Insights into the human brain proteome: Disclosing the biological meaning of protein networks in cerebrospinal fluid

<p>Cerebrospinal fluid (CSF) is an excellent source of biological information regarding the nervous system, once it is in close contact and accurately reflects alterations in this system. Several studies have analyzed differential protein profiles of CSF samples between healthy and diseased human subjects. However, the pathophysiological mechanisms and how CSF proteins relate to diseases are still poorly known. By applying bioinformatics tools, we attempted to provide new insights on the biological and functional meaning of proteomics data envisioning the identification of putative disease biomarkers. Bioinformatics analysis of data retrieved from 99 mass spectrometry (MS)-based studies on CSF profiling highlighted 1985 differentially expressed proteins across 49 diseases. A large percentage of the modulated proteins originate from exosome vesicles, and the majority are involved in either neuronal cell growth, development, maturation, migration, or neurotransmitter-mediated cellular communication. Nevertheless, some diseases present a unique CSF proteome profile, which were critically analyzed in the present study. For instance, 48 proteins were found exclusively upregulated in the CSF of patients with Alzheimer’s disease and are mainly involved in steroid esterification and protein activation cascade processes. A higher number of exclusively upregulated proteins were found in the CSF of patients with multiple sclerosis (76 proteins) and with bacterial meningitis (70 proteins). Whereas in multiple sclerosis, these proteins are mostly involved in the regulation of RNA metabolism and apoptosis, in bacterial meningitis the exclusively upregulated proteins participate in inflammation and antibacterial humoral response, reflecting disease pathogenesis. The exploration of the contribution of exclusively upregulated proteins to disease pathogenesis will certainly help to envision potential biomarkers in the CSF for the clinical management of nervous system diseases.</p

Most differential expressed proteins (fold-change > 4) between both cell lines (A549 vs SW900).

<p>Most differential expressed proteins (fold-change > 4) between both cell lines (A549 vs SW900).</p

Functional network of altered proteins present in both cell lines.

<p>ClueGO plugin of Cytoscape was used to generate a functional network (biological process). Node size is related to the degree (high degree represent important nodes in the network, also known as hubs) of the proteins and was calculated using the Cytoscape NetworkAnalyzer tool. Proteins node color is depicted as following (A549 vs SW900): green, proteins upregulated (fold-regulation > 2); red, proteins downregulated (fold-regulation > -2). Biological process node color is represented on the right side of the image. On the right bottom side of the image are shown the genes that does not fit these biological processes and that do not have any interaction with the proteins that are altered.</p

Unraveling the Phosphoproteome Dynamics in Mammal Mitochondria from a Network Perspective

With mitochondrion garnering more attention for its inextricable involvement in pathophysiological conditions, it seems imperative to understand the means by which the molecular pathways harbored in this organelle are regulated. Protein phosphorylation has been considered a central event in cellular signaling and, more recently, in the modulation of mitochondrial activity. Efforts have been made to understand the molecular mechanisms by which protein phosphorylation regulates mitochondrial signaling. With the advances in mass-spectrometry-based proteomics, there is a substantial hope and expectation in the increased knowledge of protein phosphorylation profile and its mode of regulation. On the basis of phosphorylation profiles, attempts have been made to disclose the kinases involved and how they control the molecular processes in mitochondria and, consequently, the cellular outcomes. Still, few studies have focused on mitochondrial phosphoproteome profiling, particularly in diseases. The present study reviews current data on protein phosphorylation profiling in mitochondria, the potential kinases involved and how pathophysiological conditions modulate the mitochondrial phosphoproteome. To integrate data from distinct research papers, we performed network analysis, with bioinformatic tools like Cytoscape, String, and PANTHER taking into consideration variables such as tissue specificity, biological processes, molecular functions, and pathophysiological conditions. For instance, data retrieved from these analyses evidence some homology in the mitochondrial phosphoproteome among liver and heart, with proteins from transport and oxidative phosphorylation clusters particularly susceptible to phosphorylation. A distinct profile was noticed for adipocytes, with proteins form metabolic processes, namely, triglycerides metabolism, as the main targets of phosphorylation. Regarding disease conditions, more phosphorylated proteins were observed in diabetics with some distinct phosphoproteins identified in type 2 prediabetic states and early type 2 diabetes mellitus. Heart-failure-related phosphorylated proteins are in much lower amount and are mainly involved in transport and metabolism. Nevertheless, technical considerations related to mitochondria isolation and protein separation should be considered in data comparison among different proteomic studies. Data from the present review will certainly open new perspectives of protein phosphorylation in mitochondria and will help to envisage future studies targeting the underlying regulatory mechanisms

GO analysis of the specific proteins of adenocarcinoma and squamous carcinoma cell lines: biological process and molecular function.

<p>Enriched GO terms were retrieved using DAVID database. Biological process: for the A549 cell line 141 proteins out of 239 (59%) and for the SW900 cell line 152 proteins out of 293 (52%) were classified in the GO terms and clustered. Molecular function: for the A549 cell line 114 proteins out of 239 (48%) and for the SW900 cell line 136 proteins out of 293 (46%) were classified in the GO terms clustered. The clustering was performed considering a p-value of 0.05 and a minimum number of 2 terms per cluster.</p

Integration of proteome and interactome data of the squamous carcinoma cell line.

<p>SteinerNet webserver was used to reveal hidden components in SW900 network by integrating the proteome (MS, fold-regulation) and the interactome data (HIPPIE, interaction scores). From the original network, 172 terminal nodes were excluded (21.8%) and 618 terminal nodes included (78.2%). Circular nodes denotes proteins obtained from MS, whereas diamond nodes are proteins obtained from HIPPIE database. Node and letter size are related to the betweeness centrality (high betweeness centrality represent important nodes in the network, also called bottlenecks) of the proteins and was calculated using the Cytoscape NetworkAnalyzer tool. Edge width shows the interaction score confidence. Node color is depicted as following (SW900 vs A549): green, proteins upregulated (fold-regulation > 2); red, proteins downregulated (fold-regulation > -2); yellow, unaltered proteins; violet, SW900-specific proteins.</p

Global proteome analysis of the lung cancer cell lines.

<p>Venn diagram highlighting the distribution of the identified proteins per cell line in numbers and in percentage, evidencing the overlapped and unique proteins (Venny 2.0.2).</p

Hub and bottlenecks present in the SteinerNet A549 and SW900 networks.

<p>Node color refers to the networks color code: green, proteins upregulated (fold-regulation > 2); red, proteins downregulated (fold-regulation > -2); yellow, unaltered proteins; violet, cell line-specific proteins obtained in the study; grey, proteins obtained from HIPPIE database. A threshold degree of >10 and betweenness centrality of >0.3 were used to retrieve the hubs and bottlenecks. Light green color in degree and betweenness centrality represents high values. Light orange color represents the nodes that are hub-bottlenecks.</p

Expression levels of several altered proteins in adenocarcinoma and squamous carcinoma cell lines.

<p>Four independent extracts of A549 and SW900 cell lines were prepared and analyzed using the Western blot technique for expression comparison purposes. <b>(A)</b> Western blot images of the four replicates in both cell lines. Actin was used as loading control. <b>(B)</b> Protein band densitometries were obtained, values were normalized using the internal actin control and finally averaged. In the graph (Mean ± SE, n = 4), * <i>p</i> < 0.05, ** <i>p</i> < 0.01 and *** <i>p</i> < 0.001, indicate significant changes between the analyzed cell lines following one-way ANOVA.</p

GO analysis of the specific proteins of adenocarcinoma and squamous carcinoma cell lines: cell components.

<p>Enriched GO terms were retrieved using DAVID database. For the A549 cell line 127 proteins out of 239 (53%) and for the SW900 cell line 174 proteins out of 293 (59%) were classified in the GO terms. The enrichment was performed considering a p-value of 0.05 and a minimum number of 3 genes per term. Parts of the figure were adapted from Servier Medical Art templates available at /<a href="http://www.servier.co.uk/content/servier-medical-art" target="_blank">www.servier.co.uk/content/servier-medical-art</a>. Servier Medical Art is licensed under a Creative Commons Attribution 3.0 Unported License (<a href="http://creative-commons.org/licenses/by/3.0/" target="_blank">http://creative-commons.org/licenses/by/3.0/</a>).</p