Research progress on the role and mechanism of Sirtuin family in doxorubicin cardiotoxicity.

BACKGROUND: Doxorubicin (DOX) is a widely utilized anthracycline chemotherapy drug in cancer treatment, yet its efficacy is hindered by both short-term and long-term cardiotoxicity. Although oxidative stress, inflammation and mitochondrial dysfunction are established factors in DOX-induced cardiotoxicity, the precise molecular pathways remain elusive. Further exploration of the pathogenesis and identification of novel molecular targets are imperative. Recent studies have implicated the Sirtuins family in various physiological and pathological processes, suggesting their potential in ameliorating DOX-induced cardiotoxicity. Moreover, research on Sirtuins has discovered small-molecule compounds or medicinal plants with regulatory effects, representing a notable advancement in preventing and treating DOX-induced cardiac injury. PURPOSE: In this review, we delve into the pathogenesis of DOX-induced cardiotoxicity and explore the therapeutic effects of Sirtuins in mitigating this condition, along with the associated molecular mechanisms. Furthermore, we delineate the roles and mechanisms of small-molecule regulators of Sirtuins in the prevention and treatment of DOX-induced cardiotoxicity. STUDY-DESIGN/METHODS: Data for this review were sourced from various scientific databases (such as Web of Science, PubMed and Science Direct) up to March 2024. Search terms included "Sirtuins," "DOX-induced cardiotoxicity," "DOX," "Sirtuins regulators," "histone deacetylation," among others, as well as several combinations thereof. RESULTS: Members of the Sirtuins family regulate both the onset and progression of DOX-induced cardiotoxicity through anti-inflammatory, antioxidative stress and anti-apoptotic mechanisms, as well as by maintaining mitochondrial stability. Moreover, natural plant-derived active compounds such as Resveratrol (RES), curcumin, berberine, along with synthetic small-molecule compounds like EX527, modulate the expression and activity of Sirtuins. CONCLUSION: The therapeutic role of the Sirtuins family in mitigating DOX-induced cardiotoxicity represents a potential molecular target. However, further research is urgently needed to elucidate the relevant molecular mechanisms and to assess the safety and biological activity of Sirtuins regulators. This review offers an in-depth understanding of the therapeutic role of the Sirtuins family in mitigating DOX-induced cardiotoxicity, providing a preliminary basis for the clinical application of Sirtuins regulators in this condition.

Identification of activating transcription factor 6 (ATF6) as a novel prognostic biomarker and potential target in oral squamous cell carcinoma.

BACKGROUND: Oral squamous cell carcinoma (OSCC) is originating from oral mucosal epithelial cells. Autophagy plays a crucial role in cancer treatment by promoting cellular self-degradation and eliminating damaged components, thereby enhancing therapeutic efficacy. In this study, we aim to identify a novel autophagy-related biomarker to improve OSCC therapy. METHODS: We firstly utilized Cox and Lasso analyses to identify that ATF6 is associated with OSCC prognosis, and validated the results by Kaplan-Meier survival analysis. We further identified the downstream pathways and related genes by enrichment analysis and WGCNA analysis. Subsequently, we used short interfering RNA to investigate the effects of ATF6 knockdown on proliferation, migration, apoptosis, and autophagy in SCC-9 and SCC-15 cells through cell viability assay, transwell assay, EdU incorporation assay, flow cytometry analysis, western blot analysis and immunofluorescence analysis, etc. RESULTS: Bioinformatics analyses showed that ATF6 overexpression was associated with prognosis and detrimental to survival. In vitro studies verified that ATF6 knockdown reduced OSCC cell proliferation and migration. Mechanistically, ATF6 knockdown could promote cellular autophagy and apoptosis. CONCLUSION: We propose that ATF6 holds potential as a prognostic biomarker linked to autophagy in OSCC. This study provides valuable clues for further exploration of targeted therapy against OSCC.

Targeting the Warburg effect: A revisited perspective from molecular mechanisms to traditional and innovative therapeutic strategies in cancer.

Cancer reprogramming is an important facilitator of cancer development and survival, with tumor cells exhibiting a preference for aerobic glycolysis beyond oxidative phosphorylation, even under sufficient oxygen supply condition. This metabolic alteration, known as the Warburg effect, serves as a significant indicator of malignant tumor transformation. The Warburg effect primarily impacts cancer occurrence by influencing the aerobic glycolysis pathway in cancer cells. Key enzymes involved in this process include glucose transporters (GLUTs), HKs, PFKs, LDHs, and PKM2. Moreover, the expression of transcriptional regulatory factors and proteins, such as FOXM1, p53, NF-κB, HIF1α, and c-Myc, can also influence cancer progression. Furthermore, lncRNAs, miRNAs, and circular RNAs play a vital role in directly regulating the Warburg effect. Additionally, gene mutations, tumor microenvironment remodeling, and immune system interactions are closely associated with the Warburg effect. Notably, the development of drugs targeting the Warburg effect has exhibited promising potential in tumor treatment. This comprehensive review presents novel directions and approaches for the early diagnosis and treatment of cancer patients by conducting in-depth research and summarizing the bright prospects of targeting the Warburg effect in cancer.

Modulating autophagy to treat diseases: A revisited review on in silico methods.

BACKGROUND: Autophagy refers to the conserved cellular catabolic process relevant to lysosome activity and plays a vital role in maintaining the dynamic equilibrium of intracellular matter by degrading harmful and abnormally accumulated cellular components. Accumulating evidence has recently revealed that dysregulation of autophagy by genetic and exogenous interventions may disrupt cellular homeostasis in human diseases. In silico approaches as powerful aids to experiments have also been extensively reported to play their critical roles in the storage, prediction, and analysis of massive amounts of experimental data. Thus, modulating autophagy to treat diseases by in silico methods would be anticipated. AIM OF REVIEW: Here, we focus on summarizing the updated in silico approaches including databases, systems biology network approaches, omics-based analyses, mathematical models, and artificial intelligence (AI) methods that sought to modulate autophagy for potential therapeutic purposes, which will provide a new insight into more promising therapeutic strategies. KEY SCIENTIFIC CONCEPTS OF REVIEW: Autophagy-related databases are the data basis of the in silico method, storing a large amount of information about DNA, RNA, proteins, small molecules and diseases. The systems biology approach is a method to systematically study the interrelationships among biological processes including autophagy from a macroscopic perspective. Omics-based analyses are based on high-throughput data to analyze gene expression at different levels of biological processes involving autophagy. mathematical models are visualization methods to describe the dynamic process of autophagy, and its accuracy is related to the selection of parameters. AI methods use big data related to autophagy to predict autophagy targets, design targeted small molecules, and classify diverse human diseases for potential therapeutic applications.

Carbon emissions prediction considering environment protection investment of 30 provinces in China.

In the backdrop of carbon peaking and carbon neutrality, carbon emissions have always been a major concern. The approach of the heterogeneity grey model is proposed, aiming to predict carbon emissions of 30 provinces in China. This model combines the manta ray foraging optimization algorithm to search for the optimal heterogeneity coefficient. By using the heterogeneity grey model, the carbon emissions are analyzed in 30 provinces of China from 2022 to 2030 considering different environmental protection investment scenarios. The results indicate that in 19 provinces from 2022 to 2030, there is a significant decrease in carbon emissions as government investment increases. In 11 provinces during the same period, there is a rising trend in carbon emissions with the increase of government investment. Hence, achieving a reduction in carbon emissions necessitates not only relying on government investment in environmental protection but also exploring alternative approaches to mitigate carbon emissions. The methodologies and conclusions proposed in this study can provide technical references and making decision references for provincial carbon emission efforts.

Establishment and validation of a prediction model for myocarditis in Chinese children below 14 years old: a protocol for a retrospective cohort study.

INTRODUCTION: Paediatric myocarditis, a rare inflammatory disease, often presents without clear early symptoms. Although cardiac troponin I levels can aid in diagnosing myocarditis, they are not definitive indicators. Troponin I levels frequently fluctuate within and outside the reference range, potentially causing misinterpretations by clinicians. Although a negative troponin I result is valuable for excluding myocarditis, its specificity is low. Moreover, the clinical diagnosis of paediatric myocarditis is exceptionally challenging, and accurate early-stage diagnosis and treatment pose difficulties. Currently, the Dallas criteria, involving cardiac biopsy, serves as the gold standard for myocarditis diagnosis. However, this method has several drawbacks and is unsuitable for children, resulting in its limited use. METHODS AND ANALYSIS: In this study, we will employ multiple logistic regression analysis to develop a predictive model for early childhood myocarditis. This model will assess the patient's condition at onset and provide the probability of a myocarditis diagnosis. Model performance will be evaluated for accuracy and calibration, and the results will be presented through receiver operating characteristic (ROC) curves and calibration plots. Clinical decision curve analysis, in conjunction with ROC curve analysis, will be employed to determine the optimal cut-off value and calculate the net clinical benefit value for assessing clinical effectiveness. Finally, internal model validation will be conducted using bootstrapping. ETHICS AND DISSEMINATION: Approval from the Clinical Research Ethics Committee of The Third Affiliated Hospital of Wenzhou Medical University has been obtained. The research findings will be disseminated through presentations at scientific conferences and publication in peer-reviewed journals.

Flubendazole Enhances the Inhibitory Effect of Paclitaxel via HIF1α/PI3K/AKT Signaling Pathways in Breast Cancer.

Paclitaxel, a natural anticancer drug, is widely recognized and extensively utilized in the treatment of breast cancer (BC). However, it may lead to certain side effects or drug resistance. Fortunately, combination therapy with another anti-tumor agent has been explored as an option to improve the efficacy of paclitaxel in the treatment of BC. Herein, we first evaluated the synergistic effects of paclitaxel and flubendazole through combination index (CI) calculations. Secondly, flubendazole was demonstrated to synergize paclitaxel-mediated BC cell killing in vitro and in vivo. Moreover, we discovered that flubendazole could reverse the drug resistance of paclitaxel-resistant BC cells. Mechanistically, flubendazole was demonstrated to enhance the inhibitory effect of paclitaxel via HIF1α/PI3K/AKT signaling pathways. Collectively, our findings demonstrate the effectiveness of flubendazole in combination with paclitaxel for treating BC, providing an insight into exploiting more novel combination therapies for BC in the future.

Multi-omics approaches identify novel prognostic biomarkers of autophagy in uveal melanoma.

PURPOSE: Uveal melanoma (UVM) is a rare yet malignant ocular tumor that metastases in approximately half of all patients, with the majority of those developing metastasis typically succumbing to the disease within a year. Hitherto, no effective treatment for UVM has been identified. Autophagy is a cellular mechanism that has been suggested as an emerging regulatory process for cancer-targeted therapy. Thus, identifying novel prognostic biomarkers of autophagy may help improve future treatment. METHODS: Consensus clustering and similarity network fusion approaches were performed for classifying UVM patient subgroups. Weighted correlation network analysis was performed for gene module screening and network construction. Gene set variation analysis was used to evaluate the autophagy activity of the UVM subgroups. Kaplan-Meier survival curves (Log-rank test) were performed to analyze patient prognosis. Gene set cancer analysis was used to estimate the level of immune cell infiltration. RESULTS: In this study, we employed multi-omics approaches to classify UVM patient subgroups by molecular and clinical characteristics, ultimately identifying HTR2B, EEF1A2, FEZ1, GRID1, HAP1, and SPHK1 as potential prognostic biomarkers of autophagy in UVM. High expression levels of these markers were associated with poorer patient prognosis and led to reshaping the tumor microenvironment (TME) that promotes tumor progression. CONCLUSION: We identified six novel potential prognostic biomarkers in UVM, all of which are associated with autophagy and TME. These findings will shed new light on UVM therapy with inhibitors targeting these biomarkers expected to regulate autophagy and reshape the TME, significantly improving UVM treatment outcomes.

An optimized grey model for predicting non-renewable energy consumption in China.

The large amount of the non-renewable energy consumption in China brings certain challenges to the realization of carbon neutrality. This paper proposes a new grey model to predict the consumption of non-renewable energy in China. Based on the traditional grey model, the proposed model introduces two parameters to adjust the weight of information. Simultaneously, the intelligent optimization algorithm determines the optimal parameters. Three cases verify the feasibility of the model. The forecast results show that the amount of oil and natural gas consumption will continue to grow at a faster rate. By 2026, the amount of oil consumption will exceed 37 EJ (EJ) and natural gas consumption will exceed 22 EJ. Compared to 2021, oil consumption is up nearly 24%, and natural gas consumption is up more than 60%. While the consumption of coal will maintain a small up rate and gradually be leveled off.

Forecast of natural gas consumption in 30 regions of China under dual carbon target.

Natural gas is an environmentally friendly and low-carbon clean energy. Its replacement of coal and other fossil energy sources will be important in China's carbon peaking and carbon neutrality goals. The Chinese government has also introduced many policies to encourage the development of natural gas. Therefore, it is of great significance to forecast the natural gas consumption. The grey prediction model has the unique advantage that it can perform well in the case of inadequate sample size. In this paper, the fractional cumulative grey model (FGM(1,1)) is used to forecast the natural gas consumption of 30 areas (provinces, cities, and autonomous regions) in China from 2022 to 2030. According to the reasonable forecast results, except for a few special areas, the consumption in other areas of China will continue to rise in recent years. By analyzing the results, it can also be clearly concluded that the natural gas consumption has regional characteristics. The consumption in 19 regions shows a rapid growth trend, 8 regions show a steady growth trend, and 3 regions show a downward trend. The prediction results and analysis will provide some reference for different regions to formulate natural gas-related policies.

Two novel nonlinear multivariate grey models with kernel learning for small-sample time series prediction.

For many applications, small-sample time series prediction based on grey forecasting models has become indispensable. Many algorithms have been developed recently to make them effective. Each of these methods has a specialized application depending on the properties of the time series that need to be inferred. In order to develop a generalized nonlinear multivariable grey model with higher compatibility and generalization performance, we realize the nonlinearization of traditional GM(1,N), and we call it NGM(1,N). The unidentified nonlinear function that maps the data into a better representational space is present in both the NGM(1,N) and its response function. The original optimization problem with linear equality constraints is established in terms of parameter estimation for the NGM(1,N), and two different approaches are taken to solve it. The former is the Lagrange multiplier method, which converts the optimization problem into a linear system to be solved; and the latter is the standard dualization method utilizing Lagrange multipliers, that uses a flexible estimation equation for the development coefficient. As the size of the training data increases, the estimation results of the potential development coefficient get richer and the final estimation results using the average value are more reliable. The kernel function expresses the dot product of two unidentified nonlinear functions during the solving process, greatly lowering the computational complexity of nonlinear functions. Three numerical examples show that the LDNGM(1,N) outperforms the other multivariate grey models compared in terms of generalization performance. The duality theory and framework with kernel learning are instructive for further research around multivariate grey models to follow. Supplementary Information: The online version contains supplementary material available at 10.1007/s11071-023-08296-y.

Spatial distribution of floating population in Beijing, Tianjin and Hebei Region and its correlations with synergistic development.

Utilizing statistical information from the Seventh National Population Census, statistical yearbook and sampling dynamic survey data, this study examines the distribution characteristics of the floating population in Beijing, Tianjin and Hebei Region as well as the growth trend of the floating population in each region. It also makes assessments using floating population concentration and The Moran Index Computing Methods. According to the study, the spatial distribution of the floating population has a clear clustering pattern in Beijing, Tianjin and Hebei region. Beijing, Tianjin and Hebei region's mobile population growth patterns differ substantially, and the region's inflow population is mostly made up of migrant inhabitants of domestic provinces and inflow of people from nearby regions. Most of the mobile population resides in Beijing and Tianjin, whereas the outflow of people originates in Hebei province. The diffusion impact and the spatial features of the floating population in the Beijing, Tianjin and Hebei area have a constant, positive association, according to the timeline between 2014 and 2020.

The Exosomal miR-1246 of Laryngeal Squamous Cell Carcinoma Induces Polarization of M2 Type Macrophages and Promotes the Invasiveness of Laryngeal Squamous Cell Carcinoma.

Background: The possible role and detailed mechanisms of Tumor-associated macrophages (TAMs) in laryngeal squamous cell carcinoma (LSCC) have not been revealed. Methods: The expressions of typical markers were evaluated by qRT-PCR. In macrophages cocultured with TU212 cells, CD163, and CD206 protein expressions were detected by western blot analysis; IL-10 and IL-12 expressions were detected by ELISA assay. Exosomes isolated from TU212 cells were characterized by TEM analysis. As for the TU212 cells cocultured with macrophages processed with HOK or TU212 cells derived exosomes, their viability, migration, and invasion were assessed by CCK-8 assay, wounding healing, and Transwell assays, respectively. Results: In this study, macrophages processed with exosomes from human TU212 cells notably advanced LSCC cell viability, migration, and invasion. miR-1246 inhibitor suppressed the M2 polarization of macrophages. Macrophages transfected with miR-1246 inhibitor suppressed LSCC cell viability, migration, and invasion. Conclusion: In summary, our data implied that the exosomal, miR-1246 of LSCC, induced polarization of M2 type macrophages and promoted the progression of LSCC. This trial is registered with 2020-13.

AMTDB: A comprehensive database of autophagic modulators for anti-tumor drug discovery.

Autophagy, originally described as a mechanism for intracellular waste disposal and recovery, has been becoming a crucial biological process closely related to many types of human tumors, including breast cancer, osteosarcoma, glioma, etc., suggesting that intervention of autophagy is a promising therapeutic strategy for cancer drug development. Therefore, a high-quality database is crucial for unraveling the complicated relationship between autophagy and human cancers, elucidating the crosstalk between the key autophagic pathways, and autophagic modulators with their remarkable antitumor activities. To achieve this goal, a comprehensive database of autophagic modulators (AMTDB) was developed. AMTDB focuses on 153 cancer types, 1,153 autophagic regulators, 860 targets, and 2,046 mechanisms/signaling pathways. In addition, a variety of classification methods, advanced retrieval, and target prediction functions are provided exclusively to cater to the different demands of users. Collectively, AMTDB is expected to serve as a powerful online resource to provide a new clue for the discovery of more candidate cancer drugs.

miR-1246 Promotes Laryngeal Squamous Cell Carcinoma Progression by Interacting with THBS1.

MicroRNAs (miRNAs) have been confirmed to be related to the occurrence and progress of multiple cancers, including laryngeal squamous cell carcinoma (LSCC). The present work focused on exploring the role of miR-1246 in LSCC and investigating the possible mechanisms. miR-1246 expression levels within clinical LSCC tissues and cell lines (TU212 and AMC-HN-8) were detected through quantitative reverse transcription polymerase chain reaction (qRT-PCR) assay. Then the overall survival (OS) rates of LSCC patients with different miR-1246 expressions were assessed by the Kaplan-Meier method. In addition, the roles of miR-1246 in the proliferation, apoptosis and migration of TU212 and AMC-HN-8 cells were measured by colony formation, flow cytometry, wound-healing, and Western blot (WB) assays, respectively. Moreover, TargetScan was carried out to predict the miR-1246 targets (thrombospondin-1, THBS1), further confirmed by a dual-luciferase reporter assay. Afterward, the negative relationship between miR-1246 and THBS1 was assessed by qRT-PCR and WB. Furthermore, the restoring effects of THBS1 on TU212 and AMC-HN-8 functional roles transfected with miR-1246 inhibitor were further investigated. miR-1246 expression levels were increased in clinical LSCC tissues and cell lines. Patients with low miR-1246 expression exhibited improved OS rates. In addition, miR-1246 down-regulation notably suppressed cell proliferation and migration and induced cell apoptosis of TU212 and AMC-HN-8 cells. Moreover, THBS1 was predicted and confirmed as a direct target of miR-1246 and negatively related to miR-1246 expression. Mechanically, THBS1 inhibition partially rescued the effects of the miR-1246 inhibitor on proliferation, apoptosis and migration of TU212 and AMC-HN-8 cells. This study provides an experimental basis suggesting the potential of miR-1246/THBS1 as the novel markers for LSCC.

Application of Grey Lotka-Volterra Model in Water-Economy-Industry-Technology Innovation System in Beijing-Tianjin-Hebei Region.

With the proposal of China's high-quality development strategy, how to promote regional stability and coordinated development based on a deep understanding of the main contradictions and changes in China's society has become the focus of research. High-quality development is a brand-new coordinated development concept, which aims to optimize the economic structure, transform the development model, enhance the development momentum, and take innovation as the primary driving force. How to promote the coordinated development of this region has become a hot issue considered by scholars. The Beijing-Tianjin-Hebei region is the capital economic circle of China, and the purpose of this study is to promote the coordinated and stable development of the region. On this premise, this paper firstly adopts the composite Grey Lotka-Volterra (GLV) model and Fractional GM(1,1) (FGM(1,1)) model to research Water Resources system-Economic System-Industrial System-Technology Innovation System in the Beijing-Tianjin-Hebei region. Secondly, by analyzing the research data, it is found that the relationship between the system is very complex, and the stability calculation results are all below 0. Then, the analysis of the research results shows that there is no obvious coordination among the three regions, and they have not yet reached a state of mutual promotion and stable and coordinated development. Finally, four suggestions are put forward for the coordinated development of the Beijing-Tianjin-Hebei region. This can not only provide direction for the future development of the region but also have reference significance for the development of other regions. Further, accelerate the coordination and unity of all factors of production in China and promote China's development at a deeper and higher level.

Demand forecast and influential factors of cold chain logistics based on a grey model.

Due to high requirements of storage, operation and delivery conditions, it is more difficult for cold chain logistics to meet the demand with supply in the course of disruption. And, accurate demand forecasting promotes supply efficiency for cold chain logistics in a changeable environment. This paper aims to find the main influential factors of cold chain demand and presents a prediction to support the resilience operation of cold chain logistics. After analyzing the internal relevance between potential factors and regional agricultural cold chain logistics demand, the grey model GM (1, N) with fractional order accumulation is established to forecast future agricultural cold chain logistics demand in Beijing, Tianjin, and Hebei. The following outcomes have been obtained. (1) The proportion of tertiary industry, per capita disposable income indices for urban households and general price index for farm products are the first three factors influencing the cold chain logistics demand for agricultural products in both Beijing and Tianjin. The GDP, fixed asset investment in transportation and storage, and the proportion of tertiary industry are three major influential factors in Hebei. (2) Agricultural cold chain demand in Beijing and Hebei will grow sustainably in 2021-2025, while the trend in Tianjin remains stable. In conclusion, regional developmental differences should be considered when planning policies for the Beijing-Tianjin-Hebei cold chain logistics system.

Effect of storage temperature and time on erythrocyte sedimentation rate.

OBJECTIVE: This paper explores the effect of blood sample storage temperature and time on the erythrocyte sedimentation rate (ESR) by using the Weiss method. METHODS: Whole blood samples were collected from 80 patients and diluted 1:9 with sodium citrate solution. Each sample was split into two tubes. Using the Weiss method, ESR was tested within 1 h of collection, and one sample was placed at 4 °C and the other at room temperature (23 ± 2 °C). ESR was then measured at 2, 4, 6, 8, 12, and 24 h. The data were statistically analyzed with consideration for temperature and time. RESULTS: ESR decreased gradually over 6 h at room temperature, but the results were not statistically significant. Similarly, there was no significant difference in the decline of ESR within 8 h at 4 °C. However, ESR results decreased significantly after the samples were stored at room temperature for more than 6 h or at 4 °C for more than 8 h. ESR reduction was lower in the samples stored at 4 °C than in those stored at room temperature over the same time period. CONCLUSION: Blood sample storage temperature and duration can affect the measurement of ESR using the Weiss method. ESR testing should be completed within 4 h of sample collection in clinical work.

Application of grey model in influencing factors analysis and trend prediction of carbon emission in Shanxi Province.

In recent years, global warming has attracted extensive attention. The main cause of global warming is the emission of greenhouse gases, known as carbon emissions. Therefore, it is of great significance to explore the influencing factors of carbon emissions and accurately predict carbon emissions for reducing carbon emissions and slowing down climate warming. This paper takes the carbon emissions of Shanxi Province in China as the research object. Firstly, the emission factor method is used to calculate the carbon emissions, and then the grey correlation model is used to screen out the factors that have a greater impact on carbon emissions (per capita GDP, urbanization rate, resident population, energy consumption, expenditure on R&D projects). Then, an improved grey multi-variable convolution integral model (AGMC(1, N)) is used to accurately predict carbon emissions. The results show that the application of the AGMC(1,N) model to carbon emission prediction has a good prediction effect. In addition, the carbon emissions of Shanxi Province will increase with the growth rate of per capita GDP, energy consumption, resident population, and expenditure on R&D projects, while the carbon emissions will gradually decrease with the increase of urbanization level. The prediction results provide the direction for carbon emission reduction in Shanxi Province. At the same time, theAGMC(1,N) model can also be applied to the prediction of carbon emissions in other provinces or other fields.

Intestinal changes associated with nitrite exposure in Bufo gargarizans larvae: Histological damage, immune response, and microbiota dysbiosis.

Nitrite is a ubiquitous toxic compound in aquatic ecosystems and has negative effects on aquatic organisms. The intestine and the trillions of microbes that inhabit it, play an integral role in maintaining digestive and immune functions. However, the effects of nitrite on intestinal health and microflora have been poorly investigated. Therefore, the present study evaluated the response of intestinal histology, immunity, digestive enzyme activities and microbiota to nitrite exposure in Bufo gargarizans tadpoles. The results showed that nitrite caused damage to the intestine and impaired digestive performance. Significant changes in the transcriptional profiles of genes involved in oxidative stress (sod, gpx and hsp), inflammation, and immunity (socs3, il-27, il-1ß and il-17d) were observed in the NO2-N treatment groups. In addition, exposure to nitrite induced alterations of intestinal microbial diversity, structure and composition, suggesting that nitrite may lead to intestinal microbiota dysbiosis. It is noteworthy that probiotics (e.g., Bacteroidetes and Fusobacteria) were decreased after exposure to nitrite, whereas potentially opportunistic pathogens such as Proteobacteria and Enterobacteriaceae were elevated. Functional prediction and correlation analysis suggested that the above changes may interfere with metabolic function and trigger various diseases. Taken together, we concluded that nitrite exposure induced intestinal microbial dysbiosis, which may lead to immune dysfunction and metabolic disorder, and ultimately to histological damages in B. gargarizans. Further, this study will provide a scientific basis for further understanding the risk of nitrite pollution on the intestinal health of amphibians.