Trem2 Deficiency Differentially Affects the Phenotype and Transcriptome of Human APOE3 and APOE4 mice: The Role of APOE and TREM2 in Alzheimer’s Disease

Alzheimer’s disease (AD) is the leading cause of dementia worldwide and a significant public health concern impacting not only patients, but their families and caregivers as well. Extracellular deposits of amyloid beta Aβ in the brain called amyloid plaques and intracellular tau aggregates called neurofibrillary tangles are morphological hallmarks of the disease. The risk for AD is a complicated interplay between aging, genetic risk factors, and environmental influences. The inheritance of Apolipoprotein E ε4 (APOEε4) and variants of Triggering Receptor Expressed on Myeloid cells 2 (TREM2) are major genetic risk factors for AD. Emerging evidence from protein binding assays suggest that APOE and APOE-containing lipoproteins bind to TREM2 in the brain as well as periphery. This raises the possibility of an APOE-TREM2 interaction modulating aspects of AD pathology, potentially in an isoform-specific manner. This dissertation aimed to investigate this interaction using complex AD model mice - a crossbreed of Trem2ko and APP/PSEN1dE9 mice expressing human APOE3 or APOE4 isoform, evaluating cognition, steady-state and dynamic amyloid pathology, glial response, and whole-brain transcriptomics. We found that Trem2 deletion had the following effects on the phenotype: a) reduced plaque compaction but no effect on steady-state plaque load; b) decreased microglia recruitment to plaques; c) increased plaque growth in correlation with reduced microglia barrier, an effect that is dependent on the stage of amyloid deposition; d) isoform dependent effect on plaque-associated APOE; e) worsened memory in APP but not in WT littermates. Gene expression analysis identified the Trem2 signature as a cluster of highly interconnected immune response genes commonly downregulated as a result of Trem2 deletion in all experimental groups, including Clec7a, Itgax, Cts7, Mpeg1, Csf1r, Cx3cr1 and Spi1/PU.1. Several of the Trem2 signature genes had higher expression in APP/E4 versus APP/E3 mice, a result validated for Clec7a and Csf1r by FISH, and for APOE by immunohistochemistry. In contrast, Tyrobp and several genes involved in the C1q complement cascade had higher expression levels in APP/E3 versus their APP/E4 counterparts. Collectively, this dissertation provides evidence as to the phenotypic and transcriptomic effects regarding the interplay between human APOE isoform and Trem2 deletion in association with AD pathology

Structural Prediction of Protein–Protein Interactions by Docking: Application to Biomedical Problems

A huge amount of genetic information is available thanks to the recent advances in sequencing technologies and the larger computational capabilities, but the interpretation of such genetic data at phenotypic level remains elusive. One of the reasons is that proteins are not acting alone, but are specifically interacting with other proteins and biomolecules, forming intricate interaction networks that are essential for the majority of cell processes and pathological conditions. Thus, characterizing such interaction networks is an important step in understanding how information flows from gene to phenotype. Indeed, structural characterization of protein–protein interactions at atomic resolution has many applications in biomedicine, from diagnosis and vaccine design, to drug discovery. However, despite the advances of experimental structural determination, the number of interactions for which there is available structural data is still very small. In this context, a complementary approach is computational modeling of protein interactions by docking, which is usually composed of two major phases: (i) sampling of the possible binding modes between the interacting molecules and (ii) scoring for the identification of the correct orientations. In addition, prediction of interface and hot-spot residues is very useful in order to guide and interpret mutagenesis experiments, as well as to understand functional and mechanistic aspects of the interaction. Computational docking is already being applied to specific biomedical problems within the context of personalized medicine, for instance, helping to interpret pathological mutations involved in protein–protein interactions, or providing modeled structural data for drug discovery targeting protein–protein interactions.Spanish Ministry of Economy grant number BIO2016-79960-R; D.B.B. is supported by a predoctoral fellowship from CONACyT; M.R. is supported by an FPI fellowship from the Severo Ochoa program. We are grateful to the Joint BSC-CRG-IRB Programme in Computational Biology.Peer ReviewedPostprint (author's final draft

Genome-Wide Systems Genetics of Alcohol Consumption and Dependence

Widely effective treatment for alcohol use disorder is not yet available, because the exact biological mechanisms that underlie this disorder are not completely understood. One way to gain a better understanding of these mechanisms is to examine the genetic frameworks that contribute to the risk for developing this disorder. This dissertation examines genetic association data in combination with gene expression networks in the brain to identify functional groups of genes associated with alcohol consumption and dependence. The first study took advantage of the behavioral complexity of human samples, and experimental capabilities provided by mouse models, by co-analyzing gene expression networks in the mesolimbocortical system of acute alcohol-treated mice and human genetic alcohol dependence association data. This study successfully identified ethanol-responsive gene expression networks with overrepresentation of genes suggestively associated with alcohol dependence in an independent human sample, indicating that gene expression networks in mouse models are informative for identifying mechanistic networks relevant to the risk for developing dependence. The second study aimed to identify quantitative trait loci for voluntary alcohol drinking behaviors under an intermittent ethanol access paradigm, in the genetically complex Diversity Outbred mice. After determining high heritability for alcohol consumption and dependence amongst the progenitor strains, we identified several specific genetic loci associated with these traits. One locus replicated results from a human association study of alcohol consumption, and provided insight to the potentially contributing genes. Finally, we identified alcohol consumption-correlated gene expression networks in the prefrontal cortex of these mice. We also mapped quantitative trait loci for network expression levels, some of which overlapped with the behavioral loci, indicating that the functions represented by these modules mediate the relationship between the genotypes in that region and drinking behaviors. Overall, our studies revealed neuroplastic and ubiquitin-related genes pathways involved in alcohol consumption in mice and humans, and that likely contribute to the risk for developing dependence

Dopamine perturbation of gene co-expression networks reveals differential response in schizophrenia for translational machinery.

The dopaminergic hypothesis of schizophrenia (SZ) postulates that positive symptoms of SZ, in particular psychosis, are due to disturbed neurotransmission via the dopamine (DA) receptor D2 (DRD2). However, DA is a reactive molecule that yields various oxidative species, and thus has important non-receptor-mediated effects, with empirical evidence of cellular toxicity and neurodegeneration. Here we examine non-receptor-mediated effects of DA on gene co-expression networks and its potential role in SZ pathology. Transcriptomic profiles were measured by RNA-seq in B-cell transformed lymphoblastoid cell lines from 514 SZ cases and 690 controls, both before and after exposure to DA ex vivo (100 μM). Gene co-expression modules were identified using Weighted Gene Co-expression Network Analysis for both baseline and DA-stimulated conditions, with each module characterized for biological function and tested for association with SZ status and SNPs from a genome-wide panel. We identified seven co-expression modules under baseline, of which six were preserved in DA-stimulated data. One module shows significantly increased association with SZ after DA perturbation (baseline: P = 0.023; DA-stimulated: P = 7.8 × 10-5; ΔAIC = -10.5) and is highly enriched for genes related to ribosomal proteins and translation (FDR = 4 × 10-141), mitochondrial oxidative phosphorylation, and neurodegeneration. SNP association testing revealed tentative QTLs underlying module co-expression, notably at FASTKD2 (top P = 2.8 × 10-6), a gene involved in mitochondrial translation. These results substantiate the role of translational machinery in SZ pathogenesis, providing insights into a possible dopaminergic mechanism disrupting mitochondrial function, and demonstrates the utility of disease-relevant functional perturbation in the study of complex genetic etiologies

Identification of new disease mechanisms and treatments for type 2 diabetes based on genetic variants and gene expression networks

Improved understanding of the disease mechanisms underlying type 2 diabetes (T2D) is needed, and so are new treatments.A new T2D risk variant was recently identified in ADRA2A, which encodes the α2A-adrenergic receptor. The risk allele leads to receptor overexpression in β-cells that causes increased adrenergic signaling and impaired insulin secretion. We showed that the α2A-adrenergic receptor antagonist yohimbine normalized insulin secretion in risk allele carriers with T2D, whereas it was without effect in non-risk allele carriers. These findings suggest that individualized, genotype-based treatment for T2D is possible. Next, in an attempt to identify new genes relevant for the pathogenesis of T2D and to identify new drugs for the treatment of T2D, we utilized microarray gene expression data to gain information about gene coexpression networks. Gene expression in human islets from T2D and non-diabetic donors, and gene expression in liver tissue from hyperglycemic and normoglycemic mice, was analyzed to find groups of coexpressed genes (modules) with disturbed expression in diabetes. “Disease signatures” derived from these modules were used to interrogate publically available microarray data sets. These data sets included gene expression profiles induced by a wide range of drugs and treatments. Data sets with an expression pattern similar to our islet disease signature gave clues to the underlying pathogenic process in β-cell failure, and data sets with a reverse expression pattern to our liver disease signature helped identify drug candidates for treatment of excessive hepatic glucose production. The islet disease signature was associated with β-cell dedifferentiation and loss of a mature β-cell state. We identified the transcription factor SOX5 as a regulator of the T2D-associated islet module. Overexpression of SOX5 increased the expression of β-cell specific genes in human islets and improved secretory function in islets from donors with T2D.The liver disease signature was used to rate compounds based on reverse expression compared with the disease signature. The rationale was that compounds with potential to reverse the disease signature mightaffect the pathophysiology. Sulforaphane, a sulfur-containing compound found naturally in e.g. broccoli, was identified as the top-rated compound. Sulforaphane reduced glucose production from hepatoma cells via amechanism that involves reduced expression of gluconeogenic enzymes. Sulforaphane improved glucose tolerance in animal models of diabetes. Moreover, in a small clinical study, sulforaphane-rich broccoli sproutextract reduced fasting blood glucose and HbA1c levels in obese T2D patients with poor glycemic control. Taken together, the data presented in this thesis demonstrate the opportunities of genotype-based treatment for T2D, and show the usefulness of gene network analysis to identify pathophysiological mechanisms and new potential therapies for T2D. By this approach, we have identified Sox5 as a new regulator of β-cell function, and sulforaphane as a liver-targeting therapy for T2D patients with poor glycemic control