A multi-omics dataset for the analysis of frontotemporal dementia genetic subtypes.

Understanding the molecular mechanisms underlying frontotemporal dementia (FTD) is essential for the development of successful therapies. Systematic studies on human post-mortem brain tissue of patients with genetic subtypes of FTD are currently lacking. The Risk and Modyfing Factors of Frontotemporal Dementia (RiMod-FTD) consortium therefore has generated a multi-omics dataset for genetic subtypes of FTD to identify common and distinct molecular mechanisms disturbed in disease. Here, we present multi-omics datasets generated from the frontal lobe of post-mortem human brain tissue from patients with mutations in MAPT, GRN and C9orf72 and healthy controls. This data resource consists of four datasets generated with different technologies to capture the transcriptome by RNA-seq, small RNA-seq, CAGE-seq, and methylation profiling. We show concrete examples on how to use the resulting data and confirm current knowledge about FTD and identify new processes for further investigation. This extensive multi-omics dataset holds great value to reveal new research avenues for this devastating disease.

Single-neuron whole genome sequencing identifies increased somatic mutation burden in Alzheimer's disease related genes.

Accumulation of somatic mutations in human neurons is associated with aging and neurodegeneration. To shed light on the somatic mutational burden in Alzheimer's disease (AD) neurons and get more insight into the role of somatic mutations in AD pathogenesis, we performed single-neuron whole genome sequencing to detect genome-wide somatic mutations (single nucleotide variants (SNVs) and Indels) in 96 single prefrontal cortex neurons from 8 AD patients and 8 elderly controls. We found that the mutational burden is ∼3000 somatic mutations per neuron genome in elderly subjects. AD patients have increased somatic mutation burden in AD-related annotation categories, including AD risk genes and differentially expressed genes in AD neurons. Mutational signature analysis showed somatic SNVs (sSNVs) primarily caused by aging and oxidative DNA damage processes but no significant difference was detected between AD and controls. Additionally, functional somatic mutations identified in AD patients showed significant enrichment in several AD-related pathways, including AD pathway, Notch-signaling pathway and Calcium-signaling pathway. These findings provide genetic insights into how somatic mutations may alter the function of single neurons and exert their potential roles in the pathogenesis of AD.

Absence of coding somatic single nucleotide variants within well-known candidate genes in late-onset sporadic Alzheimer's Disease based on the analysis of multi-omics data.

Somatic mutations arise randomly or are induced by environmental factors, which may increase the risk of Alzheimer's disease (AD). Identifying somatic mutations in sporadic AD (SAD) may provide new insight of the disease. To evaluate the potential contribution of somatic single nucleotide variations (SNVs), particularly that of well-known AD-candidate genes, we investigated sequencing data sets from four platforms: whole-genome sequencing (WGS), deep whole-exome sequencing (WES) on paired brain and liver samples, RNA sequencing (RNA-seq), and single-cell whole-genome sequencing (scWGS) of brain samples from 16 AD patients and 16 non-AD individuals. We found that the average number, mean variant allele fractions (VAFs) and mutational signatures of somatic SNVs have similar distributions between AD brains and non-AD brains. We did not identify any somatic SNVs within coding regions of the APP, PSEN1, PSEN2, nor in APOE. This study shows that somatic SNVs within the coding region of AD-candidate genes are unlikely to be a common causal factor for SAD.

miEAA 2.0: integrating multi-species microRNA enrichment analysis and workflow management systems.

Gene set enrichment analysis has become one of the most frequently used applications in molecular biology research. Originally developed for gene sets, the same statistical principles are now available for all omics types. In 2016, we published the miRNA enrichment analysis and annotation tool (miEAA) for human precursor and mature miRNAs. Here, we present miEAA 2.0, supporting miRNA input from ten frequently investigated organisms. To facilitate inclusion of miEAA in workflow systems, we implemented an Application Programming Interface (API). Users can perform miRNA set enrichment analysis using either the web-interface, a dedicated Python package, or custom remote clients. Moreover, the number of category sets was raised by an order of magnitude. We implemented novel categories like annotation confidence level or localisation in biological compartments. In combination with the miRBase miRNA-version and miRNA-to-precursor converters, miEAA supports research settings where older releases of miRBase are in use. The web server also offers novel comprehensive visualizations such as heatmaps and running sum curves with background distributions. We demonstrate the new features with case studies for human kidney cancer, a biomarker study on Parkinson's disease from the PPMI cohort, and a mouse model for breast cancer. The tool is freely accessible at: https://www.ccb.uni-saarland.de/mieaa2.

Comprehensive molecular profiling of 718 Multiple Myelomas reveals significant differences in mutation frequencies between African and European descent cases.

Multiple Myeloma (MM) is a plasma cell malignancy with significantly greater incidence and mortality rates among African Americans (AA) compared to Caucasians (CA). The overall goal of this study is to elucidate differences in molecular alterations in MM as a function of self-reported race and genetic ancestry. Our study utilized somatic whole exome, RNA-sequencing, and correlated clinical data from 718 MM patients from the Multiple Myeloma Research Foundation CoMMpass study Interim Analysis 9. Somatic mutational analyses based upon self-reported race corrected for ancestry revealed significant differences in mutation frequency between groups. Of interest, BCL7A, BRWD3, and AUTS2 demonstrate significantly higher mutation frequencies among AA cases. These genes are all involved in translocations in B-cell malignancies. Moreover, we detected a significant difference in mutation frequency of TP53 and IRF4 with frequencies higher among CA cases. Our study provides rationale for interrogating diverse tumor cohorts to best understand tumor genomics across populations.