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Despite advances in cancer genomics, radiotherapy is still prescribed on the basis of an empirical one-size-fits-all paradigm. Previously, we proposed a novel algorithm using the genomic-adjusted radiation dose (GARD) model to personalise prescription of radiation dose on the basis of the biological effect of a given physical dose of radiation, calculated using individual tumour genomics. We hypothesise that GARD will reveal interpatient heterogeneity associated with opportunities to improve outcomes compared with physical dose of radiotherapy alone. We aimed to test this hypothesis and investigate the GARD-based radiotherapy dosing paradigm. We did a pooled, pan-cancer analysis of 11 previously published clinical cohorts of unique patients with seven different types of cancer, which are all available cohorts with the data required to calculate GARD, together with clinical outcome. The included cancers were breast cancer, head and neck cancer, non-small-cell lung cancer, pancreatic cancer, endometrial cancer, melanoma, and glioma. Our dataset comprised 1615 unique patients, of whom 1298 (982 with radiotherapy, 316 without radiotherapy) were assessed for time to first recurrence and 677 patients (424 with radiotherapy and 253 without radiotherapy) were assessed for overall survival. We analysed two clinical outcomes of interest: time to first recurrence and overall survival. We used Cox regression, stratified by cohort, to test the association between GARD and outcome with separate models using dose of radiation and sham-GARD (ie, patients treated without radiotherapy, but modelled as having a standard-of-care dose of radiotherapy) for comparison. We did interaction tests between GARD and treatment (with or without radiotherapy) using the Wald statistic. Pooled analysis of all available data showed that GARD as a continuous variable is associated with time to first recurrence (hazard ratio [HR] 0·98 [95% CI 0·97–0·99]; p=0·0017) and overall survival (0·97 [0·95–0·99]; p=0·0007). The interaction test showed the effect of GARD on overall survival depends on whether or not that patient received radiotherapy (Wald statistic p=0·011). The interaction test for GARD and radiotherapy was not significant for time to first recurrence (Wald statistic p=0·22). The HR for physical dose of radiation was 0·99 (95% CI 0·97–1·01; p=0·53) for time to first recurrence and 1·00 (0·96–1·04; p=0·95) for overall survival. The HR for sham-GARD was 1·00 (0·97–1·03; p=1·00) for time to first recurrence and 1·00 (0·98–1·02; p=0·87) for overall survival. The biological effect of radiotherapy, as quantified by GARD, is significantly associated with time to first recurrence and overall survival for patients with cancer treated with radiation. It is predictive of radiotherapy benefit, and physical dose of radiation is not. We propose integration of genomics into radiation dosing decisions, using a GARD-based framework, as the new paradigm for personalising radiotherapy prescription dose. None. [Display omitted]

The genus Legionella comprises 65 species, among which Legionella pneumophila is a human pathogen causing severe pneumonia. To understand the evolution of an environmental to an accidental human pathogen, we have functionally analyzed 80 Legionella genomes spanning 58 species. Uniquely, an immense repository of 18,000 secreted proteins encoding 137 different eukaryotic-like domains and over 200 eukaryotic-like proteins is paired with a highly conserved type IV secretion system (T4SS). Specifically, we show that eukaryotic Rho- and Rab-GTPase domains are found nearly exclusively in eukaryotes and Legionella. Translocation assays for selected Rab-GTPase proteins revealed that they are indeed T4SS secreted substrates. Furthermore, F-box, U-box, and SET domains were present in >70% of all species, suggesting that manipulation of host signal transduction, protein turnover, and chromatin modification pathways are fundamental intracellular replication strategies for legionellae. In contrast, the Sec-7 domain was restricted to L. pneumophila and seven other species, indicating effector repertoire tailoring within different amoebae. Functional screening of 47 species revealed 60% were competent for intracellular replication in THP-1 cells, but interestingly, this phenotype was associated with diverse effector assemblages. These data, combined with evolutionary analysis, indicate that the capacity to infect eukaryotic cells has been acquired independently many times within the genus and that a highly conserved yet versatile T4SS secretes an exceptional number of different proteins shaped by interdomain gene transfer. Furthermore, we revealed the surprising extent to which legionellae have coopted genes and thus cellular functions from their eukaryotic hosts, providing an understanding of how dynamic reshuffling and gene acquisition have led to the emergence of major human pathogens.

The rapid development of therapies for severe and rare genetic conditions underlines the need to incorporate first-tier genetic testing into newborn screening (NBS) programs. A workflow was developed to screen newborns for 165 treatable pediatric disorders by deep sequencing of regions of interest in 405 genes. The prospective observational BabyDetect pilot project was launched in September 2022 in a maternity ward of a public hospital in the Liege area, Belgium. In this ongoing observational study, 4,260 families have been informed of the project, and 3,847 consented to participate. To date, 71 disease cases have been identified, 30 of which were not detected by conventional NBS. Glucose-6-phosphate dehydrogenase deficiency was the most frequent disorder detected, with 44 positive individuals. Of the remaining 27 cases, 17 were recessive disorders. We also identified one false-positive case in a newborn in whom two variants in the AGXT gene were identified, which were subsequently shown to be located on the maternal allele. Nine heterozygous variants were identified in genes associated with dominant conditions. Results from the BabyDetect project demonstrate the importance of integrating biochemical and genomic methods in NBS programs. Challenges must be addressed in variant interpretation within a presymptomatic population and in result reporting and diagnostic confirmation. The BabyDetect project offered expanded newborn genomic screening covering more than 400 genes to 4,260 families, leading to 71 clinical diagnoses.

As terabytes of multi-omics data are being generated, there is an ever-increasing need for methods facilitating the integration and interpretation of such data. Current multi-omics integration methods typically output lists, clusters, or subnetworks of molecules related to an outcome. Even with expert domain knowledge, discerning the biological processes involved is a time-consuming activity. Here we propose PathIntegrate, a method for integrating multi-omics datasets based on pathways, designed to exploit knowledge of biological systems and thus provide interpretable models for such studies. PathIntegrate employs single-sample pathway analysis to transform multi-omics datasets from the molecular to the pathway-level, and applies a predictive single-view or multi-view model to integrate the data. Model outputs include multi-omics pathways ranked by their contribution to the outcome prediction, the contribution of each omics layer, and the importance of each molecule in a pathway. Using semi-synthetic data we demonstrate the benefit of grouping molecules into pathways to detect signals in low signal-to-noise scenarios, as well as the ability of PathIntegrate to precisely identify important pathways at low effect sizes. Finally, using COPD and COVID-19 data we showcase how PathIntegrate enables convenient integration and interpretation of complex high-dimensional multi-omics datasets. PathIntegrate is available as an open-source Python package.

To gain insight into how genomic information is translated into cellular and developmental programs, the Drosophila model organism Encyclopedia of DNA Elements (modENCODE) project is comprehensively mapping transcripts, histone modifications, chromosomal proteins, transcription factors, replication proteins and intermediates, and nucleosome properties across a developmental time course and in multiple cell lines. We have generated more than 700 data sets and discovered protein-coding, noncoding, RNA regulatory, replication, and chromatin elements, more than tripling the annotated portion of the Drosophila genome. Correlated activity patterns of these elements reveal a functional regulatory network, which predicts putative new functions for genes, reveals stage- and tissue-specific regulators, and enables gene-expression prediction. Our results provide a foundation for directed experimental and computational studies in Drosophila and related species and also a model for systematic data integration toward comprehensive genomic and functional annotation.

Background Structural variants (SVs) are chromosomal segments that differ between genomes, such as deletions, duplications, insertions, inversions and translocations. The genomics revolution enabled the discovery of sub-microscopic SVs via array and whole-genome sequencing (WGS) data, paving the way to unravel the functional impact of SVs. Recent human expression QTL mapping studies demonstrated that SVs play a disproportionally large role in altering gene expression, underlining the importance of including SVs in genetic analyses. Therefore, this study aimed to generate and explore a high-quality bovine SV catalogue exploiting a unique cattle family cohort data (total 266 samples, forming 127 trios). Results We curated 13,731 SVs segregating in the population, consisting of 12,201 deletions, 1,509 duplications, and 21 multi-allelic CNVs (> 50-bp). Of these, we validated a subset of copy number variants (CNVs) utilising a direct genotyping approach in an independent cohort, indicating that at least 62% of the CNVs are true variants, segregating in the population. Among gene-disrupting SVs, we prioritised two likely high impact duplications, encompassing ORM1 and POPDC3 genes, respectively. Liver expression QTL mapping results revealed that these duplications are likely causing altered gene expression, confirming the functional importance of SVs. Although most of the accurately genotyped CNVs are tagged by single nucleotide polymorphisms (SNPs) ascertained in WGS data, most CNVs were not captured by individual SNPs obtained from a 50K genotyping array. Conclusion We generated a high-quality SV catalogue exploiting unique whole genome sequenced bovine family cohort data. Two high impact duplications upregulating the ORM1 and POPDC3 are putative candidates for postpartum feed intake and hoof health traits, thus warranting further investigation. Generally, CNVs were in low LD with SNPs on the 50K array. Hence, it remains crucial to incorporate CNVs via means other than tagging SNPs, such as investigation of tagging haplotypes, direct imputation of CNVs, or direct genotyping as done in the current study. The SV catalogue and the custom genotyping array generated in the current study will serve as valuable resources accelerating utilisation of full spectrum of genetic variants in bovine genomes.

In clinical exome sequencing (cES), the American College of Medical Genetics and Genomics recommends limiting variant interpretation to established human-disease genes. The diagnostic yield of cES in intellectual disability and/or multiple congenital anomalies (ID/MCA) is currently about 30%. Though the results may seem acceptable for rare diseases, they mean that 70% of affected individuals remain genetically undiagnosed. Further analysis extended to all mutated genes in a research environment is a valuable strategy for improving diagnostic yields. This study presents the results of systematic research reanalysis of negative cES in a cohort of 313 individuals with ID/MCA. We identified 17 new genes not related to human disease, implicated 22 non-OMIM disease-causing genes recently or previously rarely related to disease, and described 1 new phenotype associated with a known gene. Twenty-six candidate genes were identified and are waiting for future recurrence. Overall, we diagnose 15% of the individuals with initial negative cES, increasing the diagnostic yield from 30% to more than 40% (or 46% if strong candidate genes are considered). This study demonstrates the power of such extended research reanalysis to increase scientific knowledge of rare diseases. These novel findings can then be applied in the field of diagnostics.

Cancer is a leading cause of mortality worldwide, with approximately 19.6 million new cases and 10 million deaths reported in 2020. Exercise interventions have demonstrated positive effects on physical and mental health in cancer patients, yet there is limited evidence on the efficacy of tailored, high-intensity exercise programs designed using genomic data. This protocol outlines a study aimed at integrating genomic analysis and personalized exercise interventions to improve health outcomes and reduce cancer-related risk factors. This study aims to evaluate the feasibility and potential impact of a personalized exercise intervention delivered through the EXESALUS mobile application. The program integrates genomic information to tailor exercise regimens for cancer prevention, muscle strength improvement, and quality-of-life enhancement. This is a protocol for a 3-month, parallel-group, randomized controlled trial involving 500 participants, including 100 cancer patients undergoing treatment or rehabilitation and 300 non-cancer participants with elevated disease risk. Participants will engage in the EXESALUS program, which includes low-, moderate-, and high-intensity exercise tailored to genomic profiles, supported by exercise counseling and wearable device feedback. Biospecimens (blood, urine, and oral epithelial cells) will be collected at baseline, 6 weeks, and 3 months to assess genomic variations and physiological changes. Primary outcomes include physical performance (SPPB), muscle strength (1RM and peak power), and skeletal muscle mass (DXA). Secondary outcomes will evaluate mental health indicators such as fatigue (FACIT-F), resilience, anxiety, depression, and quality of life. This study will provide a detailed framework for implementing ICT-based personalized exercise interventions that incorporate genomic analysis. The EXESALUS program is expected to highlight the potential of tailored high-intensity exercise as a preventive and therapeutic strategy for cancer patients and individuals at risk of chronic diseases. The findings of this protocol will contribute to the development of precision medicine approaches for cancer prevention and management, emphasizing the scalability and utility of ICT-based solutions in health promotion. This study was registered in the Korean Clinical Trials Registry (KCT0010187).

Brain imaging genomics has manifested considerable potential in illuminating the genetic determinants of human brain structure and function. This has propelled us to develop the GIANT (Genetically Informed brAiN aTlas) that accounts for genetic and neuroanatomical variations simultaneously. Integrating voxel-wise heritability and spatial proximity, GIANT clusters brain voxels into genetically informed regions, while retaining fundamental anatomical knowledge. Compared to conventional (non-genetics) brain atlases, GIANT exhibits smaller intra-region variations and larger inter-region variations in terms of voxel-wise heritability. As a result, GIANT yields increased regional SNP heritability, enhanced polygenicity, and its polygenic risk score explains more brain volumetric variation than traditional neuroanatomical brain atlases. We provide extensive validation to GIANT and demonstrate its neuroanatomical validity, confirming its generalizability across populations with diverse genetic ancestries and various brain conditions. Furthermore, we present a comprehensive genetic architecture of the GIANT regions, covering their functional annotation at the molecular levels, their associations with other complex traits/diseases, and the genetic and phenotypic correlations among GIANT-defined imaging endophenotypes. In summary, GIANT constitutes a brain atlas that captures the complexity of genetic and neuroanatomical heterogeneity, thereby enhancing the discovery power and applicability of imaging genomics investigations in biomedical science. GIANT, a genetically informed brain atlas, integrates genetic heritability with neuroanatomy. It shows strong neuroanatomical validity and surpasses traditional atlases in discovery power for brain imaging genomics.