Experimental and computational analysis of a large protein network that controls fat storage reveals the design principles of a signaling network

Abstract

An approach combining genetic, proteomic, computational, and physiological analysis was used to define a protein network that regulates fat storage in budding yeast (Saccharomyces cerevisiae). A computational analysis of this network shows that it is not scale-free, and is best approximated by the Watts-Strogatz model, which generates "small-world" networks with high clustering and short path lengths. The network is also modular, containing energy level sensing proteins that connect to four output processes: autophagy, fatty acid synthesis, mRNA processing, and MAP kinase signaling. The importance of each protein to network function is dependent on its Katz centrality score, which is related both to the protein's position within a module and to the module's relationship to the network as a whole. The network is also divisible into subnetworks that span modular boundaries and regulate different aspects of fat metabolism. We used a combination of genetics and pharmacology to simultaneously block output from multiple network nodes. The phenotypic results of this blockage define patterns of communication among distant network nodes, and these patterns are consistent with the Watts-Strogatz model.

Fig 1. The fat storage regulation network.

(A) Nile Red/DAPI measurements (left), and TLC analysis (right) for two mutants (four replicates for each). S is lard standard, and the arrow indicates the triglyceride band. Four replicate samples are shown. (B) Fat storage-regulating yeast genes encode connected proteins. In the key at the bottom, the sizes of the circles represent the magnitude of the increase in fat levels relative to wild type, as measured by TLC (for quantification of the fat levels in each mutant see S1 Table). The molecular function of a given protein is represented by the color of the circles, while the type of reported molecular contact between two proteins is represented by the color of the connecting lines. Unfilled rectangles are proteins encoded by 8 essential genes that are likely to be part of the network (see text). (C) The network contains 7 highly connected protein communities composed of more than three proteins (enclosed by black dashed lines). Error bars in (A) are standard deviations of 10 replicas for a given genotype, and asterisks denote t-test statistical significance as compared to wild-type (wt) (**p < 0.5E-5, ***p < 0.5E-8, ****p < 0.5E-9).

Fig 2. The fat storage regulation network has a Watts-Strogatz topology in which the importance of a protein is associated with Katz centrality.

(A) Representations of the four major types of network topology. Nodes with the same color are members of the same module, while red circles in the scale-free network diagrams represent hubs. (B) The calculated P(k) distribution of the fat storage regulation network (red line) as compared to Poisson (blue line) and power-law distributions (yellow line). (C-H) The upper cartoon in each panel is a heat map of a small toy network showing the relative centrality for each node (high-low order: red-orange-yellow-green-aqua-blue). The lower diagram shows the experimental network map, with the sizes of the grey circles showing the relative impact on fat levels of mutation in the gene encoding that protein, and the sizes of the overlaid red circles represent the centrality values for each node. (H) is a map of transcriptional regulation, with the sizes of the grey circles indicating the number of genes regulated by a given transcription factor or global regulator. Upper black text: p-value for independence testing (Blomqvist β test). Lower black text: correlation.

Fig 3. Genes for which mutations produce similar LD phenotypes affect connected proteins.

(A) Fixed wild type yeast cells stained with BIODIPY 493/518, as visualized by confocal microscopy. Most cells have 3–8 LDs that are ~0.4 μm in diameter (yellow arrow). (B) Mutants with numerous and small LDs (red arrow), as exemplified by fus3Δ (left). The right sides of panels B-D show the genes for which mutants display this phenotype and their proteomic connections. (C) Mutants with a mixture of small and normal size LDs (red and yellow arrows, respectively), as exemplified by mot2Δ (left). (D) Mutants with supersized LDs, in addition to large and small LDs (yellow arrowhead, yellow arrow, and red arrow, respectively) as exemplified by snf4Δ (left). At the right side of each panel are indicated the global clustering coefficient (upper) and path length (lower) for each subnetwork (red font), presented over the mean of values from 10,000 generated simulated random networks with the same degree distribution and vertex count as the subnetwork in that panel (green font). Key for diagrams as in Fig 1. Scale bar, 4 μm.

Fig 4. Genes for which mutations affect carbon source usage affect connected proteins.

(A) TLC assays of wild-type yeast (four replicates each) show a gradual reduction in the intensity of the triglyceride band (arrows) as starvation continues for three days. This reduction is either not observed in mutants (e.g., snf1Δ) or occurs at a slower rate (e.g., opi3Δ). See Supplement for network analysis of fat store degradation. (B) Mutants that show slow or no growth on 3% glycerol, as exemplified by snf1Δ and vma2Δ mutants (image on left shows a dilution series from top to bottom). (C) Mutants that show slow or no growth on 0.1% lard and/or 0.1% palmitic acid, as exemplified by dhh1Δ and bem1Δ mutants (image on left). Mutations causing similar carbon source utilization defects affect genes that encode proteins that tend to be proteomically connected to one another (diagrams in (B) and (C). At the right side of each panel is the global clustering coefficient (upper) and path length (lower) for each subnetwork (red font), presented over the mean of values from 10,000 simulated random networks with the same degree distribution and vertex count as the subnetwork in that panel (green font). Key for diagrams as in Fig 1.

Fig 5. Genes for which mutations affect conversion of glucose or aspartic acid to fat affect connected proteins.

(A) Proteins encoded by genes for which mutation produces more than a 20% increase in the rate of conversion of ¹⁴C labeled D-glucose to fat relative to wild-type tend to be proteomically connected to one another. (B) Proteins encoded by genes for which mutation produces more than a 20% increase in the rate of conversion of ¹⁴C labeled L-aspartic acid to fat relative to wild-type tend to be proteomically connected to one another. At the right side of each panel is the global clustering coefficient (upper) and path length (lower) for each subnetwork (red font), presented over the mean of values from 10,000 simulated random networks with the same degree distribution and vertex count as the subnetwork in that panel (green font). Key for diagrams as in Fig 1.

Fig 6. Internode communication patterns deduced from drug-mutant interactions.

(A) Representations of signaling relationships between drug targets and mutant genes. In the diagrams, gene A encodes protein A, while gene B encodes protein B, which is the target of the drug. (B) TLC assays of wild-type (wt) yeast (three replicates each) showing the impact of different drug treatments on triglyceride bands (arrows). All mutants were subjected to the same treatments and analyzed in the same manner, as exemplified by the three replicas of mkk2∆ for each drug (note that U0126, which targets MAPKKs, does not increase fat levesl in mkk2∆ cells relative to untreated mutant cells). (C-D) Networks of interactions between mutants and U0126 (C) and Rap (D). Protein names are given in the corresponding larger diagram in (E). Note that the majority of proteins have signaling interactions with the drugs that range from “same pathway” (no enhancement of drug effect by mutation; dark brown), to different degrees of synergism (enhancement of drug effect by mutation, indicated by different shades of light brown), and there are fewer cases of parallel pathway (independent) relationships, in which the drug and mutant effects are additive (light grey). These parallel pathway relationships are like the synthetic negative interactions seen in double mutant studies. Corresponding data for ChQ., Conc. A, and Cer. are in S4 Fig. At the right side of panels (C) and (D) are indicated the global clustering coefficient (upper) and path length (lower) for a subnetwork of all proteins having a “same pathway” relationship to that drug (red font), presented over the mean of values from 10,000 simulated random networks with the same degree distribution and vertex count (green font). (E) Diagram of same pathway signaling relationships between drugs and network proteins. The circle color represents the number of drugs with which a protein has a same pathway relationship.