Bayesian Inference of gene-miRNA regulatory networks

Nowadays, in the post-genomics era, one of the major tasks and challenges is to decipher how genes are regulated. The miRNAs play an essential regulatory role in both plants and animals. It has been estimated that about 30% of the genes in the human genome are down-regulated by microRNAs (miRNAs), short RNA molecules which repress the translation of proteins of mRNAs in animals and plants. Genes which are regulated by a miRNA are called targets of this given miRNA. Hence, the task is to try to determine which miRNAs regulate which genes, in order then to build a network of these DNA components. Knowledge of the functional miRNAs-genes interactions can help find the source or reason of a genetic disease, then we can focus on drugs and their effects such we get more efficient treatments. In this thesis, we aim to build a Bayesian graphical model that infers a regulatory network by integrating miRNAs expression levels with their potential mRNA targets. We incorporate biological information, such as structure and sequence information, via the prior probability model. The method is broken down to 3 stages. First, a dimensionality reduction is performed; the gene expressions are narrowed down by using biological information (association scores and type of probe set), and distance similarity procedures such as clustering of correlated or co-expressed variables. Second, a Bayesian graphical model is proposed, according to which associations of gene and miRNA expressions are inferred, and an association matrix is extracted. The methodology uses simulation-based methods, as Markov Chain Monte Carlo, and benefits by managing uncertainty at a complex network. Finally, using the association matrix, the regulatory network is constructed

The correlation between inflammatory biomarkers and polygenic risk score in Alzheimer's Disease

Plasma biomarkers to aid the early diagnosis of Alzheimer’s disease (AD) or to monitor disease progression have long been sought and continue to be widely studied. Biomarkers that correlate with AD polygenic risk score, a measure of the polygenic architecture of the disease and highly predictive of AD status, would be excellent candidates. Therefore, we undertook a preliminary study to assess the association of plasma inflammatory biomarkers with an overall AD polygenic risk score as well as with an inflammation-specific AD polygenic risk score in a sample set of 93 AD cases. We measured five complement biomarkers [complement receptor 1 (CR1), clusterin, complement component 9 (C9), C1 inhibitor (C1inh), terminal complement complex (TCC)] and the benchmark inflammatory marker C-reactive protein (CRP). Plasma clusterin level showed an association with overall AD polygenic risk score, while clusterin, C1inh, and CRP levels each displayed some association with the inflammatory-specific AD polygenic risk score. The results suggest that elevated plasma levels of inflammatory biomarkers, including complement proteins, associate with polygenic risk scores in AD, further strengthening the link between genetic and biomarker disease predictors and indicating a potential role for these markers in disease prediction and patient stratification in AD

Complement system biomarkers in first episode psychosis

Several lines of evidence implicate immunological/inflammatory factors in development of schizophrenia. Complement is a key driver of inflammation, and complement dysregulation causes pathology in many diseases. Here we exploredwhether complement dysregulation occurred in first episode psychosis (FEP) andwhether this provides a source of biomarkers. Eleven complement analytes (C1q, C3, C4, C5, factor B [FB], terminal complement complex [TCC], factor H [FH], FH-related proteins [FHR125], Properdin, C1 inhibitor [C1inh], soluble complement receptor 1 [CR1]) plus C-reactive protein (CRP) were measured in serum from 136 first episode psychosis (FEP) cases and 42 mentally healthy controls using established in-house or commercial ELISA. The relationship between caseness and variables (analytes measured, sex, age, ethnicity, tobacco/cannabis smoking) was tested by multivariate logistic regression. Whenmeasured individually, only TCC was significantly different between FEP and controls (p=0.01). Stepwise selection demonstrated interdependence between some variables and revealed other variables that significantly and independently contributed to distinguishing cases and controls. The finalmodel included demographics (sex, ethnicity, age, tobacco smoking) and a subset of analytes (C3, C4, C5, TCC, C1inh, FHR125, CR1). A receiver operating curve analysis combining these variables yielded an area under the curve of 0.79 for differentiating FEP from controls. This model was confirmed by multiple replications using randomly selected sample subsets. The data suggest that complement dysregulation occurs in FEP, supporting an underlying immune/inflammatory component to the disorder. Classification of FEP cases according to biological variables rather than symptoms would help stratify cases to identify those that might most benefit from therapeuticmodification of the inflammatory response

Cerebrospinal fluid complement system biomarkers in demyelinating disease

Background: Multiple sclerosis (MS) can be difficult to differentiate from other demyelinating diseases, notably neuromyelitis optica spectrum disorder (NMOSD). We previously showed that NMOSD is distinguished from MS by plasma complement biomarkers. Objective: Here, we measure cerebrospinal fluid (CSF) complement proteins in MS, NMOSD and clinically isolated syndrome (CIS), a neurological episode that may presage MS, to test whether these distinguish NMOSD from MS and CIS. Materials and methods: CSF (53 MS, 17 CIS, 11 NMOSD, 35 controls) was obtained; complement proteins (C4, C3, C5, C9, C1, C1q, Factor B (FB)), regulators (Factor I (FI), Factor H (FH), FH-Related Proteins 1, 2 and 5 (FHR125), C1 Inhibitor (C1INH), Properdin) and activation products (terminal complement complex (TCC), iC3b) were quantified by ELISA and results expressed relative to CSF total protein (μg/mg). Results: Compared to control CSF, (1) levels of C4, C1INH and Properdin were elevated in MS; (2) TCC, iC3b, FI and FHR125 were increased in CIS; and (3) all complement biomarkers except TCC, FHR125, Properdin and C5 were higher in NMOSD CSF. A statistical model comprising six analytes (C3, C9, FB, C1q, FI, Properdin) plus age/gender optimally differentiated MS from NMOSD

Complement system biomarkers in epilepsy

Purpose To explore whether complement dysregulation occurs in a routinely recruited clinical cohort of epilepsy patients, and whether complement biomarkers have potential to be used as markers of disease severity and seizure control. Methods Plasma samples from 157 epilepsy cases (106 with focal seizures, 46 generalised seizures, 5 unclassified) and 54 controls were analysed. Concentrations of 10 complement analytes (C1q, C3, C4, factor B [FB], terminal complement complex [TCC], iC3b, factor H [FH], Clusterin [Clu], Properdin, C1 Inhibitor [C1Inh] plus C-reactive protein [CRP]) were measured using enzyme linked immunosorbent assay (ELISA). Univariate and multivariate statistical analysis were used to test whether combinations of complement analytes were predictive of epilepsy diagnoses and seizure occurrence. Correlation between number and type of anti-epileptic drugs (AED) and complement analytes was also performed. Results We found: 1) significant differences between all epilepsy patients and controls for TCC (p < 0.01) and FH (p < 0.01) after performing univariate analysis. 2) multivariate analysis combining six analytes (C3, C4, Properdin, FH, C1Inh, Clu) to give a predictive value (area under the curve) of 0.80 for differentiating epilepsy from controls. 3) significant differences in complement levels between patients with controlled seizures (n = 65) in comparison with uncontrolled seizures (n = 87). Levels of iC3b, Properdin and Clu were decreased and levels of C4 were increased in patients with uncontrolled seizures. 4) no correlation was found between the level of complement biomarkers and the number of AEDs taken, but an association between some analyte levels and drug therapy was seen in patients taking sodium valproate, clobazam, and perampanel. Conclusion This study adds to evidence implicating complement in pathogenesis of epilepsy and may allow the development of better therapeutics and prognostic markers in the future. Replication in a larger sample set is needed to validate the findings of the study

Inflammatory biomarkers in Alzheimer's disease plasma.

INTRODUCTION: Plasma biomarkers for Alzheimer's disease (AD) diagnosis/stratification are a "Holy Grail" of AD research and intensively sought; however, there are no well-established plasma markers. METHODS: A hypothesis-led plasma biomarker search was conducted in the context of international multicenter studies. The discovery phase measured 53 inflammatory proteins in elderly control (CTL; 259), mild cognitive impairment (MCI; 199), and AD (262) subjects from AddNeuroMed. RESULTS: Ten analytes showed significant intergroup differences. Logistic regression identified five (FB, FH, sCR1, MCP-1, eotaxin-1) that, age/APOε4 adjusted, optimally differentiated AD and CTL (AUC: 0.79), and three (sCR1, MCP-1, eotaxin-1) that optimally differentiated AD and MCI (AUC: 0.74). These models replicated in an independent cohort (EMIF; AUC 0.81 and 0.67). Two analytes (FB, FH) plus age predicted MCI progression to AD (AUC: 0.71). DISCUSSION: Plasma markers of inflammation and complement dysregulation support diagnosis and outcome prediction in AD and MCI. Further replication is needed before clinical translation

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements

CSF1R inhibitor JNJ-40346527 attenuates microglial proliferation and neurodegeneration in P301S mice

Neuroinflammation and microglial activation are significant processes in Alzheimer’s disease pathology. Recent genome-wide association studies have highlighted multiple immune-related genes in association with Alzheimer’s disease, and experimental data have demonstrated microglial proliferation as a significant component of the neuropathology. In this study, we tested the efficacy of the selective CSF1R inhibitor JNJ-40346527 (JNJ-527) in the P301S mouse tauopathy model. We first demonstrated the anti-proliferative effects of JNJ-527 on microglia in the ME7 prion model, and its impact on the inflammatory profile, and provided potential CNS biomarkers for clinical investigation with the compound, including pharmacokinetic/pharmacodynamics and efficacy assessment by TSPO autoradiography and CSF proteomics. Then, we showed for the first time that blockade of microglial proliferation and modification of microglial phenotype leads to an attenuation of tau-induced neurodegeneration and results in functional improvement in P301S mice. Overall, this work strongly supports the potential for inhibition of CSF1R as a target for the treatment of Alzheimer’s disease and other tau-mediated neurodegenerative diseases

Inflammatory biomarkers in Alzheimer's disease plasma

Introduction:Plasma biomarkers for Alzheimer’s disease (AD) diagnosis/stratification are a“Holy Grail” of AD research and intensively sought; however, there are no well-established plasmamarkers.Methods:A hypothesis-led plasma biomarker search was conducted in the context of internationalmulticenter studies. The discovery phase measured 53 inflammatory proteins in elderly control (CTL;259), mild cognitive impairment (MCI; 199), and AD (262) subjects from AddNeuroMed.Results:Ten analytes showed significant intergroup differences. Logistic regression identified five(FB, FH, sCR1, MCP-1, eotaxin-1) that, age/APOε4 adjusted, optimally differentiated AD andCTL (AUC: 0.79), and three (sCR1, MCP-1, eotaxin-1) that optimally differentiated AD and MCI(AUC: 0.74). These models replicated in an independent cohort (EMIF; AUC 0.81 and 0.67). Twoanalytes (FB, FH) plus age predicted MCI progression to AD (AUC: 0.71).Discussion:Plasma markers of inflammation and complement dysregulation support diagnosis andoutcome prediction in AD and MCI. Further replication is needed before clinical translatio