Multi-Omic Blood Biomarkers as Dynamic Risk Predictors in Late-Onset Alzheimer's Disease.

Late-onset Alzheimer's disease is the leading cause of dementia worldwide, accounting for a growing burden of morbidity and mortality. Diagnosing Alzheimer's disease before symptoms are established is clinically challenging, but would provide therapeutic windows for disease-modifying interventions. Blood biomarkers, including genetics, proteins and metabolites, are emerging as powerful predictors of Alzheimer's disease at various timepoints within the disease course, including at the preclinical stage. In this review, we discuss recent advances in such blood biomarkers for determining disease risk. We highlight how leveraging polygenic risk scores, based on genome-wide association studies, can help stratify individuals along their risk profile. We summarize studies analyzing protein biomarkers, as well as report on recent proteomic- and metabolomic-based prediction models. Finally, we discuss how a combination of multi-omic blood biomarkers can potentially be used in memory clinics for diagnosis and to assess the dynamic risk an individual has for developing Alzheimer's disease dementia.

Identification of expression quantitative trait loci associated with schizophrenia and affective disorders in normal brain tissue.

Schizophrenia and the affective disorders, here comprising bipolar disorder and major depressive disorder, are psychiatric illnesses that lead to significant morbidity and mortality worldwide. Whilst understanding of their pathobiology remains limited, large case-control studies have recently identified single nucleotide polymorphisms (SNPs) associated with these disorders. However, discerning the functional effects of these SNPs has been difficult as the associated causal genes are unknown. Here we evaluated whether schizophrenia and affective disorder associated-SNPs are correlated with gene expression within human brain tissue. Specifically, to identify expression quantitative trait loci (eQTLs), we leveraged disorder-associated SNPs identified from 11 genome-wide association studies with gene expression levels in post-mortem, neurologically-normal tissue from two independent human brain tissue expression datasets (UK Brain Expression Consortium (UKBEC) and Genotype-Tissue Expression (GTEx)). Utilizing stringent multi-region meta-analyses, we identified 2,224 cis-eQTLs associated with expression of 40 genes, including 11 non-coding RNAs. One cis-eQTL, rs16969968, results in a functionally disruptive missense mutation in CHRNA5, a schizophrenia-implicated gene. Importantly, comparing across tissues, we find that blood eQTLs capture < 10% of brain cis-eQTLs. Contrastingly, > 30% of brain-associated eQTLs are significant in tibial nerve. This study identifies putatively causal genes whose expression in region-specific tissue may contribute to the risk of schizophrenia and affective disorders.

Accurate and robust genomic prediction of celiac disease using statistical learning.

Practical application of genomic-based risk stratification to clinical diagnosis is appealing yet performance varies widely depending on the disease and genomic risk score (GRS) method. Celiac disease (CD), a common immune-mediated illness, is strongly genetically determined and requires specific HLA haplotypes. HLA testing can exclude diagnosis but has low specificity, providing little information suitable for clinical risk stratification. Using six European cohorts, we provide a proof-of-concept that statistical learning approaches which simultaneously model all SNPs can generate robust and highly accurate predictive models of CD based on genome-wide SNP profiles. The high predictive capacity replicated both in cross-validation within each cohort (AUC of 0.87-0.89) and in independent replication across cohorts (AUC of 0.86-0.9), despite differences in ethnicity. The models explained 30-35% of disease variance and up to ∼43% of heritability. The GRS's utility was assessed in different clinically relevant settings. Comparable to HLA typing, the GRS can be used to identify individuals without CD with ≥99.6% negative predictive value however, unlike HLA typing, fine-scale stratification of individuals into categories of higher-risk for CD can identify those that would benefit from more invasive and costly definitive testing. The GRS is flexible and its performance can be adapted to the clinical situation by adjusting the threshold cut-off. Despite explaining a minority of disease heritability, our findings indicate a genomic risk score provides clinically relevant information to improve upon current diagnostic pathways for CD and support further studies evaluating the clinical utility of this approach in CD and other complex diseases.

Genomic prediction of coronary heart disease.

AIMS: Genetics plays an important role in coronary heart disease (CHD) but the clinical utility of genomic risk scores (GRSs) relative to clinical risk scores, such as the Framingham Risk Score (FRS), is unclear. Our aim was to construct and externally validate a CHD GRS, in terms of lifetime CHD risk and relative to traditional clinical risk scores. METHODS AND RESULTS: We generated a GRS of 49 310 SNPs based on a CARDIoGRAMplusC4D Consortium meta-analysis of CHD, then independently tested it using five prospective population cohorts (three FINRISK cohorts, combined n = 12 676, 757 incident CHD events; two Framingham Heart Study cohorts (FHS), combined n = 3406, 587 incident CHD events). The GRS was associated with incident CHD (FINRISK HR = 1.74, 95% confidence interval (CI) 1.61-1.86 per S.D. of GRS; Framingham HR = 1.28, 95% CI 1.18-1.38), and was largely unchanged by adjustment for known risk factors, including family history. Integration of the GRS with the FRS or ACC/AHA13 scores improved the 10 years risk prediction (meta-analysis C-index: +1.5-1.6%, P < 0.001), particularly for individuals ≥60 years old (meta-analysis C-index: +4.6-5.1%, P < 0.001). Importantly, the GRS captured substantially different trajectories of absolute risk, with men in the top 20% of attaining 10% cumulative CHD risk 12-18 y earlier than those in the bottom 20%. High genomic risk was partially compensated for by low systolic blood pressure, low cholesterol level, and non-smoking. CONCLUSIONS: A GRS based on a large number of SNPs improves CHD risk prediction and encodes different trajectories of lifetime risk not captured by traditional clinical risk scores.

Towards a molecular systems model of coronary artery disease.

Coronary artery disease (CAD) is a complex disease driven by myriad interactions of genetics and environmental factors. Traditionally, studies have analyzed only 1 disease factor at a time, providing useful but limited understanding of the underlying etiology. Recent advances in cost-effective and high-throughput technologies, such as single nucleotide polymorphism (SNP) genotyping, exome/genome/RNA sequencing, gene expression microarrays, and metabolomics assays have enabled the collection of millions of data points in many thousands of individuals. In order to make sense of such 'omics' data, effective analytical methods are needed. We review and highlight some of the main results in this area, focusing on integrative approaches that consider multiple modalities simultaneously. Such analyses have the potential to uncover the genetic basis of CAD, produce genomic risk scores (GRS) for disease prediction, disentangle the complex interactions underlying disease, and predict response to treatment.

microRNA-21 regulates astrocytic response following spinal cord injury.

Astrogliosis following spinal cord injury (SCI) involves an early hypertrophic response that serves to repair damaged blood-brain barrier and a subsequent hyperplastic response that results in a dense scar that impedes axon regeneration. The mechanisms regulating these two phases of astrogliosis are beginning to be elucidated. In this study, we found that microRNA-21 (miR-21) increases in a time-dependent manner following SCI in mouse. Astrocytes adjacent to the lesion area express high levels of miR-21 whereas astrocytes in uninjured spinal cord express low levels of miR-21. To study the role of miR-21 in astrocytes after SCI, transgenic mice were generated that conditionally overexpress either the primary miR-21 transcript in astrocytes or a miRNA sponge designed to inhibit miR-21 function. Overexpression of miR-21 in astrocytes attenuated the hypertrophic response to SCI. Conversely, expression of the miR-21 sponge augmented the hypertrophic phenotype, even in chronic stages of SCI recovery when astrocytes have normally become smaller in size with fine processes. Inhibition of miR-21 function in astrocytes also resulted in increased axon density within the lesion site. These findings demonstrate a novel role for miR-21 in regulating astrocytic hypertrophy and glial scar progression after SCI, and suggest miR-21 as a potential therapeutic target for manipulating gliosis and enhancing functional outcome.

The emerging roles of microRNAs in CNS injuries.

The consequences of injuries to the CNS are profound and persistent, resulting in substantial burden to both the individual patient and society. Existing treatments for CNS injuries such as stroke, traumatic brain injury and spinal cord injury have proved inadequate, partly owing to an incomplete understanding of post-injury cellular and molecular changes. MicroRNAs (miRNAs) are RNA molecules composed of 20-24 nucleotides that function to inhibit mRNA translation and have key roles in normal CNS development and function, as well as in disease. However, a role for miRNAs as effectors of CNS injury has recently emerged. Use of bioinformatics to assess the mRNA targets of miRNAs enables high-order analysis of interconnected networks, and can reveal affected pathways that may not be identifiable with the use of traditional techniques such as gene knock-in or knockout approaches, or mRNA microarrays. In this Review, we discuss the findings of miRNA microarray studies of spinal cord injury, traumatic brain injury and stroke, as well as the use of gene ontological algorithms to discern global patterns of molecular and cellular changes following such injuries. Furthermore, we examine the current state of miRNA-based therapies and their potential to improve functional outcomes in patients with CNS injuries.

Generation of Mouse Spinal Cord Injury.

Spinal cord injury (SCI) is a debilitating injury with significant morbidity and mortalitiy. Understanding the pathogensis of and developing treatments for SCI requires robust animal models. Here we describe a method for generating an efficient and reproducible contusion model of SCI in adult mice.

The dynamic role of bone morphogenetic proteins in neural stem cell fate and maturation.

The bone morphogenetic proteins (BMPs) are a group of powerful morphogens that are critical for development of the nervous system. The effects of BMP signaling on neural stem cells are myriad and dynamic, changing with each stage of development. During early development inhibition of BMP signaling differentiates neuroectoderm from ectoderm, and BMP signaling helps to specify neural crest. Thus modulation of BMP signaling underlies formation of both the central and peripheral nervous systems. BMPs secreted from dorsal structures then form a gradient which helps pattern the dorsal-ventral axis of the developing spinal cord and brain. During forebrain development BMPs sequentially induce neurogenesis and then astrogliogenesis and participate in neurite outgrowth from immature neurons. BMP signaling also plays a critical role in maintaining adult neural stem cell niches in the subventricular zone (SVZ) and subgranular zone (SGZ). BMPs are able to exert such diverse effects through closely regulated temporospatial expression and interaction with other signaling pathways.