Biology subjects

Freund, E.

Publications and source records attributed to Freund, E..

3 recordsLinked to original sources

bioRxiv2024

Systematic perturbation screens decode regulators of inflammatory macrophage states and identify a role for TNF mRNA m6A modification

Macrophages adopt dynamic cell states with distinct effector functions to maintain tissue homeostasis and respond to environmental challenges. During chronic inflammation, macrophage polarization is subverted towards sustained inflammatory states which contribute to disease, but there is limited understanding of the regulatory mechanisms underlying these disease-associated states. Here, we describe a systematic functional genomics approach that combines genome-wide phenotypic screening in primary murine macrophages with transcriptional and cytokine profiling of genetic perturbations in primary human monocyte-derived macrophages (hMDMs) to uncover regulatory circuits of inflammatory macrophage states. This process identifies regulators of five distinct inflammatory states associated with key features of macrophage function. Among these, the mRNA m6A writer components emerge as novel inhibitors of a TNF-driven cell state associated with multiple inflammatory pathologies. Loss of m6A writer components in hMDMs enhances TNF transcript stability, thereby elevating macrophage TNF production. A PheWAS on SNPs predicted to impact m6A installation on TNF revealed an association with cystic kidney disease, implicating an m6A-mediated regulatory mechanism in human disease. Thus, systematic phenotypic characterization of primary human macrophages describes the regulatory circuits underlying distinct inflammatory states, revealing post-transcriptional control of TNF mRNA stability as an immunosuppressive mechanism in innate immunity.

immunology↗

bioRxiv2024

Hippocampal sharp wave ripples and coincident cortical ripples orchestrate human semantic networks

Episodic memory function is predicated upon the precise coordination between the hippocampus and widespread cortical regions. However, our understanding of the neural mechanisms involved in this process is incomplete. In this study, human subjects undergoing intracranial electroencephalography (iEEG) monitoring performed a list learning task. We show sharp-wave ripple (SWR)-locked reactivation of specific semantic processing regions during free recall. This cortical activation consists of both broadband high frequency (non-oscillatory) and cortical ripple (oscillatory) activity. SWRs and cortical ripples in the anterior temporal lobe, a major semantic hub, co-occur and increase in rate prior to recall. Coincident hippocampal-ATL ripples are associated with a greater increase in cortical reactivation, show specificity in location based on recall content, and are preceded by cortical theta oscillations. These findings may represent a reactivation of hippocampus and cortical semantic regions orchestrated by an interplay between hippocampal SWRs, cortical ripples, and theta oscillations.

neuroscience↗

bioRxiv2022

HLApollo: A superior transformer model for pan-allelic peptide-MHC-I presentation prediction, with diverse negative coverage, deconvolution and protein language features.

Antigen presentation on MHC class I (MHC-I) is key to the adaptive immune response to cancerous cells. Computational prediction of peptide presentation by MHC-I has enabled individualized cancer immunotherapies. Here, we introduce HLApollo, a transformer-based approach with end-to-end modeling of MHC-I sequence, deconvolution, and flanking sequences. To achieve this, we develop a novel training strategy, negative set switching, which greatly reduces overfitting to falsely presumed negatives that are necessarily found in presentation datasets. HLApollo shows a meaningful improvement compared to recent MHC-I models on peptide presentation (20.19% average precision (AP)) and immunogenicity (4.1% AP). As expected, adding gene expression boosts the performance of HLApollo. More interestingly, we show that introduction of features from a protein language model, ESM 1b, remarkably recoups much of the benefits of gene expression in absence of true expression measurements. Finally, we demonstrate excellent pan-allelic generalization, and introduce a framework for estimating the expected accuracy of HLApollo for untrained alleles. This guides the use of HLApollo in a clinical setting, where rare alleles may be observed in some subjects, particularly for underrepresented minorities.

immunology↗