Biology subjects

Koenig, R.

Publications and source records attributed to Koenig, R..

9 recordsLinked to original sources

bioRxiv2024

Hemispheric difference of adaptation lifetime in human auditory cortex measured with MEG

Adaptation is the attenuation of a neuronal response when a stimulus is repeatedly presented. The phenomenon has been linked to sensory memory, but its exact neuronal mechanisms are under debate. One defining feature of adaptation is its lifetime, that is, the timespan over which the attenuating effect of previous stimulation persists. This can be revealed by varying the stimulus-onset interval (SOI) of the repeated stimulus. As SOI is increased, the peak amplitude of the response grows before saturating at large SOIs. The rate of this growth can be quantified and used as an estimate of adaptation lifetime. Here, we studied whether adaptation lifetime varies across the left and the right auditory cortex of the human brain. Event-related fields of whole-head magnetoencephalograms (MEG) were measured in 14 subjects during binaural presentation of pure tone stimuli. To make statistical inferences on the single-subject level, additional event-related fields were generated by resampling the original single-trial data via bootstrapping. For each hemisphere and SOI, the peak amplitude of the N1m response was then derived from both original and bootstrap-based data sets. Finally, the N1m peak amplitudes we used for deriving subject-and hemisphere-specific estimates of adaptation lifetime. Comparing subject-specific adaptation lifetime across hemispheres, we found a significant difference, with longer adaptation lifetimes in the left than in the right auditory cortex (p = 0.004). This difference might have a functional relevance in the context of temporal binding of auditory stimuli, leading to larger integration time windows in the left than in the right hemisphere.

neuroscience↗

bioRxiv2022

Recognition of HIV-1 capsid licenses innate immune response to viral infection

Cyclic GMP-AMP synthase (cGAS) is a primary sensor of aberrant DNA that governs an innate immune signaling cascade, leading to the induction of the type-I interferon response. We have previously identified polyglutamine binding protein 1, PQBP1, as an adaptor molecule required for cGAS-mediated innate immune response of lentiviruses, including the human immunodeficiency virus 1 (HIV-1), but dispensable for the recognition of DNA viruses. HIV-1- encoded DNA is synthesized as a single copy from its RNA genome, and is subsequently integrated into the host chromatin. HIV-1 then produces progeny through amplification and packaging of its RNA genome, thus, in contrast to DNA viruses, HIV-1 DNA is both transient and of low abundance. However, the molecular basis for the detection and verification of this low abundance HIV-1 DNA pathogen-associated molecular pattern (PAMP) is not understood. Here, we elucidate a two-factor authentication strategy that is employed by the innate immune surveillance machinery to selectively respond to the low concentration of PAMP, while discerning these species from extranuclear DNA molecules. We find that, upon HIV-1 infection, PQBP1 decorates intact viral capsid, which serves as a primary verification step for the viral nucleic acid cargo. As the reverse transcription and capsid disassembly initiate, cGAS protein is then recruited to the capsid in a PQBP1-dependent manner, enabling cGAS molecules to be co-positioned at the site of PAMP generation. Thus, these data indicate that PQBP1 recognition of the HIV-1 capsid sanctions a robust cGAS-dependent response to a limited abundance and short-lived DNA PAMP. Critically, this illuminates a molecular strategy wherein the modular recruitment of co-factors to germline encoded pattern recognition receptors (PRRs) serves to enhance repertoire of pathogens that can be sensed by the innate immune surveillance machinery.

molecular biology↗

bioRxiv2021

Human transcriptional gene regulatory network compiled from 14 data resources

Transcriptional regulatory network (TRN) orchestrates spatio-temporal expression of genes to generate cellular responses for survival. The transcription factors (TF) regulating expression of their target genes (TG) are the fundamental units of TRN. Several databases have been developed to catalogue human TRN based on low- and high-throughput experimental and computational studies considering their importance in understanding cellular physiology. However, literature lacks comparative assessment on the strength and weakness of each database. In this study, we compared over 2.2 million regulatory pairs between 1,379 TF and 22,518 TG assembled from 14 data resources. Our study reveals that the TF and TG were common across data resources but not their regulatory pairs. We observed that the TF and TG of the regulatory pairs showed weak expression correlation, significant gene ontology overlap, co-citations in PubMed and low numbers of TF-TG pairs representing transcriptional repression relationships. Furthermore, each TF-TG regulatory pair assigned a combined confidence score reflecting its reliability based on its presence in multiple databases and co-expression. The TRN containing 2,246,598 TF-TG pairs, of which, 44,284 with the information on TFs activating or repressing effects on their TG is available upon request. This study brings the information about transcriptional regulation scattered over the literature and databases at one place in the form of one of the most comprehensive and complete human TRNs assembled to date, which will be a valuable resource for benchmarking TRN prediction tools, and to the scientific community working in functional genomics, gene expression and gene regulation analysis.

bioinformatics↗

bioRxiv2021

Identifying essential genes across eukaryotes by machine learning

Identifying essential genes on a genome scale is resource intensive and has been performed for only a few eukaryotes. For less studied organisms essentiality might be predicted by gene homology. However, this approach cannot be applied to non-conserved genes. Additionally, divergent essentiality information is obtained from studying single cells or whole, multi-cellular organisms, and particularly when derived from human cell line screens and human population studies. We employed machine learning across six model eukaryotes and 60,381 genes, using 41,635 features derived from sequence, gene functions and network topology. Within a leave-one-organism-out cross-validation, the classifiers showed a high generalizability with an average accuracy close to 80% in the left-out species. As a case study, we applied the method to Tribolium castaneum and validated predictions experimentally yielding similar performance. Finally, using the classifier based on the studied model organisms enabled linking the essentiality information of human cell line screens and population studies.

bioinformatics↗

bioRxiv2019

HBV DNA is a substrate for the cGAS/STING pathway but is not sensed in infected hepatocytes

HBV chronic infection is a critical risk factor for hepatocellular carcinoma. Although debated, the absence of innate immune response to HBV infection in hepatocytes is becoming the current view. However the underlying reasons are poorly understood. This study aims to define potential viral pathogen-associated molecular patterns (PAMPs) and the pattern recognition receptors (PRRs), and to elucidate whether HBV counteracts the innate pathways. The innate immune response to HBV infection was monitored by interferon-stimulated gene 54 (ISG54) mRNA, a direct downstream transcriptional target of Interferon Regulatory Factor 3 (IRF3), or IRF3 phosphorylation. The immunostimulatory potential of naked HBV DNAs or RNAs and the respective PRRs were determined upon viral nucleic acid transfection in immunocompetent cells including knockout cells lacking key molecules of innate pathways. The expression and functionality of DNA and RNA sensing pathways in primary human hepatocytes (PHH) were assessed. The inhibition of the DNA-sensing pathway by HBV was tested using IRF3 nuclear translocation assay. Our study revealed that HBV infection does not induce an innate response in infected hepatocytes, even in absence of HBV X protein. HBV relaxed-circular DNA (rcDNA) and DNA replication intermediates, but not HBV RNAs, are immunostimulatory and sensed by Cyclic Guanosine Monophosphate-Adenosine Monophosphate Synthase (cGAS) and Stimulator of Interferon Genes (STING). Although PHH express DNA sensors to reduced levels compared to myeloid cells, they can respond to naked HBV rcDNA. However, we show that the absence of innate response to HBV infection in hepatocytes is not due to an active inhibition of the DNA sensing pathway by the virus. HBV passively evades the innate immune response in infected hepatocytes by (i) producing non-immunostimulatory RNAs, (ii) avoiding sensing of its DNAs by cGAS/STING without active inhibition of the pathway, possibly through shielding of the viral DNAs by the capsid. Author summaryInnate immune responses are the first line of defense against viral infections. They lead to the production of antiviral factors after recognition of specific viral features by the infected cells. Here we show that HBV, a major cause of liver cirrhosis and cancer, avoids recognition by infected hepatocytes through different means. First, HBV RNAs, contrarily to other viral RNAs, are not immunostimulatory. Second, we show that naked HBV DNAs are recognized by cGAS/STING and induce an innate immune response. Furthermore, we demonstrate that this pathway is active in hepatocytes and is not inhibited by the virus. Instead, we propose that HBV DNAs are not accessible to cGAS/STING in the context of an infection. This might be due to shielding of the viral DNA by the viral capsid.

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

Atxn2-CAG100-KnockIn mouse spinal cord shows progressive TDP43 pathology associated with cholesterol biosynthesis suppression

Large polyglutamine expansions in Ataxin-2 (ATXN2) cause multi-system nervous atrophy in Spinocerebellar Ataxia type 2 (SCA2). Intermediate size expansions carry a risk for selective motor neuron degeneration, known as Amyotrophic Lateral Sclerosis (ALS). Conversely, the depletion of ATXN2 prevents disease progression in ALS. Although ATXN2 interacts directly with RNA, and in ALS pathogenesis there is a crucial role of RNA toxicity, the affected functional pathways remain ill defined. Here, we examined an authentic SCA2 mouse model with Atxn2-CAG100-KnockIn for a first definition of molecular mechanisms in spinal cord pathology. Neurophysiology of lower limbs detected sensory neuropathy rather than motor denervation. Triple immunofluorescence demonstrated cytosolic ATXN2 aggregates sequestrating TDP43 and TIA1 from the nucleus. In immunoblots, this was accompanied by elevated CASP3, RIPK1 and PQBP1 abundance. RT-qPCR showed increase of Grn, Tlr7 and Rnaset2 mRNA versus Eif5a2, Dcp2, Uhmk1 and Kif5a decrease. These SCA2 findings overlap well with known ALS features. Similar to other ataxias and dystonias, decreased mRNA levels for Unc80, Tacr1, Gnal, Ano3, Kcna2, Elovl5 and Cdr1 contrasted with Gpnmb increase. Preterminal stage tissue showed strongly activated microglia containing ATXN2 aggregates, with parallel astrogliosis. Global transcriptome profiles from stages of incipient motor deficit versus preterminal age identified molecules with progressive downregulation, where a cluster of cholesterol biosynthesis enzymes including Dhcr24, Msmo1, Idi1 and Hmgcs1 was prominent. Gas chromatography demonstrated a massive loss of crucial cholesterol precursor metabolites. Overall, the ATXN2 protein aggregation process affects diverse subcellular compartments, in particular stress granules, endoplasmic reticulum and receptor tyrosine kinase signaling. These findings identify new targets and potential biomarkers for neuroprotective therapies.

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↗