Analysis and Prediction of Pathways in HeLa Cells by Integrating Biological Levels of Organization with Systems-Biology Approaches

<div><p>It has recently begun to be considered that cancer is a systemic disease and that it must be studied at every level of complexity using many of the currently available approaches, including high-throughput technologies and bioinformatics. To achieve such understanding in cervical cancer, we collected information on gene, protein and phosphoprotein expression of the HeLa cell line and performed a comprehensive analysis of the different signaling pathways, transcription networks and metabolic events in which they participate. A total expression analysis by RNA-Seq of the HeLa cell line showed that 19,974 genes were transcribed. Of these, 3,360 were over-expressed, and 2,129 under-expressed when compared to the NHEK cell line. A protein-protein interaction network was derived from the over-expressed genes and used to identify central elements and, together with the analysis of over-represented transcription factor motifs, to predict active signaling and regulatory pathways. This was further validated by Metal-Oxide Affinity Chromatography (MOAC) and Tandem Mass Spectrometry (MS/MS) assays which retrieved phosphorylated proteins. The 14-3-3 family members emerge as important regulators in carcinogenesis and as possible clinical targets. We observed that the different over- and under-regulated pathways in cervical cancer could be interrelated through elements that participate in crosstalks, therefore belong to what we term “meta-pathways”. Additionally, we highlighted the relations of each one of the differentially represented pathways to one or more of the ten hallmarks of cancer. These features could be maintained in many other types of cancer, regardless of mutations or genomic rearrangements, and favor their robustness, adaptations and the evasion of tissue control. Probably, this could explain why cancer cells are not eliminated by selective pressure and why therapy trials directed against molecular targets are not as effective as expected.</p></div

Spliceosome pathway.

<p>The spliceosome pathway was reconstructed based on the KEGG database and the transcriptomics, over-represented TF networks and identified phosphoprotein analyses. The over-expressed transcripts are displayed in green, the under-expressed transcripts in red and the identified phosphoproteins in purple.</p

Meta-pathways analysis.

<p>An analysis was conducted combining the obtained signaling and transcriptional regulation pathways; the edges indicate the regulatory or hierarchical relationship, and the nodes indicate the pathway. The colors denote each of the hallmarks of cancer, with the two most representative hallmarks indicated per node. Additionally, we use a betwenness-weighed layout allowing the separation of dense clusters and the identification of elements with high centrality.</p

Network of interconnections and crosstalk among the cellular circuits.

<p>The network was made from data obtained by the analysis of signaling pathways and regulation. The nodes represent proteins that make up each of the pathways; each pathway is indicated by using different colors. The size of the node is determined by betweeness centrality measure, a larger size shows greater number of shortest paths that pass through that node.</p

Differential gene expression in HeLa Cells versus NHEK.

<p>a) A scatter plot showing the quality of the RNA-seq differential expression analysis results, using the epithelial keratinocyte cell line NHEK as a control. There were a total of 3,360 overexpressed genes and 2,129 under-expressed genes. b) The percentage distribution of the level 3 biological process domain GO terms represented by the over-expressed transcripts. c) The percentage distribution of the level 3 biological process domain GO terms represented by the under-expressed transcripts. These charts were constructed from a summary of all the similar GO terms in a functional cellular circuit.</p

Gene expression patterns in the HeLa cells.

<p>a) The distribution of total transcripts shows that there are two populations, one low-abundance population and a second larger, high-abundance population. This dichotomy shows that the parameters used to search for low abundance transcripts was successful. b) A graphical representation of the cellular process that was distinguished based on Gene Ontology (GO), using the domain of cellular components in level 3; the amount of retrieved elements was compared against the total size of the pathway.</p

Transcription factor expression networks.

<p>a) The percentage distribution of level 3 biological process domain GO terms represented by the over-represented TF network, highlighting ”Cell proliferation”, ”Metabolism of building blocks”, ”Cellular organization”, ”Angiogenesis”, ”Central metabolism” and ”Signaling”. This chart was constructed from a summary of all the similar GO terms in a functional cellular circuit. b) Hubs that were obtained from the node degree centrality measure of the over-represented TF network. The color indicates the score, with red being the highest and yellow the lowest. c) Hubs were obtained from the betweeness centrality measure of the over-represented TF networks. The color indicates the score, with red being the highest and yellow the lowest.</p

Over-represented metabolic pathways.

<p>Metabolic maps were reconstructed based on the KEGG database and the over-represented TF networks and identified phosphoprotein analyses, resulting in a map of a) pyruvate metabolism and b) glycolysis. The over-expressed transcripts are displayed in green, the under-expressed transcripts in red and the identified phosphoproteins in purple.</p

Over-represented metabolic pathways.

<p>Metabolic maps were reconstructed based on the KEGG database and the over-represented TF networks and identified phosphoprotein analyses, resulting in a map of general metabolism. The over-expressed transcripts are displayed in green, the under-expressed transcripts in red and the identified phosphoproteins in purple.</p

Pipeline.

<p>We have integrated different layers of information within biological cell dynamic that tracks the flow of information. First, we performed an analysis of all transcripts in HeLa cells; this analysis provided an overview of gene expression. Subsequently, we performed a RNA-seq differential expression analysis and a query of the over-representation of the activity of TFs. This allowed reconstruction of the metabolic pathways and the signaling and cellular transcriptional regulatory pathways. Finally, we validated this reconstruction with a phosphoproteomic analysis.</p