Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer's disease.

The genetics of complex disease produce alterations in the molecular interactions of cellular pathways whose collective effect may become clear through the organized structure of molecular networks. To characterize molecular systems associated with late-onset Alzheimer's disease (LOAD), we constructed gene-regulatory networks in 1,647 postmortem brain tissues from LOAD patients and nondemented subjects, and we demonstrate that LOAD reconfigures specific portions of the molecular interaction structure. Through an integrative network-based approach, we rank-ordered these network structures for relevance to LOAD pathology, highlighting an immune- and microglia-specific module that is dominated by genes involved in pathogen phagocytosis, contains TYROBP as a key regulator, and is upregulated in LOAD. Mouse microglia cells overexpressing intact or truncated TYROBP revealed expression changes that significantly overlapped the human brain TYROBP network. Thus the causal network structure is a useful predictor of response to gene perturbations and presents a framework to test models of disease mechanisms underlying LOAD.

Integration of peripheral transcriptomics, genomics, and interactomics following trauma identifies causal genes for symptoms of post-traumatic stress and major depression.

Posttraumatic stress disorder (PTSD) is a debilitating syndrome with substantial morbidity and mortality that occurs in the aftermath of trauma. Symptoms of major depressive disorder (MDD) are also a frequent consequence of trauma exposure. Identifying novel risk markers in the immediate aftermath of trauma is a critical step for the identification of novel biological targets to understand mechanisms of pathophysiology and prevention, as well as the determination of patients most at risk who may benefit from immediate intervention. Our study utilizes a novel approach to computationally integrate blood-based transcriptomics, genomics, and interactomics to understand the development of risk vs. resilience in the months following trauma exposure. In a two-site longitudinal, observational prospective study, we assessed over 10,000 individuals and enrolled >700 subjects in the immediate aftermath of trauma (average 5.3 h post-trauma (range 0.5-12 h)) in the Grady Memorial Hospital (Atlanta) and Jackson Memorial Hospital (Miami) emergency departments. RNA expression data and 6-month follow-up data were available for 366 individuals, while genotype, transcriptome, and phenotype data were available for 297 patients. To maximize our power and understanding of genes and pathways that predict risk vs. resilience, we utilized a set-cover approach to capture fluctuations of gene expression of PTSD or depression-converting patients and non-converting trauma-exposed controls to find representative sets of disease-relevant dysregulated genes. We annotated such genes with their corresponding expression quantitative trait loci and applied a variant of a current flow algorithm to identify genes that potentially were causal for the observed dysregulation of disease genes involved in the development of depression and PTSD symptoms after trauma exposure. We obtained a final list of 11 driver causal genes related to MDD symptoms, 13 genes for PTSD symptoms, and 22 genes in PTSD and/or MDD. We observed that these individual or combined disorders shared ESR1, RUNX1, PPARA, and WWOX as driver causal genes, while other genes appeared to be causal driver in the PTSD only or MDD only cases. A number of these identified causal pathways have been previously implicated in the biology or genetics of PTSD and MDD, as well as in preclinical models of amygdala function and fear regulation. Our work provides a promising set of initial pathways that may underlie causal mechanisms in the development of PTSD or MDD in the aftermath of trauma.

The human brainome: network analysis identifies HSPA2 as a novel Alzheimer’s disease target.

Our hypothesis is that changes in gene and protein expression are crucial to the development of late-onset Alzheimer’s disease. Previously we examined how DNA alleles control downstream expression of RNA transcripts and how those relationships are changed in late-onset Alzheimer’s disease. We have now examined how proteins are incorporated into networks in two separate series and evaluated our outputs in two different cell lines. Our pipeline included the following steps: (i) predicting expression quantitative trait loci; (ii) determining differential expression; (iii) analysing networks of transcript and peptide relationships; and (iv) validating effects in two separate cell lines. We performed all our analysis in two separate brain series to validate effects. Our two series included 345 samples in the first set (177 controls, 168 cases; age range 65–105; 58% female; KRONOSII cohort) and 409 samples in the replicate set (153 controls, 141 cases, 115 mild cognitive impairment; age range 66–107; 63% female; RUSH cohort). Our top target is heat shock protein family A member 2 (HSPA2), which was identified as a key driver in our two datasets. HSPA2 was validated in two cell lines, with overexpression driving further elevation of amyloid-β40 and amyloid-β42 levels in APP mutant cells, as well as significant elevation of microtubule associated protein tau and phosphorylated-tau in a modified neuroglioma line. This work further demonstrates that studying changes in gene and protein expression is crucial to understanding late onset disease and further nominates HSPA2 as a specific key regulator of late-onset Alzheimer’s disease processes.10.1093/brain/awy215_video1awy215media15824729224001.

Polygenic risk score analysis of pathologically confirmed Alzheimer disease.

Previous estimates of the utility of polygenic risk score analysis for the prediction of Alzheimer disease have given area under the curve (AUC) estimates of <80%. However, these have been based on the genetic analysis of clinical case-control series. Here, we apply the same analytic approaches to a pathological case-control series and show a predictive AUC of 84%. We suggest that this analysis has clinical utility and that there is limited room for further improvement using genetic data. Ann Neurol 2017;82:311-314.

Post-traumatic stress disorder is associated with PACAP and the PAC1 receptor.

Pituitary adenylate cyclase-activating polypeptide (PACAP) is known to broadly regulate the cellular stress response. In contrast, it is unclear if the PACAP-PAC1 receptor pathway has a role in human psychological stress responses, such as post-traumatic stress disorder (PTSD). Here we find, in heavily traumatized subjects, a sex-specific association of PACAP blood levels with fear physiology, PTSD diagnosis and symptoms in females. We examined 44 single nucleotide polymorphisms (SNPs) spanning the PACAP (encoded by ADCYAP1) and PAC1 (encoded by ADCYAP1R1) genes, demonstrating a sex-specific association with PTSD. A single SNP in a putative oestrogen response element within ADCYAP1R1, rs2267735, predicts PTSD diagnosis and symptoms in females only. This SNP also associates with fear discrimination and with ADCYAP1R1 messenger RNA expression in human brain. Methylation of ADCYAP1R1 in peripheral blood is also associated with PTSD. Complementing these human data, ADCYAP1R1 mRNA is induced with fear conditioning or oestrogen replacement in rodent models. These data suggest that perturbations in the PACAP-PAC1 pathway are involved in abnormal stress responses underlying PTSD. These sex-specific effects may occur via oestrogen regulation of ADCYAP1R1. PACAP levels and ADCYAP1R1 SNPs may serve as useful biomarkers to further our mechanistic understanding of PTSD.

24-hour rhythms of DNA methylation and their relation with rhythms of RNA expression in the human dorsolateral prefrontal cortex.

Circadian rhythms modulate the biology of many human tissues, including brain tissues, and are driven by a near 24-hour transcriptional feedback loop. These rhythms are paralleled by 24-hour rhythms of large portions of the transcriptome. The role of dynamic DNA methylation in influencing these rhythms is uncertain. While recent work in Neurospora suggests that dynamic site-specific circadian rhythms of DNA methylation may play a role in modulating the fungal molecular clock, such rhythms and their relationship to RNA expression have not, to our knowledge, been elucidated in mammalian tissues, including human brain tissues. We hypothesized that 24-hour rhythms of DNA methylation exist in the human brain, and play a role in driving 24-hour rhythms of RNA expression. We analyzed DNA methylation levels in post-mortem human dorsolateral prefrontal cortex samples from 738 subjects. We assessed for 24-hour rhythmicity of 420,132 DNA methylation sites throughout the genome by considering methylation levels as a function of clock time of death and parameterizing these data using cosine functions. We determined global statistical significance by permutation. We then related rhythms of DNA methylation with rhythms of RNA expression determined by RNA sequencing. We found evidence of significant 24-hour rhythmicity of DNA methylation. Regions near transcription start sites were enriched for high-amplitude rhythmic DNA methylation sites, which were in turn time locked to 24-hour rhythms of RNA expression of nearby genes, with the nadir of methylation preceding peak transcript expression by 1-3 hours. Weak ante-mortem rest-activity rhythms were associated with lower amplitude DNA methylation rhythms as were older age and the presence of Alzheimer's disease. These findings support the hypothesis that 24-hour rhythms of DNA methylation, particularly near transcription start sites, may play a role in driving 24-hour rhythms of gene expression in the human dorsolateral prefrontal cortex, and may be affected by age and Alzheimer's disease.

Genome-wide association meta-analysis of neuropathologic features of Alzheimer's disease and related dementias.

Alzheimer's disease (AD) and related dementias are a major public health challenge and present a therapeutic imperative for which we need additional insight into molecular pathogenesis. We performed a genome-wide association study and analysis of known genetic risk loci for AD dementia using neuropathologic data from 4,914 brain autopsies. Neuropathologic data were used to define clinico-pathologic AD dementia or controls, assess core neuropathologic features of AD (neuritic plaques, NPs; neurofibrillary tangles, NFTs), and evaluate commonly co-morbid neuropathologic changes: cerebral amyloid angiopathy (CAA), Lewy body disease (LBD), hippocampal sclerosis of the elderly (HS), and vascular brain injury (VBI). Genome-wide significance was observed for clinico-pathologic AD dementia, NPs, NFTs, CAA, and LBD with a number of variants in and around the apolipoprotein E gene (APOE). GalNAc transferase 7 (GALNT7), ATP-Binding Cassette, Sub-Family G (WHITE), Member 1 (ABCG1), and an intergenic region on chromosome 9 were associated with NP score; and Potassium Large Conductance Calcium-Activated Channel, Subfamily M, Beta Member 2 (KCNMB2) was strongly associated with HS. Twelve of the 21 non-APOE genetic risk loci for clinically-defined AD dementia were confirmed in our clinico-pathologic sample: CR1, BIN1, CLU, MS4A6A, PICALM, ABCA7, CD33, PTK2B, SORL1, MEF2C, ZCWPW1, and CASS4 with 9 of these 12 loci showing larger odds ratio in the clinico-pathologic sample. Correlation of effect sizes for risk of AD dementia with effect size for NFTs or NPs showed positive correlation, while those for risk of VBI showed a moderate negative correlation. The other co-morbid neuropathologic features showed only nominal association with the known AD loci. Our results discovered new genetic associations with specific neuropathologic features and aligned known genetic risk for AD dementia with specific neuropathologic changes in the largest brain autopsy study of AD and related dementias.

Natural antisense transcripts.

Recent years have seen the increasing understanding of the crucial role of RNA in the functioning of the eukaryotic genome. These discoveries, fueled by the achievements of the FANTOM, and later GENCODE and ENCODE consortia, led to the recognition of the important regulatory roles of natural antisense transcripts (NATs) arising from what was previously thought to be 'junk DNA'. Roughly defined as non-coding regulatory RNA transcribed from the opposite strand of a coding gene locus, NATs are proving to be a heterogeneous group with high potential for therapeutic application. Here, we attempt to summarize the rapidly growing knowledge about this important non-coding RNA subclass.

A coding variant in CR1 interacts with APOE-ε4 to influence cognitive decline.

Complement receptor 1 (CR1) is an Alzheimer's disease (AD) susceptibility locus that also influences AD-related traits such as episodic memory decline and neuritic amyloid plaque deposition. We implemented a functional fine-mapping approach, leveraging intermediate phenotypes to identify functional variant(s) within the CR1 locus. Using 1709 subjects (697 deceased) from the Religious Orders Study and the Rush Memory and Aging Project, we tested 41 single-nucleotide polymorphisms (SNPs) within the linkage disequilibrium block containing the published CR1 AD SNP (rs6656401) for associations with episodic memory decline, and then examined the functional consequences of the top result. We report that a coding variant in the LHR-D (long homologous repeat D) region of the CR1 gene, rs4844609 (Ser1610Thr, minor allele frequency = 0.02), is associated with episodic memory decline and accounts for the known effect of the index SNP rs6656401 (D' = 1, r(2)= 0.084) on this trait. Further, we demonstrate that the coding variant's effect is largely dependent on an interaction with APOE-ε4 and mediated by an increased burden of AD-related neuropathology. Finally, in our data, this coding variant is also associated with AD susceptibility (joint odds ratio = 1.4). Taken together, our analyses identify a CR1 coding variant that influences episodic memory decline; it is a variant known to alter the conformation of CR1 and points to LHR-D as the functional domain within the CR1 protein that mediates the effect on memory decline. We thus implicate C1q and MBL, which bind to LHR-D, as likely targets of the variant's effect and suggest that CR1 may be an important intermediate in the clearance of Aß42 particles by C1q.

Changes in expression of VGF, SPECC1L, HLA-DRA and RANBP3L act with APOE E4 to alter risk for late onset Alzheimer's disease.

While there are currently over 40 replicated genes with mapped risk alleles for Late Onset Alzheimer's disease (LOAD), the Apolipoprotein E locus E4 haplotype is still the biggest driver of risk, with odds ratios for neuropathologically confirmed E44 carriers exceeding 30 (95% confidence interval 16.59-58.75). We sought to address whether the APOE E4 haplotype modifies expression globally through networks of expression to increase LOAD risk. We have used the Human Brainome data to build expression networks comparing APOE E4 carriers to non-carriers using scalable mixed-datatypes Bayesian network (BN) modeling. We have found that VGF had the greatest explanatory weight. High expression of VGF is a protective signal, even on the background of APOE E4 alleles. LOAD risk signals, considering an APOE background, include high levels of SPECC1L, HLA-DRA and RANBP3L. Our findings nominate several new transcripts, taking a combined approach to network building including known LOAD risk loci.

Sources of technical variability in quantitative LC-MS proteomics: human brain tissue sample analysis.

To design a robust quantitative proteomics study, an understanding of both the inherent heterogeneity of the biological samples being studied as well as the technical variability of the proteomics methods and platform is needed. Additionally, accurately identifying the technical steps associated with the largest variability would provide valuable information for the improvement and design of future processing pipelines. We present an experimental strategy that allows for a detailed examination of the variability of the quantitative LC-MS proteomics measurements. By replicating analyses at different stages of processing, various technical components can be estimated and their individual contribution to technical variability can be dissected. This design can be easily adapted to other quantitative proteomics pipelines. Herein, we applied this methodology to our label-free workflow for the processing of human brain tissue. For this application, the pipeline was divided into four critical components: Tissue dissection and homogenization (extraction), protein denaturation followed by trypsin digestion and SPE cleanup (digestion), short-term run-to-run instrumental response fluctuation (instrumental variance), and long-term drift of the quantitative response of the LC-MS/MS platform over the 2 week period of continuous analysis (instrumental stability). From this analysis, we found the following contributions to variability: extraction (72%) >> instrumental variance (16%) > instrumental stability (8.4%) > digestion (3.1%). Furthermore, the stability of the platform and its suitability for discovery proteomics studies is demonstrated.

Initial assessment of the pathogenic mechanisms of the recently identified Alzheimer risk Loci.

Recent genome wide association studies have identified CLU, CR1, ABCA7 BIN1, PICALM and MS4A6A/MS4A6E in addition to the long established APOE, as loci for Alzheimer's disease. We have systematically examined each of these loci to assess whether common coding variability contributes to the risk of disease. We have also assessed the regional expression of all the genes in the brain and whether there is evidence of an eQTL explaining the risk. In agreement with other studies we find that coding variability may explain the ABCA7 association, but common coding variability does not explain any of the other loci. We were not able to show that any of the loci had eQTLs within the power of this study. Furthermore the regional expression of each of the loci did not match the pattern of brain regional distribution in Alzheimer pathology. Although these results are mainly negative, they allow us to start defining more realistic alternative approaches to determine the role of all the genetic loci involved in Alzheimer's disease.

A common polymorphism near PER1 and the timing of human behavioral rhythms.

OBJECTIVE: Circadian rhythms influence the timing of behavior, neurological diseases, and even death. Rare mutations in homologs of evolutionarily conserved clock genes are found in select pedigrees with extreme sleep timing, and there is suggestive evidence that certain common polymorphisms may be associated with self-reported day/night preference. However, no common polymorphism has been associated with the timing of directly observed human behavioral rhythms or other physiological markers of circadian timing at the population level. METHODS: We performed a candidate gene association study with replication, evaluating associations between polymorphisms in homologs of evolutionarily conserved clock genes and the timing of behavioral rhythms measured by actigraphy. For validated polymorphisms, we evaluated associations with transcript expression and time of death in additional cohorts. RESULTS: rs7221412, a common polymorphism near period homolog 1 (PER1), was associated with the timing of activity rhythms in both the discovery and replication cohorts (joint p = 2.1 × 10(-7) ). Mean activity timing was delayed by 67 minutes in rs7221412(GG) versus rs7221412(AA) homozygotes. rs7221412 also showed a suggestive time-dependent relationship with both cerebral cortex (p = 0.05) and CD14+ CD16- monocyte (p = 0.02) PER1 expression and an interesting association with time of death (p = 0.015) in which rs7221412(GG) individuals had a mean time of death nearly 7 hours later than rs7221412(AA/AG) . INTERPRETATION: A common polymorphism near PER1 is associated with the timing of human behavioral rhythms, and shows evidence of association with time of death. This may be mediated by differential PER1 expression. These results may facilitate individualized scheduling of shift work, medical treatments, or monitoring of vulnerable patient populations.

The Human Brainome: changes in expression of VGF, SPECC1L, HLA-DRA and RANBP3L act with APOE E4 to alter risk for late onset Alzheimer's disease.

While there are currently over 40 replicated genes with mapped risk alleles for Late Onset Alzheimer's disease (LOAD), the Apolipoprotein E locus E4 haplotype is still the biggest driver of risk, with odds ratios for neuropathologically confirmed E44 carriers exceeding 30 (95% confidence interval 16.59-58.75). We sought to address whether the APOE E4 haplotype modifies expression globally through networks of expression to increase LOAD risk. We have used the Human Brainome data to build expression networks comparing APOE E4 carriers to non-carriers using scalable mixed-datatypes Bayesian network (BN) modeling. We have found that VGF had the greatest explanatory weight. High expression of VGF is a protective signal, even on the background of APOE E4 alleles. LOAD risk signals, considering an APOE background, include high levels of SPECC1L, HLA-DRA and RANBP3L. Our findings nominate several new transcripts, taking a combined approach to network building including known LOAD risk loci.

What does heritability of Alzheimer's disease represent?

INTRODUCTION: Both late-onset Alzheimer's disease (AD) and ageing have a strong genetic component. In each case, many associated variants have been discovered, but how much missing heritability remains to be discovered is debated. Variability in the estimation of SNP-based heritability could explain the differences in reported heritability. METHODS: We compute heritability in five large independent cohorts (N = 7,396, 1,566, 803, 12,528 and 3,963) to determine whether a consensus for the AD heritability estimate can be reached. These cohorts vary by sample size, age of cases and controls and phenotype definition. We compute heritability a) for all SNPs, b) excluding APOE region, c) excluding both APOE and genome-wide association study hit regions, and d) SNPs overlapping a microglia gene-set. RESULTS: SNP-based heritability of late onset Alzheimer's disease is between 38 and 66% when age and genetic disease architecture are correctly accounted for. The heritability estimates decrease by 12% [SD = 8%] on average when the APOE region is excluded and an additional 1% [SD = 3%] when genome-wide significant regions were removed. A microglia gene-set explains 69-84% of our estimates of SNP-based heritability using only 3% of total SNPs in all cohorts. CONCLUSION: The heritability of neurodegenerative disorders cannot be represented as a single number, because it is dependent on the ages of cases and controls. Genome-wide association studies pick up a large proportion of total AD heritability when age and genetic architecture are correctly accounted for. Around 13% of SNP-based heritability can be explained by known genetic loci and the remaining heritability likely resides around microglial related genes.

Label-free quantitative LC-MS proteomics of Alzheimer's disease and normally aged human brains.

Quantitative proteomics analysis of cortical samples of 10 Alzheimer's disease (AD) brains versus 10 normally aged brains was performed by following the accurate mass and time tag (AMT) approach with the high resolution LTQ Orbitrap mass spectrometer. More than 1400 proteins were identified and quantitated. A conservative approach of selecting only the consensus results of four normalization methods was suggested and used. A total of 197 proteins were shown to be significantly differentially abundant (p-values <0.05, corrected for multiplicity of testing) in AD versus control brain samples. Thirty-seven of these proteins were reported as differentially abundant or modified in AD in previous proteomics and transcriptomics publications. The rest to the best of our knowledge are new. Mapping of the discovered proteins with bioinformatic tools revealed significant enrichment with differentially abundant proteins of pathways and processes known to be important in AD, including signal transduction, regulation of protein phosphorylation, immune response, cytoskeleton organization, lipid metabolism, energy production, and cell death.

Association of CR1, CLU and PICALM with Alzheimer's disease in a cohort of clinically characterized and neuropathologically verified individuals.

In this study, we assess 34 of the most replicated genetic associations for Alzheimer's disease (AD) using data generated on Affymetrix SNP 6.0 arrays and imputed at over 5.7 million markers from a unique cohort of over 1600 neuropathologically defined AD cases and controls (1019 cases and 591 controls). Testing the top genes from the AlzGene meta-analysis, we confirm the well-known association with APOE single nucleotide polymorphisms (SNPs), the CLU, PICALM and CR1 SNPs recently implicated in unusually large data sets, and previously implicated CST3 and ACE SNPs. In the cases of CLU, PICALM and CR1, as well as in APOE, the odds ratios we find are slightly larger than those previously reported in clinical samples, consistent with what we believe to be more accurate classification of disease in the clinically characterized and neuropathologically confirmed AD cases and controls.

Genetic control of human brain transcript expression in Alzheimer disease.

We recently surveyed the relationship between the human brain transcriptome and genome in a series of neuropathologically normal postmortem samples. We have now analyzed additional samples with a confirmed pathologic diagnosis of late-onset Alzheimer disease (LOAD; final n = 188 controls, 176 cases). Nine percent of the cortical transcripts that we analyzed had expression profiles correlated with their genotypes in the combined cohort, and approximately 5% of transcripts had SNP-transcript relationships that could distinguish LOAD samples. Two of these transcripts have been previously implicated in LOAD candidate-gene SNP-expression screens. This study shows how the relationship between common inherited genetic variants and brain transcript expression can be used in the study of human brain disorders. We suggest that studying the transcriptome as a quantitative endo-phenotype has greater power for discovering risk SNPs influencing expression than the use of discrete diagnostic categories such as presence or absence of disease.

CR1 is associated with amyloid plaque burden and age-related cognitive decline.

OBJECTIVE: Recently, genome-wide association studies have identified 3 new susceptibility loci for Alzheimer's disease (AD), CLU, CR1, and PICALM. We leveraged available neuropsychological and autopsy data from 2 cohort studies to investigate whether these loci are associated with cognitive decline and AD neuropathology. METHODS: The Religious Orders Study (ROS) and Rush Memory and Aging Project (MAP) are longitudinal studies that enroll nondemented subjects and include annual clinical evaluations and brain donation at death. We evaluated CR1 (rs6656401), CLU (rs11136000), and PICALM (rs7110631) in 1,666 subjects. We evaluated associations between genotypes and rate of change in cognitive function as well as AD-related pathology. Lastly, we used pathway analysis to determine whether relationships between single nucleotide polymorphisms and cognitive decline are mediated through AD pathology. RESULTS: Among our study cohort, the mean years of follow-up were 7.8 for ROS and 4.3 for MAP. Only the CR1 locus was associated with both global cognitive decline (p = 0.011) and global AD pathology (p = 0.025). More specifically, the locus affects the deposition of neuritic amyloid plaque (p = 0.009). In a mediation analysis, controlling for amyloid pathology strongly attenuated the effect of the CR1 locus on cognitive decline. INTERPRETATION: We found that common variation at the CR1 locus has a broad impact on cognition and that this effect is largely mediated by an individual's amyloid plaque burden. We therefore highlight 1 functional consequence of the CR1 susceptibility allele and generalize the role of this locus to cognitive aging in the general population.