Biology subjects

Zhang, Z.

Publications and source records attributed to Zhang, Z..

At least 685 records · Page 38Linked to original sources

bioRxiv2024

AlphaFold3, a secret sauce for predicting mutational effects on protein-protein interactions

AlphaFold3 has set the new state-of-the-art in predicting protein-protein complex structures. However, the complete picture of biomolecular interactions cannot be fully captured by static structures alone. In the field of protein engineering and antibody discovery, the connection from structure to function is often mediated by binding energy. This work benchmarks AlphaFold3 against SKEMPI, a commonly used binding energy dataset. We demonstrate that AlphaFold3 learns unique information and synergizes with force field, profile-based, and other deep learning methods in predicting the mutational effects on protein-protein interactions. We hypothesize that AlphaFold3 captures a more global effect of mutations by learning a smoother energy landscape, but it lacks the modeling of full atomic details that are better addressed by force field methods, which possess a more rugged energy landscape. Integrating both approaches could be an interesting future direction. All of our benchmark results are openly available at https://github.com/luwei0917/AlphaFold3_PPI.

biophysics↗

bioRxiv2024

The intestinal microbiome and Cetobacterium somerae inhibit viral infection through TLR2-type I IFN signaling axis in zebrafish

Accumulated evidences demonstrate that intestinal microbiome can inhibit viral infection. However, our knowledge of the signaling pathways and specific commensal microbes that mediate the antiviral response is limited. Here, a rhabdoviral infection model in zebrafish allows us to investigate the modes of action of microbiome-mediated antiviral effect. We observed that antibiotics-treated and germ-free zebrafish exhibited greater viral infection. Mechanistically, depletion of the intestinal microbiome alters TLR2-Myd88 signaling, and blunts neutrophil response and type I interferon (IFN) production. Moreover, a single commensal bacterium, Cetobacterium somerae, recapitulated TLR2- and type I IFN-dependent antiviral effect of the microbiome in gnotobiotic zebrafish, and C. somerae exopolysaccharides (CsEPS) was the effector molecule that engaged TLR2 to mediate antiviral function. Together, our results suggest a conserved role of intestinal microbiome in regulating type I IFN response among vertebrates, and reveal that the intestinal microbiome inhibits viral infection through a CsEPS-TLR2-type I IFN signaling axis in zebrafish.

microbiology↗

bioRxiv2024

Phosphodiesterase 1A physically interacts with YTHDF2 and reinforces the progression of non-small cell lung cancer

Non-small cell lung cancer (NSCLC) is the most common subtype of lung cancer, and the prognosis is poor due to distant metastasis. Thus, there is an urgent need to discover novel therapeutic targets and strategies to overcome metastasis. A series of in vitro and in vivo phenotype experiments were performed to investigate the role of PDE1A in NSCLC. The RIP assay, mRNA stability assay and LC-MS/MS were performed to investigate the molecular mechanisms of PDE1A in NSCLC progression. Phosphodiesterase 1A (PDE1A) has been shown to promote metastasis and EMT progression of NSCLC. In addition, NSCLC cells overexpressing PDE1A promoted angiogenesis by regulating exosome release. IL-6/JAK/STAT3 signaling pathway was highly enriched in PDE1A-coexpresssed genes, and PDE1A promoted NSCLC metastasis by activating the STAT3 pathway. GO enrichment analysis of PDE1A-interacting genes showed that PDE1A might interact with YTHDF2 and participate in m6A-containing RNA binding. The binding between PDE1A and YTHDF2 was verified, and PDE1A regulated the STAT3 pathway by interacting with YTHDF2. The mechanism of YTHDF2/PDE1A complex in regulating STAT3 pathway was predicted by overlapping YTHDF2-interacting-RNAs, and genes coexpressed with YTHDF2 and STAT3. The interactions between YTHDF2 and target mRNAs were predicted, and there were three predicted targets of YTHDF2 with high scores: NRF2, SOCS2, and MET. Indeed, PDE1A interacted with YTHDF2, destabilized SOCS2, and activated STAT3 pathway. The data not only uncovers a novel PDE1A/YTHDF2/STAT3 pathway in NSCLC progression but also provides therapeutic strategies for treating NSCLC patients with metastasis.

cancer biology↗

bioRxiv2024

BioInformatics Agent (BIA): Unleashing the Power of Large Language Models to Reshape Bioinformatics Workflow

Bioinformatics plays a crucial role in understanding biological phenomena, yet the exponential growth of biological data and rapid technological advancements have heightened the barriers to in-depth exploration of this domain. Thereby, we propose Bio-Informatics Agent (BIA), an intelligent agent leveraging Large Language Models (LLMs) technology, to facilitate autonomous bioinformatic analysis through natural language. The primary functionalities of BIA encompass extraction and processing of raw data and metadata, querying both locally deployed and public databases for information. It further undertakes the formulation of workflow designs, generates executable code, and delivers comprehensive reports. Focused on the single-cell RNA sequencing (scRNA-seq) data, this paper demonstrates BIAs remarkable proficiency in information processing and analysis, as well as executing sophisticated tasks and interactions. Additionally, we analyzed failed executions from the agent and demonstrate prospective enhancement strategies including selfrefinement and domain adaptation. The future outlook includes expanding BIAs practical implementations across multi-omics data, to alleviating the workload burden for the bioinformatics community and empowering more profound investigations into the mysteries of life sciences. BIA is available at: https://github.com/biagent-dev/biagent.

bioinformatics↗

bioRxiv2024

mosGraphGen: a novel tool to generate multi-omic signaling graphs to facilitate integrative and interpretable graph AI model development

Multi-omics data, i.e., genomics, epigenomics, transcriptomics, proteomics, characterize cellular complex signaling systems from multi-level and multi-view and provide a holistic view of complex cellular signaling pathways. However, it remains challenging to integrate and interpret multi-omics data for mining key disease targets and signaling pathways. Graph AI models have been widely used to analyze graph-structure datasets, and are ideal for integrative multi-omics data analysis because they can naturally integrate and represent multi-omics data as a biologically meaningful multi-level signaling graph and interpret multi-omics data via graph node and edge ranking analysis. However, it is non-trivial for graph-AI model developers to pre-analyze multi-omics data and convert the data into biologically meaningful graphs, which can be directly fed into graph-AI models. To resolve this challenge, we developed mosGraphGen (multi-omics signaling graph generator), generating Multi-omics Signaling graphs (mos-graph) of individual samples by mapping multi-omics data onto a biologically meaningful multi-level background signaling network with data normalization by aggregating measurements and aligning to the reference genome. With mosGraphGen, AI model developers can directly apply and evaluate their models using these mos-graphs. In the results, mosGraphGen was used and illustrated using two widely used multi-omics datasets of TCGA and Alzheimers disease (AD) samples. The code of mosGraphGen is open-source and publicly available via GitHub: https://github.com/FuhaiLiAiLab/mosGraphGen

systems biology↗

bioRxiv2024

Unraveling the impact of host genetics and factors on the urinary microbiome in a young population

The significance of the urinary microbiome in maintaining health and contributing to disease development is increasingly recognized. However, a comprehensive understanding of this microbiome and its influencing factors remains elusive. Utilizing whole metagenomic and whole-genome sequencing, along with detailed metadata, we characterized the urinary microbiome and its influencing factors in a cohort of 1579 Chinese individuals. Our findings unveil the distinctiveness of the urinary microbiome from other four body sites, delineating five unique urotypes dominated by Gardnerella vaginalis, Sphingobium fluviale, Lactobacillus iners, Variovorax sp PDC80 and Acinetobacter junii, respectively. We identified 108 host factors significantly influencing the urinary microbiome, collectively explaining 12.92% of the variance in microbial composition. Notably, gender-related factors, including sex hormones, emerged as key determinants in defining urotype groups, microbial composition and pathways, with the urinary microbiome exhibiting strong predictive ability for gender (AUC=0.843). Furthermore, we discovered 43 genome-wide significant associations between host genetic loci and specific urinary bacteria, Acinetobacter in particular, linked to 8 host loci (p < 5x10-8). These associations were also modulated by gender and sex hormone levels. In summary, our study provides novel insights into the impact of host genetics and other factors on the urinary microbiome, shedding light on its implications for host health and disease.

microbiology↗

bioRxiv2024

Multiple Unfolded Protein Response pathways cooperate to link cytosolic dsDNA release to Stimulator of Interferon Gene (STING) activation

The double-stranded DNA (dsDNA) sensor STING has been increasingly implicated in responses to "sterile" endogenous threats and pathogens without nominal DNA or cyclic di-nucleotide stimuli. Previous work showed an endoplasmic reticulum (ER) stress response, known as the unfolded protein response (UPR), activates STING. Herein, we sought to determine if ER stress generated a STING ligand, and to identify the UPR pathways involved. Induction of IFN-{beta} expression following stimulation with the UPR inducer thapsigargin (TPG) or oxygen glucose deprivation required both STING and the dsDNA-sensing cyclic GMP-AMP synthase (cGAS). Furthermore, TPG increased cytosolic mitochondrial DNA, and immunofluorescence visualized dsDNA punctae in murine and human cells, providing a cGAS stimulus. N-acetylcysteine decreased IFN-{beta} induction by TPG, implicating reactive oxygen species (ROS). However, mitoTEMPO, a mitochondrial oxidative stress inhibitor did not impact TPG-induced IFN. On the other hand, inhibiting the inositol requiring enzyme 1 (IRE1) ER stress sensor and its target transcription factor XBP1 decreased the generation of cytosolic dsDNA. iNOS upregulation was XBP1-dependent, and an iNOS inhibitor decreased cytosolic dsDNA and IFN-{beta}, implicating ROS downstream of the IRE1-XBP1 pathway. Inhibition of the PKR-like ER kinase (PERK) pathway also attenuated cytoplasmic dsDNA release. The PERK-regulated apoptotic factor Bim was required for both dsDNA release and IFN-{beta} mRNA induction. Finally, XBP1 and PERK pathways contributed to cytosolic dsDNA release and IFN-induction by the RNA virus, Vesicular Stomatitis Virus (VSV). Together, our findings suggest that ER stressors, including viral pathogens without nominal STING or cGAS ligands such as RNA viruses, trigger multiple canonical UPR pathways that cooperate to activate STING and downstream IFN-{beta} via mitochondrial dsDNA release.

immunology↗

bioRxiv2024

The mechanistic rules for species coexistence

Global biodiversity loss requires effective ecological community management, reliant on accurate predictions of species coexistence and community assembly1-4. Traditional methods, predicting coexistence from the effect of one species on another5-8 (i.e., phenomenologically), are sensitive to environmental context9-13. This is because they ignore the fundamental processes that can be applied across environments. While mechanistic approaches offer promise14-17, empirical tests remain rare18,19. Here, we integrated a mechanistic consumer-resource model with the growth of 12 phytoplankton species in monoculture over a range of phosphorous, nitrate or ammonium concentrations. We found that the mechanistic approach accurately predicts the composition of 960 communities across species richness and resource conditions. As confirmed by simulations, species competing for substitutable resources (ammonium vs. nitrate) exhibit greater diversity than those competing for essential resources (phosphorus vs. nitrate), especially when initial species richness is high. This is because when competing for essential resources, each species is likely to consume less of the resource that more limit its growth, violating the mechanistic rule of coexistence (each species must consume more of the resource that more limit it16). Our study highlights the power of the mechanistic approach in understanding and predicting species loss across environments and, ultimately, mitigating its pace.

ecology↗

bioRxiv2024

Hypergraph Cortical Cytoarchitectonic Parcellation with Multimodal Canine Brain Atlas

Brain atlases are vital tools in exploring the brain structure-function relationship. The burgeoning cross-species atlases have significantly accelerated our understanding of human brain development, evolution, function, and diseases. However, the existing coarse-grained macroscopic canine brain atlases greatly constrain their utility as an animal model for neurocognition research. Finer-grained brain atlas and partitions are crucial for decoding brain spatial heterogeneity and topology at different scales. Therefore, we conduct macroscopic and microscopic brain imaging to construct an interactive online dataset of multimodal canine brain atlas. Additionally, we develop a pioneering method for cortical cytoarchitectonic partitioning based on hypergraph learning. By integrating high-dimensional cytoarchitectonic features and spatial connections between cortical columns, the method leads to fine-grained partitioning patterns. This innovative approach aims to decode the biological heterogeneity of cortical microstructures, contributing to the structural annotation of canine atlas as well as public human brain atlases. The study not only offers valuable resources but also presents a novel zonation approach to investigate the cellular organization pattern and topology of the cortex.

neuroscience↗

bioRxiv2024

An in-depth analysis and exploreation with focus on the biofilm in Staphylococcus aureus

Research into the biolfilm formation in Staphylococcus aureus has benefited greatly from the generation of high-throughput sequencing data to drive molecular analyses. The accumulation of these data, particularly transcriptomic data, offers a unique opportunity to unearth the network and constituent genes involved in the biofilm formation of Staphylococcus aureus through machine learning strategies and co-expression analyses. Herein, we harnessed all available RNA sequencing data related to Staphylococcus aureus biofilm studies and identified influenced functional pathways and corresponding genes in the process of the transition of bacteria from planktonic to biofilm state via employing machine learning and differential expression analyses. By weighted gene co-expression analysis and our previously developed predictor, important functional modules, potential biofilm-associated proteins and subnetwork of biofilm formation pathway were found. By constructing a protein-protein interaction (PPI) network, we discovered several hitherto unreported novel protein interactions within these functional modules. To make these data more straightforward to experimental biologists, an online database named SAdb was developed (http://sadb.biownmcli.info/). IMPORTANCEIn this work, we conducted a comprehensive and in-depth exploration of RNA sequencing data in biofilm research through differential expression analysis, machine learning, WGCNA, and biofilm-associated protein predictive analysis, which has also illuminated novel analytical perspective for other research into bacterial phenotypes. And, to provide researchers with unimpeded access to these data, we developed a database name SAdb for the storage and analysis of Staphylococcus aureus omics data. We believe that this study will captivate the interest of researchers in the field of bacteriology, particularly those studying biofilms, which play a crucial role in bacterial growth, pathogenicity, and drug resistance.

microbiology↗

bioRxiv2024

Dopamine and acetylcholine correlations in the nucleus accumbens depend on behavioral task states

Dopamine in the nucleus accumbens ramps up as animals approach desired goals. These ramps have received intense scrutiny because they seem to violate long-held hypotheses on dopamine function. Furthermore, it has been proposed that they are driven by local acetylcholine release, i.e., that they are mechanistically separate from dopamine signals related to reward prediction errors. Here, we tested this hypothesis by simultaneously recording accumbal dopamine and acetylcholine signals in rats executing a task involving motivated approach. Contrary to recent reports, we found that dopamine ramps were not coincidental with changes in acetylcholine. Instead, we found that acetylcholine could be positively, negatively, or uncorrelated with dopamine depending on whether the task phase was determined by a salient cue, reward prediction error, or active approach, respectively. Our results suggest that accumbal dopamine and acetylcholine are largely independent but may combine to engage different postsynaptic mechanisms depending on the behavioral task states.

neuroscience↗

bioRxiv2024

Enhancement of spatial learning by 40 Hz visual stimulation requires parvalbumin interneuron-dependent hippocampal neurogenesis

Acute and short-term rhythmic 40 Hz light flicker stimulation has shown promising results in alleviating cognitive impairments in mouse models of Alzheimers disease (AD), stroke, and autism spectrum disorders (ASD). Understanding the long-term impacts and underlying mechanisms is crucial to progress this approach for potential human therapeutic applications. Here, we show that prolonged exposure to 40 Hz light flicker (1 hour per day for 30 days) significantly improved spatial learning and neurogenesis in the dentate gyrus (DG) without harmful behavioral side effects. Mice with transgenic deletion of doublecortin-positive cells (DCXDTR) in the adult hippocampus failed to exhibit enhanced neurogenesis and spatial learning with 40 Hz stimulation. Inactivation or knockout of GABAergic parvalbumin (PV) interneurons reduced the effects of 40 Hz entrainment and neurogenesis enhancement. Mechanistically, the stimulation did not alter the regional microvessel blood flow but significantly raised PV excitability and GABA levels and enhanced inhibitory transmission in the DG. Blocking GABAA receptors reversed the improvements in spatial learning and neurogenesis. These data showed that long-term exposure to 40 Hz light flicker enhances spatial learning through PV-dependent adult neurogenesis, which requires elevated GABA as a critical neurochemical mechanism for sustaining adult neurogenesis. In briefRhythmic 40 Hz light flicker alleviates cognitive impairments in diverse animal models of neurological diseases. Understanding its long-term effects and mechanisms is vital for advancing its therapeutic potential for humans. Here, we show that prolonged exposure to this flicker improves spatial learning and boosts adult neurogenesis in the dentate gyrus. Activation of PV interneurons and GABAergic support for the newborn immature neurons underlie this effect, demonstrating lasting benefits for the treatment of neurological diseases. HighlightsO_LILong-term exposure to 40 Hz light flicker significantly improved spatial learning and neurogenesis in the hippocampus, devoid of adverse effects. C_LIO_LIThe 40 Hz light flicker evoked adult neurogenesis requires the activity of GABAergic inhibitory PV interneurons. C_LIO_LIThe 40 Hz light flicker raised GABA levels and enhanced inhibitory transmission in the DG. C_LIO_LIIncreased GABA serves as a vital neurochemical mechanism to support adult neurogenesis. C_LI

neuroscience↗

bioRxiv2024

Graph Contrastive Learning as a Versatile Foundation for Advanced scRNA-seq Data Analysis

Single-cell RNA sequencing (scRNA-seq) offers unprecedented insights into transcriptome-wide gene expression at the single-cell level. Cell clustering has been long established in the analysis of scRNA-seq data to identify the groups of cells with similar expression profiles. However, cell clustering is technically challenging, as raw scRNA-seq data have various analytical issues, including high dimensionality and dropout values. Existing research has developed deep learning models, such as graph machine learning models and contrastive learning-based models, for cell clustering using scRNA-seq data and has summarized the unsupervised learning of cell clustering into a human-interpretable format. While advances in cell clustering have been profound, we are no closer to finding a simple yet effective framework for learning high-quality representations necessary for robust clustering. In this study, we propose scSimGCL, a novel framework based on the graph contrastive learning paradigm for self-supervised pretraining of graph neural networks. This framework facilitates the generation of high-quality representations crucial for cell clustering. Our scSimGCL incorporates cell-cell graph structure and contrastive learning to enhance the performance of cell clustering. Extensive experimental results on simulated and real scRNA-seq datasets suggest the superiority of the proposed scSimGCL. Moreover, clustering assignment analysis confirms the general applicability of scSimGCL, including state-of-the-art clustering algorithms. Further, ablation study and hyperparameter analysis suggest the efficacy of our network architecture with the robustness of decisions in the self-supervised learning setting. The proposed scSimGCL can serve as a robust framework for practitioners developing tools for cell clustering. The source code of scSimGCL is publicly available at https://github.com/zhangzh1328/scSimGCL.

bioinformatics↗

bioRxiv2024

De novo design of high-affinity single-domain antibodies

Antibody-based therapeutics have become indispensable in modern medicine, but traditional methods of antibody discovery often present with limitations in developability, cross-reactivity, and ethical concerns. While deep learning and generative approaches have shown promise in the design of high affinity protein binders, de novo antibody design remains challenging. Here, we present EvolveX, a structure-based computational pipeline for designing antibody fragments. EvolveX utilizes ModelX and empirical force field FoldX to optimize complementarity- determining regions (CDRs) and TANGO for aggregation analysis. We demonstrate the ability of EvolveX to redesign a single-domain VHH antibody fragment targeting mouse Vsig4 to address two challenges: enhancing stability and affinity for the original target and redesigning it for high affinity to the human ortholog. The redesigned variants of VHH fragments specific to mouse Vsig4 showed improved physicochemical properties, while retaining binding affinities comparable to the original version. Notably, EvolveX improved the binding affinity of VHHs to human Vsig4 by over 1000-fold, transforming low-affinity binders into nanomolar-affinity molecules. Structural analyses by X-ray crystallography and NMR confirmed the accuracy of the designs, which display optimized interactions with the antigen. NGS and re-modelling analysis further demonstrated the efficiency of FoldX-based design pipeline. Collectively, our study highlights EvolveXs potential to overcome current limitations in antibody design, offering a powerful tool for the development of next-generation therapeutics with enhanced specificity, stability, and efficacy.

bioinformatics↗

bioRxiv2024

MIPDB: A maize image-phenotype database with multi-angle and multi-time characteristics

Plant phenomics has become one of the most significant scientific fields in recent years. However, typical phenotyping procedures have low accuracy, low throughput, and are labor-intensive and time-consuming. Large-scale phenotypic collection equipment, on the other hand, is pricy, rigid, and inconvenient. The advancement of phenomics has been hampered by these restrictions. Lightweight picture collection equipment can now be used to capture plant phenotypic data thanks to the development of deep learning-based image identification. For the purpose of training the model, this approach needs high-quality annotated datasets. In this study, we used a handheld camera to gather multi-angle, multi-time series images and an unmanned aerial vehicle (UAV) to create a maize image phenotyping database (MIPDB). Over 30,000 high-resolution photos are available in the MIPDB, with 17,631 of those images having been carefully tagged with point-line method. The MIPDB can be accessed by the general public at http://phenomics.agis.org.cn. We anticipate that the availability of this superior dataset will stimulate a new revolution in crop breeding and advance deep learning-based phenomics research.

plant biology↗

bioRxiv2024

Matrix Vesicles from Osteoblasts Promote Atherosclerotic Calcification

BackgroundsVascular calcification often occurs with osteoporosis, a contradictory association known as "the calcification paradox". Osteoblast-derived matrix vesicles (Ost-MVs) have been implicated in bone mineralization, and also have a potential role in ectopic vascular calcification. Herein, we aim to investigate the contributions that Ost-MVs make to the bone-vascular calcification paradox and the underlying mechanisms. Methods and ResultsHyperlipidemia-induced atherosclerotic calcification in mice was accompanied with bone mineral loss, as evidenced by reduced deposition of Ost-MVs in the bone matrix and increased release of Ost-MVs into the circulation. Intravenous injection of fluorescent DiI-labeled Ost-MVs revealed a marked fluorescence accumulation in the aorta of atherogenic mice, whereas no fluorescence signals were observed in normal controls. Using proteomics to analyze proteins in non-matrix bound Ost-MVs and mineralized SMC-derived MVs (SMC-MVs), we found Lamp1 was specifically expressed in SMC-MVs, and Nid2 was exclusively expressed in Ost-MVs. We further demonstrated that both Lamp1 and Nid2 were co-localized with Collagen I within calcific plaques, indicating the involvement of both Ost-MVs and SMC-MVs in atherosclerotic calcification. Mechanistically, LPS-induced vascular injury facilitated the transendothelial transport of Ost-MVs. The recruitment of circulating Ost-MVs was regulated by remodeled Collagen I during calcification progression. Furthermore, the phenotypic transition of SMCs determined the endocytosis of Ost-MVs. Finally, we demonstrated that either recruited Ost-MVs or resident SMC-MVs accelerated atherosclerotic calcification, depending on the Ras-Raf-ERK signaling. ConclusionAtherosclerotic calcification-induced Ost-MVs are released into circulation, facilitating the transport from bone to plaque lesions and exacerbating artery calcification progression. The mechanisms of Ost-MVs recruitment include vascular injury allowing transendothelial transport of Ost-MVs, collagen I remodeling promoting Ost-MVs aggregation, and SMC phenotypic switch to facilitate Ost-MVs uptake. Our results further revealed that both recruited Ost-MVs and calcifying SMC-MVs aggravate calcification through the Ras-Raf-ERK pathway.

cell biology↗

bioRxiv2024

CCDC113 stabilizes sperm axoneme and head-tail coupling apparatus to ensure male fertility

The structural integrity of the sperm is crucial for male fertility, defects in sperm head-tail linkage and flagellar axoneme are associated with acephalic spermatozoa syndrome (ASS) and the multiple morphological abnormalities of the sperm flagella (MMAF). Notably, impaired head-tail coupling apparatus (HTCA) often accompanies defects in the flagellum structure, however, the molecular mechanisms underlying this phenomenon remain elusive. Here, we identified an evolutionarily conserved coiled-coil domain-containing (CCDC) protein, CCDC113, and found the disruption of CCDC113 produced spermatozoa with disorganized sperm flagella and HTCA, which caused male infertility. Further analysis revealed that CCDC113 could bind to CFAP57 and CFAP91, and function as an adaptor protein for the connection of radial spokes, nexin-dynein regulatory complex (N-DRC) and doublet microtubules (DMTs) in the sperm axoneme. Moreover, CCDC113 was identified as a structural component of HTCA, collaborating with SUN5 and CENTLEIN to connect sperm head to tail during spermiogenesis. Together, our studies reveal that CCDC113 serve as a critical hub for sperm axoneme and HTCA stabilization, providing insights into the potential pathogenesis of infertility associated with human CCDC113 mutations.

developmental biology↗

bioRxiv2024

FLASH Irradiation Regulates IFN-β induction by mtDNA via Cytochrome c Leakage

Ultrahigh dose rate radiotherapy (FLASH-RT) is under intensive investigation for its biological benefits. The mechanisms underlying its ability to spare normal tissues while suppress tumor growth still remain controversial. Here we reveal that compared to the low dose rate electron irradiation (0.36 Gy/s), FLASH electron irradiation at 61 or 610 Gy/s enhances the cytochrome c leakage from mitochondria in human breast cells MCF-10A, which elicits substantial caspase activation, suppresses both the cytosolic mitochondrial DNA (mtDNA) accumulation and IFN-{beta} secretion. Besides, the deletion of mtDNA severely decreases the radiation-induced cGAS-STING activation. Conversely, the cytochrome c leakage in carcinoma cells MDA-MB-231 post electron irradiation is limited, especially for the case of FLASH irradiation, resulting in less cytosolic cytochrome c but stronger cGAS-STING activation than those in MCF-10A cells. The enhanced difference of cytochrome c leakage between cancer cells and normal cells post FLASH irradiation indicates a potential mechanism of FLASH effect by regulating the apoptotic and inflammatory pathway.

biophysics↗