elegans ILPs beyond functional redundancy also extends to their gene expression. For comparison, we also analyzed the changes in expression of all 40 ILPs in mutants that impair the ILP signaling pathway, using daf-2 (e1368) (a reduction-of-function allele), and daf-16 (mu86) (a null allele) [18], [34]. Many ILPs were up-regulated in the daf-2 (e1368) background, suggesting compensation. Many of the ILPs that were regulated by other ILPs were also affected in the daf-2 (e1368) and daf-16 (mu86) backgrounds, suggesting that these changes were mediated through the canonical ILP signaling pathway. In general, daf-2 (e1368) and daf-16 (mu86) tend to cause larger effects on gene expression, suggesting that they might be closer to the upper limit of the gene expression changes, as might be expected if the central pathway for ILP signalling is disrupted. Some ILPs that were regulated by other ILPs were not affected by daf-2 (e1368) or daf-16 (mu86); this difference could be due to residual signaling activity retained in daf-2 (e1368) [34] or the use of alternative pathways for inter-ILP regulation. To understand inter-ILP communication, we built a network based on these qPCR results for graph theory analysis, treating each ILP as a node and each regulatory interaction as an edge (Figure 2B). In this network, the edges are directed (reflecting the regulation of one ILP by another) and signed (indicating positive or negative regulation) to represent the flow of information. We discovered three major properties of this network. First, the ILP network had “small world” properties defined by two key parameters: the characteristic path length that measures the average minimal number of edges between all possible pairs of ILPs, and the clustering coefficient that measures the density of local interconnections [50], [51]. Compared with random networks with the same number of edges and nodes, the ILP network has a short path length, 3. 17, and a high clustering coefficient, 0. 13 (Figures S3A to S3C). Respectively, these properties might suggest that within these genetic circuits, signals can be communicated relatively efficiently from one ILP to another because they are separated by very few intervening ILPs, and that information is processed by local genetic circuits. These are consistent with the parallel processing we observed in the dauer entry sub-network, which is discussed below. Second, the ILP expression network displayed hierarchical regulation. Plotting the number of regulators (in-degree) versus the number of targets (out-degree) of each ILP (Figure 2C) reveals a regulatory hierarchy where several ILPs had an exceptionally high number of regulators or targets. This organizational feature suggests different functional attributes for the ILPs. ILPs with few inputs and many outputs are putative upstream regulators; ILPs with similar numbers of inputs and outputs likely act in relays or processing circuits; and ILPs with many inputs and few outputs could serve as downstream integrators or effectors. Third, important nodes for network communication tend to affect more processes. We calculated the betweenness centrality for each ILP, which measures its importance as a link between other ILP pairs in the network (Figure 2D) [52]. ILPs with higher betweenness centrality were more likely to be pleiotropic (Figures 2D to 2E), similar to protein-interaction networks where proteins with high betweenness centrality tend to be essential [52]. Thus, ILPs with high betweenness centrality may act as bottlenecks during information flux in a wider range of processes. Our network analysis was robust to missing edges, such as those from subtle gene expression changes that did not rise to statistical significance. The top ranked ILPs for each network parameter were similar despite the addition or removal of 25% of random edges (Figures S3H to S3K), indicating that we have sampled the network sufficiently. To relate ILP function to network organization, we mapped the high-confidence ILPs identified in each screen onto the network, which provided three global observations. First, the ILPs with phenotypes were spread over the network (Figure 3A), suggesting that signaling across many parts of the network was important for its overall function. Second, the ILPs with more specific phenotypes from the non-sensitized screens were segregated into different locations (Figures 3B to 3F and 3J), consistent with the observations that gene expression defects in these ILP mutants do not propagate over the entire network (Figure 2A). The separation of critical nodes in the network could limit the number of physiological defects when one ILP is perturbed. Third, our sensitized screens for dauer entry revealed another functional level of non-critical ILPs distributed over much of the network (Figures 3F to 3I). This suggests distributed processing, which could reduce the severity of a phenotype by providing alternate routes of communication. Together, these mechanisms contribute to functional specificity, which is an aspect of the ILP combinatorial code. To address how ILPs combinatorially regulate a specific process, we analyzed genetic interactions among deletion mutations of ILPs involved in dauer entry. We tested 56 double mutant combinations by selecting a diverse subset of 13 ILPs identified from each of the three dauer entry screens, encompassing ILPs showing high and low penetrance (Figures 1B to 1D). To classify genetic interactions, we first determined how the fraction of dauer entry in the double mutant differed from the expected fraction in an additive model based on the single mutant phenotypes (Materials and Methods). We then subdivided the interactions based on whether the corresponding single mutants had the same or opposite phenotypes (Figure 4, Table S6). This analysis revealed a level of diversity in gene interactions not predicted by simple redundancy. Diverse genetic interactions (defined in Figure 4) were observed in 47% (26/56) of the double mutants, of which 38% (10/26) were additive or synergistic. This result indicates that while the choice between dauer arrest and reproductive growth is binary, the likelihood of a given choice is specified by a graded combination of ILP activities. Strikingly, 9 of these 10 additive or synergistic interactions were seen in double mutants with null mutations in either ins-35 or daf-28, suggesting that these ILPs are important genetic hubs in dauer entry, consistent with their strong dauer entry phenotypes. The remaining 53% (30/56) of the double mutants showed no effect or no interaction (Figure 4, Table S6), indicating that ILPs are not promiscuous in their interactions during dauer entry, even with other ILPs involved in the same process. These results reveal how signals from pairs of ILPs are integrated to regulate dauer entry. Our findings also demonstrate functional differences among ILPs that regulate dauer entry, and indicate that the effect of an ILP depends on genetic background. Information processing is strongly influenced by the signaling motifs within the network and the overall network architecture [53]. While regulatory interactions serve as a roadmap for information flow among ILPs, genetic interactions between ILPs reflect how their activities are integrated to generate a physiological outcome. To assess information flow and processing, we combined regulatory and functional data for the ILPs whose genetic interactions were extensively defined for the dauer entry phenotype (Figure 5). The connectivity and synergistic or additive genetic interactions indicate parallel signaling in the dauer entry sub-network (Figures 5A to 5B). The major signals that inhibit dauer entry come from three main branches (daf-28, ins-6/ins-33 and ins-6/ins-35), because mutants in these branches have the strongest phenotypes (Figure 1C). To generate graded probabilities of dauer entry, signals from these three branches are integrated in an additive or synergistic manner based on their genetic interactions (Figures 4 and 5B). This network organization was supported by the phenotypes observed when we disrupted the daf-28, ins-6/ins-33 and ins-6/ins-35 branches using combinations of null mutations. In the ins-33 and daf-28 double deletion mutant, we observed a strong synergistic response with a high proportion of dauers even at 25°C (Figure 5D, Table S7). Strikingly, in the ins-33; daf-28; ins-35 triple deletion mutant, up to 80% dauers were observed at 25°C (Figure 5D, Table S7), which is nearly comparable to daf-2 mutants. These results reinforce the idea that the daf-28, ins-6/ins-33 and ins-6/ins-35 branches are major pathways for regulating dauer entry. The different connectivities within each branch of the ILP network suggest that they use different information processing strategies (Figure 5). In the daf-28 branch, daf-28 inhibits ins-26, which likely serves as a compensatory regulation based on their synergistic interaction (Figures 4 and 5B). The effect of this compensation is likely to be regulation of ins-5 as both daf-28 and ins-26 inhibit ins-5. In contrast, the ins-6 branches have a bifurcated topology where ins-33 and ins-35 process inputs from ins-6. A non-additive interaction was observed between ins-6 and ins-35, as well as between ins-6 and ins-7, which is downstream of ins-35 (Figures 4 and 5C); while an additive interaction between ins-6 and ins-33 indicates compensation (Figures 4 and 5B; see below). At a downstream level, non-additive or non-synergistic interactions occur within the ins-33 or ins-35 branches, but not the daf-28 branch. Crosstalk occurs between the daf-28 and ins-6 branches (Figure 5A), which may coordinate their signaling activities. ins-3 is likely to act as a negative modulator providing feedback to the dauer entry sub-network at multiple levels; such circuits are associated with noise reduction and homeostasis. Unlike most ILPs, the ins-3 mutation decreased dauer entry in several backgrounds (Figures 1D and 4). ins-3 expression was activated by ins-6; while ins-3 in turn inhibited ins-6 expression, as well as other ILPs in the daf-28 branch (Figures 2A and 5A). While both ins-14 and ins-17 show high and low-confidence dauer entry phenotypes, respectively, they are likely to act separately as modulators in the main dauer entry sub-network (Figure 5) for two reasons. First, they are not directly connected to the ins-6 and daf-28 branches of the expression network (Figures 5A to 5C). Second, they have weaker interactions with the genes in the daf-28 and ins-6 branches (Figure 4A). One exception is an additive interaction between ins-35 and ins-14 (Figures 4 and 5B), which might represent cross-talk at the downstream level. ILPs could also exert either strong or weak effects (Figure 1). For example, although ins-6 and daf-28 both regulate dauer entry and exit, ins-6 null mutations had a stronger effect on dauer exit, whereas daf-28 null mutations had a stronger effect on dauer entry [5]. Our results reveal that this feature is common in the whole ILP system (Figure 1). These specificities are not due to some ILPs being generally strong signals, while others are generally weak, because the relative effects of the ILPs can be reversed depending on the phenotypes. Our integrated analysis provided a mechanistic explanation for the phenotypic specificity of daf-28 and ins-6 (Figure 1C) during dauer entry. Loss of daf-28 is compensated by ins-26, because ins-26 was up-regulated in daf-28 mutants (Figures 2A and 5A) and because ins-26; daf-28 double mutants have a more severe phenotype than either single mutant (Figures 4 and 5B). However, ins-26 is a weak compensator, as indicated by its weak phenotype (Figure 1C). Additionally, daf-28 mutants up-regulate ins-5 (Figures 2A and 5A), an ILP that can promote dauer entry (Figures 1 and 4). As opposed to compensation, increased ins-5 expression contributes to the mutant phenotype of daf-28, because removing ins-5 suppressed the daf-28 mutation (Figures 4 and 5C). These two targets of daf-28 therefore contribute to its strong dauer entry phenotype. In contrast, ins-6 is compensated by daf-28 and ins-33, because both daf-28 and ins-33 were up-regulated in ins-6 mutants (Figures 2A and 5A), and both ins-6; daf-28 and ins-33; ins-6 double mutants had a more severe phenotype than the respective single mutants (Figures 4 and 5B). Both daf-28 and ins-33 were strong compensators, as indicated by their strong phenotypes (Figure 1C). Thus, the weak ins-6 phenotype could be explained by compensation from two strong regulators. Together, these results show that connectivity within the ILP network serves as an important determinant of functional differences among ILPs.
Most animals, including humans, encode multiple ILPs in their genomes, which regulate multiple processes [4], [5], [14], [15], [23], [24], [26], [27], [54]–[58]. However, the biological function of large ILP ensembles remains an open question. Our systematic analysis of C. elegans ILPs revealed that they are organized into an ILP-to-ILP network that provides several regulatory mechanisms for graded signaling, functional diversity, robustness to gene perturbation and information flow. In turn, these functional properties of the ILP network generate aspects of a combinatorial code that links ILPs to developmental and physiological outputs. Thus, our findings challenge the notion that broad redundancy is the central feature of the C. elegans ILP family. Large gene families are often proposed to employ a combination of redundancy and diversity to regulate biological processes [59]. Here, we reveal the specific implementation of an ILP combinatorial code that coordinates aspects of development and physiology (Figure 1A). Different ILPs generally affect different combinations of processes, which support the idea that redundancy is not evolutionarily stable unless the genes have additional functions [59], [60]. The high-confidence phenotypes indicate that many single ILPs can significantly contribute to different phenotypic outputs. This combinatorial coding of phenotypes therefore argue against simple redundant mapping between ILPs and their outputs, but show that the complexity of these gene-phenotype relationships is generated at least in part by inter-ILP communication. The intermediate modularity of the ILP phenotypes raises the possibility that multiple ILP signaling centers exist in the animal, which could provide differential contributions to different processes. In addition to the regulatory connectivity that underlies phenotypic specificity, spatial specificity in ILP signaling could also be a complementary mechanism in achieving the specific patterns of ILP phenotypes. This model will need to be tested in the future by tissue- or cell-specific rescue of the ILPs, coupled with the elucidation of their downstream target tissues where the DAF-2 ILP receptor acts. Undirected networks have been recently used to group the C. elegans ILPs based on similarities in their expression patterns [31]. Here we show that the C. elegans ILPs are organized at the level of ILP-to-ILP regulation in a directed regulatory network, where signals in different branches are processed differently and modulated by cross-talk. This is exemplified in the different connectivities between the ins-6 and daf-28 branches of the dauer entry subnetwork, whose distinct signals are ultimately integrated to set the probability of dauer entry. This network organization thus contributes to the graded nature of the ILP combinatorial code. This property generates different probabilities of dauer entry that result in different fractions of developmentally arrested dauers versus reproductive adults within a population. Dauers can survive environmental insults that kill reproductive adults and can thus serve as a hedge at the cost of delayed reproduction. Therefore, the advantage of this graded response provided by the parallel circuit organization is the ability to optimize the trade-off between fast reproduction versus survival in response to variable environments. These findings further underscore how circuit organization in a network contributes to the phenotypic outputs of a multi-gene family. Compensation and distributed, parallel processing in the ILP network provide robustness against gene or network perturbation. Robustness in preventing dauer entry allows for rapid reproduction, ensuring that animals develop as dauers only in extreme conditions, such as when the environment impinges on more than one ILP. In addition, the connectivity of the ILP network show that specific compensatory circuits are organized to generate strong and weak regulators, an important component of the combinatorial code. Extensive genome-wide studies in yeast indicate that complete or partial functional redundancy can occur among duplicated gene pairs [61], [62] where the loss of one gene can be compensated by responsive circuits that increase the expression of a second homologous gene [63]. Although compensatory circuits are often hypothesized as a feature of gene families that lead to redundancy, we show that its actual implementation can lead to more complex outcomes than previously proposed. Instead of global redundancy, the gradation provided by the ILP network is consistent with the idea that partial redundancy, as well as overlapping and distinct functions, could serve to encode diverse inputs [59], [60]. ILP-to-ILP signaling in diverse animals uses similar signaling motifs, such as feedback, compensatory inhibition and feedforward circuitry [4], [24], [28]–[30], [64], [65], which may provide similar biological functions despite component differences [53]. Our findings suggest how simple circuits can be organized to generate complex network functions; like signaling motifs, these principles may also apply to networks in general. Because our results indicate the importance of specificity versus redundancy in multi-gene families is a consequence of network organization, we propose that large-scale connectivity-based approaches have general utility in dissecting the regulatory mechanisms employed by different families of intercellular signals in different animals. In summary, we have delineated the C. elegans ILP-to-ILP regulatory network based on functional criteria, which provides a distinct approach to existing ILP networks based on expression similarities [31]. This ILP-to-ILP regulatory network, coupled with our systematic genetic analyses, serves as a mechanistic framework for understanding information processing by ILPs. Our findings suggest that the multiple ILPs provide the ability to organize circuits into a network with diverse points of regulation, which in turn produces an intricate combinatorial code to orchestrate development and physiology. Together, this represents a new avenue to understand how hormonal systems compute the development and physiology of the organism.
C. elegans were cultivated at 20°C under standard conditions except where otherwise stated. The strains used are listed in Table S1. All ILP deletions were independently confirmed using PCR from genomic DNA with primers different from those used by the C. elegans Knockout Consortium to isolate the mutation. ins-10 (tm3498) had increased expression of the coding region from our qPCR experiments (below). PCR using genomic DNA from 6× outcrossed ins-10 (tm3498) mutants with primers that annealed to the start and end of the ins-10 coding sequence amplified a genomic fragment that contained the full ins-10 coding sequence which was verified by sequencing (data not shown). Because ins-10 (tm3498) also contained a deletion in the endogenous ins-10 locus, which we verified independently from the C. elegans Knockout Consortium, these results indicate that ins-10 (tm3498) involves at least a deletion and duplication of the ins-10 coding region that led to ins-10 overexpression. All mutant strains used in this study were obtained from the Knockout Consortium [66]. Double and triple mutants were generated by standard genetic methods. See Table S1 for strain list. Deletions were regularly verified using PCR. All the phenotypic assays were conducted on fresh NGM plates seeded with fresh OP50 unless specified otherwise, using animals that were well fed for at least 2 generations. The lifespan and dauer assays were replicated in different labs. The identity of each strain was blinded for most assays. We generated plasmids to rescue the phenotypes of the ILP mutants. These plasmids contain the entire coding region of the gene of interest and the 5′ and 3′ intergenic regions up to the next open reading frame. Genomic regions for ins-3, ins-4, ins-5, ins-14, ins-15, ins-21, ins-23, ins-26, and ins-27 were subcloned using a recombineering method [69] from the corresponding fosmids into the pQL60, a vector derived from the original pPUB in which the unc-119 marker was removed. Genomic regions for ins-31, ins-33 and ins-35 were amplified by PCR and subcloned into pCR-Blunt TOPO (Invitrogen). The transgenic lines bearing extrachromosomal arrays were generated by microinjection of the rescue construct at different concentrations (see Table S1) as well as ofm-1: : gfp as a coinjection marker (25 ng/µl) and pBluescript as a carrier DNA up to a final concentration of 100 ng/µl of DNA. For ins-12, a mini-gene was synthesized, subcloned into the MosSCI plasmid pCFJ352 [70] with the corresponding the 5′ and 3′ intergenic regions up to the next open reading frame and integrated into the QL35 strain using MosSCI [71]. Modularity of the ILP-to-phenotype and the mRNA-to-ILP matrices were estimated by rearranging the rows and columns of the matrix to find highly interconnected groups and then assessing matrix-wide the ratio of the number of inside to outside group connections. We used the adaptive BRIM (Bipartite Recursively Induced Modules) algorithm [49], [76], which is a heuristic method, implemented in MATLAB [49], [76] to maximize a bipartite modularity value Q. This Q value is dependent on modularity of the matrix; by definition, a perfectly modular matrix is comprised of clusters of completely isolated groups (), and modularity declines as the number of cross-group connections increases (). Because the modularity calculation is based on a stochastic algorithm that produced different matrix arrangement each time the algorithm is run, we performed the calculation 30 times and took the average of the modularity. The average modularity value of ILP-to-phenotype matrix is (highly reproducible) and that of mRNA-to-ILP is To evaluate the statistical significance of the modularity, we utilized two null models. The first model is a Bernoulli random null model in which the null matrix has the same total number of interactions as the original matrix, albeit randomly positioned. The second is a probabilistic degree null model in which each interaction in null model is assigned a probability. The ILP-to-phenotype and mRNA-to-ILP matrices are significantly different against the Bernoulli random null model (p<0. 001 in both cases); however, when compared against the probabilistic degree null model, which is a stronger statistical test, the p-values of both matrices are greater than 0. 05. These results suggest that both matrices are weakly modular.
Insulin signaling is widely implicated in regulating diverse physiological processes ranging from metabolism to longevity across many animal species. Many animals have multiple insulin-like peptides that can regulate the activity of this signaling pathway. For example, while humans have ten, including the well-studied insulin hormone, the nematode Caenorhabditis elegans has forty such peptides. The similarity among these insulin-like peptides led to the predominant notion that widespread redundancy occurs among these peptides. Contrary to this notion, we find that the forty insulin-like peptides in the nematode C. elegans have specific and distinct effects on eight different physiological outputs that range from development, stress responses, lifespan and reproduction. Interestingly, we also find that these peptides regulate each other at the transcriptional level to form a signaling network. In addition, we observe that this network is organized into parallel circuits, whose activities are affected by compensation, feedback and crosstalk. Finally, the organization of the network helps to explain how different combinations of peptides generate specific outputs and captures the complexity of how these peptides orchestrate an animal' s physiology through distinct peptide-to-peptide signaling circuits.
invertebrates
neurochemistry
immune physiology
caenorhabditis
gene regulation
animals
hormones
gene function
endocrine physiology
animal models
developmental biology
caenorhabditis elegans
model organisms
organism development
molecular development
molecular genetics
insulin-like growth factors
research and analysis methods
insulin
gene expression
neuroendocrinology
peptide hormones
aging
systems biology
biochemistry
signal transduction
anatomy
cell biology
gene regulatory networks
genetic screens
gene identification and analysis
physiology
endocrine system
genetics
biology and life sciences
nematoda
computational biology
cell signaling
organisms
An Insulin-to-Insulin Regulatory Network Orchestrates Phenotypic Specificity in Development and Physiology