Loss of chromosome Y in primary tumors.

Certain cancer types afflict female and male patients disproportionately. The reasons include differences in male/female physiology, effect of sex hormones, risk behavior, environmental exposures, and genetics of the sex chromosomes X and Y. Loss of Y (LOY) is common in peripheral blood cells in aging men, and this phenomenon is associated with several diseases. However, the frequency and role of LOY in tumors is little understood. Here, we present a comprehensive catalog of LOY in >5,000 primary tumors from male patients in the TCGA. We show that LOY rates vary by tumor type and provide evidence for LOY being either a passenger or driver event depending on context. LOY in uveal melanoma specifically is associated with age and survival and is an independent predictor of poor outcome. LOY creates common dependencies on DDX3X and EIF1AX in male cell lines, suggesting that LOY generates unique vulnerabilities that could be therapeutically exploited.

Genomic architecture of autism from comprehensive whole-genome sequence annotation.

Fully understanding autism spectrum disorder (ASD) genetics requires whole-genome sequencing (WGS). We present the latest release of the Autism Speaks MSSNG resource, which includes WGS data from 5,100 individuals with ASD and 6,212 non-ASD parents and siblings (total n = 11,312). Examining a wide variety of genetic variants in MSSNG and the Simons Simplex Collection (SSC; n = 9,205), we identified ASD-associated rare variants in 718/5,100 individuals with ASD from MSSNG (14.1%) and 350/2,419 from SSC (14.5%). Considering genomic architecture, 52% were nuclear sequence-level variants, 46% were nuclear structural variants (including copy-number variants, inversions, large insertions, uniparental isodisomies, and tandem repeat expansions), and 2% were mitochondrial variants. Our study provides a guidebook for exploring genotype-phenotype correlations in families who carry ASD-associated rare variants and serves as an entry point to the expanded studies required to dissect the etiology in the ∼85% of the ASD population that remain idiopathic.

A cross-disorder dosage sensitivity map of the human genome.

Rare copy-number variants (rCNVs) include deletions and duplications that occur infrequently in the global human population and can confer substantial risk for disease. In this study, we aimed to quantify the properties of haploinsufficiency (i.e., deletion intolerance) and triplosensitivity (i.e., duplication intolerance) throughout the human genome. We harmonized and meta-analyzed rCNVs from nearly one million individuals to construct a genome-wide catalog of dosage sensitivity across 54 disorders, which defined 163 dosage sensitive segments associated with at least one disorder. These segments were typically gene dense and often harbored dominant dosage sensitive driver genes, which we were able to prioritize using statistical fine-mapping. Finally, we designed an ensemble machine-learning model to predict probabilities of dosage sensitivity (pHaplo & pTriplo) for all autosomal genes, which identified 2,987 haploinsufficient and 1,559 triplosensitive genes, including 648 that were uniquely triplosensitive. This dosage sensitivity resource will provide broad utility for human disease research and clinical genetics.

Pan-genome analysis of 33 genetically diverse rice accessions reveals hidden genomic variations.

Structural variations (SVs) and gene copy number variations (gCNVs) have contributed to crop evolution, domestication, and improvement. Here, we assembled 31 high-quality genomes of genetically diverse rice accessions. Coupling with two existing assemblies, we developed pan-genome-scale genomic resources including a graph-based genome, providing access to rice genomic variations. Specifically, we discovered 171,072 SVs and 25,549 gCNVs and used an Oryza glaberrima assembly to infer the derived states of SVs in the Oryza sativa population. Our analyses of SV formation mechanisms, impacts on gene expression, and distributions among subpopulations illustrate the utility of these resources for understanding how SVs and gCNVs shaped rice environmental adaptation and domestication. Our graph-based genome enabled genome-wide association study (GWAS)-based identification of phenotype-associated genetic variations undetectable when using only SNPs and a single reference assembly. Our work provides rich population-scale resources paired with easy-to-access tools to facilitate rice breeding as well as plant functional genomics and evolutionary biology research.

Major Impacts of Widespread Structural Variation on Gene Expression and Crop Improvement in Tomato.

Structural variants (SVs) underlie important crop improvement and domestication traits. However, resolving the extent, diversity, and quantitative impact of SVs has been challenging. We used long-read nanopore sequencing to capture 238,490 SVs in 100 diverse tomato lines. This panSV genome, along with 14 new reference assemblies, revealed large-scale intermixing of diverse genotypes, as well as thousands of SVs intersecting genes and cis-regulatory regions. Hundreds of SV-gene pairs exhibit subtle and significant expression changes, which could broadly influence quantitative trait variation. By combining quantitative genetics with genome editing, we show how multiple SVs that changed gene dosage and expression levels modified fruit flavor, size, and production. In the last example, higher order epistasis among four SVs affecting three related transcription factors allowed introduction of an important harvesting trait in modern tomato. Our findings highlight the underexplored role of SVs in genotype-to-phenotype relationships and their widespread importance and utility in crop improvement.

Converting Escherichia coli to a Synthetic Methylotroph Growing Solely on Methanol.

Methanol, being electron rich and derivable from methane or CO2, is a potentially renewable one-carbon (C1) feedstock for microorganisms. Although the ribulose monophosphate (RuMP) cycle used by methylotrophs to assimilate methanol differs from the typical sugar metabolism by only three enzymes, turning a non-methylotrophic organism to a synthetic methylotroph that grows to a high cell density has been challenging. Here we reprogrammed E. coli using metabolic robustness criteria followed by laboratory evolution to establish a strain that can efficiently utilize methanol as the sole carbon source. This synthetic methylotroph alleviated a so far uncharacterized hurdle, DNA-protein crosslinking (DPC), by insertion sequence (IS)-mediated copy number variations (CNVs) and balanced the metabolic flux by mutations. Being capable of growing at a rate comparable with natural methylotrophs in a wide range of methanol concentrations, this synthetic methylotrophic strain illustrates genome editing and evolution for microbial tropism changes and expands the scope of biological C1 conversion.

Immunoglobulin germline gene polymorphisms influence the function of SARS-CoV-2 neutralizing antibodies.

The human immunoglobulin heavy-chain (IGH) locus is exceptionally polymorphic, with high levels of allelic and structural variation. Thus, germline IGH genotypes are personal, which may influence responses to infection and vaccination. For an improved understanding of inter-individual differences in antibody responses, we isolated SARS-CoV-2 spike-specific monoclonal antibodies from convalescent health care workers, focusing on the IGHV1-69 gene, which has the highest level of allelic variation of all IGHV genes. The IGHV1-69∗20-using CAB-I47 antibody and two similar antibodies isolated from an independent donor were critically dependent on allele usage. Neutralization was retained when reverting the V region to the germline IGHV1-69∗20 allele but lost when reverting to other IGHV1-69 alleles. Structural data confirmed that two germline-encoded polymorphisms, R50 and F55, in the IGHV1-69 gene were required for high-affinity receptor-binding domain interaction. These results demonstrate that polymorphisms in IGH genes can influence the function of SARS-CoV-2 neutralizing antibodies.

The Dynamic Fungal Genome: Polyploidy, Aneuploidy and Copy Number Variation in Response to Stress.

Fungal species have dynamic genomes and often exhibit genomic plasticity in response to stress. This genome plasticity often comes with phenotypic consequences that affect fitness and resistance to stress. Fungal pathogens exhibit genome plasticity in both clinical and agricultural settings and often during adaptation to antifungal drugs, posing significant challenges to human health. Therefore, it is important to understand the rates, mechanisms, and impact of large genomic changes. This review addresses the prevalence of polyploidy, aneuploidy, and copy number variation across diverse fungal species, with special attention to prominent fungal pathogens and model species. We also explore the relationship between environmental stress and rates of genomic changes and highlight the mechanisms underlying genotypic and phenotypic changes. A comprehensive understanding of these dynamic fungal genomes is needed to identify novel solutions for the increase in antifungal drug resistance.

A Highly Scalable Method for Joint Whole-Genome Sequencing and Gene-Expression Profiling of Single Cells.

To address how genetic variation alters gene expression in complex cell mixtures, we developed direct nuclear tagmentation and RNA sequencing (DNTR-seq), which enables whole-genome and mRNA sequencing jointly in single cells. DNTR-seq readily identified minor subclones within leukemia patients. In a large-scale DNA damage screen, DNTR-seq was used to detect regions under purifying selection and identified genes where mRNA abundance was resistant to copy-number alteration, suggesting strong genetic compensation. mRNA sequencing (mRNA-seq) quality equals RNA-only methods, and the low positional bias of genomic libraries allowed detection of sub-megabase aberrations at ultra-low coverage. Each cell library is individually addressable and can be re-sequenced at increased depth, allowing multi-tiered study designs. Additionally, the direct tagmentation protocol enables coverage-independent estimation of ploidy, which can be used to identify cell singlets. Thus, DNTR-seq directly links each cell's state to its corresponding genome at scale, enabling routine analysis of heterogeneous tumors and other complex tissues.

Resolving the 22q11.2 deletion using CTLR-Seq reveals chromosomal rearrangement mechanisms and individual variance in breakpoints.

We developed a generally applicable method, CRISPR/Cas9-targeted long-read sequencing (CTLR-Seq), to resolve, haplotype-specifically, the large and complex regions in the human genome that had been previously impenetrable to sequencing analysis, such as large segmental duplications (SegDups) and their associated genome rearrangements. CTLR-Seq combines in vitro Cas9-mediated cutting of the genome and pulse-field gel electrophoresis to isolate intact large (i.e., up to 2,000 kb) genomic regions that encompass previously unresolvable genomic sequences. These targets are then sequenced (amplification-free) at high on-target coverage using long-read sequencing, allowing for their complete sequence assembly. We applied CTLR-Seq to the SegDup-mediated rearrangements that constitute the boundaries of, and give rise to, the 22q11.2 Deletion Syndrome (22q11DS), the most common human microdeletion disorder. We then performed de novo assembly to resolve, at base-pair resolution, the full sequence rearrangements and exact chromosomal breakpoints of 22q11.2DS (including all common subtypes). Across multiple patients, we found a high degree of variability for both the rearranged SegDup sequences and the exact chromosomal breakpoint locations, which coincide with various transposons within the 22q11.2 SegDups, suggesting that 22q11DS can be driven by transposon-mediated genome recombination. Guided by CTLR-Seq results from two 22q11DS patients, we performed three-dimensional chromosomal folding analysis for the 22q11.2 SegDups from patient-derived neurons and astrocytes and found chromosome interactions anchored within the SegDups to be both cell type-specific and patient-specific. Lastly, we demonstrated that CTLR-Seq enables cell-type specific analysis of DNA methylation patterns within the deletion haplotype of 22q11DS.

A novel copy number variant in the murine Cdh23 gene gives rise to profound deafness and vestibular dysfunction.

Hearing loss is the most common congenital sensory deficit worldwide and exhibits high genetic heterogeneity, making molecular diagnoses elusive for most individuals. Detecting novel mutations that contribute to hearing loss is crucial to providing accurate personalized diagnoses, tailored interventions, and improving prognosis. Copy number variants (CNVs) are structural mutations that are understudied, potential contributors to hearing loss. Here, we present the Abnormal Wobbly Gait (AWG) mouse, the first documented mutant exhibiting waltzer-like locomotor dysfunction, hyperactivity, circling behaviour, and profound deafness caused by a spontaneous CNV deletion in cadherin 23 (Cdh23). We were unable to identify the causative mutation through a conventional whole-genome sequencing (WGS) and variant detection pipeline, but instead found a linked variant in hexokinase 1 (Hk1) that was insufficient to recapitulate the AWG phenotype when introduced into C57BL/6J mice using CRISPR-Cas9. Investigating nearby deafness-associated genes revealed a pronounced downregulation of Cdh23 mRNA and a complete absence of full-length CDH23 protein, which is critical for the development and maintenance of inner ear hair cells, in whole head extracts from AWG neonates. Manual inspection of WGS read depth plots of the Cdh23 locus revealed a putative 10.4 kb genomic deletion of exons 11 and 12 that was validated by PCR and Sanger sequencing. This study underscores the imperative to refine variant detection strategies to permit identification of pathogenic CNVs easily missed by conventional variant calling to enhance diagnostic precision and ultimately improve clinical outcomes for individuals with genetically heterogenous disorders such as hearing loss.

Enhancing clinical genomic accuracy with panelGC: a novel metric and tool for quantifying and monitoring GC biases in hybridization capture panel sequencing.

Accurate assessment of fragment abundance within a genome is crucial in clinical genomics applications such as the analysis of copy number variation (CNV). However, this task is often hindered by biased coverage in regions with varying guanine-cytosine (GC) content. These biases are particularly exacerbated in hybridization capture sequencing due to GC effects on probe hybridization and polymerase chain reaction (PCR) amplification efficiency. Such GC content-associated variations can exert a negative impact on the fidelity of CNV calling within hybridization capture panels. In this report, we present panelGC, a novel metric, to quantify and monitor GC biases in hybridization capture sequencing data. We establish the efficacy of panelGC, demonstrating its proficiency in identifying and flagging potential procedural anomalies, even in situations where instrument and experimental monitoring data may not be readily accessible. Validation using real-world datasets demonstrates that panelGC enhances the quality control and reliability of hybridization capture panel sequencing.

Digital microfluidics-based digital counting of single-cell copy number variation (dd-scCNV Seq).

Single-cell copy number variations (CNVs), major dynamic changes in humans, result in differential levels of gene expression and account for adaptive traits or underlying disease. Single-cell sequencing is needed to reveal these CNVs but has been hindered by single-cell whole-genome amplification (scWGA) bias, leading to inaccurate gene copy number counting. In addition, most of the current scWGA methods are labor intensive, time-consuming, and expensive with limited wide application. Here, we report a unique single-cell whole-genome library preparation approach based on digital microfluidics for digital counting of single-cell Copy Number Variation (dd-scCNV Seq). dd-scCNV Seq directly fragments the original single-cell DNA and uses these fragments as templates for amplification. These reduplicative fragments can be filtered computationally to generate the original partitioned unique identified fragments, thereby enabling digital counting of copy number variation. dd-scCNV Seq showed an increase in uniformity in the single-molecule data, leading to more accurate CNV patterns compared to other methods with low-depth sequencing. Benefiting from digital microfluidics, dd-scCNV Seq allows automated liquid handling, precise single-cell isolation, and high-efficiency and low-cost genome library preparation. dd-scCNV Seq will accelerate biological discovery by enabling accurate profiling of copy number variations at single-cell resolution.

OSCAA: A two-dimensional Gaussian mixture model for copy number variation association analysis.

Copy number variants (CNVs) are prevalent in the human genome and are found to have a profound effect on genomic organization and human diseases. Discovering disease-associated CNVs is critical for understanding the pathogenesis of diseases and aiding their diagnosis and treatment. However, traditional methods for assessing the association between CNVs and disease risks adopt a two-stage strategy conducting quantitative CNV measurements first and then testing for association, which may lead to biased association estimation and low statistical power, serving as a major barrier in routine genome-wide assessment of such variation. In this article, we developed One-Stage CNV-disease Association Analysis (OSCAA), a flexible algorithm to discover disease-associated CNVs for both quantitative and qualitative traits. OSCAA employs a two-dimensional Gaussian mixture model that is built upon the PCs from copy number intensities, accounting for technical biases in CNV detection while simultaneously testing for their effect on outcome traits. In OSCAA, CNVs are identified and their associations with disease risk are evaluated simultaneously in a single step, taking into account the uncertainty of CNV identification in the statistical model. Our simulations demonstrated that OSCAA outperformed the existing one-stage method and traditional two-stage methods by yielding a more accurate estimate of the CNV-disease association, especially for short CNVs or CNVs with weak signals. In conclusion, OSCAA is a powerful and flexible approach for CNV association testing with high sensitivity and specificity, which can be easily applied to different traits and clinical risk predictions.

Extrachromosomal circular DNA in cancer: history, current knowledge, and methods.

Extrachromosomal circular DNA (eccDNA) is a closed-circle, nuclear, nonplasmid DNA molecule found in all tested eukaryotes. eccDNA plays important roles in cancer pathogenesis, evolution of tumor heterogeneity, and therapeutic resistance. It is known under many names, including very large cancer-specific circular extrachromosomal DNA (ecDNA), which carries oncogenes and is often amplified in cancer cells. Our understanding of eccDNA has historically been limited and fragmented. To provide better a context of new and previous research on eccDNA, in this review we give an overview of the various names given to eccDNA at different times. We describe the different mechanisms for formation of eccDNA and the methods used to study eccDNA thus far. Finally, we explore the potential clinical value of eccDNA.

Tissue- and cell-type-specific molecular and functional signatures of 16p11.2 reciprocal genomic disorder across mouse brain and human neuronal models.

Chromosome 16p11.2 reciprocal genomic disorder, resulting from recurrent copy-number variants (CNVs), involves intellectual disability, autism spectrum disorder (ASD), and schizophrenia, but the responsible mechanisms are not known. To systemically dissect molecular effects, we performed transcriptome profiling of 350 libraries from six tissues (cortex, cerebellum, striatum, liver, brown fat, and white fat) in mouse models harboring CNVs of the syntenic 7qF3 region, as well as cellular, transcriptional, and single-cell analyses in 54 isogenic neural stem cell, induced neuron, and cerebral organoid models of CRISPR-engineered 16p11.2 CNVs. Transcriptome-wide differentially expressed genes were largely tissue-, cell-type-, and dosage-specific, although more effects were shared between deletion and duplication and across tissue than expected by chance. The broadest effects were observed in the cerebellum (2,163 differentially expressed genes), and the greatest enrichments were associated with synaptic pathways in mouse cerebellum and human induced neurons. Pathway and co-expression analyses identified energy and RNA metabolism as shared processes and enrichment for ASD-associated, loss-of-function constraint, and fragile X messenger ribonucleoprotein target gene sets. Intriguingly, reciprocal 16p11.2 dosage changes resulted in consistent decrements in neurite and electrophysiological features, and single-cell profiling of organoids showed reciprocal alterations to the proportions of excitatory and inhibitory GABAergic neurons. Changes both in neuronal ratios and in gene expression in our organoid analyses point most directly to calretinin GABAergic inhibitory neurons and the excitatory/inhibitory balance as targets of disruption that might contribute to changes in neurodevelopmental and cognitive function in 16p11.2 carriers. Collectively, our data indicate the genomic disorder involves disruption of multiple contributing biological processes and that this disruption has relative impacts that are context specific.

De novo genome assembly of white clover (Trifolium repens L.) reveals the role of copy number variation in rapid environmental adaptation.

BACKGROUND: White clover (Trifolium repens) is a globally important perennial forage legume. This species also serves as an eco-evolutionary model system for studying within-species chemical defense variation; it features a well-studied polymorphism for cyanogenesis (HCN release following tissue damage), with higher frequencies of cyanogenic plants favored in warmer locations worldwide. Using a newly generated haplotype-resolved genome and two other long-read assemblies, we tested the hypothesis that copy number variants (CNVs) at cyanogenesis genes play a role in the ability of white clover to rapidly adapt to local environments. We also examined questions on subgenome evolution in this recently evolved allotetraploid species and on chromosomal rearrangements in the broader IRLC legume clade. RESULTS: Integration of PacBio HiFi, Omni-C, Illumina, and linkage map data yielded a completely de novo genome assembly for white clover (created without a priori sequence assignment to subgenomes). We find that white clover has undergone extensive transposon diversification since its origin but otherwise shows highly conserved genome organization and composition with its diploid progenitors. Unlike some other clover species, its chromosomal structure is conserved with other IRLC legumes. We further find extensive evidence of CNVs at the major cyanogenesis loci; these contribute to quantitative variation in the cyanogenic phenotype and to local adaptation across wild North American populations. CONCLUSIONS: This work provides a case study documenting the role of CNVs in local adaptation in a plant species, and it highlights the value of pan-genome data for identifying contributions of structural variants to adaptation in nature.

Control-FREEC viewer: a tool for the visualization and exploration of copy number variation data.

BACKGROUND: Copy number alterations (CNAs) are genetic changes commonly found in cancer that involve different regions of the genome and impact cancer progression by affecting gene expression and genomic stability. Computational techniques can analyze copy number data obtained from high-throughput sequencing platforms, and various tools visualize and analyze CNAs in cancer genomes, providing insights into genetic mechanisms driving cancer development and progression. However, tools for visualizing copy number data in cancer research have some limitations. In fact, they can be complex to use and require expertise in bioinformatics or computational biology. While copy number data analysis and visualization provide insights into cancer biology, interpreting results can be challenging, and there may be multiple explanations for observed patterns of copy number alterations. RESULTS: We created Control-FREEC Viewer, a tool that facilitates effective visualization and exploration of copy number data. With Control-FREEC Viewer, experimental data can be easily loaded by the user. After choosing the reference genome, copy number data are displayed in whole genome or single chromosome view. Gain or loss on a specific gene can be found and visualized on each chromosome. Analysis parameters for subsequent sessions can be stored and images can be exported in raster and vector formats. CONCLUSIONS: Control-FREEC Viewer enables users to import and visualize data analyzed by the Control-FREEC tool, as well as by other tools sharing a similar tabular output, providing a comprehensive and intuitive graphical user interface for data visualization.

ProcaryaSV: structural variation detection pipeline for bacterial genomes using short-read sequencing.

BACKGROUND: Structural variations play an important role in bacterial genomes. They can mediate genome adaptation quickly in response to the external environment and thus can also play a role in antibiotic resistance. The detection of structural variations in bacteria is challenging, and the recognition of even small rearrangements can be important. Even though most detection tools are aimed at and benchmarked on eukaryotic genomes, they can also be used on prokaryotic genomes. The key features of detection are the ability to detect small rearrangements and support haploid genomes. Because of the limiting performance of a single detection tool, combining the detection abilities of multiple tools can lead to more robust results. There are already available workflows for structural variation detection for long-reads technologies and for the detection of single-nucleotide variation and indels, both aimed at bacteria. Yet we are unaware of structural variations detection workflows for the short-reads sequencing platform. Motivated by this gap we created our workflow. Further, we were interested in increasing the detection performance and providing more robust results. RESULTS: We developed an open-source bioinformatics pipeline, ProcaryaSV, for the detection of structural variations in bacterial isolates from paired-end short sequencing reads. Multiple tools, starting with quality control and trimming of sequencing data, alignment to the reference genome, and multiple structural variation detection tools, are integrated. All the partial results are then processed and merged with an in-house merging algorithm. Compared with a single detection approach, ProcaryaSV has improved detection performance and is a reproducible easy-to-use tool. CONCLUSIONS: The ProcaryaSV pipeline provides an integrative approach to structural variation detection from paired-end next-generation sequencing of bacterial samples. It can be easily installed and used on Linux machines. It is publicly available on GitHub at https://github.com/robinjugas/ProcaryaSV .

COSAP: Comparative Sequencing Analysis Platform.

BACKGROUND: Recent improvements in sequencing technologies enabled detailed profiling of genomic features. These technologies mostly rely on short reads which are merged and compared to reference genome for variant identification. These operations should be done with computers due to the size and complexity of the data. The need for analysis software resulted in many programs for mapping, variant calling and annotation steps. Currently, most programs are either expensive enterprise software with proprietary code which makes access and verification very difficult or open-access programs that are mostly based on command-line operations without user interfaces and extensive documentation. Moreover, a high level of disagreement is observed among popular mapping and variant calling algorithms in multiple studies, which makes relying on a single algorithm unreliable. User-friendly open-source software tools that offer comparative analysis are an important need considering the growth of sequencing technologies. RESULTS: Here, we propose Comparative Sequencing Analysis Platform (COSAP), an open-source platform that provides popular sequencing algorithms for SNV, indel, structural variant calling, copy number variation, microsatellite instability and fusion analysis and their annotations. COSAP is packed with a fully functional user-friendly web interface and a backend server which allows full independent deployment for both individual and institutional scales. COSAP is developed as a workflow management system and designed to enhance cooperation among scientists with different backgrounds. It is publicly available at https://cosap.bio and https://github.com/MBaysanLab/cosap/ . The source code of the frontend and backend services can be found at https://github.com/MBaysanLab/cosap-webapi/ and https://github.com/MBaysanLab/cosap_frontend/ respectively. All services are packed as Docker containers as well. Pipelines that combine algorithms can be customized and new algorithms can be added with minimal coding through modular structure. CONCLUSIONS: COSAP simplifies and speeds up the process of DNA sequencing analyses providing commonly used algorithms for SNV, indel, structural variant calling, copy number variation, microsatellite instability and fusion analysis as well as their annotations. COSAP is packed with a fully functional user-friendly web interface and a backend server which allows full independent deployment for both individual and institutional scales. Standardized implementations of popular algorithms in a modular platform make comparisons much easier to assess the impact of alternative pipelines which is crucial in establishing reproducibility of sequencing analyses.