Biology subjects

Oswald, M.

Publications and source records attributed to Oswald, M..

9 recordsLinked to original sources

bioRxiv2023

GlnA3Mt is able to glutamylate spermine but it is not essential for the detoxification of spermine in Mycobacterium tuberculosis

Mycobacterium tuberculosis is well adapted to survive and persist in the infected host, escaping the host immune response. Since polyamines, which are synthesized by infected macrophages are able to inhibit the growth of M. tuberculosis, the pathogen needs strategies to cope with toxic spermine. The actinomycete Streptomyces coelicolor, closely related to M. tuberculosis makes use of a gamma-glutamylation pathway to functionally neutralize spermine. We therefore considered whether a similar pathway would be functional in M. tuberculosis. In the current study we demonstrated that M. tuberculosis growth was inhibited by the polyamine spermine. Using a glutamine synthetase-based in vitro enzymatic activity assay we determined that GlnA3Mt (Rv1878) is a gamma-glutamylspermine synthetase. In an in vitro phosphate release assay we showed that purified His-Strep-GlnA3Mt as well as native GlnA3Mt prefer spermine as a substrate to putrescine, cadaverine, spermidine or other monoamines and amino acids, suggesting that GlnA3Mt may play a specific role in the detoxification of the polyamine spermine. However, the deletion of the glnA3 gene in M. tuberculosis did not result in growth inhibition or enhanced sensitivity of M. tuberculosis in the presence of high spermine concentrations. Subsequent RNAsequencing of M. tuberculosis bacteria revealed that the gene cluster consisting of the efflux pump-encoding rv3065-rv3066-rv3067 genes is upregulated upon spermine treatment, suggesting its involvement in bacterial survival under elevated spermine concentrations. IMPORTANCEAntibiotics for the treatment of Mycobacterium tuberculosis infections attack classical bacterial targets, such as the cell envelope or the ribosome. Upon M. tuberculosis infection macrophages synthesize the polyamine spermine which - at elevated concentrations - is toxic for M. tuberculosis. Based on our investigations of spermine resistance in the closely related actinomycete Streptomyces coelicolor, we hypothesized that the glutamyl-sperminesynthetase GlnA3 may be responsible for resistance against toxic spermine. Here we show that the mycobacterial glutamyl-sperminesynthetase indeed can inactivate spermine by glutamylation. However, GlnA3 is probably not the only resistance mechanism since a glnA3 mutant of M. tuberculosis can survive under spermine stress. Gene expression studies suggest that an efflux pump may participate in resistance. The functional role of GlnA3Mt as well as of the spermine transporter in the pathogenicity of M. tuberculosis is of special interest for their validation as new targets of novel anti-tubercular drugs.

microbiology↗

bioRxiv2023

Prevalence of Avian Influenza Virus in Synanthropic Birds Associated with an Outbreak of Highly Pathogenic Strain EA/AM H5N1

The 2022 - 2023 highly pathogenic avian influenza (HPAI) virus outbreak of H5N1 Eurasian lineage goose/Guangdong clade 2.3.4.4b is the largest in North American history and has significantly impacted wild bird populations and domestic poultry across the United States. Synanthropic birds may play an important role in transmitting the virus laterally to other wild bird species and domestic poultry. Understanding the prevalence of HPAI H5N1 in different avian orders may help inform management decisions and potential risk factors for both wild and domestic bird populations. Following the confirmation of infection of HPAI H5N1 in domestic poultry at two commercial premises in IN, USA, we sampled and tested 266 synanthropic avian species within the Columbiformes and Passeriformes orders and found no detection of the virus at either location. Additionally, laboratories within the National Animal Health Laboratory Network were queried for influenza Type A rRT-PCR assay test results from morbidity and mortality events in wild birds, consisting of 10,786 birds tested across eight orders and 1,666 avian influenza virus detections. Query results were assessed by taxonomic groups for viral prevalence and suggested that the virus most often was observed in predatory and scavenging birds. Although detections were found in non-predatory synanthropic birds including the orders Columbiformes, Galliformes, and Passeriformes, the risk of transmission from and between these groups appears comparatively low, with apparent prevalence rates of 0.0090, 0.0688, and 0.0147, respectively. The highest prevalence was observed in raptors (0.2514), with prevalence rates in exclusively scavenging Cathartidae reaching up to 0.5333. There is strong evidence that consumption of infected tissues is a key pathway for transmission of avian influenza viruses. Understanding the impact of the 2022 - 2023 HPAI outbreak in wild bird populations can provide pertinent information on viral transmission, disease ecology, and risk to humans and agriculture.

ecology↗

bioRxiv2023

SARS-CoV-2 occurrence in white-tailed deer throughout their range in the conterminous United States

Broad-scale data show SARS-CoV-2 occurrence in white-tailed deer throughout much of their range in the conterminous United States and reinforce findings of considerable SARS-CoV-2 infection and exposure. Results shed light on both current infections and prior exposure, with prevalence decreasing over time and seroprevalence increasing. One-Sentence SummaryWhite-tailed deer are infected with, and have been exposed to, SARS-CoV-2 throughout their range in the conterminous US.

ecology↗

bioRxiv2023

Machine learning on large scale perturbation screens for SARS-CoV-2 host factors identifies β-catenin/CBP inhibitor PRI-724 as a potent antiviral

Expanding antiviral treatment options against SARS-CoV-2 remains crucial as the virus evolves rapidly and drug resistant strains have emerged. Broad spectrum host-directed antivirals (HDA) are promising therapeutic options, however the robust identification of relevant host factors by CRISPR/Cas9 or RNA interference screens remains challenging due to low consistency in the resulting hits. To address this issue, we employed machine learning based on experimental data from knockout screens and a drug screen. As gold standard, we assembled perturbed genes reducing virus replication or protecting the host cells. The machines based their predictions on features describing cellular localization, protein domains, annotated gene sets from Gene Ontology, gene and protein sequences, and experimental data from proteomics, phospho-proteomics, protein interaction and transcriptomic profiles of SARS-CoV-2 infected cells. The models reached a remarkable performance with a balanced accuracy of 0.82 (knockout based classifier) and 0.71 (drugs screen based classifier), suggesting patterns of intrinsic data consistency. The predicted host dependency factors were enriched in sets of genes particularly coding for development, morphogenesis, and neural related processes. Focusing on development and morphogenesis-associated gene sets, we found {beta}-catenin to be central and selected PRI-724, a canonical {beta}-catenin/CBP disruptor, as a potential HDA. PRI-724 limited infection with SARS-CoV-2 variants, SARS-CoV-1, MERS-CoV and IAV in different cell line models. We detected a concentration-dependent reduction in CPE development, viral RNA replication, and infectious virus production in SARS-CoV-2 and SARS-CoV-1-infected cells. Independent of virus infection, PRI-724 treatment caused cell cycle deregulation which substantiates its potential as a broad spectrum antiviral. Our proposed machine learning concept may support focusing and accelerating the discovery of host dependency factors and the design of antiviral therapies. Authors summaryDrug resistance to pathogens is a well-known phenomenon which was also observed for SARS-CoV-2. Given the gradually increasing evolutionary pressure on the virus by herd immunity, we attempted to enlarge the available antiviral repertoire by focusing on host proteins that are usurped by viruses. The identification of such proteins was followed within several high throughput screens in which genes are knocked out individually. But, so far, these efforts led to very different results. Machine learning helps to identify common patterns and normalizes independent studies to their individual designs. With such an approach, we identified genes that are indispensable during embryonic development, i.e., when cells are programmed for their specific destiny. Shortlisting the hits revealed {beta}-catenin, a central player during development, and PRI-724, which inhibits the interaction of {beta}-catenin with cAMP responsive element binding (CREB) binding protein (CBP). In our work, we confirmed that the disruption of this interaction impedes virus replication and production. In A549-AT cells treated with PRI-724, we observed cell cycle deregulation which might contribute to the inhibition of virus infection, however the exact underlying mechanisms needs further investigation.

microbiology↗

bioRxiv2019

Predicting the carbon source for Bacillus subtilis by integrating gene expression profiles into a constraint-based metabolic model

Finding drug targets for antimicrobial treatment is a central focus in biomedical research. To discover new drug targets, we developed a method to identify which nutrients are essential for microorganisms. Using 13C labeled metabolites to infer metabolic fluxes is the most informative way to infer metabolic fluxes to date. However, the data can get difficult to acquire in complicated environments, for example, if the pathogen homes in host cells. Although data from gene expression profiling is less informative compared to metabolic tracer derived data, its generation is less laborious, and may still provide the relevant information. Besides this, metabolic fluxes have been successfully predicted by flux balance analysis (FBA). We developed an FBA based approach using the stoichiometric knowledge of the metabolic reactions of a cell combining them with expression profiles of the coding genes. We aimed to identify essential drug targets for specific nutritional uptakes of microorganisms. As a case study, we predicted each single carbon source out of a pool of eight different carbon sources for B. subtilis based on gene expression profiles. The models were in good agreement to models basing on 13C metabolic flux data of the same conditions. We could well predict every carbon source. Later, we applied successfully the model to unseen data from a study in which the carbon source was shifted from glucose to malate and vice versa. Technically, we present a new and fast method to reduce thermodynamically infeasible loops, which is a necessary preprocessing step for such model-building algorithms. SIGNIFICANCEIdentifying metabolic fluxes using 13C labeled tracers is the most informative way to gain insight into metabolic fluxes. However, obtaining the data can be laborious and challenging in a complex environment. Though transcriptional data is an indirect mean to estimate the fluxes, it can help to identify this. Here, we developed a new method employing constraint-based modeling to predict metabolic fluxes embedding gene expression profiles in a linear regression model. As a case study, we used the data from Bacillus subtilis grown under different carbon sources. We could well predict the correct carbon source. Additionally, we established a novel and fast method to remove thermodynamically infeasible loops.

bioinformatics↗

bioRxiv2019

Coincidence of cholinergic pauses, dopaminergic activation and depolarization drives synaptic plasticity in the striatum

Pauses in the firing of tonically-active cholinergic interneurons (ChIs) in the striatum coincide with phasic activation of dopamine neurons during reinforcement learning. However, how this pause influences cellular substrates of learning is unclear. Using two in vivo paradigms, we report that long-term potentiation (LTP) at corticostriatal synapses with spiny projection neurons (SPNs) is dependent on the temporal coincidence of ChI pause and dopamine phasic activation, critically accompanied by SPN depolarization.

neuroscience↗

bioRxiv2019

Modelling TERT regulation across 19 different cancer types based on the MIPRIP 2.0 gene regulatory network approach

BackgroundReactivation of the telomerase reverse transcriptase gene TERT is a central feature for the unlimited proliferation potential of the majority of cancers but the underlying regulatory processes are only partly understood.\n\nResultsWe assembled regulator binding information from different sources to construct a generic human and mouse regulatory network. Advancing our \"Mixed Integer linear Programming based Regulatory Interaction Predictor\" (MIPRIP) approach, we identified the most common and cancer-type specific regulators of TERT across 19 different human cancers. The results were validated by using the well-known TERT regulation by the ETS1 transcription factor in a subset of melanomas with mutations in the TERT promoter.\n\nConclusionOur improved MIPRIP2 R-package and the associated generic regulatory networks are freely available at https://github.com/network-modeling/MIPRIP. MIPRIP 2.0 identified both common as well as tumor type specific regulators of TERT. The software can be easily applied to transcriptome datasets to predict gene regulation for any gene and disease/condition under investigation.

bioinformatics↗

bioRxiv2018

Use of IFNγ/IL10 ratio for stratification of hydrocortisone therapy in patients with septic shock

BackgroundLarge clinical trials testing hydrocortisone therapy in septic shock have produced conflicting results. Subgroups may however benefit depending on their individual immune response.\n\nMethodsWe performed an exploratory analysis of the CORTICUS trial database employing machine learning to a panel of 137 variables collected from 83 patients (60 survivors, 23 non-survivors) including demographic and clinical measures, organ failure scores, leukocyte counts and circulating cytokine levels. The identified biomarker was validated against data collected from patients enrolled into a cohort of the Hellenic Sepsis Study Group (HSSG) (n=162) and two data sets of two other clinical trials. Ex vivo studies were performed on this biomarker to assess a possible mechanistic role.\n\nResultsA low serum IFN{gamma}/IL10 ratio predicted increased survival in the hydrocortisone group whereas a high ratio predicted better survival in the placebo group. Using this ratio for a decision rule, we found significant improvement in survival in the groups of patients being in compliance with the prediction rule (discovery set: OR=3.03 [95% Cl: 1.05-8.75], P=0.031, validation set: OR=2.01 [95% CI: 1.04-3.88], P=0.026). Applying the rule to two further, smaller datasets showed the same tendency. Mechanistic studies revealed that IFN{gamma}/IL10 was negatively associated with pathogen load in spiked human blood. An in silico analysis of published IFN{gamma} and IL10 values in bacteremic and non-bacteremic SIRS patients supported this association between the ratio and pathogen burden.\n\nConclusionIf confirmed prospectively, the IFN{gamma}/IL10 ratio could be used as a rapidly available theranostic for use of hydrocortisone therapy in septic shock.

immunology↗

bioRxiv2017

Dissecting telomere maintenance mechanisms in pediatric glioblastoma

Pediatric glioblastoma (pedGBM) represent a highly malignant primary brain tumor with recurrent mutations in the chromatin remodeler ATRX and the histone variant H3.3 that is typically associated with a fatal outcome. ATRX acts as suppressor of the alternative lengthening of telomeres (ALT) pathway, which is frequently activated in pedGBM. However, telomere features of pedGBMs have not been studied in detail, and ALT-positive model cell lines are lacking. Here, we systematically characterized a panel of pedGBM models that carry a representative set of recurrent genomic mutations for a variety of telomere features. These included the presence of ALT-associated promyelocytic leukemia nuclear bodies and C-circles, a specific type of extrachromosomal telomeric repeats, the telomere repeat content, and phosphorylation of histone H3.3 at serine 31. From an integrated analysis of seven pedGBM cell lines and 57 primary tumor samples we identified cell lines and tumors that represent the different telomere maintenance mechanisms and conclude the following: (i) A positive signal in the C-circle assay is a reliable ALT marker. (ii) ALT features occur heterogeneously and one pedGBM subgroup uses a non-canonical ALT mechanism in the presence of wild-type ATRX. (iii) The spreading of H3.3S31 phosphorylation during mitosis is associated with loss of ATRX but not with ALT per se. (iv) In contrast to a previous study in glioma stem cells, we did not find a hypersensitivity of ALT cells towards the ATR inhibitor VE-821. (v) ALT-positive pedGBMs can be reliably identified from a classification scheme developed here that evaluates various combinations of cytogenetic and/or genomic data. Thus, our findings elucidate further details of the ALT pathway in pedGBMs, provide valuable models for evaluating ALT targeted therapies in a preclinical setting, and introduce an ALT classification scheme for primary tumor samples.

cancer biology↗