The proteomic response in glioblastoma in young patients.

Increasing age is an important prognostic variable in glioblastoma (GBM). We have defined the proteomic response in GBM samples from 7 young patients (mean age 36 years) compared to peritumoural-control samples from 10 young patients (mean age 32 years). 2-Dimensional-gel-electrophoresis, image analysis, and protein identification (LC/MS) were performed. 68 proteins were significantly altered in young GBM samples with 29 proteins upregulated and 39 proteins downregulated. Over 50 proteins are described as altered in GBM for the first time. In a parallel analysis in old GBM (mean age 67 years), an excellent correlation could be demonstrated between the proteomic profile in young GBM and that in old GBM patients (r(2) = 0.95) with only 5 proteins altered significantly (p < 0.01). The proteomic response in young GBM patients highlighted alterations in protein-protein interactions in the immunoproteosome, NFkB signalling, and mitochondrial function and the same systems participated in the responses in old GBM patients.

Interactions among mitochondrial proteins altered in glioblastoma.

Mitochondrial dysfunction is putatively central to glioblastoma (GBM) pathophysiology but there has been no systematic analysis in GBM of the proteins which are integral to mitochondrial function. Alterations in proteins in mitochondrial enriched fractions from patients with GBM were defined with label-free liquid chromatography mass spectrometry. 256 mitochondrially-associated proteins were identified in mitochondrial enriched fractions and 117 of these mitochondrial proteins were markedly (fold-change ≥ 2) and significantly altered in GBM (p ≤ 0.05). Proteins associated with oxidative damage (including catalase, superoxide dismutase 2, peroxiredoxin 1 and peroxiredoxin 4) were increased in GBM. Protein-protein interaction analysis highlighted a reduction in multiple proteins coupled to energy metabolism (in particular respiratory chain proteins, including 23 complex-I proteins). Qualitative ultrastructural analysis in GBM with electron microscopy showed a notably higher prevalence of mitochondria with cristolysis in GBM. This study highlights the complex mitochondrial proteomic adjustments which occur in GBM pathophysiology.

Total variance should drive data handling strategies in third generation proteomic studies.

Quantitative proteomics is entering its "third generation," where intricate experimental designs aim to increase the spatial and temporal resolution of protein changes. This paper re-analyses multiple internally consistent proteomic datasets generated from whole cell homogenates and fractionated brain tissue samples providing a unique opportunity to explore the different factors influencing experimental outcomes. The results clearly indicate that improvements in data handling are required to compensate for the increased mean CV associated with complex study design and intricate upstream tissue processing. Furthermore, applying arbitrary inclusion thresholds such as fold change in protein abundance between groups can lead to unnecessary exclusion of important and biologically relevant data.

Network generation enhances interpretation of proteomic data from induced apoptosis.

Thirteen proteins (identified with 2-D gels and MALDI-TOF MS) are significantly altered during staurosporine-induced apoptosis in SH-SY5Y cells. To gain further insight into the integrated cellular response to apoptosis, we have investigated whether a network can be generated of direct and indirect interactions between these 13 proteins. A network that contains 12 out of the 13 proteins was generated using Ingenuity Pathway Analysis (IPA) and this network is dominated (89%) by direct protein-protein interactions. This network scored 34 with IPA. Bootstrapping 1000 random lists of 13 proteins suggested that the frequency of this score occurring by chance was 1 in 500. We examined whether subsets of proteins such as HSPs, which were elevated after staurosporine, had a disproportionate impact on the network generated. There was no evidence that any subset of 8 from the 13 proteins contributed disproportionately to the network. Network generation, using IPA, identified common features (such as endoplasmic reticular stress protein interactions) in apoptotic studies from different laboratories. The generation of protein interaction networks clearly enhances the interpretation of proteomic data, but only when interpreted cautiously, particularly in respect of statistical analyses.

Protein Co-Expression Analysis as a Strategy to Complement a Standard Quantitative Proteomics Approach: Case of a Glioblastoma Multiforme Study.

Although correlation network studies from co-expression analysis are increasingly popular, they are rarely applied to proteomics datasets. Protein co-expression analysis provides a complementary view of underlying trends, which can be overlooked by conventional data analysis. The core of the present study is based on Weighted Gene Co-expression Network Analysis applied to a glioblastoma multiforme proteomic dataset. Using this method, we have identified three main modules which are associated with three different membrane associated groups; mitochondrial, endoplasmic reticulum, and a vesicle fraction. The three networks based on protein co-expression were assessed against a publicly available database (STRING) and show a statistically significant overlap. Each of the three main modules were de-clustered into smaller networks using different strategies based on the identification of highly connected networks, hierarchical clustering and enrichment of Gene Ontology functional terms. Most of the highly connected proteins found in the endoplasmic reticulum module were associated with redox activity while a core of the unfolded protein response was identified in addition to proteins involved in oxidative stress pathways. The proteins composing the electron transfer chain were found differently affected with proteins from mitochondrial Complex I being more down-regulated than proteins from Complex III. Finally, the two pyruvate kinases isoforms show major differences in their co-expressed protein networks suggesting roles in different cellular locations.

Neuronal development is promoted by weakened intrinsic antioxidant defences due to epigenetic repression of Nrf2.

Forebrain neurons have weak intrinsic antioxidant defences compared with astrocytes, but the molecular basis and purpose of this is poorly understood. We show that early in mouse cortical neuronal development in vitro and in vivo, expression of the master-regulator of antioxidant genes, transcription factor NF-E2-related-factor-2 (Nrf2), is repressed by epigenetic inactivation of its promoter. Consequently, in contrast to astrocytes or young neurons, maturing neurons possess negligible Nrf2-dependent antioxidant defences, and exhibit no transcriptional responses to Nrf2 activators, or to ablation of Nrf2's inhibitor Keap1. Neuronal Nrf2 inactivation seems to be required for proper development: in maturing neurons, ectopic Nrf2 expression inhibits neurite outgrowth and aborization, and electrophysiological maturation, including synaptogenesis. These defects arise because Nrf2 activity buffers neuronal redox status, inhibiting maturation processes dependent on redox-sensitive JNK and Wnt pathways. Thus, developmental epigenetic Nrf2 repression weakens neuronal antioxidant defences but is necessary to create an environment that supports neuronal development.

Adaptive changes in the neuronal proteome: mitochondrial energy production, endoplasmic reticulum stress, and ribosomal dysfunction in the cellular response to metabolic stress.

Impaired energy metabolism in neurons is integral to a range of neurodegenerative diseases, from Alzheimer's disease to stroke. To investigate the complex molecular changes underpinning cellular adaptation to metabolic stress, we have defined the proteomic response of the SH-SY5Y human neuroblastoma cell line after exposure to a metabolic challenge of oxygen glucose deprivation (OGD) in vitro. A total of 958 proteins across multiple subcellular compartments were detected and quantified by label-free liquid chromatography mass spectrometry. The levels of 130 proteins were significantly increased (P<0.01) after OGD and the levels of 63 proteins were significantly decreased (P<0.01) while expression of the majority of proteins (765) was not altered. Network analysis identified novel protein-protein interactomes involved with mitochondrial energy production, protein folding, and protein degradation, indicative of coherent and integrated proteomic responses to the metabolic challenge. Approximately one third (61) of the differentially expressed proteins was associated with the endoplasmic reticulum and mitochondria. Electron microscopic analysis of these subcellular structures showed morphologic changes consistent with the identified proteomic alterations. Our investigation of the global cellular response to a metabolic challenge clearly shows the considerable adaptive capacity of the proteome to a slowly evolving metabolic challenge.

Proteomic analysis of mitochondria in APOE transgenic mice and in response to an ischemic challenge.

Apolipoprotein E (APOE)-ɛ4 is associated with a deleterious outcome after ischemic brain injury, which may involve abnormal regulation of mitochondrial function. We have assessed the mitochondrial proteomic response of APOE-ɛ3 and APOE-ɛ4 transgenic mice to transient global ischemic injury in the hippocampus. A genotype-dependent increase in ApoE levels in mitochondria was observed after ischemia, with APOE-ɛ4 mice showing significantly greater increases than APOE-ɛ3 mice. Quantitative analysis of the mitochondria-enriched fractions was performed using liquid-chromatography mass spectrometry coupled to label-free analysis. Of the 1,067 identified proteins, 274 were mitochondria associated. Mitochondrial protein expression was significantly different between genotypes under basal conditions as well as in response to global ischemia. A total of 12 mitochondrial proteins (including respiratory chain proteins NDUFA11, NDUFS3, NDUF5B, ATP5J, as well as ETFA, CYB5B, ATP6V1A, HSPA1B, OXR1, GLUL, IARS2, and PHYHIPL) were significantly altered with respect to genotype, global ischemia, or their interaction (P<0.01). A compelling interactome, created using proteins found to be significantly modulated by global ischemia (P<0.05), involved proteins that regulate energy production and oxidative stress. Thus, APOE genotype has a differential effect on the mitochondrial protein expression in the absence and presence of an injury, which may underlie the differing genotype susceptibility.

Glioma pathophysiology: insights emerging from proteomics.

Proteomics is increasingly employed in both neurological and oncological research to provide insight into the molecular basis of disease but rarely has a coherent, novel pathophysiological insight emerged. Gliomas account for >50% of adult primary intracranial tumors, with malignant gliomas (anaplastic astrocytomas and glioblastoma multiforme) being the most common. In glioma, the application of proteomic technology has identified altered protein expression but without consistency of these alterations or their biological significance being established. A systematic review of multiple independent proteomic analyses of glioma has demonstrated alterations of 99 different proteins. Importantly 10 of the 99 proteins found differentially expressed in glioma [PHB, Hsp20, serum albumin, epidermal growth factor receptor (EGFR), EA-15, RhoGDI, APOA1, GFAP, HSP70, PDIA3] were identified in multiple publications. An assessment of protein-protein interactions between these proteins compiled using novel web-based technology, revealed a robust and cohesive network for glioblastoma. The protein network discovered (containing TP53 and RB1 at its core) compliments recent findings in genomic studies of malignant glioma. The novel perspective provided by network analysis indicates that the potential of this technology to explore crucial aspects of glioma pathophysiology can now be realized but only if the conceptual and technical limitations highlighted in this review are addressed.

The role of mitochondria in glioma pathophysiology.

It has long been recognised that malignant tumours favour aerobic glycolysis to generate ATP and contain abnormalities of the intrinsic, mitochondria-dependent, apoptotic pathway, suggesting the involvement of dysfunctional mitochondria in tumour pathophysiology. However, the mechanisms underlying such processes in gliomas are poorly understood. Few recent studies have evaluated mitochondrial ultrastructure and proteomics in the pathophysiology of malignant gliomas. However, aberrant energy metabolism has been reported in gliomas and mitochondrial dysfunction links to glioma apoptotic signalling have been observed. Mitochondrial structural abnormalities and dysfunction in malignant gliomas is a neglected area of research. Definition of abnormalities in mitochondrial proteomics, membrane potential regulation, energy metabolism and intrinsic apoptotic pathway signalling in gliomas may open novel therapeutic opportunities.

The utility of functional interaction and cluster analysis in CNS proteomics.

Proteomic studies offer enormous potential for gaining insight into cellular dynamics and disease processes. An immediate challenge for enhancing the utility of proteomics in translational research lies in methods of handling and interpreting the large datasets generated. Publications rarely extend beyond lists of proteins, putatively altered derived from basic statistics. Here we describe two additional distinct approaches (with particular strengths and limitations) that will enhance the analysis of proteomic datasets. Arithmetic and functional cluster analyses have been performed on proteins found differentially regulated in human glioma. These two approaches highlight (i) subgroups of proteins that may be co-regulated and play a role in glioma pathophysiology, and (ii) functional protein interactions that may improve comprehension of the biological mechanisms involved. A coherent proteomic strategy which involves both arithmetic and functional clustering, (together with careful consideration of conceptual limitations), is imperative for quantitative proteomics to deliver and advance the biological understanding of disease of the CNS. A strategy which combines arithmetic analysis and bioinformatics of protein-protein interactions is both generally applicable and will facilitate the interpretation of proteomic data.