Targeted proteomics reveals strain-specific changes in the mouse insulin and central metabolic pathways after a sustained high-fat diet

High-Fructose/High-Fat Diet Downregulates the Hepatic Mitochondrial Oxidative Phosphorylation Pathway in Mice Compared with High-Fat Diet Alone

Both high-fat diet (HFD) alone and high-fructose plus HFD (HFr/HFD) cause diet-induced non-alcoholic fatty liver disease in murine models. However, the mechanisms underlying their impacts on inducing different levels of liver injury are yet to be elucidated. This study employed a proteomic approach to elucidate further on this issue. Adult male C57BL/6J mice were allocated to the HFD or the HFr/HFD group. After feeding for 12 weeks, all mice were euthanized and samples were collected. The proteomic profiles in liver tissues were analyzed using liquid chromatography-tandem mass spectrometry followed by canonical pathway analysis. We demonstrated that the mitochondrial oxidative phosphorylation (OXPHOS) pathway was the most significantly downregulated canonical pathway in the HFr/HFD group when compared with the HFD group. Within the OXPHOS pathway, the HFr/HFD group demonstrated significant downregulation of complexes I and III and significant upregulation of complex IV when compared with the HFD group. Moreover, the HFr/HFD group had lower protein levels of NADH: ubiquinone oxidoreductase subunits S3, S6, A5, and A12 in complex I (p < 0.001, =0.03, <0.001, and <0.001, respectively), lower protein level of cytochrome C in complex III (p < 0.001), and higher protein level of cytochrome C oxidase subunit 2 in complex IV (p = 0.002), when compared with the HFD group. To summarize, we have demonstrated that the hepatic mitochondrial OXPHOS pathway is significantly downregulated in long-term HFr/HFD feeding when compared with long-term HFD feeding. These data support the concept that the hepatic mitochondrial OXPHOS pathway should be involved in mediating the effects of HFr/HFD on inducing more severe liver injury than HFD alone.

Targeted Proteomics for Monitoring One-Carbon Metabolism in Liver Diseases

Liver diseases cause approximately 2 million deaths per year worldwide and had an increasing incidence during the last decade. Risk factors for liver diseases include alcohol consumption, obesity, diabetes, the intake of hepatotoxic substances like aflatoxin, viral infection, and genetic determinants. Liver cancer is the sixth most prevalent cancer and the third in mortality (second in males). The low survival rate (less than 20% in 5 years) is partially explained by the late diagnosis, which remarks the need for new early molecular biomarkers. One-carbon metabolism integrates folate and methionine cycles and participates in essential cell processes such as redox homeostasis maintenance and the regulation of methylation reactions through the production of intermediate metabolites such as cysteine and S-Adenosylmethionine. One-carbon metabolism has a tissue specific configuration, and in the liver, the participating enzymes are abundantly expressed—a requirement to maintain hepatocyte differentiation. Targeted proteomics studies have revealed significant differences in hepatocellular carcinoma and cirrhosis, suggesting that monitoring one-carbon metabolism enzymes can be useful for stratification of liver disease patients and to develop precision medicine strategies for their clinical management. Here, reprogramming of one-carbon metabolism in liver diseases is described and the role of mass spectrometry to follow-up these alterations is discussed.

Multi-Omics Approach Reveals Dysregulation of Protein Phosphorylation Correlated with Lipid Metabolism in Mouse Non-Alcoholic Fatty Liver

Obesity caused by overnutrition is a major risk factor for non-alcoholic fatty liver disease (NAFLD). Several lipid intermediates such as fatty acids, glycerophospholipids and sphingolipids are implicated in NAFLD, but detailed characterization of lipids and their functional links to proteome and phosphoproteome remain to be elucidated. To characterize this complex molecular relationship, we used a multi-omics approach by conducting comparative proteomic, phoshoproteomic and lipidomic analyses of high fat (HFD) and low fat (LFD) diet fed mice livers. We quantified 2447 proteins and 1339 phosphoproteins containing 1650 class I phosphosites, of which 669 phosphosites were significantly different between HFD and LFD mice livers. We detected alterations of proteins associated with cellular metabolic processes such as small molecule catabolic process, monocarboxylic acid, long- and medium-chain fatty acid, and ketone body metabolic processes, and peroxisome organization. We observed a significant downregulation of protein phosphorylation in HFD fed mice liver in general. Untargeted lipidomics identified upregulation of triacylglycerols, glycerolipids and ether glycerophosphocholines and downregulation of glycerophospholipids, such as lysoglycerophospholipids, as well as ceramides and acylcarnitines. Analysis of differentially regulated phosphosites revealed phosphorylation dependent deregulation of insulin signaling as well as lipogenic and lipolytic pathways during HFD induced obesity. Thus, this study reveals a molecular connection between decreased protein phosphorylation and lipolysis, as well as lipid-mediated signaling in diet-induced obesity.

Analytical comparison of absolute quantification strategies to investigate the Insulin signaling pathway in fat cells

So far, mass spectrometry based targeted proteomics is the most sensitive approach to answer and address specific biological questions in an accurate and quantitative fashion. However, the data analysis design used for such quantification varies in the field leading to discrepancies in the reported values. In this study, different quantification strategies based on calibration curves were evaluated and compared. The best accuracy and coefficient of variation was achieved by ratio to ratio calibration curves. We applied the ratio to ratio quantification approach to analyze very low abundant insulin signaling proteins such as PIK3RA (0.10-0.93 fmol/μg), AKT1 (0.1-0.39 fmol/μg) and the Insulin receptor (0.22 -2.62 fmol/μg) in a fat cell model and demonstrated the adaptation of this pathway at different states of insulin sensitivity. This article is protected by copyright. All rights reserved.

Chronic High Fat Diet Intake Impairs Hepatic Metabolic Parameters in Ovariectomized Sirt3 KO Mice

High fat diet (HFD) is an important factor in the development of metabolic diseases, with liver as metabolic center being highly exposed to its influence. However, the effect of HFD-induced metabolic stress with respect to ovary hormone depletion and sirtuin 3 (Sirt3) is not clear. Here we investigated the effect of Sirt3 in liver of ovariectomized and sham female mice upon 10 weeks of feeding with standard-fat diet (SFD) or HFD. Liver was examined by Folch, gas chromatography and lipid hydroperoxide analysis, histology and oil red staining, RT-PCR, Western blot, antioxidative enzyme and oxygen consumption analyses. In SFD-fed WT mice, ovariectomy increased Sirt3 and fatty acids synthesis, maintained mitochondrial function, and decreased levels of lipid hydroperoxides. Combination of ovariectomy and Sirt3 depletion reduced pparα, Scd-1 ratio, MUFA proportions, CII-driven respiration, and increased lipid damage. HFD compromised CII-driven respiration and activated peroxisomal ROS scavenging enzyme catalase in sham mice, whereas in combination with ovariectomy and Sirt3 depletion, increased body weight gain, expression of NAFLD- and oxidative stress-inducing genes, and impaired response of antioxidative system. Overall, this study provides evidence that protection against harmful effects of HFD in female mice is attributed to the combined effect of female sex hormones and Sirt3, thus contributing to preclinical research on possible sex-related therapeutic agents for metabolic syndrome and associated diseases.

Proteome and phosphoproteome characterization of liver in the postprandial state from diet-induced obese and lean mice

A metabolic consequence of obesity is hepatosteatosis, which can develop into more serious diseases in the non-alcoholic fatty liver disease (NAFLD) spectrum. The goal of this study was to identify the protein signature of liver in the postprandial state in obesity compared to leanness. The postprandial state is of interest due to the central role of the liver in regulating macronutrient and energy homeostasis during the fed-fast cycle and lack of previously reported controlled studies in the postprandial state. Therefore, we assessed the proteome and phosphoproteome of liver in the postprandial state from diet-induced obese (DIO) and lean mice using untargeted LC-MS/MS analysis. We identified significant alterations in the levels of proteins involved in fatty acid oxidation, activation, and transport, as well as proteins involved in energy metabolism including ketogenesis, tricarboxylic acid cycle, and electron transport chain in liver of DIO compared to lean mice. Additionally, phosphorylated proteins in liver of DIO and lean mice reflect possible regulatory mechanisms controlling fatty acid metabolism and gene expression that may contribute to hepatic metabolic alterations in obesity. Our data indicates PPARα-mediated transcriptional regulation of lipid metabolism and adaptation to hepatic lipid overload. The results of this study expand our knowledge of the molecular changes that occur in liver in the postprandial state in obesity compared to leanness. SIGNIFICANCE: Proteome and phosphoproteome studies of liver in a controlled postprandial state in obesity and leanness are lacking; however, this information is crucial to understanding how obesity-associated hepatosteatosis influences postprandial nutrient and energy metabolism. In this global shotgun proteome and phosphoproteome analysis, we identified unique protein signatures defining obesity and leanness in liver in the postprandial state and identified potential mechanisms contributing to hepatic metabolic alterations in obesity. The results of this study provide a foundation to focus future experiments on the contribution of altered protein and phosphorylation patterns to postprandial metabolism in obesity-associated hepatosteatosis.

High-fat diet in a mouse insulin-resistant model induces widespread rewiring of the phosphotyrosine signaling network

Obesity-associated type 2 diabetes and accompanying diseases have developed into a leading human health risk across industrialized and developing countries. The complex molecular underpinnings of how lipid overload and lipid metabolites lead to the deregulation of metabolic processes are incompletely understood. We assessed hepatic post-translational alterations in response to treatment of cells with saturated and unsaturated free fatty acids and the consumption of a high-fat diet by mice. These data revealed widespread tyrosine phosphorylation changes affecting a large number of enzymes involved in metabolic processes as well as canonical receptor-mediated signal transduction networks. Targeting two of the most prominently affected molecular features in our data, SRC-family kinase activity and elevated reactive oxygen species, significantly abrogated the effects of saturated fat exposure in vitro and high-fat diet in vivo. In summary, we present a comprehensive view of diet-induced alterations of tyrosine signaling networks, including proteins involved in fundamental metabolic pathways.

Deletion of the Mitochondrial Protein VWA8 Induces Oxidative Stress and an HNF4α Compensatory Response in Hepatocytes

von Willebrand A domain-containing protein 8 (VWA8) is a poorly characterized, mitochondrial matrix-targeted protein with an AAA ATPase domain and ATPase activity that increases in livers of mice fed a high-fat diet. This study was undertaken to use CRISPR/Cas9 to delete VWA8 in cultured mouse hepatocytes and gain insight into its function. Unbiased omics techniques and bioinformatics were used to guide subsequent assays, including the assessment of oxidative stress and the determination of bioenergetic capacity. Metabolomics analysis showed VWA8 null cells had higher levels of oxidative stress and protein degradation; assays of hydrogen peroxide production revealed higher levels of production of reactive oxygen species (ROS). Proteomics and transcriptomics analyses showed VWA8 null cells had higher levels of expression of mitochondrial proteins (electron transport-chain Complex I, ATP synthase), peroxisomal proteins, and lipid transport proteins. The pattern of higher protein abundance in the VWA8 null cells could be explained by a higher level of hepatocyte nuclear factor 4 α (HNF4α) expression. Bioenergetic assays showed higher rates of carbohydrate oxidation and mitochondrial and nonmitochondrial lipid oxidation in intact and permeabilized cells. Inhibitor assays localized sites of ROS production to peroxisomes and NOX1/4. The rescue of VWA8 protein restored the wild-type phenotype, and treatment with antioxidants decreased the level of HNF4α expression. Thus, loss of VWA8 produces a mitochondrial defect that may be sensed by NOX4, leading to an increase in the level of ROS that results in a higher level of HNF4α. The compensatory HNF4α response results in a higher oxidative capacity and an even higher level of ROS production. We hypothesize that VWA8 is an AAA ATPase protein that plays a role in mitochondrial protein quality.

Co-occurring KRAS mutation/LKB1 loss in non-small cell lung cancer cells results in enhanced metabolic activity susceptible to caloric restriction: an in vitro integrated multilevel approach

Background

Non–small-cell lung cancer (NSCLC) is a heterogeneous disease, with multiple different oncogenic mutations. Approximately 25–30% of NSCLC patients present KRAS mutations, which confer poor prognosis and high risk of tumor recurrence. About half of NSCLCs with activating KRAS lesions also have deletions or inactivating mutations in the serine/threonine kinase 11 (LKB1) gene. Loss of LKB1 on a KRAS-mutant background may represent a significant source of heterogeneity contributing to poor response to therapy.

Methods

Here, we employed an integrated multilevel proteomics, metabolomics and functional in-vitro approach in NSCLC H1299 isogenic cells to define their metabolic state associated with the presence of different genetic background. Protein levels were obtained by label free and single reaction monitoring (SRM)-based proteomics. The metabolic state was studied coupling targeted and untargeted mass spectrometry (MS) strategy. In vitro metabolic dependencies were evaluated using 2-deoxy glucose (2-DG) treatment or glucose/glutamine nutrient limitation.

Results

Here we demonstrate that co-occurring KRAS mutation/LKB1 loss in NSCLC cells allowed efficient exploitation of glycolysis and oxidative phosphorylation, when compared to cells with each single oncologic genotype. The enhanced metabolic activity rendered the viability of cells with both genetic lesions susceptible towards nutrient limitation.

Conclusions

Co-occurrence of KRAS mutation and LKB1 loss in NSCLC cells induced an enhanced metabolic activity mirrored by a growth rate vulnerability under limited nutrient conditions relative to cells with the single oncogenetic lesions. Our results hint at the possibility that energy stress induced by calorie restriction regimens may sensitize NSCLCs with these co-occurring lesions to cytotoxic chemotherapy.

Electronic supplementary material

The online version of this article (10.1186/s13046-018-0954-5) contains supplementary material, which is available to authorized users.

Application of targeted mass spectrometry in bottom-up proteomics for systems biology research

The enormous diversity of proteoforms produces tremendous complexity within cellular proteomes, facilitates intricate networks of molecular interactions, and constitutes a formidable analytical challenge for biomedical researchers. Currently, quantitative whole-proteome profiling often relies on non-targeted liquid chromatography-mass spectrometry (LC-MS), which samples proteoforms broadly, but can suffer from lower accuracy, sensitivity, and reproducibility compared with targeted LC-MS. Recent advances in bottom-up proteomics using targeted LC-MS have enabled previously unachievable identification and quantification of target proteins and posttranslational modifications within complex samples. Consequently, targeted LC-MS is rapidly advancing biomedical research, especially systems biology research in diverse areas that include proteogenomics, interactomics, kinomics, and biological pathway modeling. With the recent development of targeted LC-MS assays for nearly the entire human proteome, targeted LC-MS is positioned to enable quantitative proteomic profiling of unprecedented quality and accessibility to support fundamental and clinical research. Here we review recent applications of bottom-up proteomics using targeted LC-MS for systems biology research. SIGNIFICANCE: Advances in targeted proteomics are rapidly advancing systems biology research. Recent applications include systems-level investigations focused on posttranslational modifications (such as phosphoproteomics), protein conformation, protein-protein interaction, kinomics, proteogenomics, and metabolic and signaling pathways. Notably, absolute quantification of metabolic and signaling pathway proteins has enabled accurate pathway modeling and engineering. Integration of targeted proteomics with other technologies, such as RNA-seq, has facilitated diverse research such as the identification of hundreds of "missing" human proteins (genes and transcripts that appear to encode proteins but direct experimental evidence was lacking).

Differential representation of liver proteins in obese human subjects suggests novel biomarkers and promising targets for drug development in obesity

The proteome of liver biopsies from human obese (O) subjects has been compared to those of nonobese (NO) subjects using two-dimensional gel electrophoresis (2-DE). Differentially represented proteins were identified by matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS)-based peptide mass fingerprinting (PMF) and nanoflow-liquid chromatography coupled to electrospray-tandem mass spectrometry (nLC-ESI-MS/MS). Overall, 61 gene products common to all of the liver biopsies were identified within 65 spots, among which 25 ones were differently represented between O and NO subjects. In particular, over-representation of short-chain acyl-CoA dehydrogenase, Δ(3,5)-Δ(2,4)dienoyl-CoA isomerase, acetyl-CoA acetyltransferase, glyoxylate reductase/hydroxypyruvate reductase, fructose-biphosphate aldolase B, peroxiredoxin I, protein DJ-1, catalase, α- and β-hemoglobin subunits, 3-mercaptopyruvate S-transferase, calreticulin, aminoacylase 1, phenazine biosynthesis-like domain-containing protein and a form of fatty acid-binding protein, together with downrepresentation of glutamate dehydrogenase, glutathione S-transferase A1, S-adenosylmethionine synthase 1A and a form of apolipoprotein A-I, was associated with the obesity condition. Some of these metabolic enzymes and antioxidant proteins have already been identified as putative diagnostic markers of liver dysfunction in animal models of steatosis or obesity, suggesting additional investigations on their role in these syndromes. Their differential representation in human liver was suggestive of their consideration as obesity human biomarkers and for the development of novel antiobesity drugs.

System-Wide Quantitative Proteomics of the Metabolic Syndrome in Mice: Genotypic and Dietary Effects

Advances in mass spectrometry have made the quantitative measurement of proteins across multiple samples a reality, allowing for the study of complex biological systems such as the metabolic syndrome. Although the deregulation of lipid metabolism and increased hepatic storage of triacylglycerides are known to play a part in the onset of the metabolic syndrome, its molecular basis and dependency on dietary and genotypic factors are poorly characterized. Here, we used an experimental design with two different mouse strains and dietary and metabolic perturbations to generate a compendium of quantitative proteome data using three mass spectrometric techniques. The data reproduce known properties of the metabolic system and indicate differential molecular adaptation of the two mouse strains to perturbations, contributing to a better understanding of the metabolic syndrome. We show that high-quality, high-throughput proteomic data sets provide an unbiased broad overview of the behavior of complex systems after perturbation.

A large-scale targeted proteomics assay resource based on an in vitro human proteome

Targeted proteomics approaches are of value for deep and accurate quantification of protein abundance. Extending such methods to quantify large numbers of proteins requires the construction of predefined targeted assays. We developed a targeted proteomics platform-in vitro proteome-assisted multiple reaction monitoring (MRM) for protein absolute quantification (iMPAQT)-by using >18,000 human recombinant proteins, thus enabling protein absolute quantification on a genome-wide scale. Our platform comprises experimentally confirmed MRM assays of mass tag (mTRAQ)-labeled peptides to allow for rapid and straightforward measurement of the absolute abundance of predefined sets of proteins by mass spectrometry. We applied iMPAQT to delineate the quantitative metabolic landscape of normal and transformed human fibroblasts. Oncogenic transformation gave rise to relatively small but global changes in metabolic pathways resulting in aerobic glycolysis (Warburg effect) and increased rates of macromolecule synthesis. iMPAQT should facilitate quantitative biology studies based on protein abundance measurements.

Reproducible quantitative proteotype data matrices for systems biology

Historically, many mass spectrometry–based proteomic studies have aimed at compiling an inventory of protein compounds present in a biological sample, with the long-term objective of creating a proteome map of a species. However, to answer fundamental questions about the behavior of biological systems at the protein level, accurate and unbiased quantitative data are required in addition to a list of all protein components. Fueled by advances in mass spectrometry, the proteomics field has thus recently shifted focus toward the reproducible quantification of proteins across a large number of biological samples. This provides the foundation to move away from pure enumeration of identified proteins toward quantitative matrices of many proteins measured across multiple samples. It is argued here that data matrices consisting of highly reproducible, quantitative, and unbiased proteomic measurements across a high number of conditions, referred to here as quantitative proteotype maps, will become the fundamental currency in the field and provide the starting point for downstream biological analysis. Such proteotype data matrices, for example, are generated by the measurement of large patient cohorts, time series, or multiple experimental perturbations. They are expected to have a large effect on systems biology and personalized medicine approaches that investigate the dynamic behavior of biological systems across multiple perturbations, time points, and individuals.

Advances in targeted proteomics and applications to biomedical research

Targeted proteomics technique has emerged as a powerful protein quantification tool in systems biology, biomedical research, and increasing for clinical applications. The most widely used targeted proteomics approach, selected reaction monitoring (SRM), also known as multiple reaction monitoring (MRM), can be used for quantification of cellular signaling networks and preclinical verification of candidate protein biomarkers. As an extension to our previous review on advances in SRM sensitivity (Shi et al., Proteomics, 12, 1074-1092, 2012) herein we review recent advances in the method and technology for further enhancing SRM sensitivity (from 2012 to present), and highlighting its broad biomedical applications in human bodily fluids, tissue and cell lines. Furthermore, we also review two recently introduced targeted proteomics approaches, parallel reaction monitoring (PRM) and data-independent acquisition (DIA) with targeted data extraction on fast scanning high-resolution accurate-mass (HR/AM) instruments. Such HR/AM targeted quantification with monitoring all target product ions addresses SRM limitations effectively in specificity and multiplexing; whereas when compared to SRM, PRM and DIA are still in the infancy with a limited number of applications. Thus, for HR/AM targeted quantification we focus our discussion on method development, data processing and analysis, and its advantages and limitations in targeted proteomics. Finally, general perspectives on the potential of achieving both high sensitivity and high sample throughput for large-scale quantification of hundreds of target proteins are discussed.

Mass spectrometry-based absolute quantification reveals rhythmic variation of mouse circadian clock proteins

Absolute values of protein expression levels in cells are crucial information for understanding cellular biological systems. Precise quantification of proteins can be achieved by liquid chromatography (LC)-mass spectrometry (MS) analysis of enzymatic digests of proteins in the presence of isotope-labeled internal standards. Thus, development of a simple and easy way for the preparation of internal standards is advantageous for the analyses of multiple target proteins, which will allow systems-level studies. Here we describe a method, termed MS-based Quantification By isotope-labeled Cell-free products (MS-QBiC), which provides the simple and high-throughput preparation of internal standards by using a reconstituted cell-free protein synthesis system, and thereby facilitates both multiplexed and sensitive quantification of absolute amounts of target proteins. This method was applied to a systems-level dynamic analysis of mammalian circadian clock proteins, which consist of transcription factors and protein kinases that govern central and peripheral circadian clocks in mammals. Sixteen proteins from 20 selected circadian clock proteins were successfully quantified from mouse liver over a 24-h time series, and 14 proteins had circadian variations. Quantified values were applied to detect internal body time using a previously developed molecular timetable method. The analyses showed that single time-point data from wild-type mice can predict the endogenous state of the circadian clock, whereas data from clock mutant mice are not applicable because of the disappearance of circadian variation.

Signatures of Evolutionary Adaptation in Quantitative Trait Loci Influencing Trace Element Homeostasis in Liver

Essential trace elements possess vital functions at molecular, cellular, and physiological levels in health and disease, and they are tightly regulated in the human body. In order to assess variability and potential adaptive evolution of trace element homeostasis, we quantified 18 trace elements in 150 liver samples, together with the expression levels of 90 genes and abundances of 40 proteins involved in their homeostasis. Additionally, we genotyped 169 single nucleotide polymorphism (SNPs) in the same sample set. We detected significant associations for 8 protein quantitative trait loci (pQTL), 10 expression quantitative trait loci (eQTLs), and 15 micronutrient quantitative trait loci (nutriQTL). Six of these exceeded the false discovery rate cutoff and were related to essential trace elements: 1) one pQTL for GPX2 (rs10133290); 2) two previously described eQTLs for HFE (rs12346) and SELO (rs4838862) expression; and 3) three nutriQTLs: The pathogenic C282Y mutation at HFE affecting iron (rs1800562), and two SNPs within several clustered metallothionein genes determining selenium concentration (rs1811322 and rs904773). Within the complete set of significant QTLs (which involved 30 SNPs and 20 gene regions), we identified 12 SNPs with extreme patterns of population differentiation (FST values in the top 5% percentile in at least one HapMap population pair) and significant evidence for selective sweeps involving QTLs at GPX1, SELENBP1, GPX3, SLC30A9, and SLC39A8. Overall, this detailed study of various molecular phenotypes illustrates the role of regulatory variants in explaining differences in trace element homeostasis among populations and in the human adaptive response to environmental pressures related to micronutrients.

Hepatic proteomic analysis revealed altered metabolic pathways in insulin resistant Akt1(+/-)/Akt2(-/-) mice

OBJECTIVE

The aim of this study was to identify liver proteome changes in a mouse model of severe insulin resistance and markedly decreased leptin levels.

METHODS

Two-dimensional differential gel electrophoresis was utilized to identify liver proteome changes in AKT1(+/-)/AKT2(-/-) mice. Proteins with altered levels were identified with tandem mass spectrometry. Ingenuity Pathway Analysis was performed for the interpretation of the biological significance of the observed proteomic changes.

RESULTS

11 proteins were identified from 2 biological replicates to be differentially expressed by a ratio of at least 1.3 between age-matched insulin resistant (Akt1(+/-)/Akt2(-/-)) and wild type mice. Albumin and mitochondrial ornithine aminotransferase were detected from multiple spots, which suggest post-translational modifications. Enzymes of the urea cycle were common members of top regulated pathways.

CONCLUSION

Our results help to unveil the regulation of the liver proteome underlying altered metabolism in an animal model of severe insulin resistance.

Applications of targeted proteomics in systems biology and translational medicine

Biological systems are composed of numerous components of which proteins are of particularly high functional significance. Network models are useful abstractions for studying these components in context. Network representations display molecules as nodes and their interactions as edges. Because they are difficult to directly measure, functional edges are frequently inferred from suitably structured datasets consisting of the accurate and consistent quantification of network nodes under a multitude of perturbed conditions. For the precise quantification of a finite list of proteins across a wide range of samples, targeted proteomics exemplified by selected/multiple reaction monitoring (SRM, MRM) mass spectrometry has proven useful and has been applied to a variety of questions in systems biology and clinical studies. Here, we survey the literature of studies using SRM-MS in systems biology and clinical proteomics. Systems biology studies frequently examine fundamental questions in network biology, whereas clinical studies frequently focus on biomarker discovery and validation in a variety of diseases including cardiovascular disease and cancer. Targeted proteomics promises to advance our understanding of biological networks and the phenotypic significance of specific network states and to advance biomarkers into clinical use.

Global Proteomics Analysis of the Response to Starvation in C. elegans

Periodic starvation of animals induces large shifts in metabolism but may also influence many other cellular systems and can lead to adaption to prolonged starvation conditions. To date, there is limited understanding of how starvation affects gene expression, particularly at the protein level. Here, we have used mass-spectrometry-based quantitative proteomics to identify global changes in the Caenorhabditis elegans proteome due to acute starvation of young adult animals. Measuring changes in the abundance of over 5,000 proteins, we show that acute starvation rapidly alters the levels of hundreds of proteins, many involved in central metabolic pathways, highlighting key regulatory responses. Surprisingly, we also detect changes in the abundance of chromatin-associated proteins, including specific linker histones, histone variants, and histone posttranslational modifications associated with the epigenetic control of gene expression. To maximize community access to these data, they are presented in an online searchable database, the Encyclopedia of Proteome Dynamics (http://www.peptracker.com/epd/).

Rapid mass spectrometric conversion of tissue biopsy samples into permanent quantitative digital proteome maps

Clinical specimens are each inherently unique, limited and non-renewable. As such, small samples such as tissue biopsies are often completely consumed after a limited number of analyses. Here we present a method that enables fast and reproducible conversion of a small amount of tissue (approximating the quantity obtained by a biopsy) into a single, permanent digital file representing the mass spectrometry-measurable proteome of the sample. The method combines pressure cycling technology (PCT) and SWATH mass spectrometry (MS), and the resulting proteome maps can be analyzed, re-analyzed, compared and mined in silico to detect and quantify specific proteins across multiple samples. We used this method to process and convert 18 biopsy samples from 9 renal cell carcinoma patients into SWATH-MS fragment ion maps. From these proteome maps we detected and quantified more than 2,000 proteins with a high degree of reproducibility across all samples. The identified proteins clearly separated tumorous kidney tissues from healthy tissue, and differentiated distinct histomorphological kidney cancer subtypes.

Multilayered genetic and omics dissection of mitochondrial activity in a mouse reference population

The manner by which genotype and environment affect complex phenotypes is one of the fundamental questions in biology. In this study, we quantified the transcriptome--a subset of the metabolome--and, using targeted proteomics, quantified a subset of the liver proteome from 40 strains of the BXD mouse genetic reference population on two diverse diets. We discovered dozens of transcript, protein, and metabolite QTLs, several of which linked to metabolic phenotypes. Most prominently, Dhtkd1 was identified as a primary regulator of 2-aminoadipate, explaining variance in fasted glucose and diabetes status in both mice and humans. These integrated molecular profiles also allowed further characterization of complex pathways, particularly the mitochondrial unfolded protein response (UPR(mt)). UPR(mt) shows strikingly variant responses at the transcript and protein level that are remarkably conserved among C. elegans, mice, and humans. Overall, these examples demonstrate the value of an integrated multilayered omics approach to characterize complex metabolic phenotypes.

Diet-induced obesity modulates epigenetic responses to ionizing radiation in mice

Both exposure to ionizing radiation and obesity have been associated with various pathologies including cancer. There is a crucial need in better understanding the interactions between ionizing radiation effects (especially at low doses) and other risk factors, such as obesity. In order to evaluate radiation responses in obese animals, C3H and C57BL/6J mice fed a control normal fat or a high fat (HF) diet were exposed to fractionated doses of X-rays (0.75 Gy ×4). Bone marrow micronucleus assays did not suggest a modulation of radiation-induced genotoxicity by HF diet. Using MSP, we observed that the promoters of p16 and Dapk genes were methylated in the livers of C57BL/6J mice fed a HF diet (irradiated and non-irradiated); Mgmt promoter was methylated in irradiated and/or HF diet-fed mice. In addition, methylation PCR arrays identified Ep300 and Socs1 (whose promoters exhibited higher methylation levels in non-irradiated HF diet-fed mice) as potential targets for further studies. We then compared microRNA regulations after radiation exposure in the livers of C57BL/6J mice fed a normal or an HF diet, using microRNA arrays. Interestingly, radiation-triggered microRNA regulations observed in normal mice were not observed in obese mice. miR-466e was upregulated in non-irradiated obese mice. In vitro free fatty acid (palmitic acid, oleic acid) administration sensitized AML12 mouse liver cells to ionizing radiation, but the inhibition of miR-466e counteracted this radio-sensitization, suggesting that the modulation of radiation responses by diet-induced obesity might involve miR-466e expression. All together, our results suggested the existence of dietary effects on radiation responses (especially epigenetic regulations) in mice, possibly in relationship with obesity-induced chronic oxidative stress.

Influence of the digestion technique, protease, and missed cleavage peptides in protein quantitation

Quantitative determination of absolute and relative protein amounts is an essential requirement for most current bottom-up proteomics applications, but protein quantitation estimates are affected by several sources of variability such as sample preparation, mass spectrometric acquisition, and data analysis. Among them, sample digestion has attracted much attention from the proteomics community, as protein quantitation by bottom-up proteomics relies on the efficiency and reproducibility of protein enzymatic digestion, with the presence of missed cleavages, nonspecific cleavages, or even the use of different proteases having been postulated as important sources of variation in protein quantitation. Here we evaluated both in-solution and filter-aided digestion protocols and assessed their influence in the estimation of protein abundances using five E. coli mixtures with known amounts of spiked proteins. We observed that replicates of trypsin specificity digestion protocols are highly reproducible in terms of peptide quantitation, with digestion technique and the chosen proteolytic enzyme being the major sources of variability in peptide quantitation. Finally, we also evaluated the result of including peptides with missed cleavages in protein quantitation and observed no significant differences in precision, accuracy, specificity, and sensitivity compared with the use of fully tryptic peptides.

Parallel universes of Black Six biology

Creation of lethal and synthetic lethal mutations in an experimental organism is a cornerstone of genetic dissection of gene function, and is related to the concept of an essential gene. Common inbred mouse strains carry background mutations, which can act as genetic modifiers, interfering with the assignment of gene essentiality. The inbred strain C57BL/6J, commonly known as "Black Six", stands out, as it carries a spontaneous homozygous deletion in the nicotinamide nucleotide transhydrogenase (Nnt) gene [GenBank: AH009385.2], resulting in impairment of steroidogenic mitochondria of the adrenal gland, and a multitude of indirect modifier effects, coming from alteration of glucocorticoid-regulated processes. Over time, the popular strain has been used, by means of gene targeting technology, to assign "essential" and "redundant" qualifiers to numerous genes, thus creating an internally consistent "parallel universe" of knowledge. It is unrealistic to suggest phasing-out of this strain, given the scope of shared resources built around it, however, continuing on the road of "strain-unawareness" will result in profound waste of effort, particularly where translational research is concerned. The review analyzes the historical roots of this phenomenon and proposes that building of "parallel universes" should be urgently made visible to a critical reader by obligatory use of unambiguous and persistent tags in publications and databases, such as hypertext links, pointing to a vendor's strain description web page, or to a digital object identifier (d.o.i.) of the original publication, so that any research done exclusively in C57BL/6J, could be easily identified.

REVIEWERS

This article was reviewed by Dr. Neil Smalheiser and Dr. Miguel Andrade-Navarro.

Intelligent data acquisition blends targeted and discovery methods

A mass spectrometry (MS) method is described here that can reproducibly identify hundreds of peptides across multiple experiments. The method uses intelligent data acquisition to precisely target peptides while simultaneously identifying thousands of other, nontargeted peptides in a single nano-LC–MS/MS experiment. We introduce an online peptide elution order alignment algorithm that targets peptides based on their relative elution order, eliminating the need for retention-time-based scheduling. We have applied this method to target 500 mouse peptides across six technical replicate nano-LC–MS/MS experiments and were able to identify 440 of these in all six, compared with only 256 peptides using data-dependent acquisition (DDA). A total of 3757 other peptides were also identified within the same experiment, illustrating that this hybrid method does not eliminate the novel discovery advantages of DDA. The method was also tested on a set of mice in biological quadruplicate and increased the number of identified target peptides in all four mice by over 80% (826 vs 459) compared with the standard DDA method. We envision real-time data analysis as a powerful tool to improve the quality and reproducibility of proteomic data sets.

Quantification of ErbB network proteins in three cell types using complementary approaches identifies cell-general and cell-type-specific signaling proteins

Relating protein concentration to cell-type-specific responses is one of the remaining challenges for obtaining a quantitative systems level understanding of mammalian signaling. Here we used mass-spectrometry (MS)- and antibody-based quantitative proteomic approaches to measure protein abundances for 75% of a hand-curated reconstructed ErbB network of 198 proteins, in two established cell types (HEK293 and MCF-7) and in primary keratinocyte cells. Comparison with other quantitative studies allowed building a set of ErbB network proteins expressed in all cells and another which are cell-specific and could impart specific properties to the network. As a proof-of-concept of the importance of protein concentration, we generated a small simplified mathematical model encompassing ligand binding, followed by receptor dimerization, activation, and degradation. The model predicts ErbB phosphorylation in HEK293, MCF-7, and keratinocyte cells simply by incorporating cell-type-specific ErbB1, ErbB2, and caveolin-1 abundances but otherwise contains similar rate constants. Altogether, the data provide a resource for protein abundances and localization to be included in larger mathematical models, enabling the generation of cell-type-specific computational models. MS data have been deposited to the ProteomeXchange via PRIDE (with identifier PXD000623) and PASSEL (with identifier PASS00372).