Whole-genome doubling drives oncogenic loss of chromatin segregation.

Whole-genome doubling (WGD) is a recurrent event in human cancers and it promotes chromosomal instability and acquisition of aneuploidies1-8. However, the three-dimensional organization of chromatin in WGD cells and its contribution to oncogenic phenotypes are currently unknown. Here we show that in p53-deficient cells, WGD induces loss of chromatin segregation (LCS). This event is characterized by reduced segregation between short and long chromosomes, A and B subcompartments and adjacent chromatin domains. LCS is driven by the downregulation of CTCF and H3K9me3 in cells that bypassed activation of the tetraploid checkpoint. Longitudinal analyses revealed that LCS primes genomic regions for subcompartment repositioning in WGD cells. This results in chromatin and epigenetic changes associated with oncogene activation in tumours ensuing from WGD cells. Notably, subcompartment repositioning events were largely independent of chromosomal alterations, which indicates that these were complementary mechanisms contributing to tumour development and progression. Overall, LCS initiates chromatin conformation changes that ultimately result in oncogenic epigenetic and transcriptional modifications, which suggests that chromatin evolution is a hallmark of WGD-driven cancer.

Investigating proton tunneling dynamics in the time-dependent Schrödinger equation.

Understanding the temporal evolution of the wave function in the proton tunneling reactions allows us to make theoretical predictions on the possible femtosecond spectroscopy patterns. However, the analytical solution of the time-dependent Schrödinger equation of complex molecules is challenging and requires a high computational cost. In this study, we solve the time-dependent Schrödinger equation using the Fourier grid Hamiltonian method, highlighting its simplicity of calculation, even for multidimensional tunneling reactions. The obtained model is applied to studying malonaldehyde's two-dimensional intramolecular proton tunneling, comparing the results with those obtained using other computational methods.

Federated sharing and processing of genomic datasets for tertiary data analysis.

MOTIVATION: With the spreading of biological and clinical uses of next-generation sequencing (NGS) data, many laboratories and health organizations are facing the need of sharing NGS data resources and easily accessing and processing comprehensively shared genomic data; in most cases, primary and secondary data management of NGS data is done at sequencing stations, and sharing applies to processed data. Based on the previous single-instance GMQL system architecture, here we review the model, language and architectural extensions that make the GMQL centralized system innovatively open to federated computing. RESULTS: A well-designed extension of a centralized system architecture to support federated data sharing and query processing. Data is federated thanks to simple data sharing instructions. Queries are assigned to execution nodes; they are translated into an intermediate representation, whose computation drives data and processing distributions. The approach allows writing federated applications according to classical styles: centralized, distributed or externalized. AVAILABILITY: The federated genomic data management system is freely available for non-commercial use as an open source project at http://www.bioinformatics.deib.polimi.it/FederatedGMQLsystem/. CONTACT: {arif.canakoglu, pietro.pinoli}@polimi.it.

Exploring chromatin conformation and gene co-expression through graph embedding.

MOTIVATION: The relationship between gene co-expression and chromatin conformation is of great biological interest. Thanks to high-throughput chromosome conformation capture technologies (Hi-C), researchers are gaining insights on the tri-dimensional organization of the genome. Given the high complexity of Hi-C data and the difficult definition of gene co-expression networks, the development of proper computational tools to investigate such relationship is rapidly gaining the interest of researchers. One of the most fascinating questions in this context is how chromatin topology correlates with gene co-expression and which physical interaction patterns are most predictive of co-expression relationships. RESULTS: To address these questions, we developed a computational framework for the prediction of co-expression networks from chromatin conformation data. We first define a gene chromatin interaction network where each gene is associated to its physical interaction profile; then, we apply two graph embedding techniques to extract a low-dimensional vector representation of each gene from the interaction network; finally, we train a classifier on gene embedding pairs to predict if they are co-expressed. Both graph embedding techniques outperform previous methods based on manually designed topological features, highlighting the need for more advanced strategies to encode chromatin information. We also establish that the most recent technique, based on random walks, is superior. Overall, our results demonstrate that chromatin conformation and gene regulation share a non-linear relationship and that gene topological embeddings encode relevant information, which could be used also for downstream analysis. AVAILABILITY AND IMPLEMENTATION: The source code for the analysis is available at: https://github.com/marcovarrone/gene-expression-chromatin. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Processing of big heterogeneous genomic datasets for tertiary analysis of Next Generation Sequencing data.

MOTIVATION: We previously proposed a paradigm shift in genomic data management, based on the Genomic Data Model (GDM) for mediating existing data formats and on the GenoMetric Query Language (GMQL) for supporting, at a high level of abstraction, data extraction and the most common data-driven computations required by tertiary data analysis of Next Generation Sequencing datasets. Here, we present a new GMQL-based system with enhanced accessibility, portability, scalability and performance. RESULTS: The new system has a well-designed modular architecture featuring: (i) an intermediate representation supporting many different implementations (including Spark, Flink and SciDB); (ii) a high-level technology-independent repository abstraction, supporting different repository technologies (e.g., local file system, Hadoop File System, database or others); (iii) several system interfaces, including a user-friendly Web-based interface, a Web Service interface, and a programmatic interface for Python language. Biological use case examples, using public ENCODE, Roadmap Epigenomics and TCGA datasets, demonstrate the relevance of our work. AVAILABILITY AND IMPLEMENTATION: The GMQL system is freely available for non-commercial use as open source project at: http://www.bioinformatics.deib.polimi.it/GMQLsystem/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PyGMQL: scalable data extraction and analysis for heterogeneous genomic datasets.

BACKGROUND: With the growth of available sequenced datasets, analysis of heterogeneous processed data can answer increasingly relevant biological and clinical questions. Scientists are challenged in performing efficient and reproducible data extraction and analysis pipelines over heterogeneously processed datasets. Available software packages are suitable for analyzing experimental files from such datasets one by one, but do not scale to thousands of experiments. Moreover, they lack proper support for metadata manipulation. RESULTS: We present PyGMQL, a novel software for the manipulation of region-based genomic files and their relative metadata, built on top of the GMQL genomic big data management system. PyGMQL provides a set of expressive functions for the manipulation of region data and their metadata that can scale to arbitrary clusters and implicitly apply to thousands of files, producing millions of regions. PyGMQL provides data interoperability, distribution transparency and query outsourcing. The PyGMQL package integrates scalable data extraction over the Apache Spark engine underlying the GMQL implementation with native Python support for interactive data analysis and visualization. It supports data interoperability, solving the impedance mismatch between executing set-oriented queries and programming in Python. PyGMQL provides distribution transparency (the ability to address a remote dataset) and query outsourcing (the ability to assign processing to a remote service) in an orthogonal way. Outsourced processing can address cloud-based installations of the GMQL engine. CONCLUSIONS: PyGMQL is an effective and innovative tool for supporting tertiary data extraction and analysis pipelines. We demonstrate the expressiveness and performance of PyGMQL through a sequence of biological data analysis scenarios of increasing complexity, which highlight reproducibility, expressive power and scalability.

Dried plasma spot as an innovative approach to therapeutic drug monitoring of apixaban: Development and validation of a novel liquid chromatography-tandem mass spectrometry method.

Apixaban, a direct oral anticoagulant drug (DOAC), typically does not require routine therapeutic drug monitoring (TDM), yet recent guidelines propose its use in specific clinical scenarios. While various antifactor Xa (anti-FXa) chromogenic assays serve as useful proxies for measuring plasma exposure to apixaban in emergencies, they lack specificity compared with chromatographic methods. This research project is intended to the development and validation of a standardized protocol of liquid chromatography-tandem mass spectrometry (LC-MS/MS) in conformity with the ICH guidelines M10 for the measurement of apixaban in both plasma and dried plasma spots (DPSs). Samples preparation included protein precipitation after the addition of a deuterated internal standard (IS), and the chromatographic separation was carried out on a Thermo Scientific™ Accucore™ Polar Premium column (50 mm × 2.1 mm, i.d. 2.6 m). The newly developed LC-MS/MS method for apixaban mesurement from both plasma and DPS resulted linear over a wide concentration range (31.25-500 ng/mL), accurate, and reproducible without matrix effects, allowing for specific and rapid quantification. Stability was assessed on quality controls and a real sample, allowing the setting up of a robust TDM protocol that was applied to five anonymized plasma samples obtained from adult patients undergoing apixaban treatment at steady-state. In conclusion our novel LC-MS/MS method is adequate for accurate apixaban quantitation from both plasma and DPS matrixes, and may thus facilitate the guidelines suggested implementation of apixaban TDM, even in peripheral hospitals through shipment of DPS at reference laboratories.

Long follow-up of BNT162b2 mRNA vaccine in healthcare workers (2020-2022): A retrospective longitudinal SARS-CoV-2 serological surveillance.

SARS-CoV-2 anti-spike IgG production and protection from severe respiratory illness should be explored in greater depth after COVID-19 booster vaccination. This longitudinal observational retrospective study investigated the anti-spike IgG response elicited by the first, second and booster doses of BNT162b2 mRNA vaccine in healthcare workers (HCW) at San Martino IRCCS Policlinico Hospital (Genoa) up to the 12th month. Sequential blood sampling was performed at T0 (prior to vaccination), T1 (21 days after the 1st dose of vaccine), T2, T3, T4, T5, T6 (7 days and 1, 3, 6 and 9 months after the 2nd dose, respectively), T7 and T8 (1 and 3 months after a booster dose). A SARS-CoV-2 IgG panel (Bio-Rad, Marnes-la-Coquette, France) was used to determine levels of receptor-binding domain (RBD), spike-1 (S1), spike-2 and nucleocapsid structural proteins of SARS-CoV-2. In the 51 HCWs evaluated, seroprevalence was 96% (49/51) at T1 and 100% (51/51) from T2 to T5 for RBD and S1. At T6, only one HCW was negative. T2 [RBD = 2945 (IQR:1693-5364); S1 = 1574 (IQR:833-3256) U/mL], and T7 [RBD = 8204 (IQR:4129-11,912); S1 = 4124 (IQR:2124-6326) U/mL] were characterized by the highest antibody values. Significant humoral increases in RBD and S1 were documented at T7 and T8 compared to T2 and T4, respectively (p-value < .001). Following vaccination with BNT162b2 and a booster dose in the 9th month, naïve and healthy subjects show high antibody titers up to 12 months and a protective humoral response against COVID-19 disease lasting up to 20 months after the last booster.

The ENCODE Imputation Challenge: a critical assessment of methods for cross-cell type imputation of epigenomic profiles.

A promising alternative to comprehensively performing genomics experiments is to, instead, perform a subset of experiments and use computational methods to impute the remainder. However, identifying the best imputation methods and what measures meaningfully evaluate performance are open questions. We address these questions by comprehensively analyzing 23 methods from the ENCODE Imputation Challenge. We find that imputation evaluations are challenging and confounded by distributional shifts from differences in data collection and processing over time, the amount of available data, and redundancy among performance measures. Our analyses suggest simple steps for overcoming these issues and promising directions for more robust research.

Investigating Deep Learning Based Breast Cancer Subtyping Using Pan-Cancer and Multi-Omic Data.

Breast Cancer comprises multiple subtypes implicated in prognosis. Existing stratification methods rely on the expression quantification of small gene sets. Next Generation Sequencing promises large amounts of omic data in the next years. In this scenario, we explore the potential of machine learning and, particularly, deep learning for breast cancer subtyping. Due to the paucity of publicly available data, we leverage on pan-cancer and non-cancer data to design semi-supervised settings. We make use of multi-omic data, including microRNA expressions and copy number alterations, and we provide an in-depth investigation of several supervised and semi-supervised architectures. Obtained accuracy results show simpler models to perform at least as well as the deep semi-supervised approaches on our task over gene expression data. When multi-omic data types are combined together, performance of deep models shows little (if any) improvement in accuracy, indicating the need for further analysis on larger datasets of multi-omic data as and when they become available. From a biological perspective, our linear model mostly confirms known gene-subtype annotations. Conversely, deep approaches model non-linear relationships, which is reflected in a more varied and still unexplored set of representative omic features that may prove useful for breast cancer subtyping.

Systematic inference and comparison of multi-scale chromatin sub-compartments connects spatial organization to cell phenotypes.

Chromatin compartmentalization reflects biological activity. However, inference of chromatin sub-compartments and compartment domains from chromosome conformation capture (Hi-C) experiments is limited by data resolution. As a result, these have been characterized only in a few cell types and systematic comparisons across multiple tissues and conditions are missing. Here, we present Calder, an algorithmic approach that enables the identification of multi-scale sub-compartments at variable data resolution. Calder allows to infer and compare chromatin sub-compartments and compartment domains in >100 cell lines. Our results reveal sub-compartments enriched for poised chromatin states and undergoing spatial repositioning during lineage differentiation and oncogenic transformation.

Antiphospholipid antibodies and anticoagulant therapy: capillaroscopic findings.

BACKGROUND: Antiphospholipid syndrome (APS) is a systemic autoimmune disease characterized by specific vascular and obstetric manifestations and by antiphospholipid antibodies (aPL) positivity. Microvascular damage in the course of APS and "aPL carrier" patients without symptoms is poorly investigated. OBJECTIVES: This study aims to compare nailfold videocapillaroscopy (NVC) microvascular parameters in APS patients and non-symptomatic "aPL carriers" and to investigate their possible correlations with different aPL subtypes. METHODS: NVC was performed during standard evaluations in 18 APS patients (mean age 50 ± 13.8 years), 24 "aPL carriers" without symptoms (mean age 46.4 ± 16.4 years), and 18 control patients (CTR) (mean age 74 ± 12.5 years) taking oral anticoagulants for non-immunological indications (i.e., cardiovascular accidents). All patients were investigated for the presence of dilated capillaries, giant capillaries, microhemorrhages, capillary loss, and further non-specific/specific abnormalities (i.e., branched "bushy" capillaries, sign of neoangiogenesis) by NVC. Every alteration was also classified according to a semi-quantitative score. Lupus anticoagulant, anticardiolipin antibodies, and antibeta2 glycoprotein I antibodies were tested in each patient. RESULTS: APS patients showed at NVC increased frequency of microhemorrhages (p = 0.039)-particularly a "comb-like" pattern (parallel hemorrhages) (p = 0.002)-than "aPL carriers". Of note, there were no significant differences concerning the isolated number of microhemorrhages between APS and the CTR group (p = 0.314), but "comb-like" hemorrhages were significantly more frequent in the APS group (p = 0.034). Not any significant correlation was found between the aPL subtypes and NVC parameters. CONCLUSIONS: APS patients showed significantly a greater number of non-specific NVC abnormalities than "aPL carriers", particularly the "comb-like" NVC pattern. Oral anticoagulants may represent a confounding factor for isolated microhemorrhages. Not any correlation was found between aPL subtypes and NVC parameters. Further investigations are needed to better characterize the microvascular endothelium damage induced by aPL.

Spatial patterns of CTCF sites define the anatomy of TADs and their boundaries.

BACKGROUND: Topologically associating domains (TADs) are genomic regions of self-interaction. Additionally, it is known that TAD boundaries are enriched in CTCF binding sites. In turn, CTCF sites are known to be asymmetric, whereby the convergent configuration of a pair of CTCF sites leads to the formation of a chromatin loop in vivo. However, to date, it has been unclear how to reconcile TAD structure with CTCF-based chromatin loops. RESULTS: We approach this problem by analysing CTCF binding site strengths and classifying clusters of CTCF sites along the genome on the basis of their relative orientation. Analysis of CTCF site orientation classes as a function of their spatial distribution along the human genome reveals that convergent CTCF site clusters are depleted while divergent CTCF clusters are enriched in the 5- to 100-kb range. We then analyse the distribution of CTCF binding sites as a function of TAD boundary conservation across seven primary human blood cell types. This reveals divergent CTCF site enrichment at TAD boundaries. Furthermore, convergent arrays of CTCF sites separate the left and right sections of TADs that harbour internal CTCF sites, resulting in unequal TAD 'halves'. CONCLUSIONS: The orientation-based CTCF binding site cluster classification that we present reconciles TAD boundaries and CTCF site clusters in a mechanistically elegant fashion. This model suggests that the emergent structure of nuclear chromatin in the form of TADs relies on the obligate alternation of divergent and convergent CTCF site clusters that occur at different length scales along the genome.

Extensive epigenomic integration of the glucocorticoid response in primary human monocytes and in vitro derived macrophages.

Glucocorticoid receptor is a transcription factor that is ubiquitously expressed. Glucocorticoids are circadian steroids that regulate a wide range of bodily functions, including immunity. Here we report that synthetic glucocorticoids affect 1035 mRNAs in isolated healthy human blood monocytes but only 165 in the respective six day-old monocyte-derived macrophages. The majority of the glucocorticoid response in monocytes concerns genes that are dynamic upon monocyte to macrophage differentiation, whereby macrophage-like mRNA levels are often reached in monocytes within four hours of treatment. Concomitantly, over 5000 chromosomal H3K27ac regions undergo remodelling, of which 60% involve increased H3K27ac signal. We find that chromosomal glucocorticoid receptor binding sites correlate with positive but not with negative local epigenomic effects. To investigate further we assigned our data to topologically associating domains (TADs). This shows that about 10% of macrophage TADs harbour at least one GR binding site and that half of all the glucocorticoid-induced H3K27ac regions are confined to these TADs. Our analyses are therefore consistent with the notion that TADs naturally accommodate information from sets of distal glucocorticoid response elements.