Integration of chronological omics data reveals mitochondrial regulatory mechanisms during the development of hepatocellular carcinoma.

Mitochondria participate in multiple functions in eukaryotic cells. Although disruption of mitochondrial function has been associated with energetic deregulation in cancer, the chronological changes in mitochondria during cancer development remain unclear. With the aim to assess the role of mitochondria throughout cancer development, we analyzed samples chronologically obtained from induced hepatocellular carcinoma (HCC) in rats. In our analyses, we integrated mitochondrial proteomic data, mitochondrial metabolomic data and nuclear genome transcriptomic data. We used pathway over-representation and weighted gene co-expression network analysis (WGCNA) to integrate expression profiles of genes, miRNAs, proteins and metabolite levels throughout HCC development. Our results show that mitochondria are dynamic organelles presenting specific modifications in different stages of HCC development. We also found that mitochondrial proteomic profiles from tissues adjacent to nodules or tumor are determined more by the stage of HCC development than by tissue type, and we evaluated two models to predict HCC stage of the samples using proteomic profiles. Finally, we propose an omics integration pipeline to massively identify molecular features that could be further evaluated as key regulators, biomarkers or therapeutic targets. As an example, we show a group of miRNAs and transcription factors as candidates, responsible for mitochondrial metabolic modification in HCC.

Signatures of co-evolutionary host-pathogen interactions in the genome of the entomopathogenic nematode Steinernema carpocapsae.

BACKGROUND: The entomopathogenic nematode Steinernema carpocapsae has been used worldwide as a biocontrol agent for insect pests, making it an interesting model for understanding parasite-host interactions. Two models propose that these interactions are co-evolutionary processes in such a way that equilibrium is never reached. In one model, known as "arms race", new alleles in relevant genes are fixed in both host and pathogens by directional positive selection, producing recurrent and alternating selective sweeps. In the other model, known as"trench warfare", persistent dynamic fluctuations in allele frequencies are sustained by balancing selection. There are some examples of genes evolving according to both models, however, it is not clear to what extent these interactions might alter genome-level evolutionary patterns and intraspecific diversity. Here we investigate some of these aspects by studying genomic variation in S. carpocapsae and other pathogenic and free-living nematodes from phylogenetic clades IV and V. RESULTS: To look for signatures of an arms-race dynamic, we conducted massive scans to detect directional positive selection in interspecific data. In free-living nematodes, we detected a significantly higher proportion of genes with sites under positive selection than in parasitic nematodes. However, in these genes, we found more enriched Gene Ontology terms in parasites. To detect possible effects of dynamic polymorphisms interactions we looked for signatures of balancing selection in intraspecific genomic data. The observed distribution of Tajima's D values in S. carpocapsae was more skewed to positive values and significantly different from the observed distribution in the free-living Caenorhabditis briggsae. Also, the proportion of significant positive values of Tajima's D was elevated in genes that were differentially expressed after induction with insect tissues as compared to both non-differentially expressed genes and the global scan. CONCLUSIONS: Our study provides a first portrait of the effects that lifestyle might have in shaping the patterns of selection at the genomic level. An arms-race between hosts and pathogens seems to be affecting specific genetic functions but not necessarily increasing the number of positively selected genes. Trench warfare dynamics seem to be acting more generally in the genome, likely focusing on genes responding to the interaction, rather than targeting specific genetic functions.