Mechanism of ceramide synthase inhibition by fumonisin B1.

Ceramide synthases (CerSs) play crucial roles in sphingolipid metabolism and have emerged as promising drug targets for metabolic diseases, cancers, and antifungal therapy. However, the therapeutic targeting of CerSs has been hindered by a limited understanding of their inhibition mechanisms by small molecules. Fumonisin B1 (FB1) has been extensively studied as a potent inhibitor of eukaryotic CerSs. In this study, we characterize the inhibition mechanism of FB1 on yeast CerS (yCerS) and determine the structures of both FB1-bound and N-acyl-FB1-bound yCerS. Through our structural analysis and the observation of N-acylation of FB1 by yCerS, we propose a potential ping-pong catalytic mechanism for FB1 N-acylation by yCerS. Lastly, we demonstrate that FB1 exhibits lower binding affinity for yCerS compared to the C26- coenzyme A (CoA) substrate, suggesting that the potent inhibitory effect of FB1 on yCerS may primarily result from the N-acyl-FB1 catalyzed by yCerS, rather than through direct binding of FB1.

Collaborative regulation of yeast SPT-Orm2 complex by phosphorylation and ceramide.

The homeostatic regulation of serine palmitoyltransferase (SPT) activity in yeast involves N-terminal phosphorylation of Orm proteins, while higher eukaryotes lack these phosphorylation sites. Although recent studies have indicated a conserved ceramide-mediated feedback inhibition of the SPT-ORM/ORMDL complex in higher eukaryotes, its conservation and relationship with phosphorylation regulation in yeast remain unclear. Here, we determine the structure of the yeast SPT-Orm2 complex in a dephosphomimetic state and identify an evolutionarily conserved ceramide-sensing site. Ceramide stabilizes the dephosphomimetic Orm2 in an inhibitory conformation, facilitated by an intramolecular ß-sheet between the N- and C-terminal segments of Orm2. Moreover, we find that a phosphomimetic mutant of Orm2, positioned adjacent to its intramolecular ß-sheet, destabilizes the inhibitory conformation of Orm2. Taken together, our findings suggest that both Orm dephosphorylation and ceramide binding are crucial for suppressing SPT activity in yeast. This highlights a distinctive regulatory mechanism in yeast involving the collaborative actions of phosphorylation and ceramide.

Structure and mechanism of a eukaryotic ceramide synthase complex.

Ceramide synthases (CerS) catalyze ceramide formation via N-acylation of a sphingoid base with a fatty acyl-CoA and are attractive drug targets for treating numerous metabolic diseases and cancers. Here, we present the cryo-EM structure of a yeast CerS complex, consisting of a catalytic Lac1 subunit and a regulatory Lip1 subunit, in complex with C26-CoA substrate. The CerS holoenzyme exists as a dimer of Lac1-Lip1 heterodimers. Lac1 contains a hydrophilic reaction chamber and a hydrophobic tunnel for binding the CoA moiety and C26-acyl chain of C26-CoA, respectively. Lip1 interacts with both the transmembrane region and the last luminal loop of Lac1 to maintain the proper acyl chain binding tunnel. A lateral opening on Lac1 serves as a potential entrance for the sphingoid base substrate. Our findings provide a template for understanding the working mechanism of eukaryotic ceramide synthases and may facilitate the development of therapeutic CerS modulators.

Mortality among papillary thyroid cancer patients by detection route: a hospital-based retrospective cohort study.

Background: Incidence rates of papillary thyroid cancer (PTC) have increased rapidly, with incidentally detected cancers contributing a large proportion. We aimed to explore the impact of incidental detection on thyroid cancer-specific and competing mortality among PTC patients. Methods: We conducted a retrospective cohort study of PTC patients at a cancer center in Guangzhou. Baseline information on detection route and other covariates were collected between 2010 and 2018, and death outcome was followed up for each patient. Cumulative incidence functions were used to estimate the mortality risk of thyroid cancer and competing risk. Cause-specific hazard models were then utilized to explore the association between detection routes and PTC-specific and competing mortality. Results: Of the 2874 patients included, 2011 (70.0%) were detected incidentally, and the proportion increased from 36.9% in 2011 to 82.3% in 2018. During a median follow-up of 5.6 years, 42 deaths occurred, with 60% of them due to competing causes. The probability of competing mortality at 5 years in the non-incidental group and incidental group was 1.4% and 0.4%, respectively, and PTC-specific mortality in the non-incidental group and incidental group was 1.0% and 0.1%, respectively. After adjusting for covariates, the HRs of incidental detection were 0.13 (95% CI: 0.04-0.46; P = 0.01) and 0.47 (95% CI: 0.20-1.10; P = 0.10) on PTC-specific mortality and competing mortality, respectively. Conclusions: Incidental detection is associated with a lower risk of PTC-specific and competing mortality. Under the context of increasing magnitude of overdiagnosis, incorporation of detection route in clinical decision-making might be helpful to identify patients who might benefit from more extensive or conservative therapeutic strategies.

Identifying Vital Nodes in Hypergraphs Based on Von Neumann Entropy.

Hypergraphs have become an accurate and natural expression of high-order coupling relationships in complex systems. However, applying high-order information from networks to vital node identification tasks still poses significant challenges. This paper proposes a von Neumann entropy-based hypergraph vital node identification method (HVC) that integrates high-order information as well as its optimized version (semi-SAVC). HVC is based on the high-order line graph structure of hypergraphs and measures changes in network complexity using von Neumann entropy. It integrates s-line graph information to quantify node importance in the hypergraph by mapping hyperedges to nodes. In contrast, semi-SAVC uses a quadratic approximation of von Neumann entropy to measure network complexity and considers only half of the maximum order of the hypergraph's s-line graph to balance accuracy and efficiency. Compared to the baseline methods of hyperdegree centrality, closeness centrality, vector centrality, and sub-hypergraph centrality, the new methods demonstrated superior identification of vital nodes that promote the maximum influence and maintain network connectivity in empirical hypergraph data, considering the influence and robustness factors. The correlation and monotonicity of the identification results were quantitatively analyzed and comprehensive experimental results demonstrate the superiority of the new methods. At the same time, a key non-trivial phenomenon was discovered: influence does not increase linearly as the s-line graph orders increase. We call this the saturation effect of high-order line graph information in hypergraph node identification. When the order reaches its saturation value, the addition of high-order information often acts as noise and affects propagation.

Ceramide sensing by human SPT-ORMDL complex for establishing sphingolipid homeostasis.

The ORM/ORMDL family proteins function as regulatory subunits of the serine palmitoyltransferase (SPT) complex, which is the initiating and rate-limiting enzyme in sphingolipid biosynthesis. This complex is tightly regulated by cellular sphingolipid levels, but the sphingolipid sensing mechanism is unknown. Here we show that purified human SPT-ORMDL complexes are inhibited by the central sphingolipid metabolite ceramide. We have solved the cryo-EM structure of the SPT-ORMDL3 complex in a ceramide-bound state. Structure-guided mutational analyses reveal the essential function of this ceramide binding site for the suppression of SPT activity. Structural studies indicate that ceramide can induce and lock the N-terminus of ORMDL3 into an inhibitory conformation. Furthermore, we demonstrate that childhood amyotrophic lateral sclerosis (ALS) variants in the SPTLC1 subunit cause impaired ceramide sensing in the SPT-ORMDL3 mutants. Our work elucidates the molecular basis of ceramide sensing by the SPT-ORMDL complex for establishing sphingolipid homeostasis and indicates an important role of impaired ceramide sensing in disease development.

Complexity of Government response to COVID-19 pandemic: a perspective of coupled dynamics on information heterogeneity and epidemic outbreak.

This study aims at modeling the universal failure in preventing the outbreak of COVID-19 via real-world data from the perspective of complexity and network science. Through formalizing information heterogeneity and government intervention in the coupled dynamics of epidemic and infodemic spreading, first, we find that information heterogeneity and its induced variation in human responses significantly increase the complexity of the government intervention decision. The complexity results in a dilemma between the socially optimal intervention that is risky for the government and the privately optimal intervention that is safer for the government but harmful to the social welfare. Second, via counterfactual analysis against the COVID-19 crisis in Wuhan, 2020, we find that the intervention dilemma becomes even worse if the initial decision time and the decision horizon vary. In the short horizon, both socially and privately optimal interventions agree with each other and require blocking the spread of all COVID-19-related information, leading to a negligible infection ratio 30 days after the initial reporting time. However, if the time horizon is prolonged to 180 days, only the privately optimal intervention requires information blocking, which would induce a catastrophically higher infection ratio than that in the counterfactual world where the socially optimal intervention encourages early-stage information spread. These findings contribute to the literature by revealing the complexity incurred by the coupled infodemic-epidemic dynamics and information heterogeneity to the governmental intervention decision, which also sheds insight into the design of an effective early warning system against the epidemic crisis in the future.

Mechanism of sphingolipid homeostasis revealed by structural analysis of Arabidopsis SPT-ORM1 complex.

The serine palmitoyltransferase (SPT) complex catalyzes the first and rate-limiting step in sphingolipid biosynthesis in all eukaryotes. ORM/ORMDL proteins are negative regulators of SPT that respond to cellular sphingolipid levels. However, the molecular basis underlying ORM/ORMDL-dependent homeostatic regulation of SPT is not well understood. We determined the cryo-electron microscopy structure of Arabidopsis SPT-ORM1 complex, composed of LCB1, LCB2a, SPTssa, and ORM1, in an inhibited state. A ceramide molecule is sandwiched between ORM1 and LCB2a in the cytosolic membrane leaflet. Ceramide binding is critical for the ORM1-dependent SPT repression, and dihydroceramides and phytoceramides differentially affect this repression. A hybrid ß sheet, formed by the amino termini of ORM1 and LCB2a and induced by ceramide binding, stabilizes the amino terminus of ORM1 in an inhibitory conformation. Our findings provide mechanistic insights into sphingolipid homeostatic regulation via the binding of ceramide to the SPT-ORM/ORMDL complex that may have implications for plant-specific processes such as the hypersensitive response for microbial pathogen resistance.

Vital node identification in hypergraphs via gravity model.

Hypergraphs that can depict interactions beyond pairwise edges have emerged as an appropriate representation for modeling polyadic relations in complex systems. With the recent surge of interest in researching hypergraphs, the centrality problem has attracted much attention due to the challenge of how to utilize higher-order structure for the definition of centrality metrics. In this paper, we propose a new centrality method (HGC) on the basis of the gravity model as well as a semi-local HGC, which can achieve a balance between accuracy and computational complexity. Meanwhile, two comprehensive evaluation metrics, i.e., a complex contagion model in hypergraphs, which mimics the group influence during the spreading process and network s-efficiency based on the higher-order distance between nodes, are first proposed to evaluate the effectiveness of our methods. The results show that our methods can filter out nodes that have fast spreading ability and are vital in terms of hypergraph connectivity.

Quantification of network structural dissimilarities based on network embedding.

Quantifying structural dissimilarities between networks is a fundamental and challenging problem in network science. Previous network comparison methods are based on the structural features, such as the length of shortest path and degree, which only contain part of the topological information. Therefore, we propose an efficient network comparison method based on network embedding, which considers the global structural information. In detail, we first construct a distance matrix for each network based on the distances between node embedding vectors derived from DeepWalk. Then, we define the dissimilarity between two networks based on Jensen-Shannon divergence of the distance distributions. Experiments on both synthetic and empirical networks show that our method outperforms the baseline methods and can distinguish networks well. In addition, we show that our method can capture network properties, e.g., average shortest path length and link density. Moreover, the experiment of modularity further implies the functionality of our method.

Cryo-EM structures of the human surfactant lipid transporter ABCA3.

The adenosine 5'-triphosphate (ATP)-binding cassette (ABC) transporter ABCA3 plays a critical role in pulmonary surfactant biogenesis. Mutations in human ABCA3 have been recognized as the most frequent causes of inherited surfactant dysfunction disorders. Despite two decades of research, in vitro biochemical and structural studies of ABCA3 are still lacking. Here, we report the cryo-EM structures of human ABCA3 in two distinct conformations, both at resolution of 3.3 Å. In the absence of ATP, ABCA3 adopts a "lateral-opening" conformation with the lateral surfaces of transmembrane domains (TMDs) exposed to the membrane and features two positively charged cavities within the TMDs as potential substrate binding sites. ATP binding induces pronounced conformational changes, resulting in the collapse of the potential substrate binding cavities. Our results help to rationalize the disease-causing mutations in human ABCA3 and suggest a conserved "lateral access and extrusion" mechanism for both lipid export and import mediated by ABCA transporters.

Enhancing Cancer Driver Gene Prediction by Protein-Protein Interaction Network.

With the advances in gene sequencing technologies, millions of somatic mutations have been reported in the past decades, but mining cancer driver genes with oncogenic mutations from these data remains a critical and challenging area of research. In this study, we proposed a network-based classification method for identifying cancer driver genes with merging the multi-biological information. In this method, we construct a cancer specific genetic network from the human protein-protein interactome (PPI) to mine the network structure attributes, and combine biological information such as mutation frequency and differential expression of genes to achieve accurate prediction of cancer driver genes. Across seven different cancer types, the proposed algorithm always achieves high prediction accuracy, which is superior to the existing advanced methods. In the analysis of the predicted results, about 40 percent of the top 10 candidate genes overlap with the Cancer Gene Census database. Interestingly, the feature comparison indicates that the network based features are still more important than the biological features, including the mutation frequency and genetic differential expression. Further analyses also show that the integration of network structure attributes and biological information is valuable for predicting new cancer driver genes.

Structural basis of substrate recognition and translocation by human ABCA4.

Human ATP-binding cassette (ABC) subfamily A (ABCA) transporters mediate the transport of various lipid compounds across the membrane. Mutations in human ABCA transporters have been described to cause severe hereditary disorders associated with impaired lipid transport. However, little is known about the mechanistic details of substrate recognition and translocation by ABCA transporters. Here, we present three cryo-EM structures of human ABCA4, a retina-specific ABCA transporter, in distinct functional states at resolutions of 3.3-3.4 Å. In the nucleotide-free state, the two transmembrane domains (TMDs) exhibit a lateral-opening conformation, allowing the lateral entry of substrate from the lipid bilayer. The N-retinylidene-phosphatidylethanolamine (NRPE), the physiological lipid substrate of ABCA4, is sandwiched between the two TMDs in the luminal leaflet and is further stabilized by an extended loop from extracellular domain 1. In the ATP-bound state, the two TMDs display a closed conformation, which precludes the substrate binding. Our study provides a molecular basis to understand the mechanism of ABCA4-mediated NRPE recognition and translocation, and suggests a common 'lateral access and extrusion' mechanism for ABCA-mediated lipid transport.

A network-based deep learning methodology for stratification of tumor mutations.

MOTIVATION: Tumor stratification has a wide range of biomedical and clinical applications, including diagnosis, prognosis and personalized treatment. However, cancer is always driven by the combination of mutated genes, which are highly heterogeneous across patients. Accurately subdividing the tumors into subtypes is challenging. RESULTS: We developed a network-embedding based stratification (NES) methodology to identify clinically relevant patient subtypes from large-scale patients' somatic mutation profiles. The central hypothesis of NES is that two tumors would be classified into the same subtypes if their somatic mutated genes located in the similar network regions of the human interactome. We encoded the genes on the human protein-protein interactome with a network embedding approach and constructed the patients' vectors by integrating the somatic mutation profiles of 7344 tumor exomes across 15 cancer types. We firstly adopted the lightGBM classification algorithm to train the patients' vectors. The AUC value is around 0.89 in the prediction of the patient's cancer type and around 0.78 in the prediction of the tumor stage within a specific cancer type. The high classification accuracy suggests that network embedding-based patients' features are reliable for dividing the patients. We conclude that we can cluster patients with a specific cancer type into several subtypes by using an unsupervised clustering algorithm to learn the patients' vectors. Among the 15 cancer types, the new patient clusters (subtypes) identified by the NES are significantly correlated with patient survival across 12 cancer types. In summary, this study offers a powerful network-based deep learning methodology for personalized cancer medicine. AVAILABILITY AND IMPLEMENTATION: Source code and data can be downloaded from https://github.com/ChengF-Lab/NES. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Analysis of COVID-19 transmission in Shanxi Province with discrete time imported cases.

Since December 2019, an outbreak of a novel coronavirus pneumonia (WHO named COVID-19) swept across China. In Shanxi Province, the cumulative confirmed cases finally reached 133 since the first confirmed case appeared on January 22, 2020, and most of which were imported cases from Hubei Province. Reasons for this ongoing surge in Shanxi province, both imported and autochthonous infected cases, are currently unclear and demand urgent investigation. In this paper, we developed a SEIQR difference-equation model of COVID-19 that took into account the transmission with discrete time imported cases, to perform assessment and risk analysis. Our findings suggest that if the lock-down date in Wuhan is earlier, the infectious cases are fewer. Moreover, we reveal the effects of city lock-down date on the final scale of cases: if the date is advanced two days, the cases may decrease one half (67, 95% CI: 66-68); if the date is delayed for two days, the cases may reach about 196 (95% CI: 193-199). Our investigation model could be potentially helpful to study the transmission of COVID-19, in other provinces of China except Hubei. Especially, the method may also be used in countries with the first confirmed case is imported.

Safflower yellow alleviates osteoarthritis and prevents inflammation by inhibiting PGE2 release and regulating NF-κB/SIRT1/AMPK signaling pathways.

BACKGROUND: Safflower yellow (SY) is the main active ingredient of safflower, with various pharmacological effects such as anticoagulating, antioxidant, and anti-arthritis effects. PURPOSE: To investigate the anti-inflammatory and chondrocyte protecting role of SY, which subsequently leads to the inhibition of cartilage degradation. METHODS: Rat chondrocytes were stimulated with tumor necrosis factor α (TNF-α) with or without SY treatment. Following this, CCK-8 assay was performed to detect cytotoxicity. RT-qPCR, Western blotting, and immunofluorescence staining were used to detect the gene/protein expression of typical cartilage matrix genes and related inflammatory markers. Subsequently, EdU assay was used to evaluate cell proliferation. RNA sequencing, online target prediction, and molecular docking were performed to determine the possible molecular targets and pathways. RESULTS: The results showed that SY restored the TNF-α-induced up-regulation of IL-1ß, PTGS2, and MMP-13 and down-regulation of COL2A1 and ACAN. Furthermore, it recovered cell proliferation by suppressing TNF-α. Gene expression profiles identified 717 differentially expressed genes (DEGs) in the cells cultured with or without SY under TNF-α stimulation. After pathway enrichment, PI3K-Akt, TNF, Cytokine-cytokine receptor interaction, NF-κB, NOD-like receptor, and Chemokine signaling pathways were notably selected to highlight NFKBIA, CCL5, CCL2, IL6, and TNF as potential targets in osteoarthritis (OA). SY inhibited TNF-α-induced activation of NF-κB and endoplasmic reticulum (ER) stress by promoting AMPK phosphorylation along with SIRT1 expression. Further, SY reduced MMP-13 expression and targeted COX-2 for decreasing PGE2 release. In addition, anterior cruciate ligament transection-induced OA was ameliorated by local administration of SY. CONCLUSION: These results demonstrate that SY protects chondrocytes and inhibits inflammation by regulating the NF-κB/SIRT1/AMPK pathways and ER stress, thus preventing cartilage degeneration in OA.

Ursolic acid protects chondrocytes, exhibits anti-inflammatory properties via regulation of the NF-κB/NLRP3 inflammasome pathway and ameliorates osteoarthritis.

Inflammation and poor viability of chondrocytes result in the degradation of cartilage as osteoarthritis (OA) progresses. The purpose of the present study was to investigate whether ursolic acid (UA) can protect chondrocytes and alleviate OA. Following stimulation with tumor necrosis factor-α (TNF-α), 5 µM UA displayed no cytotoxicity and reversed the up-regulation of the inflammatory factors MMP13, IL-1ß, IL-6 and PTGS2, and down-regulation of the cartilaginous genes/proteins type II collagen and Aggrecan. RNA sequencing identified 533 common deferentially expressed genes (DEGs) of which TNF, PI3K-AKT, NOD-like receptor, cytokine receptor interaction and NF-κB pathways were of potential importance. Further notable DEGs in the most-highly expressed 10 pathways contributed to maintenance of cartilaginous ECM homeostasis and were involved in an inflammatory response. The expression of these most-enriched DEGs was reversed by UA following stimulation with TNF-α. Additional investigation demonstrated that treatment with UA inhibited TNF-α-induced nuclear translocation of p65 and phosphorylation of IκBα and AKT, and reversed TNF-α-induced up-regulation of P20, ACS and NLRP3. Furthermore, rat anterior cruciate ligament transection (ACLT) induced-OA was ameliorated by treatment with UA. In conclusion, these results suggest that UA activates chondrocytes through the NF-κB/NLRP3 inflammasome pathway, thus preventing cartilage degeneration in osteoarthritis.

A Large-Scale High-Density Weighted Structural Connectome of the Macaque Brain Acquired by Predicting Missing Links.

As a substrate for function, large-scale brain structural networks are crucial for fundamental and systems-level understanding of primate brains. However, it is challenging to acquire a complete primate whole-brain structural connectome using track tracing techniques. Here, we acquired a weighted brain structural network across 91 cortical regions of a whole macaque brain hemisphere with a connectivity density of 59% by predicting missing links from the CoCoMac-based binary network with a low density of 26.3%. The prediction model combines three factors, including spatial proximity, topological similarity, and cytoarchitectural similarity-to predict missing links and assign connection weights. The model was tested on a recently obtained high connectivity density yet partial-coverage experimental weighted network connecting 91 sources to 29 target regions; the model showed a prediction sensitivity of 74.1% in the predicted network. This predicted macaque hemisphere-wide weighted network has module segregation closely matching functional domains. Interestingly, the areas that act as integrators linking the segregated modules are mainly distributed in the frontoparietal network and correspond to the regions with large wiring costs in the predicted weighted network. This predicted weighted network provides a high-density structural dataset for further exploration of relationships between structure, function, and metabolism in the primate brain.

Prevalence of Enteropathogens in Outpatients with Acute Diarrhea from Urban and Rural Areas, Southeast China, 2010-2014.

Acute diarrhea is an important public health issue. Here, we focused on the differences of enteropathogens in acute diarrhea between urban and rural areas in southeast China. Laboratory- and sentinel-based surveillance of acute diarrhea (≥ 3 loose or liquid stools/24 hours) was conducted at 16 hospitals. Fecal specimens were tested for bacterial (Aeromonas sp., Campylobacter sp., diarrheagenic Escherichia coli, Plesiomonas shigelloides, non-typhoidal Salmonella, Shigella sp., Vibrio sp., and Yersinia sp.) and viral (adenovirus, astrovirus, Norovirus, Rotavirus, and Sapovirus) pathogens. Descriptive statistics were used. Between January 1, 2010, and December 31, 2014, 4,548 outpatients with acute diarrhea were enrolled (urban, n = 3,220; rural, n = 1,328). Pathogens were identified in 2,074 (45.6%) patients. Norovirus (25.7%), Vibrio parahaemolyticus (10.2%), enteroaggregative Escherichia coli (EAEC) (8.8%), group A Rotavirus (7.0%), and enterotoxigenic Escherichia coli (ETEC) (5.6%) were the most common pathogens. Enteropathogens were less common in urban than in rural areas (42.0% versus 54.4%, P < 0.001). In urban areas, EAEC and ETEC were more common in high-income than in middle-income regions. Interventions targeting the most common enteropathogens can substantially reduce the burden of acute diarrhea in southeast China.

Temporal information gathering process for node ranking in time-varying networks.

Many systems are dynamic and time-varying in the real world. Discovering the vital nodes in temporal networks is more challenging than that in static networks. In this study, we proposed a temporal information gathering (TIG) process for temporal networks. The TIG-process, as a node's importance metric, can be used to do the node ranking. As a framework, the TIG-process can be applied to explore the impact of temporal information on the significance of the nodes. The key point of the TIG-process is that nodes' importance relies on the importance of its neighborhood. There are four variables: temporal information gathering depth n, temporal distance matrix D, initial information c, and weighting function f. We observed that the TIG-process can degenerate to classic metrics by a proper combination of these four variables. Furthermore, the fastest arrival distance based TIG-process ( fad-tig) is performed optimally in quantifying nodes' efficiency and nodes' spreading influence. Moreover, for the fad-tig process, we can find an optimal gathering depth n that makes the TIG-process perform optimally when n is small.