Inference of RNA decay rate from transcriptional profiling highlights the regulatory programs of Alzheimer's disease.

The abundance of mRNA is mainly determined by the rates of RNA transcription and decay. Here, we present a method for unbiased estimation of differential mRNA decay rate from RNA-sequencing data by modeling the kinetics of mRNA metabolism. We show that in all primary human tissues tested, and particularly in the central nervous system, many pathways are regulated at the mRNA stability level. We present a parsimonious regulatory model consisting of two RNA-binding proteins and four microRNAs that modulate the mRNA stability landscape of the brain, which suggests a new link between RBFOX proteins and Alzheimer's disease. We show that downregulation of RBFOX1 leads to destabilization of mRNAs encoding for synaptic transmission proteins, which may contribute to the loss of synaptic function in Alzheimer's disease. RBFOX1 downregulation is more likely to occur in older and female individuals, consistent with the association of Alzheimer's disease with age and gender."mRNA abundance is determined by the rates of transcription and decay. Here, the authors propose a method for estimating the rate of differential mRNA decay from RNA-seq data and model mRNA stability in the brain, suggesting a link between mRNA stability and Alzheimer's disease.

Genetic implications of individual intervention and neuronal dysfunction in neurodevelopmental disorders

Neurodevelopmental disorders (NDDs) are a group of conditions appearing in childhood, with developmental deficits that produce impairments of functioning. Autism spectrum disorder (ASD) is a common NDD with a high heritability affected by complex genetic factors, including both common and rare variants. Behavior interventions such as social skills group training (SSGT) have been widely used in school-aged autistic individuals to relieve social communication difficulties in a group setting. Studies have confirmed that intervention outcomes can be influenced by sex and age, but how the genetic risk contributes to the outcome variability remains elusive. Furthermore, although large population cohorts have been well studied and have found numerous genes associated with ASD and NDDs, the molecular and neuronal outcomes of risk variants and genes are unclear. Therefore, this thesis included four studies in which the effects of genetic factors on intervention outcomes and cellular level neuronal functions were investigated. Results from this thesis may provide a genetic perspective for further studies to explore potential individualized treatments for ASD and other NDDs. Specifically, In STUDY 1-3, exome sequencing and microarray were performed on individuals from a randomized controlled trial of SSGT (KONTAKT®). Common and rare variants, including copy number variations (CNVs) and exome variants, were tested for association effects with SSGT and standard care intervention outcomes. Polygenic risk scores (PRSs) were calculated from common variants, and clinically significant rare CNVs and rare exome variants were prioritized. Molecular diagnoses were identified in 12.6% of the autistic participants. PRSs and carrier status of clinically significant rare variants were associated with intervention outcomes, although with varied effects on both SSGT and standard care. In addition, genetic scores representing variant loads in specific gene sets were obtained from rare and common variants in ASD-related pathways. Outcomes of interventions were differentially associated with genetic scores for ASD-related gene sets including synaptic transmission and transcription regulation from RNA polymerase II. After combining genetic information and behavior measures, a machine learning model was able to select important features and confirm that the intervention outcomes were predictable. In STUDY 4, genetic variants affecting Calcium/Calmodulin Dependent Serine Protein Kinase (CASK) gene, a risk gene for NDDs, were examined using human induced pluripotent stem cell-derived neuronal models to identify the cellular effects of these mutation consequences. CASK protein was reduced in maturing neurons from mutation carriers. Bulk RNA sequencing results revealed that the global expression of genes from presynaptic development and CASK network were downregulated in CASK-deficient neurons compared to controls. Neuronal cells influenced by CASK mutations showed a decrease of inhibitory presynapse size and changed excitatory-inhibitory (E/I) balance in developing neural circuitries. In summary, this is the first study to investigate the association of genome-wide rare and common variants with ASD intervention outcomes. Differential variant effects were found for individuals receiving SSGT or standard care. Future studies should include genetic information at different levels to improve molecular genetic testing for diagnoses and intervention plans. Presynapses and E/I imbalance could be an option to be developed for the treatment of CASK-related disorders

A profile of differential DNA methylation in sporadic human prion disease blood: precedent, implications and clinical promise

Sporadic Creutzfeldt-Jakob Disease (sCJD) is a rare but devastating neurodegenerative disorder characterised by misfolding, propagation and deposition of the prion protein in the brain, leading to neuronal death and rapid cognitive and functional decline. As there is no obvious genetic cause of sCJD, the epigenetic status of sCJD patients may clarify spontaneous prion disease aetiology or reveal biomarkers of the disease. Blood from patients was profiled to document genome-wide differential DNA methylation. // 38 loci were identified as being differentially methylated in sCJD blood, including two which associated with disease severity as measured by the MRC Scale score. Of 7 loci considered for replication, 5 showed similar effects in a second cohort of patients, but not in patients of Alzheimer’s disease, iatrogenic CJD, or inherited prion disease, suggesting these effects are specific to the sporadic form of CJD. Notably hypomethylation at a site in the promoter of AIM2, an inflammasome component, retained its association with disease severity. // Hypomethylation of FKBP5, a gene known to regulate the cellular response to cortisol, prompted further investigation which revealed that circulating cortisol is indeed elevated in sCJD patients. Profiling of frontal cortex-derived DNA showed that differential methylation observed in blood is absent from the brain methylome. // Machine learning classification of sCJD based on genome-wide methylation data was able to classify sCJD and healthy control status with an accuracy of 87.04%. This is an appreciable level of accuracy but importantly sets precedence for further classification of prion patients in more complex clinical and research settings, as well as assisting differential diagnosis of less conventional rapid dementias

Development of New Bioinformatic Approaches for Human Genetic Studies

The development of bioinformatics methods for human genetic studies utilizes the vast amount of data to generate new valuable information. Machine learning and statistical coupling analysis can be used in the study of human diseases. These diseases include intellectual disabilities (ID), prevalent in 1-3% of the population and caused primarily by genetics. Although many cases of ID are caused by mutations in protein-coding genes, the possible involvement of long non-coding RNAs (lncRNAs) in ID due to their role in gene expression regulation, has been explored. In this study, we used machine learning to develop a new expression-based model trained using ID genes encoded with the developing brain transcriptome. The model was fine-tuned using the class-balancing approach of synthetic over-sampling of the minority class, resulting in improved performance. We used the model to predict candidate ID-associated lncRNAs. Our model identified several candidates that overlapped with previously reported ID-associated lncRNAs, enriched with neurodevelopmental functions, and highly expressed in brain tissues. Machine learning was also used to predict protein stability changes caused by missense mutations, which can lead to disease conditions including ID. We tested Random Forests, Support Vector Machines (SVM) and Naïve Bayes to find the best-performing algorithm to develop a multi-class classifier. We developed an SVM model using relevant physico-chemical features after feature selection. Our work identified new features for predicting the effect of amino acid substitutions on protein stability and a well-performing multi-class classifier solely based on sequence information. Statistical approaches were used to analyze the association between mutations and phenotypes. In this study, we used statistical coupling analysis (SCA) to cluster disease-causing mutations and ID phenotypes. Using SCA we identified groups of co-evolving residues, known as protein sectors, in ID protein families. Within each distinct sector, mutations associated with different phenotypic manifestations associated with a syndromic ID were identified. Our results suggest that protein sector analysis can be used to associate mutations with phenotypic manifestations in human diseases. The bioinformatic methods developed in this dissertation can be used in human genetic research to understand the role of new genes and proteins in human disease

Systems Analytics and Integration of Big Omics Data

A “genotype"" is essentially an organism's full hereditary information which is obtained from its parents. A ""phenotype"" is an organism's actual observed physical and behavioral properties. These may include traits such as morphology, size, height, eye color, metabolism, etc. One of the pressing challenges in computational and systems biology is genotype-to-phenotype prediction. This is challenging given the amount of data generated by modern Omics technologies. This “Big Data” is so large and complex that traditional data processing applications are not up to the task. Challenges arise in collection, analysis, mining, sharing, transfer, visualization, archiving, and integration of these data. In this Special Issue, there is a focus on the systems-level analysis of Omics data, recent developments in gene ontology annotation, and advances in biological pathways and network biology. The integration of Omics data with clinical and biomedical data using machine learning is explored. This Special Issue covers new methodologies in the context of gene–environment interactions, tissue-specific gene expression, and how external factors or host genetics impact the microbiome

Computational Logic for Biomedicine and Neurosciences

We advocate here the use of computational logic for systems biology, as a \emph{unified and safe} framework well suited for both modeling the dynamic behaviour of biological systems, expressing properties of them, and verifying these properties. The potential candidate logics should have a traditional proof theoretic pedigree (including either induction, or a sequent calculus presentation enjoying cut-elimination and focusing), and should come with certified proof tools. Beyond providing a reliable framework, this allows the correct encodings of our biological systems. % For systems biology in general and biomedicine in particular, we have so far, for the modeling part, three candidate logics: all based on linear logic. The studied properties and their proofs are formalized in a very expressive (non linear) inductive logic: the Calculus of Inductive Constructions (CIC). The examples we have considered so far are relatively simple ones; however, all coming with formal semi-automatic proofs in the Coq system, which implements CIC. In neuroscience, we are directly using CIC and Coq, to model neurons and some simple neuronal circuits and prove some of their dynamic properties. % In biomedicine, the study of multi omic pathway interactions, together with clinical and electronic health record data should help in drug discovery and disease diagnosis. Future work includes using more automatic provers. This should enable us to specify and study more realistic examples, and in the long term to provide a system for disease diagnosis and therapy prognosis

Hybrid ACO and SVM algorithm for pattern classification

Ant Colony Optimization (ACO) is a metaheuristic algorithm that can be used to solve a variety of combinatorial optimization problems. A new direction for ACO is to optimize continuous and mixed (discrete and continuous) variables. Support Vector Machine (SVM) is a pattern classification approach originated from statistical approaches. However, SVM suffers two main problems which include feature subset selection and parameter tuning. Most approaches related to tuning SVM parameters discretize the continuous value of the parameters which will give a negative effect on the classification performance. This study presents four algorithms for tuning the SVM parameters and selecting feature subset which improved SVM classification accuracy with smaller size of feature subset. This is achieved by performing the SVM parameters’ tuning and feature subset selection processes simultaneously. Hybridization algorithms between ACO and SVM techniques were proposed. The first two algorithms, ACOR-SVM and IACOR-SVM, tune the SVM parameters while the second two algorithms, ACOMV-R-SVM and IACOMV-R-SVM, tune the SVM parameters and select the feature subset simultaneously. Ten benchmark datasets from University of California, Irvine, were used in the experiments to validate the performance of the proposed algorithms. Experimental results obtained from the proposed algorithms are better when compared with other approaches in terms of classification accuracy and size of the feature subset. The average classification accuracies for the ACOR-SVM, IACOR-SVM, ACOMV-R and IACOMV-R algorithms are 94.73%, 95.86%, 97.37% and 98.1% respectively. The average size of feature subset is eight for the ACOR-SVM and IACOR-SVM algorithms and four for the ACOMV-R and IACOMV-R algorithms. This study contributes to a new direction for ACO that can deal with continuous and mixed-variable ACO

Multi-Modal Magnetic Resonance Imaging Predicts Regional Amyloid Burden in the Brain

Alzheimer’s disease (AD) is the most common cause of dementia and identifying early markers of this disease is important for prevention and treatment strategies. Amyloid- β (Aβ) protein deposition is one of the earliest detectable pathological changes in AD. But in-vivo detection of Aβ using positron emission tomography (PET) is hampered by high cost and limited geographical accessibility. These factors can become limiting when PET is used to screen large numbers of subjects into prevention trials when only a minority are expected to be amyloid-positive. Structural MRI is advantageous; as it is non-invasive, relatively inexpensive and more accessible. Thus it could be widely used in large studies, even when frequent or repetitive imaging is necessary. We used a machine learning, pattern recognition, approach using intensity-based features from individual and combination of MR modalities (T1 weighted, T2 weighted, T2 fluid attenuated inversion recovery [FLAIR], susceptibility weighted imaging) to predict voxel-level amyloid in the brain. The MR- Aβ relation was learned within each subject and generalized across subjects using subject–specific features (demographic, clinical, and summary MR features). When compared to other modalities, combination of T1-weighted, T2-weighted FLAIR, and SWI performed best in predicting the amyloid status as positive or negative. A combination of T2-weighted and SWI imaging performed the best in predicting change in amyloid over two timepoints. Overall, our results show feasibility of amyloid prediction by MRI and its potential use as an amyloid-screening tool