Complex Structural Variants Research Articles

Resolving complex duplication variants in autism spectrum disorder using long-read genome sequencing.

Rare or de novo structural variation, primarily in the form of copy number variants, is detected in 5%-10% of autism spectrum disorder (ASD) families. While complex structural variants involving duplications can generally be detected using microarray or short-read genome sequencing (GS), these methods frequently fail to characterize breakpoints at nucleotide resolution, requiring additional molecular methods for validation and fine-mapping. Here, we use Oxford Nanopore Technologies PromethION long-read GS to characterize complex genomic rearrangements (CGRs) involving large duplications that segregate with ASD in five families. In total, we investigated 13 CGR carriers and were able to resolve all breakpoint junctions at nucleotide resolution. While all breakpoints were identified, the precise genomic architecture of one rearrangement remained unresolved with three different potential structures. The findings in two families include potential fusion genes formed through duplication rearrangements, involving IL1RAPL1-DMD and SUPT16H-CHD8 In two of the families originating from the same geographical region, an identical rearrangement involving ANK2 was identified, which likely represents a founder variant. In addition, we analyze methylation status directly from the long-read data, allowing us to assess the activity of rearranged genes and regulatory regions. Investigation of methylation across the CGRs reveals aberrant methylation status in carriers across a rearrangement affecting the CREBBP locus. In aggregate, our results demonstrate the utility of nanopore sequencing to pinpoint CGRs associated with ASD in five unrelated families, and highlight the importance of a gene-centric description of disease-associated complex chromosomal rearrangements.

Identification of novel 3D-genome altering and complex structural variants underlying retinitis pigmentosa type 17 through a multistep and high-throughput approach

IntroductionAutosomal dominant retinitis pigmentosa type 17 (adRP, type RP17) is caused by complex structural variants (SVs) affecting a locus on chromosome 17 (chr17q22). The SVs disrupt the 3D regulatory landscape by altering the topologically associating domain (TAD) structure of the locus, creating novel TAD structures (neo-TADs) and ectopic enhancer-gene contacts. Currently, screening for RP17-associated SVs is not included in routine diagnostics given the complexity of the variants and a lack of cost-effective detection methods. The aim of this study was to accurately detect novel RP17-SVs by establishing a systematic and efficient workflow.MethodsGenetically unexplained probands diagnosed with adRP (n = 509) from an international cohort were screened using a smMIPs or genomic qPCR-based approach tailored for the RP17 locus. Suspected copy number changes were validated using high-density SNP-array genotyping, and SV breakpoint characterization was performed by mutation-specific breakpoint PCR, genome sequencing and, if required, optical genome mapping. In silico modeling of novel SVs was performed to predict the formation of neo-TADs and whether ectopic contacts between the retinal enhancers and the GDPD1-promoter could be formed.ResultsUsing this workflow, potential RP17-SVs were detected in eight probands of which seven were confirmed. Two novel SVs were identified that are predicted to cause TAD rearrangement and retinal enhancer-GDPD1 contact, one from Germany (DE-SV9) and three with the same SV from the United States (US-SV10). Previously reported RP17-SVs were also identified in three Australian probands, one with UK-SV2 and two with SA-SV3.DiscussionIn summary, we describe a validated multi-step pipeline for reliable and efficient RP17-SV discovery and expand the range of disease-associated SVs. Based on these data, RP17-SVs can be considered a frequent cause of adRP which warrants the inclusion of RP17-screening as a standard diagnostic test for this disease.

Advanced terahertz refractive index sensor in all-dielectric metasurface utilizing second order toroidal quasi-BIC modes for biochemical environments

We present a novel design for an all-dielectric metasurface ultra-sensitive refractive index (RI) sensor, characterized by a high-quality factor (Qf) and figure of merit (FOM). This design incorporates two high-order toroidal modes, including electric toroidal quadrupole (\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{Q}_{T}^{e}$$\\end{document}​) and magnetic toroidal quadrupole (\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{Q}_{T}^{m}$$\\end{document}​) based on quasi‑bound states in the continuum (q-BIC) resonances. Our findings demonstrate the feasibility of switching between \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{Q}_{T}^{e}$$\\end{document} and \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{Q}_{T}^{m}$$\\end{document}​ through various symmetry-breaking mechanisms. To excite these high-order toroidal modes, we explored several symmetry-breaking strategies, including complex structural designs, symmetry disruption, and variations in the incident wave angle. Symmetry breaking in the structure induces new modes in the transmission spectrum, which are highly advantageous for sensing applications due to the presence of dark modes. The designed metasurface exhibits the capability to sense a broad range of RI in diverse environments, particularly in biochemical fields. Sensitivity (S) is significantly enhanced by the excitation of new resonance modes and adjustments in the incidence angle, increasing from 217 GHz/RIU in a symmetrical structure to 512.3 GHz/RIU for \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{Q}_{T}^{e}$$\\end{document}​. The FOM improves from 197 RIU 1 to 8538 RIU− 1 for \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{Q}_{T}^{e}$$\\end{document}​ and 152,395 RIU− 1 for \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{Q}_{T}^{m}$$\\end{document}​. Additionally, the Qf rises from 872 to 17,983 and 921,351 for \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{Q}_{T}^{e}$$\\end{document}​ and \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{Q}_{T}^{m}$$\\end{document}​, respectively.

Comprehensive Analysis of Complex Copy Number Variations in the Plasmodium falciparum Genome via Long-Read Sequencing: Implications for Antimalarial Resistance and Broad Genomic Insights

Abstract Introduction/Objective The genome of Plasmodium falciparum, characterized by a high AT content and a propensity for copy number variations (CNVs), presents a model for understanding how genomes adapt to stress. Our research focused on the prevalence and distribution of CNVs highlights long-read sequencing as a tool for detecting complex structural variants in other models. Methods/Case Report P. falciparum parasites were cultured and treated with DSM1 and aphidicolin that induce replication stress. Long-read sequencing was conducted using the Oxford Nanopore Technology Minion, with an emphasis on an optimizing DNA extraction method for preserving intact chromosomes. Visualization of CNVs, including tandem and inverted duplications, was achieved through a custom R Shiny application that allowed both quantitative and qualitative assessments. Results (if a Case Study enter NA) Long read sequencing identified an enrichment of multiple CNV types in treated samples, with a significant enrichment of inverted duplications as well as other types of structural variants. Aphidicolin and DSM1-treated samples demonstrated significant accumulation of these variations compared to controls (2.0 and 5.6-fold, respectively with a p value of < 0.0001 in both treatment groups). These occurred mainly in core genomic regions that do not contain variable gene copies. An increase in the number of inverted duplications may be related to stress response to the treatment and may be a potential adaptive mechanism of the parasite. Our manual single-read visualization technique made it possible to visualize the structure of various CNV types, which provides unique insight on CNVs as they arise in single genomes. Conclusion We hypothesize that inverted duplications represent stalled replication forks, and we are currently assessing their frequency in replicating and non-replicating cells. This study highlights the role of long-read sequencing in detecting complex genomic structures in P. falciparum, which may influence the parasite’s resistance to drug treatment. These findings not only advance our understanding of malaria pathology but also aid our comprehension of how cancer cells might react to drug-induced stress and the mechanisms behind CNV formation in neoplastic cells.

Targeted long-read sequencing identifies missing pathogenic variant in unsolved 11β-hydroxylase deficiency

Background11β-hydroxylase deficiency (11β-OHD), caused by homozygosity or compound heterozygosity CYP11B1 variants, is the second most common cause of congenital adrenal hyperplasia (CAH). Due to the high degree of sequence identity between CYP11B1 and CYP11B2, chimeric genes, and complex structural variants (SVs), the conventional approach to gene testing for 11β-OHD is facing challenges. The study aimed to clarify the underlying genetic causes of two siblings of a Chinese family with 11β-OHD.MethodsPeripheral blood samples and clinical information were collected from subjects and their family members. Sex steroid concentrations were measured using LC-MS/MS. Long-range PCR-based next-generation sequencing (NGS), PCR assay and target long-read sequencing were used to detect the pathogenic variants.ResultsEarly onset hypertension, increased serum levels of adrenocorticotropin (ACTH), progesterone, testosterone, and decreased cortisol and potassium were detected in both affected siblings. Long-range PCR-based NGS identified a heterozygous missense variant (NM_000497.4:c.281 C > T, p.P94> L) in CYP11B1 gene in the two siblings. PCR detected no chimeric CYP11B2/CYP11B1 gene. We finally identified a second pathogenic variant in CYP11B1 gene via target long-read sequencing (T-LRS). This novel variant was a deletion-insertion variant and located chr8:143957269–143,957,579 (hg19) with the insertion of ‘ACAG’ (NM_000497.4:c.954 + 78_980delinsACAG), which was in trans with CYP11B1: c.281 C > T.ConclusionsOur study suggests that the integrated long-range PCR-based NGS and T-LRS seem to be the most reliable and accurate method for 11β-OHD genetic diagnosis and carrier sequencing.

Real-time genomic characterization of pediatric acute leukemia using adaptive sampling.

Effective treatment of pediatric acute leukemia is dependent on accurate genomic classification, typically derived from a combination of multiple time-consuming and costly techniques such as flow cytometry, fluorescence in situ hybridization (FISH), karyotype analysis, targeted PCR, and microarrays. We investigated the feasibility of a comprehensive single-assay classification approach using long-read sequencing, with real-time genome target enrichment, to classify chromosomal abnormalities and structural variants characteristic of acute leukemia. We performed whole genome sequencing on DNA from diagnostic peripheral blood or bone marrow for 54 pediatric acute leukemia cases with diverse genomic subtypes. We demonstrated the characterization of known, clinically relevant karyotype abnormalities and structural variants concordant with standard-of-care clinical testing. Subtype-defining genomic alterations were identified in all cases following a maximum of forty-eight hours of sequencing. In 18 cases, we performed real-time analysis - concurrent with sequencing - and identified the driving alteration in as little as fifteen minutes (for karyotype) or up to six hours (for complex structural variants). Whole genome nanopore sequencing with adaptive sampling has the potential to provide detailed genomic classification of acute leukemia specimens with reduced cost and turnaround time compared to the current standard of care.

The interplay of mutagenesis and ecDNA shapes urothelial cancer evolution.

Advanced urothelial cancer is a frequently lethal disease characterized by marked genetic heterogeneity1. In this study, we investigated the evolution of genomic signatures caused by endogenous and external mutagenic processes and their interplay with complex structural variants (SVs). We superimposed mutational signatures and phylogenetic analyses of matched serial tumours from patients with urothelial cancer to define the evolutionary dynamics of these processes. We show that APOBEC3-induced mutations are clonal and early, whereas chemotherapy induces mutational bursts of hundreds of late subclonal mutations. Using a genome graph computational tool2, we observed frequent high copy-number circular amplicons characteristic of extrachromosomal DNA (ecDNA)-forming SVs. We characterized the distinct temporal patterns of APOBEC3-induced and chemotherapy-induced mutations within ecDNA-forming SVs, gaining new insights into the timing of these mutagenic processes relative to ecDNA biogenesis. We discovered that most CCND1 amplifications in urothelial cancer arise within circular ecDNA-forming SVs. ecDNA-forming SVs persisted and increased in complexity, incorporating additional DNA segments and contributing to the evolution of treatment resistance. Oxford Nanopore Technologies long-read whole-genome sequencing followed by de novo assembly mapped out CCND1 ecDNAstructure. Experimental modelling of CCND1 ecDNA confirmed its role as a driver of treatment resistance. Our findings define fundamental mechanisms that drive urothelial cancer evolution and have important therapeutic implications.

Complex structural variation is prevalent and highly pathogenic in pediatric solid tumors.

In pediatric cancer, structural variants (SVs) and copy-number alterations contribute to cancer initiation as well as progression, thereby aiding diagnosis and treatment stratification. Although suggested to be of importance, the prevalence and biological relevance of complex genomic rearrangements (CGRs) across pediatric solid tumors is largely unexplored. In a cohort of 120 primary tumors, we systematically characterized patterns of extrachromosomal DNA, chromoplexy, and chromothripsis across five pediatric solid cancer types. CGRs were identified in 56 tumors (47%), and in 42 of these tumors, CGRs affect cancer driver genes or result in unfavorable chromosomal alterations. This demonstrates that CGRs are prevalent and pathogenic in pediatric solid tumors and suggests that selection likely contributes to the structural variation landscape. Moreover, carrying CGRs is associated with more adverse clinical events. Our study highlights the potential for CGRs to be incorporated in risk stratification or exploited for targeted treatments.

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

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. 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. 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 .

Complex genetic variation in nearly complete human genomes.

Diverse sets of complete human genomes are required to construct a pangenome reference and to understand the extent of complex structural variation. Here, we sequence 65 diverse human genomes and build 130 haplotype-resolved assemblies (130 Mbp median continuity), closing 92% of all previous assembly gaps1,2 and reaching telomere-to-telomere (T2T) status for 39% of the chromosomes. We highlight complete sequence continuity of complex loci, including the major histocompatibility complex (MHC), SMN1/SMN2, NBPF8, and AMY1/AMY2, and fully resolve 1,852 complex structural variants (SVs). In addition, we completely assemble and validate 1,246 human centromeres. We find up to 30-fold variation in α-satellite high-order repeat (HOR) array length and characterize the pattern of mobile element insertions into α-satellite HOR arrays. While most centromeres predict a single site of kinetochore attachment, epigenetic analysis suggests the presence of two hypomethylated regions for 7% of centromeres. Combining our data with the draft pangenome reference1 significantly enhances genotyping accuracy from short-read data, enabling whole-genome inference3 to a median quality value (QV) of 45. Using this approach, 26,115 SVs per sample are detected, substantially increasing the number of SVs now amenable to downstream disease association studies.

Abstract C102: Polyethnic-1000: Advancing cancer genomics by studying ethnically diverse, underserved patient populations in New York

Abstract Prompted by the considerable biases toward European ancestry cases in large cancer genomics repositories, the New York Genome Center initiated an effort to diversify the public datasets that are forming the foundations of current cancer research, in order to optimize the unbiased applications of modern genomically-driven discoveries to a larger population. Our effort was focused on 8 cancer types (breast, prostate, bladder, lung, pancreatic, colon and endometrial cancer, as well as multiple myeloma) and generated Illumina tumor-normal whole-genome sequencing and tumor transcriptome sequencing. The majority of the samples were retrospectively obtained in clinics and hospitals of the New York region and the analyses are conducted with collaborators in these institutions. Our dataset is currently composed of 851 tumor-normal WGS and 471 RNASeq, and is progressively being distributed to the scientific community in partnership with ICGC-ARGO. Overall, 75% of the cohort is predominantly of non-European ancestry, as estimated by genetic ancestry analysis using the 1000Genomes continental references.We analyzed the samples with NYGC’s somatic variant calling pipeline and will present results from the 8 cancer types related to germline and somatic variants (single-nucleotide variants, indels, copy-number alterations, complex structural variants) as well as mutational signatures, neoantigen prediction, fusion genes and expression profiles, in relation to genetic ancestry estimation and clinical features of the patients. We anticipate that the dataset will significantly diversify the genetic ancestry of tumor profiles in public databases, and serve to form hypotheses regarding the contribution of ancestry on cancer disparities. The experience of the Polyethnic-1000 project informs our contribution to the Cancer Grand Challenge SAMBAI, which focuses on breast, prostate and pancreatic cancers in the African Diaspora. SAMBAI will further investigate the causes of cancer disparity by combining genomic data with social determinants of health and exposomics information, deeper immune profiling and new genomics technologies such as long reads sequencing and the use of a pangenome reference. Citation Format: Nicolas Robine, Lara Winterkorn, Tim Chu, Zoe Goldstein, Will Hooper, Ali Oku, Heather M. Geiger, Melissa B. Davis, Onyinye Balogun. Polyethnic-1000: Advancing cancer genomics by studying ethnically diverse, underserved patient populations in New York [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr C102.

SVbyEye: A visual tool to characterize structural variation among whole-genome assemblies.

We are now in the era of being able to routinely generate highly contiguous (near telomere-to-telomere) genome assemblies of human and nonhuman species. Complex structural variation and regions of rapid evolutionary turnover are being discovered for the first time. Thus, efficient and informative visualization tools are needed to evaluate and directly observe structural differences between two or more genomes. We developed SVbyEye, an open-source R package to visualize and annotate sequence-to-sequence alignments along with various functionalities to process alignments in PAF format. The tool facilitates the characterization of complex structural variants in the context of sequence homology helping resolve the mechanisms underlying their formation. SVbyEye is available at https://github.com/daewoooo/SVbyEye.

Impact and characterization of serial structural variations across humans and great apes

Modern sequencing technology enables the systematic detection of complex structural variation (SV) across genomes. However, extensive DNA rearrangements arising through a series of mutations, a phenomenon we refer to as serial SV (sSV), remain underexplored, posing a challenge for SV discovery. Here, we present NAHRwhals (https://github.com/WHops/NAHRwhals), a method to infer repeat-mediated series of SVs in long-read genomic assemblies. Applying NAHRwhals to haplotype-resolved human genomes from 28 individuals reveals 37 sSV loci of various length and complexity. These sSVs explain otherwise cryptic variation in medically relevant regions such as the TPSAB1 gene, 8p23.1, 22q11 and Sotos syndrome regions. Comparisons with great ape assemblies indicate that most human sSVs formed recently, after the human-ape split, and involved non-repeat-mediated processes in addition to non-allelic homologous recombination. NAHRwhals reliably discovers and characterizes sSVs at scale and independent of species, uncovering their genomic abundance and suggesting broader implications for disease.

GGTyper: genotyping complex structural variants using short-read sequencing data.

Complex structural variants (SVs) are genomic rearrangements that involve multiple segments of DNA. They contribute to human diversity and have been shown to cause Mendelian disease. Nevertheless, our abilities to analyse complex SVs are very limited. As opposed to deletions and other canonical types of SVs, there are no established tools that have explicitly been designed for analysing complex SVs. Here, we describe a new computational approach that we specifically designed for genotyping complex SVs in short-read sequenced genomes. Given a variant description, our approach computes genotype-specific probability distributions for observing aligned read pairs with a wide range of properties. Subsequently, these distributions can be used to efficiently determine the most likely genotype for any set of aligned read pairs observed in a sequenced genome. In addition, we use these distributions to compute a genotyping difficulty for a given variant, which predicts the amount of data needed to achieve a reliable call. Careful evaluation confirms that our approach outperforms other genotypers by making reliable genotype predictions across both simulated and real data. On up to 7829 human genomes, we achieve high concordance with population-genetic assumptions and expected inheritance patterns. On simulated data, we show that precision correlates well with our prediction of genotyping difficulty. This together with low memory and time requirements makes our approach well-suited for application in biomedical studies involving small to very large numbers of short-read sequenced genomes. Source code is available at https://github.com/kehrlab/Complex-SV-Genotyping.

Detection and analysis of complex structural variation in human genomes across populations and in brains of donors with psychiatric disorders.

Complex structural variations (cxSVs) are often overlooked in genome analyses due to detection challenges. We developed ARC-SV, a probabilistic and machine-learning-based method that enables accurate detection and reconstruction of cxSVs from standard datasets. By applying ARC-SV across 4,262 genomes representing all continental populations, we identified cxSVs as a significant source of natural human genetic variation. Rare cxSVs have a propensity to occur in neural genes and loci that underwent rapid human-specific evolution, including those regulating corticogenesis. By performing single-nucleus multiomics in postmortem brains, we discovered cxSVs associated with differential gene expression and chromatin accessibility across various brain regions and cell types. Additionally, cxSVs detected in brains of psychiatric cases are enriched for linkage with psychiatric GWAS risk alleles detected in the same brains. Furthermore, our analysis revealed significantly decreased brain-region- and cell-type-specific expression of cxSV genes, specifically for psychiatric cases, implicating cxSVs in the molecular etiology of major neuropsychiatric disorders.

3D Vessel Segmentation With Limited Guidance of 2D Structure-Agnostic Vessel Annotations.

Delineating 3D blood vessels of various anatomical structures is essential for clinical diagnosis and treatment, however, is challenging due to complex structure variations and varied imaging conditions. Although recent supervised deep learning models have demonstrated their superior capacity in automatic 3D vessel segmentation, the reliance on expensive 3D manual annotations and limited capacity for annotation reuse among different vascular structures hinder their clinical applications. To avoid the repetitive and costly annotating process for each vascular structure and make full use of existing annotations, this paper proposes a novel 3D shape-guided local discrimination (3D-SLD) model for 3D vascular segmentation under limited guidance from public 2D vessel annotations. The primary hypothesis is that 3D vessels are composed of semantically similar voxels and often exhibit tree-shaped morphology. Accordingly, the 3D region discrimination loss is firstly proposed to learn the discriminative representation measuring voxel-wise similarities and cluster semantically consistent voxels to form the candidate 3D vascular segmentation in unlabeled images. Secondly, the shape distribution from existing 2D structure-agnostic vessel annotations is introduced to guide the 3D vessels with the tree-shaped morphology by the adversarial shape constraint loss. Thirdly, to enhance training stability and prediction credibility, the highlighting-reviewing-summarizing (HRS) mechanism is proposed. This mechanism involves summarizing historical models to maintain temporal consistency and identifying credible pseudo labels as reliable supervision signals. Only guided by public 2D coronary artery annotations, our method achieves results comparable to SOTA barely-supervised methods in 3D cerebrovascular segmentation, and the best DSC in 3D hepatic vessel segmentation, demonstrating the effectiveness of our method.

Enhancing point cloud registration with transformer: cultural heritage protection of the Terracotta Warriors

Point cloud registration technology, by precisely aligning repair components with the original artifacts, can accurately record the geometric shape of cultural heritage objects and generate three-dimensional models, thereby providing reliable data support for the digital preservation, virtual exhibition, and restoration of cultural relics. However, traditional point cloud registration methods face challenges when dealing with cultural heritage data, including complex morphological and structural variations, sparsity and irregularity, and cross-dataset generalization. To address these challenges, this paper introduces an innovative method called Enhancing Point Cloud Registration with Transformer (EPCRT). Firstly, we utilize local geometric perception for positional encoding and combine it with a dynamic adjustment mechanism based on local density information and geometric angle encoding, enhancing the flexibility and adaptability of positional encoding to better characterize the complex local morphology and structural variations of artifacts. Additionally, we introduce a convolutional-Transformer hybrid module to facilitate interactive learning of artifact point cloud features, effectively achieving local–global feature fusion and enhancing detail capture capabilities, thus effectively handling the sparsity and irregularity of artifact point cloud data. We conduct extensive evaluations on the 3DMatch, ModelNet, KITTI, and MVP-RG datasets, and validate our method on the Terracotta Warriors cultural heritage dataset. The results demonstrate that our method has significant performance advantages in handling the complexity of morphological and structural variations, sparsity and irregularity of relic data, and cross-dataset generalization.

Uncovering hidden genetic variations: long-read sequencing reveals new insights into tuberous sclerosis complex.

Tuberous sclerosis is a multi-system disorder caused by mutations in either TSC1 or TSC2. The majority of affected patients (85%-90%) have heterozygous variants, and a smaller number (around 5%) have mosaic variants. Despite using various techniques, some patients still have "no mutation identified" (NMI). We hypothesized that the causal variants of patients with NMI may be structural variants or deep intronic variants. To investigate this, we sequenced the DNA of 26 tuberous sclerosis patients with NMI using targeted long-read sequencing. We identified likely pathogenic/pathogenic variants in 13 of the cases, of which 6 were large deletions, four were InDels, two were deep intronic variants, one had retrotransposon insertion in either TSC1 or TSC2, and one was complex rearrangement. Furthermore, there was a de novo Alu element insertion with a high suspicion of pathogenicity that was classified as a variant of unknown significance. Our findings expand the current knowledge of known pathogenic variants related to tuberous sclerosis, particularly uncovering mosaic complex structural variations and retrotransposon insertions that have not been previously reported in tuberous sclerosis. Our findings suggest a higher prevalence of mosaicism among tuberous sclerosis patients than previously recognized. Our results indicate that long-read sequencing is a valuable approach for tuberous sclerosis cases with no mutation identified (NMI).

An Integrated Transcriptomics and Genomics Approach Detects an X/Autosome Translocation in a Female with Duchenne Muscular Dystrophy.

Duchenne and Becker muscular dystrophies, caused by pathogenic variants in DMD, are the most common inherited neuromuscular conditions in childhood. These diseases follow an X-linked recessive inheritance pattern, and mainly males are affected. The most prevalent pathogenic variants in the DMD gene are copy number variants (CNVs), and most patients achieve their genetic diagnosis through Multiplex Ligation-dependent Probe Amplification (MLPA) or exome sequencing. Here, we investigated a female patient presenting with muscular dystrophy who remained genetically undiagnosed after MLPA and exome sequencing. RNA sequencing (RNAseq) from the patient's muscle biopsy identified an 85% reduction in DMD expression compared to 116 muscle samples included in the cohort. A de novo balanced translocation between chromosome 17 and the X chromosome (t(X;17)(p21.1;q23.2)) disrupting the DMD and BCAS3 genes was identified through trio whole genome sequencing (WGS). The combined analysis of RNAseq and WGS played a crucial role in the detection and characterisation of the disease-causing variant in this patient, who had been undiagnosed for over two decades. This case illustrates the diagnostic odyssey of female DMD patients with complex structural variants that are not detected by current panel or exome sequencing analysis.

The Importance of Copy Number Variant Analysis in Patients with Monogenic Kidney Disease

IntroductionGenetic testing can reveal monogenic causes of kidney diseases, offering diagnostic, therapeutic, and prognostic benefits. While single nucleotide variants (SNVs) and copy number variants (CNVs) can result in kidney disease, CNV analysis is not always included in genetic testing. MethodsWe investigated the diagnostic value of CNV analysis in 2,432 kidney disease patients genetically tested at the University Medical Centre Utrecht between 2014 and May 2022. We combined previous diagnostic testing results, encompassing SNVs and CNVs, with newly acquired results based on retrospective CNV analysis. The reported yield considers both ACMG classification and whether the genotype actually results in disease. ResultsWe report a diagnostic yield of at least 23% for our complete diagnostic cohort. The total diagnostic yield based solely on CNVs was 2.4%. The overall contribution of CNV analysis, defined as the proportion of positive genetic tests requiring CNV analysis, was 10.5% and varied among different disease subcategories, with the highest impact seen in congenital anomalies of the kidney and urinary tract and chronic kidney disease at a young age. We highlight the efficiency of exome-based CNV calling, which reduces the need for additional diagnostic tests. Furthermore, a complex structural variant, likely a COL4A4 founder variant, was identified. Additional findings unrelated to kidney diseases were reported in a small percentage of cases. ConclusionIn summary, this study demonstrates the substantial diagnostic value of CNV analysis, providing insights into its contribution to the diagnostic yield and advocating for its routine inclusion in genetic testing of kidney disease patients.