A sperm-specific proteome-scale metabolic network model identifies non-glycolytic genes for energy deficiency in asthenozoospermia

<p>About 15% of couples experience difficulty in conceiving a child, of which half of the cases are thought to be male-related. Asthenozoospermia, or low sperm motility, is one of the frequent types of male infertility. Although energy metabolism is suggested to be central to the etiology of asthenozoospermia, very few attempts have been made to identify its underlying metabolic pathways. Here, we reconstructed SpermNet, the first proteome-scale model of the sperm cell by using whole-proteome data and the mCADRE algorithm. The reconstructed model was then analyzed using the COBRA toolbox. Genes were knocked-out in the model to investigate their effect on ATP production. A total of 78 genes elevated ATP production rate considerably of which most encode components of oxidative phosphorylation, fatty acid oxidation, the Krebs cycle, and members of the solute carrier 25 family. Among them, we identified 11 novel genes which have previously not been associated with sperm cell energy metabolism and may thus be implicated in asthenozoospermia. We further examined the reconstructed model by <i>in silico</i> knock out of currently known asthenozoospermia implicated-genes that were not predicted by our model. The pathways affected by knocking out these genes were also related to energy metabolism, confirming previous findings. Therefore, our model not only predicts the known pathways, it also identifies several non-glycolytic genes for deficient energy metabolism in asthenozoospermia. Finally, this model supports the notion that metabolic pathways besides glycolysis such as oxidative phosphorylation and fatty acid oxidation are essential for sperm energy metabolism and if validated, may form a basis for fertility recovery.</p> <p><b>Abbreviations</b>: mCADRE: metabolic context-specificity assessed by deterministic reaction evaluation; ATP: adenosine triphosphate; RNA: ribonucleic acid; FBA: flux balance analysis; FVA: flux variability analysis; DAVID: database for annotation, visualization and integrated discovery; OXPHOS: oxidative phosphorylation; ETC: electron transfer chain; SLC: solute carrier; DLD: dihydrolypoamide dehydrogenase; DLST: dihydrolypoamide S-succinyl transferase; OGDH: oxoglutarate dehydrogenase; CS: citrate synthase; FH: fumarate hydratase; IDH: isocitrate dehydrogenase; SUCLG1: succinate-CoA ligase; SD: succinate dehydrogenase; HADHA: hydroxyacyl-CoA dehydrogenase/3-ketoacyl-CoA thiolase/enoyl-CoA hydratase, subunit A; HADHB: hydroxyacyl-CoA dehydrogenase/3-ketoacyl-CoA thiolase/enoyl-CoA hydratase, subunit B; PPA2: pyrophosphatase (inorganic) 2; PP_i: inorganic phosphate; GALT: galactose-1-phosphate uridylyltransferase</p

Three-way interaction model to trace the mechanisms involved in Alzheimer’s disease transgenic mice

<div><p>Alzheimer's disease (AD) is the most common cause for dementia in human. Currently, more than 46 million people in the world suffer from AD and it is estimated that by 2050 this number increases to more than 131 million. AD is considered as a complex disease. Therefore, understanding the mechanism of AD is a universal challenge. Nowadays, a huge number of disease-related high-throughput “omics” datasets are freely available. Such datasets contain valuable information about disease-related pathways and their corresponding gene interactions. In the present work, a three-way interaction model is used as a novel approach to understand AD-related mechanisms. This model can trace the dynamic nature of co-expression relationship between two genes by introducing their link to a third gene. Apparently, such relationships cannot be traced by the classical two-way interaction model. Liquid association method was applied to capture the statistically significant triplets which are involved in three-way interaction. Subsequently, gene set enrichment analysis (GSEA) and gene regulatory network (GRN) inference were applied to analyze the biological relevance of the statistically significant triplets. The results of this study suggest that the innate immunity processes are important in AD. Specifically, our results suggest that <i>H2-Ob</i> as the switching gene and the gene pair {<i>Csf1r</i>, <i>Milr1</i>} form a statistically significant and biologically relevant triplet, which may play an important role in AD. We propose that the homeostasis-related link between mast cells and microglia is presumably controlled with <i>H2-Ob</i> expression levels as a switching gene.</p></div

Biologically relevant triplets.

<p>By tracing statistically significant triplets in the enriched terms, 12 triplets in which X₁ and X₂ are involved in the same biological process or pathway were determined.</p

Gene set enrichment analysis.

<p>Enriched terms based on (A) “biological process”; (B) “cellular component”; and (C) “KEGG pathway” for two gene groups, genes in X₃ position and all of the genes involved in the triplets. The common terms in these two groups are shown in red. The high frequency of common terms suggest that the results of liquid association method are consistent with the biological expectation from three-way interactions, that is, the presence of switching and switched genes in the same biological pathway.</p

FDR vs. -log (<i>p</i>-value).

<p>The changes in FDR (BH-corrected <i>p</i>-value) versus -log (<i>p</i>-value) for the first 300000 results of fastLA [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0184697#pone.0184697.ref021" target="_blank">21</a>]. As shown FDR = 0.001 corresponds to -log (<i>p</i>- value) = 6.817.</p

An example of three-way interaction.

<p>In a three-way interaction with switching mechanism the correlation between two genes, namely X₁ and X₂ is considered. Then, it is assumed that there is a third gene, namely the "switching gene" denoted by X₃, whose expression level affects the co-expression relationship of the two other genes. In other words, based on the expression levels of the third gene (X₃), the expression levels of the two other genes ({X₁, X₂}) are either directly or inversely correlated. Here, the three-way interaction with switching mechanism between <i>H2-Ob</i> (as the switching gene) and {<i>Csf1r</i>, <i>Milr1</i>} (as {X₁, X₂}) is shown. (A) When <i>H2-Ob</i> gene is up-regulated (i.e., its normalized expression level is between 0.37 and 1.84), there is a direct correlation between <i>Milr1</i> and <i>Csf1r</i> expression levels (red); (B) When <i>H2-Ob</i> gene is in the intermediate state (i.e., its normalized expression level is between 0.37 and -0.37), expression levels of <i>Milr1</i> and <i>Csf1r</i> are not correlated (grey); (C) When <i>H2-Ob</i> gene is down-regulated (normalized expression level of it is between -1.84 and -0.37), there is an inverse correlation between <i>Milr1</i> and <i>Csf1r</i> expression levels (green). This triplet will be further explained in the Discussion section.</p

Examples of the statistically triplets.

<p>In each case, a considerable change in the correlation of X₁ and X₂ occurs as a result of change in X₃.</p

Regulatory relationships within triplets.

<p>The regulatory relationships of significant triplets obtained from liquid association were traced in the GRN. The more the intensity of the red color, the more the up-regulation of the gene in Alzheimer’ disease. As shown there are regulatory relationships between <i>Milr1</i>, <i>Csf1r</i> and <i>H2-Ob</i> in triplet 72. Additionally, regulatory relationships are observed between <i>Slc14a1</i> and <i>Slamf6</i> in the triplet 97.</p