Biology subjects

Gehrke, S.

Publications and source records attributed to Gehrke, S..

4 recordsLinked to original sources

bioRxiv2024

The Unified Phenotype Ontology (uPheno): A framework for cross-species integrative phenomics

Phenotypic data are critical for understanding biological mechanisms and consequences of genomic variation, and are pivotal for clinical use cases such as disease diagnostics and treatment development. For over a century, vast quantities of phenotype data have been collected in many different contexts covering a variety of organisms. The emerging field of phenomics focuses on integrating and interpreting these data to inform biological hypotheses. A major impediment in phenomics is the wide range of distinct and disconnected approaches to recording the observable characteristics of an organism. Phenotype data are collected and curated using free text, single terms or combinations of terms, using multiple vocabularies, terminologies, or ontologies. Integrating these heterogeneous and often siloed data enables the application of biological knowledge both within and across species. Existing integration efforts are typically limited to mappings between pairs of terminologies; a generic knowledge representation that captures the full range of cross-species phenomics data is much needed. We have developed the Unified Phenotype Ontology (uPheno) framework, a community effort to provide an integration layer over domain-specific phenotype ontologies, as a single, unified, logical representation. uPheno comprises (1) a system for consistent computational definition of phenotype terms using ontology design patterns, maintained as a community library; (2) a hierarchical vocabulary of species-neutral phenotype terms under which their species-specific counterparts are grouped; and (3) mapping tables between species-specific ontologies. This harmonized representation supports use cases such as cross-species integration of genotype-phenotype associations from different organisms and cross-species informed variant prioritization.

bioinformatics↗

bioRxiv2023

The Ontology of Biological Attributes (OBA) - Computational Traits for the Life Sciences

Existing phenotype ontologies were originally developed to represent phenotypes that manifest as a character state in relation to a wild-type or other reference. However, these do not include the phenotypic trait or attribute categories required for the annotation of genome-wide association studies (GWAS), Quantitative Trait Loci (QTL) mappings or any population-focused measurable trait data. Moreover, variations in gene expression in response to environmental disturbances even without any genetic alterations can also be associated with particular biological attributes. The integration of trait and biological attribute information with an ever increasing body of chemical, environmental and biological data greatly facilitates computational analyses and it is also highly relevant to biomedical and clinical applications. The Ontology of Biological Attributes (OBA) is a formalised, species-independent collection of interoperable phenotypic trait categories that is intended to fulfil a data integration role. OBA is a standardised representational framework for observable attributes that are characteristics of biological entities, organisms, or parts of organisms. OBA has a modular design which provides several benefits for users and data integrators, including an automated and meaningful classification of trait terms computed on the basis of logical inferences drawn from domain-specific ontologies for cells, anatomical and other relevant entities. The logical axioms in OBA also provide a previously missing bridge that can computationally link Mendelian phenotypes with GWAS and quantitative traits. The term components in OBA provide semantic links and enable knowledge and data integration across specialised research community boundaries, thereby breaking silos.

bioinformatics↗

bioRxiv2023

Investigating the Unbinding of Muscarinic Antagonists from the Muscarinic 3 Receptor.

Patient symptom relief is often heavily influenced by the residence time of the inhibitor-target complex. For the human muscarinic receptor 3 (hMR3), tiotropium is a long-acting bronchodylator used in conditions such as asthma or chronic obstructive pulmonary disease (COPD). The mechanistic insights of this inhibitor remain unclear, specifically, elucidation of the main factors determining the unbinding rates could help develop the next generation of antimuscarinic agents. Using our novel unbinding algorithm, we were able to investigate ligand dissociation from hMR3. The unbinding paths of tiotropium and two of its analogues, N-methylscopolamin and homatropine methylbromide show a consistent qualitative mechanism and allowed us to identify the structural bottleneck of the process. Furthermore, our machine learning-based analysis identified key roles of the ECL2/TM5 junction involved at the transition state. Additionally, our results point at relevant changes at the intracellular end of the TM6 helix leading to the ICL3 kinase domain, highlighting the closest residue L482. This residue is located right between two main protein binding sites involved in signal transduction for hMR3s activation and regulation. We also highlight key pharmacophores of tiotropium that play determining roles in the unbinding kinetics and could aid towards drug design and lead optimization. Description O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC="FIGDIR/small/522558v2_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@2598b2org.highwire.dtl.DTLVardef@4bf425org.highwire.dtl.DTLVardef@89dfa8org.highwire.dtl.DTLVardef@1ba9f48_HPS_FORMAT_FIGEXP M_FIG C_FIG Graphical abstract of the work, showing the unbinding for ligands 1 (tiotropium, TTP), 2 (N-methylscopolamin, NMS) and 3 (homatropine methylbromide, CPD2). Using TTPs downhill simulations from its unbinding transition state, different protein-ligand and proteinprotein interactions were analyzed with MLTSA to find relevant CVs driving the different outcomes.

biophysics↗

bioRxiv2018

Targeting glutamine metabolism and redox state for leukemia therapy

Acute myeloid leukemia (AML) is a hematological malignancy characterized by the accumulation of immature myeloid precursor cells. AML is poorly responsive to conventional genotoxic chemotherapy and a diagnosis of AML is usually fatal. More effective and less toxic forms of therapy are desperately needed. AML cells are known to be highly dependent on the amino acid glutamine for their survival. Here, we show that blocking glutamine metabolism through the use of a glutaminase inhibitor (CB-839) significantly impairs antioxidant glutathione production in multiple types of AML, resulting in accretion of mitochondrial reactive oxygen species (mitoROS) and apoptotic cell death. Moreover, glutaminase inhibition makes AML cells susceptible to adjuvant drugs that further perturb mitochondrial redox state, such as arsenic trioxide (ATO) and homoharringtonine (HHT). Indeed, the combination of ATO or HHT with CB-839 exacerbates mitoROS and apoptosis, and leads to more complete cell death in AML cell lines, primary AML patient samples and in vivo using mouse models of AML. In addition, these redox-targeted combination therapies are effective in eradicating acute lymphoblastic leukemia cells in vitro and in vivo. Thus, targeting glutamine metabolism in combination with drugs that perturb mitochondrial redox state represents an effective and potentially widely applicable therapeutic strategy for treating multiple types of leukemia.\n\nKey PointsO_LIGlutaminase inhibition commonly impairs glutathione metabolism and induces mitochondrial oxidative stress in acute myeloid leukemia cells\nC_LIO_LIA glutaminase inhibitor synergizes with pro-oxidant drugs in inducing apoptosis and eliminating leukemia cells in vitro and in vivo\nC_LI

cancer biology↗