Comparison and evaluation of integrative methods for the analysis of multilevel omics data: a study based on simulated and experimental cancer data.

Integrative analysis aims to identify the driving factors of a biological process by the joint exploration of data from multiple cellular levels. The volume of omics data produced is constantly increasing, and so too does the collection of tools for its analysis. Comparative studies assessing performance and the biological value of results, however, are rare but in great demand. We present a comprehensive comparison of three integrative analysis approaches, sparse canonical correlation analysis (sCCA), non-negative matrix factorization (NMF) and logic data mining MicroArray Logic Analyzer (MALA), by applying them to simulated and experimental omics data. We find that sCCA and NMF are able to identify differential features in simulated data, while the Logic Data Mining method, MALA, falls short. Applied to experimental data, we show that MALA performs best in terms of sample classification accuracy, and in general, the classification power of prioritized feature sets is high (97.1-99.5% accuracy). The proportion of features identified by at least one of the other methods, however, is approximately 60% for sCCA and NMF and nearly 30% for MALA, and the proportion of features jointly identified by all methods is only around 16%. Similarly, the congruence on functional levels (Gene Ontology, Reactome) is low. Furthermore, the agreement of identified feature sets with curated gene signatures relevant to the investigated disease is modest. We discuss possible reasons for the moderate overlap of identified feature sets with each other and with curated cancer signatures. The R code to create simulated data, results and figures is provided at https://github.com/ThallingerLab/IamComparison.

Myristic acid induces proteomic and secretomic changes associated with steatosis, cytoskeleton remodeling, endoplasmic reticulum stress, protein turnover and exosome release in HepG2 cells.

Myristic acid, the 14-carbon saturated fatty acid (C14:0), is associated to an increased cardiovascular disease risk. Since it is found in low concentration in cells, its specific properties have not been fully analyzed. The aim of this study was to explore the cell response to this fatty acid to help explaining clinical findings on the relationship between C14:0 and cardiovascular disease. The human liver HepG2 cell line was used to investigate the hepatic response to C14:0 in a combined proteomic and secretomic approach. A total of 47 intracellular and 32 secreted proteins were deregulated after treatments with different concentrations of C14:0. Data are available via ProteomeXchange (PXD007902). In addition, C14:0 treatment of primary murine hepatocytes confirmed that C14:0 induces lipid droplet accumulation and elevates perilipin-2 levels. Functional enrichment analysis revealed that C14:0 modulates lipid droplet formation and cytoskeleton organization, induce ER stress, changes in exosome and extracellular miRNA sorting in HepG2cells. Our data provide for the first time a proteomic profiling of the effects of C14:0 in human hepatoma cells and contribute to the elucidation of molecular mechanisms through which this fatty acid may cause adverse health effects. BIOLOGICAL SIGNIFICANCE: Myristic acid is correlated with an increase in plasma cholesterol and mortality due to cardiovascular diseases. This study is the first example of an integration of proteomic and secretomic analysis of HepG2 cells to investigate the specific properties and functional roles of myristic acid on hepatic cells. Our analyses will lead to a better understanding of the myristic acid induced effects and can elicit new diagnostic and treatment strategies based on altered proteins.

Deletion of Adipose Triglyceride Lipase Links Triacylglycerol Accumulation to a More-Aggressive Phenotype in A549 Lung Carcinoma Cells.

Adipose triglyceride lipase (ATGL) catalyzes the rate limiting step in triacylglycerol breakdown in adipocytes but is expressed in most tissues. The enzyme was shown to be lost in many human tumors, and its loss may play a role in early stages of cancer development. Here, we report that loss of ATGL supports a more-aggressive cancer phenotype in a model system in which ATGL was deleted in A549 lung cancer cells by CRISPR/Cas9. We observed that loss of ATGL led to triacylglycerol accumulation in lipid droplets and higher levels of cellular phospholipid and bioactive lipid species (lyso- and ether-phospholipids). Label-free quantitative proteomics revealed elevated expression of the pro-oncogene SRC kinase in ATGL depleted cells, which was also found on mRNA level and confirmed on protein level by Western blot. Consistently, higher expression of phosphorylated (active) SRC (Y416 phospho-SRC) was observed in ATGL-KO cells. Cells depleted of ATGL migrated faster, which was dependent on SRC kinase activity. We propose that loss of ATGL may thus increase cancer aggressiveness by activation of pro-oncogenic signaling via SRC kinase and increased levels of bioactive lipids.