Precision Preventive Medicine of Relapse in Smoking Cessation: Can MRI Inform the Search of Intermediate Phenotypes?

Chronic tobacco smoking remains a major health problem worldwide. Numerous smokers wish to quit but most fail, even if they are helped. The possibility of identifying neuro-biomarkers in smokers at high risk of relapse could be of incredible progress toward personalized prevention therapy. Our aim is to provide a scoping review of this research topic in the field of Magnetic Resonance Imaging (MRI) and to review the studies that investigated if MRI defined markers predicted smoking cessation treatment outcome (abstainers versus relapsers). Based on the available literature, a meta-analysis could not be conducted. We thus provide an overview of the results obtained and take stock of methodological issues that will need to be addressed to pave the way toward precision medicine. Based on the most consistent findings, we discuss the pivotal role of the insula in light of the most recent neurocognitive models of addiction.

Improving Between-Group Effect Size for Multi-Site Functional Connectivity Data via Site-Wise De-Meaning

Background: Multi-site functional MRI (fMRI) databases are becoming increasingly prevalent in the study of neurodevelopmental and psychiatric disorders. However, multi-site databases are known to introduce site effects that may confound neurobiological and measures such as functional connectivity (FC). Although studies have been conducted to mitigate site effects, these methods often result in reduced effect size in FC comparisons between controls and patients.Methods: We present a site-wise de-meaning (SWD) strategy in multi-site FC analysis and compare its performance with two common site-effect mitigation methods, i.e., generalized linear model (GLM) and Combining Batches (ComBat) Harmonization. For SWD, after FC was calculated and Fisher z-transformed, the site-wise FC mean was removed from each subject before group-level statistical analysis. The above methods were tested on two multi-site psychiatric consortiums [Autism Brain Imaging Data Exchange (ABIDE) and Bipolar and Schizophrenia Network on Intermediate Phenotypes (B-SNIP)]. Preservation of consistent FC alterations in patients were evaluated for each method through the effect sizes (Hedge’s g) of patients vs. controls.Results: For the B-SNIP dataset, SWD improved the effect size between schizophrenic and control subjects by 4.5–7.9%, while GLM and ComBat decreased the effect size by 22.5–42.6%. For the ABIDE dataset, SWD improved the effect size between autistic and control subjects by 2.9–5.3%, while GLM and ComBat decreased the effect size by up to 11.4%.Conclusion: Compared to the original data and commonly used methods, the SWD method demonstrated superior performance in preserving the effect size in FC features associated with disorders.

Autoimmune Inflammation and Insulin Resistance: Hallmarks So Far and Yet So Close to Explain Diabetes Endotypes

Purpose of Review Diabetes mellitus can be categorized into two major variants, type 1 and type 2. A number of traits such as clinical phenotype, age at disease onset, genetic background, and underlying pathogenesis distinguish the two forms. Recent Findings Recent evidence indicates that type 1 diabetes can be accompanied by insulin resistance and type 2 diabetes exhibits self-reactivity. These two previously unknown conditions can influence the progression and outcome of the disease. Unlike most conventional considerations, diabetes appears to consist of a spectrum of intermediate phenotypes that includes monogenic and polygenic loci linked to inflammatory processes including autoimmunity, beta cell impairment, and insulin resistance. Summary Here we discuss why a shift of the classical bi-modal view of diabetes (autoimmune vs. non-autoimmune) is necessary in favor of a model of an immunological continuum of endotypes lying between the two extreme “insulin-resistant” and “autoimmune beta cell targeting,” shaped by environmental and genetic factors which contribute to determine specific immune-conditioned outcomes.

Over-representation of potential SP4 target genes within schizophrenia-risk genes

Reduction of Sp4 expression causes age-dependent hippocampal vacuolization and many other intermediate phenotypes of schizophrenia in Sp4 hypomorphic mice. Recent human genetic studies from both the Schizophrenia Exome Sequencing Meta-Analysis (SCHEMA) and the Genome-Wide Association Study (GWAS) validated SP4 as a schizophrenia-risk gene over the exome-wide or the genome-wide significance. Truncation of the human SP4 gene has an odds ratio of 9.37 (3.38–29.7) for schizophrenia. Despite successful identification of many schizophrenia-risk genes, it is unknown whether and how these risk genes may interact with each other in the development of schizophrenia. By taking advantage of the specific localization of the GC-boxes bound by SP4 transcription factors, I analyzed the relative abundance of these GC-boxes in the proximal promoter regions of schizophrenia-risk genes. I found that the GC-box containing genes are significantly over-represented within schizophrenia-risk genes, suggesting that SP4 is not only a high-risk gene for schizophrenia, but may also act as a hub of network in the regulation of many other schizophrenia-risk genes via these GC-boxes in the pathogenesis of schizophrenia.

Towards Building a Quantitative Proteomics Toolbox in Precision Medicine: A Mini-Review

Precision medicine as a framework for disease diagnosis, treatment, and prevention at the molecular level has entered clinical practice. From the start, genetics has been an indispensable tool to understand and stratify the biology of chronic and complex diseases in precision medicine. However, with the advances in biomedical and omics technologies, quantitative proteomics is emerging as a powerful technology complementing genetics. Quantitative proteomics provide insight about the dynamic behaviour of proteins as they represent intermediate phenotypes. They provide direct biological insights into physiological patterns, while genetics accounting for baseline characteristics. Additionally, it opens a wide range of applications in clinical diagnostics, treatment stratification, and drug discovery. In this mini-review, we discuss the current status of quantitative proteomics in precision medicine including the available technologies and common methods to analyze quantitative proteomics data. Furthermore, we highlight the current challenges to put quantitative proteomics into clinical settings and provide a perspective to integrate proteomics data with genomics data for future applications in precision medicine.

Estimation of total mediation effect for high-dimensional omics mediators

Background Environmental exposures can regulate intermediate molecular phenotypes, such as gene expression, by different mechanisms and thereby lead to various health outcomes. It is of significant scientific interest to unravel the role of potentially high-dimensional intermediate phenotypes in the relationship between environmental exposure and traits. Mediation analysis is an important tool for investigating such relationships. However, it has mainly focused on low-dimensional settings, and there is a lack of a good measure of the total mediation effect. Here, we extend an R-squared (R$$^2$$ 2 ) effect size measure, originally proposed in the single-mediator setting, to the moderate- and high-dimensional mediator settings in the mixed model framework. Results Based on extensive simulations, we compare our measure and estimation procedure with several frequently used mediation measures, including product, proportion, and ratio measures. Our R$$^2$$ 2 -based second-moment measure has small bias and variance under the correctly specified model. To mitigate potential bias induced by non-mediators, we examine two variable selection procedures, i.e., iterative sure independence screening and false discovery rate control, to exclude the non-mediators. We establish the consistency of the proposed estimation procedures and introduce a resampling-based confidence interval. By applying the proposed estimation procedure, we found that 38% of the age-related variations in systolic blood pressure can be explained by gene expression profiles in the Framingham Heart Study of 1711 individuals. An R package “RsqMed” is available on CRAN. Conclusion R-squared (R$$^2$$ 2 ) is an effective and efficient measure for total mediation effect especially under high-dimensional setting.

Over-Representation of Potential SP4 Target Genes within Schizophrenia-Risk Genes

Reduction of Sp4 expression causes age-dependent hippocampal vacuolization and many other intermediate phenotypes of schizophrenia in Sp4 hypomorphic mice. Recent human genetic studies from both the Schizophrenia Exome Sequencing Meta-Analysis (SCHEMA) and the Genome-Wide Association Study (GWAS) validated SP4 as a schizophrenia-risk gene over the exome-wide or the genome-wide significance. Truncation of human SP4 gene has an odds ratio of 9.37 (3.38-29.7) for schizophrenia. Despite successful identification of many schizophrenia-risk genes, it is unknown whether and how these risk genes may interact with each other in the development of schizophrenia. By taking advantage of the specific localization of the GC-boxes bound by SP4 transcription factors, I analyzed the relative abundance of these GC-boxes in the proximal promoter regions of schizophrenia-risk genes. I found that the GC-box containing genes are significantly over-represented within schizophrenia-risk genes, suggesting that SP4 is not only a high-risk gene for schizophrenia, but may also act as a hub of network in regulation of many other schizophrenia-risk genes via these GC-boxes in the pathogenesis of schizophrenia.

Neurodevelopment regulators miR-137 and miR-34 family as biomarkers for early and adult onset schizophrenia

Early-onset schizophrenia (EOS) may have stronger familial aggregation and a more severe outcome than adult-onset schizophrenia (AOS). MicroRNA (miRNA) takes on dual roles as a genetic and epigenetic modulator, which may mediate the influence of genetic risk. Neurological soft signs (NSS) are neurological abnormalities that may be intermediate phenotypes or endophenotypes for schizophrenia. Our previous study found poorer performance on NSS tests from patients with EOS and their unaffected first-degree relatives. Thus, we aimed to identify a set of aberrant neurodevelopmental-related miRNAs that could serve as potential biomarkers for EOS or schizophrenia with NSS. This study included 215 schizophrenia patients (104 EOS and 111 AOS), 72 unaffected first-degree relatives, 31 patients with bipolar disorder, and 100 healthy controls. Differential expression analysis revealed that miR-137, miR-34b, and miR-34c were significantly up-regulated in patients with schizophrenia and their unaffected first-degree relatives compared to healthy controls. Receiver operating characteristic (ROC) analysis showed that the miR-137 expression signature could be used to discriminate between patients with EOS and healthy controls (AUC = 0.911). Additionally, miR-34b had the highest ability to discriminate between EOS and AOS (AUC = 0.810), which may indicate different aetiological pathways to disease onset. Moreover, miR-137 dysregulation was correlated with almost all NSS subscales (i.e., sensory integration, motor sequencing, etc.) and, when EOS patients with NSS, miR-137 expression discriminated these patients from healthy controls to a greater extent (AUC = 0.957). These findings support the potential for neurodevelopmental-related miRNAs to be used as indicators of vulnerability to EOS.

Toxoplasma gondii, Suicidal Behavior, and Intermediate Phenotypes for Suicidal Behavior

Within the general literature on infections and suicidal behavior, studies on Toxoplasma gondii (T. gondii) occupy a central position. This is related to the parasite's neurotropism, high prevalence of chronic infection, as well as specific and non-specific behavioral alterations in rodents that lead to increased risk taking, which are recapitulated in humans by T. gondii's associations with suicidal behavior, as well as trait impulsivity and aggression, mental illness and traffic accidents. This paper is a detailed review of the associations between T. gondii serology and suicidal behavior, a field of study that started 15 years ago with our publication of associations between T. gondii IgG serology and suicidal behavior in persons with mood disorders. This “legacy” article presents, chronologically, our primary studies in individuals with mood disorders and schizophrenia in Germany, recent attempters in Sweden, and in a large cohort of mothers in Denmark. Then, it reviews findings from all three meta-analyses published to date, confirming our reported associations and overall consistent in effect size [ranging between 39 and 57% elevation of odds of suicide attempt in T. gondii immunoglobulin (IgG) positives]. Finally, the article introduces certain links between T. gondii and biomarkers previously associated with suicidal behavior (kynurenines, phenylalanine/tyrosine), intermediate phenotypes of suicidal behavior (impulsivity, aggression) and state-dependent suicide risk factors (hopelessness/dysphoria, sleep impairment). In sum, an abundance of evidence supports a positive link between suicide attempts (but not suicidal ideation) and T. gondii IgG (but not IgM) seropositivity and serointensity. Trait impulsivity and aggression, endophenotypes of suicidal behavior have also been positively associated with T. gondii seropositivity in both the psychiatrically healthy as well as in patients with Intermittent Explosive Disorder. Yet, causality has not been demonstrated. Thus, randomized interventional studies are necessary to advance causal inferences and, if causality is confirmed, to provide hope that an etiological treatment for a distinct subgroup of individuals at an increased risk for suicide could emerge.