SNAPR: a bioinformatics pipeline for efficient and accurate RNA-seq alignment and analysis

Allele-specific analysis reveals exon- and cell-type-specific regulatory effects of Alzheimer's disease-associated genetic variants

Elucidating regulatory effects of Alzheimer's disease (AD)-associated genetic variants is critical for unraveling their causal pathways and understanding the pathology. However, their cell-type-specific regulatory mechanisms in the brain remain largely unclear. Here, we conducted an analysis of allele-specific expression quantitative trait loci (aseQTLs) for 33 AD-associated variants in four brain regions and seven cell types using ~3000 bulk RNA-seq samples and >0.25 million single nuclei. We first develop a flexible hierarchical Poisson mixed model (HPMM) and demonstrate its superior statistical power to a beta-binomial model achieved by unifying samples in both allelic and genotype-level expression data. Using the HPMM, we identified 24 (~73%) aseQTLs in at least one brain region, including three new eQTLs associated with CA12, CHRNE, and CASS4. Notably, the APOE ε4 variant reduces APOE expression across all regions, even in AD-unaffected controls. Our results reveal region-dependent and exon-specific effects of multiple aseQTLs, such as rs2093760 with CR1, rs7982 with CLU, and rs3865444 with CD33. In an attempt to pinpoint the cell types responsible for the observed tissue-level aseQTLs using the snRNA-seq data, we detected many aseQTLs in microglia or monocytes associated with immune-related genes, including HLA-DQB1, HLA-DQA2, CD33, FCER1G, MS4A6A, SPI1, and BIN1, highlighting the regulatory role of AD-associated variants in the immune response. These findings provide further insights into potential causal pathways and cell types mediating the effects of the AD-associated variants.

Genetics of Gene Expression in the Aging Human Brain Reveal TDP-43 Proteinopathy Pathophysiology

Here, we perform a genome-wide screen for variants that regulate the expression of gene co-expression modules in the aging human brain; we discover and replicate such variants in the TMEM106B and RBFOX1 loci. The TMEM106B haplotype is known to influence the accumulation of TAR DNA-binding protein 43 kDa (TDP-43) proteinopathy, and the haplotype's large-scale transcriptomic effects include the dysregulation of lysosomal genes and alterations in synaptic gene splicing that are also seen in the pathophysiology of TDP-43 proteinopathy. Further, a variant near GRN, another TDP-43 proteinopathy susceptibility gene, shows concordant effects with the TMEM106B haplotype. Leveraging neuropathology data from the same participants, we also show that TMEM106B and APOE-amyloid-β effects converge to alter myelination and lysosomal gene expression, which then contributes to TDP-43 accumulation. These results advance our mechanistic understanding of the TMEM106B TDP-43 risk haplotype and uncover a transcriptional program that mediates the converging effects of APOE-amyloid-β and TMEM106B on TDP-43 aggregation in older adults.

Mitochondrial tRNA methylation in Alzheimer's disease and progressive supranuclear palsy

BACKGROUND

Methylation of mitochondrial tRNAs (mt-tRNA) at the 9th position ("p9 site") is known to impact translational efficiency and downstream mitochondrial function; however, direct assessment of mt-RNA methylation is challenging. Recent RNA sequence-based methods have been developed to reliably identify post-transcriptional methylation. Though p9 methylation has been studied in healthy human populations and in the context of cancer, it has not yet been analyzed in neurodegenerative disease, where mitochondrial dysfunction is a prominent and early hallmark of disease progression.

METHODS

Mitochondrial p9 methylation was inferred from multi-allelic calls in RNA-seq data. Gene-based association studies were performed in FUMA. Correlations between nuclear gene expression and p9 methylation were tested using Spearman's rho. Fisher's Exact test was used in PANTHER and IPA to test for overrepresentation and enrichment of biological processes and pathways in the top nuclear genes correlated with p9 methylation.

RESULTS

Variable methylation was observed at 11 p9 sites in post-mortem cerebellar tissue of elderly subjects who were either healthy or diagnosed with Alzheimer's disease (AD), progressive supranuclear palsy (PSP) or pathological aging (PA). Similarities in degree of methylation were observed between AD and PSP. Certain nuclear encoded genes were identified as significantly associated with p9 methylation. Expression of 5300 nuclear encoded genes was significantly correlated with p9 methylation, with AD and PSP subjects exhibiting similar expression profiles. Overrepresentation and enrichment testing using the top transcripts revealed enrichment for a number of molecular processes, terms and pathways including many of which that were mitochondrial-related.

CONCLUSION

With mitochondrial dysfunction being an established hallmark of neurodegenerative disease pathophysiology, this work sheds light on the potential molecular underpinnings of this dysfunction. Here we show overlap in cerebellar pathophysiology between common tauopathies such as Alzheimer's disease and progressive supranuclear palsy. Whether p9 hypermethylation is a cause or consequence of pathology remains an area of focus.

Computational Oncology in the Multi-Omics Era: State of the Art

Cancer is the quintessential complex disease. As technologies evolve faster each day, we are able to quantify the different layers of biological elements that contribute to the emergence and development of malignancies. In this multi-omics context, the use of integrative approaches is mandatory in order to gain further insights on oncological phenomena, and to move forward toward the precision medicine paradigm. In this review, we will focus on computational oncology as an integrative discipline that incorporates knowledge from the mathematical, physical, and computational fields to further the biomedical understanding of cancer. We will discuss the current roles of computation in oncology in the context of multi-omic technologies, which include: data acquisition and processing; data management in the clinical and research settings; classification, diagnosis, and prognosis; and the development of models in the research setting, including their use for therapeutic target identification. We will discuss the machine learning and network approaches as two of the most promising emerging paradigms, in computational oncology. These approaches provide a foundation on how to integrate different layers of biological description into coherent frameworks that allow advances both in the basic and clinical settings.

Accelerated brain aging towards transcriptional inversion in a zebrafish model of the K115fs mutation of human PSEN2

Background

The molecular changes involved in Alzheimer’s disease (AD) progression remain unclear since we cannot easily access antemortem human brains. Some non-mammalian vertebrates such as the zebrafish preserve AD-relevant transcript isoforms of the PRESENILIN genes lost from mice and rats. One example is PS2V, the alternative transcript isoform of the PSEN2 gene. PS2V is induced by hypoxia/oxidative stress and shows increased expression in late onset, sporadic AD brains. A unique, early onset familial AD mutation of PSEN2, K115fs, mimics the PS2V coding sequence suggesting that forced, early expression of PS2V-like isoforms may contribute to AD pathogenesis. Here we use zebrafish to model the K115fs mutation to investigate the effects of forced PS2V-like expression on the transcriptomes of young adult and aged adult brains.

Methods

We edited the zebrafish genome to model the K115fs mutation. To explore its effects at the molecular level, we analysed the brain transcriptome and proteome of young (6-month-old) and aged (24-month-old) wild type and heterozygous mutant female sibling zebrafish. Finally, we used gene co-expression network analysis (WGCNA) to compare molecular changes in the brains of these fish to human AD.

Results

Young heterozygous mutant fish show transcriptional changes suggesting accelerated brain aging and increased glucocorticoid signalling. These early changes precede a transcriptional ‘inversion’ that leads to glucocorticoid resistance and other likely pathological changes in aged heterozygous mutant fish. Notably, microglia-associated immune responses regulated by the ETS transcription factor family are altered in both our zebrafish mutant model and in human AD. The molecular changes we observe in aged heterozygous mutant fish occur without obvious histopathology and possibly in the absence of Aβ.

Conclusions

Our results suggest that forced expression of a PS2V-like isoform contributes to immune and stress responses favouring AD pathogenesis. This highlights the value of our zebrafish genetic model for exploring molecular mechanisms involved in AD pathogenesis.

Improving data workflow systems with cloud services and use of open data for bioinformatics research

Data workflow systems (DWFSs) enable bioinformatics researchers to combine components for data access and data analytics, and to share the final data analytics approach with their collaborators. Increasingly, such systems have to cope with large-scale data, such as full genomes (about 200 GB each), public fact repositories (about 100 TB of data) and 3D imaging data at even larger scales. As moving the data becomes cumbersome, the DWFS needs to embed its processes into a cloud infrastructure, where the data are already hosted. As the standardized public data play an increasingly important role, the DWFS needs to comply with Semantic Web technologies. This advancement to DWFS would reduce overhead costs and accelerate the progress in bioinformatics research based on large-scale data and public resources, as researchers would require less specialized IT knowledge for the implementation. Furthermore, the high data growth rates in bioinformatics research drive the demand for parallel and distributed computing, which then imposes a need for scalability and high-throughput capabilities onto the DWFS. As a result, requirements for data sharing and access to public knowledge bases suggest that compliance of the DWFS with Semantic Web standards is necessary. In this article, we will analyze the existing DWFS with regard to their capabilities toward public open data use as well as large-scale computational and human interface requirements. We untangle the parameters for selecting a preferable solution for bioinformatics research with particular consideration to using cloud services and Semantic Web technologies. Our analysis leads to research guidelines and recommendations toward the development of future DWFS for the bioinformatics research community.

Motivational, proteostatic and transcriptional deficits precede synapse loss, gliosis and neurodegeneration in the B6.HttQ111/+ model of Huntington's disease

We investigated the appearance and progression of disease-relevant signs in the B6.Htt^Q111/+ mouse, a genetically precise model of the mutation that causes Huntington’s disease (HD). We find that B6.Htt^Q111/+ mice are healthy, show no overt signs of central or peripheral inflammation, and no gross motor impairment as late as 12 months of age. Behaviorally, we find that 4–9 month old B6.Htt^Q111/+ mice have normal activity levels and show no clear signs of anxiety or depression, but do show clear signs of reduced motivation. The neuronal density, neuronal size, synaptic density and number of glia is normal in B6.Htt^Q111/+ striatum, the most vulnerable brain region in HD, up to 12 months of age. Despite this preservation of the synaptic and cellular composition of the striatum, we observe clear progressive, striatal-specific transcriptional dysregulation and accumulation of neuronal intranuclear inclusions (NIIs). Simulation studies suggest these molecular endpoints are sufficiently robust for future preclinical studies, and that B6.Htt^Q111/+ mice are a useful tool for modeling disease-modifying or neuroprotective strategies for disease processes before the onset of overt phenotypes.

Human whole genome genotype and transcriptome data for Alzheimer's and other neurodegenerative diseases

Previous genome-wide association studies (GWAS), conducted by our group and others, have identified loci that harbor risk variants for neurodegenerative diseases, including Alzheimer's disease (AD). Human disease variants are enriched for polymorphisms that affect gene expression, including some that are known to associate with expression changes in the brain. Postulating that many variants confer risk to neurodegenerative disease via transcriptional regulatory mechanisms, we have analyzed gene expression levels in the brain tissue of subjects with AD and related diseases. Herein, we describe our collective datasets comprised of GWAS data from 2,099 subjects; microarray gene expression data from 773 brain samples, 186 of which also have RNAseq; and an independent cohort of 556 brain samples with RNAseq. We expect that these datasets, which are available to all qualified researchers, will enable investigators to explore and identify transcriptional mechanisms contributing to neurodegenerative diseases.