Applications of semantic similarity measures

There has been much interest in uncovering protein-protein interactions and their underlying domain-domain interactions. Many experimental techniques have been developed, for example yeast-two-hybrid screening and tandem affinity purification. Since it is time consuming and expensive to perform exhaustive experimental screens, in silico methods are used for predicting interactions. However, all experimental and computational methods have considerable false positive and false negative rates. Therefore, it is necessary to validate experimentally determined and predicted interactions. One possibility for the validation of interactions is the comparison of the functions of the proteins or domains. Gene Ontology (GO) is widely accepted as a standard vocabulary for functional terms, and is used for annotating proteins and protein families with biological processes and their molecular functions. This annotation can be used for a functional comparison of interacting proteins or domains using semantic similarity measures. Another application of semantic similarity measures is the prioritization of disease genes. It is know that functionally similar proteins are often involved in the same or similar diseases. Therefore, functional similarity is used for predicting disease associations of proteins. In the first part of my talk, I will introduce some semantic and functional similarity measures that can be used for comparison of GO terms and proteins or protein families. Then, I will show their application for determining a confidence threshold for domain-domain interaction predictions. Additionally, I will present FunSimMat (http://www.funsimmat.de/), a comprehensive resource of functional similarity values available on the web. In the last part, I will introduce the problem of comparing diseases, and a first attempt to apply functional similarity measures based on GO to this problem

HIV-1 fitness landscape models for indinavir treatment pressure using observed evolution in longitudinal sequence data are predictive for treatment failure

We previously modeled the in vivo evolution of human immunodeficiency virus-1 (HIV-1) under drug selective pressure from cross-sectional viral sequences. These fitness landscapes (FLs) were made by using first a Bayesian network (BN) to map epistatic substitutions, followed by scaling the fitness landscape based on an HIV evolution simulator trying to evolve the sequences from treatment naïve patients into sequences from patients failing treatment. In this study, we compared four FLs trained with different sequence populations. Epistatic interactions were learned from three different cross-sectional BNs, trained with sequence from patients experienced with indinavir (BNT), all protease inhibitors (PIs) (BNP) or all PI except indinavir (BND). Scaling the fitness landscape was done using cross-sectional data from drug naïve and indinavir experienced patients (Fcross using BNT) and using longitudinal sequences from patients failing indinavir (FlongT using BNT, FlongP using BNP, FlongD using BND). Evaluation to predict the failing sequence and therapy outcome was performed on independent sequences of patients on indinavir. Parameters included estimated fitness (LogF), the number of generations (GF) or mutations (MF) to reach the fitness threshold (average fitness when a major resistance mutation appeared), the number of generations (GR) or mutations (MR) to reach a major resistance mutation and compared to genotypic susceptibility score (GSS) from Rega and HIVdb algorithms. In pairwise FL comparisons we found significant correlation between fitness values for individual sequences, and this correlation improved after correcting for the subtype. Furthermore, FLs could predict the failing sequence under indinavir-containing combinations. At 12 and 48 weeks, all parameters from all FLs and indinavir GSS (both for Rega and HIVdb) were predictive of therapy outcome, except MR for FlongT and FlongP. The fitness landscapes have similar predictive power for treatment response under indinavir-containing regimen as standard rules-based algorithms, and additionally allow predicting genetic evolution under indinavir selective pressure

Gut Microbiome Dysbiosis in Antibiotic-Treated COVID-19 Patients is Associated with Microbial Translocation and Bacteremia

Although microbial populations in the gut microbiome are associated with COVID-19 severity, a causal impact on patient health has not been established. Here we provide evidence that gut microbiome dysbiosis is associated with translocation of bacteria into the blood during COVID-19, causing life-threatening secondary infections. We first demonstrate SARS-CoV-2 infection induces gut microbiome dysbiosis in mice, which correlated with alterations to Paneth cells and goblet cells, and markers of barrier permeability. Samples collected from 96 COVID-19 patients at two different clinical sites also revealed substantial gut microbiome dysbiosis, including blooms of opportunistic pathogenic bacterial genera known to include antimicrobial-resistant species. Analysis of blood culture results testing for secondary microbial bloodstream infections with paired microbiome data indicates that bacteria may translocate from the gut into the systemic circulation of COVID-19 patients. These results are consistent with a direct role for gut microbiome dysbiosis in enabling dangerous secondary infections during COVID-19

Verbesserung und Validierung von datenbasierten genotypischen Interpretationssystemen zur Auswahl von antiretroviralen Therapien

Infection with Human immunodeficiency vir type 1 (HIV-1) requires treatment with a combination of antiretroviral drugs. This combination of drugs must be selected under consideration of its prospects for attaining sustained therapeutic success. Genotypic therapy-success interpretation systems can be used for selecting a combination of antiretroviral compounds. However, a number of shortcomings of these systems have prevented them from reaching the bedside. In this work, I present and validate novel methods for deriving interpretable genotype interpretation systems that are trained on HIV-1 data from routine clinical practice. One method produces scores that are correlated with previous exposure of the virus to the drug and with drug resistance. A further, novel genotype interpretation system produces a prognostic score correlated with the time for which the antiretroviral therapy with a certain drug combination will remain effective. The methods presented in this work represent an important advance in techniques for the interpretation of viral genotypes. Validation of the methods shows that their performance is comparable or, most frequently, superior to that of previously available methods. The methods are interpretable and can be retrained without the need for expert intervention. Last but not least, long-term therapeutic success is considered by the methods such that their predictions are in line with the results of clinical studies.Eine Infektion mit dem Humanen Immunodefizienz-Virus Typ 1 (HIV-1) erfordert die Behandlung des Patienten mit einer Kombination von antiretroviralen Wirkstoffen. Die Auswahl dieser Wirkstoffkombination muss unter Berücksichtigung der Aussichten für einen lang anhaltenden Behandlungserfolg stattfinden. Bei der Auswahl von Wirkstoffkombinationen können Systeme zur Vorhersage des Behandlungserfolgs eingesetzt werden. Bisher verfügbare Systeme weisen jedoch mehrere Defizite auf, sodass sie in der klinischen Praxis kaum Verwendung finden. In dieser Arbeit werden neuartige Methoden zur Aufstellung von Systemen zur Genotypinterpretation präsentiert und validiert. Eine dieser Methoden bewertet einen HIV-1-Genotyp bezüglich der vorhergehenden viralen Wirkstoffexposition und der Wirkstoffresistenzen. Eine weitere Genotypinterpretationsmethode errechnet eine prognostische Zahl, welche mit der Zeit korreliert, die eine antiretrovirale Therapie effektiv sein wird. Diese Arbeit stellt eine wichtige Weiterentwicklung der Methoden zur Interpretation von viralen Genotypen dar. Zum Einen ist das Vorhersagemögen der Modelle dieser Arbeit vergleichbar oder sogar höher als diejenige von bisher verfügbaren Modellen. Zum Anderen sind die Modelle dieser Arbeit interpretierbar und können ohne Expertensupervision neu trainiert werden. Darüber hinaus berücksichtigen die Methoden den Langzeittherapieerfolg, sodass ihre Vorhersagen mit den Ergebnissen klinischer Studien übereinstimmen

Improved therapy-success prediction with GSS estimated from clinical HIV-1 sequences

Introduction: Rules-based HIV-1 drug-resistance interpretation (DRI) systems disregard many amino-acid positions of the drug's target protein. The aims of this study are (1) the development of a drug-resistance interpretation system that is based on HIV-1 sequences from clinical practice rather than hard-to-get phenotypes, and (2) the assessment of the benefit of taking all available amino-acid positions into account for DRI. Materials and Methods: A dataset containing 34,934 therapy-naïve and 30,520 drug-exposed HIV-1 pol sequences with treatment history was extracted from the EuResist database and the Los Alamos National Laboratory database. 2,550 therapy-change-episode baseline sequences (TCEB) were assigned to test set A. Test set B contains 1,084 TCEB from the HIVdb TCE repository. Sequences from patients absent in the test sets were used to train three linear support vector machines to produce scores that predict drug exposure pertaining to each of 20 antiretrovirals: the first one uses the full amino-acid sequences (DEfull), the second one only considers IAS drug-resistance positions (DEonlyIAS), and the third one disregards IAS drug-resistance positions (DEnoIAS). For performance comparison, test sets A and B were evaluated with DEfull, DEnoIAS, DEonlyIAS, geno2pheno[resistance], HIVdb, ANRS, HIV-GRADE, and REGA. Clinically-validated cut-offs were used to convert the continuous output of the first four methods into susceptible-intermediate-resistant (SIR) predictions. With each method, a genetic susceptibility score (GSS) was calculated for each therapy episode in each test set by converting the SIR prediction for its compounds to integer: S=2, I=1, and R=0. The GSS were used to predict therapy success as defined by the EuResist standard datum definition. Statistical significance was assessed using a Wilcoxon signed-rank test. Results: A comparison of the therapy-success prediction performances among the different interpretation systems for test set A can be found in Table 1, while those for test set B are found in Figure 1. Therapy-success prediction of first-line therapies with DEnoIAS performed better than DEonlyIAS (p<10–16). Conclusions: Therapy success prediction benefits from the consideration of all available mutations. The increase in performance was largest in first-line therapies with transmitted drug-resistance mutations

Effects of sequence alterations on results from genotypic tropism testing

Background: geno2pheno([coreceptor]) is a bioinformatic method for genotypic tropism determination (GTD) which has been extensively validated. Objectives: GTD can be affected by sequencing/base-calling variability and unreliable representation of minority populations in Sanger bulk sequencing. This study aims at quantifying the robustness of geno2pheno([coreceptor]) with respect to these issues. GTD with a single amplification or in triplicate (henceforth singleton/triplicate) is considered. Study Design: From a dataset containing 67,997HIV-1 V3 nucleotide sequences, two datasets simulating sequencing variability were created. Further two datasets were created to simulate unreliable representation of minority variants. After interpretation of all sequences with geno2pheno[coreceptor], probabilities of change of predicted tropism were calculated. Results: geno2pheno([coreceptor]) tends to report reduced false-positive rates (FPRs) when sequence alterations are present. Triplicate FPRs tend to be lower than singleton FPRs, resulting in a bias towards classifying viruses as X4-capable. Alterations introduced into nucleotide sequences by simulation change singleton predicted tropism with a probability <= 2%. Triplicate prediction lowers this probability for predicted X4 tropism, but raises it for predicted R5 tropism <= 6%. Simulated limited detection of minority variants in X4 sequences resulted in unchanged predicted tropism with probability above 90% as compared to probability above 98% with triplicate FPRs. Conclusions: geno2pheno([coreceptor]) proved to be robust when sequence alterations are present and when detectable minorities are missed by bulk sequencing. Changes in tropism prediction due to sequence alterations as well as triplicate prediction are much more likely to result in false X4-capable predictions than in false R5 predictions. (C) 2015 Published by Elsevier B.V

Metabolic flux analysis gives an insight on verapamil induced changes in central metabolism of HL-1 cells.

Verapamil has been shown to inhibit glucose transport in several cell types. However, the consequences of this inhibition on central metabolism are not well known. In this study we focused on verapamil induced changes in metabolic fluxes in a murine atrial cell line (HL-1 cells). These cells were adapted to serum free conditions and incubated with 4 muM verapamil and [U-(1)(3)C(5)] glutamine. Specific extracellular metabolite uptake/production rates together with mass isotopomer fractions in alanine and glutamate were implemented into a metabolic network model to calculate metabolic flux distributions in the central metabolism. Verapamil decreased specific glucose consumption rate and glycolytic activity by 60%. Although the HL-1 cells show Warburg effect with high lactate production, verapamil treated cells completely stopped lactate production after 24 h while maintaining growth comparable to the untreated cells. Calculated fluxes in TCA cycle reactions as well as NADH/FADH(2) production rates were similar in both treated and untreated cells. This was confirmed by measurement of cell respiration. Reduction of lactate production seems to be the consequence of decreased glucose uptake due to verapamil. In case of tumors, this may have two fold effects; firstly depriving cancer cells of substrate for anaerobic glycolysis on which their growth is dependent; secondly changing pH of the tumor environment, as lactate secretion keeps the pH acidic and facilitates tumor growth. The results shown in this study may partly explain recent observations in which verapamil has been proposed to be a potential anticancer agent. Moreover, in biotechnological production using cell lines, verapamil may be used to reduce glucose uptake and lactate secretion thereby increasing protein production without introduction of genetic modifications and application of more complicated fed-batch processes

Using drug exposure for predicting drug resistance - A data-driven genotypic interpretation tool

Antiretroviral treatment history and past HIV-1 genotypes have been shown to be useful predictors for the success of antiretroviral therapy. However, this information may be unavailable or inaccurate, particularly for patients with multiple treatment lines often attending different clinics. We trained statistical models for predicting drug exposure from current HIV-1 genotype. These models were trained on 63,742 HIV-1 nucleotide sequences derived from patients with known therapeutic history, and on 6,836 genotype-phenotype pairs (GPPs). The mean performance regarding prediction of drug exposure on two test sets was 0.78 and 0.76 (ROC-AUC), respectively. The mean correlation to phenotypic resistance in GPPs was 0.51 (PhenoSense) and 0.46 (Antivirogram). Performance on prediction of therapy-success on two test sets based on genetic susceptibility scores was 0.71 and 0.63 (ROC-AUC), respectively. Compared to geno2pheno[resistance], our novel models display a similar or superior performance. Our models are freely available on the internet via www.geno2pheno.org. They can be used for inferring which drug compounds have previously been used by an HIV-1-infected patient, for predicting drug resistance, and for selecting an optimal antiretroviral therapy. Our data-driven models can be periodically retrained without expert intervention as clinical HIV-1 databases are updated and therefore reduce our dependency on hard-to-obtain GPPs