Liver fibrosis interferes with normal liver function and facilitates hepatocellular carcinoma (HCC) development, representing a major threat to human health. Here, we present a comprehensive perspective of microRNA (miRNA) function on targeting the fibrotic microenvironment. Starting from a murine HCC model, we identify a miRNA network composed of 8 miRNA hubs and 54 target genes. We show that let-7, miR-30, miR-29c, miR-335, and miR-338 (collectively termed antifibrotic microRNAs [AF-miRNAs]) down-regulate key structural, signaling, and remodeling components of the extracellular matrix. During fibrogenic transition, these miRNAs are transcriptionally regulated by the transcription factor Pparγ and thus we identify a role of Pparγ as regulator of a functionally related class of AF-miRNAs. The miRNA network is active in human HCC, breast, and lung carcinomas, as well as in 2 independent mouse liver fibrosis models. Therefore, we identify a miRNA:mRNA network that contributes to formation of fibrosis in tumorous and nontumorous organs of mice and humans.

19The coordination of metabolism and growth with cell division is crucial for proliferation. While it 20 has long been known that cell metabolism regulates the cell division cycle, it is becoming 21 increasingly clear that the cell division cycle also regulates metabolism. In budding yeast, we 22 previously showed that over half of all measured metabolites change concentration through the 23 cell cycle indicating that metabolic fluxes are extensively regulated during cell cycle progression. 24 However, how this regulation is achieved still remains poorly understood. Since both the cell cycle 25 and metabolism are regulated to a large extent by protein phosphorylation, we here decided to 26 measure the phosphoproteome through the budding yeast cell cycle. Specifically, we chose a cell 27 cycle synchronisation strategy that avoids stress and nutrient-related perturbations of metabolism, 28 and we grew the yeast on ethanol minimal medium to force cells to utilize their full biosynthetic 29 repertoire. Using a tandem-mass-tagging approach, we found over 200 sites on metabolic enzymes 30 and transporters to be phospho-regulated. These sites were distributed among many pathways 31 including carbohydrate catabolism, lipid metabolism and amino acid synthesis and therefore likely 32 contribute to changing metabolic fluxes through the cell cycle. Among all one thousand sites 33 whose phosphorylation increases through the cell cycle, the CDK consensus motif and an arginine-34 directed motif were highly enriched. This arginine-directed R-R-x-S motif is associated with 35 protein-kinase A, which regulates metabolism and promotes growth. Finally, we also found over 36 one thousand sites that are dephosphorylated through the G1/S transition. We speculate that the 37 phosphatase Glc7/ PP1, known to regulate both the cell cycle and carbon metabolism, may play 38 an important role because its regulatory subunits are phospho-regulated in our data. In summary, 39 our results identify extensive cell cycle dependent phosphorylation and dephosphorylation of 40 metabolic enzymes and suggest multiple mechanisms through which the cell division cycle 41 regulates metabolic signalling pathways to temporally coordinate biosynthesis with distinct phases 42 of the cell division cycle.43 45division cycle, which ensures that DNA and other crucial cellular components are duplicated and 46 divided between two daughter cells. In budding yeast, it was viewed that cell metabolism and 47 growth proceed largely independently of the cell cycle. This assumption comes from the 48 observation that mutants arrested in distinct phases of the cell cycle continued to grow and became 49 extremely large and irregularly shaped (Hartwell et al., 1974;Johnston et al., 1977; Pringle and 50 Hartwell, 1981). This showed clearly that a cell cycle arrest does not stop metabolism and mass 51 accumulation, which led to the text book model that in budding yeast growth controls division, but 52 not vice versa (Morgan, 2007). 53While the hierarchy of metaboli...

Metabolism and the Cell Cycle cell cycle dependent phosphorylation and dephosphorylation of metabolic enzymes and suggest multiple mechanisms through which the cell division cycle regulates metabolic signaling pathways to temporally coordinate biosynthesis with distinct phases of the cell division cycle.

With a growing amount of (multi-)omics data being available, the extraction of knowledge from these datasets is still a difficult problem. Classical enrichment-style analyses require predefined pathways or gene sets that are tested for significant deregulation to assess whether the pathway is functionally involved in the biological process under study. De novo identification of these pathways can reduce the bias inherent in predefined pathways or gene sets. At the same time, the definition and efficient identification of these pathways de novo from large biological networks is a challenging problem. We present a novel algorithm, DeRegNet, for the identification of maximally deregulated subnetworks on directed graphs based on deregulation scores derived from (multi-)omics data. DeRegNet can be interpreted as maximum likelihood estimation given a certain probabilistic model for de-novo subgraph identification. We use fractional integer programming to solve the resulting combinatorial optimization problem. We can show that the approach outperforms related algorithms on simulated data with known ground truths. On a publicly available liver cancer dataset we can show that DeRegNet can identify biologically meaningful subgraphs suitable for patient stratification. DeRegNet is freely available as open-source software.

Background With a growing amount of (multi-)omics data being available, the extraction of knowledge from these datasets is still a difficult problem. Classical enrichment-style analyses require predefined pathways or gene sets that are tested for significant deregulation to assess whether the pathway is functionally involved in the biological process under study. De novo identification of these pathways can reduce the bias inherent in predefined pathways or gene sets. At the same time, the definition and efficient identification of these pathways de novo from large biological networks is a challenging problem. Results We present a novel algorithm, DeRegNet, for the identification of maximally deregulated subnetworks on directed graphs based on deregulation scores derived from (multi-)omics data. DeRegNet can be interpreted as maximum likelihood estimation given a certain probabilistic model for de-novo subgraph identification. We use fractional integer programming to solve the resulting combinatorial optimization problem. We can show that the approach outperforms related algorithms on simulated data with known ground truths. On a publicly available liver cancer dataset we can show that DeRegNet can identify biologically meaningful subgraphs suitable for patient stratification. DeRegNet can also be used to find explicitly multi-omics subgraphs which we demonstrate by presenting subgraphs with consistent methylation-transcription patterns. DeRegNet is freely available as open-source software. Conclusion The proposed algorithmic framework and its available implementation can serve as a valuable heuristic hypothesis generation tool contextualizing omics data within biomolecular networks.