Person:

Calvo, Sarah

Background: D-bifunctional protein deficiency, caused by recessive mutations in HSD17B4, is a severe, infantile-onset disorder of peroxisomal fatty acid oxidation. Few affected patients survive past two years of age. Compound heterozygous mutations in HSD17B4 have also been reported in two sisters diagnosed with Perrault syndrome (MIM # 233400), who presented in adolescence with ovarian dysgenesis, hearing loss, and ataxia. Case presentation: An adult male presented with cerebellar ataxia, peripheral neuropathy, hearing loss, and azoospermia. The clinical presentation, in combination with biochemical findings in serum, urine, and muscle biopsy, suggested a mitochondrial disorder. Commercial genetic testing of 18 ataxia and mitochondrial disease genes was negative. Targeted exome sequencing followed by analysis of single nucleotide variants and small insertions/deletions failed to reveal a genetic basis of disease. Application of a computational algorithm to infer copy number variants (CNVs) from exome data revealed a heterozygous 12 kb deletion of exons 10–13 of HSD17B4 that was compounded with a rare missense variant (p.A196V) at a highly conserved residue. Retrospective review of patient records revealed mildly elevated ratios of pristanic:phytanic acid and arachidonic:docosahexaenoic acid, consistent with dysfunctional peroxisomal fatty acid oxidation. Conclusion: Our case expands the phenotypic spectrum of HSD17B4-deficiency, representing the first male case reported with infertility. Furthermore, it points to crosstalk between mitochondria and peroxisomes in HSD17B4-deficiency and Perrault syndrome.

In recent years, there has been a huge rise in the number of publicly available transcriptional profiling datasets. These massive compendia comprise billions of measurements and provide a special opportunity to predict the function of unstudied genes based on co-expression to well-studied pathways. Such analyses can be very challenging, however, since biological pathways are modular and may exhibit co-expression only in specific contexts. To overcome these challenges we introduce CLIC, CLustering by Inferred Co-expression. CLIC accepts as input a pathway consisting of two or more genes. It then uses a Bayesian partition model to simultaneously partition the input gene set into coherent co-expressed modules (CEMs), while assigning the posterior probability for each dataset in support of each CEM. CLIC then expands each CEM by scanning the transcriptome for additional co-expressed genes, quantified by an integrated log-likelihood ratio (LLR) score weighted for each dataset. As a byproduct, CLIC automatically learns the conditions (datasets) within which a CEM is operative. We implemented CLIC using a compendium of 1774 mouse microarray datasets (28628 microarrays) or 1887 human microarray datasets (45158 microarrays). CLIC analysis reveals that of 910 canonical biological pathways, 30% consist of strongly co-expressed gene modules for which new members are predicted. For example, CLIC predicts a functional connection between protein C7orf55 (FMC1) and the mitochondrial ATP synthase complex that we have experimentally validated. CLIC is freely available at www.gene-clic.org. We anticipate that CLIC will be valuable both for revealing new components of biological pathways as well as the conditions in which they are active.