Whole-blood expression of inflammasome- and glucocorticoid-related mRNAs correctly separates treatment-resistant depressed patients from drug-free and responsive patients in the BIODEP study

Cattaneo, Annamaria; Ferrari, Clarissa; Turner, Lorinda; Mariani, Nicole; Enache, Daniela; Hastings, Caitlin; Kose, Melisa; Lombardo, Giulia; McLaughlin, Anna; Nettis, Maria Antonietta; Nikkheslat, Naghmeh; Sforzini, Luca; Worrell, Courtney; Zajkowska, Zuzanna; Cattane, Nadia; Lopizzo, Nicola; Mazzelli, Monica; Pointon, Linda; Cowen, Philip J.; Cavanagh, Jonathan; Harrison, Neil A.; Boer, P. de; Jones, Declan N.C.; Drevets, Wayne C.; Mondelli, Valeria; Bullmore, Edward T.; Pariante, Carmine M.

doi:10.1038/s41398-020-00874-7

Cited by 76 publications

(72 citation statements)

References 74 publications

(107 reference statements)

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“…Evidence suggests that at around 30% of patients with depression do not respond to antidepressant treatment, with most of them having sub-chronic levels of inflammation [1][2][3], a process which potentially impacts depression-relevant brain pathways. However, despite the role of inflammation in depression, there is still a lack of anti-inflammatory strategies that are effective for these patients, safe for everyday use, and with a clear mechanism of action.…”

Section: Introductionmentioning

confidence: 99%

Omega-3 polyunsaturated fatty acids protect against inflammation through production of LOX and CYP450 lipid mediators: relevance for major depression and for human hippocampal neurogenesis

et al. 2021

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Eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) can exert antidepressant, anti-inflammatory and neuroprotective properties, but the exact molecular mechanism underlying their effects is still not fully understood. We conducted both in vitro and clinical investigations to test which EPA or DHA metabolites are involved in these anti-inflammatory, neuroprotective and antidepressant effects. In vitro, we used the human hippocampal progenitor cell line HPC0A07/03C, and pre-treated cells with either EPA or DHA, followed by interleukin 1beta (IL1β), IL6 and interferon-alpha (IFN-α). Both EPA and DHA prevented the reduction in neurogenesis and the increase in apoptosis induced by these cytokines; moreover, these effects were mediated by the lipoxygenase (LOX) and cytochrome P450 (CYP450) EPA/DHA metabolites, 5-hydroxyeicosapentaenoic acid (HEPE), 4-hydroxydocosahexaenoic acid (HDHA), 18-HEPE, 20-HDHA, 17(18)-epoxyeicosatetraenoic acid (EpETE) and 19(20)-epoxydocosapentaenoic acid (EpDPA), detected here for the first time in human hippocampal neurones using mass spectrometry lipidomics of the supernatant. In fact, like EPA/DHA, co-treatment with these metabolites prevented cytokines-induced reduction in neurogenesis and apoptosis. Moreover, co-treatment with 17(18)-EpETE and 19(20)-EpDPA and the soluble epoxide hydroxylase (sEH) inhibitor, TPPU (which prevents their conversion into dihydroxyeicosatetraenoic acid (DiHETE)/ dihydroxydocosapentaenoic acid (DiHDPA) metabolites) further enhanced their neurogenic and anti-apoptotic effects. Interestingly, these findings were replicated in a sample of n = 22 patients with a DSM-IV Major Depressive Disorder, randomly assigned to treatment with either EPA (3.0 g/day) or DHA (1.4 g/day) for 12 weeks, with exactly the same LOX and CYP450 lipid metabolites increased in the plasma of these patients following treatment with their precursor, EPA or DHA, and some evidence that higher levels of these metabolites were correlated with less severe depressive symptoms. Overall, our study provides the first evidence for the relevance of LOX- and CYP450-derived EPA/DHA bioactive lipid metabolites as neuroprotective molecular targets for human hippocampal neurogenesis and depression, and highlights the importance of sEH inhibitors as potential therapeutic strategy for patients suffering from depressive symptoms.

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Section: Introductionmentioning

confidence: 99%

Omega-3 polyunsaturated fatty acids protect against inflammation through production of LOX and CYP450 lipid mediators: relevance for major depression and for human hippocampal neurogenesis

et al. 2021

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“…Regarding immunological complexity, studies have reported various associations of serum inflammatory proteins with depression, including among others CRP, interleukin (IL)-6, IL-10, and tumour necrosis factor (TNF)-α (Goldsmith et al, 2016;Haapakoski et al, 2015;. Evidence from in-depth immunophenotyping further suggests that there may be distinct subgroups of inflammation-related depression as shown by immune cell count clustering and transcriptome analyses (Cattaneo et al, 2020;Lynall et al, 2020). These studies suggest that acutely elevated levels of inflammatory markers are associated with depression, but associations of depression with genetic/lifetime predisposition to higher inflammatory markers has been studied less frequently and primarily for CRP (Badini et al, 2020;Kappelmann et al, 2020;Milaneschi et al, 2017bMilaneschi et al, , 2016.…”

Section: Introductionmentioning

confidence: 99%

Polygenic risk for immuno-metabolic markers and specific depressive symptoms: A multi-sample network analysis study

Kappelmann

Rost

Moser

et al. 2021

Preprint

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BackgroundAbout every fourth patient with major depressive disorder (MDD) shows evidence of systemic inflammation. Previous studies have shown inflammation-depression associations of multiple serum inflammatory markers and multiple specific depressive symptoms. It remains unclear, however, if these associations extend to genetic/lifetime predisposition to higher inflammatory marker levels and what role metabolic factors such as Body Mass Index (BMI) play. It is also unclear whether inflammation-symptom associations reflect direct or indirect associations, which can be disentangled using network analysis.MethodsThis study examined associations of polygenic risk scores (PRSs) for immuno-metabolic markers (C-reactive protein [CRP], interleukin [IL]-6, IL-10, tumour necrosis factor [TNF]-α, BMI) with seven depressive symptoms in one general population sample, the UK Biobank study (n=110,010), and two patient samples, the Munich Antidepressant Response Signature (MARS, n=1,058) and Sequenced Treatment Alternatives to Relieve Depression (STAR*D, n=1,143) studies. Network analysis was applied jointly for these samples using fused graphical least absolute shrinkage and selection operator (FGL) estimation as primary analysis and, individually, using unregularized model search estimation. Stability of results was assessed using bootstrapping and three quality criteria were defined to appraise consistency of results across estimation methods, network bootstrapping, and samples.ResultsNetwork analysis results displayed to-be-expected PRS-PRS and symptom-symptom associations (termed edges), respectively, that were mostly positive. Using FGL estimation, results further suggested 28, 29, and six PRS-symptom edges in MARS, STAR*D, and UK Biobank samples, respectively. Unregularized model search estimation suggested three PRS-symptom edges in the UK Biobank sample. Applying our quality criteria to these associations indicated that only the association of higher CRP PRS with greater changes in appetite fulfilled all three criteria. Four additional associations fulfilled at least two quality criteria; specifically, higher CRP PRS was associated with greater fatigue and reduced anhedonia, higher TNF-α PRS was associated with greater fatigue, and higher BMI PRS with greater changes in appetite and anhedonia. Associations of the BMI PRS with anhedonia, however, showed an inconsistent valence across estimation methods.ConclusionsOur findings align with previous studies suggesting that systemic inflammatory markers are primarily associated with somatic/neurovegetative symptoms of depression such as changes in appetite and fatigue. We extend these findings by providing evidence that associations are direct (using network analysis) and extend to genetic predisposition to immuno-metabolic markers (using PRSs). Our findings can inform selection of patients with inflammation-related symptoms into clinical trials of immune-modulating drugs for MDD.

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“…Genetic polymorphisms within the gene were found to be significantly associated with MDD (Zhao et al, 2020). Similarly, there are reports of increased A2M expression in the whole blood of MDD patients (Cattaneo et al, 2020). Other genes worth highlighting include CRYBA1, GALR3, GPR88, and DDC-AS1.…”

Section: Lessons On Pathophysiologymentioning

confidence: 83%

Machine learning and bioinformatic analysis of brain and blood mRNA profiles in major depressive disorder: A case–control study

Ramamurthy

Bennani

et al. 2021

American J of Med Genetics Pt B

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This study analyzed gene expression messenger RNA data, from cases with major depressive disorder (MDD) and controls, using supervised machine learning (ML). We built on the methodology of prior studies to obtain more generalizable/reproducible results. First, we obtained a classifier trained on gene expression data from the dorsolateral prefrontal cortex of post‐mortem MDD cases (n = 126) and controls (n = 103). An average area‐under‐the‐receiver‐operating‐characteristics‐curve (AUC) from 10‐fold cross‐validation of 0.72 was noted, compared to an average AUC of 0.55 for a baseline classifier (p = .0048). The classifier achieved an AUC of 0.76 on a previously unused testing‐set. We also performed external validation using DLPFC gene expression values from an independent cohort of matched MDD cases (n = 29) and controls (n = 29), obtained from Affymetrix microarray (vs. Illumina microarray for the original cohort) (AUC: 0.62). We highlighted gene sets differentially expressed in MDD that were enriched for genes identified by the ML algorithm. Next, we assessed the ML classification performance in blood‐based microarray gene expression data from MDD cases (n = 1,581) and controls (n = 369). We observed a mean AUC of 0.64 on 10‐fold cross‐validation, which was significantly above baseline (p = .0020). Similar performance was observed on the testing‐set (AUC: 0.61). Finally, we analyzed the classification performance in covariates subgroups. We identified an interesting interaction between smoking and recall performance in MDD case prediction (58% accurate predictions in cases who are smokers vs. 43% accurate predictions in cases who are non‐smokers). Overall, our results suggest that ML in combination with gene expression data and covariates could further our understanding of the pathophysiology in MDD.

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Whole-blood expression of inflammasome- and glucocorticoid-related mRNAs correctly separates treatment-resistant depressed patients from drug-free and responsive patients in the BIODEP study

Cited by 76 publications

References 74 publications

Omega-3 polyunsaturated fatty acids protect against inflammation through production of LOX and CYP450 lipid mediators: relevance for major depression and for human hippocampal neurogenesis

Omega-3 polyunsaturated fatty acids protect against inflammation through production of LOX and CYP450 lipid mediators: relevance for major depression and for human hippocampal neurogenesis

Polygenic risk for immuno-metabolic markers and specific depressive symptoms: A multi-sample network analysis study

Machine learning and bioinformatic analysis of brain and blood mRNA profiles in major depressive disorder: A case–control study

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