Biomolecules. 2023 Nov 11;13(11):1638. doi: 10.3390/biom13111638.

ABSTRACT

Mitochondria are ancient endosymbiotic double membrane organelles that support a wide range of eukaryotic cell functions through energy, metabolism, and cellular control. There are over 1000 known proteins that either reside within the mitochondria or are transiently associated with it. These mitochondrial proteins represent a functional subcellular protein network (mtProteome) that is encoded by mitochondrial and nuclear genomes and significantly varies between cell types and conditions. In neurons, the high metabolic demand and differential energy requirements at the synapses are met by specific modifications to the mtProteome, resulting in alterations in the expression and functional properties of the proteins involved in energy production and quality control, including fission and fusion. The composition of mtProteomes also impacts the localization of mitochondria in axons and dendrites with a growing number of neurodegenerative diseases associated with changes in mitochondrial proteins. This review summarizes the findings on the composition and properties of mtProteomes important for mitochondrial energy production, calcium and lipid signaling, and quality control in neural cells. We highlight strategies in mass spectrometry (MS) proteomic analysis of mtProteomes from cultured cells and tissue. The research into mtProteome composition and function provides opportunities in biomarker discovery and drug development for the treatment of metabolic and neurodegenerative disease.

PMID:38002320 | DOI:10.3390/biom13111638

Biomolecules. 2023 Nov 1;13(11):1604. doi: 10.3390/biom13111604.

ABSTRACT

The ability of cancer cells to detach from the primary site and metastasize is the main cause of cancer- related death among all cancer types. Epithelial-to-mesenchymal transition (EMT) is the first event of the metastatic cascade, resulting in the loss of cell-cell adhesion and the acquisition of motile and stem-like phenotypes. A critical modulator of EMT in cancer cells is the stromal tumor microenvironment (TME), which can promote the acquisition of a mesenchymal phenotype through direct interaction with cancer cells or changes to the broader microenvironment. In this review, we will explore the role of stromal cells in modulating cancer cell EMT, with particular emphasis on the function of mesenchymal stromal/stem cells (MSCs) through the activation of EMT-inducing pathways, extra cellular matrix (ECM) remodeling, immune cell alteration, and metabolic rewiring.

PMID:38002286 | DOI:10.3390/biom13111604

Biomolecules. 2023 Oct 28;13(11):1587. doi: 10.3390/biom13111587.

ABSTRACT

Several studies in the last few years have determined that, in contrast to the prevailing dogma that drug resistance is simply due to Darwinian evolution-the selection of mutant clones in response to drug treatment-non-genetic changes can also lead to drug resistance whereby tolerant, reversible phenotypes are eventually relinquished by resistant, irreversible phenotypes. Here, using KRAS as a paradigm, we illustrate how this nexus between genetic and non-genetic mechanisms enables cancer cells to evade the harmful effects of drug treatment. We discuss how the conformational dynamics of the KRAS molecule, that includes intrinsically disordered regions, is influenced by the binding of the targeted therapies contributing to conformational noise and how this noise impacts the interaction of KRAS with partner proteins to rewire the protein interaction network. Thus, in response to drug treatment, reversible drug-tolerant phenotypes emerge via non-genetic mechanisms that eventually enable the emergence of irreversible resistant clones via genetic mutations. Furthermore, we also discuss the recent data demonstrating how combination therapy can help alleviate KRAS drug resistance in lung cancer, and how new treatment strategies based on evolutionary principles may help minimize or even preclude the emergence of drug resistance.

PMID:38002269 | DOI:10.3390/biom13111587

Foods. 2023 Nov 17;12(22):4156. doi: 10.3390/foods12224156.

ABSTRACT

The proliferation of genetically modified organisms (GMOs) presents challenges to GMO testing laboratories and policymakers. Traditional methods, like quantitative real-time PCR (qPCR), face limitations in quantifying the increasing number of GMOs in a single sample. Digital PCR (dPCR), specifically multiplexing, offers a solution by enabling simultaneous quantification of multiple GMO targets. This study explores the use of the Naica six-color Crystal dPCR platform for quantifying five GM soybean lines within a single six-plex assay. Two four-color assays were also developed for added flexibility. These assays demonstrated high specificity, sensitivity (limit of detection or LOD < 25 copies per reaction) and precision (bias to an estimated copy number concentration <15%). Additionally, two approaches for the optimization of data analysis were implemented. By applying a limit-of-blank (LOB) correction, the limit of quantification (LOQ) and LOD could be more precisely determined. Pooling of reactions additionally lowered the LOD, with a two- to eight-fold increase in sensitivity. Real-life samples from routine testing were used to confirm the assays' applicability for quantifying GM soybean lines in complex samples. This study showcases the potential of the six-color Crystal dPCR platform to revolutionize GMO testing, facilitating comprehensive analysis of GMOs in complex samples.

PMID:38002213 | DOI:10.3390/foods12224156

Biomedicines. 2023 Nov 14;11(11):3042. doi: 10.3390/biomedicines11113042.

ABSTRACT

In dentistry, various animal models are used to evaluate adhesive systems, dental caries and periodontal diseases. Metalloproteinases (MMPs) are enzymes that degrade collagen in the dentin matrix and are categorized in over 20 different classes. Collagenases and gelatinases are intrinsic constituents of the human dentin organic matrix fibrillar network and are the most abundant MMPs in this tissue. Understanding such enzymes' action on dentin is important in the development of approaches that could reduce dentin degradation and provide restorative procedures with extended longevity. This in silico study is based on dentistry's most used animal models and intends to search for the most suitable, evolutionarily close to Homo sapiens. We were able to retrieve 176,077 mammalian MMP sequences from the UniProt database. These sequences were manually curated through a three-step process. After such, the remaining 3178 sequences were aligned in a multifasta file and phylogenetically reconstructed using the maximum likelihood method. Our study inferred that the animal models most evolutionarily related to Homo sapiens were Orcytolagus cuniculus (MMP-1 and MMP-8), Canis lupus (MMP-13), Rattus norvegicus (MMP-2) and Orcytolagus cuniculus (MMP-9). Further research will be needed for the biological validation of our findings.

PMID:38002041 | DOI:10.3390/biomedicines11113042

Cancers (Basel). 2023 Nov 7;15(22):5314. doi: 10.3390/cancers15225314.

ABSTRACT

Radiation treatment (RT) is a mainstay treatment for many types of cancer. Recommendations for RT and the radiation plan are individualized to each patient, taking into consideration the patient's tumor pathology, staging, anatomy, and other clinical characteristics. Information on germline mutations and somatic tumor mutations is at present rarely used to guide specific clinical decisions in RT. Many genes, such as ATM, and BRCA1/2, have been identified in the laboratory to confer radiation sensitivity. However, our understanding of the clinical significance of mutations in these genes remains limited and, as individual mutations in such genes can be rare, their impact on tumor response and toxicity remains unclear. Current guidelines, including those from the National Comprehensive Cancer Network (NCCN), provide limited guidance on how genetic results should be integrated into RT recommendations. With an increasing understanding of the molecular underpinning of radiation response, genomically-guided RT can inform decisions surrounding RT dose, volume, concurrent therapies, and even omission to further improve oncologic outcomes and reduce risks of toxicities. Here, we review existing evidence from laboratory, pre-clinical, and clinical studies with regard to how genetic alterations may affect radiosensitivity. We also summarize recent data from clinical trials and explore potential future directions to utilize genetic data to support clinical decision-making in developing a pathway toward personalized RT.

PMID:38001574 | DOI:10.3390/cancers15225314

Commun Biol. 2023 Nov 24;6(1):1195. doi: 10.1038/s42003-023-05580-w.

ABSTRACT

Type VI secretion systems (T6SSs) deliver effectors into target cells. Besides structural and effector proteins, many other proteins, such as adaptors, co-effectors and accessory proteins, are involved in this process. MIX domains can assist in the delivery of T6SS effectors when encoded as a stand-alone gene or fused at the N-terminal of the effector. However, whether there are other conserved domains exhibiting similar encoding forms to MIX in T6SS remains obscure. Here, we scanned publicly available bacterial genomes and established a database which include 130,825 T6SS vgrG loci from 45,041 bacterial genomes. Based on this database, we revealed six domain families encoded within vgrG loci, which are either fused at the C-terminus of VgrG/N-terminus of T6SS toxin or encoded by an independent gene. Among them, DUF2345 was further validated and shown to be indispensable for the T6SS effector delivery and LysM was confirmed to assist the interaction between VgrG and the corresponding effector. Together, our results implied that these widely distributed domain families with similar genetic configurations may be required for the T6SS effector recruitment process.

PMID:38001377 | DOI:10.1038/s42003-023-05580-w

Cell Death Differ. 2023 Nov 24. doi: 10.1038/s41418-023-01236-8. Online ahead of print.

ABSTRACT

Multiple Myeloma is a typical example of a neoplasm that shows significant differences in incidence, age of onset, type, and frequency of genetic alterations between patients of African and European ancestry. This perspective explores the hypothesis that both genetic polymorphisms and spontaneous somatic mutations in the TP53 tumor suppressor gene are determinants of these differences. In the US, the rates of occurrence of MM are at least twice as high in African Americans (AA) as in Caucasian Americans (CA). Strikingly, somatic TP53 mutations occur in large excess (at least 4-6-fold) in CA versus AA. On the other hand, TP53 contains polymorphisms specifying amino-acid differences that are under natural selection by the latitude of a population and have evolved during the migrations of humans over several hundred thousand years. The p53 protein plays important roles in DNA strand break repair and, therefore, in the surveillance of aberrant DNA recombination, leading to the B-cell translocations that are causal in the pathogenesis of MM. We posit that polymorphisms in one region of the TP53 gene (introns 2 and 3, and the proline-rich domain) specify a concentration of the p53 protein with a higher capacity to repress translocations in CA than AA patients. This, in turn, results in a higher risk of acquiring inactivating, somatic mutations in a different region of the TP53 gene (DNA binding domain) in CA than in AA patients. Such a mechanism, by which the polymorphic status of a gene influencing its own "spontaneous" mutation frequency, may provide a genetic basis to address ethnicity-related differences in the incidence and phenotypes of many different forms of cancer.

PMID:38001255 | DOI:10.1038/s41418-023-01236-8

Sci Rep. 2023 Nov 24;13(1):20703. doi: 10.1038/s41598-023-47752-y.

ABSTRACT

Dietary patterns strongly correlate with non-alcoholic fatty liver disease (NAFLD), which is a leading cause of chronic liver disease in developed societies. In this study, we introduce a new definition, the co-consumption network (CCN), which depicts the common consumption patterns of food groups through network analysis. We then examine the relationship between dietary patterns and NAFLD by analyzing this network. We selected 1500 individuals living in Tehran, Iran, cross-sectionally. They completed a food frequency questionnaire and underwent scanning via the FibroScan for liver stiffness, using the CAP score. The food items were categorized into 40 food groups. We reconstructed the CCN using the Spearman correlation-based connection. We then created healthy and unhealthy clusters using the label propagation algorithm. Participants were assigned to two clusters using the hypergeometric distribution. Finally, we classified participants into two healthy NAFLD networks, and reconstructed the gender and disease differential CCNs. We found that the sweet food group was the hub of the proposed CCN, with the largest cliques of size 5 associated with the unhealthy cluster. The unhealthy module members had a significantly higher CAP score (253.7 ± 47.8) compared to the healthy module members (218.0 ± 46.4) (P < 0.001). The disease differential CCN showed that in the case of NAFLD, processed meat had been co-consumed with mayonnaise and soft drinks, in contrast to the healthy participants, who had co-consumed fruits with green leafy and yellow vegetables. The CCN is a powerful method for presenting food groups, their consumption quantity, and their interactions efficiently. Moreover, it facilitates the examination of the relationship between dietary patterns and NAFLD.

PMID:38001137 | DOI:10.1038/s41598-023-47752-y

Nat Commun. 2023 Nov 24;14(1):7690. doi: 10.1038/s41467-023-43549-9.

ABSTRACT

Surveillance programs for managing antimicrobial resistance (AMR) have yielded thousands of genomes suited for data-driven mechanism discovery. We present a workflow integrating pangenomics, gene annotation, and machine learning to identify AMR genes at scale. When applied to 12 species, 27,155 genomes, and 69 drugs, we 1) find AMR gene transfer mostly confined within related species, with 925 genes in multiple species but just eight in multiple phylogenetic classes, 2) demonstrate that discovery-oriented support vector machines outperform contemporary methods at recovering known AMR genes, recovering 263 genes compared to 145 by Pyseer, and 3) identify 142 AMR gene candidates. Validation of two candidates in E. coli BW25113 reveals cases of conditional resistance: ΔcycA confers ciprofloxacin resistance in minimal media with D-serine, and frdD V111D confers ampicillin resistance in the presence of ampC by modifying the overlapping promoter. We expect this approach to be adaptable to other species and phenotypes.

PMID:38001096 | DOI:10.1038/s41467-023-43549-9

Sci Data. 2023 Nov 24;10(1):822. doi: 10.1038/s41597-023-02548-7.

ABSTRACT

Transferable and mechanistic understanding of cross-scale interactions is necessary to predict how coastal systems respond to global change. Cohesive datasets across geographically distributed sites can be used to examine how transferable a mechanistic understanding of coastal ecosystem control points is. To address the above research objectives, data were collected by the EXploration of Coastal Hydrobiogeochemistry Across a Network of Gradients and Experiments (EXCHANGE) Consortium - a regionally distributed network of researchers that collaborated on experimental design, methodology, collection, analysis, and publication. The EXCHANGE Consortium collected samples from 52 coastal terrestrial-aquatic interfaces (TAIs) during Fall of 2021. At each TAI, samples collected include soils from across a transverse elevation gradient (i.e., coastal upland forest, transitional forest, and wetland soils), surface waters, and nearshore sediments across research sites in the Great Lakes and Mid-Atlantic regions (Chesapeake and Delaware Bays) of the continental USA. The first campaign measures surface water quality parameters, bulk geochemical parameters on water, soil, and sediment samples, and physicochemical parameters of sediment and soil.

PMID:38001085 | DOI:10.1038/s41597-023-02548-7

Bioinformatics. 2023 Nov 24:btad717. doi: 10.1093/bioinformatics/btad717. Online ahead of print.

ABSTRACT

MOTIVATION: Large-scale clinical proteomics datasets of infectious pathogens, combined with antimicrobial resistance outcomes, have recently opened the door for machine learning models which aim to improve clinical treatment by predicting resistance early. However, existing prediction frameworks typically train a separate model for each antimicrobial and species in order to predict a pathogen's resistance outcome, resulting in missed opportunities for chemical knowledge transfer and generalizability.

RESULTS: We demonstrate the effectiveness of multimodal learning over proteomic and chemical features by exploring two clinically relevant tasks for our proposed deep learning models: drug recommendation and generalized resistance prediction. By adopting this multi-view representation of the pathogenic samples and leveraging the scale of the available datasets, our models outperformed the previous single-drug and single-species predictive models by statistically significant margins. We extensively validated the multi-drug setting, highlighting the challenges in generalizing beyond the training data distribution, and quantitatively demonstrate how suitable representations of antimicrobial drugs constitute a crucial tool in the development of clinically relevant predictive models.

AVAILABILITY AND IMPLEMENTATION: The code used to produce the results presented in this paper is available at https://github.com/BorgwardtLab/MultimodalAMR.

PMID:38001023 | DOI:10.1093/bioinformatics/btad717

J Mol Cell Cardiol. 2023 Nov 23:S0022-2828(23)00180-3. doi: 10.1016/j.yjmcc.2023.11.004. Online ahead of print.

NO ABSTRACT

PMID:38000978 | DOI:10.1016/j.yjmcc.2023.11.004

J Chem Inf Model. 2023 Nov 24. doi: 10.1021/acs.jcim.3c01278. Online ahead of print.

ABSTRACT

We performed exhaustive torsion sampling on more than 3 million compounds using the GFN2-xTB method and performed a comparison of experimental crystallographic and gas-phase conformers. Many conformer sampling methods derive torsional angle distributions from experimental crystallographic data, limiting the torsion preferences to molecules that must be stable, synthetically accessible, and able to be crystallized. In this work, we evaluate the differences in torsional preferences of experimental crystallographic geometries and gas-phase computed conformers from a broad selection of compounds to determine whether torsional angle distributions obtained from semiempirical methods are suitable priors for conformer sampling. We find that differences in torsion preferences can be mostly attributed to a lack of available experimental crystallographic data with small deviations derived from gas-phase geometry differences. GFN2 demonstrates the ability to provide accurate and reliable torsional preferences that can provide a basis for new methods free from the limitations of experimental data collection. We provide Gaussian-based fits and sampling distributions suitable for torsion sampling and propose an alternative to the widely used "experimental torsion and knowledge distance geometry" (ETKDG) method using quantum torsion-derived distance geometry (QTDG) methods.

PMID:38000780 | DOI:10.1021/acs.jcim.3c01278

Eur J Pharmacol. 2023 Nov 22:176176. doi: 10.1016/j.ejphar.2023.176176. Online ahead of print.

ABSTRACT

One of the cost-effective alternative methods to find new inhibitors has been the repositioning approach of existing drugs. The advantage of computational drug repositioning method is saving time and cost to remove the pre-clinical step and accelerate the drug discovery process. Hence, an ensemble computational-experimental approach, consisting of three different steps, a machine learning model, simulation of drug-target interaction and experimental characterization, was developed. The machine learning type used here was different tree classification method, which is one of the best randomize machine learning model to identify potential inhibitors from weak inhibitors. This model was trained more than one-hundred times, and forty top trained models were extracted for the drug repositioning step. The machine learning step aimed to discover the approved drugs with the highest possible success rate in the experimental step. Therefore, among all the identified molecules with more than 0.9 probability in more than 70% of the models, nine compounds, were selected. Besides, out of the nine chosen drugs, seven compounds have been confirmed to inhibit EGF in the published articles since 2019. Hence, two identified compounds, in addition to gefitinib, as a positive control, five weak-inhibitors and one neutral, were considered via molecular docking study. Finally, eight proposed drugs, including gefitinib, were investigated using MTT assay and In-Cell ELISA to characterize the drugs effect on A431 cell growth and EGF-signaling. From our experiments, we could conclude that salicylic acid and piperazine could play an EGF-inhibitor role like gefitinib.

PMID:38000720 | DOI:10.1016/j.ejphar.2023.176176

Biol Psychiatry Cogn Neurosci Neuroimaging. 2023 Nov 22:S2451-9022(23)00311-7. doi: 10.1016/j.bpsc.2023.11.001. Online ahead of print.

ABSTRACT

BACKGROUND: MiR-137 is a microRNA involved in brain development, regulating neurogenesis and neuronal maturation. Genome-Wide Association Studies implicate miR-137 in schizophrenia risk but do not explain its involvement in brain function and underlying biology. Polygenic risk for schizophrenia mediated by miR-137 targets is associated with working memory, although other evidence points to emotion processing. We characterized the functional brain correlates of miR-137 target genes associated with schizophrenia while disentangling previously reported associations of miR-137 targets with working memory and emotion processing.

METHODS: Using RNA-sequencing data from postmortem prefrontal cortex (N=522), we identified a co-expression gene set enriched for miR-137 targets and schizophrenia risk genes. We validated the relationship of this set to miR-137 in-vitro by manipulating miR-137 expression in neuroblastoma cells. We translated this gene set into polygenic scores of co-expression prediction and associated them with fMRI activation in healthy volunteers (N1=214; N2=136; N3=2,075; N4=1,800) and with short-term treatment response in patients with schizophrenia (N=427).

RESULTS: In 4,652 human subjects, we found that (i) schizophrenia risk genes are co-expressed in a biologically validated set enriched for miR-137 targets, (ii) increased expression of miR-137 target risk genes is mediated by low prefrontal miR-137 expression, (iii) alleles predicting greater gene-set co-expression are associated with greater prefrontal activation during emotion processing in three independent healthy cohorts (N1-2-3), in interaction with age (N4), (iv) these alleles predict less improvement in negative symptoms following antipsychotic treatment in patients with schizophrenia.

CONCLUSIONS: The functional translation of miR-137 target gene expression linked with schizophrenia involves emotion processing.

PMID:38000716 | DOI:10.1016/j.bpsc.2023.11.001

Mol Cell Proteomics. 2023 Nov 22:100685. doi: 10.1016/j.mcpro.2023.100685. Online ahead of print.

ABSTRACT

Environment pH (pHe) is a key parameter dictating a surfeit of conditions critical to plant survival and fitness. To elucidate the mechanisms that recalibrate cytoplasmic and apoplastic pH homeostasis, we conducted a comprehensive proteomic/phosphoproteomic inventory of plants subjected to transient exposure to acidic or alkaline pH, an approach that covered the majority of protein-coding genes of the reference plant Arabidopsis thaliana. Our survey revealed a large set so far undocumented pHe-dependent phospho-sites, indicative of extensive post-translational regulation of proteins involved in the acclimation to pHe. Changes in pHe altered both electrogenic H+ pumping via P-type ATPases and H+/anion co-transport processes, putatively leading to altered net trans-plasma membrane translocation of H+ ions. In pH 7.5 plants, the transport (but not the assimilation) of nitrogen via NRT2-type nitrate and AMT1-type ammonium transporters was induced, conceivably to increase the cytosolic H+ concentration. Exposure to both acidic and alkaline pH resulted in a marked repression of primary root elongation. No such cessation was observed in nrt2.1 mutants. Alkaline pH decreased the number of root hairs in the wild type but not in nrt2.1 plants, supporting a role of NRT2.1 in developmental signaling. Sequestration of iron into the vacuole via alterations in protein abundance of the vacuolar iron transporter VTL5 was inversely regulated in response to high and low pHe, presumptively in anticipation of associated changes in iron availability. A pH-dependent phospho-switch was also observed for the ABC transporter PDR7, suggesting changes in activity and, possibly, substrate specificity. Unexpectedly, the effect of pHe was not restricted to roots and provoked pronounced changes in the leaf proteome. In both roots and shoots, the plant-specific TPLATE complex components AtEH1 and AtEH2-essential for clathrin-mediated endocytosis-were differentially phosphorylated at multiple sites in response to pHe, indicating that the endocytic cargo protein trafficking is orchestrated by pHe.

PMID:38000714 | DOI:10.1016/j.mcpro.2023.100685

Bioresour Technol. 2023 Nov 22:130084. doi: 10.1016/j.biortech.2023.130084. Online ahead of print.

ABSTRACT

Laccase-like multicopper oxidases are recognized for their potential to alter the reactivity of lignins for application in value-added products. Typically, model compounds are employed to discover such enzymes; however, they do not represent the complexity of industrial lignin substrates. In this work, a screening pipeline was developed to test enzymes simultaneously on model compounds and industrial lignins. A total of 12 lignin-active fungal multicopper oxidases were discovered, including 9 enzymes active under alkaline conditions (pH 11.0). Principal component analysis revealed the poor ability of model compounds to predict enzyme performance on industrial lignins. Additionally, sequence similarity analyses grouped these enzymes with auxiliary activity 1 sub-families with few previously characterized members, underscoring their taxonomic novelty. Correlation between the lignin-activity of these enzymes and their taxonomic origin, however, was not observed. These are critical insights to bridge the gap between enzyme discovery and application for industrial lignin valorization.

PMID:38000639 | DOI:10.1016/j.biortech.2023.130084

Nucleic Acids Res. 2023 Nov 24:gkad1082. doi: 10.1093/nar/gkad1082. Online ahead of print.

ABSTRACT

Bridging the gap between genetic variations, environmental determinants, and phenotypic outcomes is critical for supporting clinical diagnosis and understanding mechanisms of diseases. It requires integrating open data at a global scale. The Monarch Initiative advances these goals by developing open ontologies, semantic data models, and knowledge graphs for translational research. The Monarch App is an integrated platform combining data about genes, phenotypes, and diseases across species. Monarch's APIs enable access to carefully curated datasets and advanced analysis tools that support the understanding and diagnosis of disease for diverse applications such as variant prioritization, deep phenotyping, and patient profile-matching. We have migrated our system into a scalable, cloud-based infrastructure; simplified Monarch's data ingestion and knowledge graph integration systems; enhanced data mapping and integration standards; and developed a new user interface with novel search and graph navigation features. Furthermore, we advanced Monarch's analytic tools by developing a customized plugin for OpenAI's ChatGPT to increase the reliability of its responses about phenotypic data, allowing us to interrogate the knowledge in the Monarch graph using state-of-the-art Large Language Models. The resources of the Monarch Initiative can be found at monarchinitiative.org and its corresponding code repository at github.com/monarch-initiative/monarch-app.

PMID:38000386 | DOI:10.1093/nar/gkad1082

Sci Adv. 2023 Nov 24;9(47):eadj4846. doi: 10.1126/sciadv.adj4846. Epub 2023 Nov 24.

ABSTRACT

Patients with advanced chronic kidney disease (CKD) mostly die from sudden cardiac death and recurrent heart failure. The mechanisms of cardiac remodeling are largely unclear. To dissect molecular and cellular mechanisms of cardiac remodeling in CKD in an unbiased fashion, we performed left ventricular single-nuclear RNA sequencing in two mouse models of CKD. Our data showed a hypertrophic response trajectory of cardiomyocytes with stress signaling and metabolic changes driven by soluble uremia-related factors. We mapped fibroblast to myofibroblast differentiation in this process and identified notable changes in the cardiac vasculature, suggesting inflammation and dysfunction. An integrated analysis of cardiac cellular responses to uremic toxins pointed toward endothelin-1 and methylglyoxal being involved in capillary dysfunction and TNFα driving cardiomyocyte hypertrophy in CKD, which was validated in vitro and in vivo. TNFα inhibition in vivo ameliorated the cardiac phenotype in CKD. Thus, interventional approaches directed against uremic toxins, such as TNFα, hold promise to ameliorate cardiac remodeling in CKD.

PMID:38000021 | DOI:10.1126/sciadv.adj4846