Processing human frontal cortex brain tissue for population-scale Oxford Nanopore long-read DNA sequencing SOP v1

Processing human frontal cortex brain tissue for population-scale Oxford Nanopore long-read DNA sequencing SOP At the NIH's Center for Alzheimer's and Related Dementias (CARD) https://card.nih.gov/research-programs/long-read-sequencing we will generate long-read sequencing data from roughly 4000 patients with Alzheimer's disease, frontotemporal dementia, Lewy body dementia, and healthy subjects. With this research, we will build a public resource consisting of long-read genome sequencing data from a large number of confirmed people with Alzheimer's disease and related dementias and healthy individuals. To generate this large-scale nanopore sequencing data we have developed a protocol for processing and long-read sequencing human frontal cortex brain tissue, targeting an N50 of ~30kb and ~30X coverage. †Correspondence to: Kimberley Billingsley [email protected] and Cornelis Blauwendraat [email protected] Acknowledgements: We would like to thank the Nanopore team (Androo Markham &Hannah Lucio), Circulomics Inc team (Jeffrey Burke, Michelle Kim, Duncan Kilburn & Kelvin Liu) and the whole CARD long-read team listed below => UCSC: Benedict Paten, Mikhail Kolmogorov, Miten Jain, Kishwar Shafin, Trevor Pesout; NHGRI: Adam Phillippy, Arang Rhie; Baylor: Fritz Sedlazeck; JHU: Winston Timp; NINDS: Sonja Scholz; NIA: Cornelis Blauwendraat, Kimberley Billingsley, Frank Grenn, Pilar Alvarez Jerez, Bryan Traynor, Shannon Ballard, Caroline Pantazis; CZI: Paolo Carnevali.

Severe COVID-19 induces molecular signatures of aging in the human brain

Coronavirus disease 2019 (COVID-19) is predominantly an acute respiratory disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and remains a significant threat to public health. COVID-19 is accompanied by neurological symptoms and cognitive decline, but the molecular mechanisms underlying this effect remain unclear. As aging induces distinct molecular signatures in the brain associated with cognitive decline in healthy populations, we hypothesized that COVID-19 may induce molecular signatures of aging. Here, we performed whole transcriptomic analysis of human frontal cortex, a critical area for cognitive function, in 12 COVID-19 cases and age- and sex-matched uninfected controls. COVID-19 induces profound changes in gene expression, despite the absence of detectable virus in brain tissue. Pathway analysis shows downregulation of genes involved in synaptic function and cognition and upregulation of genes involved in immune processes. Comparison with five independent transcriptomic datasets of aging human frontal cortex reveals striking similarities between aged individuals and severe COVID-19 patients. Critically, individuals below 65 years of age exhibit profound transcriptomic changes not observed among older individuals in our patient cohort. Our data indicate that severe COVID-19 induces molecular signatures of aging in the human brain and emphasize the value of neurological follow-up in recovered individuals.

Application of Machine Learning and Weighted Gene Co-expression Network Algorithm to Explore the Hub Genes in the Aging Brain

Aging is a major risk factor contributing to neurodegeneration and dementia. However, it remains unclarified how aging promotes these diseases. Here, we use machine learning and weighted gene co-expression network (WGCNA) to explore the relationship between aging and gene expression in the human frontal cortex and reveal potential biomarkers and therapeutic targets of neurodegeneration and dementia related to aging. The transcriptional profiling data of the human frontal cortex from individuals ranging from 26 to 106 years old was obtained from the GEO database in NCBI. Self-Organizing Feature Map (SOM) was conducted to find the clusters in which gene expressions downregulate with aging. For WGCNA analysis, first, co-expressed genes were clustered into different modules, and modules of interest were identified through calculating the correlation coefficient between the module and phenotypic trait (age). Next, the overlapping genes between differentially expressed genes (DEG, between young and aged group) and genes in the module of interest were discovered. Random Forest classifier was performed to obtain the most significant genes in the overlapping genes. The disclosed significant genes were further identified through network analysis. Through WGCNA analysis, the greenyellow module is found to be highly negatively correlated with age, and functions mainly in long-term potentiation and calcium signaling pathways. Through step-by-step filtering of the module genes by overlapping with downregulated DEGs in aged group and Random Forest classifier analysis, we found that MAPT, KLHDC3, RAP2A, RAP2B, ELAVL2, and SYN1 were co-expressed and highly correlated with aging.

Detection of Alu Exonization Events in Human Frontal Cortex From RNA-Seq Data

Alu exonization events functionally diversify the transcriptome, creating alternative mRNA isoforms and accounting for an estimated 5% of the alternatively spliced (skipped) exons in the human genome. We developed computational methods, implemented into a software called Alubaster, for detecting incorporation of Alu sequences in mRNA transcripts from large scale RNA-seq data sets. The approach detects Alu sequences derived from both fixed and polymorphic Alu elements, including Alu insertions missing from the reference genome. We applied our methods to 117 GTEx human frontal cortex samples to build and characterize a collection of Alu-containing mRNAs. In particular, we detected and characterized Alu exonizations occurring at 870 fixed Alu loci, of which 237 were novel, as well as hundreds of putative events involving Alu elements that are polymorphic variants or rare alleles not present in the reference genome. These methods and annotations represent a unique and valuable resource that can be used to understand the characteristics of Alu-containing mRNAs and their tissue-specific expression patterns.

Segmental duplications and their variation in a complete human genome

Despite their importance in disease and evolution, highly identical segmental duplications (SDs) have been among the last regions of the human reference genome (GRCh38) to be finished. Based on a complete telomere-to-telomere human genome (T2T CHM13), we present the first comprehensive view of human SD organization. SDs account for nearly one-third of the additional sequence increasing the genome-wide estimate from 5.4% to 7.0% (218 Mbp). An analysis of 266 human genomes shows that 91% of the new T2T CHM13 SD sequence (68.3 Mbp) better represents human copy number. We find that SDs show increased single-nucleotide variation diversity when compared to unique regions; we characterize methylation signatures that correlate with duplicate gene transcription and predict 182 novel protein-coding gene candidates. We find that 63% (35.11/55.7 Mbp) of acrocentric chromosomes consist of SDs distinct from rDNA and satellite sequences. Acrocentric SDs are 1.75-fold longer (p=0.00034) than other SDs, are frequently shared with autosomal pericentromeric regions, and are heteromorphic among human chromosomes. Comparing long-read assemblies from other human (n=12) and nonhuman primate (n=5) genomes, we use the T2T CHM13 genome to systematically reconstruct the evolution and structural haplotype diversity of biomedically relevant (LPA, SMN) and duplicated genes (TBC1D3, SRGAP2C, ARHGAP11B) important in the expansion of the human frontal cortex. The analysis reveals unprecedented patterns of structural heterozygosity and massive evolutionary differences in SD organization between humans and their closest living relatives.

Similarity in sensory modality and information domain impair processing in a dual-task context: Evidence from behavior, pupillometry, and EEG

Perception and navigation frequently require us to maintain sensory information in memory, while also processing new sensory inputs as they arise. Recent fMRI research has uncovered regions in human frontal cortex that coordinate these diverse processes. Across various attention and working memory (WM) tasks, these regions can be separated into two distinct networks. Each shows a response preference for either auditory or visual stimuli, yet in addition, each can be flexibly recruited based on the information domain (i.e., temporal or spatial) of the task, regardless of sensory modality. Motivated by the sensory and functional specializations these networks display, we investigated whether dual-task interference is affected by similarity between the tasks in sensory modality (auditory or visual) and information domain. Participants performed a novel dual-task paradigm involving different combinations of WM and Intervening tasks, while two temporally sensitive physiological signals, pupillometry and electroencephalography (EEG), were measured. Convergent evidence from behavioral performance, pupil dilation amplitudes, and event-related potentials (ERPs) indicates that dual-task interference is greatest when the tasks match in both sensory modality and information domain. However, differences also arise in the patterns of dual-task interference across these metrics, highlighting the differential strengths and sensitivities of each. The results are consistent with increased interference when multiple tasks compete for shared cognitive control resources due to a common sensory modality or information domain.

Extended frontal networks for visual and auditory working memory

Working memory (WM) supports the persistent representation of transient sensory information. Visual and auditory stimuli place different demands on WM and recruit different brain networks. Separate auditory- and visual-biased WM networks extend into the frontal lobes, but several challenges confront attempts to parcellate human frontal cortex, including fine-grained organization and between-subject variability. Here, we use differential intrinsic functional connectivity from two visual-biased and two auditory-biased frontal structures to identify additional candidate sensory-biased regions in frontal cortex. We then examine direct contrasts of task fMRI during visual vs. auditory 2-back WM to validate those candidate regions. Three visual-biased and five auditory-biased regions are robustly activated bilaterally in the frontal lobes of individual subjects (N=14, 7 women). These regions exhibit a sensory preference during passive exposure to task stimuli, and that preference is stronger during WM. Hierarchical clustering analysis of intrinsic connectivity among novel and previously identified bilateral sensory-biased regions confirms that they functionally segregate into visual and auditory networks, even though the networks are anatomically interdigitated. We also observe that the fronto-temporal auditory WM network is highly selective and exhibits strong functional connectivity to structures serving non-WM functions, while the fronto-parietal visual WM network hierarchically merges into the multiple-demand cognitive system.