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Orthobunyavirus neurovirulence is a complex trait involving all three genome segments.

La Crosse orthobunyavirus (LACV) is a tri-segmented negative sense RNA virus and is the leading cause of pediatric arboviral encephalitis in the USA. The viral factors that mediate LACV's ability to replicate and cause damage and disease in the brain (neurovirulence) are not fully understood. We previously characterized the neurovirulence of LACV and closely related Inkoo virus (INKV) and discovered they have opposing neurovirulence phenotypes in mice and human neuronal cells: LACV has high neurovirulence and INKV has low neurovirulence. We therefore generated reassortant viruses between LACV and INKV to map the genome segments that mediate LACV's high neurovirulence phenotype. We recovered all six possible reassortant viruses of the L, M, and S genome segments using coinfection and reverse genetics approaches. We evaluated the neurovirulence of these reassortant viruses in mice in vivo and in human neuronal cells in vitro. Our results show that no single LACV genome segment alone was sufficient to cause wildtype LACV-like neurological disease in mice, and in fact all six reassortant viruses were attenuated from wildtype LACV. We found that the LACV M and S segments together were the primary drivers of neurological disease in mice, whereas the LACV L segment played a minor role. Our in vitro results indicate that the LACV M segment is crucial for efficient replication in neurons, but the LACV L segment appears to mediate slightly more efficient neuronal replication than the INKV L segment. The LACV M and S segments together induced wildtype LACV-like levels of neuronal death, indicating the LACV M and S are the primary mediators of neuronal death, and the L segment is not required. Together, these results indicate that LACV neurovirulence is a complex trait mediated by viral proteins on all three genome segments.

Journal Article

Predicting host tropism in influenza a viruses: insights from multi-segment nucleotide signatures.

BACKGROUND: Influenza A virus (IAV) poses a significant public health threat due to its cross-species transmission and complex host adaptation mechanisms. This study integrated whole-genome data from avian, human, swine, and bovine IAV strains, using machine learning to predict viral host tropism based on nucleotide site features and to identify key sites driving host adaptation along with their synergistic effects. METHODS: A total of 64,000 IAV sequences from avian, human, swine, and bovine hosts were analyzed to build host-prediction models. A four-class classification framework (avian, human, swine, bovine) was constructed using nucleotide site features from all eight genomic segments (PB2, PB1, PA, HA, NP, NA, MP, NS). Eight machine learning algorithms (logistic regression, decision tree, random forest, SVM, KNN, gradient boosting, XGBoost, LightGBM) were benchmarked via 10-fold stratified cross-validation. Model performance was evaluated using accuracy, precision, recall, F1-score, AUPRC, and AUC. SHAP (SHapley Additive exPlanations) analysis prioritized critical nucleotide sites, while bivariate association tests identified synergistic/antagonistic interactions between sites. Nucleotide composition profiles were compared across host groups using hierarchical clustering and heatmap visualization. RESULTS: The XGBoost algorithm demonstrated the best and most stable performance, achieving an AUC value of over 0.95 in distinguishing human-derived sequences from non-human ones. SHAP analysis identified the top 20 critical nucleotide sites for each gene segment, such as sites 46 and 698 in the NS segment. Nucleotide composition analysis revealed high similarity between human and swine sequences in the HA and PB2 segments, and between avian and bovine sequences. The HA segment was particularly challenging in differentiating human from swine strains. Bivariate site association analysis uncovered significant synergistic or antagonistic effects between key sites within gene segments, forming complex networks. For instance, in the NS segment, a positive prediction contribution was observed when sites 371, 698, and 419 were all G. CONCLUSIONS: This study advances our mechanistic understanding of IAV host adaptation, identifies molecular determinants for zoonotic risk stratification, and establishes a scalable machine learning framework for predicting viral host tropism through nucleotide signature analysis, thereby enhancing surveillance strategies and informing preventive measures against emerging viral threats.

Influenza A virus

Construction and Segmental Reconstitution of Full-Length Infectious Clones of Milk Vetch Dwarf Virus.

The construction of infectious clones (ICs) is essential for studying viral replication, pathogenesis, and host interactions. Milk vetch dwarf virus (MDV), a nanovirus with a multipartite, single-stranded DNA genome, presents unique challenges for IC development due to its segmented genome organization. To enable functional analysis of its genome, we constructed full-length tandem-dimer-based ICs for all eight MDV genomic segments. Each segment was cloned into a binary vector and co-delivered into Nicotiana benthamiana, Nicotiana tabacum, Vicia faba, and Vigna unguiculata plants via Agrobacterium-mediated inoculation. Systemic infection was successfully reconstituted in all host plants, with PCR-based detection confirming the presence of all viral segments in the infected leaves of nearly all tested plants. Segmental accumulation in infected plants was quantified using qPCR, revealing non-equimolar distribution across hosts. This study establishes the first complete IC system for MDV, enabling reproducible infection, replication analysis, and quantitative segment profiling. It provides a foundational tool for future molecular investigations into MDV replication, host interactions, and viral movement, advancing our understanding of nanovirus biology and transmission dynamics.

Nicotiana

Ramu stunt virus genome reveals previously unreported segments and nucleocapsid domain duplication in Mechlorovirus.

Ramu stunt virus (RmSV), a member of the genus Mechlorovirus within the family Phenuiviridae, was previously described as a six-segmented RNA virus infecting sugarcane. In this study, we re-examined type material and additional isolates using high-throughput sequencing and RT-PCR validation, revealing that RmSV possesses a nine-segmented genome, making it the largest reported in the Phenuiviridae. This expanded architecture includes duplicated RNA segments (RNA 2a and RNA 2b) encoding nucleocapsid-like proteins and two novel segments (RNA 7 and RNA 8). Comparative analysis showed that RNA 2a and 2b share about 84% amino acid identity, while RNA 5 encodes a third nucleocapsid homolog, indicating unprecedented domain redundancy. Structural modeling confirmed that all three nucleocapsid proteins maintain a conserved fold despite low sequence identity, with electrostatic mapping suggesting differential RNA-binding potential. Additionally, RNA 6 encodes a hypothetical protein structurally similar to the rice stripe virus disease-specific S-protein, implicating a role in symptom development. Transcript abundance analysis revealed RNA 6 as the most highly expressed segment across isolates. These findings revise the genomic composition of RmSV, highlight mechanisms of genome plasticity and adaptive evolution in plant-infecting bunyaviruses, and underscore practical implications for diagnostic assay design, resistance breeding, and biosecurity surveillance.

Genome, Viral

FUSE: data-driven functional segmentation of DNA methylation data.

SUMMARY: DNA methylation (DNAm) of neighbouring CpG sites is highly correlated, making DNAm function in terms of blocks. DNAm patterns and functionality are linked to both chromatin structure of DNA and gene regulation. Defining biologically meaningful DNA methylation blocks from whole-genome bisulfite sequencing (WGBS) data remains challenging, as most existing methods rely on fixed genomic windows rather than the observed methylation pattern. We present FUSE, a data-driven segmentation method that captures intrinsic methylation segments directly from WGBS data by jointly analyzing multiple samples. FUSE identifies spatially homogeneous methylation blocks shared across the input cohort while allowing different methylation states across samples. Applied to 61 WGBS samples from the ENCODE database, FUSE identified segments which overlap significantly with promoters, enhancers, and repetitive elements. FUSE was able to recover the true segment breakpoints in synthetic data with high sensitivity under increased levels of noise. As such, FUSE facilitates post hoc methylation analyses by aggregating coherent CpG sites into candidate segments for downstream differential methylation testing or other comparative studies. AVAILABILITY AND IMPLEMENTATION: FUSE is implemented as an R-package methFuse, available at https://github.com/holmsusa/methFuse and https://cran.r-project.org/package=methFuse. A GenomeSpy visualization of the data is available at https://csbi.ltdk.helsinki.fi/p/fuse_encode_gs/.

DNA Methylation

An archaic reference-free method to jointly infer Neanderthal and Denisovan introgressed segments in modern human genomes.

Admixture between populations is a common feature of human history. Admixture events introduce new genetic variation that can fuel evolution. Characterizing the significance of admixture events on the evolution of populations across various species is of great interest to evolutionary geneticists. Local Ancestry Inference (LAI) methods infer genetic ancestry of an individual at a particular chromosomal location. Certain methods specialize in detecting archaic introgression, which consists of interbreeding between modern and archaic humans like Neanderthals and Denisovans. Most current LAI methods allow the detection of a single archaic ancestry, and post-processing may distinguish between multiple waves of introgression. These methods vary in how they choose archaic or modern reference genomes for the inference. Here, we present a new HMM-based method (DAIseg), which has the advantage of simultaneously distinguishing between multiple waves of ancient and recent admixture, using only modern human reference genomes. Simulations demonstrate that DAIseg achieves higher overall performance than state-of-the-art methods. We also apply DAIseg to Papuan populations to jointly detect Denisovan and Neanderthal introgressed segments, and identify a higher number of archaic segments than previous methods. Analysis of inferred introgressed segments, shows that we can identify evidence for two Denisovan introgression events in Papuans. Overall, on top of being able to deal with both Archaic and recent admixture, DAIseg provides a more principled approach for detecting and classifying Denisovan and Neanderthal segments which will improve downstream analysis of introgressed segments to infer the impact of archaic introgression in humans.

Denisovan

Absolute copy number aware CNV calling of sub-megabase segments in ultra-low coverage single-cell DNA sequencing data.

Recent advances in ultra-low coverage whole-genome sequencing (WGS) of single cells have enabled detailed analysis of copy number variation at a throughput approaching that of single-cell RNA sequencing. However, downstream computational methods have not seen comparable advances and are largely adaptations of deep sequencing methodology with reduced precision. Here, we present ASCENT, a computational method built to take full advantage of modern direct tagmentation-based WGS at ultra-low depth. Using joint segmentation with high-resolution bins, we accurately detect small segments, achieving accurate copy number profiles even at 100 000 reads per cell. ASCENT implements true absolute copy state inference for single cells, based on statistical modeling of coverage rather than comparison to a reference, while taking variable segment copy state into account. Further, ASCENT implements per-segment copy-neutral loss of heterozygosity (LOH) calling without the need for non-tumor or bulk WGS reference. When applied to a pediatric B-ALL sample, ASCENT finds copy-neutral LOH in a small segment and a minor subclone defined by breakpoints missed in bulk WGS. Thus, by applying appropriate computational methods, single-cell WGS provides clear advantages over bulk, even at a relatively low cell number and sequencing depth.

DNA Copy Number Variations

Posterior Segment Risk Factors for Penetrating Keratoplasty Failure.

PURPOSE: To analyze the relationship between intraoperative and postpenetrating keratoplasty (PK) posterior segment variables and PK graft survival. DESIGN: Retrospective clinical cohort study. SUBJECTS: Patients undergoing PK between May 1, 2007 and September 1, 2018 at a single tertiary center. METHODS: Chart review for PKs performed was conducted, and the first PK completed at the institution for each patient was included for analysis. Data collected included demographics, medical and ocular history, preoperative and intraoperative findings, and intraoperative and postoperative posterior segment factors (pars plana vitrectomy [PPV], endolaser, retinal detachment [RD], and vitreous hemorrhage [VH]). After univariable analysis, variables were selected for multivariable Cox regression analysis. MAIN OUTCOME MEASURE: Graft failure, defined as irreversible and visually significant corneal edema, haze, or scarring. RESULTS: Eight hundred and thirty-five eyes of 835 patients were included. Mean age was 57.1 ± 22.0 (range: 0-100) years, and mean time from PK to final follow-up or graft failure was 3.2 ± 2.9 (range: 0.01-16.1) years. Graft failure occurred in 35.0% of cases with a mean onset of 1.9 ± 2.0 (range: 0.04-11.4) years after PK. After multivariable analysis, 9 variables had significant associations with failure. Two posterior segment variables were significant: intraoperative VH at the time of PK (hazard ratio [HR] 6.6, 95% confidence interval [CI] 1.6-27.7, P = .010) and silicone oil (SO) tamponade after the PK (HR 3.2, 95% CI 1.4-7.4, P = .007). CONCLUSIONS: Graft failure is a serious complication of PK. VH at the time of the PK and SO tamponade after the PK were associated with graft failure. In complex eyes that are undergoing PK grafts and that may also require posterior segment interventions, these findings may guide patient counseling and discussion of graft prognosis.

Humans

A comparative evaluation of multiple enlarged perivascular space segmentation tools.

BACKGROUND: Enlarged perivascular spaces (ePVS) are a marker of cerebral small vessel disease, potentially reflecting reduced waste clearance. Because manual quantification is unfeasible in large datasets, we developed and evaluated an automated tool. METHODS: Detection Of Regions of Enlarged perivascular Spaces (DORES), a 3D nnU-Net-based deep learning algorithm was developed for ePVS segmentation using T1-weighted and fluid-attenuated inversion recovery magnetic resonance imaging (MRI). DORES was developed in two stages: an initial model trained on 35 manually segmented scans and a final model on 1460 pseudo-labeled sessions from the Vanderbilt Memory and Aging Project (VMAP). A subset of VMAP participants with 3 T brain MRI underwent whole-brain manual ePVS tracing (n = 35, 73 ± 9 years, 51% male) and visual rating (n = 388, 71 ± 8 years, 54% male) by a neuroradiologist. DORES was evaluated and compared against three other segmentation tools using Dice and F1 scores, absolute volume and element differences, correlation, and agreement. External validation used an Alzheimer's Disease Neuroimaging Initiative 3 subset with manual tracings (ADNI3, n = 18, 73 ± 9 years, 67% female). RESULTS: DORES achieved Dice scores of 0.61 ± 0.16 (white matter) and 0.72 ± 0.08 (basal ganglia) in VMAP, with strong correlations and agreement for ePVS count and volume. Performances modestly declined in ADNI3 across algorithms. Scanner-stratified analyses showed stronger correlations for Philips versus Siemens images in the basal ganglia, indicating scanner-dependent differences in measurement consistency. CONCLUSIONS: DORES provides a multimodal nnU-Net-based pipeline for ePVS segmentation in older adults. The model demonstrates robust within-cohort performance and reasonable external validity, though scanner-related effects limit application across sites.

Humans

Molecular characterization of a novel partitivirus harboring an additional third dsRNA segment from Trichoderma harzianum.

We report the complete genome sequence of a novel partitivirus identified from Trichoderma harzianum NFCF092 strain, designated Trichoderma harzianum partitivirus 4 (ThPV4). Unlike canonical members of the family Partitiviridae, which possess a bipartite genome consisting of two double-stranded RNA (dsRNA) segments encoding an RNA-dependent RNA polymerase (RdRP) and a capsid protein (CP), ThPV4 harbors a third dsRNA segment encoding a protein of unknown function. The complete genome consists of dsRNA1 (1,950 bp; encoding the RdRP), dsRNA2 (1,772 bp; encoding the CP), and dsRNA3 (1,629 bp; encoding a protein with unknown function). Sequence analysis shows that each segment possesses a single open reading frame (ORF). The deduced amino acid sequence of the RdRP shows the highest similarity (90.5% identity) to that of Trichoderma gamsii alphapartitivirus 1. Phylogenetic analyses based on the RdRP indicate that ThPV4 clusters within the genus Alphapartitivirus of the family Partitiviridae. To our knowledge. ThPV4 is the first member of the genus Alphapartitivirus identified from T. harzianum to possess an additional, conserved third dsRNA segment.

Phylogeny

A noncanonical role of glycolytic metabolites controlling the timing of mouse embryo segmentation.

Studies on the impact of metabolism on cell fate decisions are seeing a renaissance. However, a key challenge remains to distinguish signaling functions of metabolism from its canonical bioenergetic and biosynthetic roles, which underlie cellular homeostasis. Here, we tackled this challenge using mouse embryonic axis segmentation as an experimental model. First, we found that energetically subminimal amounts of glucose can support ongoing segmentation clock activity, providing evidence that glycolysis exerts a signaling function. Using a dynamical systems approach based on entrainment, we identified fructose 1,6-bisphosphate (FBP) as the potential signaling metabolite. Functionally, we demonstrated that glycolytic flux/FBP control the segmentation clock period and Wnt signaling in an anticorrelated manner. Critically, we showed that the slow segmentation clock phenotype caused by elevated glycolysis is mediated by Wnt signaling rather than cellular bioenergetic and biosynthetic state. Combined, our results demonstrate a modular organization of metabolic functions, revealing a signaling module of glycolysis that can be decoupled from its canonical metabolic functions.

Animals

Performance Profiles of Short DNA Barcode Segments for Family Level Detection of Asteraceae Within Asterales.

Short DNA barcodes may facilitate sequence recovery from degraded material, but their ability to retain target-family identity while excluding related taxa varies among genomic regions. We computationally evaluated 16 nuclear, plastid, and mitochondrial marker regions from 11 Asterales families using 279,956 NCBI locus-record matches and an accession-disjoint discovery/test design. Thirty-one candidate segments of 50-200 bp (mean, 98.55 bp) were screened in discovery data and evaluated for within-Asteraceae sequence recall, differentiation from non-Asteraceae Asterales, in silico primer behavior, phylogenetic placement, and exploratory matching across 808 metadata-defined metagenomic samples. Conserved regions such as matR and rbcL showed high within-Asteraceae identity, whereas ITS1, ITS, and trnH-psbA showed larger differences from related-family backgrounds; ITS2 and ycf1 showed intermediate profiles. Candidate segments were placed within or immediately adjacent to Asteraceae reference branches in segment-specific maximum-likelihood analyses, although support and topology varied among regions. Metadata-defined target-containing groups had higher mean query coverage and identity than background groups; because target presence was not independently verified and no classifier was fitted, these comparisons were descriptive and did not estimate diagnostic accuracy. Definitionally linked sequence statistics were interpreted as structural associations rather than evidence of causal evolutionary mechanisms. These results provide a family-level computational comparison of candidate short segments for Asteraceae detection within Asterales. Species identification, operational marker combinations, threshold robustness, and laboratory performance require validation using taxonomically dense, voucher-linked, and experimentally characterized datasets.

Asteraceae

SGLF-Net:Staged Global-to-Local Cross-Scale Fusion Network for Colonoscopic Polyp Segmentation.

Polyp segmentation in colonoscopy images plays a pivotal role in computer-aided medical diagnosis and the early prevention of colorectal cancer. However, existing methods often suffer from performance degradation when confronted with extreme polyp scale variation and polyp boundary ambiguity. To address these challenges, we propose the Staged Global-to-Local Cross-Scale Fusion Network (SGLF-Net), which adopts a novel staged global-to-local learning paradigm to progressively refine segmentation from coarse global semantics to fine-grained local details. Specifically, the Global Semantic Perception Stage integrates a Swin Transformer Encoder and a Dynamic Attentive Decoder (DAD) to construct comprehensive multi-scale contextual representations. The Local Detail Refinement Stage employs an Edge-aware Dynamic Attentive Decoder (E-DAD) to enhance structural fidelity and boundary precision through explicit edge-guided supervision. Furthermore, we introduce the Cross Spatial-Scale Feature Aggregation and Reconstitution (CSSAR) module, equipped with hybrid attention mechanisms, to facilitate efficient semantic structural interaction between the two cascaded stages. Extensive experiments on five public benchmark datasets demonstrate that SGLF-Net consistently outperforms state-of-the-art methods in both segmentation accuracy and boundary preservation.

Journal Article

SLB-msSIM: A Spectral Library-Based Multiplex Segmented SIM Platform for Single-Cell Proteomic Analysis.

Mass spectrometry (MS)-based single-cell proteomics, while highly challenging, offers unique potential for a wide range of applications to interrogate cellular heterogeneity, trajectories, and phenotypes at a functional level. We report here the development of the spectral library-based multiplex segmented selected ion monitoring (SLB-msSIM) method, a conceptually unique approach with significantly enhanced sensitivity and robustness for single-cell analysis. The single-cell MS data is acquired by a multiplex segmented selected ion monitoring (msSIM) technique, which sequentially applies multiple isolation cycles with the quadrupole using a wide isolation window in each cycle to accumulate and store precursor ions in the C-trap for a single scan in the Orbitrap. Proteomic identification is achieved through spectral matching using a well-defined spectral library. We applied the SLB-msSIM method to interrogate cellular heterogeneity in various pancreatic cancer cell lines, revealing common and distinct functional traits among PANC-1, MIA-PaCa2, AsPc-1, HPAF, and normal HPDE cells. Furthermore, for the first time, our novel data revealed the diverse cell trajectories of individual PANC-1 cells during the induction and reversal of epithelial-mesenchymal transition (EMT). Collectively, our results demonstrate that SLB-msSIM is a highly sensitive and robust platform, applicable to a wide range of instruments for single-cell proteomic studies. SUMMARY: We present the SLB-msSIM method, a conceptually unique approach in mass spectrometry-based single-cell proteomics that significantly enhances sensitivity and robustness. This innovative platform enables detailed analysis of the proteome landscape, capturing cellular heterogeneity, trajectories, and phenotypes at a single-cell resolution. Utilizing the SLB-msSIM technique, we identified both common and distinct functional traits among various pancreatic cancer cell lines and normal cells. Moreover, our study unveiled new insights into the diverse cell trajectories of individual cancer cells during the induction and reversal of epithelial-mesenchymal transition (EMT). In summary, the SLB-msSIM method offers a highly sensitive and robust platform for single-cell proteomic studies, with broad applicability across different instruments.

Single-Cell Analysis

Automated segmentation and length measurement of metacarpal and phalangeal bones for hand radiograph evaluation.

Evaluating hand and wrist radiographs is essential in pediatric endocrinology and clinical genetics, particularly for the assessment of suspected skeletal anomalies. In this study, we present Auto-Bone-Caliper, an automated system for the segmentation and length measurement of metacarpal and phalangeal (M&P) bones, trained and evaluated on public datasets comprising both normal and dysmorphic cases. We first introduce InstanceSAM, a two-stage framework that detects and segments all 19 M&P bones in pediatric hand radiographs, achieving Dice scores of 98.7% for normal bones and 95.0% for dysmorphic bones. We further develop and evaluate three methods for bone-length estimation, identifying a k-means-based approach as the most accurate, with relative errors of 2.2% for normal bones and 4.5% for dysmorphic bones. Our automated pipeline, Auto-Bone-Caliper, integrates InstanceSAM with the k-means-based length-estimation method. To enable scale-independent downstream analyses, we derive relative bone-length measures from the automated measurements. Using these relative measures, we statistically compare measurements obtained using Auto-Bone-Caliper on an independent dataset with a healthy reference catalog of normal bone morphologies, observing a high level of agreement (Wasserstein-1 distance = 0.012). Finally, we demonstrate a potential clinical use case of Auto-Bone-Caliper by obtaining relative metacarpophalangeal pattern profiles for three genetic conditions, namely Turner syndrome, achondroplasia, and pseudohypoparathyroidism. Our results highlight the potential of the Auto-Bone-Caliper to streamline and standardize M&P length measurement, providing an objective and reproducible tool suitable for clinical application.

Humans

Combinatorial genome engineering of pseudorabies virus Bartha by developing a reverse genetic system based on three overlapping genomic segments.

INTRODUCTION: The 138-kilobase genome of pseudorabies virus vaccine strain Bartha K61 harbors many nonessential genes for replication and exhibits remarkable capacity for incorporating foreign genes for therapeutic applications. However, the large size of the Bartha genome complicates its efficient engineering. OBJECTIVES: Development of a reverse genetic system for pseudorabies virus Bartha based on three overlapping genomic segments to facilitate multiplex genome engineering. METHODS: The 138-kb genome of Bartha was split into three overlapping segments (42 kb, 43 kb, and 53 kb), each cloned in a bacterial artificial chromosome (BAC) to facilitate genome engineering. The infectious virus was reconstituted by transfecting the 3 genomic fragments released from the BACs into Vero cells in which a complete virus genome was assembled using 2-kb overlaps between adjacent pieces. RESULTS: Employing the reverse genetic system, we individually deleted 15 candidate nonessential genes and confirmed that 10 were dispensable for viral growth in cell culture. Deletion of 7 nonessential genes had no impact on viral growth, whereas UL47 deletion reduced viral growth rate and deletions of UL44, UL47, or US3 resulted in smaller viral plaques. A total of 45 viral genomes with double deletions of nonessential genes were constructed, among which 22 were successfully rescued into infectious virions. Fifteen double-deletion mutant viruses had a viral titer comparable with the wild-type Bartha, while the remaining 7 showed a lower titer. Additionally, expressions of the mNeonGreen reporter gene at nonessential gene loci were evaluated. Cells infected with recombinant viruses carrying mNeonGreen at 8 loci showed strong green fluorescence, whereas those with mNeonGreen at 2 loci exhibited very weak fluorescence. CONCLUSION: The reverse genetic system developed in this study enables rapid and combinatorial engineering of viruses with the large DNA genome, and will accelerate development of large DNA virus-based therapeutics including live-attenuated vaccines, vector vaccines, and oncolytic herpesviruses.

Herpesvirus 1, Suid

Stromal Hedgehog Signaling Drives Segment-Specific Malignant Transformation of Gastrointestinal Stem Cells by Producing Bone Morphogenetic Protein Antagonists.

BACKGROUND & AIMS: Hedgehog signaling plays a complex role in epithelial-stromal interactions, but its effects on gastrointestinal stem cells mediated by heterogeneous stromal cell populations remain incompletely defined. Here, we investigate how stromal Hedgehog signaling regulates gastric stem cells and tumorigenesis in a segment-specific manner. METHODS: We genetically activated Hedgehog signaling in distinct stromal cell lineages using Col1a2-, Pdgfra-, Gli1-, Acta2-, and Prrx1-CreERT mouse lines, combined with lineage tracing, RNA sequencing, chromatin immunoprecipitation-quantitative polymerase chain reaction, and pharmacologic interventions. Human gastric cancer data from The Cancer Genome Atlas were also analyzed. RESULTS: We show that genetic activation of Hedgehog signaling in stromal cells marked by Col1a2, Pdgfra, or Gli1, but not by Acta2, induces tumorigenesis in the stomach and gastroesophageal junction, but not in the small or large intestine. Hedgehog signaling increases the expression of multiple bone morphogenetic protein antagonists in gastric but not colonic stromal cells, via Gli1-mediated transcription. These bone morphogenetic protein antagonists, in turn, activate Wnt/β-catenin signaling in gastric stem cells, driving their proliferation and initiating gastric cancer expressing CD44 and Sox9, but not Lgr5. Activating bone morphogenetic protein or inhibiting Wnt signaling blocks tumor initiation. Analysis of patient data from The Cancer Genome Atlas reveals elevated Hedgehog signaling in gastric cancers, which correlates with suppressed bone morphogenetic protein signaling. CONCLUSIONS: These findings uncover a gastrointestinal segment-specific oncogenic role for Hedgehog signaling in Col1a2+Acta2- stromal cells, mediated through the bone morphogenetic protein-Wnt-β-catenin axis.

BMP Antagonists

Strategic targeting of Cas9 nickase induces large segmental duplications.

Gene/segmental duplications play crucial roles in genome evolution and variation. Here, we introduce paired nicking-induced amplification (PNAmp) for their experimental induction. PNAmp strategically places two Cas9 nickases upstream and downstream of a replication origin on opposite strands. This configuration directs the sister replication forks initiated from the origin to break at the nicks, generating a pair of one-ended double-strand breaks. If homologous sequences flank the two break sites, then end resection converts them to single-stranded DNAs that readily anneal to drive duplication of the region bounded by the homologous sequences. PNAmp induces duplication of segments as large as ∼1 Mb with efficiencies exceeding 10% in the budding yeast Saccharomyces cerevisiae. Furthermore, appropriate splint DNAs allow PNAmp to duplicate/multiplicate even segments not bounded by homologous sequences. We also provide evidence for PNAmp in mammalian cells. Therefore, PNAmp provides a prototype method to induce structural variations by manipulating replication fork progression.

Saccharomyces cerevisiae