Quantifying the phenotypic information in mRNA abundance

Quantifying the dependency between mRNA abundance and downstream cellular phenotypes is a fundamental open problem in biology. Advances in multimodal single‐cell measurement technologies provide an opportunity to apply new computational frameworks to dissect the contribution of individual genes and gene combinations to a given phenotype. Using an information theory approach, we analyzed multimodal data of the expression of 83 genes in the Ca²⁺ signaling network and the dynamic Ca²⁺ response in the same cell. We found that the overall expression levels of these 83 genes explain approximately 60% of Ca²⁺ signal entropy. The average contribution of each single gene was 17%, revealing a large degree of redundancy between genes. Using different heuristics, we estimated the dependency between the size of a gene set and its information content, revealing that on average, a set of 53 genes contains 54% of the information about Ca²⁺ signaling. Our results provide the first direct quantification of information content about complex cellular phenotype that exists in mRNA abundance measurements.     

Understanding protein trafficking in plant cells through proteomics

The functions of approximately one-third of the proteins encoded by the Arabidopsis thaliana genome are completely unknown. Moreover, many annotations of the remainder of the genome supply tentative functions, at best. Knowing the ultimate localization of these proteins, as well as the pathways used for getting there, may provide clues as to their functions. The putative localization of most proteins currently relies on in silico-based bioinformatics approaches, which, unfortunately, often result in erroneous predictions. Emerging proteomics techniques coupled with other systems biology approaches now provide researchers with a plethora of methods for elucidating the final location of these proteins on a large scale, as well as the ability to dissect protein-sorting pathways in plants.     

Quantifying intraclass correlations for count and time‐to‐event data

The intraclass correlation is commonly used with clustered data. It is often estimated based on fitting a model to hierarchical data and it leads, in turn, to several concepts such as reliability, heritability, inter‐rater agreement, etc. For data where linear models can be used, such measures can be defined as ratios of variance components. Matters are more difficult for non‐Gaussian outcomes. The focus here is on count and time‐to‐event outcomes where so‐called combined models are used, extending generalized linear mixed models, to describe the data. These models combine normal and gamma random effects to allow for both correlation due to data hierarchies as well as for overdispersion. Furthermore, because the models admit closed‐form expressions for the means, variances, higher moments, and even the joint marginal distribution, it is demonstrated that closed forms of intraclass correlations exist. The proposed methodology is illustrated using data from agricultural and livestock studies.     

Cellular and extracellular miRNAs are blood‐compartment‐specific diagnostic targets in sepsis

Septic shock is a common medical condition with a mortality approaching 50% where early diagnosis and treatment are of particular importance for patient survival. Novel biomarkers that serve as prompt indicators of sepsis are urgently needed. High‐throughput technologies assessing circulating microRNAs represent an important tool for biomarker identification, but the blood‐compartment specificity of these miRNAs has not yet been investigated. We characterized miRNA profiles from serum exosomes, total serum and blood cells (leukocytes, erythrocytes, platelets) of sepsis patients by next‐generation sequencing and RT‐qPCR (n = 3 × 22) and established differences in miRNA expression between blood compartments. In silico analysis was used to identify compartment‐specific signalling functions of differentially regulated miRNAs in sepsis‐relevant pathways. In septic shock, a total of 77 and 103 miRNAs were down‐ and up‐regulated, respectively. A majority of these regulated miRNAs (14 in serum, 32 in exosomes and 73 in blood cells) had not been previously associated with sepsis. We found a distinctly compartment‐specific regulation of miRNAs between sepsis patients and healthy volunteers. Blood cellular miR‐199b‐5p was identified as a potential early indicator for sepsis and septic shock. miR‐125b‐5p and miR‐26b‐5p were uniquely regulated in exosomes and serum, respectively, while one miRNA (miR‐27b‐3p) was present in all three compartments. The expression of sepsis‐associated miRNAs is compartment‐specific. Exosome‐derived miRNAs contribute significant information regarding sepsis diagnosis and survival prediction and could serve as newly identified targets for the development of novel sepsis biomarkers.     

EBprot: Statistical analysis of labeling‐based quantitative proteomics data

Labeling‐based proteomics is a powerful method for detection of differentially expressed proteins (DEPs). The current data analysis platform typically relies on protein‐level ratios, which is obtained by summarizing peptide‐level ratios for each protein. In shotgun proteomics, however, some proteins are quantified with more peptides than others, and this reproducibility information is not incorporated into the differential expression (DE) analysis. Here, we propose a novel probabilistic framework EBprot that directly models the peptide‐protein hierarchy and rewards the proteins with reproducible evidence of DE over multiple peptides. To evaluate its performance with known DE states, we conducted a simulation study to show that the peptide‐level analysis of EBprot provides better receiver‐operating characteristic and more accurate estimation of the false discovery rates than the methods based on protein‐level ratios. We also demonstrate superior classification performance of peptide‐level EBprot analysis in a spike‐in dataset. To illustrate the wide applicability of EBprot in different experimental designs, we applied EBprot to a dataset for lung cancer subtype analysis with biological replicates and another dataset for time course phosphoproteome analysis of EGF‐stimulated HeLa cells with multiplexed labeling. Through these examples, we show that the peptide‐level analysis of EBprot is a robust alternative to the existing statistical methods for the DE analysis of labeling‐based quantitative datasets. The software suite is freely available on the Sourceforge website http://ebprot.sourceforge.net/ . All MS data have been deposited in the ProteomeXchange with identifier PXD001426 ( http://proteomecentral.proteomexchange.org/dataset/PXD001426/ ).     

Isobaric tag for relative and absolute quantitation based quantitative proteomics reveals unique urinary protein profiles in patients with preeclampsia

Preeclampsia (PE) is one of the most significant pregnancy‐related hypertensive disorders. Currently, there are no useful markers to predict the onset of the condition in pregnant women. To provide further insights into the pathogenesis of PE and identify biomarkers of the condition, we used isobaric tags for relative and absolute quantitation (iTRAQ) proteomics coupled with 2‐D LC‐MS/MS, to analyze urinary protein profiles from 7 PE patients and 7 normotensive pregnant women. A total of 294 proteins were abnormally expressed in PE patients. Of these, 233 were significantly down‐regulated and 61 proteins were significantly up‐regulated. Bioinformatics analysis using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) database, found that the most differentially expressed proteins (DEPs) were involved in coagulation and complement pathways, the renin‐angiotensin system and cell adhesion molecules (CAMs) pathways. We further validated three of the DEPs, including serotransferrin (TF) and complement factor B (CFB) by immunoblottingand serum paraoxonase/arylesterase 1 (PON1) by ELISA using 14 pairs of urine samples from PE patients and normal pregnant women. Taken together, our results provide the basis for further understanding the pathogenesis of PE and identifying predictive biomarkers.     

Conserved abundance and topological features in chromatin‐remodeling protein interaction networks

The study of conserved protein interaction networks seeks to better understand the evolution and regulation of protein interactions. Here, we present a quantitative proteomic analysis of 18 orthologous baits from three distinct chromatin‐remodeling complexes in Saccharomyces cerevisiae and Homo sapiens. We demonstrate that abundance levels of orthologous proteins correlate strongly between the two organisms and both networks have highly similar topologies. We therefore used the protein abundances in one species to cross‐predict missing protein abundance levels in the other species. Lastly, we identified a novel conserved low‐abundance subnetwork further demonstrating the value of quantitative analysis of networks.     

Arsenite‐induced changes in hepatic protein abundance in cynomolgus monkeys (Macaca fascicularis)

Arsenic is an environmental pollutant, and its liver toxicity has long been recognized. The effect of arsenic on liver protein expression was analyzed using a proteomic approach in monkeys. Monkeys were orally administered sodium arsenite (SA) for 28 days. As shown by 2D‐PAGE in combination with MS, the expression levels of 16 proteins were quantitatively changed in SA‐treated monkey livers compared to control‐treated monkey livers. Specifically, the levels of two proteins, mortalin and tubulin beta chain, were increased, and 14 were decreased, including plastin‐3, cystathionine‐beta‐synthase, selenium‐binding protein 1, annexin A6, alpha‐enolase, phosphoenolpyruvate carboxykinase‐M, erlin‐2, and arginase‐1. In view of their functional roles, differential expression of these proteins may contribute to arsenic‐induced liver toxicity, including cell death and carcinogenesis. Among the 16 identified proteins, four were selected for validation by Western blot and immunohistochemistry. Additional Western blot analyses indicated arsenic‐induced dysregulation of oxidative stress related, genotoxicity‐related, and glucose metabolism related proteins in livers from SA‐treated animals. Many changes in the abundance of toxicity‐related proteins were also demonstrated in SA‐treated human hepatoma cells. These data on the arsenic‐induced regulation of proteins with critical roles may help elucidate the specific mechanisms underlying arsenic‐induced liver toxicity.     

High‐throughput serum proteomics for the identification of protein biomarkers of mortality in older men

The biological perturbations associated with incident mortality are not well elucidated, and there are limited biomarkers for the prediction of mortality. We used a novel high‐throughput proteomics approach to identify serum peptides and proteins associated with 5‐year mortality in community‐dwelling men age ≥65 years who participated in a longitudinal observational study of musculoskeletal aging (Osteoporotic Fractures in Men: MrOS). In a discovery phase, serum specimens collected at baseline in 2473 men were analyzed using liquid chromatography–ion mobility–mass spectrometry, and incident mortality in the subsequent 5 years was ascertained by tri‐annual questionnaire. Rigorous statistical methods were utilized to identify 56 peptides (31 proteins) that were associated with 5‐year mortality. In an independent replication phase, selected reaction monitoring was used to examine 21 of those peptides in baseline serum from 750 additional men; 81% of those peptides remained significantly associated with mortality. Mortality‐associated proteins included a variety involved in inflammation or complement activation; several have been previously linked to mortality (e.g., C‐reactive protein, alpha 1‐antichymotrypsin) and others are not previously known to be associated with mortality. Other novel proteins of interest included pregnancy‐associated plasma protein, VE‐cadherin, leucine‐rich α‐2 glycoprotein 1, vinculin, vitronectin, mast/stem cell growth factor receptor, and Saa4. A panel of peptides improved the predictive value of a commonly used clinical predictor of mortality. Overall, these results suggest that complex inflammatory pathways, and proteins in other pathways, are linked to 5‐year mortality risk. This work may serve to identify novel biomarkers for near‐term mortality.     

Miniaturized fluid array for high‐throughput protein expression

We describe a miniaturized fluid array device for high‐throughput cell‐free protein synthesis (CFPS), aiming to match the throughput and scale of gene discovery. Current practice of using E. coli cells for production of recombinant proteins is difficult and cost‐prohibitive to implement in a high‐throughput format. As more and more new genes are being identified, there is a considerable need to have high‐throughput methods to produce a large number of proteins for studying structures and functions of the corresponding genes. The device consists of 96 units and each unit is for expression of one protein; thus up to 96 proteins can be produced simultaneously. The function of the fluid array was demonstrated by expression of a variety of proteins, with more than two orders of magnitude reduction in reagent consumption compared with a commercially available CFPS instrument. The protein expression yield in the device was up to 87 times higher for β‐glucoronidase than that in a conventional microplate. The concentration of β‐galactosidase expressed in the device was determined at 5.5 μg/μL. The feasibility of using the device for drug screening was demonstrated by measuring the inhibitory effects of mock drug compounds on synthesized β‐lactamase without the need for harvesting proteins, which enabled us to reduce the analysis time from days to hours. © 2010 American Institute of Chemical Engineers Biotechnol. Prog., 2010     

Examining post‐translational modification‐mediated protein–protein interactions using a chemical proteomics approach

Post‐translational modifications (PTM) of proteins can control complex and dynamic cellular processes via regulating interactions between key proteins. To understand these regulatory mechanisms, it is critical that we can profile the PTM‐dependent protein–protein interactions. However, identifying these interactions can be very difficult using available approaches, as PTMs can be dynamic and often mediate relatively weak protein–protein interactions. We have recently developed CLASPI (cross‐linking‐assisted and stable isotope labeling in cell culture‐based protein identification), a chemical proteomics approach to examine protein–protein interactions mediated by methylation in human cell lysates. Here, we report three extensions of the CLASPI approach. First, we show that CLASPI can be used to analyze methylation‐dependent protein–protein interactions in lysates of fission yeast, a genetically tractable model organism. For these studies, we examined trimethylated histone H3 lysine‐9 (H3K9Me₃)‐dependent protein–protein interactions. Second, we demonstrate that CLASPI can be used to examine phosphorylation‐dependent protein–protein interactions. In particular, we profile proteins recognizing phosphorylated histone H3 threonine‐3 (H3T3‐Phos), a mitotic histone “mark” appearing exclusively during cell division. Our approach identified survivin, the only known H3T3‐Phos‐binding protein, as well as other proteins, such as MCAK and KIF2A, that are likely to be involved in weak but selective interactions with this histone phosphorylation “mark”. Finally, we demonstrate that the CLASPI approach can be used to study the interplay between histone H3T3‐Phos and trimethylation on the adjacent residue lysine 4 (H3K4Me₃). Together, our findings indicate the CLASPI approach can be broadly applied to profile protein–protein interactions mediated by PTMs.     

Identification of lncRNA‐associated differential subnetworks in oesophageal squamous cell carcinoma by differential co‐expression analysis

Differential expression analysis has led to the identification of important biomarkers in oesophageal squamous cell carcinoma (ESCC). Despite enormous contributions, it has not harnessed the full potential of gene expression data, such as interactions among genes. Differential co‐expression analysis has emerged as an effective tool that complements differential expression analysis to provide better insight of dysregulated mechanisms and indicate key driver genes. Here, we analysed the differential co‐expression of lncRNAs and protein‐coding genes (PCGs) between normal oesophageal tissue and ESCC tissues, and constructed a lncRNA‐PCG differential co‐expression network (DCN). DCN was characterized as a scale‐free, small‐world network with modular organization. Focusing on lncRNAs, a total of 107 differential lncRNA‐PCG subnetworks were identified from the DCN by integrating both differential expression and differential co‐expression. These differential subnetworks provide a valuable source for revealing lncRNA functions and the associated dysfunctional regulatory networks in ESCC. Their consistent discrimination suggests that they may have important roles in ESCC and could serve as robust subnetwork biomarkers. In addition, two tumour suppressor genes (AL121899.1 and ELMO2), identified in the core modules, were validated by functional experiments. The proposed method can be easily used to investigate differential subnetworks of other molecules in other cancers.     

SWATH‐based proteomics reveals processes associated with immune evasion and metastasis in poor prognosis colorectal tumours

Newly emerged proteomic methodologies, particularly data‐independent acquisition (DIA) analysis–related approaches, would improve current gene expression–based classifications of colorectal cancer (CRC). Therefore, this study was aimed to identify protein expression signatures using SWATH‐MS DIA and targeted data extraction, to aid in the classification of molecular subtypes of CRC and advance in the diagnosis and development of new drugs. For this purpose, 40 human CRC samples and 7 samples of healthy tissue were subjected to proteomic and bioinformatic analysis. The proteomic analysis identified three different molecular CRC subtypes: P1, P2 and P3. Significantly, P3 subtype showed high agreement with the mesenchymal/stem‐like subtype defined by gene expression signatures and characterized by poor prognosis and survival. The P3 subtype was characterized by decreased expression of ribosomal proteins, the spliceosome, and histone deacetylase 2, as well as increased expression of osteopontin, SERPINA 1 and SERPINA 3, and proteins involved in wound healing, acute inflammation and complement pathway. This was also confirmed by immunodetection and gene expression analyses. Our results show that these tumours are characterized by altered expression of proteins involved in biological processes associated with immune evasion and metastasis, suggesting new therapeutic options in the treatment of this aggressive type of CRC.     

A proteomics approach to identifying key protein targets involved in VEGF inhibitor mediated attenuation of bleomycin‐induced pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is a progressive lung disease with a life expectancy of less than 5 years post diagnosis for most patients. Poor molecular characterization of IPF has led to insufficient understanding of the pathogenesis of the disease, resulting in lack of effective therapies. In this study, we have integrated a label‐free LC‐MS based approach with systems biology to identify signaling pathways and regulatory nodes within protein interaction networks that govern phenotypic changes that may lead to IPF. Ingenuity Pathway Analysis of proteins modulated in response to bleomycin treatment identified PI3K/Akt and Wnt signaling as the most significant profibrotic pathways. Similar analysis of proteins modulated in response to vascular endothelial growth factor (VEGF) inhibitor (CBO‐P11) treatment identified natural killer cell signaling and PTEN signaling as the most significant antifibrotic pathways. Mechanistic/mammalian target of rapamycin (mTOR) and extracellular signal‐regulated kinase (ERK) were identified to be key mediators of pro‐ and antifibrotic response, where bleomycin (BLM) treatment resulted in increased expression and VEGF inhibitor treatment attenuated expression of mTOR and ERK. Using a BLM mouse model of pulmonary fibrosis and VEGF inhibitor CBO‐P11 as a therapeutic measure, we identified a comprehensive set of signaling pathways and proteins that contribute to the pathogenesis of pulmonary fibrosis that can be targeted for therapy against this fatal disease.