Protein co-expression network analysis (ProCoNA)

Abstract Background Biological networks are important for elucidating disease etiology due to their ability to model complex high dimensional data and biological systems. Proteomics provides a critical data source for such models, but currently lacks robust de novo methods for network construction, which could bring important insights in systems biology. Results We have evaluated the construction of network models using methods derived from weighted gene co-expression network analysis (WGCNA). We show that approximately scale-free peptide networks, composed of statistically significant modules, are feasible and biologically meaningful using two mouse lung experiments and one human plasma experiment. Within each network, peptides derived from the same protein are shown to have a statistically higher topological overlap and concordance in abundance, which is potentially important for inferring protein abundance. The module representatives, called eigenpeptides, correlate significantly with biological phenotypes. Furthermore, within modules, we find significant enrichment for biological function and known interactions (gene ontology and protein-protein interactions). Conclusions Biological networks are important tools in the analysis of complex systems. In this paper we evaluate the application of weighted co-expression network analysis to quantitative proteomics data. Protein co-expression networks allow novel approaches for biological interpretation, quality control, inference of protein abundance, a framework for potentially resolving degenerate peptide-protein mappings, and a biomarker signature discovery

Network based analysis to identify master regulators in prostate carcinogenesis

Prostate cancer (PCa) is the second most common tumor diagnosed in man, for which robust prognostic markers and novel targets for therapy are lacking. Major challenges in PCa therapeutical management arise from the marked intra and inter-tumors heterogeneity, hampering the discernment of molecular subtypes that can be used to guide treatment decisions. For this reason, virtually all patients undergoing standard of care androgen deprivation therapy for locally advanced or metastatic cancer, will eventually progress into the more aggressive and currently incurable form of PCa, referred to as castration resistant prostate cancer (CRPC). By exploiting the richness of information stored in gene-gene interactions, I tested the hypothesis that a gene regulatory network derived from transcriptomic profiles of PCa orthografts can reveal transcriptional regulators to be subsequently adopted as robust biomarkers or as target for novel therapies. Among the 1308 regulons identified from the preclinical models, Cox regression analysis coherently associated JMJD6 regulon activity with disease-free survival in three clinical cohorts, outperforming three published prognostic gene signatures (TMCC11, BROMO-10 and HYPOXIA-28). Given its potential role in a number of cancers, in-depth investigations of JMJD6 mediated function in PCa is warranted to test if it has a driver role in tumor progression. Encouraged by the predictive abilities of the gene regulatory network inferred from transcriptomics data, I explored the possibility of integrating the regulons structure with data from the proteomes of the same preclinical orthografts studied by RNA sequencing. This approach leverages the complementarity between gene and protein expression, to increase the robustness of the statistical analysis. Similar to gene-gene co-expression profiles, protein-protein co-expression data can provide a distinct representation of the molecular alterations underlying a biological phenotype. By implementing a pipeline to integrate modules derived from transcriptomic based regulons and proteinprotein interactions respectively from matched RNA-seq and quantitative proteomic data, I obtained 516 joint modules entailing a median of four protein complexes (range 1-41) per individual transcription factor regulon, providing new insight into its regulatory mechanisms. In the final step of the analysis, a permutation-based enrichment of the genes/proteins integrative modules implicated MID1 (an E3 ubiquitin ligase belonging to the family of tripartite motif containing protein) to be a driver transcriptional regulator in CRPC. In fact, MID1 module was the only candidate for which gene-gene and proteinprotein interactions were supported (p-value < 0.05) by both differentially expressed genes and proteins obtained from the CRPC vs PC contrast. Finally, I wished to test the usefulness of a network based investigation as a tool to identify predictors of treatment response. To this end, I obtained transcriptomics data from an in vivo subcutaneous xenograft treatment experiment (namely mychophenolic acid or abiraterone/ARN-509 as stand alone treatment or in combination) and determined which regulons were inferred to be active in the tumours following treatment. The androgen receptor positive human LNCaP C4-2b prostate cancer cells were injected into mice. The effects of treatment were assessed by collecting serial tumor sizes and by performing RNAseq at the designed endpoint of the study. Noteworthy, the gene graph enrichment analysis provided novel hypothesisbehind the anti- proliferative effect of mychophenolic acid (MPA), suggesting the SET proto-oncogene to be a target for MPA mediated suppression of proliferation. Of note, standard gene-set enrichment analysis, without input on specific gene-gene interactions, was not effective in prioritising the SET protooncogene, demonstrating the usefulness of the network based investigation. Collectively, data presented in this thesis provides an alternative perspective for the analysis of multi-omics profiles from PCa and highlights the importance of gene-gene and protein protein interactions in prostate cancer growth and progression

Microglial exosome secretion coupled to TREM2: implications on neuron-like cells

Gene-wide association studies have implicated microglia, the immune cells of the brain, in the development and progression of Alzheimer’s disease (AD), the most common form of dementia. Genetic variants on the triggering receptor expressed on myeloid cells-2 (TREM2) are associated with an increased risk of developing AD. TREM2 fulfils a range of different functions in microglia and this thesis investigated the contribution of TREM2 variants on the secretion, content and effect of small extracellular vesicles, exosomes, from microglia. To study this, patient-derived induced pluripotent stem cells (iPSC) carrying TREM2 variants were differentiated into microglia-like cells (iPS-Mg), using a newly developed protocol. These cells displayed microglia like functions and were shown to secrete exosomes. Exosome secretion was decreased from iPS-Mg carrying TREM2 variants, a deficit that could be rescued with increasing the energy availability in the iPS-Mg. Independent of the secretion rate, exosomal protein content was also influenced by the disease-relevant R47Hhet TREM2 variant. Proteomic analysis of exosomes, through mass spectrometry, revealed differences between exosomes from common variant (Cv) and R47Hhet iPS-Mg. Changes in the exosomal proteome were studied after iPS-Mg were exposed to either lipopolysaccharide (LPS), a classical treatment to activate microglia, or apoptotic neurons, a more physiological stimulus. Exosomes from Cv iPS-Mg showed a stimulus-specific shift in protein content, a response that was reduced in exosomes from R47Hhet iPS-Mg. The differences in exosomal protein translated to differential responses in neuron-like cells to these exosomes. As neuronal models SH-SY5Y and iPS-neurons were used and the effect of exosomes was analysed. The effect of exosomes on cell death and cell stress pathways appeared to be dependent on the treatment of iPS-Mg, whilst the TREM2 status of exosome-secreting iPS-Mg also played a role in inducing effects in metabolism and synaptic functioning in neuron-like cells

Multi-omic network signatures of disease

To better understand dynamic disease processes, integrated multi-omic methods are needed, yet comparing different types of omic data remains difficult. Integrative solutions benefit experimenters by eliminating potential biases that come with single omic analysis. We have developed the methods needed to explore whether a relationship exists between co-expression network models built from transcriptomic and proteomic data types, and whether this relationship can be used to improve the disease signature discovery process. A naïve, correlation based method is utilized for comparison. Using publicly available infectious disease time series data, we analyzed the related co-expression structure of the transcriptome and proteome in response to SARS-CoV infection in mice. Transcript and peptide expression data was filtered using quality scores and subset by taking the intersection on mapped Entrez IDs. Using this data set, independent co-expression networks were built. The networks were integrated by constructing a bipartite module graph based on module member overlap, module summary correlation, and correlation to phenotypes of interest. Compared to the module level results, the naïve approach is hindered by a lack of correlation across data types, less significant enrichment results, and little functional overlap across data types. Our module graph approach avoids these problems, resulting in an integrated omic signature of disease progression, which allows prioritization across data types for down-stream experiment planning. Integrated modules exhibited related functional enrichments and could suggest novel interactions in response to infection. These disease and platform-independent methods can be used to realize the full potential of multi-omic network signatures. The data (experiment SM001) are publically available through the NIAID Systems Virology (https://www.systemsvirology.org) and PNNL (http://omics.pnl.gov) web portals. Phenotype data is found in the supplementary information. The ProCoNA package is available as part of Bioconductor 2.13

Differential stimulation of pluripotent stem cell-derived human microglia leads to exosomal proteomic changes affecting neurons

Microglial exosomes are an emerging communication pathway, implicated in fulfilling homeostatic microglial functions and transmitting neurodegenerative signals. Gene variants of triggering receptor expressed on myeloid cells-2 (TREM2) are associated with an increased risk of developing dementia. We investigated the influence of the TREM2 Alzheimer’s disease risk variant, R47Hhet, on the microglial exosomal proteome consisting of 3019 proteins secreted from human iPS-derived microglia (iPS-Mg). Exosomal protein content changed according to how the iPS-Mg were stimulated. Thus lipopolysaccharide (LPS) induced microglial exosomes to contain more inflammatory signals, whilst stimulation with the TREM2 ligand phosphatidylserine (PS+) increased metabolic signals within the microglial exosomes. We tested the effect of these exosomes on neurons and found that the exosomal protein changes were functionally relevant and influenced downstream functions in both neurons and microglia. Exosomes from R47Hhet iPS-Mg contained disease-associated microglial (DAM) signature proteins and were less able to promote the outgrowth of neuronal processes and increase mitochondrial metabolism in neurons compared with exosomes from the common TREM2 variant iPS-Mg. Taken together, these data highlight the importance of microglial exosomes in fulfilling microglial functions. Additionally, variations in the exosomal proteome influenced by the R47Hhet TREM2 variant may underlie the increased risk of Alzheimer’s disease associated with this variant

Metabolization and sequestration of plant specialized metabolites in insect herbivores: Current and emerging approaches

Herbivorous insects encounter diverse plant specialized metabolites (PSMs) in their diet, that have deterrent, anti-nutritional, or toxic properties. Understanding how they cope with PSMs is crucial to understand their biology, population dynamics, and evolution. This review summarizes current and emerging cutting-edge methods that can be used to characterize the metabolic fate of PSMs, from ingestion to excretion or sequestration. It further emphasizes a workflow that enables not only to study PSM metabolism at different scales, but also to tackle and validate the genetic and biochemical mechanisms involved in PSM resistance by herbivores. This review thus aims at facilitating research on PSM-mediated plant-herbivore interactions

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

MERS-CoV and H5N1 influenza virus antagonize antigen presentation by altering the epigenetic landscape

Convergent evolution dictates that diverse groups of viruses will target both similar and distinct host pathways to manipulate the immune response and improve infection. In this study, we sought to leverage this uneven viral antagonism to identify critical host factors that govern disease outcome. Utilizing a systems-based approach, we examined differential regulation of IFN-γ–dependent genes following infection with robust respiratory viruses including influenza viruses [A/influenza/Vietnam/1203/2004 (H5N1-VN1203) and A/influenza/California/04/2009 (H1N1-CA04)] and coronaviruses [severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome CoV (MERS-CoV)]. Categorizing by function, we observed down-regulation of gene expression associated with antigen presentation following both H5N1-VN1203 and MERS-CoV infection. Further examination revealed global down-regulation of antigen-presentation gene expression, which was confirmed by proteomics for both H5N1-VN1203 and MERS-CoV infection. Importantly, epigenetic analysis suggested that DNA methylation, rather than histone modification, plays a crucial role in MERS-CoV–mediated antagonism of antigen-presentation gene expression; in contrast, H5N1-VN1203 likely utilizes a combination of epigenetic mechanisms to target antigen presentation. Together, the results indicate a common mechanism utilized by H5N1-VN1203 and MERS-CoV to modulate antigen presentation and the host adaptive immune response

Metabolization and sequestration of plant specialized metabolites in insect herbivores: Current and emerging approaches.

Herbivorous insects encounter diverse plant specialized metabolites (PSMs) in their diet, that have deterrent, anti-nutritional, or toxic properties. Understanding how they cope with PSMs is crucial to understand their biology, population dynamics, and evolution. This review summarizes current and emerging cutting-edge methods that can be used to characterize the metabolic fate of PSMs, from ingestion to excretion or sequestration. It further emphasizes a workflow that enables not only to study PSM metabolism at different scales, but also to tackle and validate the genetic and biochemical mechanisms involved in PSM resistance by herbivores. This review thus aims at facilitating research on PSM-mediated plant-herbivore interactions