Maternal obesity alters offspring liver and skeletal muscle metabolism in early post-puberty despite maintaining a normal post-weaning dietary lifestyle.

Maternal obesity (MO) during pregnancy is linked to increased and premature risk of age-related metabolic diseases in the offspring. However, the underlying molecular mechanisms still remain not fully understood. Using a well-established nonhuman primate model of MO, we analyzed tissue biopsies and plasma samples obtained from post-pubertal offspring (3-6.5 y) of MO mothers (n = 19) and from control animals born to mothers fed a standard diet (CON, n = 13). All offspring ate a healthy chow diet after weaning. Using untargeted gas chromatography-mass spectrometry metabolomics analysis, we quantified a total of 351 liver, 316 skeletal muscle, and 423 plasma metabolites. We identified 58 metabolites significantly altered in the liver and 46 in the skeletal muscle of MO offspring, with 8 metabolites shared between both tissues. Several metabolites were changed in opposite directions in males and females in both liver and skeletal muscle. Several tissue-specific and 4 shared metabolic pathways were identified from these dysregulated metabolites. Interestingly, none of the tissue-specific metabolic changes were reflected in plasma. Overall, our study describes characteristic metabolic perturbations in the liver and skeletal muscle in MO offspring, indicating that metabolic programming in utero persists postnatally, and revealing potential novel mechanisms that may contribute to age-related metabolic diseases later in life.

Assessment of label-free quantification and missing value imputation for proteomics in non-human primates.

BACKGROUND: Reliable and effective label-free quantification (LFQ) analyses are dependent not only on the method of data acquisition in the mass spectrometer, but also on the downstream data processing, including software tools, query database, data normalization and imputation. In non-human primates (NHP), LFQ is challenging because the query databases for NHP are limited since the genomes of these species are not comprehensively annotated. This invariably results in limited discovery of proteins and associated Post Translational Modifications (PTMs) and a higher fraction of missing data points. While identification of fewer proteins and PTMs due to database limitations can negatively impact uncovering important and meaningful biological information, missing data also limits downstream analyses (e.g., multivariate analyses), decreases statistical power, biases statistical inference, and makes biological interpretation of the data more challenging. In this study we attempted to address both issues: first, we used the MetaMorphues proteomics search engine to counter the limits of NHP query databases and maximize the discovery of proteins and associated PTMs, and second, we evaluated different imputation methods for accurate data inference. We used a generic approach for missing data imputation analysis without distinguising the potential source of missing data (either non-assigned m/z or missing values across runs). RESULTS: Using the MetaMorpheus proteomics search engine we obtained quantitative data for 1622 proteins and 10,634 peptides including 58 different PTMs (biological, metal and artifacts) across a diverse age range of NHP brain frontal cortex. However, among the 1622 proteins identified, only 293 proteins were quantified across all samples with no missing values, emphasizing the importance of implementing an accurate and statiscaly valid imputation method to fill in missing data. In our imputation analysis we demonstrate that Single Imputation methods that borrow information from correlated proteins such as Generalized Ridge Regression (GRR), Random Forest (RF), local least squares (LLS), and a Bayesian Principal Component Analysis methods (BPCA), are able to estimate missing protein abundance values with great accuracy. CONCLUSIONS: Overall, this study offers a detailed comparative analysis of LFQ data generated in NHP and proposes strategies for improved LFQ in NHP proteomics data.

Integrated omics analysis reveals sirtuin signaling is central to hepatic response to a high fructose diet.

BACKGROUND: Dietary high fructose (HFr) is a known metabolic disruptor contributing to development of obesity and diabetes in Western societies. Initial molecular changes from exposure to HFr on liver metabolism may be essential to understand the perturbations leading to insulin resistance and abnormalities in lipid and carbohydrate metabolism. We studied vervet monkeys (Clorocebus aethiops sabaeus) fed a HFr (n=5) or chow diet (n=5) for 6 weeks, and obtained clinical measures of liver function, blood insulin, cholesterol and triglycerides. In addition, we performed untargeted global transcriptomics, proteomics, and metabolomics analyses on liver biopsies to determine the molecular impact of a HFr diet on coordinated pathways and networks that differed by diet. RESULTS: We show that integration of omics data sets improved statistical significance for some pathways and networks, and decreased significance for others, suggesting that multiple omics datasets enhance confidence in relevant pathway and network identification. Specifically, we found that sirtuin signaling and a peroxisome proliferator activated receptor alpha (PPARA) regulatory network were significantly altered in hepatic response to HFr. Integration of metabolomics and miRNAs data further strengthened our findings. CONCLUSIONS: Our integrated analysis of three types of omics data with pathway and regulatory network analysis demonstrates the usefulness of this approach for discovery of molecular networks central to a biological response. In addition, metabolites aspartic acid and docosahexaenoic acid (DHA), protein ATG3, and genes ATG7, and HMGCS2 link sirtuin signaling and the PPARA network suggesting molecular mechanisms for altered hepatic gluconeogenesis from consumption of a HFr diet.

Efficiency of whole-exome sequencing in old world and new world primates using human capture reagents.

BACKGROUND: Whole-exome sequencing (WES) can expedite research on genetic variation in non-human primate (NHP) models of human diseases. However, NHP-specific reagents for exome capture are not available. This study reports the use of human-specific capture reagents in WES for olive baboons, marmosets, and vervet monkeys. METHODS: Exome capture was carried out using the SureSelect Human All Exon V6 panel from Agilent Technologies, followed by high-throughput sequencing. Capture of protein-coding genes and detection of single nucleotide variants were evaluated. RESULTS: Exome capture and sequencing results showed that more than 97% of old world and 93% of new world monkey protein coding genes were detected. Single nucleotide variants were detected across the genomes and missense variants were found in genes associated with human diseases. CONCLUSIONS: A cost-effective approach based on commercial, human-specific reagents can be used to perform WES for the discovery of genetic variants in these NHP species.

High Resolution GC-Orbitrap-MS Metabolomics Using Both Electron Ionization and Chemical Ionization for Analysis of Human Plasma.

Gas chromatography-mass spectrometry (GC-MS) platforms are typically run in electron ionization (EI) mode for mass spectral matching and metabolite annotation. With the advent of high resolution mass spectrometry (HRMS), soft ionization techniques such as chemical ionization (CI) may provide additional coverage for compound identification. We evaluated NIST SRM 1950 pooled plasma reference sample using a HRGC-MS instrument [GC-Orbitrap-MS with electron ionization (EI), positive chemical ionization (PCI), and negative CI (NCI) capabilities] for metabolite annotation and quantification to assess the suitability of the platform for routine discovery metabolomics. Using both open source and vendor workflows, we validated the spectral matches with an in-house spectral library (Wake Forest CPM GC-MS spectral and retention time libraries) of EI-MS and CI-MS/MS spectra obtained from chemical standards. We confidently [metabolomics standards initiative (MSI) confidence level 2] identified 263, 93, and 65 metabolites using EI, PCI, and NCI modes, respectively, of which 270 metabolites (64%) were validated using our Wake Forest CPM GC-MS spectral libraries. When compared to published LC-MS-based efforts using the same NIST SRM 1950 plasma sample, there was only 17% overlap between the two platforms. In addition, the metabolomics analysis of community approved standard human plasma demonstrated the ability of EI- and CI-MS modes of analysis using a HRGC-MS platform to enable reproducible and interoperable spectral matching.

The Need for Multi-Omics Biomarker Signatures in Precision Medicine.

Recent advances in omics technologies have led to unprecedented efforts characterizing the molecular changes that underlie the development and progression of a wide array of complex human diseases, including cancer. As a result, multi-omics analyses-which take advantage of these technologies in genomics, transcriptomics, epigenomics, proteomics, metabolomics, and other omics areas-have been proposed and heralded as the key to advancing precision medicine in the clinic. In the field of precision oncology, genomics approaches, and, more recently, other omics analyses have helped reveal several key mechanisms in cancer development, treatment resistance, and recurrence risk, and several of these findings have been implemented in clinical oncology to help guide treatment decisions. However, truly integrated multi-omics analyses have not been applied widely, preventing further advances in precision medicine. Additional efforts are needed to develop the analytical infrastructure necessary to generate, analyze, and annotate multi-omics data effectively to inform precision medicine-based decision-making.

Proteomic characterization of high-density lipoprotein particles in patients with non-alcoholic fatty liver disease.

BACKGROUND: Metabolic diseases such as obesity and diabetes are associated with changes in high-density lipoprotein (HDL) particles, including changes in particle size and protein composition, often resulting in abnormal function. Recent studies suggested that patients with non-alcoholic fatty liver disease (NAFLD), including individuals with non-alcoholic steatohepatitis (NASH), have smaller HDL particles when compared to individuals without liver pathologies. However, no studies have investigated potential changes in HDL particle protein composition in patients with NAFLD, in addition to changes related to obesity, to explore putative functional changes of HDL which may increase the risk of cardiovascular complications. METHODS: From a cohort of morbidly obese females who were diagnosed with simple steatosis (SS), NASH, or normal liver histology, we selected five matched individuals from each condition for a preliminary pilot HDL proteome analysis. HDL particles were enriched using size-exclusion chromatography, and the proteome of the resulting fraction was analyzed by liquid chromatography tandem mass spectrometry. Differences in the proteomes between the three conditions (normal, SS, NASH) were assessed using label-free quantitative analysis. Gene ontology term analysis was performed to assess the potential impact of proteomic changes on specific functions of HDL particles. RESULTS: Of the 95 proteins identified, 12 proteins showed nominally significant differences between the three conditions. Gene ontology term analysis revealed that severity of the liver pathology may significantly impact the anti-thrombotic functions of HDL particles, as suggested by changes in the abundance of HDL-associated proteins such as antithrombin III and plasminogen. CONCLUSIONS: The pilot data from this study suggest that changes in the HDL proteome may impact the functionality of HDL particles in NAFLD and NASH patients. These proteome changes may alter cardio-protective properties of HDL, potentially contributing to the increased cardiovascular disease risk in affected individuals. Further validation of these protein changes by orthogonal approaches is key to confirming the role of alterations in the HDL proteome in NAFLD and NASH. This will help elucidate the mechanistic effects of the altered HDL proteome on cardioprotective properties of HDL particles.

Optimized GC-MS metabolomics for the analysis of kidney tissue metabolites.

INTRODUCTION: Metabolomics is a promising approach for discovery of relevant biomarkers in cells, tissues, organs, and biofluids for disease identification and prediction. The field has mostly relied on blood-based biofluids (serum, plasma, urine) as non-invasive sources of samples as surrogates of tissue or organ-specific conditions. However, the tissue specificity of metabolites pose challenges in translating blood metabolic profiles to organ-specific pathophysiological changes, and require further downstream analysis of the metabolites. OBJECTIVES: As part of this project, we aim to develop and optimize an efficient extraction protocol for the analysis of kidney tissue metabolites representative of key primate metabolic pathways. METHODS: Kidney cortex and medulla tissues of a baboon were homogenized and extracted using eight different extraction protocols including methanol/water, dichloromethane/methanol, pure methanol, pure water, water/methanol/chloroform, methanol/chloroform, methanol/acetonitrile/water, and acetonitrile/isopropanol/water. The extracts were analyzed by a two-dimensional gas chromatography time-of-flight mass-spectrometer (2D GC-ToF-MS) platform after methoximation and silylation. RESULTS: Our analysis quantified 110 shared metabolites in kidney cortex and medulla tissues from hundreds of metabolites found among the eight different solvent extractions spanning low to high polarities. The results revealed that medulla is metabolically richer compared to the cortex. Dichloromethane and methanol mixture (3:1) yielded highest number of metabolites across both the tissue types. Depending on the metabolites of interest, tissue type, and the biological question, different solvents can be used to extract specific groups of metabolites. CONCLUSION: This investigation provides insights into selection of extraction solvents for detection of classes of metabolites in renal cortex and medulla, which is fundamentally important for identification of prognostic and diagnostic metabolic kidney biomarkers for future therapeutic applications.

High-resolution gas chromatography/mass spectrometry metabolomics of non-human primate serum.

RATIONALE: Metabolomics analyses using gas chromatography/mass spectrometry (GC/MS)-based metabolomics are heavily impeded by the lack of high-resolution mass spectrometers and limited spectral libraries to complement the excellent chromatography that GC platforms offer, a challenge that is being addressed with the implementation of high-resolution (HR) platforms such as 1D-GC/Orbitrap-MS. METHODS: We used serum samples from a non-human primate (NHP), a baboon (Papio hamadryas), with suitable quality controls to quantify the chemical space using an advanced HRMS platform for confident metabolite identification and robust quantification to assess the suitability of the platform for routine clinical metabolomics research. In a complementary approach, we also analyzed the same serum samples using two-dimensional gas chromatography/time-of-flight mass spectrometry (2D-GC/TOF-MS) for metabolite identification and quantification following established standard protocols. RESULTS: Overall, the 2D-GC/TOF-MS (~5000 peaks per sample) and 1D-GC/Orbitrap-MS (~500 peaks per sample) analyses enabled identification and quantification of a total of 555 annotated metabolites from the NHP serum with a spectral similarity score Rsim  ≥ 900 and signal-to-noise (S/N) ratio of >25. A common set of 30 metabolites with HMDB and KEGG IDs was quantified in the serum samples by both platforms where 2D-GC/TOF-MS enabled quantification of a total 384 metabolites (118 HMDB IDs) and 1D-GC/Orbitrap-MS analysis quantification of a total 200 metabolites (47 HMDB IDs). Thus, roughly 30-70% of the peaks remain unidentified or un-annotated across both platforms. CONCLUSIONS: Our study provides insights into the benefits and limitations of the use of a higher mass resolution and mass accuracy instrument for untargeted GC/MS-based metabolomics with multi-dimensional chromatography in future studies addressing clinical conditions or exposome studies.

Elucidating Protein-DNA Interactions in Human Alphoid Chromatin via Hybridization Capture and Mass Spectrometry.

The centromere is the chromosomal locus where the kinetochore forms and is critical for ensuring proper segregation of sister chromatids during cell division. A substantial amount of effort has been devoted to understanding the characteristic features and roles of the centromere, yet some fundamental aspects of the centromere, such as the complete list of elements that define it, remain obscure. It is well-known that human centromeres include a highly repetitive class of DNA known as alpha satellite, or alphoid, DNA. We present here the first DNA-centric examination of human protein-alpha satellite interactions, employing an approach known as HyCCAPP (hybridization capture of chromatin-associated proteins for proteomics) to identify the protein components of alphoid chromatin in a human cell line. Using HyCCAPP, cross-linked alpha satellite chromatin was isolated from cell lysate, and captured proteins were analyzed via mass spectrometry. After being compared to proteins identified in control pulldown experiments, 90 proteins were identified as enriched at alphoid DNA. This list included many known centromere-binding proteins in addition to multiple novel alpha satellite-binding proteins, such as LRIF1, a heterochromatin-associated protein. The ability of HyCCAPP to reveal both known as well as novel alphoid DNA-interacting proteins highlights the validity and utility of this approach.

Proteomics in non-human primates: utilizing RNA-Seq data to improve protein identification by mass spectrometry in vervet monkeys.

BACKGROUND: Shotgun proteomics utilizes a database search strategy to compare detected mass spectra to a library of theoretical spectra derived from reference genome information. As such, the robustness of proteomics results is contingent upon the completeness and accuracy of the gene annotation in the reference genome. For animal models of disease where genomic annotation is incomplete, such as non-human primates, proteogenomic methods can improve the detection of proteins by incorporating transcriptional data from RNA-Seq to improve proteomics search databases used for peptide spectral matching. Customized search databases derived from RNA-Seq data are capable of identifying unannotated genetic and splice variants while simultaneously reducing the number of comparisons to only those transcripts actively expressed in the tissue. RESULTS: We collected RNA-Seq and proteomic data from 10 vervet monkey liver samples and used the RNA-Seq data to curate sample-specific search databases which were analyzed in the program Morpheus. We compared these results against those from a search database generated from the reference vervet genome. A total of 284 previously unannotated splice junctions were predicted by the RNA-Seq data, 92 of which were confirmed by peptide spectral matches. More than half (53/92) of these unannotated splice variants had orthologs in other non-human primates, suggesting that failure to match these peptides in the reference analyses likely arose from incomplete gene model information. The sample-specific databases also identified 101 unique peptides containing single amino acid substitutions which were missed by the reference database. Because the sample-specific searches were restricted to actively expressed transcripts, the search databases were smaller, more computationally efficient, and identified more peptides at the empirically derived 1 % false discovery rate. CONCLUSION: Proteogenomic approaches are ideally suited to facilitate the discovery and annotation of proteins in less widely studies animal models such as non-human primates. We expect that these approaches will help to improve existing genome annotations of non-human primate species such as vervet.

HyCCAPP as a tool to characterize promoter DNA-protein interactions in Saccharomyces cerevisiae.

Currently available methods for interrogating DNA-protein interactions at individual genomic loci have significant limitations, and make it difficult to work with unmodified cells or examine single-copy regions without specific antibodies. In this study, we describe a physiological application of the Hybridization Capture of Chromatin-Associated Proteins for Proteomics (HyCCAPP) methodology we have developed. Both novel and known locus-specific DNA-protein interactions were identified at the ENO2 and GAL1 promoter regions of Saccharomyces cerevisiae, and revealed subgroups of proteins present in significantly different levels at the loci in cells grown on glucose versus galactose as the carbon source. Results were validated using chromatin immunoprecipitation. Overall, our analysis demonstrates that HyCCAPP is an effective and flexible technology that does not require specific antibodies nor prior knowledge of locally occurring DNA-protein interactions and can now be used to identify changes in protein interactions at target regions in the genome in response to physiological challenges.

Discovery of Chromatin-Associated Proteins via Sequence-Specific Capture and Mass Spectrometric Protein Identification in Saccharomyces cerevisiae.

DNA-protein interactions play critical roles in the control of genome expression and other fundamental processes. An essential element in understanding how these systems function is to identify their molecular components. We present here a novel strategy, Hybridization Capture of Chromatin Associated Proteins for Proteomics (HyCCAPP), to identify proteins that are interacting with any given region of the genome. This technology identifies and quantifies the proteins that are specifically interacting with a genomic region of interest by sequence-specific hybridization capture of the target region from in vivo cross-linked chromatin, followed by mass spectrometric identification and quantification of associated proteins. We demonstrate the utility of HyCCAPP by identifying proteins associated with three multicopy and one single-copy loci in yeast. In each case, a locus-specific pattern of target-associated proteins was revealed. The binding of previously unknown proteins was confirmed by ChIP in 11 of 17 cases. The identification of many previously known proteins at each locus provides strong support for the ability of HyCCAPP to correctly identify DNA-associated proteins in a sequence-specific manner, while the discovery of previously unknown proteins provides new biological insights into transcriptional and regulatory processes at the target locus.

Advanced methods for the analysis of chromatin-associated proteins.

DNA-protein interactions are central to gene expression and chromatin regulation and have become one of the main focus areas of the ENCODE consortium. Advances in mass spectrometry and associated technologies have facilitated studies of these interactions, revealing many novel DNA-interacting proteins and histone posttranslational modifications. Proteins interacting at a single locus or at multiple loci have been targeted in these recent studies, each requiring a separate analytical strategy for isolation and analysis of DNA-protein interactions. The enrichment of target chromatin fractions occurs via a number of methods including immunoprecipitation, affinity purification, and hybridization, with the shared goal of using proteomics approaches as the final readout. The result of this is a number of exciting new tools, with distinct strengths and limitations that can enable highly robust and novel chromatin studies when applied appropriately. The present review compares and contrasts these methods to help the reader distinguish the advantages of each approach.

Cochlear Implant Outcomes in Patients With TMTC2 -Associated Sensorineural Hearing Loss and Auditory Neuropathy/Auditory Dys-Synchrony.

OBJECTIVE: To investigate the outcomes of cochlear implantation in patients with TMTC2 -associated sensorineural hearing loss and auditory neuropathy/auditory dys-synchrony. PATIENTS: Adult and pediatric cochlear implant (CI) patients followed in an academic center who tested positive for TMTC2 genetic variant rs35725509. INTERVENTION: Cochlear implantation. MAIN OUTCOME MEASURES: Speech perception scores in quiet. RESULTS: Ten CI patients were identified with TMTC2 variant rs35725509 out of 157 patients who underwent genetic testing (i.e., 6.3% of patients tested). All demonstrated progressive, bilateral hearing loss with severe-to-profound audiometric thresholds preoperatively. Pre-CI and 1-year post-CI speech recognition percent correct scores were compared. Post-CI speech perception (mean 61.0%, standard deviation 31.4%) was significantly higher than pre-CI speech perception (mean 21.0%, standard deviation 27.0%) ( p = 0.002). Individually, 9 of the 10 subjects experienced significant improvements in speech perception pre- to post-CI ( p < 0.05). Electrically evoked compound action potential measures were available for five patients, and all showed normal electrically evoked compound action potential thresholds. CONCLUSION: Patients with TMTC2 -associated sensorineural hearing loss and auditory neuropathy/auditory dys-synchrony have significantly improved speech perception outcomes with cochlear implantation and should be considered candidates for this intervention if there are no other contraindications.

Genetic regulatory effects in response to a high-cholesterol, high-fat diet in baboons.

Steady-state expression quantitative trait loci (eQTLs) explain only a fraction of disease-associated loci identified through genome-wide association studies (GWASs), while eQTLs involved in gene-by-environment (GxE) interactions have rarely been characterized in humans due to experimental challenges. Using a baboon model, we found hundreds of eQTLs that emerge in adipose, liver, and muscle after prolonged exposure to high dietary fat and cholesterol. Diet-responsive eQTLs exhibit genomic localization and genic features that are distinct from steady-state eQTLs. Furthermore, the human orthologs associated with diet-responsive eQTLs are enriched for GWAS genes associated with human metabolic traits, suggesting that context-responsive eQTLs with more complex regulatory effects are likely to explain GWAS hits that do not seem to overlap with standard eQTLs. Our results highlight the complexity of genetic regulatory effects and the potential of eQTLs with disease-relevant GxE interactions in enhancing the understanding of GWAS signals for human complex disease using non-human primate models.

Perinatal maternal undernutrition in baboons modulates hepatic mitochondrial function but not metabolites in aging offspring.

We previously demonstrated in baboons that maternal undernutrition (MUN), achieved by 70 % of control nutrition, impairs fetal liver function, but long-term changes associated with aging in this model remain unexplored. Here, we assessed clinical phenotypes of liver function, mitochondrial bioenergetics, and protein abundance in adult male and female baboons exposed to MUN during pregnancy and lactation and their control counterparts. Plasma liver enzymes were assessed enzymatically. Liver glycogen, choline, and lipid concentrations were quantified by magnetic resonance spectroscopy. Mitochondrial respiration in primary hepatocytes under standard culture conditions and in response to metabolic (1 mM glucose) and oxidative (100 µM H2O2) stress were assessed with Seahorse XFe96. Hepatocyte mitochondrial membrane potential (MMP) and protein abundance were determined by tetramethylrhodamine ethyl ester staining and immunoblotting, respectively. Liver enzymes and metabolite concentrations were largely unaffected by MUN, except for higher aspartate aminotransferase levels in MUN offspring when male and female data were combined. Oxygen consumption rate, extracellular acidification rate, and MMP were significantly higher in male MUN offspring relative to control animals under standard culture. However, in females, cellular respiration was similar in control and MUN offspring. In response to low glucose challenge, only control male hepatocytes were resistant to low glucose-stimulated increase in basal and ATP-linked respiration. H2O2 did not affect hepatocyte mitochondrial respiration. Protein markers of mitochondrial respiratory chain subunits, biogenesis, dynamics, and antioxidant enzymes were unchanged. Male-specific increases in mitochondrial bioenergetics in MUN offspring may be associated with increased energy demand in these animals. The similarity in systemic liver parameters suggests that changes in hepatocyte bioenergetics capacity precede detectable circulatory hepatic defects in MUN offspring and that the mitochondria may be an orchestrator of liver programming outcome.

Current analysis platforms and methods for detecting copy number variation.

Copy number variation (CNV), generated through duplication or deletion events that affect one or more loci, is widespread in the human genomes and is often associated with functional consequences that may include changes in gene expression levels or fusion of genes. Genome-wide association studies indicate that some disease phenotypes and physiological pathways might be impacted by CNV in a small number of characterized genomic regions. However, the pervasiveness and full impact of such variation remains unclear. Suitable analytic methods are needed to thoroughly mine human genomes for genomic structural variation, and to explore the interplay between observed CNV and disease phenotypes, but many medical researchers are unfamiliar with the features and nuances of recently developed technologies for detecting CNV. In this article, we evaluate a suite of commonly used and recently developed approaches to uncovering genome-wide CNVs and discuss the relative merits of each.

Quantitative characterization of changes in the cardiac mitochondrial proteome during anesthetic preconditioning and ischemia.

Changes in mitochondrial bioenergetics have been proposed to be critical for triggering and effecting anesthetic-induced preconditioning (APC) against cardiac ischemia and reperfusion injury. The objective of this study was to analyze changes in mitochondrial protein levels and link those changes to potential functional changes. A (18)O-labeling method was applied for relative comparison of cardiac mitochondrial samples from control and isoflurane exposed rats before and after ischemia and reperfusion. Wistar rats were exposed to isoflurane for 30 min (APC) or did not receive the anesthetic (control). Rats were subjected to 30 min coronary occlusion and 15 min reperfusion without (ischemia) or after APC (ischemia + APC). The following comparisons were made: control vs. APC, control vs. ischemia, and APC vs. ischemia + APC. Proteins were analyzed by liquid chromatography-mass spectrometry. A total of 98 proteins currently annotated as mitochondrial proteins in the UniProt database were positively identified from three replicate experiments. Most of the changes during APC and ischemia occur in complexes of the electron transport chain. Overall, fewer changes in ETC complexes were found when comparing APC with APC + ischemia than when comparing control and ischemia. This corresponds to the preservation of bioenergetics due to APC after ischemia and reperfusion as indicated by preserved ATP level and generation. APC itself induced changes in complex I, but those changes were not correlated with activity changes in mitochondria after APC. Thus, a proteomic mass spectral approach does not only assess quantitative changes without prior knowledge of proteins, but also allows insight into the mechanisms of ischemia and reperfusion injury and APC.

Variant discovery in targeted resequencing using whole genome amplified DNA.

BACKGROUND: Next generation sequencing and advances in genomic enrichment technologies have enabled the discovery of the full spectrum of variants from common to rare alleles in the human population. The application of such technologies can be limited by the amount of DNA available. Whole genome amplification (WGA) can overcome such limitations. Here we investigate applicability of using WGA by comparing SNP and INDEL variant calls from a single genomic/WGA sample pair from two capture separate experiments: a 50 Mbp whole exome capture and a custom capture array of 4 Mbp region on chr12. RESULTS: Our results comparing variant calls derived from genomic and WGA DNA show that the majority of variant SNP and INDEL calls are common to both callsets, both at the site and genotype level and suggest that allele bias plays a minimal role when using WGA DNA in re-sequencing studies. CONCLUSIONS: Although the results of this study are based on a limited sample size, they suggest that using WGA DNA allows the discovery of the vast majority of variants, and achieves high concordance metrics, when comparing to genomic DNA calls.