Genomic Coordinates Research Articles

DeCGR: an interactive toolkit for deciphering complex genomic rearrangements from Hi-C data

BackgroundComplex genomic rearrangements (CGRs) drive the restructuring of chromatin architecture, resulting in significant interactions among rearranged fragments, visible as anomalous interaction blocks in chromatin contact maps generated by chromosome conformation capture technologies such as Hi-C. These blocks not only offer the orientation and genome coordinates of rearranged fragments but also filter out false positive CGRs, thereby facilitating CGR assembly. Despite this, there is a lack of interactive graphical software tailored for this purpose.ResultsWe present DeCGR, a user-friendly Python toolbox specifically designed for deciphering CGRs in Hi-C data. DeCGR consists of four independent execution components. The Breakpoint Filtering module identifies and filters simple rearrangements, providing the coordinates of rearrangement breakpoints. The Fragment Assembly module automatically assembles CGRs and visualizes the assembly process, facilitating the direct association between anomalous interaction blocks and CGR events. The Validation CGRs module verifies the completeness and accuracy of CGRs by generating the Hi-C map with CGRs through a simulation process and examines the difference from the original Hi-C maps. This module displays both the original and the simulated Hi-C map with highlighted rearranged fragment boundaries for rapid review to assess the CGRs. Finally, the Reconstruct Hi-C Map module provides the reconstructed Hi-C map based on the determined CGRs, allowing users to directly observe the impact of rearrangements on chromatin structure.ConclusionsDeCGR is designed specifically for biologists who aim to explore CGRs from Hi-C data. It provides a validation module to ensure the completeness and correctness of CGRs. Additionally, it allows users to generate CGR assembly results and reconstruct the Hi-C map with just one click. DeCGR provides intuitive visualization results for each module, allowing users to easily associate CGRs with Hi-C maps. DeCGR is operable through a user-friendly graphical interface. Source codes are freely available at https://github.com/GaoLabXDU/DeCGR.

Abstract PR004: Biological insights into tissue-agnostic plasma cfDNA methylation signature for surveillance of head and neck tumor recurrence

Abstract BACKGROUND: We recently described a clinical validation of a tissue-agnostic genome-wide methylome enrichment molecular residual disease (MRD) assay for head and neck cancers (HNC) (Liu, ESMO 2024). Herein, we investigate the cfDNA methylation signature utilized by this test to demonstrate the biological relevance with respect to the tissue of origin and epigenetic mechanisms of dysregulation that alter normal cellular activity. We evaluate the signature to gain insights and to characterize the biology of this biomarker and to investigate the application of cfMeDIP-seq assay as a diagnostic tool. METHODS: From unblinded training set of 163 HNC patients, including HPV+ (n = 76), HPV- (n = 43), and HPV-unknown (n = 44) individuals, a cfDNA methylation classifier signature was developed to detect recurrence (n MRD+ = 62, n MRD- = 101) in samples from blood draws approximately at 3, 12, and 24 months post curative intent treatment using cfMeDIP-seq assay. Clinical performance of the signature was then successfully validated in a blinded HNC validation cohort (n = 162), including HPV+ and HPV- patients (Liu, ESMO 2024). Here, the signature’s ability to detect ctDNA was assessed using HNC tumor tissue (n = 523), adjacent normal tissue (n = 45), and healthy peripheral blood leukocytes (n = 93) DNA methylation data from TCGA. The signature’s ability to provide biological insights was assessed by evaluating enrichment in CpG elements, epigenetic regulators, regulatory pathways, and other biological pathways. RESULTS: Identified differentially methylated regions (DMRs) were significantly hypermethylated in HNC tumor tissue samples when compared to adjacent normal and healthy peripheral blood leukocytes (P = 1.3e-131), and the mean signature signal within tumor tissue samples correlated with tumor purity (P = 2.47e-21). These DMRs were also significantly enriched in CpG islands, shores, and shelves and could classify HNC patients into identifiable molecular subtypes. Further analysis of the DMRs, based on their genomic coordinates, shows association with known cancer genes and significant enrichment in pathways known to be associated with HNC, for example, epithelial to mesenchymal transition (P = 1.76e-4), and multiple functional pathways involved in DNA and transcription factor binding, transcriptional regulation, and chromatin remodeling. CONCLUSIONS: These results, along with the previous clinical validation, provide strong evidence that the cfDNA methylation signature developed in HNC detects ctDNA shed from residual tumor and provide insights into relevant tumor biology and epigenetic mechanisms, including CpG island hypermethylation and epithelial to mesenchymal transition. Furthermore, these data support the generalization of biomarker detection utilizing cfMeDIP-seq to a broad spectrum of indications. Citation Format: Yulia Newton, Justin Burgener, Margaret Gruca, Collin Melton, Jun Min, Abel Licon, Scott Bratman, Alan Williams, Daniel D De Carvalho. Biological insights into tissue-agnostic plasma cfDNA methylation signature for surveillance of head and neck tumor recurrence [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr PR004.

Predicting somatic mutation origins in cell-free DNA by semi-supervised GAN models

MotivationDistinguishing between pathogenic cancer-associated mutations and other somatic variants present in cell-free DNA (cfDNA) is one of the challenges in the field of liquid biopsy. This distinction is critical, since the misclassification of mutations stemming from clonal hematopoiesis (CH) as tumor-derived and vice versa could result in inaccurate diagnoses and inappropriate therapeutic interventions for patients. ResultsWe addressed this by developing a specialized machine learning technique to differentiate tumor- or CH-related mutations in cfDNA. We established a comprehensive in-house reference catalog, comprising approximately 25,000 single nucleotide variants (SNVs), each linked to either tumor or CH origin. This reference serves as a foundation for training a deep learning model, which is structured on the semi-supervised generative adversarial network (SSGAN) architecture. By analyzing genomic coordinates and nucleotide composition of cfDNA variants, our model attains 95 % area under the curve (AUC) in classifying uncharacterized variants as CH or tumor-derived. In conclusion, our research emphasizes the potential of genomic feature prediction, using cfDNA data, to stand as a robust alternative to conventional multi-analyte sequencing methods. This approach not only enhances the accuracy of distinguishing CH from tumor mutations in liquid biopsy data, but also highlights the potential of advanced data analysis techniques and machine learning in genomics and personalized medicine. Availability: https://github.com/FPalizban/SSGAN.

The 5-Fluorouracil RNA Expression Viewer (5-FUR) Facilitates Interpreting the Effects of Drug Treatment and RRP6 Deletion on the Transcriptional Landscape in Yeast.

Saccharomyces cerevisiae is an excellent model to study the effect of external cues on cell division and stress response. 5-Fluorocuracil (5-FU) has been used to treat solid tumors since several decades. The drug was initially designed to interfere with DNA replication but was later found to exert its antiproliferative effect also via RNA-dependent processes. Since 5-FU inhibits the activity of the 3'-5'-exoribonuclease Rrp6 in yeast and mammals, earlier work has compared the effect of 5-FU treatment and RRP6 deletion at the transcriptome level in diploid synchronized yeast cells. To facilitate interpreting the expression data we have developed an improved 5-Fluorouracil RNA (5-FUR) expression viewer. Users can access information via genome coordinates and systematic or standard names for mRNAs and Xrn1-dependent-, stable-, cryptic-, and meiotic unannotated transcripts (XUTs, SUTs, CUTs, and MUTs). Normalized log2-transformed or linear data can be displayed as filled diagrams, line graphs or color-coded heatmaps. The expression data are useful for researchers interested in processes such as cell cycle regulation, mitotic repression of meiotic genes, the effect of 5-FU treatment and Rrp6 deficiency on the transcriptome and expression profiles of sense/antisense loci that encode overlapping transcripts. The viewer is accessible at http://5fur.genouest.org.

Confirmation and pathogenicity of small copy number variations incidentally detected via a targeted next-generation sequencing–based preimplantation genetic testing for aneuploidy platform

ObjectiveTo evaluate the technical accuracy, inheritance, and pathogenicity of small copy number variants (CNVs) detected by a targeted next-generation sequencing (NGS)-based PGT-A platform. DesignRetrospective observational study performed between 2020-2022. Subjects12,157 patients who underwent clinical PGT-A performed by targeted NGS for whole chromosome and large segmental aneuploidies. ExposureAn incidental finding was reported when a CNV gain/loss of at least three consecutive amplicons appeared in at least two embryos from the same IVF cycle. Main Outcome MeasuresThe primary outcome measures were the specificity, incidence, inheritance, and pathogenicity of small CNVs detected by the PGT-A platform. Accuracy of the PGT-A platform CNV calls was assessed via concordance with the CNV calls (size and genomic location) on chromosomal microarray of the gamete provider(s). Parental origin of the CNV and pathogenicity classifications were also reported. ResultsIn 75 of 12,157 unique PGT-A patients (0.62%;95%CI:0.5-0.8%), an incidental finding that met reporting criteria was identified. Chromosomal microarray follow-up was requested for all cases and results were received for one or both members of 65 reproductive couples. In all cases, one of the gamete providers was confirmed to have the CNV identified in the embryos (100.0%: N=65/65 95%CI:94.5-100). The identified CNV was of maternal origin in 34 cases (52.3%) and of paternal origin in 31 cases (47.7%). A significant correlation was identified between PGT-A-predicted CNV sizes and chromosomal microarray detected sizes (r=0.81) and genomic coordinates on parental DNA. Twenty-six (40%) of the CNVs were classified as benign/likely benign, 30 (46.2%) as a variant of uncertain significance (VUS), and 9 (13.8%) as pathogenic/likely pathogenic. ConclusionCertain PGT-A platforms may enable the detection of inherited, small CNVs with extremely high specificity without prior knowledge of parental status. The majority of CNVs in this data set were confirmed to be benign/likely benign or a VUS; however, pathogenic/likely pathogenic CNVs associated with a broad range of phenotypic features may also be detected, although a reliable negative predictive value for small CNVs with current PGT-A technologies is unknown due to the many technical challenges.

DORA: an interactive map for the visualization and analysis of ancient human DNA and associated data.

The ability to sequence ancient genomes has revolutionized the way we study evolutionary history by providing access to the most important aspect of evolution-time. Until recently, studying human demography, ecology, biology, and history using population genomic inference relied on contemporary genomic datasets. Over the past decade, the availability of human ancient DNA (aDNA) has increased rapidly, almost doubling every year, opening the way for spatiotemporal studies of ancient human populations. However, the multidimensionality of aDNA, with genotypes having temporal, spatial and genomic coordinates, and integrating multiple sources of data, poses a challenge for developing meta-analyses pipelines. To address this challenge, we developed a publicly-available interactive tool, DORA, which integrates multiple data types, genomic and non-genomic, in a unified interface. This web-based tool enables browsing sample metadata alongside additional layers of information, such as population structure, climatic data, and unpublished samples. Users can perform analyses on genotypes of these samples, or export sample subsets for external analyses. DORA integrates analyses and visualizations in a single intuitive interface, resolving the technical issues of combining datasets from different sources and formats, and allowing researchers to focus on the scientific questions that can be addressed through analysis of aDNA datasets.

TULIPs decorate the three-dimensional genome of PFA ependymoma

Posterior fossa group A (PFA) ependymoma is a lethal brain cancer diagnosed in infants and young children. The lack of driver events in the PFA linear genome led us to search its 3D genome for characteristic features. Here, we reconstructed 3D genomes from diverse childhood tumor types and uncovered a global topology in PFA that is highly reminiscent of stem and progenitor cells in a variety of human tissues. A remarkable feature exclusively present in PFA are type B ultra long-range interactions in PFAs (TULIPs), regions separated by great distances along the linear genome that interact with each other in the 3D nuclear space with surprising strength. TULIPs occur in all PFA samples and recur at predictable genomic coordinates, and their formation is induced by expression of EZHIP. The universality of TULIPs across PFA samples suggests a conservation of molecular principles that could be exploited therapeutically.

SpliceAPP: an interactive web server to predict splicing errors arising from human mutations

BackgroundSplicing variants are a major class of pathogenic mutations, with their severity equivalent to nonsense mutations. However, redundant and degenerate splicing signals hinder functional assessments of sequence variations within introns, particularly at branch sites. We have established a massively parallel splicing assay to assess the impact on splicing of 11,191 disease-relevant variants. Based on the experimental results, we then applied regression-based methods to identify factors determining splicing decisions and their respective weights.ResultsOur statistical modeling is highly sensitive, accurately annotating the splicing defects of near-exon intronic variants, outperforming state-of-the-art predictive tools. We have incorporated the algorithm and branchpoint information into a web-based tool, SpliceAPP, to provide an interactive application. This user-friendly website allows users to upload any genetic variants with genome coordinates (e.g., chr15 74,687,208 A G), and the tool will output predictions for splicing error scores and evaluate the impact on nearby splice sites. Additionally, users can query branch site information within the region of interest.ConclusionsIn summary, SpliceAPP represents a pioneering approach to screening pathogenic intronic variants, contributing to the development of precision medicine. It also facilitates the annotation of splicing motifs. SpliceAPP is freely accessible using the link https://bc.imb.sinica.edu.tw/SpliceAPP. Source code can be downloaded at https://github.com/hsinnan75/SpliceAPP.

Hadge: a comprehensive pipeline for donor deconvolution in single-cell studies

Single-cell multiplexing techniques (cell hashing and genetic multiplexing) combine multiple samples, optimizing sample processing and reducing costs. Cell hashing conjugates antibody-tags or chemical-oligonucleotides to cell membranes, while genetic multiplexing allows to mix genetically diverse samples and relies on aggregation of RNA reads at known genomic coordinates. We develop hadge (hashing deconvolution combined with genotype information), a Nextflow pipeline that combines 12 methods to perform both hashing- and genotype-based deconvolution. We propose a joint deconvolution strategy combining best-performing methods and demonstrate how this approach leads to the recovery of previously discarded cells in a nuclei hashing of fresh-frozen brain tissue.

Genome-wide annotation of transcript boundaries using bacterial Rend-seq datasets.

Accurate annotation to single-nucleotide resolution of the transcribed regions in genomes is key to optimally analyse RNA-seq data, understand regulatory events and for the design of experiments. However, currently most genome annotations provided by GenBank generally lack information about untranslated regions. Additionally, information regarding genomic locations of non-coding RNAs, such as sRNAs, or anti-sense RNAs is frequently missing. To provide such information, diverse RNA-seq technologies, such as Rend-seq, have been developed and applied to many bacterial species. However, incorporating this vast amount of information into annotation files has been limited and is bioinformatically challenging, resulting in UTRs and other non-coding elements being overlooked or misrepresented. To overcome this problem, we present pyRAP (python Rend-seq Annotation Pipeline), a software package that analyses Rend-seq datasets to accurately resolve transcript boundaries genome-wide. We report the use of pyRAP to find novel transcripts, transcript isoforms, and RNase-dependent sRNA processing events. In Bacillus subtilis we uncovered 63 novel transcripts and provide genomic coordinates with single-nucleotide resolution for 2218 5'UTRs, 1864 3'UTRs and 161 non-coding RNAs. In Escherichia coli, we report 117 novel transcripts, 2429 5'UTRs, 1619 3'UTRs and 91 non-coding RNAs, and in Staphylococcus aureus, 16 novel transcripts, 664 5'UTRs, 696 3'UTRs, and 81 non-coding RNAs. Finally, we use pyRAP to produce updated annotation files for B. subtilis 168, E. coli K-12 MG1655, and S. aureus 8325 for use in the wider microbial genomics research community.

MutationExplorer: a webserver for mutation of proteins and 3D visualization of energetic impacts.

The possible effects of mutations on stability and function of a protein can only be understood in the context of protein 3D structure. The MutationExplorerwebserver maps sequence changes onto protein structures and allows users to study variation by inputting sequence changes. As the user enters variants, the 3D model evolves, and estimated changes in energy are highlighted. In addition to a basic per-residue input format, MutationExplorercan also upload an entire replacement sequence. Previously the purview of desktop applications, such an upload can back-mutate PDB structures to wildtype sequence in a single step. Another supported variation source is human single nucelotide polymorphisms (SNPs), genomic coordinates input in VCF format. Structures are flexibly colorable, not only by energetic differences, but also by hydrophobicity, sequence conservation, or other biochemical profiling. Coloring by interface score reveals mutation impacts on binding surfaces. MutationExplorerstrives for efficiency in user experience. For example, we have prepared 45000 PDB depositions for instant retrieval and initial display. All modeling steps are performed by Rosetta. Visualizations leverage MDsrv/Mol*. MutationExplorer is available at: http://proteinformatics.org/mutation_explorer/.

Abstract 2935: NGS panel customization for comprehensive genomic profiling

Abstract Introduction: We have developed a custom SureSelect Cancer design pipeline for the creation of target enrichment gene panels for comprehensive profiling of single nucleotide variations (SNP/INDEL), gene-level copy number variations (CNV), and DNA translocations (TL) as well as content for measuring of microsatellite instability (MSI) and tumor mutational burden (TMB). Methods: The performance of individual probes was measured for the 679-gene SureSelect Cancer CGP panel and the information used to generate a reference database. When creating a new design, the gene list is cross-referenced with the database and when applicable, the probe information is applied to the new design. If genes are included in the custom design that are not present in the database, a second design algorithm selects the optimal probe based on its sequence characteristics (% GC, homology, etc.). Content is selected based on the biomarker to be measured and can be defined based on gene name (SNV/indel and CNV) or genomic coordinates (SNV/indel, TL). Pre-defined content for MSI measurement can be added to any design to cover 288 microsatellite sites. The performance of these sites in determining MSI status was demonstrated by comparing to the MSI-PCR reference method. To assess the performance of the design methodology, designs of various sizes (50 to 650+ genes) and content type were generated and assayed on reference samples and FFPE with known biomarkers. Sequencing metrics were evaluated for capture specificity and coverage of the target regions. Results: The design methodology allowed for sub-selection of content present in the 679-gene SureSelect Cancer CGP assay as well as addition of de-novo content. The design algorithm uses empirical data on probe performance to provide robust and reproducible results for the range of supported design sizes. Biomarkers were reliably detected with SNV detection down to 1% allele frequency, detection of gene amplifications and translocations concordant with FISH, and MSI and TMB determination concordant with PCR and Exome sequencing respectively. The design workspace was implemented in the SureDesign platform, providing a web-based interface for creating the designs. Conclusions: We demonstrated full customization capability of the SureSelect Cancer target enrichment platform that pairs user-defined content with an optimized probe design methodology to enable the sensitive detection of the common types of DNA alterations found in FFPE samples. Citation Format: Michael Ruvolo, Akanksha Khare, Nishita Matcha-Balaguruvappa, Katherine Wilkins, Linus Forsmark, Swati Gupta. NGS panel customization for comprehensive genomic profiling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2935.

Abstract 2997: Identification of novel long non-coding RNAs in mantle cell lymphoma patients

Abstract Mantle Cell Lymphoma (MCL) is a rare and incurable type of B-cell non-Hodgkin lymphoma that affects about 4,000 people per year in the United States. Despite intense research to identify novel therapeutic approaches, the median progression-free survival after first-line treatment is four years. For this reason, the discovery of novel therapeutic targets remains crucial. Over half of the human genome is actively transcribed as non-coding RNAs. Among them, the non-coding RNAs that are longer than 200 nucleotides are named long non-coding RNAs (lncRNAs). lncRNAs have been shown to play essential roles in several human diseases, including cancer, by modulating cell cycle, immune response, or stemness through their interactions with DNA, protein, and RNA. For this reason, we decided to perform an RNA sequencing (RNA-Seq) profiling to identify the dysregulated lncRNAs in MCL. In more detail, we performed RNA-Seq in a cohort of 18 MCL patients compared to normal B-cells from the peripheral blood of healthy donors (HD). Reads were first trimmed for quality filtering, and adapters were removed using Trim Galore. Trimmed reads were mapped to the human genome (HG38 assembly) using HISAT 2. All the reads mapped inside the genomic coordinates of lncRNAs, reported in the GTF annotation file of GENCODE v43, were counted by featureCounts. Differential expression analysis between MCL samples vs HD was performed by LIMMA. To validate the results obtained from our internal MCL cohort, we also downloaded publicly available RNA-Seq data of 16 MCL patients and analyzed them using the previously described pipeline. Our analyses revealed several dysregulations in lncRNA expression occurring in MCL. Precisely, we identified 385 upregulated and 387 downregulated lncRNAs (|log2FC| > 0.58 and adjusted p-value < 0.05) in our internal MCL cohort, and 63% of them were also confirmed in the external cohort. Notably, the lncRNA called MALAT1 showed the highest significant upregulation in both cohorts, reinforcing this lncRNA's pivotal role in cancer development. To study the potential involvement of MALAT1 in MCL, we compared the MCL samples with higher MALAT1 expression (upper quartile) against those with lower expression (lower quartile) in both cohorts. The identified differentially expressed genes were used as input for a mechanistic network-based topological pathway analysis using MITHrIL. The analysis revealed that the KEGG pathway "Cell Cycle" was among the top upregulated pathways in MCL samples with high MALAT1 expression, suggesting the relevance of this lncRNA in promoting cellular proliferation, as also reported in the literature for other tumor types. In conclusion, our is the first study to describe the novel dysregulation signature of lncRNAs in MCL in two independent cohorts. Citation Format: Alessandro LaFerlita, Satishkumar Singh, Anuvrat Sircar, Robert Baiocchi, Natarajan Muthusamy, Lapo Alinari, Narendranath Epperla, Lalit Sehgal. Identification of novel long non-coding RNAs in mantle cell lymphoma patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2997.

Identification of germline population variants misclassified as cancer-associated somatic variants.

Databases used for clinical interpretation in oncology rely on genetic data derived primarily from patients of European ancestry, leading to biases in cancer genetics research and clinical practice. One practical issue that arises in this context is the potential misclassification of multi-ancestral population variants as tumor-associated because they are not represented in reference genomes against which tumor sequencing data is aligned. To systematically find misclassified variants, we compared somatic variants in census genes from the Catalogue of Somatic Mutations in Cancer (COSMIC) V99 with multi-ancestral population variants from the Genome Aggregation Databases' Linkage Disequilibrium (GnomAD). By comparing genomic coordinates, reference, and alternate alleles, we could identify misclassified variants in genes associated with cancer. We found 192 of 208 genes in COSMIC's cancer-associated census genes (92.31%) to be associated with variant misclassifications. Among the 1,906,732 variants in COSMIC, 6,957 variants (0.36%) aligned with normal population variants in GnomAD, concerning for misclassification. The African / African American ancestral population included the greatest number of misclassified variants and also had the greatest number of unique misclassified variants. The direct, systematic comparison of variants from COSMIC for co-occurrence in GnomAD supports a more accurate interpretation of tumor sequencing data and reduces bias related to genomic ancestry.

BCFtools/liftover: an accurate and comprehensive tool to convert genetic variants across genome assemblies.

Many genetics studies report results tied to genomic coordinates of a legacy genome assembly. However, as assemblies are updated and improved, researchers are faced with either realigning raw sequence data using the updated coordinate system or converting legacy datasets to the updated coordinate system to be able to combine results with newer datasets. Currently available tools to perform the conversion of genetic variants have numerous shortcomings, including poor support for indels and multi-allelic variants, that lead to a higher rate of variants being dropped or incorrectly converted. As a result, many researchers continue to work with and publish using legacy genomic coordinates. Here we present BCFtools/liftover, a tool to convert genomic coordinates across genome assemblies for variants encoded in the variant call format with improved support for indels represented by different reference alleles across genome assemblies and full support for multi-allelic variants. It further supports variant annotation fields updates whenever the reference allele changes across genome assemblies. The tool has the lowest rate of variants being dropped with an order of magnitude less indels dropped or incorrectly converted and is an order of magnitude faster than other tools typically used for the same task. It is particularly suited for converting variant callsets from large cohorts to novel telomere-to-telomere assemblies as well as summary statistics from genome-wide association studies tied to legacy genome assemblies. The tool is written in C and freely available under the MIT open source license as a BCFtools plugin available at http://github.com/freeseek/score.

Novel intertypic recombination of Echovirus 11 in the Enterovirus species B.

Enteroviruses (EVs), single-stranded, positive-sense RNA viruses, can be classified into four species (A-D), which have previously been linked to a diverse range of disease manifestations and infections affecting the central nervous system. In the Enterovirus species B (EV-B), Echovirus type 11 (E11) has been observed to occasionally circulate in Taiwan, which was responsible for an epidemic of enterovirus infections in 2018. Here, 48 clinical specimens isolated in 2003, 2004, 2009, and 2018 were collected for the high-throughput sequencing. Notably, we identified 2018 Taiwanese strains having potential recombinations in the 3D gene, as well as one 2003 strain having a double recombination with E6 and Coxsackievirus B5 in the P2 and P3 regions, respectively. Additionally, one amino acid signature mutated from the Histidine (H) in throat swab specimens to the Tyrosine (Y) in cerebral spinal fluid specimens was detected at position 1496 (or 57) of the genomic coordinate (or 3A gene) to further demonstrate intra-host evolution in different organs. In conclusion, this study identifies potential intertypic recombination events and an intra-host signature mutation in E11 strains, isolated during a 2018 neurological disease outbreak in Taiwan, contributing to our understanding of its evolution and pathogenesis.

HER2/ERBB2 copy number analysis by targeted next-generation sequencing in breast cancer.

A combination of immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH) is the current standard of care for HER2 evaluation in breast cancer. Here, we investigate the potential clinical utility of next-generation sequencing (NGS)-derived HER2/ERBB2 copy number (CN) data for predicting HER2 status as defined by American Society of Clinical Oncology (ASCO)/College of American Pathologists (CAP) guidelines. In total, 294 locally recurrent and metastatic breast cancers previously tested by targeted hybrid capture-based NGS and by HER2 IHC/FISH were included. Analyses focused on the ERBB2 median log2 ratios and start-end genomic coordinates from NGS, average HER2 CN and HER2/CEP17 ratios from FISH, and the HER2 IHC scores. We also determined a more stringent log2 ratio cutoff to predict HER2-positive status with 100% specificity. Sixty-four (22%) cases were HER2 positive and 230 (78%) were HER2 negative by ASCO/CAP guidelines. The ERBB2 median log2 ratios from NGS strongly correlated with HER2 status by IHC/FISH (area under receiver operator characteristic curve = 0.951). ERBB2 log2 ratio more than 1.7 was 100% specific for HER2-positive results by IHC/FISH. Start and end genomic coordinates for regions of gain near ERBB2 by NGS also predicted HER2 status. Copy number data from our NGS panel strongly correlate with HER2 status. Using a stringent cutoff, ERBB2 log2 ratio accurately predicts HER2 positivity with high specificity. The NGS CN assessment may have utility in determining HER2 status in certain clinical settings.

NK-2 Homeobox Transcription Factor NKX2-3 Confers Differentiation Block in NPM1-Mutated in Acute Myeloid Leukaemia

Dysregulation of HOX homeobox family transcription factor (TF) gene expression is important in the pathogenesis of AML in particular in NPM1-mutated and MLL rearranged molecular subtypes. However, little is known about the role of NK2-homeobox (NKX2) family TF genes. NKX2 genes have critical roles in normal development of the central nervous system, thyroid, lung and pancreas. We observed high expression of NKX2-3 in patient AML samples and hypothesised the transcription factor might have a functional role in the disease. By qPCR, we found that NKX2-3 is highly expressed in normal HSCs but is rapidly down regulated as cells differentiate. In primary AML samples, it is highly expressed in 21-37% cases, including within the immunophenotypic LSC compartment, in particular in cases with NPM1 and/or FLT3-ITD mutations. Forced expression of NKX2-3 in normal murine KIT + bone marrow (BM) HSPCs transiently enhanced their clonogenic activity and impaired differentiation: NKX2-3-expressing HSPCs expressed higher levels of E2F or MYC target genes, and leukemia stem cell (LSC) maintenance genes. Congenic transplant experiments revealed that forced expression of NKX2-3 in normal BM HSPCs was sufficient to enhance multilineage engraftment across four months of follow up without inducing leukemia. In keeping with this, transplantation of Nkx2-3 -/- knockout BM cells led to significantly inferior engraftment compared with BM cells from litter mate control mice. Thus, while increased expression of Nkx2-3 enhances engraftment of normal stem cells in transplantation assays, loss of Nkx2-3 impairs it. Analysis of NKX2-3 high primary human AML cases demonstrated that HOXA9 was the most highly upregulated transcription factor gene compared with NKX2-3 low cases. We expressed both factors singly and together in murine KIT+ BM HSPCs: Hoxa9/NKX2-3 cells exhibited significantly higher clonogenic activity and reduced morphologic and immunophenotypic differentiation compared with Hoxa9/MTV cells. Congenic BM transplant experiments demonstrated that Hoxa9/NKX2-3 recipients developed leukemias significantly earlier than Hoxa9/MTV recipients (median 82 vs 155 days). BM morphology and immunophenotyping confirmed that Hoxa9/NKX2-3AMLs exhibited a greater degree of differentiation block than Hoxa9/MTV AMLs. Leukemia cells from Hoxa9/NKX2-3 recipients coordinately expressed higher levels of E2F or MYC target genes, and LSC maintenance genes. NKX2-3 KD to ~30% of control values reduced proliferation and induced morphologic and immunophenotypic differentiation in primary patient NPM1-mutated AML cells. NKX2-3 KD led to down regulation of E2F and MYC target genes and normal human CD34 + stem cell genes; and up regulation of myeloid differentiation genes. Using a CRISPR ribonucleofection approach to relocate NPM1c to the nucleus we confirmed that NKX2-3 expression in NPM1-mutant primary AML cells is sustained by mutant NPM1c. ChIPseq in NPM1-mutated primary patient and KO52 AML cells identified NKX2-3 binding peaks. As expected, DNA sequences under peak apices were strongly enriched for NKX2-3 binding motifs; there was also significant enrichment for RUNX and ETS family motifs, but not HOX/MEIS/PBX motifs. To identify genes potentially regulated by NKX2-3, genomic coordinates of strong binding peaks were mapped to the single nearest gene. Remarkably, among genes up regulated following NKX2-3 knockdown in primary human AML cells there was a highly significant enrichment of genes with putative regulatory elements bound by NKX2-3. Likewise, among genes down regulated in NKX2-3-expressing KIT+ murine HSPCs, there was a similar significant enrichment of genes with putative regulatory elements bound by NKX2-3. These data suggest that NKX2-3 functions as a transcription repressor at bound regulatory elements in primary AML cells. In summary, NKX2-3 is highly expressed in NPM1 & FLT3 mutated molecular subtypes of AML and serves to enhance self-renewal and the level of differentiation block through binding to and repressing regulatory elements of myeloid differentiation genes.

An Analysis of a Transposable Element Expression Atlas during 27 Developmental Stages in Porcine Skeletal Muscle: Unveiling Molecular Insights into Pork Production Traits.

The development and growth of porcine skeletal muscle determine pork quality and yield. While genetic regulation of porcine skeletal muscle development has been extensively studied using various omics data, the role of transposable elements (TEs) in this context has been less explored. To bridge this gap, we constructed a comprehensive atlas of TE expression throughout the developmental stages of porcine skeletal muscle. This was achieved by integrating porcine TE genomic coordinates with whole-transcriptome RNA-Seq data from 27 developmental stages. We discovered that in pig skeletal muscle, active Tes are closely associated with active epigenomic marks, including low levels of DNA methylation, high levels of chromatin accessibility, and active histone modifications. Moreover, these TEs include 6074 self-expressed TEs that are significantly enriched in terms of muscle cell development and myofibril assembly. Using the TE expression data, we conducted a weighted gene co-expression network analysis (WGCNA) and identified a module that is significantly associated with muscle tissue development as well as genome-wide association studies (GWAS) of the signals of pig meat and carcass traits. Within this module, we constructed a TE-mediated gene regulatory network by adopting a unique multi-omics integration approach. This network highlighted several established candidate genes associated with muscle-relevant traits, including HES6, CHRNG, ACTC1, CHRND, MAMSTR, and PER2, as well as novel genes like ENSSSCG00000005518, ENSSSCG00000033601, and PIEZO2. These novel genes hold promise for regulating muscle-related traits in pigs. In summary, our research not only enhances the TE-centered dissection of the genetic basis underlying pork production traits, but also offers a general approach for constructing TE-mediated regulatory networks to study complex traits or diseases.

COSMIC:a curated database of somatic variants and clinical data for cancer.

The Catalogue Of Somatic Mutations In Cancer (COSMIC), https://cancer.sanger.ac.uk/cosmic, is an expert-curated knowledgebase providing data on somatic variants in cancer, supported by a comprehensive suite of tools for interpreting genomic data, discerning the impact of somatic alterations on disease, and facilitating translational research. The catalogue is accessed and used by thousands of cancer researchers and clinicians daily, allowing them to quickly access information from an immense pool of data curated from over 29 thousand scientific publications and large studies. Within the last 4 years, COSMIC has substantially expanded its utility by adding new resources: the Mutational Signatures catalogue, the Cancer Mutation Census, and Actionability. To improve data accessibility and interoperability, somatic variants have received stable genomic identifiers that are associated with their genomic coordinates in GRCh37 and GRCh38, and new export files with reduced data redundancy have been made available for download.