Timothy E. Sweeney

Heme Oxygenase-1 Couples Activation of Mitochondrial Biogenesis to Anti-inflammatory Cytokine Expression

The induction of heme oxygenase-1 (HO-1; Hmox1) by inflammation, for instance in sepsis, is associated both with an anti-inflammatory response and with mitochondrial biogenesis. Here, we tested the idea that HO-1, acting through the Nfe2l2 (Nrf2) transcription factor, links anti-inflammatory cytokine expression to activation of mitochondrial biogenesis. HO-1 induction after LPS stimulated anti-inflammatory IL-10 and IL-1 receptor antagonist (IL-1Ra) expression in mouse liver, human HepG2 cells, and mouse J774.1 macrophages but blunted tumor necrosis factor-α expression. This was accompanied by nuclear Nfe2l2 accumulation and led us to identify abundant Nfe2l2 and other mitochondrial biogenesis transcription factor binding sites in the promoter regions of IL10 and IL1Ra compared with pro-inflammatory genes regulated by NF-κΒ. Mechanistically, HO-1, through its CO product, enabled these transcription factors to bind the core IL10 and IL1Ra promoters, which for IL10 included Nfe2l2, nuclear respiratory factor (NRF)-2 (Gabpa), and MEF2, and for IL1Ra, included NRF-1 and MEF2. In cells, Hmox1 or Nfe2l2 RNA silencing prevented IL-10 and IL-1Ra up-regulation, and HO-1 induction failed post-LPS in Nfe2l2-silenced cells and post-sepsis in Nfe2l2−/− mice. Nfe2l2−/− mice compared with WT mice, showed more liver damage, higher mortality, and ineffective CO rescue in sepsis. Nfe2l2−/− mice in sepsis also generated higher hepatic TNF-α mRNA levels, lower NRF-1 and PGC-1α mRNA levels, and no enhancement of anti-inflammatory Il10, Socs3, or bcl-xL gene expression. These findings disclose a highly structured transcriptional network that couples mitochondrial biogenesis to counter-inflammation with major implications for immune suppression in sepsis.

A comprehensive time-course–based multicohort analysis of sepsis and sterile inflammation reveals a robust diagnostic gene set

Five publicly available gene expression cohorts comparing SIRS/trauma to sepsis were split into time-matched subcohorts and summarized via multicohort analysis, which yielded an 11-gene set that was validated for discriminating patients with sepsis or infection in 15 independent cohorts. Uncovering the tracks of sepsis Sepsis, or systemic inflammation resulting from infection, is notoriously difficult to distinguish from other causes of inflammation, which can be a nonspecific response to noninfectious conditions. A delay in recognition of sepsis and initiation of antibiotics can be deadly. Conversely, preemptively starting antibiotics in a patient misdiagnosed with sepsis exposes the patient to side effects of the drugs. Sweeney et al. evaluated a large collection of data from patients with sepsis or sterile inflammation and identified a set of genes that distinguish the two conditions. The authors also discovered that gene expression in these conditions changes over time, and thus any attempts to identify sepsis patients by gene expression would need to consider the time since the onset of disease. Although several dozen studies of gene expression in sepsis have been published, distinguishing sepsis from a sterile systemic inflammatory response syndrome (SIRS) is still largely up to clinical suspicion. We hypothesized that a multicohort analysis of the publicly available sepsis gene expression data sets would yield a robust set of genes for distinguishing patients with sepsis from patients with sterile inflammation. A comprehensive search for gene expression data sets in sepsis identified 27 data sets matching our inclusion criteria. Five data sets (n = 663 samples) compared patients with sterile inflammation (SIRS/trauma) to time-matched patients with infections. We applied our multicohort analysis framework that uses both effect sizes and P values in a leave-one-data set-out fashion to these data sets. We identified 11 genes that were differentially expressed (false discovery rate ≤1%, inter–data set heterogeneity P > 0.01, summary effect size >1.5-fold) across all discovery cohorts with excellent diagnostic power [mean area under the receiver operating characteristic curve (AUC), 0.87; range, 0.7 to 0.98]. We then validated these 11 genes in 15 independent cohorts comparing (i) time-matched infected versus noninfected trauma patients (4 cohorts), (ii) ICU/trauma patients with infections over the clinical time course (3 cohorts), and (iii) healthy subjects versus sepsis patients (8 cohorts). In the discovery Glue Grant cohort, SIRS plus the 11-gene set improved prediction of infection (compared to SIRS alone) with a continuous net reclassification index of 0.90. Overall, multicohort analysis of time-matched cohorts yielded 11 genes that robustly distinguish sterile inflammation from infectious inflammation.

Metabolic surgery: Action via hormonal milieu changes, changes in bile acids or gut microbiota? A summary of the literature

Obesity and type 2 diabetes remain epidemic problems. Different bariatric surgical techniques causes weight loss and diabetes remission to varying degrees. The underlying mechanisms of the beneficial effects of bariatric surgery are complex, and include changes in diet and behaviour, as well as changes in hormones, bile acid flow, and gut bacteria. We summarized the effects of multiple different bariatric procedures, and their resulting effects on several hormones (leptin, ghrelin, adiponectin, glucagon-like peptide 1 (GLP-1), peptide YY, and glucagon), bile acid changes in the gut and the serum, and resulting changes to the gut microbiome. As much as possible, we have tried to incorporate multiple studies to try to explain underlying mechanistic changes. What emerges from the data is a picture of clear differences between restrictive and metabolic procedures. The latter, in particular the roux-en-Y gastric bypass, induces large and distinctive changes in most measured fat and gut hormones, including early and sustained increase in GLP-1, possible through intestinal bile acid signalling. The changes in bile flow and the gut microbiome are causally inseparable so far, but new studies show that each contributes to the effects of weight loss and diabetes resolution.

Genome-wide expression for diagnosis of pulmonary tuberculosis: a multicohort analysis

BACKGROUND Active pulmonary tuberculosis is difficult to diagnose and treatment response is difficult to effectively monitor. A WHO consensus statement has called for new non-sputum diagnostics. The aim of this study was to use an integrated multicohort analysis of samples from publically available datasets to derive a diagnostic gene set in the peripheral blood of patients with active tuberculosis. METHODS We searched two public gene expression microarray repositories and retained datasets that examined clinical cohorts of active pulmonary tuberculosis infection in whole blood. We compared gene expression in patients with either latent tuberculosis or other diseases versus patients with active tuberculosis using our validated multicohort analysis framework. Three datasets were used as discovery datasets and meta-analytical methods were used to assess gene effects in these cohorts. We then validated the diagnostic capacity of the three gene set in the remaining 11 datasets. FINDINGS A total of 14 datasets containing 2572 samples from 10 countries from both adult and paediatric patients were included in the analysis. Of these, three datasets (N=1023) were used to discover a set of three genes (GBP5, DUSP3, and KLF2) that are highly diagnostic for active tuberculosis. We validated the diagnostic power of the three gene set to separate active tuberculosis from healthy controls (global area under the ROC curve (AUC) 0·90 [95% CI 0·85-0·95]), latent tuberculosis (0·88 [0·84-0·92]), and other diseases (0·84 [0·80-0·95]) in eight independent datasets composed of both children and adults from ten countries. Expression of the three-gene set was not confounded by HIV infection status, bacterial drug resistance, or BCG vaccination. Furthermore, in four additional cohorts, we showed that the tuberculosis score declined during treatment of patients with active tuberculosis. INTERPRETATION Overall, our integrated multicohort analysis yielded a three-gene set in whole blood that is robustly diagnostic for active tuberculosis, that was validated in multiple independent cohorts, and that has potential clinical application for diagnosis and monitoring treatment response. Prospective laboratory validation will be required before it can be used in a clinical setting. FUNDING National Institute of Allergy and Infectious Diseases, National Library of Medicine, the Stanford Child Health Research Institute, the Society for University Surgeons, and the Bill and Melinda Gates Foundation.

Robust classification of bacterial and viral infections via integrated host gene expression diagnostics

An 18-gene panel distinguishes patients with bacterial and viral infections as well as those with inflammation not caused by infection. Making a gene list, checking it twice Sepsis, a severe inflammation caused by infection, is a common and deadly medical condition. Sepsis therapy combines supportive treatment with interventions directed at the underlying cause of the illness, especially antibiotics for bacterial infections. Unfortunately, it can be difficult to distinguish patients with noninfectious inflammation from those with bacterial and viral infections, and only those with bacterial sepsis derive any benefit from antibiotics. Sweeney et al. have previously analyzed large numbers of patients across many cohorts to derive a blood test identifying which patients with sepsis-like symptoms have an underlying infection. Now, the authors expanded their analysis to create an integrated score that not only identifies infected patients but also classifies their infection as bacterial or viral, suggesting appropriate treatment. Improved diagnostics for acute infections could decrease morbidity and mortality by increasing early antibiotics for patients with bacterial infections and reducing unnecessary antibiotics for patients without bacterial infections. Several groups have used gene expression microarrays to build classifiers for acute infections, but these have been hampered by the size of the gene sets, use of overfit models, or lack of independent validation. We used multicohort analysis to derive a set of seven genes for robust discrimination of bacterial and viral infections, which we then validated in 30 independent cohorts. We next used our previously published 11-gene Sepsis MetaScore together with the new bacterial/viral classifier to build an integrated antibiotics decision model. In a pooled analysis of 1057 samples from 20 cohorts (excluding infants), the integrated antibiotics decision model had a sensitivity and specificity for bacterial infections of 94.0 and 59.8%, respectively (negative likelihood ratio, 0.10). Prospective clinical validation will be needed before these findings are implemented for patient care.

Integrated, Multi-cohort Analysis Identifies Conserved Transcriptional Signatures across Multiple Respiratory Viruses

Summary Respiratory viral infections are a significant burden to healthcare worldwide. Many whole genome expression profiles have identified different respiratory viral infection signatures, but these have not translated to clinical practice. Here, we performed two integrated, multi-cohort analyses of publicly available transcriptional data of viral infections. First, we identified a common host signature across different respiratory viral infections that could distinguish (1) individuals with viral infections from healthy controls and from those with bacterial infections, and (2) symptomatic from asymptomatic subjects prior to symptom onset in challenge studies. Second, we identified an influenza-specific host response signature that (1) could distinguish influenza-infected samples from those with bacterial and other respiratory viral infections, (2) was a diagnostic and prognostic marker in influenza-pneumonia patients and influenza challenge studies, and (3) was predictive of response to influenza vaccine. Our results have applications in the diagnosis, prognosis, and identification of drug targets in viral infections.

Differential regulation of the PGC family of genes in a mouse model of Staphylococcus aureus sepsis.

The PGC family of transcriptional co-activators (PGC-1α [Ppargc1a], PGC-1β [Ppargc1b], and PRC [Pprc]) coordinates the upregulation of mitochondrial biogenesis, and Ppargc1a is known to be activated in response to mitochondrial damage in sepsis. Therefore, we postulated that the PGC family is regulated by the innate immune system. We investigated whether mitochondrial biogenesis and PGC gene expression are disrupted in an established model of Staphylococcus aureus sepsis both in mice with impaired innate immune function (TLR2−/− and TLR4−/−) and in wild-type controls. We found an early up-regulation of Ppargc1a and Ppargc1b post-infection (at 6 h) in WT mice, but the expression of both genes was concordantly dysregulated in TLR2−/− mice (no increase at 6 h) and in TLR4−/− mice (amplified at 6 h). However, the third family member, PRC, was regulated differently, and its expression increased significantly at 24 h in all three mouse strains (WT, TLR2−/−, and TLR4−/−). In silico analyses showed that Ppargc1a and Ppargc1b share binding sites for microRNA mmu-mir-202-3p. Thus, miRNA-mediated post-transcriptional mRNA degradation could account for the failure to increase the expression of both genes in TLR2−/− mice. The expression of mmu-mir-202-3p was measured by real-time PCR and found to be significantly increased in TLR2−/− but not in WT or TLR4−/− mice. In addition, it was found that mir-202-3p functionally decreases Ppargc1a mRNA in vitro. Thus, both innate immune signaling through the TLRs and mir-202-3p-mediated mRNA degradation are implicated in the co-regulation of Ppargc1a and Ppargc1b during inflammation. Moreover, the identification of mir-202-3p as a potential factor for Ppargc1a and Ppargc1b repression in acute inflammation may open new avenues for mitochondrial research and, potentially, therapy.

Co-regulation of nuclear respiratory factor-1 by NFκB and CREB links LPS-induced inflammation to mitochondrial biogenesis

The nuclear respiratory factor-1 (NRF1) gene is activated by lipopolysaccharide (LPS), which might reflect TLR4-mediated mitigation of cellular inflammatory damage via initiation of mitochondrial biogenesis. To test this hypothesis, we examined NRF1 promoter regulation by NFκB, and identified interspecies-conserved κB-responsive promoter and intronic elements in the NRF1 locus. In mice, activation of Nrf1 and its downstream target, Tfam, by Escherichia coli was contingent on NFκB, and in LPS-treated hepatocytes, NFκB served as an NRF1 enhancer element in conjunction with NFκB promoter binding. Unexpectedly, optimal NRF1 promoter activity after LPS also required binding by the energy-state-dependent transcription factor CREB. EMSA and ChIP assays confirmed p65 and CREB binding to the NRF1 promoter and p65 binding to intron 1. Functionality for both transcription factors was validated by gene-knockdown studies. LPS regulation of NRF1 led to mtDNA-encoded gene expression and expansion of mtDNA copy number. In cells expressing plasmid constructs containing the NRF-1 promoter and GFP, LPS-dependent reporter activity was abolished by cis-acting κB-element mutations, and nuclear accumulation of NFκB and CREB demonstrated dependence on mitochondrial H2O2. These findings indicate that TLR4-dependent NFκB and CREB activation co-regulate the NRF1 promoter with NFκB intronic enhancement and redox-regulated nuclear translocation, leading to downstream target-gene expression, and identify NRF-1 as an early-phase component of the host antibacterial defenses.

Staphylococcus aureus Sepsis and Mitochondrial Accrual of the 8-Oxoguanine DNA Glycosylase DNA Repair Enzyme in Mice

RATIONALE Damage to mitochondrial DNA (mtDNA) by the production of reactive oxygen species during inflammatory states, such as sepsis, is repaired by poorly understood mechanisms. OBJECTIVES To test the hypothesis that the DNA repair enzyme, 8-oxoguanine DNA glycosylase (OGG1), contributes to mtDNA repair in sepsis. METHODS Using a well-characterized mouse model of Staphylococcus aureus sepsis, we analyzed molecular markers for mitochondrial biogenesis and OGG1 translocation into liver mitochondria as well as OGG1 mRNA expression at 0, 24, 48, and 72 hours after infection. The effects of OGG1 RNA silencing on mtDNA content were determined in control, tumor necrosis factor-α, and peptidoglycan-exposed rat hepatoma cells. Based on in situ analysis of the OGG1 promoter region, chromatin immunoprecipitation assays were performed for nuclear respiratory factor (NRF)-1 and NRF-2α GA-binding protein (GABP) binding to the promoter of OGG1. MEASUREMENTS AND MAIN RESULTS Mice infected with 10(7) cfu S. aureus intraperitoneally demonstrated hepatic oxidative mtDNA damage and significantly lower hepatic mtDNA content as well as increased mitochondrial OGG1 protein and enzyme activity compared with control mice. The infection also caused increases in hepatic OGG1 transcript levels and NRF-1 and NRF-2α transcript and protein levels. A bioinformatics analysis of the Ogg1 gene locus identified several promoter sites containing NRF-1 and NRF-2α DNA binding motifs, and chromatin immunoprecipitation assays confirmed in situ binding of both transcription factors to the Ogg1 promoter within 24 hours of infection. CONCLUSIONS These studies identify OGG1 as an early mitochondrial response protein during sepsis under regulation by the NRF-1 and NRF-2α transcription factors that regulate mitochondrial biogenesis.

Methods to increase reproducibility in differential gene expression via meta-analysis

Findings from clinical and biological studies are often not reproducible when tested in independent cohorts. Due to the testing of a large number of hypotheses and relatively small sample sizes, results from whole-genome expression studies in particular are often not reproducible. Compared to single-study analysis, gene expression meta-analysis can improve reproducibility by integrating data from multiple studies. However, there are multiple choices in designing and carrying out a meta-analysis. Yet, clear guidelines on best practices are scarce. Here, we hypothesized that studying subsets of very large meta-analyses would allow for systematic identification of best practices to improve reproducibility. We therefore constructed three very large gene expression meta-analyses from clinical samples, and then examined meta-analyses of subsets of the datasets (all combinations of datasets with up to N/2 samples and K/2 datasets) compared to a ‘silver standard’ of differentially expressed genes found in the entire cohort. We tested three random-effects meta-analysis models using this procedure. We showed relatively greater reproducibility with more-stringent effect size thresholds with relaxed significance thresholds; relatively lower reproducibility when imposing extraneous constraints on residual heterogeneity; and an underestimation of actual false positive rate by Benjamini–Hochberg correction. In addition, multivariate regression showed that the accuracy of a meta-analysis increased significantly with more included datasets even when controlling for sample size.