Background Neurological complications are a significant concern of Coronavirus Disease 2019(COVID-19).However,the pathogenic mechanism of neurological symptoms associated with severe acute respiratory syndrome coronav...Background Neurological complications are a significant concern of Coronavirus Disease 2019(COVID-19).However,the pathogenic mechanism of neurological symptoms associated with severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)infection is poorly understood.Methods We used Drosophila as a model to systematically analyze SARS-CoV-2 genes encoding structural and accessory proteins and identified the membrane protein(M)that disrupted mitochondrial functions in vivo.The M protein was stereotaxically injected to further assess its effects in the brains of wild-type(WT)and 5×FAD mice.Omics technologies,including RNA sequencing and interactome analysis,were performed to explore the mechanisms of the effects of M protein both in vitro and in vivo.Results Systematic analysis of SARS-CoV-2 structural and accessory proteins in Drosophila identified that the M protein induces mitochondrial fragmentation and dysfunction,leading to reduced ATP production,ROS overproduction,and eventually cell death in the indirect flight muscles.In WT mice,M caused hippocampal atrophy,neural apoptosis,glial activation,and mitochondrial damage.These changes were further aggravated in 5×FAD mice.M was localized to the Golgi apparatus and genetically interacted with four wheel drive(FWD,a Drosophila homolog of mammalian PI4KIIIβ)to regulate Golgi functions in flies.Fwd RNAi,but not PI4KIIIαRNAi,reversed the M-induced Golgi abnormality,mitochondrial fragmentation,and ATP reduction.Inhibition of PI4KIIIβactivity suppressed the M-induced neuronal cell death.Therefore,M induced mitochondrial fragmentation and apoptosis likely through disruption of Golgi-derived PI(4)P-containing vesicles.Conclusions M disturbs the distribution and function of Golgi,leading to mitochondrial abnormality and eventually neurodegeneration via a PI4KIIIβ-mediated mechanism.This study reveals a potential mechanism for COVID-19 neurological symptoms and opens a new avenue for development of therapeutic strategies targeting SARS-CoV-2 M or mitochondria.展开更多
Massive sequence view (MSV) is a classic timeline-based dynamic network visualization approach. However, it is vulnerable to visual clutter caused by overlapping edges, thereby leading to unexpected misunderstanding o...Massive sequence view (MSV) is a classic timeline-based dynamic network visualization approach. However, it is vulnerable to visual clutter caused by overlapping edges, thereby leading to unexpected misunderstanding of time-varying trends of network communications. This study presents a new edge sampling algorithm called edge-based multi-class blue noise (E-MCBN) to reduce visual clutter in MSV. Our main idea is inspired by the multi-class blue noise (MCBN) sampling algorithm, commonly used in multi-class scatterplot decluttering. First, we take a node pair as an edge class, which can be regarded as an analogy to classes in multi-class scatterplots. Second, we propose two indicators, namely, class overlap and inter-class conflict degrees, to measure the overlapping degree and mutual exclusion, respectively, between edge classes. These indicators help construct the foundation of migrating the MCBN sampling from multi-class scatterplots to dynamic network samplings. Finally, we propose three strategies to accelerate MCBN sampling and a partitioning strategy to preserve local high-density edges in the MSV. The result shows that our approach can effectively reduce visual clutters and improve the readability of MSV. Moreover, our approach can also overcome the disadvantages of the MCBN sampling (i.e., long-running and failure to preserve local high-density communication areas in MSV). This study is the first that introduces MCBN sampling into a dynamic network sampling.展开更多
基金supported by grants from the National Natural Science Foundation of China(81429002,31330031,31872778,82201412)the Discipline Innovative Engineering Plan(111 Program)of China(B13036)+3 种基金a Key Laboratory Grant from Hunan Provincial Science and Technology Department(2016TP1006)a Science and Technology Major Project of Hunan Provincial Science and Technology Department(2018SK1030)Science and Technology Innovation Program of Hunan Province(2021SK1014)the China Postdoctoral Science Foundation(271004).
文摘Background Neurological complications are a significant concern of Coronavirus Disease 2019(COVID-19).However,the pathogenic mechanism of neurological symptoms associated with severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)infection is poorly understood.Methods We used Drosophila as a model to systematically analyze SARS-CoV-2 genes encoding structural and accessory proteins and identified the membrane protein(M)that disrupted mitochondrial functions in vivo.The M protein was stereotaxically injected to further assess its effects in the brains of wild-type(WT)and 5×FAD mice.Omics technologies,including RNA sequencing and interactome analysis,were performed to explore the mechanisms of the effects of M protein both in vitro and in vivo.Results Systematic analysis of SARS-CoV-2 structural and accessory proteins in Drosophila identified that the M protein induces mitochondrial fragmentation and dysfunction,leading to reduced ATP production,ROS overproduction,and eventually cell death in the indirect flight muscles.In WT mice,M caused hippocampal atrophy,neural apoptosis,glial activation,and mitochondrial damage.These changes were further aggravated in 5×FAD mice.M was localized to the Golgi apparatus and genetically interacted with four wheel drive(FWD,a Drosophila homolog of mammalian PI4KIIIβ)to regulate Golgi functions in flies.Fwd RNAi,but not PI4KIIIαRNAi,reversed the M-induced Golgi abnormality,mitochondrial fragmentation,and ATP reduction.Inhibition of PI4KIIIβactivity suppressed the M-induced neuronal cell death.Therefore,M induced mitochondrial fragmentation and apoptosis likely through disruption of Golgi-derived PI(4)P-containing vesicles.Conclusions M disturbs the distribution and function of Golgi,leading to mitochondrial abnormality and eventually neurodegeneration via a PI4KIIIβ-mediated mechanism.This study reveals a potential mechanism for COVID-19 neurological symptoms and opens a new avenue for development of therapeutic strategies targeting SARS-CoV-2 M or mitochondria.
基金supported in part by the National Key Research and Development Program of China(2018YFB1700403)the Special Funds for the Construction of an Innovative Province of Hunan(2020GK2028)+1 种基金the National Natural Science Foundation of China(Grant Nos.61872388,62072470)the Natural Science Foundation of Hunan Province(2020JJ4758).
文摘Massive sequence view (MSV) is a classic timeline-based dynamic network visualization approach. However, it is vulnerable to visual clutter caused by overlapping edges, thereby leading to unexpected misunderstanding of time-varying trends of network communications. This study presents a new edge sampling algorithm called edge-based multi-class blue noise (E-MCBN) to reduce visual clutter in MSV. Our main idea is inspired by the multi-class blue noise (MCBN) sampling algorithm, commonly used in multi-class scatterplot decluttering. First, we take a node pair as an edge class, which can be regarded as an analogy to classes in multi-class scatterplots. Second, we propose two indicators, namely, class overlap and inter-class conflict degrees, to measure the overlapping degree and mutual exclusion, respectively, between edge classes. These indicators help construct the foundation of migrating the MCBN sampling from multi-class scatterplots to dynamic network samplings. Finally, we propose three strategies to accelerate MCBN sampling and a partitioning strategy to preserve local high-density edges in the MSV. The result shows that our approach can effectively reduce visual clutters and improve the readability of MSV. Moreover, our approach can also overcome the disadvantages of the MCBN sampling (i.e., long-running and failure to preserve local high-density communication areas in MSV). This study is the first that introduces MCBN sampling into a dynamic network sampling.
