Linear interaction between replication and transcription shapes DNA break dynamics at recurrent DNA break Clusters.

Recurrent DNA break clusters (RDCs) are replication-transcription collision hotspots; many are unique to neural progenitor cells. Through high-resolution replication sequencing and a capture-ligation assay in mouse neural progenitor cells experiencing replication stress, we unravel the replication features dictating RDC location and orientation. Most RDCs occur at the replication forks traversing timing transition regions (TTRs), where sparse replication origins connect unidirectional forks. Leftward-moving forks generate telomere-connected DNA double-strand breaks (DSBs), while rightward-moving forks lead to centromere-connected DSBs. Strand-specific mapping for DNA-bound RNA reveals co-transcriptional dual-strand DNA:RNA hybrids present at a higher density in RDC than in other actively transcribed long genes. In addition, mapping RNA polymerase activity uncovers that head-to-head interactions between replication and transcription machinery result in 60% DSB contribution to the head-on compared to 40% for co-directional. Taken together we reveal TTR as a fragile class and show how the linear interaction between transcription and replication impacts genome stability.

Persistent complement dysregulation with signs of thromboinflammation in active Long Covid.

Long Covid is a debilitating condition of unknown etiology. We performed multimodal proteomics analyses of blood serum from COVID-19 patients followed up to 12 months after confirmed severe acute respiratory syndrome coronavirus 2 infection. Analysis of >6500 proteins in 268 longitudinal samples revealed dysregulated activation of the complement system, an innate immune protection and homeostasis mechanism, in individuals experiencing Long Covid. Thus, active Long Covid was characterized by terminal complement system dysregulation and ongoing activation of the alternative and classical complement pathways, the latter associated with increased antibody titers against several herpesviruses possibly stimulating this pathway. Moreover, markers of hemolysis, tissue injury, platelet activation, and monocyte-platelet aggregates were increased in Long Covid. Machine learning confirmed complement and thromboinflammatory proteins as top biomarkers, warranting diagnostic and therapeutic interrogation of these systems.

Linear Interaction Between Replication and Transcription Shapes DNA Break Dynamics at Recurrent DNA Break Clusters.

Recurrent DNA break clusters (RDCs) are replication-transcription collision hotspots; many are unique to neural progenitor cells. Through high-resolution replication sequencing and a capture-ligation assay in mouse neural progenitor cells experiencing replication stress, we unraveled the replication features dictating RDC location and orientation. Most RDCs occur at the replication forks traversing timing transition regions (TTRs), where sparse replication origins connect unidirectional forks. Leftward-moving forks generate telomere-connected DNA double-strand breaks (DSBs), while rightward-moving forks lead to centromere-connected DSBs. Strand-specific mapping for DNA-bound RNA revealed co-transcriptional dual-strand DNA:RNA hybrids present at a higher density in RDC than in other actively transcribed long genes. In addition, mapping RNA polymerase activity revealed that head-to-head interactions between replication and transcription machinery resulted in 60% DSB contribution to the head-on compared to 40% for co-directional. Our findings revealed TTR as a novel fragile class and highlighted how the linear interaction between transcription and replication impacts genome stability.

networkGWAS: a network-based approach to discover genetic associations.

MOTIVATION: While the search for associations between genetic markers and complex traits has led to the discovery of tens of thousands of trait-related genetic variants, the vast majority of these only explain a small fraction of the observed phenotypic variation. One possible strategy to overcome this while leveraging biological prior is to aggregate the effects of several genetic markers and to test entire genes, pathways or (sub)networks of genes for association to a phenotype. The latter, network-based genome-wide association studies, in particular suffer from a vast search space and an inherent multiple testing problem. As a consequence, current approaches are either based on greedy feature selection, thereby risking that they miss relevant associations, or neglect doing a multiple testing correction, which can lead to an abundance of false positive findings. RESULTS: To address the shortcomings of current approaches of network-based genome-wide association studies, we propose networkGWAS, a computationally efficient and statistically sound approach to network-based genome-wide association studies using mixed models and neighborhood aggregation. It allows for population structure correction and for well-calibrated P-values, which are obtained through circular and degree-preserving network permutations. networkGWAS successfully detects known associations on diverse synthetic phenotypes, as well as known and novel genes in phenotypes from Saccharomycescerevisiae and Homo sapiens. It thereby enables the systematic combination of gene-based genome-wide association studies with biological network information. AVAILABILITY AND IMPLEMENTATION: https://github.com/BorgwardtLab/networkGWAS.git.

Higher-order genetic interaction discovery with network-based biological priors.

MOTIVATION: Complex phenotypes, such as many common diseases and morphological traits, are controlled by multiple genetic factors, namely genetic mutations and genes, and are influenced by environmental conditions. Deciphering the genetics underlying such traits requires a systemic approach, where many different genetic factors and their interactions are considered simultaneously. Many association mapping techniques available nowadays follow this reasoning, but have some severe limitations. In particular, they require binary encodings for the genetic markers, forcing the user to decide beforehand whether to use, e.g. a recessive or a dominant encoding. Moreover, most methods cannot include any biological prior or are limited to testing only lower-order interactions among genes for association with the phenotype, potentially missing a large number of marker combinations. RESULTS: We propose HOGImine, a novel algorithm that expands the class of discoverable genetic meta-markers by considering higher-order interactions of genes and by allowing multiple encodings for the genetic variants. Our experimental evaluation shows that the algorithm has a substantially higher statistical power compared to previous methods, allowing it to discover genetic mutations statistically associated with the phenotype at hand that could not be found before. Our method can exploit prior biological knowledge on gene interactions, such as protein-protein interaction networks, genetic pathways, and protein complexes, to restrict its search space. Since computing higher-order gene interactions poses a high computational burden, we also develop a more efficient search strategy and support computation to make our approach applicable in practice, leading to substantial runtime improvements compared to state-of-the-art methods. AVAILABILITY AND IMPLEMENTATION: Code and data are available at https://github.com/BorgwardtLab/HOGImine.

Biological network analysis with deep learning.

Recent advancements in experimental high-throughput technologies have expanded the availability and quantity of molecular data in biology. Given the importance of interactions in biological processes, such as the interactions between proteins or the bonds within a chemical compound, this data is often represented in the form of a biological network. The rise of this data has created a need for new computational tools to analyze networks. One major trend in the field is to use deep learning for this goal and, more specifically, to use methods that work with networks, the so-called graph neural networks (GNNs). In this article, we describe biological networks and review the principles and underlying algorithms of GNNs. We then discuss domains in bioinformatics in which graph neural networks are frequently being applied at the moment, such as protein function prediction, protein-protein interaction prediction and in silico drug discovery and development. Finally, we highlight application areas such as gene regulatory networks and disease diagnosis where deep learning is emerging as a new tool to answer classic questions like gene interaction prediction and automatic disease prediction from data.

Histone Deacetylases (HDACs): Evolution, Specificity, Role in Transcriptional Complexes, and Pharmacological Actionability.

Histone deacetylases (HDACs) are evolutionary conserved enzymes which operate by removing acetyl groups from histones and other protein regulatory factors, with functional consequences on chromatin remodeling and gene expression profiles. We provide here a review on the recent knowledge accrued on the zinc-dependent HDAC protein family across different species, tissues, and human pathologies, specifically focusing on the role of HDAC inhibitors as anti-cancer agents. We will investigate the chemical specificity of different HDACs and discuss their role in the human interactome as members of chromatin-binding and regulatory complexes.

Results for tricuspid valve surgery in adults with congenital heart disease other than Ebstein's anomaly†.

OBJECTIVES: Tricuspid valve (TV) surgery in the adult with congenital heart disease (ACHD) is a frequently performed procedure. The aim of this study was to analyse postoperative and medium-term outcomes. METHODS: We conducted a single-centre retrospective study of patients with ACHD who underwent TV surgery (January 2000-December 2016); patients with Ebstein's anomalies were excluded. Operative and clinical records were reviewed. Outcomes considered were survival, grade of insufficiency/stenosis and TV reoperation at follow-up. RESULTS: A total of 128 patients with ACHD had TV surgery for functional regurgitation (n = 95), dysplasia (n = 23) and systemic TV (n = 10). Median age was 40.8 years [interquartile range (IQR) 25.3]; 55.5% were men. Preoperative regurgitation was classified as mild (n = 8), moderate (n = 47) and severe (n = 70). The TV was repaired in 109 as follows: ring annuloplasty (n = 43), de Vega annuloplasty (n = 29), Wooler annuloplasty (n = 13), commissural plasty (n = 9), Kay annuloplasty (n = 7) and others (n = 8). The TV was replaced in 19 patients with biological (n = 10) and mechanical (n = 9) prostheses. The median hospital stay was 12 days (IQR 10). The overall mortality rate was 8.6% (n = 11): 2 hospital deaths (1.6%) and 9 late deaths. Survival was 93% [95% confidence interval (CI) 85-97%] at 5 years and 83% (95% CI 70-91%) at 10 years. The median follow-up period was 4.95 years (IQR 7.7) with 1 TV reoperation. Echocardiographic assessment showed ≥moderate regurgitation in 34 (34.3%) patients. Suture plasty had a significantly higher incidence of TV regurgitation ≥moderate compared to ring annuloplasty (48.9% vs 26.3%; P = 0.033). CONCLUSIONS: TV surgery in the ACHD is frequently associated with other main procedures. Stabilizing the TV annulus with a prosthetic ring guarantees lower recurrence of moderate to severe regurgitation compared to suture plasty repair.