scCaT: An explainable capsulating architecture for sepsis diagnosis transferring from single-cell RNA sequencing.

Sepsis is a life-threatening condition characterized by an exaggerated immune response to pathogens, leading to organ damage and high mortality rates in the intensive care unit. Although deep learning has achieved impressive performance on prediction and classification tasks in medicine, it requires large amounts of data and lacks explainability, which hinder its application to sepsis diagnosis. We introduce a deep learning framework, called scCaT, which blends the capsulating architecture with Transformer to develop a sepsis diagnostic model using single-cell RNA sequencing data and transfers it to bulk RNA data. The capsulating architecture effectively groups genes into capsules based on biological functions, which provides explainability in encoding gene expressions. The Transformer serves as a decoder to classify sepsis patients and controls. Our model achieves high accuracy with an AUROC of 0.93 on the single-cell test set and an average AUROC of 0.98 on seven bulk RNA cohorts. Additionally, the capsules can recognize different cell types and distinguish sepsis from control samples based on their biological pathways. This study presents a novel approach for learning gene modules and transferring the model to other data types, offering potential benefits in diagnosing rare diseases with limited subjects.

Multimorbidity patterns, leisure activities, and cognitive function: A population-based longitudinal study.

BACKGROUND: There is limited knowledge about the effect of leisure activities on cognitive decline related to different multimorbidity patterns. The study aimed to examine the role of leisure activities in the association between multimorbidity patterns and cognitive function. METHODS: We conducted a community-based cohort study based on the 2002-2018 Chinese Longitudinal Health Longevity Survey (CLHLS). Multimorbidity patterns were examined by exploratory factor analysis. Multivariable linear and logistic regression models were used to assess the associations between multimorbidity, leisure activities and cognitive function. RESULTS: The study included 14,093 older adults. Those with specific multimorbidity patterns had lower MMSE scores. Compared to individuals with cardio-metabolic and sensory patterns who frequently engaged in activities such as housework, garden work, and watching TV/listening to the radio, those who participated in these activities less regularly had lower MMSE scores. Furthermore, a higher frequency change of participation and a greater variety of leisure activities were associated with better cognitive function. CONCLUSIONS: The older individuals with multimorbidity are associated with lower MMSE scores, while those who participated in more leisure activities had higher cognitive function. Diverse, and frequent leisure activities may help delay cognitive decline in Chinese older adults with different multimorbidities.

Multimorbidity measures associated with cognitive function among community-dwelling older Chinese adults.

INTRODUCTION: Older adults with multimorbidity are at high risk of cognitive impairment development. There is a lack of research on the associations between different multimorbidity measures and cognitive function among older Chinese adults living in the community. METHODS: We used the Chinese Longitudinal Healthy Longevity Survey from 2002 to 2018 and included data on dementia-free participants aged ≥65 years. Multimorbidity measures included condition counts, multimorbidity patterns, and trajectories. The association of multimorbidity measures with cognitive function was examined by generalized estimating equation and linear and logistic regression models. RESULTS: Among 14,093 participants at baseline, 43.2% had multimorbidity. Multimorbidity patterns were grouped into cancer-inflammatory, cardiometabolic, and sensory patterns. Multimorbidity trajectories were classified as "onset-condition," "newly developing," and "severe condition." The Mini-Mental State Examination scores were significantly lower for participants with more chronic conditions, with cancer-inflammatory/cardiometabolic/sensory patterns, and with developing multimorbidity trajectories. DISCUSSION: Condition counts, sensory pattern, cardiometabolic pattern, cancer-inflammatory pattern, and multimorbidity developmental trajectories were prospectively associated with cognitive function. HIGHLIGHTS: Elderly individuals with a higher number of chronic conditions were associated with lower MMSE scores in the Chinese Longitudinal Healthy Longevity Survey data. MMSE scores were significantly lower for participants with specific multimorbidity patterns. Individuals with developing trajectories of multimorbidity were associated with lower MMSE scores and a higher risk of mild cognitive impairment.

Deep learning model to discriminate diverse infection types based on pairwise analysis of host gene expression.

Accurate detection of pathogens, particularly distinguishing between Gram-positive and Gram-negative bacteria, could improve disease treatment. Host gene expression can capture the immune system's response to infections caused by various pathogens. Here, we present a deep neural network model, bvnGPS2, which incorporates the attention mechanism based on a large-scale integrated host transcriptome dataset to precisely identify Gram-positive and Gram-negative bacterial infections as well as viral infections. We performed analysis of 4,949 blood samples across 40 cohorts from 10 countries using our previously designed omics data integration method, iPAGE, to select discriminant gene pairs and train the bvnGPS2. The performance of the model was evaluated on six independent cohorts comprising 374 samples. Overall, our deep neural network model shows robust capability to accurately identify specific infections, paving the way for precise medicine strategies in infection treatment and potentially also for identifying subtypes of other diseases.

Integrative machine learning and neural networks for identifying PANoptosis-related lncRNA molecular subtypes and constructing a predictive model for head and neck squamous cell carcinoma.

PURPOSE: PANoptosis is considered a novel type of cell death that plays important roles in tumor progression. In this study, we applied machine learning algorithms to explore the relationships between PANoptosis-related lncRNAs (PRLs) and head and neck squamous cell carcinoma (HNSCC) and established a neural network model for prognostic prediction. METHODS: Information about the HNSCC cohort was downloaded from the TCGA database, and the differentially expressed prognostic PRLs between tumor and normal samples were assessed in patients with different tumor subtypes via nonnegative matrix factorization (NMF) analysis. Subsequently, five kinds of machine-learning algorithms were used to select the core PRLs across the subtypes, and the interactive features were pooled into a neural network model to establish a PRL-related risk score (PLRS) system. Survival differences were compared via Kaplan‒Meier analysis, and the predictive effects were assessed with the areas under the ROCs. Moreover, functional enrichment analysis, immune infiltration, tumor mutation burden (TMB) and clinical therapeutic response were also conducted to further evaluate the novel predictive model. RESULTS: A total of 347 PRLs were identified, 225 of which were differentially expressed between tumor and normal samples. Patients were divided into two clusters via NMF analysis, in which cluster 1 had a better prognosis and more immune cells and functional infiltrates. With the application of five machine learning algorithms, we selected 13 interactive PRLs to construct the predictive model. The AUCs for the ROCs in the entire set were 0.735, 0.740 and 0.723, respectively. Patients in the low-PLRS group exhibited a better prognosis, greater immune cell enrichment, greater immune function activation, lower TMB and greater sensitivity to immunotherapy. CONCLUSION: In this study, we established a novel neural network prognostic model to predict survival and identify tumor subtypes in HNSCC patients. This novel assessment system is useful for prediction, providing ideas for clinical treatment.

Commonly used software tools produce conflicting and overly-optimistic AUPRC values.

The precision-recall curve (PRC) and the area under the precision-recall curve (AUPRC) are useful for quantifying classification performance. They are commonly used in situations with imbalanced classes, such as cancer diagnosis and cell type annotation. We evaluate 10 popular tools for plotting PRC and computing AUPRC, which were collectively used in more than 3000 published studies. We find the AUPRC values computed by the tools rank classifiers differently and some tools produce overly-optimistic results.

Targeting adipocyte ESRRA promotes osteogenesis and vascular formation in adipocyte-rich bone marrow.

Excessive bone marrow adipocytes (BMAds) accumulation often occurs under diverse pathophysiological conditions associated with bone deterioration. Estrogen-related receptor α (ESRRA) is a key regulator responding to metabolic stress. Here, we show that adipocyte-specific ESRRA deficiency preserves osteogenesis and vascular formation in adipocyte-rich bone marrow upon estrogen deficiency or obesity. Mechanistically, adipocyte ESRRA interferes with E2/ESR1 signaling resulting in transcriptional repression of secreted phosphoprotein 1 (Spp1); yet positively modulates leptin expression by binding to its promoter. ESRRA abrogation results in enhanced SPP1 and decreased leptin secretion from both visceral adipocytes and BMAds, concertedly dictating bone marrow stromal stem cell fate commitment and restoring type H vessel formation, constituting a feed-forward loop for bone formation. Pharmacological inhibition of ESRRA protects obese mice against bone loss and high marrow adiposity. Thus, our findings highlight a therapeutic approach via targeting adipocyte ESRRA to preserve bone formation especially in detrimental adipocyte-rich bone milieu.

Deregulation of immune response contributing to fulminant hepatitis in HEV infected pregnant women.

Hepatitis E virus (HEV) infection in pregnant women is associated with a wide spectrum of adverse consequences for both mother and fetus. The high mortality in this population appears to be associated with hormonal changes and consequent immunological changes. This study conducted an analysis of immune responses in pregnant women infected with HEV manifesting varying severity. Data mining analysis of the GSE79197 was utilized to examine differentially biological functions in pregnant women with HEV infection (P-HEV) versus without HEV infection (P-nHEV), P-HEV progressing to ALF (P-ALF) versus P-HEV, and P-HEV versus non-pregnant women with HEV infection (nP-HEV). We found cellular response to interleukin and immune response-regulating signalings were activated in P-HEV compared with P-nHEV. However, there was a significant decrease of immune responses, such as T cell activation, leukocyte cell-cell adhesion, regulation of lymphocyte activation, and immune response-regulating signaling pathway in P-ALF patient than P-HEV patient. Compared with nP-HEV, MHC protein complex binding function was inhibited in P-HEV. Further microRNA enrichment analysis showed that MAPK and T cell receptor signaling pathways were inhibited in P-HEV compared with nP-HEV. In summary, immune responses were activated during HEV infection while being suppressed when developing ALF during pregnancy, heightening the importance of immune mediation in the pathogenesis of severe outcome in HEV infected pregnant women.

Analysis of differential metabolites in serum metabolomics of patients with aortic dissection.

BACKGROUND: Pathogenesis and diagnostic biomarkers of aortic dissection (AD) can be categorized through the analysis of differential metabolites in serum. Analysis of differential metabolites in serum provides new methods for exploring the early diagnosis and treatment of aortic dissection. OBJECTIVES: This study examined affected metabolic pathways to assess the diagnostic value of metabolomics biomarkers in clients with AD. METHOD: The serum from 30 patients with AD and 30 healthy people was collected. The most diagnostic metabolite markers were determined using metabolomic analysis and related metabolic pathways were explored. RESULTS: In total, 71 differential metabolites were identified. The altered metabolic pathways included reduced phospholipid catabolism and four different metabolites considered of most diagnostic value including N2-gamma-glutamylglutamine, PC(phocholines) (20:4(5Z,8Z,11Z,14Z)/15:0), propionyl carnitine, and taurine. These four predictive metabolic biomarkers accurately classified AD patient and healthy control (HC) samples with an area under the curve (AUC) of 0.9875. Based on the value of the four different metabolites, a formula was created to calculate the risk of aortic dissection. Risk score = (N2-gamma-glutamylglutamine × -0.684) + (PC (20:4(5Z,8Z,11Z,14Z)/15:0) × 0.427) + (propionyl carnitine × 0.523) + (taurine × -1.242). An additional metabolic pathways model related to aortic dissection was explored. CONCLUSION: Metabolomics can assist in investigating the metabolic disorders associated with AD and facilitate a more in-depth search for potential metabolic biomarkers.

Intra-sample reversed pairs based on differentially ranked genes reveal biosignature for ovarian cancer.

Ovarian cancer, a major gynecological malignancy, often remains undetected until advanced stages, necessitating more effective early screening methods. Existing biomarker based on differential genes often suffers from variations in clinical practice. To overcome the limitations of absolute gene expression values including batch effects and biological heterogeneity, we introduced a pairwise biosignature leveraging intra-sample differentially ranked genes (DRGs) and machine learning for ovarian cancer detection across diverse cohorts. We analyzed ten cohorts comprising 872 samples with 796 ovarian cancer and 76 normal. Our method, DRGpair, involves three stages: intra-sample ranking differential analysis, reversed gene pair analysis, and iterative LASSO regression. We identified four DRG pairs, demonstrating superior diagnostic performance compared to current state-of-the-art biomarkers and differentially expressed genes in seven independent cohorts. This rank-based approach not only reduced computational complexity but also enhanced the specificity and effectiveness of biomarkers, revealing DRGs as promising candidates for ovarian cancer detection and offering a scalable model adaptable to varying cohort characteristics.

Commonly used software tools produce conflicting and overly-optimistic AUPRC values.

The precision-recall curve (PRC) and the area under it (AUPRC) are useful for quantifying classification performance. They are commonly used in situations with imbalanced classes, such as cancer diagnosis and cell type annotation. We evaluated 10 popular tools for plotting PRC and computing AUPRC, which were collectively used in >3,000 published studies. We found the AUPRC values computed by the tools rank classifiers differently and some tools produce overly-optimistic results.

DeepLocRNA: an interpretable deep learning model for predicting RNA subcellular localization with domain-specific transfer-learning.

MOTIVATION: Accurate prediction of RNA subcellular localization plays an important role in understanding cellular processes and functions. Although post-transcriptional processes are governed by trans-acting RNA binding proteins (RBPs) through interaction with cis-regulatory RNA motifs, current methods do not incorporate RBP-binding information. RESULTS: In this article, we propose DeepLocRNA, an interpretable deep-learning model that leverages a pre-trained multi-task RBP-binding prediction model to predict the subcellular localization of RNA molecules via fine-tuning. We constructed DeepLocRNA using a comprehensive dataset with variant RNA types and evaluated it on the held-out dataset. Our model achieved state-of-the-art performance in predicting RNA subcellular localization in mRNA and miRNA. It has also demonstrated great generalization capabilities, performing well on both human and mouse RNA. Additionally, a motif analysis was performed to enhance the interpretability of the model, highlighting signal factors that contributed to the predictions. The proposed model provides general and powerful prediction abilities for different RNA types and species, offering valuable insights into the localization patterns of RNA molecules and contributing to our understanding of cellular processes at the molecular level. A user-friendly web server is available at: https://biolib.com/KU/DeepLocRNA/.

RNA trafficking and subcellular localization-a review of mechanisms, experimental and predictive methodologies.

RNA localization is essential for regulating spatial translation, where RNAs are trafficked to their target locations via various biological mechanisms. In this review, we discuss RNA localization in the context of molecular mechanisms, experimental techniques and machine learning-based prediction tools. Three main types of molecular mechanisms that control the localization of RNA to distinct cellular compartments are reviewed, including directed transport, protection from mRNA degradation, as well as diffusion and local entrapment. Advances in experimental methods, both image and sequence based, provide substantial data resources, which allow for the design of powerful machine learning models to predict RNA localizations. We review the publicly available predictive tools to serve as a guide for users and inspire developers to build more effective prediction models. Finally, we provide an overview of multimodal learning, which may provide a new avenue for the prediction of RNA localization.

Co-expression module analysis reveals high expression homogeneity for both coding and non-coding genes in sepsis.

Sepsis is a life-threatening condition characterized by a harmful host response to infection with organ dysfunction. Annually about 20 million people are dead owing to sepsis and its mortality rates is as high as 20%. However, no studies have been carried out to investigate sepsis from the system biology point of view, as previous research predominantly focused on individual genes without considering their interactions and associations. Here, we conducted a comprehensive exploration of genome-wide expression alterations in both mRNAs and long non-coding RNAs (lncRNAs) in sepsis, using six microarray datasets. Co-expression networks were conducted to identify mRNA and lncRNA modules, respectively. Comparing these sepsis modules with normal modules, we observed a homogeneous expression pattern within the mRNA/lncRNA members, with the majority of them displaying consistent expression direction. Moreover, we identified consistent modules across diverse datasets, consisting of 20 common mRNA members and two lncRNAs, namely CHRM3-AS2 and PRKCQ-AS1, which are potential regulators of sepsis. Our results reveal that the up-regulated common mRNAs are mainly involved in the processes of neutrophil mediated immunity, while the down-regulated mRNAs and lncRNAs are significantly overrepresented in T-cell mediated immunity functions. This study sheds light on the co-expression patterns of mRNAs and lncRNAs in sepsis, providing a novel perspective and insight into the sepsis transcriptome, which may facilitate the exploration of candidate therapeutic targets and molecular biomarkers for sepsis.

5-Methylcytosine-related lncRNAs: predicting prognosis and identifying hot and cold tumor subtypes in head and neck squamous cell carcinoma.

BACKGROUND: 5-Methylcytosine (m5C) methylation is recognized as an mRNA modification that participates in biological progression by regulating related lncRNAs. In this research, we explored the relationship between m5C-related lncRNAs (mrlncRNAs) and head and neck squamous cell carcinoma (HNSCC) to establish a predictive model. METHODS: RNA sequencing and related information were obtained from the TCGA database, and patients were divided into two sets to establish and verify the risk model while identifying prognostic mrlncRNAs. Areas under the ROC curves were assessed to evaluate the predictive effectiveness, and a predictive nomogram was constructed for further prediction. Subsequently, the tumor mutation burden (TMB), stemness, functional enrichment analysis, tumor microenvironment, and immunotherapeutic and chemotherapeutic responses were also assessed based on this novel risk model. Moreover, patients were regrouped into subtypes according to the expression of model mrlncRNAs. RESULTS: Assessed by the predictive risk model, patients were distinguished into the low-MLRS and high-MLRS groups, showing satisfactory predictive effects with AUCs of 0.673, 0.712, and 0.681 for the ROCs, respectively. Patients in the low-MLRS groups exhibited better survival status, lower mutated frequency, and lower stemness but were more sensitive to immunotherapeutic response, whereas the high-MLRS group appeared to have higher sensitivity to chemotherapy. Subsequently, patients were regrouped into two clusters: cluster 1 displayed immunosuppressive status, but cluster 2 behaved as a hot tumor with a better immunotherapeutic response. CONCLUSIONS: Referring to the above results, we established a m5C-related lncRNA model to evaluate the prognosis, TME, TMB, and clinical treatments for HNSCC patients. This novel assessment system is able to precisely predict the patients' prognosis and identify hot and cold tumor subtypes clearly for HNSCC patients, providing ideas for clinical treatment.

Porcine epidemic diarrhea virus (PEDV) ORF3 protein inhibits cellular type I interferon signaling through down-regulating proteins expression in RLRs-mediated pathway.

Porcine epidemic diarrhea virus (PEDV) is an entero-pathogenic coronavirus, which belongs to the genus Alphacoronavirus in the family Coronaviridae, causing lethal watery diarrhea in piglets. Previous studies have shown that PEDV has developed an antagonistic mechanism by which it evades the antiviral activities of interferon (IFN), such as the sole accessory protein open reading frame 3 (ORF3) being found to inhibit IFN-ß promoter activities, but how this mechanism used by PEDV ORF3 inhibits activation of the type I signaling pathway remains not fully understood. Thus, in this present study, we showed that PEDV ORF3 inhibited both polyinosine-polycytidylic acid (poly(I:C))- and IFNα2b-stimulated transcription of IFN-ß and interferon-stimulated genes (ISGs) mRNAs. The expression levels of antiviral proteins in the retinoic acid-inducible gene I (RIG-I)-like receptors (RLRs)-mediated pathway was down-regulated in cells with over-expression of PEDV ORF3 protein, but global protein translation remained unchanged and the association of ORF3 with RLRs-related antiviral proteins was not detected, implying that ORF3 only specifically suppressed the expression of these signaling molecules. At the same time, we also found that the PEDV ORF3 protein inhibited interferon regulatory factor 3 (IRF3) phosphorylation and poly(I:C)-induced nuclear translocation of IRF3, which further supported the evidence that type I IFN production was abrogated by PEDV ORF3 through interfering with RLRs signaling. Furthermore, PEDV ORF3 counteracted transcription of IFN-ß and ISGs mRNAs, which were triggered by over-expression of signal proteins in the RLRs-mediated pathway. However, to our surprise, PEDV ORF3 initially induced, but subsequently reduced the transcription of IFN-ß and ISGs mRNAs to normal levels. Additionally, mRNA transcriptional levels of signaling molecules located at IFN-ß upstream were not inhibited, but elevated by PEDV ORF3 protein. Collectively, these results demonstrate that inhibition of type I interferon signaling by PEDV ORF3 can be realized through down-regulating the expression of signal molecules in the RLRs-mediated pathway, but not via inhibiting their mRNAs transcription. This study points to a new mechanism evolved by PEDV through blockage of the RLRs-mediated pathway by ORF3 protein to circumvent the host's antiviral immunity.

Deciphering associations between gut microbiota and clinical factors using microbial modules.

MOTIVATION: Human gut microbiota plays a vital role in maintaining body health. The dysbiosis of gut microbiota is associated with a variety of diseases. It is critical to uncover the associations between gut microbiota and disease states as well as other intrinsic or environmental factors. However, inferring alterations of individual microbial taxa based on relative abundance data likely leads to false associations and conflicting discoveries in different studies. Moreover, the effects of underlying factors and microbe-microbe interactions could lead to the alteration of larger sets of taxa. It might be more robust to investigate gut microbiota using groups of related taxa instead of the composition of individual taxa. RESULTS: We proposed a novel method to identify underlying microbial modules, i.e. groups of taxa with similar abundance patterns affected by a common latent factor, from longitudinal gut microbiota and applied it to inflammatory bowel disease (IBD). The identified modules demonstrated closer intragroup relationships, indicating potential microbe-microbe interactions and influences of underlying factors. Associations between the modules and several clinical factors were investigated, especially disease states. The IBD-associated modules performed better in stratifying the subjects compared with the relative abundance of individual taxa. The modules were further validated in external cohorts, demonstrating the efficacy of the proposed method in identifying general and robust microbial modules. The study reveals the benefit of considering the ecological effects in gut microbiota analysis and the great promise of linking clinical factors with underlying microbial modules. AVAILABILITY AND IMPLEMENTATION: https://github.com/rwang-z/microbial_module.git.

bvnGPS: a generalizable diagnostic model for acute bacterial and viral infection using integrative host transcriptomics and pretrained neural networks.

MOTIVATION: The confusion of acute inflammation infected by virus and bacteria or noninfectious inflammation will lead to missing the best therapy occasion resulting in poor prognoses. The diagnostic model based on host gene expression has been widely used to diagnose acute infections, but the clinical usage was hindered by the capability across different samples and cohorts due to the small sample size for signature training and discovery. RESULTS: Here, we construct a large-scale dataset integrating multiple host transcriptomic data and analyze it using a sophisticated strategy which removes batch effect and extracts the common information from different cohorts based on the relative expression alteration of gene pairs. We assemble 2680 samples across 16 cohorts and separately build gene pair signature (GPS) for bacterial, viral, and noninfected patients. The three GPSs are further assembled into an antibiotic decision model (bacterial-viral-noninfected GPS, bvnGPS) using multiclass neural networks, which is able to determine whether a patient is bacterial infected, viral infected, or noninfected. bvnGPS can distinguish bacterial infection with area under the receiver operating characteristic curve (AUC) of 0.953 (95% confidence interval, 0.948-0.958) and viral infection with AUC of 0.956 (0.951-0.961) in the test set (N = 760). In the validation set (N = 147), bvnGPS also shows strong performance by attaining an AUC of 0.988 (0.978-0.998) on bacterial-versus-other and an AUC of 0.994 (0.984-1.000) on viral-versus-other. bvnGPS has the potential to be used in clinical practice and the proposed procedure provides insight into data integration, feature selection and multiclass classification for host transcriptomics data. AVAILABILITY AND IMPLEMENTATION: The codes implementing bvnGPS are available at https://github.com/Ritchiegit/bvnGPS. The construction of iPAGE algorithm and the training of neural network was conducted on Python 3.7 with Scikit-learn 0.24.1 and PyTorch 1.7. The visualization of the results was implemented on R 4.2, Python 3.7, and Matplotlib 3.3.4.

GPGPS: a robust prognostic gene pair signature of glioma ensembling IDH mutation and 1p/19q co-deletion.

MOTIVATION: Many studies have shown that IDH mutation and 1p/19q co-deletion can serve as prognostic signatures of glioma. Although these genetic variations affect the expression of one or more genes, the prognostic value of gene expression related to IDH and 1p/19q status is still unclear. RESULTS: We constructed an ensemble gene pair signature for the risk evaluation and survival prediction of glioma based on the prior knowledge of the IDH and 1p/19q status. First, we separately built two gene pair signatures IDH-GPS and 1p/19q-GPS and elucidated that they were useful transcriptome markers projecting from corresponding genome variations. Then, the gene pairs in these two models were assembled to develop an integrated model named Glioma Prognostic Gene Pair Signature (GPGPS), which demonstrated high area under the curves (AUCs) to predict 1-, 3- and 5-year overall survival (0.92, 0.88 and 0.80) of glioma. GPGPS was superior to the single GPSs and other existing prognostic signatures (avg AUC = 0.70, concordance index = 0.74). In conclusion, the ensemble prognostic signature with 10 gene pairs could serve as an independent predictor for risk stratification and survival prediction in glioma. This study shed light on transferring knowledge from genetic alterations to expression changes to facilitate prognostic studies. AVAILABILITY AND IMPLEMENTATION: Codes are available at https://github.com/Kimxbzheng/GPGPS.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Phospholipase A2 induces acute kidney injury by complement mediated mitochondrial apoptosis via TNF-α/NF-κB signaling pathway.

OBJECTIVE: Acute kidney injury (AKI) is one of common complications of wasp/bee stings. Phospholipase A2 (PLA2) is a vital pathogenic composition of wasp/bee venom. We aimed to investigate the role of complement mediated mitochondrial apoptosis in PLA2 induced AKI. MATERIALS AND METHODS: PLA2 induced AKI model was established by injecting PLA2 into via tail vein on mice. The pathological changes and the microstructural changes of kidney, complement activation, inflammation and apoptosis were detected in vitro and in vivo respectively. RESULTS: The results showed that PLA2 induced AKI models were successfully established in vivo and vitro. Compared with control, serum creatinine and urea nitrogen levels were elevated. Complement system activation and mitochondrial damage were observed. Expressions of IL-6, TNF-α, cleaved caspase-3 and cleaved caspase-9, and Bax/Bcl-2 increased in PLA2 induced AKI models. TNF-α/NF-κB signaling pathway activation in AKI models. CONCLUSION: In the present study, PLA2 induced AKI model was first successfully established to our knowledge. The role of complement mediated mitochondrial apoptosis pathway in renal tubular epithelial cells in PLA2 induced AKI were verified in vitro and vivo.