Abstract Here, we present a multi-modal deep generative model, the single-cell Multi-View Profiler (scMVP), which is designed for handling sequencing data that simultaneously measure gene expression and chromatin accessibility in the same cell, including SNARE-seq, sci-CAR, Paired-seq, SHARE-seq, and Multiome from 10X Genomics. scMVP generates common latent representations for dimensionality reduction, cell clustering, and developmental trajectory inference and generates separate imputations for differential analysis and cis-regulatory element identification. scMVP can help mitigate data sparsity issues with imputation and accurately identify cell groups for different joint profiling techniques with common latent embedding, and we demonstrate its advantages on several realistic datasets.
Lakes of meltwater in the Artic have become one of the transforming landscape changes under global warming. We herein compared microbial communities between sediments and bank soils at an arctic lake post land submergence using geochemistry, 16S rRNA amplicons, and metagenomes. The results obtained showed that each sample had approximately 2,609 OTUs on average and shared 1,716 OTUs based on the 16S rRNA gene V3-V4 region. Dominant phyla in sediments and soils included Proteobacteria, Acidobacteria, Actinobacteria, Gemmatimonadetes, and Nitrospirae; sediments contained a unique phylum, Euryarchaeota, with the phylum Thaumarchaeota being primarily present in bank soils. Among the top 35 genera across all sites, 17 were more abundant in sediments, while the remaining 18 were more abundant in bank soils; seven out of the top ten genera across all sites were only from sediments. A redundancy analysis separated sediment samples from soil samples based on the components of nitrite and ammonium. Metagenome results supported the role of nitrite because most of the genes for denitrification and methane metabolic genes were more abundant in sediments than in soils, while the abundance of phosphorus-utilizing genes was similar and, thus, was not a significant explanatory factor. We identified several modules from the global networks of OTUs that were closely related to some geochemical factors, such as pH and nitrite. Collectively, the present results showing consistent changes in geochemistry, microbiome compositions, and functional genes suggest an ecological mechanism across molecular and community levels that structures microbiomes post land submergence.
Gut microbiota plays a crucial role in the pathophysiology of depression. This study aimed to explore the antidepressant effect of mature whole C. aurantium fruit extract (FEMC) in the chronic unpredictable mild stress (CUMS) model. The behavioral tests were applied to assess antidepressant effect and 16S rRNA sequencing was used to analyze the changes of gut microbiota. The results showed that the major components of FEMC were naringin and neohesperidin and significantly increased the sucrose preference index of the mice. FEMC also could reduce the feeding latency in an open field test and the rest time in a novelty suppressed feeding test. In addition, FEMC could increase CUMS-induced reduction in the levels of BDNF, PSD95, and SYN in the hippocampus. Moreover, FEMC intervention slightly decreased the ratio of Firmicutes to Bacteroidota. Meanwhile, FEMC reduced the abundance of the Prevotellaceae_Ga6A1_group, [Ruminococcus]_torques_group, which have been reported to be closely related to inflammation. Bioinformatics analysis revealed that MAPK signaling pathway and lipopolysaccharide biosynthesis were involved in the anti-inflammatory effect of FEMC in the CUMS animal model. Finally, the ELISA results showed that FEMC could significantly reduce the expression of pro-inflammatory cytokines IL-6 and TNF-α in the serum of depressive mice. Our results suggest FEMC can ameliorate depressive behavior by inhibiting gut microbiota-mediated inflammation in mice.
Peanut protein prepared by ultrasound pretreatment was hydrolyzed with alkaline,papaya protease,trypsin,pepsin,neutral protease,and flavourzyme using DPPH free radicals scavenging activity as the indicator.The results showed that the optimal enzymes were alkaline and trypsin and the combined ratio was 8∶2.The optimal hydrolysis conditions of multi-enzyme and flavourzyme were investigated by means of response surface methodology(RSM).The correlation between degree of hydrolysis(DH) and DPPH free radical scavenging rate of hydrolyzed peanut protein isolate was further studied.The optimal hydrolysis condition for antioxidant peptides using multi-enzyme was pH 8.5,hydrolysis temperature 49.36 ℃,the amount of enzyme 3.40%(m/m),and hydrolysis time 203.59 min.
In classical genetic algorithm, fitness evaluations are often very expensive or highly time-consuming, especially for some engineering optimization problems. We present an efficient genetic algorithm (GA) by combining clustering methods with an empirical fitness estimating formula. The new individuals are clustered at first, and then only the cluster representatives are really evaluated by its original time-consuming fitness computing processes, and other individuals undergo high efficient fitness evaluating processes by using the empirical fitness estimating formula. To further improve the accuracy of fitness estimations, we present a schema discovery strategy by extracting the common encoding characters from both high-fitness individual group and low-fitness individual group, and then adjust the estimated fitness for each individual based on the matching with the discovered schema. Experiments show that the schema discovery strategy contributes remarkably to the accuracy of fitness estimation. Numerical experiments of some well-known benchmark problems and a practical engineering problem demonstrate that the proposed method could improve the efficiency by over 30% in terms of the times of real fitness evaluations at the similar optimization accuracy of classical genetic algorithm.
Tetracycline pollution is common in Chinese arable soils, and vermicomposting is an effective approach to accelerate tetracycline bioremediation. However, current studies mainly focus on the impacts of soil physicochemical properties, microbial degraders and responsive degradation/resistance genes on tetracycline degradation efficiencies, and limited information is known about tetracycline speciation in vermicomposting. This study explored the roles of epigeic E. fetida and endogeic A. robustus in altering tetracycline speciation and accelerating tetracycline degradation in a laterite soil. Both earthworms significantly affected tetracycline profiles in soils and promoted the transformation of exchangeable and bound tetracycline to water soluble tetracycline, thereby facilitating tetracycline degradation efficiencies. Although earthworms increased soil cation exchange capacity and enhanced tetracycline adsorption on soil particles, the significantly elevated soil pH and dissolved organic carbon benefited faster tetracycline degradation, attributing to the consumption of soil organic matters and humus by earthworms. Different from endogeic A. robustus which promoted both abiotic and biotic degradation of tetracycline, epigeic E. foetida preferently accelerated abiotic tetracyline degradation. Our findings uncovered the change of tetracycline speciation during vermicompsiting process, unraveled the mechanisms of different earthworm types in tetracycline speciation and metabolisms, and offered clues for effective vermiremediation application at tetracycline contaminated sites.
Extranodal natural killer/T cell lymphoma, nasal type (ENKTL) is an aggressive lymphoid malignancy with a poor prognosis and lacks standard treatment. Targeted therapies are urgently needed. Here we systematically investigated the druggable mechanisms through chemogenomic screening and identified that Bcl-xL-specific BH3 mimetics effectively induced ENKTL cell apoptosis. Notably, the specific accumulation of Bcl-xL, but not other Bcl-2 family members, was verified in ENKTL cell lines and patient tissues. Furthermore, Bcl-xL high expression was shown to be closely associated with worse patient survival. The critical role of Bcl-xL in ENKTL cell survival was demonstrated utilizing selective inhibitors, genetic silencing, and a specific degrader. Additionally, the IL2-JAK1/3-STAT5 signaling was implicated in Bcl-xL dysregulation. In vivo, Bcl-xL inhibition reduced tumor burden, increased apoptosis, and prolonged survival in ENKTL cell line xenograft and patient-derived xenograft models. Our study indicates Bcl-xL as a promising therapeutic target for ENKTL, warranting monitoring in ongoing clinical trials by targeting Bcl-xL.
Urine excretory proteins are among the most commonly used biomarkers in body fluids. Computational identification of urine excretory proteins can provide very useful information for identifying targeted disease biomarkers in urine by linking transcriptome or proteomics data. There are few methods based on conventional machine learning algorithms for predicting urine excretory proteins, and most of these methods strongly depend on the extraction of features from urine excretory proteins. An end-to-end model for urine excretory protein prediction, called DeepUEP, is presented using deep neural networks relying on only amino acid sequence information. The model achieves good performance and outperforms existing methods on training and testing sets. By comparing known urinary protein biomarkers with the results of the model, we find that the model can achieve a true-positive rate of over 80% for urinary protein biomarkers that have been detected in more than one study. We also combine our model with transcriptome and proteomics data from lung cancer patients to predict the potential urinary protein biomarkers of lung cancer. A web server is developed for the prediction of urine excretory proteins, and it can be accessed at the following URL: http://www.csbg-jlu.info/DeepUEP/ . We believe that our prediction model and web server are useful for biomedical researchers who are interested in identifying urinary protein biomarkers, especially for candidate proteins in transcriptome or proteomics analyses of diseased tissues.
Plantation of saline-alkali tolerant rice in coastal areas is proposed as a strategy to improve rice yield and ensure global food security. The soil nitrification process driven by ammonia-oxidizing bacteria (AOB) and ammonia-oxidizing archaea (AOA) is an integral part of the soil nitrogen cycle and is correlated with coastal solonchak quality. However, little is known about the effects of saline-alkali tolerant rice cultivation on the soil nitrification process in coastal solonchaks. In this study, we investigated the community structures of AOA/AOB and nitrification rate (NR) in the rice rhizosphere along salinity gradients. Saline-alkali-tolerant rice cultivation significantly decreased the soil pH, organic carbon content, and salinity in the rhizosphere. The abundance of amoA genes from AOA and AOB increased in the rhizosphere from (3.09-11.5)×10 6 and (7.76-52.0)×10 5 copies g -1 in soils to (16.2-50.9)×10 6 and (69.2-423)×10 5 copies g -1 in the rhizosphere, respectively. Compared with bulk soils, the NR of AOA in the rhizosphere increased by 41.67% in the high-salt treatment (6 g kg -1 ), and that of AOB increased from 0.09-2.00 mg N kg -1 soil d -1 to 0.70-2.56 mg N kg -1 soil d -1 . Salinity elevation significantly reduced the relative abundance of Nitrosocaldus and Nitrosospira , and increased that of Nitrosocosmicus and Nitrosomonas . The Nitrosospira and amoA genes from AOA/AOB were negatively correlated with soil pH, organic carbon and salinity. Our findings offer new clues on the accelerated nitrification process in the saline-alkali tolerant rice rhizosphere in coastal solonchaks and reveal its roles in shaping AOA/AOB communities, providing suggestions for saline-alkali tolerant rice plantations in coastal solonchaks.
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