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To examine the length of a hexanucleotide expansion in C9orf72, which represents the most frequent genetic cause of frontotemporal lobar degeneration and motor neuron disease, we employed a targeted amplification-free long-read sequencing technology: No-Amp sequencing. In our cross-sectional study, we assessed cerebellar tissue from 28 well-characterized C9orf72 expansion carriers. We obtained 3507 on-target circular consensus sequencing reads, of which 814 bridged the C9orf72 repeat expansion (23%). Importantly, we observed a significant correlation between expansion sizes obtained using No-Amp sequencing and Southern blotting (P = 5.0 × 10−4). Interestingly, we also detected a significant survival advantage for individuals with smaller expansions (P = 0.004). Additionally, we uncovered that smaller expansions were significantly associated with higher levels of C9orf72 transcripts containing intron 1b (P = 0.003), poly(GP) proteins (P = 1.3 × 10− 5), and poly(GA) proteins (P = 0.005). Thorough examination of the composition of the expansion revealed that its GC content was extremely high (median: 100%) and that it was mainly composed of GGGGCC repeats (median: 96%), suggesting that expanded C9orf72 repeats are quite pure. Taken together, our findings demonstrate that No-Amp sequencing is a powerful tool that enables the discovery of relevant clinicopathological associations, highlighting the important role played by the cerebellar size of the expanded repeat in C9orf72-linked diseases., DeJesus-Hernandez et al. employ an innovative long-read sequencing method to examine the length of the expanded C9orf72 repeat that represents the most common genetic cause of frontotemporal dementia and amyotrophic lateral sclerosis. Smaller expansion size confers a survival benefit for patients.

Protein–protein functional interactions arise from either transitory or permanent biomolecular associations and often lead to the coevolution of the interacting residues. Although mutual information has traditionally been used to identify coevolving residues within the same protein, its application between coevolving proteins remains largely uncharacterized. Therefore, we developed the Protein Interactions Calculator (PIC) to efficiently identify coevolving residues between two protein sequences using mutual information. We verified the algorithm using 2102 known human protein interactions and 233 known bacterial protein interactions, with a respective 1975 and 252 non-interacting protein controls. The average PIC score for known human protein interactions was 4.5 times higher than non-interacting proteins (p = 1.03 × 10−108) and 1.94 times higher in bacteria (p = 1.22 × 10−35). We then used the PIC scores to determine the probability that two proteins interact. Using those probabilities, we paired 37 Alzheimer’s disease-associated proteins with 8608 other proteins and determined the likelihood that each pair interacts, which we report through a web interface. The PIC had significantly higher sensitivity and residue-specific resolution not available in other algorithms. Therefore, we propose that the PIC can be used to prioritize potential protein interactions, which can lead to a better understanding of biological processes and additional therapeutic targets belonging to protein interaction groups.

Ancestral RNA polymerase III (Pol III) is a multi-subunit polymerase responsible for transcription of short non-coding RNA, such as double-stranded short interspersed nuclear elements (SINEs). Although SINE ncRNAs are generally transcriptionally repressed, they can be induced in response to viral infections and can stimulate immune signaling pathways. Indeed, mutations in RNA Pol III have been associated with poor antiviral interferon response following infection with varicella zoster virus (VZV). In this study, we probed the role of Pol III transcripts in the detection and initial immune response to VZV by characterizing the transcriptional response following VZV infection of wild type A549 lung epithelial cells as well as A549 cells lacking specific RNA sensors MAVS and TLR3, or interferon-stimulated genes RNase L and PKR in presence or absence of functional RNA Pol III. Multiple components of the antiviral sensing and interferon signaling pathways were involved in restricting VZV replication in lung epithelial cells thus suggesting an innate defense system with built-in redundancy. In addition, RNA Pol III silencing altered the antiviral transcriptional program indicating that it plays an essential role in the sensing of VZV infection.

Ramp sequences occur when the average translational efficiency of codons near the 5′ end of highly expressed genes is significantly lower than the rest of the gene sequence, which counterintuitively increases translational efficiency by decreasing downstream ribosomal collisions. Here, we show that the relative codon adaptiveness within different tissues changes the existence of a ramp sequence without altering the underlying genetic code. We present the first comprehensive analysis of tissue and cell type-specific ramp sequences and report 3108 genes with ramp sequences that change between tissues and cell types, which corresponds with increased gene expression within those tissues and cells. The Ramp Atlas (https://ramps.byu.edu/) allows researchers to query precomputed ramp sequences in 18 388 genes across 62 tissues and 66 cell types and calculate tissue-specific ramp sequences from user-uploaded FASTA files through an intuitive web interface. We used The Ramp Atlas to identify seven SARS-CoV-2 genes and seven human SARS-CoV-2 entry factor genes with tissue-specific ramp sequences that may help explain viral proliferation within those tissues. We anticipate that The Ramp Atlas will facilitate personalized and creative tissue-specific ramp sequence analyses for both human and viral genes that will increase our ability to utilize this often-overlooked regulatory region.

The Alzheimer’s Disease Neuroimaging Initiative (ADNI) contains extensive patient measurements (e.g., magnetic resonance imaging [MRI], biometrics, RNA expression, etc.) from Alzheimer’s disease (AD) cases and controls that have recently been used by machine learning algorithms to evaluate AD onset and progression. While using a variety of biomarkers is essential to AD research, highly correlated input features can significantly decrease machine learning model generalizability and performance. Additionally, redundant features unnecessarily increase computational time and resources necessary to train predictive models. Therefore, we used 49,288 biomarkers and 793,600 extracted MRI features to assess feature correlation within the ADNI dataset to determine the extent to which this issue might impact large scale analyses using these data. We found that 93.457% of biomarkers, 92.549% of the gene expression values, and 100% of MRI features were strongly correlated with at least one other feature in ADNI based on our Bonferroni corrected α (p-value ≤ 1.40754 × 10−13). We provide a comprehensive mapping of all ADNI biomarkers to highly correlated features within the dataset. Additionally, we show that significant correlation within the ADNI dataset should be resolved before performing bulk data analyses, and we provide recommendations to address these issues. We anticipate that these recommendations and resources will help guide researchers utilizing the ADNI dataset to increase model performance and reduce the cost and complexity of their analyses.

Motivation: Ramp sequences are an understudied evolutionarily-conserved mechanism for regulating protein translational efficiency. Slowly-translated codons concentrated at the 5' end of genes form ramp sequences that counterintuitively increase overall translational efficiency by evenly spacing ribosomes at initiation, which limits downstream ribosomal collisions. We previously developed ExtRamp, which is the only algorithm to identify translational ramp sequences in single genes. ExtRamp currently lacks a web interface to facilitate wider adoption and application for non-programmers. Additionally, ExtRamp currently identifies ramp sequences using only species-wide codon efficiencies that may lack the specificity of tissue and cell type-specific codon usage biases.Results: We present an online interface for ExtRamp to facilitate wider adoption and application for non-programmers, along with a significant improvement to the underlying algorithm to calculate tissue and cell type-specific ramp sequences (https://ramps.byu.edu/ExtRampOnline). ExtRamp Online contains all options available in the original ExtRamp algorithm with additional pre-set default values to enable researchers to calculate human tissue-specific or genome-wide ramp sequences on any web browser. Human tissue and cell type-specific codon usage biases have been precomputed and can be applied with a simple drop-down menu. Hover-over hints provide users with detailed information on all available options, which will help facilitate future creative analyses using ramp sequences. Availability: ExtRamp Online is publicly available at https://ramps.byu.edu/ExtRampOnline. All associated scripts are publicly available at https://github.com/ridgelab/ExtRampOnline.

The process of identifying suitable genome-wide association (GWA) studies and formatting the data to calculate multiple polygenic risk scores on a single genome can be laborious. Here, we present a centralized polygenic risk score calculator currently containing over 250,000 genetic variant associations from the NHGRI-EBI GWAS Catalog for users to easily calculate sample-specific polygenic risk scores with comparable results to other available tools. Polygenic risk scores are calculated either online through the Polygenic Risk Score Knowledge Base (PRSKB; https://prs.byu.edu) or via a command-line interface. We report study-specific polygenic risk scores across the UK Biobank, 1000 Genomes, and the Alzheimer’s Disease Neuroimaging Initiative (ADNI), contextualize computed scores, and identify potentially confounding genetic risk factors in ADNI. We introduce a streamlined analysis tool and web interface to calculate and contextualize polygenic risk scores across various studies, which we anticipate will facilitate a wider adaptation of polygenic risk scores in future disease research.

Translational ramp sequences are essential regulatory elements that have yet to be characterized in specific tissues. Ramp sequences increase gene expression by evenly spacing ribosomes and slowing initial translation. Therefore, the relative codon adaptiveness within different tissues changes the existence of a ramp sequence without altering the underlying genetic code. Here, we present the first comprehensive analysis of tissue and cell type-specific ramp sequences, and report 3,108 genes with ramp sequences that change between tissues and cell types. The Ramp Atlas (https://ramps.byu.edu/) is an accompanying web portal that allows researchers to query ramp sequences in 18,388 genes across 62 tissues and 66 cell types. We also identified seven SARS-CoV-2 genes and seven human SARS-CoV-2 entry factor genes with tissue-specific ramp sequences that may help explain viral proliferation within those tissues. We anticipate that The Ramp Atlas will facilitate future tissue-specific ramp sequence analyses to develop targeted therapeutics for human disease.

The repetitive nature and complexity of multiple medically important genes make them intractable to accurate analysis, despite the maturity of short-read sequencing, resulting in a gap in clinical applications of genome sequencing. The Genome in a Bottle Consortium has provided benchmark variant sets, but these excluded some medically relevant genes due to their repetitiveness or polymorphic complexity. In this study, we characterize 273 of these 395 challenging autosomal genes that have multiple implications for medical sequencing. This extended, curated benchmark reports over 17,000 SNVs, 3,600 INDELs, and 200 SVs each for GRCh37 and GRCh38 across HG002. We show that false duplications in either GRCh37 or GRCh38 result in reference-specific, missed variants for short- and long-read technologies in medically important genes including CBS, CRYAA, and KCNE1. Our proposed solution improves variant recall in these genes from 8% to 100%. This benchmark will significantly improve the comprehensive characterization of these medically relevant genes and guide new method development.

The repetitive nature and complexity of some medically relevant genes poses a challenge for their accurate analysis in a clinical setting. The Genome in a Bottle Consortium has provided variant benchmark sets, but these exclude nearly 400 medically relevant genes due to their repetitiveness or polymorphic complexity. Here, we characterize 273 of these 395 challenging autosomal genes using a haplotype-resolved whole-genome assembly. This curated benchmark reports over 17,000 single-nucleotide variations, 3,600 insertions and deletions and 200 structural variations each for human genome reference GRCh37 and GRCh38 across HG002. We show that false duplications in either GRCh37 or GRCh38 result in reference-specific, missed variants for short- and long-read technologies in medically relevant genes, including CBS, CRYAA and KCNE1. When masking these false duplications, variant recall can improve from 8% to 100%. Forming benchmarks from a haplotype-resolved whole-genome assembly may become a prototype for future benchmarks covering the whole genome.

Microglia are central players in homeostasis and disease. Kang et al. reveal a novel ApoE-driven microglial pathway shared between aging, amyloidosis, and tauopathy that is exacerbated in females, suggesting a convergent mechanism for altering microglial reactivity during Alzheimer’s disease., Alzheimer’s disease (AD) is an age-associated neurodegenerative disease characterized by amyloidosis, tauopathy, and activation of microglia, the brain resident innate immune cells. We show that a RiboTag translational profiling approach can bypass biases due to cellular enrichment/cell sorting. Using this approach in models of amyloidosis, tauopathy, and aging, we revealed a common set of alterations and identified a central APOE-driven network that converged on CCL3 and CCL4 across all conditions. Notably, aged females demonstrated a significant exacerbation of many of these shared transcripts in this APOE network, revealing a potential mechanism for increased AD susceptibility in females. This study has broad implications for microglial transcriptomic approaches and provides new insights into microglial pathways associated with different pathological aspects of aging and AD.

We previously found C9orf72-associated (c9ALS) and sporadic amyotrophic lateral sclerosis (sALS) brain transcriptomes comprise thousands of defects, among which, some are likely key contributors to ALS pathogenesis. We have now generated complementary methylome data and combine these two data sets to perform a comprehensive "multi-omic" analysis to clarify the molecular mechanisms initiating RNA misregulation in ALS. We found that c9ALS and sALS patients have generally distinct but overlapping methylome profiles, and that the c9ALS- and sALS-affected genes and pathways have similar biological functions, indicating conserved pathobiology in disease. Our results strongly implicate SERPINA1 in both C9orf72 repeat expansion carriers and non-carriers, where expression levels are greatly increased in both patient groups across the frontal cortex and cerebellum. SERPINA1 expression is particularly pronounced in C9orf72 repeat expansion carriers for both brain regions, where SERPINA1 levels are strictly down regulated across most human tissues, including the brain, except liver and blood, and are not measurable in E18 mouse brain. The altered biological networks we identified contain critical molecular players known to contribute to ALS pathology, which also interact with SERPINA1. Our comprehensive combined methylation and transcription study identifies new genes and highlights that direct genetic and epigenetic changes contribute to c9ALS and sALS pathogenesis.

Significant transcriptome alterations are detected in the brain of patients with amyotrophic lateral sclerosis (ALS), including carriers of the C9orf72 repeat expansion and C9orf72-negative sporadic cases. Recently, the expression of repetitive element transcripts has been associated with toxicity and, while increased repetitive element expression has been observed in several neurodegenerative diseases, little is known about their contribution to ALS. To assess whether aberrant expression of repetitive element sequences are observed in ALS, we analysed RNA sequencing data from C9orf72-positive and sporadic ALS cases, as well as healthy controls. Transcripts from multiple classes and subclasses of repetitive elements (LINEs, endogenous retroviruses, DNA transposons, simple repeats, etc.) were significantly increased in the frontal cortex of C9orf72 ALS patients. A large collection of patient samples, representing both C9orf72 positive and negative ALS, ALS/FTLD, and FTLD cases, was used to validate the levels of several repetitive element transcripts. These analyses confirmed that repetitive element expression was significantly increased in C9orf72-positive compared to C9orf72-negative or control cases. While previous studies suggest an important link between TDP-43 and repetitive element biology, our data indicate that TDP-43 pathology alone is insufficient to account for the observed changes in repetitive elements in ALS/FTLD. Instead, we found that repetitive element expression positively correlated with RNA polymerase II activity in postmortem brain, and pharmacologic modulation of RNA polymerase II activity altered repetitive element expression in vitro. We conclude that increased RNA polymerase II activity in ALS/FTLD may lead to increased repetitive element transcript expression, a novel pathological feature of ALS/FTLD.