FDR Analysis Research Articles

Investigating the shared genetic architecture between depression and subcortical volumes

Depression, a widespread and highly heritable mental health condition, profoundly affects millions of individuals worldwide. Neuroimaging studies have consistently revealed volumetric abnormalities in subcortical structures associated with depression. However, the genetic underpinnings shared between depression and subcortical volumes remain inadequately understood. Here, we investigate the extent of polygenic overlap using the bivariate causal mixture model (MiXeR), leveraging summary statistics from the largest genome-wide association studies for depression (N = 674,452) and 14 subcortical volumetric phenotypes (N = 33,224). Additionally, we identify shared genomic loci through conditional/conjunctional FDR analyses. MiXeR shows that subcortical volumetric traits share a substantial proportion of genetic variants with depression, with 44 distinct shared loci identified by subsequent conjunctional FDR analysis. These shared loci are predominantly located in intronic regions (58.7%) and non-coding RNA intronic regions (25.4%). The 269 protein-coding genes mapped by these shared loci exhibit specific developmental trajectories, with the expression level of 55 genes linked to both depression and subcortical volumes, and 30 genes linked to cognitive abilities and behavioral symptoms. These findings highlight a shared genetic architecture between depression and subcortical volumetric phenotypes, enriching our understanding of the neurobiological underpinnings of depression.

Identifying miRNA as biomarker for breast cancer subtyping using association rule

— This paper presents a comprehensive study focused on breast cancer subtyping, utilizing a multifaceted approach that integrates feature selection, machine learning classifiers, and miRNA regulatory networks. The feature selection process begins with the CFS algorithm, followed by the Apriori algorithm for association rule generation, resulting in the identification of significant features tailored to Luminal A, Luminal B, HER-2 enriched, and Basal-like subtypes. The subsequent application of Random Forest (RF) and Support Vector Machine (SVM) classifiers yielded promising results, with the SVM model achieving an overall accuracy of 76.60 % and the RF model demonstrating robust performance at 80.85 %. Detailed accuracy metrics revealed strengths and areas for refinement, emphasizing the potential for optimizing subtype-specific recall. To explore the regulatory landscape in depth, an analysis of selected miRNAs was conducted using MIENTURNET, a tool for visualizing miRNA-target interactions. While FDR analysis raised concerns for HER-2 and Basal-like subtypes, Luminal A and Luminal B subtypes showcased significant miRNA-gene interactions. Functional enrichment analysis for Luminal A highlighted the role of Ovarian steroidogenesis, implicating specific miRNAs such as hsa-let-7c-5p and hsa-miR-125b-5p as potential diagnostic biomarkers and regulators of Luminal A breast cancer. Luminal B analysis uncovered associations with the MAPK signaling pathway, with miRNAs like hsa-miR-203a-3p and hsa-miR-19a-3p exhibiting potential diagnostic and therapeutic significance. In conclusion, this integrative approach combines machine learning techniques with miRNA analysis to provide a holistic understanding of breast cancer subtypes. The identified miRNAs and associated pathways offer insights into potential diagnostic biomarkers and therapeutic targets, contributing to the ongoing efforts to improve breast cancer diagnostics and personalized treatment strategies.

Identification of genetic loci that overlap between schizophrenia and metabolic syndrome

Patients with schizophrenia (SCZ) frequently exhibit an elevated risk of metabolic syndrome (MetS), which may lead to a worse clinical outcome. Even though these two phenotypes are genetically linked, little is known about their shared genetic determinants. Here, we investigated whether SCZ and MetS share genetic risk factors. To examine the genetic overlap between the two disorders, we applied a comprehensive genetic overlap analysis by integrating GWAS data for SCZ (n = 320,404) and MetS (n = 291,107) at the genome, genetic variants, and gene levels. At the genome level, we observed polygenic overlap between SCZ and MetS by utilizing LDSC (rg=−0.09, P = 1 × 10−4) and GNOVA (rho=−0.04, P = 1.39 × 10−8) analysis. At the SNP level, we performed conjunctional FDR (conjFDR) analysis to identify genetic variants simultaneously associated with two phenotypes. Based on conjFDR < 0.05, we identified 22 loci shared between SCZ and MetS. At the gene level, we further demonstrated that SCZ- and MetS-inferred gene expression overlapped across 49 GTEx tissues and highlighted the PCCB and KCTD13 genes as putative mediators of the genetic association. Overall, these findings shed novel light on the association between SCZ and MetS, and potentially enhance our knowledge of the high comorbidity and genetic processes that overlap between the two disorders.

Characterizing the polygenic overlaps of bipolar disorder subtypes with schizophrenia and major depressive disorder

BackgroundLarge-scale studies have shown that bipolar I disorder (BD-I) and bipolar II disorder (BD-II) have differences in genetic association with schizophrenia (SCZ) and major depressive disorder (MDD). However, the underlying shared genetic architectures between BD subtypes and both SCZ and MDD remain largely unknown. MethodsWe applied univariate and bivariate causal mixture models (MiXeR) to estimate the polygenicity and polygenic overlaps on large GWASs summary statistics of BD-I (n = 25,060), BD-II (n = 6781), SCZ (n = 69,369) and MDD (n = 170,756). Then, conjunctional false discovery rate approach was used to identify specific shared genetic loci between BD subtypes and both SCZ and MDD. ResultsUnivariate MiXeR revealed that BD-II was substantially more polygenic (22.37 K causal variants) as compared to BD-I, SCZ and MDD (7.87–12.43 K causal variants). Bivariate MiXeR revealed substantial polygenic overlaps between BD-I and SCZ (Dice-coefficient = 0.83) and between BD-I and MDD (Dice-coefficient = 0.76), which are beyond the genetic correlation (rg = 0.71 and 0.36). Conjunctional FDR analysis identified 236 distinct shared loci between BD-I and BD-II (2 loci), BD-I and SCZ (227 loci), BD-I and MDD (19 loci), BD-II and SCZ (1 locus), and BD-II and MDD (3 loci). Most of these shared loci have concordant effect directions among BD subtypes, SCZ and MDD. LimitationsThe bivariate MiXeR model was not applied for the BD-II because of insufficient power and inadequate model fit. ConclusionsThese findings provide evidence for extensive polygenic effects across BD subtypes, SCZ and MDD, which further our understanding of the potential genetic basis for the comorbid symptoms across these disorders.

Detection of Differentially Expressed Genes In Small Sets of cDNA Microarrays

Methods for testing the equality of two means are of critical importance in many areas of applied statistics. In the microarray context, it is often necessary to apply this kind of testing to small samples containing no more than a dozen elements, when inevitably the power of these tests is low. We suggest an augmentation of the classical t-test by introducing a new test statistic which we call “bio-weight.” We show by simulation that in practically important cases of small sample size, the test based on this statistic is substantially more powerful than that of the classical t-test. The power computations are accompanied by ROC and FDR analysis of the simulated microarray data.

Performance of an automatic inspection system for classification of Fusarium Moniliforme damaged corn seeds by image analysis

This paper presents algorithms for pre-processing, feature selection and classifier design which are used for Parameter Refining in the task of development of an automatic system for recognition and grading of corn seeds with external signs of Fusarium Moniliforme disease. The abilities of several feature selection methods – FDR, Scatter matrices and Stepwise Discriminant Analysis and two classification methods - Support Vector Machine (SVM) and K-Nearest Neighbours (K-NN) are investigated. Design and implementation of the system also has been showed. The system could continually present one by one positioned corn kernels to CCD camera, perform a classification procedure of captured images and discharge seeds to assigned containers. The software was developed in LabVIEW environment including image analysis and classification procedures performed using MATLAB Script. Results for total error rate of 8.4% - 7.2% from preliminary classification related to 8480 seeds from16 Bulgarian varieties and total error rate of 6.95% - 20.4% for experimental results obtained with the system during the control measurements of the seed sample are obtained.

An improvement on local FDR analysis applied to functional MRI data

BackgroundDiscovering effective connectivity between brain regions gained a lot of attention recently. A vector autoregressive model is a simple and flexible approach for exploratory structural modeling where the involvement of a large number of brain regions is crucial to avoid confounding. The non-zero coefficients of the VAR model are interpreted as actual effective connectivity between brain regions. Thus methods for a higher correct discovery rate are crucial for neuroscience. New methodWe propose an improved version of the FDR analysis procedure which would be more suitable to fMRI data. The estimates of the VAR coefficients are often not symmetric about 0 with non-zero modes. In this case, we suggest to estimate the null distribution of the estimates which is assumed symmetric about 0 in two steps: use one side of the estimates and then both sides under some condition. ResultsA theoretical argument is provided for the proposed procedure with a theorem and two types of experiments are made. In a simulation experiment, we show via ROC curves improvement over previous methods. We apply the proposed method to analyze real fMRI data with results interpreted in the language of cognitive neuroscience. Comparison with existing method(s)The proposed method outperforms the standard method in the simulation experiment with a VAR model of dimension up to 100 over a wide range of sample sizes. The improvement is made in the context of the true positive rate and performance consistency. ConclusionsThe proposed method is more appropriate for analyzing fMRI data with VAR models when the estimates of the VAR coefficients are not symmetric about 0 and have non-zero modes.

Solution to Statistical Challenges in Proteomics Is More Statistics, Not Less.

In any high-throughput scientific study, it is often essential to estimate the percent of findings that are actually incorrect. This percentage is called the false discovery rate (abbreviated "FDR"), and it is an invariant (albeit, often unknown) quantity for any well-formed study. In proteomics, it has become common practice to incorrectly conflate the protein FDR (the percent of identified proteins that are actually absent) with protein-level target-decoy, a particular method for estimating the protein-level FDR. In this manner, the challenges of one approach have been used as the basis for an argument that the field should abstain from protein-level FDR analysis altogether or even the suggestion that the very notion of a protein FDR is flawed. As we demonstrate in simple but accurate simulations, not only is the protein-level FDR an invariant concept, when analyzing large data sets, the failure to properly acknowledge it or to correct for multiple testing can result in large, unrecognized errors, whereby thousands of absent proteins (and, potentially every protein in the FASTA database being considered) can be incorrectly identified.

Aberrantly expressed mRNAs and long non-coding RNAs in patients with invasive ductal breast carcinoma: a pilot study.

Invasive ductal breast carcinoma (IDBC) is the most prevalent type of invasive breast cancer in females; however, the pathogenesis of IDBC remains to be elucidated. Therefore, the identification of novel markers may enhance current understanding of the initiation and development of IDBC as well as elucidate potential therapeutic targets for effective treatment of IDBC. In the present study, a pilot study was conducted to screen for potential mRNAs and long non-coding (lnc)RNAs that exhibit aberrantly altered expression in patients with IDBC. Fresh breast cancer specimens and normal breast tissues were obtained from three female patients with IDBC aged ≥60 years following a modified radical mastectomy without chemotherapy. Expression levels of 44,244 probes were detected and included in the analysis, of which 22,078 (49.9%) were mRNAs and 22,166 (50.1%) were lncRNAs. Potential marker screening was performed using paired t-tests (criterion 1), false discovery rates (FDR; criterion 2) and sure independence screening procedures based on distance correlations (DC-SIS; criterion 3). The results showed that in IDBC tissues 3,510 probes had a ≥2-fold statistically significant change in expression levels compared to those in the corresponding normal breast tissue (P<0.05); in addition, following FDR analysis, 353 probes were found to have significantly altered expression levels. Furthermore, DC-SIS analysis identified 18 probes (12 mRNA and 6 lncRNAs) with significantly altered expression levels in IDBC tissue; these 18 probes therefore demonstrated significant results in all three criteria. Several of the mRNAs identified have been previously reported to be involved in signal transduction, protein binding, and cancer pathways, and the present study revealed that the majority of their gene products were located in the cytoplasm. Two of the six identified lncRNAs demonstrated a >10-fold decrease in expression levels in IDBC tissues compared to that in the normal breast tissue. However, further studies are required in order to elucidate the biological functions of the identified probes.

Plasma metabolomic profiling in adults with cystic fibrosis and cystic fibrosis‐related diabetes (248.1)

Background: Advances in metabolomics enable broad exploration of metabolism in cystic fibrosis (CF) and cystic fibrosis‐related diabetes (CFRD).Methods: Plasma from 30 CF adults hospitalized for pulmonary symptoms (15 with CFRD) and 17 healthy controls were analyzed by high‐resolution LC‐MS. Data was analyzed in R using an in‐house pipeline that performs log2‐transformation, quantile normalization, FDR analysis using limma, and hierarchal clustering analysis (HCA). The METLIN Database was used to match ions to known metabolites. A targeted metabolome‐wide association study (MWAS) with serum 25‐hydroxyvitamin D (25(OH)D) levels was also done.Results: Of 11,736 metabolites, 4,522 significantly differed between CF and controls (q = 0.01). HCA revealed 55 distinct metabolomic clusters differentiating CF and control subjects. Derivatives of vitamin D3 and several acylcarnitines were lower in CF. MWAS identified 268 metabolites associated with serum 25(OH)D levels, including octanoylcarnitine (r = 0.51, P = 0.01). A total of 74 metabolites significantly differentiated CF patients ± CFRD (q = 0.05), including coenzyme A and ATP.Conclusions: Plasma metabolomic profiling can distinguish adults with or without CF and CF patients ± CFRD; other metabolites are linked with vitamin D status. These pilot data suggest fatty acid β‐oxidation pathways may be perturbed in certain CF patients.Grant Funding Source: Supported by National Institutes of Health (T32 DK007298‐32S1, K23 AR054334, and K24 RR023356).

An unsupervised approach to predict functional relations between genes based on expression data.

This work presents a novel approach to predict functional relations between genes using gene expression data. Genes may have various types of relations between them, for example, regulatory relations, or they may be concerned with the same protein complex or metabolic/signaling pathways and obviously gene expression data should contain some clues to such relations. The present approach first digitizes the log-ratio type gene expression data of S. cerevisiae to a matrix consisting of 1, 0, and −1 indicating highly expressed, no major change, and highly suppressed conditions for genes, respectively. For each gene pair, a probability density mass function table is constructed indicating nine joint probabilities. Then gene pairs were selected based on linear and probabilistic relation between their profiles indicated by the sum of probability density masses in selected points. The selected gene pairs share many Gene Ontology terms. Furthermore a network is constructed by selecting a large number of gene pairs based on FDR analysis and the clustering of the network generates many modules rich with similar function genes. Also, the promoters of the gene sets in many modules are rich with binding sites of known transcription factors indicating the effectiveness of the proposed approach in predicting regulatory relations.

The Host Genetic Background of DNA Repair Mechanisms Represents An Independent Predictor of Progression and Survival in Diffuse Large B-Cell Lymphoma Treated with R-CHOP.

Abstract 442Scant information is availabel on the impact of the host genetic background in predicting outcome of diffuse large B cell lymphoma (DLBCL). We recently documented that host single nucleotide polymorphisms (SNPs) affecting doxorubicin pharmacodynamics and alkylator detoxification may predict outcome in R-CHOP-treated DLBCL (Rossi et al, Leukemia 2009; 23:118-26). Several DNA repair mechanisms are involved in resistance to R-CHOP drugs. We verified whether SNPs of DNA repair genes may further refine prognostic stratification and toxicity prediction in DLBCL treated with R-CHOP. The study was based on 163 consecutive DLBCL treated with R-CHOP and provided with a prospectively collected dataset. At diagnosis, age >60 years was observed in 104/163 (63.8%) cases, ECOG PS >1 in 21/163 (12.9%), extranodal sites >1 in 41/163 (25.2%), Ann Arbor stage III-IV in 85/163 (52.1%), bulky disease in 46/163 (28.2%), B symptoms in 38/163 (23.3%), LDH elevation in 75/163 (46.0%), IPI >2 in 55/163 (33.7%). Median follow up was 40 months. Candidate SNPs selected for the analysis belonged to genes involved in repairing DNA damage produced by R-CHOP drugs, and included SNPs affecting the following DNA repair mechanisms: i) base excision repair (OGG1 rs1052133); ii) nucleotide excision repair (ERCC2 rs1799793, ERCC2 rs13181, ERCC6 rs3793784, ERCC6 rs2228528, ERCC6 rs2228529, XPA rs1800975, XPC rs2227999, XPC rs2228000); iii) mismatch repair (MLH1 rs1799977); iv) double strand break repair (LIG4 rs1805388, XRCC6 rs132788, XRCC6 rs55751129, BRCA1 rs799917). Genotyping was performed by SNP-minisequencing on DNA extracted from granulocytes. Clinical endpoints considered in the study were overall survival (OS), progression free survival (PFS) and toxicity. Associations of SNPs with clinical endpoints were controlled for multiple testing by FDR analysis. Among the SNPs included in the study, univariate log-rank analysis controlled for multiple comparisons by FDR testing identified MLH1 rs1799977 as a predictor of both PFS and OS in DLBCL treated with R-CHOP. MLH1 encodes a DNA mismatch repair gene involved in alkylating agents and doxorubicin resistance. DLBCL patients carrying the MLH1 rs1799977 AG/GG genotypes displayed a poorer PFS (Events/N: 23/52; 3-year PFS: 51.9%) compared to DLBCL patients carrying the MLH1 rs1799977 AA genotype (Events/N: 33/111; 3-year PFS: 72.0%) (p=.020; q=.240) (Fig. 1). Other variables predicting PFS were ECOG PS (p=.042), Ann Arbor stage (p=.035), bulky disease (p=.001), number of extranodal sites (p=.009), LDH (p=.007), and IPI (p=.002). Multivariate analysis selected MLH1 rs1799977 AG/GG (HR: 1.17; p=.034) as an independent predictor of PFS, along with IPI (HR: 1.44; p=.002) and bulky disease (HR: 2.20, p=.004). The MLH1 rs1799977 genotype was also relevant for predicting DLBCL survival, since DLBCL patients carrying the MLH1 rs1799977 AG/GG genotypes displayed a poorer OS (Events/N: 19/52; 3-year OS: 61.8%) compared to DLBCL patients carrying the MLH1 rs1799977 AA genotype (Events/N: 18/111; 3-year OS: 83.3%) (p=.001; q=.013) (Fig. 2). Other variables predicting OS were age (p=.026), ECOG PS (p=.002), bulky disease (p<.001), number of extranodal sites (p=.027), IPI (p=.020), relative dose intensity of doxorubicin (p=.017), and comorbidity score (p=.003). Multivariate analysis selected MLH1 rs1799977 AG/GG (HR: 2.84; p=.002) as an independent predictor of OS, along with IPI (HR: 1.40; p=.013) and bulky disease (HR: 3.20; p=.001). The impact of SNPs was also evaluated for toxicity in 803 R-CHOP courses. A multivariate model for toxicity was built by generalized estimating equations (GEE), which adjust for the clustering of treatment courses within a patient. After correcting for multiple comparisons and adjusting for potentially confounding variables, none of the DNA repair SNPs analysed was found to be associated with R-CHOP toxicity. Overall, MLH1 rs1799977 SNP is an independent predictor of progression and survival in DLBCL treated with R-CHOP. The biologic plausibility of this association is supported by two lines evidence: i) MLH1 rs1799977 G variant allele is known to reduce MLH1 protein expression and function; ii) loss of MLH1 DNA mismatch repair function is known to result in in vitro resistance to doxorubicin and alkylating agents. Consistently, DLBCL carriers of the MLH1 rs1799977 AG/GG genotypes displayed poor PFS and OS possibly due to altered MLH1 function. [Display omitted] [Display omitted] Disclosures:No relevant conflicts of interest to declare.

Factoring local sequence composition in motif significance analysis.

We recently introduced a biologically realistic and reliable significance analysis of the output of a popular class of motif finders. In this paper we further improve our significance analysis by incorporating local base composition information. Relying on realistic biological data simulation, as well as on FDR analysis applied to real data, we show that our method is significantly better than the increasingly popular practice of using the normal approximation to estimate the significance of a finder's output. Finally we turn to leveraging our reliable significance analysis to improve the actual motif finding task. Specifically, endowing a variant of the Gibbs Sampler with our improved significance analysis we demonstrate that de novo finders can perform better than has been perceived. Significantly, our new variant outperforms all the finders reviewed in a recently published comprehensive analysis of the Harbison genome-wide binding location data. Interestingly, many of these finders incorporate additional information such as nucleosome positioning and the significance of binding data.

A unified framework for finding differentially expressed genes from microarray experiments.

BackgroundThis paper presents a unified framework for finding differentially expressed genes (DEGs) from the microarray data. The proposed framework has three interrelated modules: (i) gene ranking, ii) significance analysis of genes and (iii) validation. The first module uses two gene selection algorithms, namely, a) two-way clustering and b) combined adaptive ranking to rank the genes. The second module converts the gene ranks into p-values using an R-test and fuses the two sets of p-values using the Fisher's omnibus criterion. The DEGs are selected using the FDR analysis. The third module performs three fold validations of the obtained DEGs. The robustness of the proposed unified framework in gene selection is first illustrated using false discovery rate analysis. In addition, the clustering-based validation of the DEGs is performed by employing an adaptive subspace-based clustering algorithm on the training and the test datasets. Finally, a projection-based visualization is performed to validate the DEGs obtained using the unified framework.ResultsThe performance of the unified framework is compared with well-known ranking algorithms such as t-statistics, Significance Analysis of Microarrays (SAM), Adaptive Ranking, Combined Adaptive Ranking and Two-way Clustering. The performance curves obtained using 50 simulated microarray datasets each following two different distributions indicate the superiority of the unified framework over the other reported algorithms. Further analyses on 3 real cancer datasets and 3 Parkinson's datasets show the similar improvement in performance. First, a 3 fold validation process is provided for the two-sample cancer datasets. In addition, the analysis on 3 sets of Parkinson's data is performed to demonstrate the scalability of the proposed method to multi-sample microarray datasets.ConclusionThis paper presents a unified framework for the robust selection of genes from the two-sample as well as multi-sample microarray experiments. Two different ranking methods used in module 1 bring diversity in the selection of genes. The conversion of ranks to p-values, the fusion of p-values and FDR analysis aid in the identification of significant genes which cannot be judged based on gene ranking alone. The 3 fold validation, namely, robustness in selection of genes using FDR analysis, clustering, and visualization demonstrate the relevance of the DEGs. Empirical analyses on 50 artificial datasets and 6 real microarray datasets illustrate the efficacy of the proposed approach. The analyses on 3 cancer datasets demonstrate the utility of the proposed approach on microarray datasets with two classes of samples. The scalability of the proposed unified approach to multi-sample (more than two sample classes) microarray datasets is addressed using three sets of Parkinson's Data. Empirical analyses show that the unified framework outperformed other gene selection methods in selecting differentially expressed genes from microarray data.

Size, power and false discovery rates

Modern scientific technology has provided a new class of large-scale simultaneous inference problems, with thousands of hypothesis tests to consider at the same time. Microarrays epitomize this type of technology, but similar situations arise in proteomics, spectroscopy, imaging, and social science surveys. This paper uses false discovery rate methods to carry out both size and power calculations on large-scale problems. A simple empirical Bayes approach allows the false discovery rate (fdr) analysis to proceed with a minimum of frequentist or Bayesian modeling assumptions. Closed-form accuracy formulas are derived for estimated false discovery rates, and used to compare different methodologies: local or tail-area fdr’s, theoretical, permutation, or empirical null hypothesis estimates. Two microarray data sets as well as simulations are used to evaluate the methodology, the power diagnostics showing why nonnull cases might easily fail to appear on a list of “significant” discoveries.

Warm Ischemia-induced Alterations in Oxidative and Inflammatory Proteins in Hepatic Kupffer Cells in Rats

The aim of the study was to investigate the impact of ischemia/reperfusion injury on the proteome of Kupffer cells. Lean Zucker rats (n = 6 each group) were randomized to 75 min of warm ischemia or sham operation. After reperfusion for 8 h, Kupffer cells were isolated by enzymatic perfusion and density gradient centrifugation. Proteins were tryptically digested into peptides and differentially labeled with iTRAQ (isobaric tags for relative and absolute quantitation) reagent. After fractionation by cation exchange chromatography, peptides were identified by mass spectrometry (ESI-LC-MS/MS). Spectra were interrogated against the Swiss-Prot database and quantified using ProteinProspector. The results for heat shock protein 70 and myeloperoxidase were validated by ELISA. Quantitative information for more than 1559 proteins was obtained. In the ischemia group proteins involved in inflammation were significantly up-regulated. The ratio for calgranulin B in the ischemia/sham group was 1.81 +/- 0.97 (p < 0.0001), for complement C3 the ratio was 1.81 +/- 0.49 (p < 0.0001), and for myeloperoxidase the ratio was 1.30 +/- 0.32. Myeloperoxidase was only recently documented in Kupffer cells. The antioxidative proteins Cu,Zn-superoxide dismutase (1.34 +/- 0.19; p < 0.001) and catalase (1.23 +/- 0.43; p < 0.001) were also elevated. In conclusion, ischemia/reperfusion injury induces alterations in the Kupffer cell proteome. Isotope ratio mass spectrometry is a powerful tool to investigate these reactions. The ability to simultaneously monitor several pathways involved in reperfusion stress may result in important mechanistic insight and possibly new treatment options.