Neuronal DNA double-strand breaks lead to genome structural variations and 3D genome disruption in neurodegeneration.

Persistent DNA double-strand breaks (DSBs) in neurons are an early pathological hallmark of neurodegenerative diseases including Alzheimer's disease (AD), with the potential to disrupt genome integrity. We used single-nucleus RNA-seq in human postmortem prefrontal cortex samples and found that excitatory neurons in AD were enriched for somatic mosaic gene fusions. Gene fusions were particularly enriched in excitatory neurons with DNA damage repair and senescence gene signatures. In addition, somatic genome structural variations and gene fusions were enriched in neurons burdened with DSBs in the CK-p25 mouse model of neurodegeneration. Neurons enriched for DSBs also had elevated levels of cohesin along with progressive multiscale disruption of the 3D genome organization aligned with transcriptional changes in synaptic, neuronal development, and histone genes. Overall, this study demonstrates the disruption of genome stability and the 3D genome organization by DSBs in neurons as pathological steps in the progression of neurodegenerative diseases.

Unified views on variant impact across many diseases.

Genomic studies of human disorders are often performed by distinct research communities (i.e., focused on rare diseases, common diseases, or cancer). Despite underlying differences in the mechanistic origin of different disease categories, these studies share the goal of identifying causal genomic events that are critical for the clinical manifestation of the disease phenotype. Moreover, these studies face common challenges, including understanding the complex genetic architecture of the disease, deciphering the impact of variants on multiple scales, and interpreting noncoding mutations. Here, we highlight these challenges in depth and argue that properly addressing them will require a more unified vocabulary and approach across disease communities. Toward this goal, we present a unified perspective on relating variant impact to various genomic disorders.

Complex chromosomal 6q rearrangements revealed by combined long-molecule genomics technologies.

Technologies for detecting structural variation (SV) have advanced with the advent of long-read sequencing, which enables the validation of SV at a nucleotide level. Optical genome mapping (OGM), a technology based on physical mapping, can also provide comprehensive SVs analysis. We applied long-read whole genome sequencing (LRWGS) to accurately reconstruct breakpoint (BP) segments in a patient with complex chromosome 6q rearrangements that remained elusive by conventional karyotyping. Although all BPs were precisely identified by LRWGS, there were two possible ways to construct the BP segments in terms of their orders and orientations. Thus, we also used OGM analysis. Notably, OGM recognized entire inversions exceeding 500 kb in size, which LRWGS could not characterize. Consequently, here we successfully unveil the full genomic structure of this complex chromosomal 6q rearrangement and cryptic SVs through combined long-molecule genomic analyses, showcasing how LRWGS and OGM can complement each other in SV analysis.

FindCSV: a long-read based method for detecting complex structural variations.

BACKGROUND: Structural variations play a significant role in genetic diseases and evolutionary mechanisms. Extensive research has been conducted over the past decade to detect simple structural variations, leading to the development of well-established detection methods. However, recent studies have highlighted the potentially greater impact of complex structural variations on individuals compared to simple structural variations. Despite this, the field still lacks precise detection methods specifically designed for complex structural variations. Therefore, the development of a highly efficient and accurate detection method is of utmost importance. RESULT: In response to this need, we propose a novel method called FindCSV, which leverages deep learning techniques and consensus sequences to enhance the detection of SVs using long-read sequencing data. Compared to current methods, FindCSV performs better in detecting complex and simple structural variations. CONCLUSIONS: FindCSV is a new method to detect complex and simple structural variations with reasonable accuracy in real and simulated data. The source code for the program is available at https://github.com/nwpuzhengyan/FindCSV .

Hi-C as a molecular rangefinder to examine genomic rearrangements.

The mammalian genome is highly packed into the nucleus. Over the past decade, the development of Hi-C has contributed significantly to our understanding of the three-dimensional (3D) chromatin structure, uncovering the principles and functions of higher-order chromatin organizations. Recent studies have repositioned its property in spatial proximity measurement to address challenging problems in genome analyses including genome assembly, haplotype phasing, and the detection of genomic rearrangements. In particular, the power of Hi-C in detecting large-scale structural variations (SVs) in the cancer genome has been demonstrated, which is challenging to be addressed solely with short-read-based whole-genome sequencing analyses. In this review, we first provide a comprehensive view of Hi-C as an intuitive and effective SV detection tool. Then, we introduce recently developed bioinformatics tools utilizing Hi-C to investigate genomic rearrangements. Finally, we discuss the potential application of single-cell Hi-C to address the heterogeneity of genomic rearrangements and sub-population identification in the cancer genome.

Systematic evaluation of multiple NGS platforms for structural variants detection.

Structural variations (SV) are critical genome changes affecting human diseases. Although many hybridization-based methods exist, evaluating SVs through next-generation sequencing (NGS) data is still necessary for broader research exploration. Here, we comprehensively compared the performance of 16 SV callers and multiple NGS platforms using NA12878 whole genome sequencing (WGS) datasets. The results indicated that several SV callers performed well relatively, such as Manta, GRIDSS, LUMPY, TARDIS, FermiKit, and Wham. Meanwhile, all NGS platforms have a similar performance using a single software. Additionally, we found that the source of undetected SVs was mostly from long reads datasets, therefore, the more appropriate strategy for accurate SV detection will be an integration of long and shorter reads in the future. At present, in the period of NGS as a mainstream method in bioinformatics, our study would provide helpful and comprehensive guidelines for specific categories of SV research.

Mechanism of ERBB2 gene overexpression by the formation of super-enhancer with genomic structural abnormalities in lung adenocarcinoma without clinically actionable genetic alterations.

BACKGROUND: In an extensive genomic analysis of lung adenocarcinomas (LUADs), driver mutations have been recognized as potential targets for molecular therapy. However, there remain cases where target genes are not identified. Super-enhancers and structural variants are frequently identified in several hundred loci per case. Despite this, most cancer research has approached the analysis of these data sets separately, without merging and comparing the data, and there are no examples of integrated analysis in LUAD. METHODS: We performed an integrated analysis of super-enhancers and structural variants in a cohort of 174 LUAD cases that lacked clinically actionable genetic alterations. To achieve this, we conducted both WGS and H3K27Ac ChIP-seq analyses using samples with driver gene mutations and those without, allowing for a comprehensive investigation of the potential roles of super-enhancer in LUAD cases. RESULTS: We demonstrate that most genes situated in these overlapped regions were associated with known and previously unknown driver genes and aberrant expression resulting from the formation of super-enhancers accompanied by genomic structural abnormalities. Hi-C and long-read sequencing data further corroborated this insight. When we employed CRISPR-Cas9 to induce structural abnormalities that mimicked cases with outlier ERBB2 gene expression, we observed an elevation in ERBB2 expression. These abnormalities are associated with a higher risk of recurrence after surgery, irrespective of the presence or absence of driver mutations. CONCLUSIONS: Our findings suggest that aberrant gene expression linked to structural polymorphisms can significantly impact personalized cancer treatment by facilitating the identification of driver mutations and prognostic factors, contributing to a more comprehensive understanding of LUAD pathogenesis.

Quantitative genetics: pan-genomes, SVs, and k-mers for GWAS.

For identification of marker-trait associations (MTAs) for complex traits in animals and plants, thousands of genome-wide association studies (GWAS) were conducted during the past two decades. This involved regular improvement in methodology. Initially, a reference genome and SNPs were used; more recently pan-genomes and the markers structural variations (SVs)/k-mers are also being used.

Genome editing in rice using CRISPR/Cas12i3.

The CRISPR/Cas type V-I is a family of programmable nuclease systems that prefers a T-rich protospacer adjacent motif (PAM) and is guided by a short crRNA. In this study, the genome-editing application of Cas12i3, a type V-I family endonuclease, was characterized in rice. We developed a CRIPSR/Cas12i3-based Multiplex direct repeats (DR)-spacer Array Genome Editing (iMAGE) system that was efficient in editing various genes in rice. Interestingly, iMAGE produced chromosomal structural variations with a higher frequency than CRISPR/Cas9. In addition, we developed base editors using deactivated Cas12i3 and generated herbicide-resistant rice plants using the base editors. These CRIPSR/Cas12i3-based genome editing systems will facilitate precision molecular breeding in plants.

MAMnet: detecting and genotyping deletions and insertions based on long reads and a deep learning approach.

Structural variations (SVs) play important roles in human genetic diversity; deletions and insertions are two common types of SVs that have been proven to be associated with genetic diseases. Hence, accurately detecting and genotyping SVs is significant for disease research. Despite the fact that long-read sequencing technologies have improved the field of SV detection and genotyping, there are still some challenges that prevent satisfactory results from being obtained. In this paper, we propose MAMnet, a fast and scalable SV detection and genotyping method based on long reads and a combination of convolutional neural network and long short-term network. MAMnet uses a deep neural network to implement sensitive SV detection with a novel prediction strategy. On real long-read sequencing datasets, we demonstrate that MAMnet outperforms Sniffles, SVIM, cuteSV and PBSV in terms of their F1 scores while achieving better scaling performance. The source code is available from https://github.com/micahvista/MAMnet.