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Human immunodeficiency virus (HIV) can develop resistance to all antiretroviral drugs. Multidrug resistance, however, is a rare event in modern HIV treatment, but can be life-threatening, particular in patients with very long therapy histories and in areas with limited access to novel drugs. To understand the evolution of multidrug resistance, we analyzed the EuResist database to uncover the accumulation of mutations over time. We hypothesize that the accumulation of resistance mutations is not acquired simultaneously and randomly across viral genotypes but rather tends to follow a predetermined order. The knowledge of this order might help to elucidate potential mechanisms of multidrug resistance. Our evolutionary model shows an almost monotonic increase of resistance with each acquired mutation, including less well-known nucleoside reverse transcriptase (RT) inhibitor-related mutations like K223Q, L228H, and Q242H. Mutations within the integrase (IN) (T97A, E138A/K G140S, Q148H, N155H) indicate high probability of multidrug resistance. Hence, these IN mutations also tend to be observed together with mutations in the protease (PR) and RT. We followed up with an analysis of the mutation-specific error rates of our model given the data. We identified several mutations with unusual rates (PR: M41L, L33F, IN: G140S). This could imply the existence of previously unknown virus variants in the viral quasispecies. In conclusion, our bioinformatics model supports the analysis and understanding of multidrug resistance., (© 2022 The Authors. Journal of Medical Virology published by Wiley Periodicals LLC.)

Objectives: Bovine pericardium - native, fixed as well as decellularized - is one of the most common implant materials in modern cardiovascular surgery. Although used in everyday procedures, there are no recommendations in regard to which part of the pericardium to prefer. It was the aim of this study, to identify areas of the pericardium with consistent properties and high durability., Methods: Fresh bovine pericardia were collected from a local slaughterhouse. The native pericardia were analyzed at 140 spots in regard to thickness and fiber orientation. Based on these results, five promising areas were selected for further evaluation. The pericardia were decellularized with detergents (0.5% sodiumdesoxycholate/0.5% sodiumdodecylsulfate) and subsequently incubated in DNAse. The two investigation groups native und DC consisted of 20 samples each. The efficiency of the decellularization was evaluated by DNA quantification, as well as DAPI and H&E staining. Biomechanical properties were determined using uniaxial tensile tests. To evaluate the microstructure, scanning electron microscopy, Picrosirius Red- and Movat's Pentachrome staining were utilized. To assess the long-term durability, patches were tested in a high-cycle system for a duration equaling the stress of three months in-vivo. Commercially available, fixed pericardium patches served as control group., Results: Only a limited part of the pericardium showed a homogenous and usable thickness. The decellularization removed all cell nuclei, proven by negative DAPI and H&E staining, and also significantly reduced the DNA amount by 84%. The mechanical testing revealed that two investigated areas had an inconsistent tensile strength. Microscopical observations showed that the integrity of the extracellular matrix did not suffer by the decellularization procedure. During the long-term testing, most of the pericardia slowly lost tautness, though none of them got measurably damaged. Especially one area showed no decline of tensile strength after durability testing at all. Decellularized patches and fixed patches achieved comparable results in mechanical testing and microscopical evaluation after the durability testing., Conclusion: We could clearly document significant, location-based differences within single pericardia. Only one area showed consistent properties and a high durability. We highly recommend taking this into account for future implant material selections., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Identifying resistance to antiretroviral drugs is crucial for ensuring the successful treatment of patients infected with viruses such as human immunodeficiency virus (HIV) or hepatitis C virus (HCV). In contrast to Sanger sequencing, next-generation sequencing (NGS) can detect resistance mutations in minority populations. Thus, genotypic resistance testing based on NGS data can offer novel, treatment-relevant insights. Since existing web services for analyzing resistance in NGS samples are subject to long processing times and follow strictly rules-based approaches, we developed geno2pheno[ngs-freq], a web service for rapidly identifying drug resistance in HIV-1 and HCV samples. By relying on frequency files that provide the read counts of nucleotides or codons along a viral genome, the time-intensive step of processing raw NGS data is eliminated. Once a frequency file has been uploaded, consensus sequences are generated for a set of user-defined prevalence cutoffs, such that the constructed sequences contain only those nucleotides whose codon prevalence exceeds a given cutoff. After locally aligning the sequences to a set of references, resistance is predicted using the well-established approaches of geno2pheno[resistance] and geno2pheno[hcv]. geno2pheno[ngs-freq] can assist clinical decision making by enabling users to explore resistance in viral populations with different abundances and is freely available at http://ngs.geno2pheno.org.

ADAM17, a prominent member of the "Disintegrin and Metalloproteinase" (ADAM) family, is an important regulator of endothelial cell proliferation and cell survival. The protease controls vital cellular functions through cleavage of growth factors, cytokines and their receptors including transforming growth factor-alpha (TGF-α), tumor necrosis factor-alpha (TNF-α) and TNF-α receptor 1 (TNFR1). TNF-α is the major inducer of endothelial cell death in cardiovascular diseases. The latter are also characterized by elevated plasma and tissue levels of extracellular sphingomyelinase (SMase). Whether the SMase affects ADAM activity and thus endothelial cell function has not been addressed to date. Here, we analyzed the effect of SMase on ADAM17-mediated shedding in COS7 cells and in human umbilical vein endothelial cells (HUVECs). Exposure to SMase significantly increased ADAM17-mediated release of alkaline-phosphatase (AP)-tagged TGF-α in COS7 cells and shedding of endogenously expressed TNFR1 in HUVECs. We previously presented evidence that surface exposure of phosphatidylserine (PS) is pivotal for ADAM17 to exert sheddase function. We found that SMase treatment led to PS externalization in both cell types. Transient non-apoptotic PS exposure is often mediated by Ca 2+ -dependent phospholipid scramblases. Accordingly, the Ca 2+ -chelator EGTA markedly reduced the breakdown of phospholipid asymmetry and shedding of TGF-α and TNFR1. Moreover, sheddase activity was significantly diminished in the presence of the competing PS-headgroup OPLS. SMase-stimulated TNFR1 shedding strikingly diminished TNF-α-induced signalling cascades and endothelial cell death. Taken together, our data suggest that SMase activity might act as protective factor for endothelial cells in cardiovascular diseases., Competing Interests: CONFLICTS OF INTEREST The authors declare no competing financial interests.

Background: HIV-1 drug resistance can be measured with phenotypic drug-resistance tests. However, the output of these tests, the resistance factor (RF), requires interpretation with respect to the in vivo activity of the tested variant. Specifically, the dynamic range of the RF for each drug has to be divided into a suitable number of clinically meaningful intervals., Methods: We calculated a susceptible-to-intermediate and an intermediate-to-resistant cutoff per drug for RFs predicted by geno2pheno[resistance]. Probability densities for therapeutic success and failure were estimated from 10,444 treatment episodes. The density estimation procedure corrects for the activity of the backbone drug compounds and for therapy failure without drug resistance. For estimating the probability of therapeutic success given an RF, we fit a sigmoid function. The cutoffs are given by the roots of the third derivative of the sigmoid function., Results: For performance assessment, we used geno2pheno[resistance] RF predictions and the cutoffs for predicting therapeutic success in 2 independent sets of therapy episodes. HIVdb was used for performance comparison. On one test set (n = 807), our cutoffs and HIVdb performed equally well receiver operating characteristic curve [(ROC)-area under the curve (AUC): 0.68]. On the other test set (n = 917), our cutoffs (ROC-AUC: 0.63) and HIVdb (ROC-AUC: 0.65) performed comparatively well., Conclusions: Our method can be used for calculating clinically relevant cutoffs for (predicted) RFs. The method corrects for the activity of the backbone drug compounds and for therapy failure without drug resistance. Our method's performance is comparable with that of HIVdb. RF cutoffs for the latest version of geno2pheno[resistance] have been estimated with this method.

Background: CCR5-coreceptor antagonists can be used for treating HIV-2 infected individuals. Before initiating treatment with coreceptor antagonists, viral coreceptor usage should be determined to ensure that the virus can use only the CCR5 coreceptor (R5) and cannot evade the drug by using the CXCR4 coreceptor (X4-capable). However, until now, no online tool for the genotypic identification of HIV-2 coreceptor usage had been available. Furthermore, there is a lack of knowledge on the determinants of HIV-2 coreceptor usage. Therefore, we developed a data-driven web service for the prediction of HIV-2 coreceptor usage from the V3 loop of the HIV-2 glycoprotein and used the tool to identify novel discriminatory features of X4-capable variants., Results: Using 10 runs of tenfold cross validation, we selected a linear support vector machine (SVM) as the model for geno2pheno[coreceptor-hiv2], because it outperformed the other SVMs with an area under the ROC curve (AUC) of 0.95. We found that SVMs were highly accurate in identifying HIV-2 coreceptor usage, attaining sensitivities of 73.5% and specificities of 96% during tenfold nested cross validation. The predictive performance of SVMs was not significantly different (p value 0.37) from an existing rules-based approach. Moreover, geno2pheno[coreceptor-hiv2] achieved a predictive accuracy of 100% and outperformed the existing approach on an independent data set containing nine new isolates with corresponding phenotypic measurements of coreceptor usage. geno2pheno[coreceptor-hiv2] could not only reproduce the established markers of CXCR4-usage, but also revealed novel markers: the substitutions 27K, 15G, and 8S were significantly predictive of CXCR4 usage. Furthermore, SVMs trained on the amino-acid sequences of the V1 and V2 loops were also quite accurate in predicting coreceptor usage (AUCs of 0.84 and 0.65, respectively)., Conclusions: In this study, we developed geno2pheno[coreceptor-hiv2], the first online tool for the prediction of HIV-2 coreceptor usage from the V3 loop. Using our method, we identified novel amino-acid markers of X4-capable variants in the V3 loop and found that HIV-2 coreceptor usage is also influenced by the V1/V2 region. The tool can aid clinicians in deciding whether coreceptor antagonists such as maraviroc are a treatment option and enables epidemiological studies investigating HIV-2 coreceptor usage. geno2pheno[coreceptor-hiv2] is freely available at http://coreceptor-hiv2.geno2pheno.org .

The next generation sequencing technologies produce unprecedented amounts of data on the genetic sequence of individual organisms. These sequences carry a substantial amount of variation that may or may be not related to a phenotype. Phenotypically important part of this variation often comes in form of protein-sequence altering (non-synonymous) single nucleotide variants (nsSNVs). Here we present StructMAn, a Web-based tool for annotation of human and non-human nsSNVs in the structural context. StructMAn analyzes the spatial location of the amino acid residue corresponding to nsSNVs in the three-dimensional (3D) protein structure relative to other proteins, nucleic acids and low molecular-weight ligands. We make use of all experimentally available 3D structures of query proteins, and also, unlike other tools in the field, of structures of proteins with detectable sequence identity to them. This allows us to provide a structural context for around 20% of all nsSNVs in a typical human sequencing sample, for up to 60% of nsSNVs in genes related to human diseases and for around 35% of nsSNVs in a typical bacterial sample. Each nsSNV can be visualized and inspected by the user in the corresponding 3D structure of a protein or protein complex. The StructMAn server is available at http://structman.mpi-inf.mpg.de., (© The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.)

ADAM17, a prominent member of the 'Disintegrin and Metalloproteinase' (ADAM) family, controls vital cellular functions through cleavage of transmembrane substrates. Here we present evidence that surface exposure of phosphatidylserine (PS) is pivotal for ADAM17 to exert sheddase activity. PS exposure is tightly coupled to substrate shedding provoked by diverse ADAM17 activators. PS dependency is demonstrated in the following: (a) in Raji cells undergoing apoptosis; (b) in mutant PSA-3 cells with manipulatable PS content; and (c) in Scott syndrome lymphocytes genetically defunct in their capacity to externalize PS in response to intracellular Ca(2+) elevation. Soluble phosphorylserine but not phosphorylcholine inhibits substrate cleavage. The isolated membrane proximal domain (MPD) of ADAM17 binds to PS but not to phosphatidylcholine liposomes. A cationic PS-binding motif is identified in this domain, replacement of which abrogates liposome-binding and renders the protease incapable of cleaving its substrates in cells. We speculate that surface-exposed PS directs the protease to its targets where it then executes its shedding function.

In recent years, we have witnessed a substantial increase of the amount of available protein interaction data. However, most data are currently not readily accessible to the biologist at a single site, but scattered over multiple online repositories. Therefore, we have developed the DASMIweb server that affords the integration, analysis and qualitative assessment of distributed sources of interaction data in a dynamic fashion. Since DASMIweb allows for querying many different resources of protein and domain interactions simultaneously, it serves as an important starting point for interactome studies and assists the user in finding publicly accessible interaction data with minimal effort. The pool of queried resources is fully configurable and supports the inclusion of own interaction data or confidence scores. In particular, DASMIweb integrates confidence measures like functional similarity scores to assess individual interactions. The retrieved results can be exported in different file formats like MITAB or SIF. DASMIweb is freely available at http://www.dasmiweb.de.

Motivation: Ever increasing amounts of biological interaction data are being accumulated worldwide, but they are currently not readily accessible to the biologist at a single site. New techniques are required for retrieving, sharing and presenting data spread over the Internet., Results: We introduce the DASMI system for the dynamic exchange, annotation and assessment of molecular interaction data. DASMI is based on the widely used Distributed Annotation System (DAS) and consists of a data exchange specification, web servers for providing the interaction data and clients for data integration and visualization. The decentralized architecture of DASMI affords the online retrieval of the most recent data from distributed sources and databases. DASMI can also be extended easily by adding new data sources and clients. We describe all DASMI components and demonstrate their use for protein and domain interactions., Availability: The DASMI tools are available at http://www.dasmi.de/ and http://ipfam.sanger.ac.uk/graph. The DAS registry and the DAS 1.53E specification is found at http://www.dasregistry.org/.

Background: Expert-based genotypic interpretation systems are standard methods for guiding treatment selection for patients infected with human immunodeficiency virus type 1. We previously introduced the software pipeline geno2pheno-THEO (g2p-THEO), which on the basis of viral sequence predicts the response to treatment with a combination of antiretroviral compounds by applying methods from statistical learning and the estimated potential of the virus to escape from drug pressure., Methods: We retrospectively validated the statistical model used by g2p-THEO in approximately 7600 independent treatment-sequence pairs extracted from the EuResist integrated database, ranging from 1990 to 2007. Results were compared with the 3 most widely used expert-based interpretation systems: Stanford HIVdb, ANRS, and Rega., Results: The difference in receiver operating characteristic curves between g2p-THEO and expert-based approaches was significant (P < .001; paired Wilcoxon test). Indeed, at 80% specificity, g2p-THEO found 16.2%-19.8% more successful regimens than did the expert-based approaches. The increased performance of g2p-THEO was confirmed in a 2001-2007 data set from which most obsolete therapies had been removed., Conclusion: Finding drug combinations that increase the chances of therapeutic success is the main reason for using decision support systems. The present analysis of a large data set derived from clinical practice demonstrates that g2p-THEO solves this task significantly better than state-of-the-art expert-based systems. The tool is available at http://www.geno2pheno.org.

The EpiGRAPH web service http://epigraph.mpi-inf.mpg.de/ enables biologists to uncover hidden associations in vertebrate genome and epigenome datasets. Users can upload sets of genomic regions and EpiGRAPH will test multiple attributes (including DNA sequence, chromatin structure, epigenetic modifications and evolutionary conservation) for enrichment or depletion among these regions. Furthermore, EpiGRAPH learns to predictively identify similar genomic regions. This paper demonstrates EpiGRAPH's practical utility in a case study on monoallelic gene expression and describes its novel approach to reproducible bioinformatic analysis.

Background: We compared several statistical learning methods for the prediction of HIV coreceptor use from clonal HIV third hypervariable (V3) loop sequences, and evaluated and improved their effectiveness on clinical samples., Methods: Support vector machines (SVM), artificial neural networks, position-specific scoring matrices (PSSM) and mixtures of localized rules were estimated and tested using 10x ten-fold cross-validation on a clonal dataset consisting of 1,100 matched clonal genotype-phenotype pairs from 332 patients. Different SVMs were also trained and tested on a clinically derived dataset, representing 920 patient samples from British Columbia, Canada. Methods were evaluated using receiver operating characteristic (ROC) curves., Results: In the clonal analysis, the sensitivity of the 11/25 rule at 92.5% specificity was 59.5%. PSSMs and SVMs increased sensitivity to 71.9% and 76.4%, respectively, at the same specificity (P < < 0.05). In clinical samples, the sensitivity of the 11/25 rule and SVM decreased to 25.9% (specificity 93.9%) and 39.8% (specificity 93.5%), respectively. However, the integration of clinical data resulted in a further 2.4-fold increase in sensitivity over the 11/25 rule (63%). Univariate analyses identified 41 V3 mutations significantly associated with coreceptor usage., Conclusion: For all methods tested, a substantial sensitivity decrease is observed on clinical data, probably owing to the heterogeneity of the viral population in vivo. In response to these complications, we present an SVM-based approach that integrates sequence information with clinical and host data, resulting in improved performance and sensitivity compared with purely sequence-based approaches.