R5hmCFDV: computational identification of RNA 5-hydroxymethylcytosine based on deep feature fusion and deep voting.

RNA 5-hydroxymethylcytosine (5hmC) is a kind of RNA modification, which is related to the life activities of many organisms. Studying its distribution is very important to reveal its biological function. Previously, high-throughput sequencing was used to identify 5hmC, but it is expensive and inefficient. Therefore, machine learning is used to identify 5hmC sites. Here, we design a model called R5hmCFDV, which is mainly divided into feature representation, feature fusion and classification. (i) Pseudo dinucleotide composition, dinucleotide binary profile and frequency, natural vector and physicochemical property are used to extract features from four aspects: nucleotide composition, coding, natural language and physical and chemical properties. (ii) To strengthen the relevance of features, we construct a novel feature fusion method. Firstly, the attention mechanism is employed to process four single features, stitch them together and feed them to the convolution layer. After that, the output data are processed by BiGRU and BiLSTM, respectively. Finally, the features of these two parts are fused by the multiply function. (iii) We design the deep voting algorithm for classification by imitating the soft voting mechanism in the Python package. The base classifiers contain deep neural network (DNN), convolutional neural network (CNN) and improved gated recurrent unit (GRU). And then using the principle of soft voting, the corresponding weights are assigned to the predicted probabilities of the three classifiers. The predicted probability values are multiplied by the corresponding weights and then summed to obtain the final prediction results. We use 10-fold cross-validation to evaluate the model, and the evaluation indicators are significantly improved. The prediction accuracy of the two datasets is as high as 95.41% and 93.50%, respectively. It demonstrates the stronger competitiveness and generalization performance of our model. In addition, all datasets and source codes can be found at https://github.com/HongyanShi026/R5hmCFDV.

Microcephaly Gene Mcph1 Deficiency Induces p19ARF-Dependent Cell Cycle Arrest and Senescence.

MCPH1 has been identified as the causal gene for primary microcephaly type 1, a neurodevelopmental disorder characterized by reduced brain size and delayed growth. As a multifunction protein, MCPH1 has been reported to repress the expression of TERT and interact with transcriptional regulator E2F1. However, it remains unclear whether MCPH1 regulates brain development through its transcriptional regulation function. This study showed that the knockout of Mcph1 in mice leads to delayed growth as early as the embryo stage E11.5. Transcriptome analysis (RNA-seq) revealed that the deletion of Mcph1 resulted in changes in the expression levels of a limited number of genes. Although the expression of some of E2F1 targets, such as Satb2 and Cdkn1c, was affected, the differentially expressed genes (DEGs) were not significantly enriched as E2F1 target genes. Further investigations showed that primary and immortalized Mcph1 knockout mouse embryonic fibroblasts (MEFs) exhibited cell cycle arrest and cellular senescence phenotype. Interestingly, the upregulation of p19ARF was detected in Mcph1 knockout MEFs, and silencing p19Arf restored the cell cycle and growth arrest to wild-type levels. Our findings suggested it is unlikely that MCPH1 regulates neurodevelopment through E2F1-mediated transcriptional regulation, and p19ARF-dependent cell cycle arrest and cellular senescence may contribute to the developmental abnormalities observed in primary microcephaly.

Hydrophobic and Homogeneous Conductive Carbon Matrix for High-Rate Non-Alkaline Zinc-Air Batteries.

Non-alkaline zinc-air batteries (ZABs) that use reversible O2 /ZnO2 chemistry exhibit excellent stability and superior reversibility compared to conventional alkaline ZABs. Unlike alkaline ZABs, ZnO2 discharge products are generated on the surface of the air cathodes in non-alkaline ZABs, requiring more gas-liquid-solid three-phase reaction interfaces. However, the kinetics of reported ZABs based on carbon black (CB) is far from satisfactory due to the insufficient reaction areas. The rational structural design of the air cathode is an effective way to increase active surfaces to further enhance the performance of non-alkaline ZABs. In this study, multi-walled carbon nanotubes (MW-CNTs) with unique mesoporous structures and high pore volumes are selected to replace CB in the air cathode preparation. Due to the larger electrochemically active surface area, superior hydrophobicity, and uniform electroconductibility of MW-CNTs-based cathodes, primary ZABs exhibit high specific capacity (704 mAh gZn-1 ) with a Zn utilization ratio of 85.85% at 1.0 mA cm-2 , excellent discharge rate performance, and negligible self-discharge. Furthermore, rechargeable ZABs also demonstrate outstanding rate capability and excellent cycling stability at various current densities. This work provides a fundamental understanding of the criteria for the cathode design of non-alkaline ZABs, thus opening a new pathway for more sustainable ZABs.

An improved residual network using deep fusion for identifying RNA 5-methylcytosine sites.

MOTIVATION: 5-Methylcytosine (m5C) is a crucial post-transcriptional modification. With the development of technology, it is widely found in various RNAs. Numerous studies have indicated that m5C plays an essential role in various activities of organisms, such as tRNA recognition, stabilization of RNA structure, RNA metabolism and so on. Traditional identification is costly and time-consuming by wet biological experiments. Therefore, computational models are commonly used to identify the m5C sites. Due to the vast computing advantages of deep learning, it is feasible to construct the predictive model through deep learning algorithms. RESULTS: In this study, we construct a model to identify m5C based on a deep fusion approach with an improved residual network. First, sequence features are extracted from the RNA sequences using Kmer, K-tuple nucleotide frequency component (KNFC), Pseudo dinucleotide composition (PseDNC) and Physical and chemical property (PCP). Kmer and KNFC extract information from a statistical point of view. PseDNC and PCP extract information from the physicochemical properties of RNA sequences. Then, two parts of information are fused with new features using bidirectional long- and short-term memory and attention mechanisms, respectively. Immediately after, the fused features are fed into the improved residual network for classification. Finally, 10-fold cross-validation and independent set testing are used to verify the credibility of the model. The results show that the accuracy reaches 91.87%, 95.55%, 92.27% and 95.60% on the training sets and independent test sets of Arabidopsis thaliana and M.musculus, respectively. This is a considerable improvement compared to previous studies and demonstrates the robust performance of our model. AVAILABILITY AND IMPLEMENTATION: The data and code related to the study are available at https://github.com/alivelxj/m5c-DFRESG.

Long-term variations and potency of neutralizing antibodies against Omicron subvariants after CoronaVac-inactivated booster: A 7-month follow-up study.

The long-term protective efficacy of neutralizing antibodies (Nabs) against Omicron subvariants after inactivated booster vaccines remains elusive. During the follow-up study, 54 healthy volunteers aged 20-31 years received inactivated CoronaVac booster vaccinations and were monitored for 221 days. The dynamic efficacy and durability of Nab against Omicron subvariants BA.1, BA.2, BA.2.12.2, and BA4/5 were assessed using a pseudotyped virus neutralization assay at up to nine time points post immunization. The antibody response against Omicron subvariants was substantially weaker than D614G, with BA.4/5 being the least responsive. The geometric mean titer (GMT) of Nab against Omicron subvariants BA.1, BA.2, BA.2.12.1, and BA.4/5 was 2.2-, 1.7-, 1.8-, and 2.2-fold lower than that against D614G (ps < 0.0001). The gap in Nab response between Omicron subvariants was pronounced during the 2 weeks-2 months following booster vaccination (ps < 0.05). Seven months post booster, the antibody potency against D614G was maintained at 100% (50% for Nab titers ≥ 100 50% inhibitory dilution [EC50 ]), whereas at 77.3% for BA.1, 90.9% for BA.2, 86.4% for BA.2.12.1, and 86.4% for BA.4/5 (almost 20% for Nab titers ≥ 100 EC50 ). Despite the inevitable immune escape, Omicron subvariants maintained sustained and measurable antibody potency post-booster vaccination during long-term monitoring, which could help optimize immunization strategies.

Long-term dynamic shifts in genomic base content and evolutionary trajectories of SARS-CoV-2 variants.

The rapid spread and remarkable mutations of SARS-CoV-2 variants, particularly Omicron, necessitate an understanding of their evolutionary characteristics. In this study, we analyzed representative high-quality whole-genome sequences of 2008 SARS-CoV-2 variants to explore long-term dynamic changes in genomic base (especially GC) content and variations during viral evolution. Our results demonstrated a highly negative correlation between GC content and variant emergence time (r = -0.765, p < 2.22e-16). Major gene partitions (S, N, ORF1ab) displayed similar trends. Omicron exhibited a significantly lower GC content than non-Omicron variants (p < 2.22e-16). Notably, we observed a robust negative correlation between C and T content (r = -0.778, p < 2.22e-16) and between G and A content (r = -0.773, p < 2.22e-16). Among all strains, Omicron showed the greatest base variation, with C->T mutations being the most frequent (median [interquartile range [IQR]]: 29 (27, 31), 37.67%), succeeded by G->A mutations (11 (9, 13), 14.63%). Over a 3-year span, an annual decline rate of 0.12% in SARS-CoV-2 GC content was observed and could become more pronounced in future emerging variants. These findings provided insights into the evolutionary trajectory of SARS-CoV-2, underscoring the significance of continuous genomic surveillance for effective prediction of and response to future variants.

Epidemiology and Association Rules Analysis for Pulmonary Tuberculosis Cases with Extrapulmonary Tuberculosis from Age and Gender Perspective: A Large-Scale Retrospective Multicenter Observational Study in China.

Background: Tuberculosis (TB), a multisystemic disease with protean presentation, remains a major global health problem. Although concurrent pulmonary tuberculosis (PTB) and extrapulmonary tuberculosis (EPTB) cases are commonly observed clinically, knowledge regarding concurrent PTB-EPTB is limited. Here, a large-scale multicenter observational study conducted in China aimed to study the epidemiology of concurrent PTB-EPTB cases by diagnostically defining TB types and then implementing association rules analysis. Methods: The retrospective study was conducted at 21 hospitals in 15 provinces in China and included all inpatients with confirmed TB diagnoses admitted from Jan 2011 to Dec 2017. Association rules analysis was conducted for cases with concurrent PTB and various types of EPTB using the Apriori algorithm. Results: Evaluation of 438,979TB inpatients indicated PTB was the most commonly diagnosed (82.05%) followed by tuberculous pleurisy (23.62%). Concurrent PTB-EPTB was found in 129,422 cases (29.48%) of which tuberculous pleurisy was the most common concurrent EPTB type observed. The multivariable logistic regression models demonstrated that odds ratios of concurrent PTB-EPTB cases varied by gender and age group. For PTB cases with concurrent EPTB, the strongest association was found between PTB and concurrent bronchial tuberculosis (lift = 1.09). For EPTB cases with concurrent PTB, the strongest association was found between pharyngeal/laryngeal tuberculosis and concurrent PTB (lift = 1.11). Confidence and lift values of concurrent PTB-EPTB cases varied with gender and age. Conclusions: Numerous concurrent PTB-EPTB case types were observed, with confidence and lift values varying with gender and age. Clinicians should screen for concurrent PTB-EPTB in order to improve treatment outcomes.

Dosage impact of submerged plants extracts on Microcystis aeruginosa growth: From hormesis to inhibition.

Allelopathy has been demonstrated to be an environmentally friendly way to control harmful algal blooms. Allelochemicals of submerged plants have attracted extensive research due to their bioavailability. The dose-response of submerged plant extracts on algae growth is worth further study to improve the efficiency of bioremediation. In this study, the ultrasonic-enzymatic assistance method was utilized to extract allelochemicals from Ceratophyllum, Myriophyllum spicatum, and Vallisneria. The effects of low-dosage and high-dosage extracts on the growth of Microcystis aeruginosa were compared based on cell biomass and morphology, photosynthetic parameters, reactive oxygen species (ROS), superoxide dismutase (SOD), catalase (CAT), and malondialdehyde (MDA) levels. The results showed that the three submerged plant extracts exhibited hormetic effects at low dosages and inhibitory effects at high dosages on algal growth. Within 48 h of cultivation, the enzymatic activities of Microcystis aeruginosa fluctuated, suggesting that the extracts of the three submerged plants induced different oxidative reactions. After 120 h of cultivation with high-dosage extracts, the physiological and biochemical reactions of Microcystis aeruginosa significantly decreased, indicating the effectiveness of the allelopathy of Ceratophyllum, Myriophyllum spicatum, and Vallisneria extracts in controlling algal blooms. The phenomenon of hormesis and inhibition effect confirmed a significant dose-response relationship between the allelochemicals of submerged plant extracts and Microcystis aeruginosa, which could be attributed to the composition and content of allelochemicals. These findings highlight the importance of the relative concentration of the biological algaecide and will benefit other researchers in determining the safe dosage of plant allelochemicals when used in water.

In Situ Electrochemical Transformation toward Structure Optimized VEG@MXene Cathode for Enhanced Zinc-Ion Storage.

Vanadium-based derivatives, featuring affordable cost and high theoretical capacity, have gathered widespread interest in the context of aqueous zinc-ion batteries (ZIBs). However, the further application of vanadium-based materials is hindered by the limited electrical conductivity and cycling lifespan. Herein, 1D chain-like structure vanadyl ethylene glycolate (VEG, (VO(CH2 O)2 )), growing on the Ti3 C2 Tx MXene nanosheets, is synthesized via a one-step oil-bath heating process as cathode materials for ZIBs. Benefiting from the hybrid structure with high conductivity and abundant reactive sites, the VEG@MXene cathode exhibits a remarkable specific capacity (360.3 mAh g-1 at 0.5 A g-1 ), and impressive capacity retention (up to 85.2% after 3000 cycles at 10 A g-1 ). Mechanism analysis reveals a gradual phase transition from the original VEG on MXene to the stable Zn3 V2 O7 (OH)2 ·2H2 O nanoflakes accompanied by continuous zinc ion intercalation/deintercalation, offering more pathways for zinc ion transport. This work suggests that engineering conductivity-enhanced vanadium-based materials is a rational approach for developing promising cathode materials of ZIBs.

A preliminary study on early postmortem submersion interval (PMSI) estimation and cause-of-death discrimination based on nontargeted metabolomics and machine learning algorithms.

Postmortem submersion interval (PMSI) estimation and cause-of-death discrimination of corpses in water have long been challenges in forensic practice. Recently, many studies have linked postmortem metabolic changes with PMI extension, providing a potential strategy for estimating PMSI using the metabolome. Additionally, there is a lack of potential indicators with high sensitivity and specificity for drowning identification. In the present study, we profiled the untargeted metabolome of blood samples from drowning and postmortem submersion rats at different PMSIs within 24 h by liquid chromatography-tandem mass spectrometry (LC-MS/MS). A total of 601 metabolites were detected. Four different machine learning algorithms, including random forest (RF), partial least squares (PLS), support vector machine (SVM), and neural network (NN), were used to compare the efficiency of the machine learning methods. Nineteen metabolites with obvious temporal regularity were selected as candidate biomarkers according to "IncNodePurity." Robust models were built with these biomarkers, which yielded a mean absolute error of 1.067 h. Additionally, 36 other metabolites were identified to build the classifier model for discriminating drowning and postmortem submersion (AUC = 1, accuracy = 95%). Our results demonstrated the potential application of metabolomics combined with machine learning in PMSI estimation and cause-of-death discrimination.

Cross dehydrogenation coupling reaction of purine derivatives with thioethers.

A metal-free cross-dehydrogenation coupling method was established to synthesize N9 alkylated purine derivatives. Using PhI(OAc)2 as the oxidant, versatile thioethers were successfully employed as alkylation reagents. Under the optimized conditions, a variety of alkylated purine derivatives and other aromatic N-heterocycles were obtained in moderate to good yields. The regioselectivity of this protocol which involves the reaction of unsymmetrical thioethers with purine derivatives was also studied.

A Rapid Near-Infrared Fluorescent Probe for Cysteine Based on Isophorone and its Application in B16 Cell Imaging.

A novel near-infrared fluorescent probe SWJT-5 based on dicyanoisophorone was synthesized. It achieved the rapid (within 40 s) and discriminative detection of Cys over Hcy and GSH with a large Stokes shift (205 nm). It showed high selectivity and sensitivity for Cys, and had an obvious enhancement of fluorescence emission. The detection limit was 0.43 µM. This probe also had low background interference and little damage to biological samples. Therefore, SWJT-5 had been applied to bioimaging in living cells successfully.

Inferring Postmortem Submersion Interval in Rats Found in Water Based on Vitreous Humor Metabolites.

OBJECTIVES: The metabolomics technique of LC-MS/MS combined with data analysis was used to detect changes and differences in metabolic profiles in the vitreous humor of early rat carcasses found in water, and to explore the feasibility of its use for early postmortem submersion interval (PMSI) estimation and the cause of death determination. METHODS: The experimental model was established in natural lake water with 100 SD rats were randomly divided into a drowning group (n=50) and a postmortem (CO2 suffocation) immediately submersion group (n=50). Vitreous humor was extracted from 10 rats in each group at 0, 6, 12, 18 and 24 h postmortem for metabolomics analyses, of which 8 were used as the training set to build the model, and 2 were used as test set. PCA and PLS multivariate statistical analysis were performed to explore the differences in metabolic profiles among PMSI and causes of death in the training set samples. Then random forest (RF) algorithm was used to screen several biomarkers to establish a model. RESULTS: PCA and PLS analysis showed that the metabolic profiles had time regularity, but no differences were found among different causes of death. Thirteen small molecule biomarkers with good temporal correlation were selected by RF algorithm. A simple PMSI estimation model was constructed based on this indicator set, and the data of the test samples showed the mean absolute error (MAE) of the model was 0.847 h. CONCLUSIONS: The 13 metabolic markers screened in the vitreous humor of rat corpses in water had good correlations with the early PMSI. The simplified PMSI estimation model constructed by RF can be used to estimate the PMSI. Additionally, the metabolic profiles of vitreous humor cannot be used for early identification of cause of death in water carcasses.

The prevalence and risks of major comorbidities among inpatients with pulmonary tuberculosis in China from a gender and age perspective: a large-scale multicenter observational study.

In clinical practice, PTB patients have concurrent many types of comorbidities such as pneumonia, liver disorder, diabetes mellitus, hematological disorder, and malnutrition. Detecting and treating specific comorbidities and preventing their development are important for PTB patients. However, the prevalence of most comorbid conditions in patients with PTB is not well described. We conducted a large-scale, multicenter, observational study to elucidate and illustrate the prevalence rates of major comorbidities in inpatients at 21 hospitals in China. The 19 specific comorbidities were selected for analysis in this patient cohort, and stratified the inpatient cohort according to age and gender. A total of 355,929 PTB inpatients were included, with a male:female ratio of 1.98 and the proportion of ≥ 65 years PTB inpatients was the most. Approximately 70% of PTB inpatients had at least one defined type of comorbidity. The prevalence of 19 specific comorbidities in inpatients with PTB was analyzed, with pneumonia being the most common comorbidity. The prevalence of most comorbidities was higher in males with PTB except thyroid disorders, mental health disorders, etc. The prevalence of defined most comorbidities in patients with PTB tended to increase with increasing age, although some specific comorbidities tended to increase initially then decrease with increasing age. Our study describes multiple clinically important comorbidities among PTB inpatients, and their prevalence between different gender and age groups. The results will enhance the clinical aptitude of physicians who treat patients with PTB to recognize, diagnose, and treat PTB comorbidities early.

Cardiac Troponin I association with critical illness and death risk in 726 seriously ill COVID-19 patients: A retrospective cohort study.

Background: For coronavirus disease 2019 (COVID-19), early identification of patients with serious symptoms at risk of critical illness and death is important for personalized treatment and balancing medical resources. Methods: Demographics, clinical characteristics, and laboratory tests data from 726 patients with serious COVID-19 at Tongji Hospital (Wuhan, China) were analyzed. Patients were classified into critical group (n = 174) and severe group (n= 552), the critical group was sub-divided into survivors (n = 47) and non-survivors (n = 127). Results: Multivariable analyses revealed the risk factors associated with critical illness in serious patients were: Advanced age, high respiratory rate (RR), high lactate dehydrogenase (LDH) level, high hypersensitive cardiac troponin I (hs-cTnI) level, and thrombocytopenia on admission. High hs-cTnI level was the independent risk factor of mortality among critically ill patients in the unadjusted and adjusted models. ROC curves demonstrated that hs-cTnI and LDH were predictive factors for critical illness in patients with serious COVID-19 whereas procalcitonin and D-Dimer with hs-cTnI and LDH were predictive parameters in mortality risk. Conclusions: Advanced age, high RR, LDH, hs-cTnI, and thrombocytopenia, constitute risk factors for critical illness among patients with serious COVID-19, and the hs-cTnI level helps predict fatal outcomes in critically ill patients.

Predictive indicators of severe COVID-19 independent of comorbidities and advanced age: a nested case-control study.

To determine what exacerbate severity of the COVID-19 among patients without comorbidities and advanced age and investigate potential clinical indicators for early surveillance, we adopted a nested case-control study, design in which severe cases (case group, n = 67) and moderate cases (control group, n = 67) of patients diagnosed with COVID-19 without comorbidities, with ages ranging from 18 to 50 years who admitted to Wuhan Tongji Hospital were matched based on age, sex and BMI. Demographic and clinical characteristics, and risk factors associated with severe symptoms were analysed. Percutaneous oxygen saturation (SpO2), lymphocyte counts, C-reactive protein (CRP) and IL-10 were found closely associated with severe COVID-19. The adjusted multivariable logistic regression analyses revealed that the independent risk factors associated with severe COVID-19 were CRP (OR 2.037, 95% CI 1.078-3.847, P = 0.028), SpO2 (OR 1.639, 95% CI 0.943-2.850, P = 0.080) and lymphocyte (OR 1.530, 95% CI 0.850-2.723, P = 0.148), whereas the changes exhibited by indicators influenced incidence of disease severity. Males exhibited higher levels of indicators associated with inflammation, myocardial injury and kidney injury than the females. This study reveals that increased CRP levels and decreased SpO2 and lymphocyte counts could serve as potential indicators of severe COVID-19, independent of comorbidities, advanced age and sex. Males could at higher risk of developing severe symptoms of COVID-19 than females.

Calculation of evapotranspiration in different climatic zones combining the long-term monitoring data with bootstrap method.

Evapotranspiration (ET) is a central process in the climate system that plays a crucial role in the regional water cycle and climate regulation. However, estimating the effects of regional ET on the regional water cycle and climate regulation remains challenging due to the lack of quantitative methods and large-scale direct observational data. This study develops a new method to estimate evapotranspiration at regional scales using long-term monitoring data and the bootstrap resampling approach to calculate the ET unit area per year for China. This study applies the deviance information criterion as a goodness-of-fit index to select the most optimal formula for estimating regional ET for different climatic zones in China. The bootstrap resampling method was used to estimate parameter distribution in different climatic zones based on the outcome of 2000 trials. The results show that the predicted ET of adjacent climates overlaps with each other. The subtropical monsoonal climatic zone had the widest range of predicted ET (0-8000 mm/year), followed by the temperate and monsoonal climatic zones (0-1500 mm/year), mountain plateau climatic zone (0-1000 mm/year), and temperate continental climatic zone (0-500 mm/year). The probability distributions and isopleths of regionally predicted ET were also determined for China. The methods used in this study provide a promising tool to assess the effects of introducing large-scale forestation or restoration of trees on local water resources management.

All-fiber-optic vector magnetic field sensor based on side-polished fiber and magnetic fluid.

A kind of compact all-fiber-optic vector magnetic sensor is proposed and demonstrated. The sensor consists of a side-polished-fiber (SPF)-integrated with singlemode-no core-singlemode (SNS) fiber structure. A section of side-polished fiber breaks the axially symmetry of the composite structure. The as-fabricated sensor supports vector sensing and has a magnetic field strength sensitivity of up to -2370 pm/mT over 2-6 mT range. The physical mechanism is that the modal interference is strongly influenced by the refractive index (RI) near the side-polished surface. The advantages of the proposed sensor lie in low cost, simple structure and easy manufacture, which make it attractive in the field of magnetic field vector sensing.

Gold-Film-Thickness Dependent SPR Refractive Index and Temperature Sensing with Hetero-Core Optical Fiber Structure.

A simple hetero-core optical fiber (MMF-NCF-MMF) surface plasmon resonance (SPR) sensing structure was proposed. The SPR spectral sensitivity, full width of half peak (FWHM), valley depth (VD), and figure of merit (FOM) were defined to evaluate the sensing performance comprehensively. The effect of gold film thickness on the refractive index and temperature sensing performance was studied experimentally. The optimum gold film thickness was found. The maximum sensitivities for refractive index and temperature measurement were obtained to be 2933.25 nm/RIU and -0.91973 nm/°C, respectively. The experimental results are helpful to design the SPR structure with improved sensing performance. The proposed SPR sensing structure has the advantages of simple structure, easy implementation, and good robustness, which implies a broad application prospect.

Tiansi Liquid Modulates Gut Microbiota Composition and Tryptophan⁻Kynurenine Metabolism in Rats with Hydrocortisone-Induced Depression.

Tiansi Liquid is a traditional Chinese herbal medicine used to treat depression; however, the underlying mechanisms remain unclear. Here, we examined the effect of Tiansi Liquid in a rat model of hydrocortisone-induced depression using behavioral testing, 16S rRNA high-throughput pyrosequencing and high-performance liquid chromatography-mass spectrometry-based metabolomics of the tryptophan (TRP)⁻kynurenine (KYN) pathway. Tiansi Liquid significantly improved the sucrose preference and exploratory behavior of the depressive rats. The richness of intestinal mucosa samples from the model (depressive) group tended to be higher than that from the control group, while the richness was higher in the Tiansi Liquid-treated group than in the model group. Tiansi Liquid increased the relative abundance of some microbiota (Ruminococcaceae, Lactococcus, Lactobacillus, Lachnospiraceae_NK4A136_group). Metabolomics showed that Tiansi Liquid reduced the levels of tryptophan 2,3 dioxygenase, indoleamine 2,3-dioxygenase, quinoline and the KYN/TRP ratio, while increasing kynurenic acid and 5-HT levels. Correlation analysis revealed a negative relationship between the relative abundance of the Lachnospiraceae_NK4A136_group and quinoline content. Collectively, these findings suggest that Tiansi Liquid ameliorates depressive symptoms in rats by modulating the gut microbiota composition and metabolites in the TRP⁻KYN pathway.