Effects of different puncture approaches on the curative effect and safety of patients with combined cardiovascular and cerebrovascular angiography.

Introduction: The relationship between different puncture points and perioperative complications and length of stay in hospital (LOS) in SCCAG patients has rarely been reported. Aim: To compare the curative effect and safety of the transradial artery approach and the transfemoral artery approach in combined heart-brain angiography. Material and methods: 120 patients who received combined cardio-cerebral angiography in our hospital were selected and divided into a transradial artery approach group (TRA) and a transfemoral artery approach group (TFA) according to a random number table. The postoperative efficacy and safety of the 2 groups were compared. Results: There was no statistically significant difference in puncture time and operation time between the 2 groups (p > 0.05). Postoperative bed rest time, hospitalization time, and X-ray exposure time in the TRA group were shorter than those in the TFA group, and the difference was statistically significant (p < 0.05). Before operation and 3 days after operation, there was no significant difference in left ventricle ejection fraction between the 2 groups (p > 0. 05). The overall incidence of complications in the TFA group was higher than that in the TRA group. The incidence between haematoma and pseudoaneurysm in the TFA group was higher, and the difference was statistically significant (p < 0.05). Conclusions: For simultaneous heart-brain angiography, interventional therapy via radial artery and femoral artery has good curative effect and can improve cardiac function. However, interventional therapy through the radial artery can shorten the postoperative bed rest time and hospitalization time, and reduce the incidence of complications.

Acutalibacter caecimuris sp. nov., Acutalibacter intestini sp. nov. and Neglectibacter caecimuris sp. nov., three novel species of the family Oscillospiraceae isolated from caecal contents of C57BL/6J mice.

The intestines of mice are colonized by diverse, as-yet-uncultivated bacteria. In this report, we describe the isolation, culture, genotypic and phenotypic characterization, as well as taxonomic classification of three novel anaerobic bacterial strains derived from the caecal contents of C57BL/6J male mice. According to the phenotypic and genotype-based polyphasic taxonomy, we propose three novel species within the family Oscillospiraceae. They are Acutalibacter caecimuris sp. nov. (type strain M00118T=CGMCC 1.18042T=KCTC 25739T), Acutalibacter intestini sp. nov. (type strain M00204T=CGMCC 1.18044T=KCTC 25741T) and Neglectibacter caecimuris sp. nov. (type strain M00184T=CGMCC 1.18043T=KCTC 25740T).

Glutathione determines chronic myeloid leukemia vulnerability to an inhibitor of CMPK and TMPK.

Bcr-Abl transformation leads to chronic myeloid leukemia (CML). The acquirement of T315I mutation causes tyrosine kinase inhibitors (TKI) resistance. This study develops a compound, JMF4073, inhibiting thymidylate (TMP) and cytidylate (CMP) kinases, aiming for a new therapy against TKI-resistant CML. In vitro and in vivo treatment of JMF4073 eliminates WT-Bcr-Abl-32D CML cells. However, T315I-Bcr-Abl-32D cells are less vulnerable to JMF4073. Evidence is presented that ATF4-mediated upregulation of GSH causes T315I-Bcr-Abl-32D cells to be less sensitive to JMF4073. Reducing GSH biosynthesis generates replication stress in T315I-Bcr-Abl-32D cells that require dTTP/dCTP synthesis for survival, thus enabling JMF4073 susceptibility. It further shows that the levels of ATF4 and GSH in several human CML blast-crisis cell lines are inversely correlated with JMF4073 sensitivity, and the combinatory treatment of JMF4073 with GSH reducing agent leads to synthetic lethality in these CML blast-crisis lines. Altogether, the investigation indicates an alternative option in CML therapy.

AttABseq: an attention-based deep learning prediction method for antigen-antibody binding affinity changes based on protein sequences.

The optimization of therapeutic antibodies through traditional techniques, such as candidate screening via hybridoma or phage display, is resource-intensive and time-consuming. In recent years, computational and artificial intelligence-based methods have been actively developed to accelerate and improve the development of therapeutic antibodies. In this study, we developed an end-to-end sequence-based deep learning model, termed AttABseq, for the predictions of the antigen-antibody binding affinity changes connected with antibody mutations. AttABseq is a highly efficient and generic attention-based model by utilizing diverse antigen-antibody complex sequences as the input to predict the binding affinity changes of residue mutations. The assessment on the three benchmark datasets illustrates that AttABseq is 120% more accurate than other sequence-based models in terms of the Pearson correlation coefficient between the predicted and experimental binding affinity changes. Moreover, AttABseq also either outperforms or competes favorably with the structure-based approaches. Furthermore, AttABseq consistently demonstrates robust predictive capabilities across a diverse array of conditions, underscoring its remarkable capacity for generalization across a wide spectrum of antigen-antibody complexes. It imposes no constraints on the quantity of altered residues, rendering it particularly applicable in scenarios where crystallographic structures remain unavailable. The attention-based interpretability analysis indicates that the causal effects of point mutations on antibody-antigen binding affinity changes can be visualized at the residue level, which might assist automated antibody sequence optimization. We believe that AttABseq provides a fiercely competitive answer to therapeutic antibody optimization.

Crocin's role in modulating MMP2/TIMP1 and mitigating hypoxia-induced pulmonary hypertension in mice.

To explore the molecular pathogenesis of pulmonary arterial hypertension (PAH) and identify potential therapeutic targets, we performed transcriptome sequencing of lung tissue from mice with hypoxia-induced pulmonary hypertension. Our Gene Ontology analysis revealed that "extracellular matrix organization" ranked high in the biological process category, and matrix metallopeptidases (MMPs) and other proteases also played important roles in it. Moreover, compared with those in the normoxia group, we confirmed that MMPs expression was upregulated in the hypoxia group, while the hub gene Timp1 was downregulated. Crocin, a natural MMP inhibitor, was found to reduce inflammation, decrease MMPs levels, increase Timp1 expression levels, and attenuate hypoxia-induced pulmonary hypertension in mice. In addition, analysis of the cell distribution of MMPs and Timp1 in the human lung cell atlas using single-cell RNAseq datasets revealed that MMPs and Timp1 are mainly expressed in a population of fibroblasts. Moreover, in vitro experiments revealed that crocin significantly inhibited myofibroblast proliferation, migration, and extracellular matrix deposition. Furthermore, we demonstrated that crocin inhibited TGF-ß1-induced fibroblast activation and regulated the pulmonary arterial fibroblast MMP2/TIMP1 balance by inhibiting the TGF-ß1/Smad3 signaling pathway. In summary, our results indicate that crocin attenuates hypoxia-induced pulmonary hypertension in mice by inhibiting TGF-ß1-induced myofibroblast activation.

DrugFlow: An AI-Driven One-Stop Platform for Innovative Drug Discovery.

Artificial intelligence (AI)-aided drug design has demonstrated unprecedented effects on modern drug discovery, but there is still an urgent need for user-friendly interfaces that bridge the gap between these sophisticated tools and scientists, particularly those who are less computer savvy. Herein, we present DrugFlow, an AI-driven one-stop platform that offers a clean, convenient, and cloud-based interface to streamline early drug discovery workflows. By seamlessly integrating a range of innovative AI algorithms, covering molecular docking, quantitative structure-activity relationship modeling, molecular generation, ADMET (absorption, distribution, metabolism, excretion and toxicity) prediction, and virtual screening, DrugFlow can offer effective AI solutions for almost all crucial stages in early drug discovery, including hit identification and hit/lead optimization. We hope that the platform can provide sufficiently valuable guidance to aid real-word drug design and discovery. The platform is available at https://drugflow.com.

Visfatin Facilitates VEGF-D-Induced Lymphangiogenesis through Activating HIF-1α and Suppressing miR-2277-3p in Human Chondrosarcoma.

Chondrosarcoma is a malignant bone tumor that arises from abnormalities in cartilaginous tissue and is associated with lung metastases. Lymphangiogenesis plays an essential role in cancer metastasis. Visfatin is an adipokine reported to enhance tumor metastasis, but its relationship with VEGF-D generation and lymphangiogenesis in chondrosarcoma remains undetermined. Our results from clinical samples reveal that VEGF-D levels are markedly higher in chondrosarcoma patients than in normal individuals. Visfatin stimulation promotes VEGF-D-dependent lymphatic endothelial cell lymphangiogenesis. We also found that visfatin induces VEGF-D production by activating HIF-1α and reducing miR-2277-3p generation through the Raf/MEK/ERK signaling cascade. Importantly, visfatin controls chondrosarcoma-related lymphangiogenesis in vivo. Therefore, visfatin is a promising target in the treatment of chondrosarcoma lymphangiogenesis.

Antioxidant and Anti-Inflammatory Properties of Hydrolyzed Royal Jelly Peptide in Human Dermal Fibroblasts: Implications for Skin Health and Care Applications.

Hydrolyzed royal jelly peptide (RJP) has garnered attention for its health-promoting functions. However, the potential applications of RJP in skincare have not been fully explored. In this study, we prepared RJP through the enzymatic hydrolysis of royal jelly protein with trypsin and investigated its antioxidant and anti-inflammatory properties on primary human dermal fibroblasts (HDFs). Our results demonstrate that RJP effectively inhibits oxidative damage induced by H2O2 and lipid peroxidation triggered by AAPH and t-BuOOH in HDFs. This effect may be attributed to the ability of RJP to enhance the level of glutathione and the activities of catalase and glutathione peroxidase 4, as well as its excellent iron chelating capacity. Furthermore, RJP modulates the NLRP3 inflammasome-mediated inflammatory response in HDFs, suppressing the mRNA expressions of NLRP3 and IL-1ß in the primer stage induced by LPS and the release of mature IL-1ß induced by ATP, monosodium urate, or nigericin in the activation stage. RJP also represses the expressions of COX2 and iNOS induced by LPS. Finally, we reveal that RJP exhibits superior antioxidant and anti-inflammatory properties over unhydrolyzed royal jelly protein. These findings suggest that RJP exerts protective effects on skin cells through antioxidative and anti-inflammatory mechanisms, indicating its promise for potential therapeutic avenues for managing oxidative stress and inflammation-related skin disorders.

Air Change Rate and SARS-CoV-2 Exposure in Hospitals and Residences: A Meta-Analysis.

As SARS-CoV-2 swept across the globe, increased ventilation and implementation of air cleaning were emphasized by the US CDC and WHO as important strategies to reduce the risk of inhalation exposure to the virus. To assess whether higher ventilation and air cleaning rates lead to lower exposure risk to SARS-CoV-2, 1274 manuscripts published between April 2020 and September 2022 were screened using key words "airborne SARS-CoV-2 or "SARS-CoV-2 aerosol". Ninety-three studies involved air sampling at locations with known sources (hospitals and residences) were selected and associated data were compiled. Two metrics were used to assess exposure risk: SARS-CoV-2 concentration and SARS-CoV-2 detection rate in air samples. Locations were categorized by type (hospital or residence) and proximity to the sampling location housing the isolated/quarantined patient (primary or secondary). The results showed that hospital wards had lower airborne virus concentrations than residential isolation rooms. A negative correlation was found between airborne virus concentrations in primary-occupancy areas and air changes per hour (ACH). In hospital settings, sample positivity rates were significantly reduced in secondary-occupancy areas compared to primary-occupancy areas, but they were similar across sampling locations in residential settings. ACH and sample positivity rates were negatively correlated, though the effect was diminished when ACH values exceeded 8. While limitations associated with diverse sampling protocols exist, data considered by this meta-analysis support the notion that higher ACH may reduce exposure risks to the virus in ambient air.

Use of biologics in Chinese pregnant patients with deficiency of interleukin-36 receptor antagonist (DITRA): A case series.

OBJECTIVE: Impetigo herpetiformis (IH) is a rare form of pustular psoriasis which may result in maternal and fetal morbidity and even mortality. Deficiency of interleukin-36 receptor antagonist (DITRA) is the most frequently identified genetic defect of IH. Currently there are no biologics approved for IH despite the revolutionary role of biologics in the treatment of plaque and pustular psoriasis. Anecdotal reports of biologics use in DITRA patients with IH are also limited. CASE REPORTS: We present herein a case series of 6 Chinese IH patients harboring IL36RN gene c.115+6T>C mutation during 8 pregnancies, treated with various biologics, including adalimumab, etanercept and secukinumab. CONCLUSION: Most pregnancy courses were uneventful, except for one woman who had recurrent episodes of decreased fetal heart rate variability after adalimumab injections, which subsided after switching to etanercept. The treatment effectiveness and safety demonstrated in our cases suggested the role of biologics for the treatment of IH in patients with DITRA.

The BioCascade Impactor: A novel device for direct collection of size-fractionated bioaerosols into liquid medium.

The ability to collect size-fractionated airborne particles that contain viable bacteria and fungi directly into liquid medium while also maintaining their viability is critical for assessing exposure risks. In this study, we present the BioCascade impactor, a novel device designed to collect airborne particles into liquid based on their aerodynamic diameter in three sequential stages (>9.74 µm, 3.94-9.74 µm, and 1.38-3.94 µm when operated at 8.5 L/min). Aerosol samples containing microorganisms - either Saccharomyces kudriavzevii or Micrococcus luteus, were used to evaluate the performance of the BioCascade (BC) paired with either the VIable Virus Aerosol Sampler (VIVAS) or a gelatin filter (GF) as stage 4 to collect particles <1.38 µm. Stages 2 and 3 collected the largest fractions of viable S. kudriavzevii when paired with VIVAS (0.468) and GF (0.519), respectively. Stage 3 collected the largest fraction of viable M. luteus particles in both BC+VIVAS (0.791) and BC+GF (0.950) configurations. The distribution function of viable microorganisms was consistent with the size distributions measured by the Aerodynamic Particle Sizer. Testing with both bioaerosol species confirmed no internal loss and no re-aerosolization occurred within the BC. Irrespective of the bioaerosol tested, stages 1, 3 and 4 maintained ≥80% of viability, while stage 2 maintained only 37% and 73% of viable S. kudriavzevii and M. luteus, respectively. The low viability that occurred in stage 2 warrants further investigation. Our work shows that the BC can efficiently size-classify and collect bioaerosols without re-aerosolization and effectively maintain the viability of collected microorganisms.

Dynamics of respiratory infectious diseases under rapid urbanization and COVID-19 pandemic in the subcenter of Beijing during 2014-2022.

Objective: The study analyzed the impact of urbanization on epidemiological characteristics of respiratory infectious disease in Tongzhou District, Beijing during 2014-2022 to provide reference for prevention and control priorities of respiratory infectious diseases during the innovative urbanization process in China. Methods: The incidence data of notifiable respiratory infectious diseases (NRIDs) in Tongzhou Beijing during 2014-2022 were summarized. The trend of incidence rate was analyzed by Joinpoint regression model, and entropy method was performed to construct the comprehensive index of urbanization (CIU) and generalized linear model was used to analyze the influence of CIU on the incidence rate of respiratory infectious diseases. Results: Totally 72616 NRIDs cases were reported in Tongzhou District during 2014-2022, and the incidence rate of NRIDs was higher during 2017-2019 (153/100 000) than during 2014-2016 (930/100 000) and during 2020-2022 (371/100 000), respectively (both P < 0.001). The CIU constantly increased with slight fluctuation in 2016 and 2018, respectively. The incidence rate of NRIDs showed an increase along with the CIU during 2014-2019 (r = 0.95, P = 0.004), while the incidence rate's tendency was interrupted by COVID-19 during 2020 with slight decrease in 2020-2021 and rebounded in 2022. For the patients aged <15 years, the incidence rate of NRIDs revealed a very sharp rise at the urbanization period without COVID-19 pandemic compared with that under pre-urbanization period (RR = 7.93, 95 % CI 7.63-8.24), and dropped off to the similar level as of pre-urbanization period when COVID-19 pandemic spread. Conclusions: Urbanization process may increase the incidence of NRIDs but constrained by COVID-19. Certain measures should be taken to prevent and control the effects by urbanization process, such as good natural environment with less population density, ecological environment with good air quality, promoted hand hygiene, mask wearing, keeping interpersonal distance, vaccination, media publicity for NRIDs' prevention and control.

A general model for predicting enzyme functions based on enzymatic reactions.

Accurate prediction of the enzyme comission (EC) numbers for chemical reactions is essential for the understanding and manipulation of enzyme functions, biocatalytic processes and biosynthetic planning. A number of machine leanring (ML)-based models have been developed to classify enzymatic reactions, showing great advantages over costly and long-winded experimental verifications. However, the prediction accuracy for most available models trained on the records of chemical reactions without specifying the enzymatic catalysts is rather limited. In this study, we introduced BEC-Pred, a BERT-based multiclassification model, for predicting EC numbers associated with reactions. Leveraging transfer learning, our approach achieves precise forecasting across a wide variety of Enzyme Commission (EC) numbers solely through analysis of the SMILES sequences of substrates and products. BEC-Pred model outperformed other sequence and graph-based ML methods, attaining a higher accuracy of 91.6%, surpassing them by 5.5%, and exhibiting superior F1 scores with improvements of 6.6% and 6.0%, respectively. The enhanced performance highlights the potential of BEC-Pred to serve as a reliable foundational tool to accelerate the cutting-edge research in synthetic biology and drug metabolism. Moreover, we discussed a few examples on how BEC-Pred could accurately predict the enzymatic classification for the Novozym 435-induced hydrolysis and lipase efficient catalytic synthesis. We anticipate that BEC-Pred will have a positive impact on the progression of enzymatic research.

Qingfei mixture modulates the immune responses in lung cancer through modulating mTOR signaling and gut microbiota-derived short-chain fatty acids.

Lung cancer ranks among the primary contributors to cancer-related fatalities on a global scale. Multiple research investigations have demonstrated that there exists a dysbiosis within the intestinal bacteria and short-chain fatty acids (SCFAs) is linked with immune responses in lung cancer. Qingfei mixture (QFM) has been widely used in treating lung cancer, yet the active ingredients and roles of the QFM on immune responses by targeting gut microbiota remain to be elucidated. The chemical constituents of QFM were qualitatively examined by UPLC/Q-TOF-MS. Additionally, we evaluated the therapeutic impact of the organic substance QFM on lung cancer, aiming to elucidate its mechanisms for improving the tumor-immune microenvironment. Herein, we constructed a Lewis lung carcinoma (LLC)-bearing mice model with QFM treatment to observe tumor growth and immune cell changes. Then, the feces were collected and a combinatory study using metagenomes, non-targeted metabonomics, and targeted metabonomics of SCFAs was performed. In vitro experiments have been conducted to estimate the roles of acetate and sodium propionate in CD8+ T cells. Furthermore, we treated tumor-bearing mice with QFM, QFM + MHY1485 (an mTOR activator), and QFM + an antibiotic mixture (ABX) to explore the potential therapeutic benefit of regulation of the tumor microenvironment. A total of 96 compounds were obtained from QFM by UPLC/Q-TOF-MS. Besides, the findings demonstrated that QFM exhibited significant efficacy against lung cancer, manifesting in reduced tumor growth and improved immune responses. In investigating its mechanisms, we integrated gut microbiota sequencing and fecal metabolomics, revealing that QFM effectively restored disruptions in gut microbiota and SCFAs in mice with lung cancer. QFM, acetate, or sodium propionate contributed to the up-regulation of IFN-γ, Gzms-B, perforin, IL-17, IL-6, IL-12, TNF-α expressions and decreased HDAC and IL-10 levels in vitro and in vivo. Moreover, MHY1485 and ABX weakened the effects of QFM on immunomodulation. Collectively, these results suggest that QFM may facilitate immune responses in the LLC-bearing mice via regulating the gut microbiota-derived SCFAs at least partially through targeting the mTOR signaling pathway.

The BioCascade-VIVAS system for collection and delivery of virus-laden size-fractionated airborne particles.

The size of virus-laden particles determines whether aerosol or droplet transmission is dominant in the airborne transmission of pathogens. Determining dominant transmission pathways is critical to implementing effective exposure risk mitigation strategies. The aerobiology discipline greatly needs an air sampling system that can collect virus-laden airborne particles, separate them by particle diameter, and deliver them directly onto host cells without inactivating virus or killing cells. We report the use of a testing system that combines a BioAerosol Nebulizing Generator (BANG) to aerosolize Human coronavirus (HCoV)-OC43 (OC43) and an integrated air sampling system comprised of a BioCascade impactor (BC) and Viable Virus Aerosol Sampler (VIVAS), together referred to as BC-VIVAS, to deliver the aerosolized virus directly onto Vero E6 cells. Particles were collected into four stages according to their aerodynamic diameter (Stage 1: >9.43 µm, Stage 2: 3.81-9.43 µm, Stage 3: 1.41-3.81 µm and Stage 4: <1.41 µm). OC43 was detected by reverse-transcription quantitative polymerase chain reaction (RT-qPCR) analyses of samples from all BC-VIVAS stages. The calculated OC43 genome equivalent counts per cm3 of air ranged from 0.34±0.09 to 70.28±12.56, with the highest concentrations in stage 3 (1.41-3.81 µm) and stage 4 (<1.41 µm). Virus-induced cytopathic effects appeared only in cells exposed to particles collected in stages 3 and 4, demonstrating the presence of viable OC43 in particles <3.81 µm. This study demonstrates the dual utility of the BC-VIVAS as particle size-fractionating air sampler and a direct exposure system for aerosolized viruses. Such utility may help minimize conventional post-collection sample processing time required to assess the viability of airborne viruses and increase the understanding about transmission pathways for airborne pathogens.

Advancing Ligand Docking through Deep Learning: Challenges and Prospects in Virtual Screening.

Molecular docking, also termed ligand docking (LD), is a pivotal element of structure-based virtual screening (SBVS) used to predict the binding conformations and affinities of protein-ligand complexes. Traditional LD methodologies rely on a search and scoring framework, utilizing heuristic algorithms to explore binding conformations and scoring functions to evaluate binding strengths. However, to meet the efficiency demands of SBVS, these algorithms and functions are often simplified, prioritizing speed over accuracy.The emergence of deep learning (DL) has exerted a profound impact on diverse fields, ranging from natural language processing to computer vision and drug discovery. DeepMind's AlphaFold2 has impressively exhibited its ability to accurately predict protein structures solely from amino acid sequences, highlighting the remarkable potential of DL in conformation prediction. This groundbreaking advancement circumvents the traditional search-scoring frameworks in LD, enhancing both accuracy and processing speed and thereby catalyzing a broader adoption of DL algorithms in binding pose prediction. Nevertheless, a consensus on certain aspects remains elusive.In this Account, we delineate the current status of employing DL to augment LD within the VS paradigm, highlighting our contributions to this domain. Furthermore, we discuss the challenges and future prospects, drawing insights from our scholarly investigations. Initially, we present an overview of VS and LD, followed by an introduction to DL paradigms, which deviate significantly from traditional search-scoring frameworks. Subsequently, we delve into the challenges associated with the development of DL-based LD (DLLD), encompassing evaluation metrics, application scenarios, and physical plausibility of the predicted conformations. In the evaluation of LD algorithms, it is essential to recognize the multifaceted nature of the metrics. While the accuracy of binding pose prediction, often measured by the success rate, is a pivotal aspect, the scoring/screening power and computational speed of these algorithms are equally important given the pivotal role of LD tools in VS. Regarding application scenarios, early methods focused on blind docking, where the binding site is unknown. However, recent studies suggest a shift toward identifying binding sites rather than solely predicting binding poses within these models. In contrast, LD with a known pocket in VS has been shown to be more practical. Physical plausibility poses another significant challenge. Although DLLD models often achieve higher success rates compared to traditional methods, they may generate poses with implausible local structures, such as incorrect bond angles or lengths, which are disadvantageous for postprocessing tasks like visualization. Finally, we discuss the future perspectives for DLLD, emphasizing the need to improve generalization ability, strike a balance between speed and accuracy, account for protein conformation flexibility, and enhance physical plausibility. Additionally, we delve into the comparison between generative and regression algorithms in this context, exploring their respective strengths and potential.

The stimulation of exosome generation by visfatin polarizes M2 macrophages and enhances the motility of chondrosarcoma.

Chondrosarcoma is a malignant bone tumor that arises from abnormalities in cartilaginous tissue and is associated with lung metastases. Extracellular vesicles called exosomes are primarily used as mediators of intercellular signal transmission to control tumor metastasis. Visfatin is an adipokine reported to enhance tumor metastasis, but its relationship with exosome generation in chondrosarcoma motility remains undetermined. Our results found that overexpressing visfatin augments the production of exosomes from chondrosarcoma cells. Visfatin-treated chondrosarcoma exosomes educate macrophage polarization towards the M2 but not M1 phenotype. Interestingly, M2 macrophages polarized by exosomes return to chondrosarcoma cells to facilitate cell motility. According to these findings, chondrosarcoma cells emit more exosomes when treated with visfatin. The stimulation of exosome generation by visfatin polarizes M2 macrophages and enhances the motility of chondrosarcoma.

NME3 is a gatekeeper for DRP1-dependent mitophagy in hypoxia.

NME3 is a member of the nucleoside diphosphate kinase (NDPK) family localized on the mitochondrial outer membrane (MOM). Here, we report a role of NME3 in hypoxia-induced mitophagy dependent on its active site phosphohistidine but not the NDPK function. Mice carrying a knock-in mutation in the Nme3 gene disrupting NME3 active site histidine phosphorylation are vulnerable to ischemia/reperfusion-induced infarction and develop abnormalities in cerebellar function. Our mechanistic analysis reveals that hypoxia-induced phosphatidic acid (PA) on mitochondria is essential for mitophagy and the interaction of DRP1 with NME3. The PA binding function of MOM-localized NME3 is required for hypoxia-induced mitophagy. Further investigation demonstrates that the interaction with active NME3 prevents DRP1 susceptibility to MUL1-mediated ubiquitination, thereby allowing a sufficient amount of active DRP1 to mediate mitophagy. Furthermore, MUL1 overexpression suppresses hypoxia-induced mitophagy, which is reversed by co-expression of ubiquitin-resistant DRP1 mutant or histidine phosphorylatable NME3. Thus, the site-specific interaction with active NME3 provides DRP1 a microenvironment for stabilization to proceed the segregation process in mitophagy.