Biology subjects

Mehl, F.

Publications and source records attributed to Mehl, F..

4 recordsLinked to original sources

bioRxiv2026

ACE-2-like Enzymatic Activity in Anti-SARS-CoV-2 Spike Protein Monoclonal Antibodies

Many people with acute COVID-19 have clinical disease not clearly attributable to viral replication and many COVID-19 convalescents are affected by post-acute sequelae of COVID-19 (PASC, or long COVID, LC). LC has severely affected public health and economies worldwide. Features of LC including blood pressure dysregulation, coagulopathies, high levels of inflammation, and neuropsychiatric complaints. The mechanisms responsible for the pathogenesis of some of COVID-19s clinical features and LC have not been well established. The host cell receptor for SARS-CoV-2 is human angiotensin converting enzyme 2 (ACE2), which binds the SARS-CoV-2 spike protein receptor-binding domain (RBD) to initiate infection. We hypothesized that some people may produce anti-RBD antibodies that sufficiently resemble ACE2 structure to have ACE2-like catalytic activity after infection. Those antibodies, ACE2-like abzymes, may contribute to the pathogenesis of LC. Our previous studies showed that ACE2-like activity was associated with immunoglobulin in some acute and convalescent COVID-19 patients. ACE2-like catalytic activity correlated with blood pressure changes following a moderate exercise challenge in people convalescing from COVID-19. To further establish that ACE2-like activity could be attributed to antibodies, we screened human monoclonal antibodies (mAbs) against SARS-CoV-2 spike protein from 3 different research centers and others purchased from a commercial source for ACE2-like catalytic activity. We identified 4 human monoclonal antibodies with ACE2-like catalytic activity. The ACE2-like catalytic activity of these mAbs was not inhibited by MLN-4760, a compound that inhibits native human ACE2 catalytic activity, nor by EDTA, unlike native ACE2, a Zinc metalloprotease, but was inhibited by an overlapping pool of spike peptides. Enzyme kinetic studies showed that the mAbs had substantially lower Vmax and Km values than native ACE2. The data therefore suggested that the antibodies cleave ACE2 substrate via a different mechanism than native ACE2. The identification of specific mAbs with ACE2-like catalytic activity supports the hypothesis that antibodies induced by SARS-CoV-2 infection could help mediate the pathogenesis of COVID-19 and LC, and more generally, the hypothesis that catalytic antibodies induced by infectious agents can contribute to disease pathogenesis.

microbiology↗

bioRxiv2020

Multi-omics profiling of living human pancreatic islet donors reveals heterogeneous beta cell trajectories toward type 2 diabetes

Existing studies do not sufficiently describe the molecular changes of pancreatic islet beta cells leading to their deficient insulin secretion in type 2 diabetes (T2D). Here we address this deficiency with a comprehensive multi-omics analysis of metabolically profiled pancreatectomized living human donors stratified along the glycemic continuum from normoglycemia to T2D. Islet pools isolated from surgical samples by laser-capture microdissection had remarkably heterogeneous transcriptomic and proteomic profiles in diabetics, but not in non-diabetic controls. Transcriptomics analysis of this unique cohort revealed islet genes already dysregulated in prediabetic individuals with impaired glucose tolerance. Our findings demonstrate a progressive but disharmonic remodeling of mature beta cells, challenging current hypotheses of linear trajectories toward precursor or trans-differentiation stages in T2D. Further, integration of islet transcriptomics and pre-operative blood plasma lipidomics data enabled us to define the relative importance of gene co-expression modules and lipids positively or negatively associated with HbA1c levels, pointing to potential prognostic markers.

bioinformatics↗

bioRxiv2020

MetaNetX/MNXref - unified namespace for metabolites and biochemical reactions in the context of metabolic models

MetaNetX/MNXref is a reconciliation of metabolites and biochemical reactions providing cross-links between major public biochemistry and Genome-Scale Metabolic Network (GSMN) databases. The new release brings several improvements with respect to the quality of the reconciliation, with particular attention dedicated to preserving the intrinsic properties of GSMN models. The MetaNetX website (https://www.metanetx.org/) provides access to the full database and online services. A major improvement is for mapping of user-provided GSMNs to MXNref, which now provides diagnostic messages about model content. In addition to the website and flat files, the resource can now be accessed through a SPARQL endpoint (https://rdf.metanetx.org).

systems biology↗

bioRxiv2019

Mechanistic insights into bacterial metabolic reprogramming from omics-integrated genome-scale models

Understanding the adaptive responses of individual bacterial strains is crucial for microbiome engineering approaches that introduce new functionalities into complex microbiomes, such as xenobiotic compound metabolism for soil bioremediation. Adaptation requires metabolic reprogramming of the cell, which can be captured by multi-omics, but this data remains formidably challenging to interpret and predict. Here we present a new approach that combines genome-scale metabolic modeling with transcriptomics and exometabolomics, both of which are common tools for studying dynamic population behavior. As a realistic demonstration, we developed a genome-scale model of Pseudomonas veronii 1YdBTEX2, a candidate bioaugmentation agent for accelerated metabolism of mono-aromatic compounds in soil microbiomes, while simultaneously collecting experimental data of P. veronii metabolism during growth phase transitions. Predictions of the P. veronii growth rates and specific metabolic processes from the integrated model closely matched experimental observations. We conclude that integrative and network-based analysis can help build predictive models that accurately capture bacterial adaptation responses. Further development and testing of such models may considerably improve the successful establishment of bacterial inoculants in more complex systems.

systems biology↗