Biology subjects

He, F.

Publications and source records attributed to He, F..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

Ultra-low-illumination, high-fidelity longitudinal monitoring of cerebral perfusion via deep learning-enhanced laser speckle contrast imaging

Laser Speckle Contrast Imaging (LSCI) is a non-contact, label-free optical technique widely used in biomedical research and clinical applications. It enables real-time visualization and quantification of microvascular blood flow by analyzing the temporal fluctuations of laser speckles induced by moving red blood cells. However, conventional LSCI uses visible or near-infrared illumination, which--during prolonged exposure (e.g., >1{square}hr)--can induce sublethal neural stress and cause signal drift, compromising physiological relevance and raising ethical concerns. To mitigate these limitations, we introduce TunLSCI--a TransUNet-based recovery network designed to reconstruct high-fidelity mouse cerebral blood flow (CBF) indices from ultra-low-illumination LSCI. We train our network on paired ultra-low-illumination (1.27 {micro}W/mm2) and conventional LSCI data ([~]200 {micro}W/mm2 illumination, the latter as reference), and demonstrate that it outperforms the conventional standard analytical LSCI processing pipeline based on stLASCA, particularly in reconstructing fine vasculature from few frames, suppressing speckle noise, and maintaining robustness against exposure variations. We validate that the proposed TunLSCI reduces illumination power density by [~]157-fold compared with conventional stLASCA, well below the safety threshold for cortical exposure in mice and markedly improves stability during a 2-hour continuous mouse CBF monitoring. Our method significantly minimizes the phototoxic burden of LSCI while preserving spatiotemporal fidelity and quantitative accuracy, thus enabling longitudinal, high-biosafety cerebral perfusion tracking in vivo over multi-hours.

bioengineering↗

bioRxiv2026

High-throughput Single-cell Proteomics Enabled by Integrating nPOP-workflow with Quantitative Hyperplexing

Single-cell proteomics (scProteomics) has emerged as a powerful approach to dissect cellular heterogeneity and dynamic molecular mechanisms at unprecedented resolution. However, achieving high proteome coverage and quantitative accuracy while maintaining high-throughput remains a major challenge. In this study, we established a high-throughput scProteomics workflow that integrates a modified nano-proteomic sample preparation (nPOP) workflow with an IBT16 and TMT16 quantitative hyperplexing strategy. Through systematic optimization of chromatographic and mass spectrometric conditions, we established a label-free workflow for high-sensitivity and high quantification accuracy. On average, more than 3,000 protein groups were identified from individual 293T and HeLa cells on timsTOF SCP. When applied to single cholangiocarcinoma (CCA) cells and matched paracancerous cells dissociated from human fresh-frozen CCA tissue, approximately 2,000 protein groups were quantified per cell, revealing distinct metabolic and translational regulation patterns consistent with previously reported molecular features of CCA subtypes. To achieve high-throughput scProteomics, we then established an nPOP-based IBT16-TMT16 quantitative hyperplexing workflow. Across four human cell lines (293T, HeLa, A549, and LM3), our quantitative hyperplexing strategy achieved over 95% labelling efficiency and consistently identified 1,400 to 2,000 protein groups from single cells with a dynamic range spanning five orders of magnitude. In comparison to conventional multiplexing methods, our hyperplexing strategy not only enhanced proteome depth but also achieved ultrahigh-throughput ([~] 2,000 single cells per day if use Orbitrap Astral Zoom or timsUltra AIP etc.). Overall, our label-free and quantitative hyperplexing workflows provide an efficient and scalable platform for large scale scProteomics studies and clinical applications.

cell biology↗

bioRxiv2026

Ultra-flexible PINE arrays for month-long, continuous intracellular ion flux monitoring in plants with nanomolar accuracy

High-precision in vivo monitoring of ion fluxes is essential yet challenging studying plant electrophysiology such as growth regulation, signal transduction and stress responses. Existing methods for probing ion dynamics are limited by low sensitivity, high invasiveness that interferes physiological processes, and the inability to accurately resolve ion homeostasis with required spatial and temporal resolution. Here, we introduce ultraflexible, plant implantable nanoelectrode (PINE) arrays manufactured on 1.2-m-thick polymer substrates, which enable ultrasensitive and selective measurement of ionic current for month-long via scalable nanofabrication techniques. The fabricated PINE arrays have a smaller dimension than typical plant cells as well as less stiffness, facilitating minimally invasive integration with living plant cells. This subcellular-scale plant-electronic interface allows for reliable, selective detection of K+ flux with a detection limit of [~]10- M, and thus allows continuous, stable monitoring of tomato stem cells over six weeks, capturing dynamic potassium fluctuations during all key growth stages. More importantly, the method permits long-term, real-time tracking of ion-specific dynamics without disrupting plant cellular structure or altering endogenous ion concentrations. Therefore, PINE provides unprecedented access to ion homeostasis and signaling networks, making it an excellent platform for precision agriculture and a foundational tool for future digital plant engineering.

plant biology↗

bioRxiv2026

quantms-rescoring enables deep proteome coverage across protein quantification, immunopeptidomics, and post-translational modifications experiments.

The growing volume of public proteomics datasets and the advent of novel machine learning (ML)-based methods create unprecedented opportunities for discovery through large-scale reanalysis. However, traditional desktop tools are increasingly insufficient for processing and integrating data at this scale. To address this challenge, we present a novel package, quantms-rescoring, that extends the cloud-native quantms workflow with a machine learning-based rescoring module. Unlike prior tools that rescore single-engine outputs, quantms-rescoring seamlessly integrates multiple search engines (SAGE, COMET, and MSGF+), performs automatic model selection, model fine-tuning, and scales reproducibly on cloud infrastructures. In quantms-rescoring, we rely on multiple fragment-ion intensity (AlphaPeptDeep and MS2PIP) and retention-time prediction (DeepLC) methods to improve results from multiple peptide database search engines. It features automatic model selection, fine-tuning, and retraining for MS/MS intensity and retention time prediction to select the best model for a given dataset. We applied the novel workflow to five representative datasets spanning DDA label-free quantification, TMT 10-plex isobaric labelling of tumor proteomics data, immunopeptidomics, phospho-proteomics, and unseen lysine malonylation experiments. We achieved a 16-22.8% increase in identified spectra, along with the quantification of 2191 additional phosphorylated peptides and 1337 phosphosites. In the tandem mass tag (TMT)-labeled clear cell renal cell carcinoma dataset, 76 novel differentially expressed multiple search engines identified proteins with quantms-rescoring. Additionally, novel 11,688 HLA-II potential binders were detected in the immunopeptidomics dataset by multiple search engines with quantms-rescoring. For unseen malonylation data, we reported more than 58.8% malonylation PSMs and 30.5% modification sites than COMET alone. Together, these results show that integrating multi-engine searches with machine learning-derived features can be combined in a scalable workflow that enhances identification, PTM localization, and quantification performance.

bioinformatics↗

bioRxiv2025

ACE2 Phosphorylation Modulates Angiogenesis via the Activator Protein-1

BackgroundAngiogenesis plays a crucial role in organ development. However, aberrant blood vessel growth is involved in various diseases, including tumors and neovascular eye diseases. Angiotensin-converting enzyme 2 (ACE2) is a critical enzyme regulating the health of cardiovascular system, and its post-translational modifications (PTMs) are crucial to determine ACE2 expression level and activity. Here, we studied how the PTM of ACE2 in vascular endothelial cells (ECs) affect pathological retinal neovascularization and tumor angiogenesis. MethodsThe angiogenic capabilities of ECs were assessed by tube formation, sprouting assays, and 5-ethynyl-2-deoxyuridine (EdU) and filopodia staining. EC angiogenesis was examined by poteome profiler array and aortic ring assays in vitro and by the oxygen-induced retinopathy (OIR) and tumor angiogenesis models in vivo. High-throughput screening involving data from RNA-seq, ATAC-seq, and ChIP-seq were used to explore the epigenetic and transcriptional regulations of pro-angiogenic genes regualtged by ACE2 PTMs. ResultsACE2 Ser-680 dephosphorylation in connection with Lys-788 ubiquitination increased EC angiogenic phenotype, which were manifested by aberrant vascularization in mouse OIR and tumor models. ACE2 Ser-680 dephosphorylation led to the activation of activator protein-1 (AP-1), which transactivated multiple genes involved in angiogenesis. AP- 1 inhibition mitigated such angiogenesis in vivo. ConclusionOur findings show a novel PTM mechanism of ACE2 involved in pathological angiogeneis. Specifically, ACE2 Ser-680 dephosphorylation facilitated AP-1 transactivation of the downstream pro-angiogenic genes in ECs.

cell biology↗

bioRxiv2025

Simple, Fast and Highly Efficient One-or Two-step Proteomic Preparation Enables Deep Profiling of Microgram-level FF and FFPE Tissues

Large-scale tissue proteomics requires workflows that are efficient, rapid, and repeatable across diverse samples. Herein, we present Simple Workflow for Integrated and Fast Tissue-preparation (SWIFT), which enables complete processing of fresh- frozen (FF) and formalin-fixed, paraffin-embedded (FFPE) tissues in either one- or two-step formats, while maintaining deep proteome coverage with high repeatability from low to microgram-level tissues. For FF tissues, an incubation process integrating lysis, reduction, alkylation, and digestion generates peptide samples directly from tissues in [≤] 1.5 h. For FFPE tissues, concurrent deparaffinization, rehydration, and de- crosslinking is achieved within 0.5 h, followed by one-step peptide preparation. Furthermore, our workflows eliminate desalting and offline cleanup steps, thereby reducing variability and total processing time. Using our methods, we identified up to [~]10,000 protein groups and [~]150,000 peptides across multiple mouse organs on timsTOF Pro. Repeatability was high (pairwise Pearsons r > 0.96 across six experimental replicates), with dynamic ranges spanning 6-7 orders of magnitude. Organ-enriched protein analysis identified functionally distinct proteins unique to each tissue Paired FF and FFPE analyses revealed preservation-induced shifts, with FFPE tissues showing reduced detection of membrane-associated and respiratory proteins, including mitochondrial Complex I. Together, our fast and simplified workflows enable deep tissue proteomics for large-scale clinical and translational studies in a cost- effective and widely-accessible manner.

biochemistry↗

bioRxiv2025

Stress granule sequestration of OXPHOS gene transcripts exacerbates glycolytic restriction

Stress granules (SGs) are dynamic organelles formed under cellular stress and they are generally regarded as protective entities. Meanwhile, their role in pathogenesis is becoming increasingly recognized, but the underlying mechanisms remain elusive due to the diverse nature of both stress types and biological contexts. Here we investigate SG dynamics and temporal changes in bulk and SG-associated transcriptomes under different regimens that inhibit glycolysis. We subject cells to either single assaults of glucose depletion (GD) or 2-deoxy-D-glucose addition (2DG) or a combined treatment (GD+2DG). We find that SGs formed under these conditions exhibit distinct properties, including eIF2 phosphorylation dependency, mRNA composition, and capacity to disassembly. Our results show that SGs induced by GD+2DG uniquely trap oxidative phosphorylation (OXPHOS) gene transcripts, leading to mitochondrial dysfunction. We provide evidence suggesting that the persistency of SGs formed under GD+2DG treatment is interwoven with mitochondrial dysfunction resulting in heightened apoptosis, effects that can also be recreated under single assaults when combined with mitochondrial inhibition. Our findings suggest that SG formation induced by inhibiting a single metabolic pathway can widen its impact in intensifying cellular metabolic stress under specific conditions, providing mechanistic insights into the paradoxical dual nature of SGs in stress response and pathology.

cell biology↗

bioRxiv2025

High-throughput Single-cell Proteomics Enabled by an Integrated Hyperplexing and Automatic Labelling Approach without Fractionation

Single-cell proteomics (scProteomics) enables comprehensive analysis of protein composition, expression and functions at the single-cell level. While label-free techniques have generated promising results, achieving high-throughput scProteomics remains a substantial challenge. In this study, we present an approach that increases scProteomics throughput by approximately 30-fold through the true integration of isobaric tags (IBT) with tandem mass tags (TMT), a method we term Integral-Hyperplex. Due to remarkable differences in signal response between TMT16 and IBT16 reporter ions, we implemented two normalization strategies to ensure accurate and integrated quantification: (1) the inclusion of a normalization channel in both IBT and TMT groups to serve as internal standards; and (2) the generation of protein-specific conversion factors between IBT16 and TMT16 signals. We validated the quantification accuracy of the Integral-Hyperplex method by labeling HeLa digests at varying ratios. Furthermore, we demonstrated fully automated labeling on the active-matrix digital microfluidics chip, consuming only 12 nL of labeling reagent per single cell and with a reaction volume as low as 20 nL. Our approach was validated through proof-of-principle quantification experiments across three types of single cells. Approximately 2,000 protein groups were quantified per cell, and the three cell types can be well clustered, confirming the reliability of the Integral-Hyperplex approach. Approximately 300 samples can be analyzed per day due to insufficient resolution of timsTOF SCP. Throughput can be further enhanced using TMT32, additional labeling reagents, and more advanced mass spectrometers such as Orbitrap Astral and timsTOF Ultra 2, and our approach is theoretically capable of analyzing up to [~]2,000 single cells per day.

biochemistry↗

bioRxiv2025

Expression of β-catenin, LEF-1, MMP7 and SFRP5 associated with poor outcome in pancreatic cancer

Pancreatic cancer is highly aggressive and lethal malignancies worldwide. {beta}-catenin, LEF-1, MMP7 and SFRP5 are key regulatory proteins in the Wnt signaling pathway. It has been confirmed that Wnt signaling pathway is involved in the biological regulation of pancreatic cancer cells and plays an important role. However, its effect on the malignant phenotypes of pancreatic cancer cell and the corresponding molecular details remains unknown. In the current study, the expression of {beta}-catenin, LEF-1 and MMP7 was up-regulated and the expression of SFRP5 was down-regulated in pancreatic cancer cell lines and tissues by RNA sequencing, quantitative PCR (qPCR) and immunohistochemical analysis. {beta}-catenin, LEF-1 and MMP7 positive expression correlates with poor prognosis in PDAC patients, and SFRP5 positive expression correlates with longer survival times. Subsequently r software was used to establish a rosette model to predict the prognosis of pancreatic cancer according to the results of multi-factor analysis. After statistical analysis, age, CA-199, {beta}-catenin, MMP7 and SFRP5 could serve as promising biomarkers for predicting prognosis in PDAC patients. This study also suggests that survival probability of patients can be reliably predicted by a nomogram-based method.

cancer biology↗

bioRxiv2025

GABA-independent activation of GABAB receptor by mechanical forces

The heterodimeric GABAB receptor, composed of GB1 and GB2 subunits, is a metabotropic G protein-coupled receptor (GPCR) activated by the neurotransmitter GABA. GABA binds to the extracellular domain of GB1 to activate G proteins through GB2. Here we show that GABAB receptors can be activated by mechanical forces, such as traction force and shear stress, in a GABA-independent manner. This GABA-independent mechano-activation of GABAB receptor is mediated by a direct interaction between integrins and the extracellular domain of GB1, indicating that GABAB receptor and integrin form a novel type of mechano-transduction complex. Mechanistically, shear stress promotes the binding of integrin to GB1 and induces an allosteric re-arrangement of GABAB receptor transmembrane domains towards an active conformation, culminating in receptor activation. Furthermore, we demonstrate that shear stress-induced GABAB receptor activation plays a crucial role in astrocyte remodeling. These findings reveal a previously unrecognized function of GABAB receptor in mechano-transduction, uncovering a novel ligand-independent activation mechanism for GPCRs.

neuroscience↗

bioRxiv2025

Tetraploid turnips (Brassica rapa ssp. rapa) are highly tolerant of tetravalent formation and aneuploidy

Prior to 1980, experimental induction of polyploidy (chromosome doubling) led to the release of several tetraploid Brassica rapa ssp. rapa as fodder turnip cultivars. Most experimentally induced polyploids are meiotically unstable and show reduced fertility. However, we hypothesized that based on the requirement to produce large amounts of commercial seed to sell to farmers, these turnip lines should have managed to restore fertility and stabilize meiosis. We collected and tested all B. rapa listed as or referred to as tetraploid from the IPK Gatersleben, CGN Wageningen and Nordgen germplasm banks, and subsequently investigated chromosome karyotypes, meiotic chromosome behaviour and fertility in confirmed tetraploid turnip accessions, using a combination of resequencing and fluorescent in situ hybridisation. Contrary to our expectations, all accessions showed unstable meiosis: the average tetravalent frequency per meiosis per plant ranged from 4.8 to 6.4 per line, and these tetravalent associations also persisted from diakinesis to metaphase I. Using chromosome-specific fluorescence in situ hybridisation probes, we found that most chromosomes showed similar frequencies of tetravalent formation except for chromosomes A03 and A06, which predominantly formed tetravalents (>90%). Of the 21 individuals sequenced (one per accession), approximately half (9/21) were aneuploid (loss or gain of a whole chromosome), and two displayed additional chromosomal rearrangements. We nevertheless observed no significant phenotypic abnormalities or reductions in fertility (although all accessions were self-incompatible). Our findings indicate that stabilizing meiosis may not always be necessary to produce relatively fertile and homogeneous outcrossing polyploid populations.

genetics↗

bioRxiv2025

Towards Unmanned Proteomics Data Generation: A Fully Automated Sample-to-Data System for Proteomic Experiments

The {pi}-HuB project requires proteomic measurements of millions of low-input samples that are challenging with current technology. Here, we introduce a pilot {pi}-HuB data factory that features a fully automated sample-to-data system to enable high-throughput digitization of human samples. Our platform, as a proof-of-concept towards unmanned proteomics, was demonstrated to show longitudinal robustness and applied for spatial proteomics and the profiling of plasma and cell lines, thereby serving as the workhorse to drive {pi}-HuB.

biochemistry↗

bioRxiv2025

Temporal coding carries more stable cortical visual representations than firing rate over time

The brains ability to stably represent recurring visual scenes is crucial for behavior. Previous studies have used slow dynamic (1-5 seconds) rate code measurements to study visual tuning, revealing varying degrees of gradual activity changes over time or "representational drifts." However, it remains unclear if there is an underlying neural code that maintains the encoding of information stable over time. In this study, we extracted structures in fast (tens of milliseconds) temporal responses and explored the role of such temporal codes in supporting the stability of visual representations. We tracked the spiking activity of the same visual cortical populations in male mice for 15 consecutive days using custom-developed, large-scale, ultraflexible electrode arrays. Across various types of stimuli, we found that neurons exhibited varying degrees of day-to-day stability in their firing rate-based tuning. The across day stability correlated with tuning reliability. Notably, accounting for spiking temporal dynamics increased single neuron tuning stability, especially for less reliable neurons. Temporal coding further improved population representation discriminability and decoding accuracy. The stability of temporal codes was more correlated with network functional connectivity than rate coding. These results show that temporal coding is crucial for stably encoding sensory stimuli, suggesting its significant role in ensuring consistent sensory experiences.

neuroscience↗

bioRxiv2025

Harnessing the Power of Single-Cell Large Language Models with Parameter Efficient Fine-Tuning using scPEFT

Single-cell large language models (scLLMs) capture essential biological insights from vast single-cell atlases but struggle in out-of-context applications, where zero-shot predictions can be unreliable. To address this, we introduce a single-cell parameter-efficient fine-tuning (scPEFT) framework that integrates learnable, low-dimensional adapters into scLLMs. By freezing the backbone model and updating only the adapter parameters, scPEFT efficiently adapts to specific tasks using limited custom data. This approach mitigates catastrophic forgetting, reduces parameter tuning by over 96%, and decreases GPU memory usage by more than half, significantly enhancing scLLMss accessibility for resource-constrained researchers. Validated across diverse datasets, scPEFT outperformed zero-shot models and traditional fine-tuning in disease-specific, cross-species, and under-characterized cell population tasks. Its attention-mechanism analysis identified COVID-related genes associated with specific cell states and uncovered unique blood cell subpopulations, demonstrating scPEFTs capacity for condition-specific interpretations. These findings position scPEFT as an efficient solution for improving scLLMs utilities in general single-cell analyses.

cell biology↗

bioRxiv2025

A biGWAS strategy reveals the genetic architecture of the interaction between wheat and Blumeria graminis f. sp. tritici

BackgroundWheat powdery mildew, caused by Blumeria graminis f. sp. tritici (Bgt) is one of the most significant diseases affecting global wheat production. Breeding for disease resistance (R) genes against Bgt often follows a boom-bust cycles, where cultivars with effective resistance are widely deployed on an expanding area until virulence emerges in the avirulence (Avr) pathogen population. While extensive effort has been devoted to identifying and cloning R genes, Avr genes remain relatively understudied, limiting our understanding of R-Avr interactions. That said, understanding R-Avr interactions is crucial for developing durable resistance strategies. ResultsWe conducted whole genome sequencing on 245 Bgt isolates collected from major wheat-growing regions in China, identifying 120 genetically unique isolates. Each of these unique isolates was inoculated and phenotyped on a diverse panel of wheat germplasm. Through Genome-Wide Association Analysis (GWAS) in both Bgt and wheat, we identified 65 Avr loci and 251 R loci overlapping with nine and eight cloned Avr or R genes, respectively. On average, each isolate carries eight Avr alleles, ranging from one to 17. Little geographical preference for Avr alleles or their combinations was observed, suggesting that disease resistance breeding against this pathogen should be coordinated at the national level. The level of resistance level is positively correlated with the number of R alleles carried by a wheat line, and the average frequency of an R allele is 2% among the wheat panel, indicating the potential for accumulating R alleles in breeding programs. We mapped 212 R-Avr interactions based on joint GWAS using both plant and pathogen genomes and cross-species epistasis, where a network of interactions was formed between wheat and Bgt. These interactions indicate that pyramiding five major R loci has the potential to confer resistance to half of the Avr loci. As a proof of concept, we successfully verified two previously described R-Avr pairs using tobacco experiments. Furthermore, we provided molecular validation evidence for three new Avr genes, including Bgt-50651, BgtE-5826 and BgtE-20009, and two new R-Avr interactions. Among them, Bgt-50651 interacts with Pm1a, Pm2a, and another unidentified R gene located near the Pm6/Pm52 interval. ConclusionOur study provides a framework for understanding the genetic interaction between plants and pathogens. The discovery of novel R/Avr loci and their complex interaction network underscores the need to integrate crop and pathogen genetic background, particularly the R/Avr allele composition, into breeding program design. These findings have significant implications for developing durable resistance strategies in wheat alongside offering valuable insights into the broader dynamics of plant-pathogen interactions.

genomics↗

bioRxiv2025

Reshaping epistatic network modules enhances wheat yield potential

Increasing wheat yield is a key approach to ensuring global food security and an enduring focus in crop breeding. Here, we assembled a panel of 3,030 wheat lines to dissect the genetic mechanisms of yield improvement. We conducted large-scale field trials across five ecological environments over two consecutive years to evaluate yield performance and constructed a haplotype atlas using the Wheat 660K genotype array. 234 quantitative trait loci (QTLs) and 522 haplotype blocks (HBs) were identified for 14 traits through genome-wide association studies (GWAS), including 10 QTLs and 35 HBs associated with grain yield. Genomic analysis revealed that wheat yield improvement is primarily driven by changes in the epistatic network rather than the introduction of new haplotype segments. SNPs or HBs explain 53.3%-62.9% of the phenotypic variation in yield, whereas epistasis explains 70.4%, highlighting the role of epistasis in yield improvement. Moreover, yield improvement is significantly correlated with the accumulation of favorable epistatic modules. Pedigree-based analysis revealed that the modules remained relatively stable under the pressure of breeding selection, while new favorable modules were also created during hybrid breeding, highlighting that crop design breeding should be based on genetic modules. This study provides insights into genetic mechanism of wheat yield improvement and guidance for designing future wheat in the era of artificial intelligence.

genetics↗

bioRxiv2025

The tectum transversum(TTR) maintains patency of the developing coronal suture

Craniosynostosis is a congenital defect characterized by the premature fusion of calvarial bones, often attributed to the loss of fibrous sutures or deregulated bone formation. Recent studies have reported abnormal cartilage formation in multiple synostosis models, suggesting a potential role for cartilage in suture formation and maintenance. The tectum transversum (TTR) is a transient cartilage located between the coronal suture and dura, adjacent to the frontal and parietal bones. Abnormal TTR formation has been observed in several models; however, its role in coronal suture development remains unclear. In this study, we investigated the developmental process of TTR in a mouse model and characterized its formation in relation to adjacent tissues, including the calvarial bones and the coronal suture. Through genetic ablation of TTR, we demonstrated its essential role in maintaining coronal suture patency. Furthermore, spatial transcriptomics data suggest that TTR may function as a barrier to BMP signaling activation in the coronal suture, a process potentially influenced by the dura. These findings provide new insights into the mechanisms regulating coronal suture development and the etiology of coronal synostosis.

developmental biology↗

bioRxiv2025

Obesity Increases Atherosclerosis Susceptibility via Inter-tissue miR-30e-SLC7A11 Axis

BackgroundWith obesity as a risk factor for atherosclerotic disease, recent research suggests that adipose tissue in obese animal models and humans can generate endocrine-like molecules that affect arterial health later on. Previous studies showed that microRNA-30e (miR-30e) level is elevated in atherosclerosis and solute carrier family 7 member 11 (SLC7A11), a cystine/glutamate transporter, is involved in atherogenesis. However, whether an endocrine-like link between the adipose-derived miR-30e-5p and SLC7A11 in the vascular endothelium can lead to obesity-caused atherosclerosis is unknown. MethodsMiRNA data mining and RT-PCR validations were used to demonstrate the positive association among serum level of miR-30e-5p, obesity, and coronary arterial disease in human patients. Transcriptomics (RNA-seq and single-nucleus RNA-seq), metabolomics, and in silico analysis were used to establish a miR-30e-5p-SLC7A11 regulation of central carbon metabolism, mitochondrial and endothelial cell (EC) function. Mouse models with EC-specific Slc7a11 knockout (EC-Slc7a11-/-) and gain or loss of function of miR-30e-5p were used to elucidate the detrimental role of this endocrine-like axis in obesity-related atherosclerosis. ResultsThe level of adipocyte-derived miR-30e-5p was significantly upregulated in obese humans with coronary artery disease and obese and atherosclerotic mice. Via serum exosomes, the adipocyte-generated miR-30e-5p targeted SLC7A11 mRNA in vascular ECs. SLC7A11 deficiency due to miR-30e-5p targeting dysregulated glutamate/cystine metabolism increased glycolysis, reduced oxidative phosphorylation, and impaired mitochondrial function. The EC dysfunction could be rectified by SLC7A11 overexpression or miR-30e-5p antagonism. Exogenously delivered miR-30e-5p phenocopied the increased atherosclerosis in EC-Slc7a11-/- mice. In contrast, miR-30e-5p antagomir treatment reduced atherosclerosis in Apoe-/- and ob/ob mice. ConclusionOur multi-omics approaches demonstrates that the adipose-derived miR-30e-5p downregulated SLC7A11 mRNA in ECs via tissue crosstalk. The resulting EC dysfunction led to obesity-related atherosclerosis in mice. These findings underscore a causality between obesity and atherosclerosis in the context of cardiovascular-kidney-metabolic syndrome.

molecular biology↗