Transcriptomic and genomic studies classify NKL54 as a histone deacetylase inhibitor with indirect influence on MEF2-dependent transcription.

In leiomyosarcoma class IIa HDACs (histone deacetylases) bind MEF2 and convert these transcription factors into repressors to sustain proliferation. Disruption of this complex with small molecules should antagonize cancer growth. NKL54, a PAOA (pimeloylanilide o-aminoanilide) derivative, binds a hydrophobic groove of MEF2, which is used as a docking site by class IIa HDACs. However, NKL54 could also act as HDAC inhibitor (HDACI). Therefore, it is unclear which activity is predominant. Here, we show that NKL54 and similar derivatives are unable to release MEF2 from binding to class IIa HDACs. Comparative transcriptomic analysis classifies these molecules as HDACIs strongly related to SAHA/vorinostat. Low expressed genes are upregulated by HDACIs, while abundant genes are repressed. This transcriptional resetting correlates with a reorganization of H3K27 acetylation around the transcription start site (TSS). Among the upregulated genes there are several BH3-only family members, thus explaining the induction of apoptosis. Moreover, NKL54 triggers the upregulation of MEF2 and the downregulation of class IIa HDACs. NKL54 also increases the binding of MEF2D to promoters of genes that are upregulated after treatment. In summary, although NKL54 cannot outcompete MEF2 from binding to class IIa HDACs, it supports MEF2-dependent transcription through several actions, including potentiation of chromatin binding.

Equated hospitalization rate of patients with inflammatory rheumatic diseases as compared to normal population in 2°wave SARSCoV2 infection.

OBJECTIVE: To investigate the clinical manifestations and outcome of COVID-19 in patients with inflammatory rheumatic and musculoskeletal disease (iRMD) as compared with the general population. METHODS: This is a case-control study of patients selected from the South-Tyrol public health service-Italy with and without iRMD affected by COVID-19. We included patients ≥18 years and with a positive SARS-CoV-2 PCR test between 1.10.2020 and 01.03.2021. Cases were identified by linking the diagnosis of a rheumatic disease with PCR test positivity; these were matched in a 1:1.8 (planned 1:2) ratio for age, sex, and date of COVID-19 diagnosis with people from the general population. The outcomes of primary interest were hospitalization and severe course (intensive care unit, mechanical ventilation/extracorporeal membrane oxygenation, death). RESULTS: The study population consisted of 561 COVID-19 patients, of which 201 (mean age 60.4 years; 65.2% female) were patients with iRMD and 360 were controls from the general population (59.8 years; 64.7% female). The majority of iRMD patients (88.6%) received an immunosuppressive drug at time of COVID-19 diagnosis, 36.3% were under glucocorticoids. COVID-19 related hospitalization (12.4% vs 10.6%, p= 0.49), severe course (5.0% vs 5.3%, p= 1.00), and mortality (3.5% vs 4.4%, p= 0.66) were similar between groups. Among hospitalized patients, mechanic ventilation was more common in iRMD patients than in controls [n = 5 (20.0%) vs n = 1 (2.6%), p= 0.035]. CONCLUSIONS: Our study indicates similar rates for admission, severe course and mortality between patients with iRMD and controls affected by COVID-19. Among hospitalized patients, mechanical ventilation was more frequently required in the iRMD group.

Prevalence and determinants of serum antibodies to SARS-CoV-2 in the general population of the Gardena valley.

Estimating the spread of SARS-CoV-2 infection in communities is critical. We surveyed 2244 stratified random sample community members of the Gardena valley, a winter touristic area, amidst the first expansion phase of the COVID-19 pandemic in Europe. We measured agreement between Diasorin and Abbott serum bioassay outputs and the Abbott optimal discriminant threshold of serum neutralisation titres with recursive receiver operating characteristic curve. We analytically adjusted serum antibody tests for unbiased seroprevalence estimate and analysed the determinants of infection with non-response weighted multiple logistic regression. SARS-CoV-2 seroprevalence was 26.9% (95% CI 25.2-28.6) by June 2020. The bioassays had a modest agreement with each other. At a lower threshold than the manufacturer's recommended level, the Abbott assay reflected greater discrimination of serum neutralisation capacity. Seropositivity was associated with place and economic activity, not with sex or age. Symptoms like fever and weakness were age-dependent. SARS-CoV-2 mitigation strategies should account for context in high prevalence areas.

ensembldb: an R package to create and use Ensembl-based annotation resources.

SUMMARY: Bioinformatics research frequently involves handling gene-centric data such as exons, transcripts, proteins and their positions relative to a reference coordinate system. The ensembldb Bioconductor package retrieves and stores Ensembl-based genetic annotations and positional information, and furthermore offers identifier conversion and coordinates mappings for gene-associated data. In support of reproducible research, data are tied to Ensembl releases and are kept separately from the software. Premade data packages are available for a variety of genomes and Ensembl releases. Three examples demonstrate typical use cases of this software. AVAILABILITY AND IMPLEMENTATION: ensembldb is part of Bioconductor (https://bioconductor.org/packages/ensembldb). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Comparative assessment of different familial aggregation methods in the context of large and unstructured pedigrees.

Motivation: Familial aggregation analysis is an important early step for characterizing the genetic determinants of phenotypes in epidemiological studies. To facilitate this analysis, a collection of methods to detect familial aggregation in large pedigrees has been made available recently. However, efficacy of these methods in real world scenarios remains largely unknown. Here, we assess the performance of five aggregation methods to identify individuals or groups of related individuals affected by a Mendelian trait within a large set of decoys. We investigate method performance under a representative set of combinations of causal variant penetrance, trait prevalence and number of affected generations in the pedigree. These methods are then applied to assess familial aggregation of familial hypercholesterolemia and stroke, in the context of the Cooperative Health Research in South Tyrol (CHRIS) study. Results: We find that in some situations statistical hypothesis testing with a binomial null distribution achieves performance similar to methods that are based on kinship information, while kinship based methods perform better when information is available on fewer generations. Potential case families from the CHRIS study are reported and the results are discussed taking into account insights from the performance assessment. Availability and implementation: The familial aggregation analysis package is freely available at the Bioconductor repository, http://www.bioconductor.org/packages/FamAgg. Supplementary information: Supplementary data are available at Bioinformatics online.

Inclusion of biological knowledge in a Bayesian shrinkage model for joint estimation of SNP effects.

With the aim of improving detection of novel single-nucleotide polymorphisms (SNPs) in genetic association studies, we propose a method of including prior biological information in a Bayesian shrinkage model that jointly estimates SNP effects. We assume that the SNP effects follow a normal distribution centered at zero with variance controlled by a shrinkage hyperparameter. We use biological information to define the amount of shrinkage applied on the SNP effects distribution, so that the effects of SNPs with more biological support are less shrunk toward zero, thus being more likely detected. The performance of the method was tested in a simulation study (1,000 datasets, 500 subjects with ∼200 SNPs in 10 linkage disequilibrium (LD) blocks) using a continuous and a binary outcome. It was further tested in an empirical example on body mass index (continuous) and overweight (binary) in a dataset of 1,829 subjects and 2,614 SNPs from 30 blocks. Biological knowledge was retrieved using the bioinformatics tool Dintor, which queried various databases. The joint Bayesian model with inclusion of prior information outperformed the standard analysis: in the simulation study, the mean ranking of the true LD block was 2.8 for the Bayesian model versus 3.6 for the standard analysis of individual SNPs; in the empirical example, the mean ranking of the six true blocks was 8.5 versus 9.3 in the standard analysis. These results suggest that our method is more powerful than the standard analysis. We expect its performance to improve further as more biological information about SNPs becomes available.

Seventy-five genetic loci influencing the human red blood cell.

Anaemia is a chief determinant of global ill health, contributing to cognitive impairment, growth retardation and impaired physical capacity. To understand further the genetic factors influencing red blood cells, we carried out a genome-wide association study of haemoglobin concentration and related parameters in up to 135,367 individuals. Here we identify 75 independent genetic loci associated with one or more red blood cell phenotypes at P < 10(-8), which together explain 4-9% of the phenotypic variance per trait. Using expression quantitative trait loci and bioinformatic strategies, we identify 121 candidate genes enriched in functions relevant to red blood cell biology. The candidate genes are expressed preferentially in red blood cell precursors, and 43 have haematopoietic phenotypes in Mus musculus or Drosophila melanogaster. Through open-chromatin and coding-variant analyses we identify potential causal genetic variants at 41 loci. Our findings provide extensive new insights into genetic mechanisms and biological pathways controlling red blood cell formation and function.

HDAC Inhibition Improves the Sarcoendoplasmic Reticulum Ca2+-ATPase Activity in Cardiac Myocytes.

SERCA2a is the Ca2+ ATPase playing the major contribution in cardiomyocyte (CM) calcium removal. Its activity can be regulated by both modulatory proteins and several post-translational modifications. The aim of the present work was to investigate whether the function of SERCA2 can be modulated by treating CMs with the histone deacetylase (HDAC) inhibitor suberanilohydroxamic acid (SAHA). The incubation with SAHA (2.5 µM, 90 min) of CMs isolated from rat adult hearts resulted in an increase of SERCA2 acetylation level and improved ATPase activity. This was associated with a significant improvement of calcium transient recovery time and cell contractility. Previous reports have identified K464 as an acetylation site in human SERCA2. Mutants were generated where K464 was substituted with glutamine (Q) or arginine (R), mimicking constitutive acetylation or deacetylation, respectively. The K464Q mutation ameliorated ATPase activity and calcium transient recovery time, thus indicating that constitutive K464 acetylation has a positive impact on human SERCA2a (hSERCA2a) function. In conclusion, SAHA induced deacetylation inhibition had a positive impact on CM calcium handling, that, at least in part, was due to improved SERCA2 activity. This observation can provide the basis for the development of novel pharmacological approaches to ameliorate SERCA2 efficiency.

FamAgg: an R package to evaluate familial aggregation of traits in large pedigrees.

UNLABELLED: Familial aggregation analysis is the first fundamental step to perform when assessing the extent of genetic background of a disease. However, there is a lack of software to analyze the familial clustering of complex phenotypes in very large pedigrees. Such pedigrees can be utilized to calculate measures that express trait aggregation on both the family and individual level, providing valuable directions in choosing families for detailed follow-up studies. We developed FamAgg, an open source R package that contains both established and novel methods to investigate familial aggregation of traits in large pedigrees. We demonstrate its use and interpretation by analyzing a publicly available cancer dataset with more than 20 000 participants distributed across approximately 400 families. AVAILABILITY AND IMPLEMENTATION: The FamAgg package is freely available at the Bioconductor repository, http://www.bioconductor.org/packages/FamAgg CONTACT: Christian.Weichenberger@eurac.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Dintor: functional annotation of genomic and proteomic data.

BACKGROUND: During the last decade, a great number of extremely valuable large-scale genomics and proteomics datasets have become available to the research community. In addition, dropping costs for conducting high-throughput sequencing experiments and the option to outsource them considerably contribute to an increasing number of researchers becoming active in this field. Even though various computational approaches have been developed to analyze these data, it is still a laborious task involving prudent integration of many heterogeneous and frequently updated data sources, creating a barrier for interested scientists to accomplish their own analysis. RESULTS: We have implemented Dintor, a data integration framework that provides a set of over 30 tools to assist researchers in the exploration of genomics and proteomics datasets. Each of the tools solves a particular task and several tools can be combined into data processing pipelines. Dintor covers a wide range of frequently required functionalities, from gene identifier conversions and orthology mappings to functional annotation of proteins and genetic variants up to candidate gene prioritization and Gene Ontology-based gene set enrichment analysis. Since the tools operate on constantly changing datasets, we provide a mechanism to unambiguously link tools with different versions of archived datasets, which guarantees reproducible results for future tool invocations. We demonstrate a selection of Dintor's capabilities by analyzing datasets from four representative publications. The open source software can be downloaded and installed on a local Unix machine. For reasons of data privacy it can be configured to retrieve local data only. In addition, the Dintor tools are available on our public Galaxy web service at http://dintor.eurac.edu . CONCLUSIONS: Dintor is a computational annotation framework for the analysis of genomic and proteomic datasets, providing a rich set of tools that cover the most frequently encountered tasks. A major advantage is its capability to consistently handle multiple versions of tool-associated datasets, supporting the researcher in delivering reproducible results.

The solvent component of macromolecular crystals.

The mother liquor from which a biomolecular crystal is grown will contain water, buffer molecules, native ligands and cofactors, crystallization precipitants and additives, various metal ions, and often small-molecule ligands or inhibitors. On average, about half the volume of a biomolecular crystal consists of this mother liquor, whose components form the disordered bulk solvent. Its scattering contributions can be exploited in initial phasing and must be included in crystal structure refinement as a bulk-solvent model. Concomitantly, distinct electron density originating from ordered solvent components must be correctly identified and represented as part of the atomic crystal structure model. Herein, are reviewed (i) probabilistic bulk-solvent content estimates, (ii) the use of bulk-solvent density modification in phase improvement, (iii) bulk-solvent models and refinement of bulk-solvent contributions and (iv) modelling and validation of ordered solvent constituents. A brief summary is provided of current tools for bulk-solvent analysis and refinement, as well as of modelling, refinement and analysis of ordered solvent components, including small-molecule ligands.

Importance of different types of prior knowledge in selecting genome-wide findings for follow-up.

Biological plausibility and other prior information could help select genome-wide association (GWA) findings for further follow-up, but there is no consensus on which types of knowledge should be considered or how to weight them. We used experts' opinions and empirical evidence to estimate the relative importance of 15 types of information at the single-nucleotide polymorphism (SNP) and gene levels. Opinions were elicited from 10 experts using a two-round Delphi survey. Empirical evidence was obtained by comparing the frequency of each type of characteristic in SNPs established as being associated with seven disease traits through GWA meta-analysis and independent replication, with the corresponding frequency in a randomly selected set of SNPs. SNP and gene characteristics were retrieved using a specially developed bioinformatics tool. Both the expert and the empirical evidence rated previous association in a meta-analysis or more than one study as conferring the highest relative probability of true association, whereas previous association in a single study ranked much lower. High relative probabilities were also observed for location in a functional protein domain, although location in a region evolutionarily conserved in vertebrates was ranked high by the data but not by the experts. Our empirical evidence did not support the importance attributed by the experts to whether the gene encodes a protein in a pathway or shows interactions relevant to the trait. Our findings provide insight into the selection and weighting of different types of knowledge in SNP or gene prioritization, and point to areas requiring further research.

SNP prioritization using a Bayesian probability of association.

Prioritization is the process whereby a set of possible candidate genes or SNPs is ranked so that the most promising can be taken forward into further studies. In a genome-wide association study, prioritization is usually based on the P-values alone, but researchers sometimes take account of external annotation information about the SNPs such as whether the SNP lies close to a good candidate gene. Using external information in this way is inherently subjective and is often not formalized, making the analysis difficult to reproduce. Building on previous work that has identified 14 important types of external information, we present an approximate Bayesian analysis that produces an estimate of the probability of association. The calculation combines four sources of information: the genome-wide data, SNP information derived from bioinformatics databases, empirical SNP weights, and the researchers' subjective prior opinions. The calculation is fast enough that it can be applied to millions of SNPS and although it does rely on subjective judgments, those judgments are made explicit so that the final SNP selection can be reproduced. We show that the resulting probability of association is intuitively more appealing than the P-value because it is easier to interpret and it makes allowance for the power of the study. We illustrate the use of the probability of association for SNP prioritization by applying it to a meta-analysis of kidney function genome-wide association studies and demonstrate that SNP selection performs better using the probability of association compared with P-values alone.

Ten years of probabilistic estimates of biocrystal solvent content: new insights via nonparametric kernel density estimate.

The probabilistic estimate of the solvent content (Matthews probability) was first introduced in 2003. Given that the Matthews probability is based on prior information, revisiting the empirical foundation of this widely used solvent-content estimate is appropriate. The parameter set for the original Matthews probability distribution function employed in MATTPROB has been updated after ten years of rapid PDB growth. A new nonparametric kernel density estimator has been implemented to calculate the Matthews probabilities directly from empirical solvent-content data, thus avoiding the need to revise the multiple parameters of the original binned empirical fit function. The influence and dependency of other possible parameters determining the solvent content of protein crystals have been examined. Detailed analysis showed that resolution is the primary and dominating model parameter correlated with solvent content. Modifications of protein specific density for low molecular weight have no practical effect, and there is no correlation with oligomerization state. A weak, and in practice irrelevant, dependency on symmetry and molecular weight is present, but cannot be satisfactorily explained by simple linear or categorical models. The Bayesian argument that the observed resolution represents only a lower limit for the true diffraction potential of the crystal is maintained. The new kernel density estimator is implemented as the primary option in the MATTPROB web application at http://www.ruppweb.org/mattprob/.

Changes in chromatin accessibility and transcriptional landscape induced by HDAC inhibitors in TP53 mutated patient-derived colon cancer organoids.

Here we present the generation and characterization of patient-derived organoids (PDOs) from colorectal cancer patients. PDOs derived from two patients with TP53 mutations were tested with two different HDAC inhibitors (SAHA and NKL54). Cell death induction, transcriptome, and chromatin accessibility changes were analyzed. HDACIs promote the upregulation of low expressed genes and the downregulation of highly expressed genes. A similar differential effect is observed at the level of chromatin accessibility. Only SAHA is a potent inducer of cell death, which is characterized by the upregulation of BH3-only genes BIK and BMF. Up-regulation of BIK is associated with increased accessibility in an intronic region that has enhancer properties. SAHA, but not NKL54, also causes downregulation of BCL2L1 and decreases chromatin accessibility in three distinct regions of the BCL2L1 locus. Both inhibitors upregulate the expression of innate immunity genes and members of the MHC family. In summary, our exploratory study indicates a mechanism of action for SAHA and demonstrate the low efficacy of NKL54 as a single agent for apoptosis induction, using two PDOs. These observations need to be validated in a larger cohort of PDOs.

Techniques, tools and best practices for ligand electron-density analysis and results from their application to deposited crystal structures.

As a result of substantial instrumental automation and the continuing improvement of software, crystallographic studies of biomolecules are conducted by non-experts in increasing numbers. While improved validation almost ensures that major mistakes in the protein part of structure models are exceedingly rare, in ligand-protein complex structures, which in general are most interesting to the scientist, ambiguous ligand electron density is often difficult to interpret and the modelled ligands are generally more difficult to properly validate. Here, (i) the primary technical reasons and potential human factors leading to problems in ligand structure models are presented; (ii) the most common categories of building errors or overinterpretation are classified; (iii) a few instructive and specific examples are discussed in detail, including an electron-density-based analysis of ligand structures that do not contain any ligands; (iv) means of avoiding such mistakes are suggested and the implications for database validity are discussed and (v) a user-friendly software tool that allows non-expert users to conveniently inspect ligand density is provided.

Visualizing ligand molecules in Twilight electron density.

Three-dimensional models of protein structures determined by X-ray crystallography are based on the interpretation of experimentally derived electron-density maps. The real-space correlation coefficient (RSCC) provides an easily comprehensible, objective measure of the residue-based fit of atom coordinates to electron density. Among protein structure models, protein-ligand complexes are of special interest, given their contribution to understanding the molecular underpinnings of biological activity and to drug design. For consumers of such models, it is not trivial to determine the degree to which ligand-structure modelling is biased by subjective electron-density interpretation. A standalone script, Twilight, is presented for the analysis, visualization and annotation of a pre-filtered set of 2815 protein-ligand complexes deposited with the PDB as of 15 January 2012 with ligand RSCC values that are below a threshold of 0.6. It also provides simplified access to the visualization of any protein-ligand complex available from the PDB and annotated by the Uppsala Electron Density Server. The script runs on various platforms and is available for download at http://www.ruppweb.org/twilight/.

Determinants of SARS-CoV-2 nasopharyngeal testing in a rural community sample susceptible of first infection: the CHRIS COVID-19 study.

To characterize COVID-19 epidemiology, numerous population-based studies have been undertaken to model the risk of SARS-CoV-2 infection. Less is known about what may drive the probability to undergo testing. Understanding how much testing is driven by contextual or individual conditions is important to delineate the role of individual behavior and to shape public health interventions and resource allocation. In the Val Venosta/Vinschgau district (South Tyrol, Italy), we conducted a population-representative longitudinal study on 697 individuals susceptible to first infection who completed 4,512 repeated online questionnaires at four-week intervals between September 2020 and May 2021. Mixed-effects logistic regression models were fitted to investigate associations of self-reported SARS-CoV-2 testing with individual characteristics (social, demographic, and biological) and contextual determinants. Testing was associated with month of reporting, reflecting the timing of both the pandemic intensity and public health interventions, COVID-19-related symptoms (odds ratio, OR:8.26; 95% confidence interval, CI:6.04-11.31), contacts with infected individuals within home (OR:7.47, 95%CI:3.81-14.62) or outside home (OR:9.87, 95%CI:5.78-16.85), and being retired (OR:0.50, 95%CI:0.34-0.73). Symptoms and next within- and outside-home contacts were the leading determinants of swab testing predisposition in the most acute phase of the pandemics. Testing was not associated with age, sex, education, comorbidities, or lifestyle factors. In the study area, contextual determinants reflecting the course of the pandemic were predominant compared to individual sociodemographic characteristics in explaining the SARS-CoV-2 probability of testing. Decision makers should evaluate whether the intended target groups were correctly prioritized by the testing campaign.

Trends and symptoms of SARS-CoV-2 infection: a longitudinal study on an Alpine population representative sample.

OBJECTIVES: The continuous monitoring of SARS-CoV-2 infection waves and the emergence of novel pathogens pose a challenge for effective public health surveillance strategies based on diagnostics. Longitudinal population representative studies on incident events and symptoms of SARS-CoV-2 infection are scarce. We aimed at describing the evolution of the COVID-19 pandemic during 2020 and 2021 through regular monitoring of self-reported symptoms in an Alpine community sample. DESIGN: To this purpose, we designed a longitudinal population representative study, the Cooperative Health Research in South Tyrol COVID-19 study. PARTICIPANTS AND OUTCOME MEASURES: A sample of 845 participants was retrospectively investigated for active and past infections with swab and blood tests, by August 2020, allowing adjusted cumulative incidence estimation. Of them, 700 participants without previous infection or vaccination were followed up monthly until July 2021 for first-time infection and symptom self-reporting: COVID-19 anamnesis, social contacts, lifestyle and sociodemographic data were assessed remotely through digital questionnaires. Temporal symptom trajectories and infection rates were modelled through longitudinal clustering and dynamic correlation analysis. Negative binomial regression and random forest analysis assessed the relative importance of symptoms. RESULTS: At baseline, the cumulative incidence of SARS-CoV-2 infection was 1.10% (95% CI 0.51%, 2.10%). Symptom trajectories mimicked both self-reported and confirmed cases of incident infections. Cluster analysis identified two groups of high-frequency and low-frequency symptoms. Symptoms like fever and loss of smell fell in the low-frequency cluster. Symptoms most discriminative of test positivity (loss of smell, fatigue and joint-muscle aches) confirmed prior evidence. CONCLUSIONS: Regular symptom tracking from population representative samples is an effective screening tool auxiliary to laboratory diagnostics for novel pathogens at critical times, as manifested in this study of COVID-19 patterns. Integrated surveillance systems might benefit from more direct involvement of citizens' active symptom tracking.

The Class IIA Histone Deacetylase (HDAC) Inhibitor TMP269 Downregulates Ribosomal Proteins and Has Anti-Proliferative and Pro-Apoptotic Effects on AML Cells.

Acute myeloid leukemia (AML) is a hematopoietic malignancy characterized by altered myeloid progenitor cell proliferation and differentiation. As in many other cancers, epigenetic transcriptional repressors such as histone deacetylases (HDACs) are dysregulated in AML. Here, we investigated (1) HDAC gene expression in AML patients and in different AML cell lines and (2) the effect of treating AML cells with the specific class IIA HDAC inhibitor TMP269, by applying proteomic and comparative bioinformatic analyses. We also analyzed cell proliferation, apoptosis, and the cell-killing capacities of TMP269 in combination with venetoclax compared to azacitidine plus venetoclax, by flow cytometry. Our results demonstrate significantly overexpressed class I and class II HDAC genes in AML patients, a phenotype which is conserved in AML cell lines. In AML MOLM-13 cells, TMP269 treatment downregulated a set of ribosomal proteins which are overexpressed in AML patients at the transcriptional level. TMP269 showed anti-proliferative effects and induced additive apoptotic effects in combination with venetoclax. We conclude that TMP269 exerts anti-leukemic activity when combined with venetoclax and has potential as a therapeutic drug in AML.