Explore the vast range of titles available.

The platinum-based drug cisplatin is a widely used first-line therapy for several cancers. Cisplatin interacts with DNA mainly in the form of Pt-d(GpG) di-adduct, which stalls cell proliferation and activates DNA damage response. Although cisplatin shows a broad spectrum of anticancer activity, its utility is limited due to acquired drug resistance and toxicity to non-targeted tissues. Here, by integrating genome-wide high-throughput Damage-seq, XR-seq, and RNA-seq approaches, along with publicly available epigenomic data, we systematically study the genome-wide profiles of cisplatin damage formation and excision repair in mouse kidney, liver, lung and spleen. We find different DNA damage and repair spectra across mouse organs, which are associated with tissue-specific transcriptomic and epigenomic profiles. The framework and the multi-omics data we present here constitute an unbiased foundation for understanding the mechanisms of cellular response to cisplatin. Our approach should be applicable for studying drug resistance and for tailoring cancer chemotherapy regimens. Cisplatin, a platinum chemotherapeutic agent, is widely used to treat several cancers. Here Yimit et al. revert to genome-wide approaches to map and analyze cisplatin damage formation and excision repair with single nucleotide resolution across different mice organs following platinum treatment.

Cancer is a disease driven by evolutionary selection on somatic genetic and epigenetic alterations. Here, we propose Canopy, a method for inferring the evolutionary phylogeny of a tumor using both somatic copy number alterations and single-nucleotide alterations from one or more samples derived from a single patient. Canopy is applied to bulk sequencing datasets of both longitudinal and spatial experimental designs and to a transplantable metastasis model derived from human cancer cell line MDA-MB-231. Canopy successfully identifies cell populations and infers phylogenies that are in concordance with existing knowledge and ground truth. Through simulations, we explore the effects of key parameters on deconvolution accuracy and compare against existing methods. Canopy is an open-source R package available at https://cran.r-project.org/web/packages/Canopy/.

More than a decade of genome-wide association studies (GWASs) have identified genetic risk variants that are significantly associated with complex traits. Emerging evidence suggests that the function of trait-associated variants likely acts in a tissue- or cell-type-specific fashion. Yet, it remains challenging to prioritize trait-relevant tissues or cell types to elucidate disease etiology. Here, we present EPIC (cEll tyPe enrIChment), a statistical framework that relates large-scale GWAS summary statistics to cell-type-specific gene expression measurements from single-cell RNA sequencing (scRNA-seq). We derive powerful gene-level test statistics for common and rare variants, separately and jointly, and adopt generalized least squares to prioritize trait-relevant cell types while accounting for the correlation structures both within and between genes. Using enrichment of loci associated with four lipid traits in the liver and enrichment of loci associated with three neurological disorders in the brain as ground truths, we show that EPIC outperforms existing methods. We apply our framework to multiple scRNA-seq datasets from different platforms and identify cell types underlying type 2 diabetes and schizophrenia. The enrichment is replicated using independent GWAS and scRNA-seq datasets and further validated using PubMed search and existing bulk case-control testing results.

With the continuous advancement of 3D scanning technology, point cloud data of mechanical components has found widespread applications in industrial design, manufacturing, and repair. However, due to limitations in scanning precision and acquisition conditions, point cloud data often exhibit sparsity and missing information. This issue is particularly challenging when dealing with mechanically complex geometric shapes, where the missing portions frequently contain crucial details, posing significant difficulties for data completion. To effectively recover these missing parts while maintaining the accuracy of both global morphology and local details, this paper proposes a Hierarchical Shape-Preserving Completion Network (HSPC-Net). This approach integrates a multi-receptive field Transformer with a cross-modal geometric information fusion strategy, enabling the precise restoration of local details of mechanical components at multiple scales. Additionally, it leverages 2D image information to assist in the completion of 3D point clouds, significantly enhancing completion accuracy and robustness. Experimental results on ShapeNet and mechanical component point cloud datasets demonstrate that HSPC-Net outperforms existing state-of-the-art methods in terms of completion accuracy, structural consistency, and detail recovery.

Allele-specific expression is traditionally studied by bulk RNA sequencing, which measures average expression across cells. Single-cell RNA sequencing allows the comparison of expression distribution between the two alleles of a diploid organism and the characterization of allele-specific bursting. Here, we propose SCALE to analyze genome-wide allele-specific bursting, with adjustment of technical variability. SCALE detects genes exhibiting allelic differences in bursting parameters and genes whose alleles burst non-independently. We apply SCALE to mouse blastocyst and human fibroblast cells and find that cis control in gene expression overwhelmingly manifests as differences in burst frequency.

Artificial intelligence provides an opportunity to try to redefine disease subtypes based on similar pathobiology. Using a machine-learning algorithm (Subtype and Stage Inference) with cross-sectional MRI from 296 individuals with focal epilepsy originating from the temporal lobe (TLE) and 91 healthy controls, we show phenotypic heterogeneity in the pathophysiological progression of TLE. This study was registered in the Chinese Clinical Trials Registry (number: ChiCTR2200062562). We identify two hippocampus-predominant phenotypes, characterized by atrophy beginning in the left or right hippocampus; a third cortex-predominant phenotype, characterized by hippocampus atrophy after the neocortex; and a fourth phenotype without atrophy but amygdala enlargement. These four subtypes are replicated in the independent validation cohort (109 individuals). These subtypes show differences in neuroanatomical signature, disease progression and epilepsy characteristics. Five-year follow-up observations of these individuals reveal differential seizure outcomes among subtypes, indicating that specific subtypes may benefit from temporal surgery or pharmacological treatment. These findings suggest a diverse pathobiological basis underlying focal epilepsy that potentially yields to stratification and prognostication – a necessary step for precise medicine. Brain imaging-based disease progression modelling is a promising technique for disease stratification. Here the authors characterize distinct ‘trajectories’ of brain atrophy in temporal lobe epilepsy and identify four subtypes with distinct neuroanatomical signatures.

Bulky DNA adducts such as those induced by ultraviolet light are removed from the genomes of multicellular organisms by nucleotide excision repair, which occurs through two distinct mechanisms, global repair, requiring the DNA damage recognition-factor XPC (xeroderma pigmentosum complementation group C), and transcription-coupled repair (TCR), which does not. TCR is initiated when elongating RNA polymerase II encounters DNA damage, and thus analysis of genome-wide excision repair in XPC-mutants only repairing by TCR provides a unique opportunity to map transcription events missed by methods dependent on capturing RNA transcription products and thus limited by their stability and/or modifications (5’-capping or 3’-polyadenylation). Here, we have performed eXcision Repair-sequencing (XR-seq) in the model organism Caenorhabditis elegans to generate genome-wide repair maps in a wild-type strain with normal excision repair, a strain lacking TCR ( csb-1 ), and a strain that only repairs by TCR ( xpc-1 ). Analysis of the intersections between the xpc-1 XR-seq repair maps with RNA-mapping datasets (RNA-seq, long- and short-capped RNA-seq) reveal previously unrecognized sites of transcription and further enhance our understanding of the genome of this important model organism.

Complete loss of BRCA1 or BRCA2 function is associated with sensitivity to DNA damaging agents. However, not all BRCA1 and BRCA2 germline mutation-associated tumors respond. Herein we report analyses of 160 BRCA1 and BRCA2 germline mutation-associated breast and ovarian tumors. Retention of the normal BRCA1 or BRCA2 allele (absence of locus-specific loss of heterozygosity (LOH)) is observed in 7% of BRCA1 ovarian, 16% of BRCA2 ovarian, 10% of BRCA1 breast, and 46% of BRCA2 breast tumors. These tumors have equivalent homologous recombination deficiency scores to sporadic tumors, significantly lower than scores in tumors with locus-specific LOH (ovarian, P  = 0.0004; breast P  < 0.0001, two-tailed Student’s t -test). Absence of locus-specific LOH is associated with decreased overall survival in ovarian cancer patients treated with platinum chemotherapy ( P  = 0.01, log-rank test). Locus-specific LOH may be a clinically useful biomarker to predict primary resistance to DNA damaging agents in patients with germline BRCA1 and BRCA2 mutations. Most tumours associated with germline BRCA1/BRCA2 loss of function mutations respond to DNA damaging agents, however, some do not. Herein, the authors identify that a subset of breast/ovarian tumors retain a normal allele, which is associated with decreased overall survival after DNA damage-inducing platinum chemotherapy.

Trim24 has been implicated in inflammatory processes and lipid metabolism, yet its role in atherosclerosis (AS) remains insufficiently explored. This study aimed to evaluate the diagnostic value of serum Trim24 in AS and its association with disease severity. This prospective case-control study included 137 AS patients and 137 healthy controls. Serum Trim24 and sCD163 levels were measured using ELISA. Clinical characteristics, including lipid profiles, inflammatory markers, and the Gensini score, were collected. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors for AS, severe AS, and severe stenosis. Receiver operating characteristic (ROC) curve analysis was employed to evaluate the diagnostic performance of Trim24. Serum Trim24 levels were significantly higher in AS patients compared to healthy controls. Within the AS group, Trim24 levels were higher in patients with severe AS and severe stenosis compared to their non-severe counterparts. Trim24 exhibited strong diagnostic performance for AS (AUC = 0.928), severe AS (AUC = 0.826), and severe stenosis (AUC = 0.928). Additionally, sCD163 levels were significantly lower in AS patients, especially in those with high Trim24 expression, severe AS, and severe stenosis. ROC analysis showed moderate diagnostic value of sCD163 for AS and its severity (AUC = 0.708–0.778). Curve Estimation revealed a negative correlation between Trim24 and sCD163. Multivariate logistic regression identified Trim24 was an independent risk factors for AS, severe AS, and severe stenosis, respectively. Elevated serum Trim24 levels are associated with increased AS risk and severity, likely through modulation of macrophage polarization. Trim24 demonstrates strong potential as a diagnostic biomarker for AS and its progression, supporting further investigation in larger, more diverse populations.

High-throughput DNA sequencing enables detection of copy number variations (CNVs) on the genome-wide scale with finer resolution compared to array-based methods but suffers from biases and artifacts that lead to false discoveries and low sensitivity. We describe CODEX2, as a statistical framework for full-spectrum CNV profiling that is sensitive for variants with both common and rare population frequencies and that is applicable to study designs with and without negative control samples. We demonstrate and evaluate CODEX2 on whole-exome and targeted sequencing data, where biases are the most prominent. CODEX2 outperforms existing methods and, in particular, significantly improves sensitivity for common CNVs.