Resource diversity disturbs marine Vibrio diversity and community stability, but loss of Vibrio diversity enhances community stability.

Vibrio is a salt-tolerant heterotrophic bacterium that occupies an important ecological niche in marine environments. However, little is known about the contribution of resource diversity to the marine Vibrio diversity and community stability. In this study, we investigated the association among resource diversity, taxonomic diversity, phylogenetic diversity, and community stability of marine Vibrio in the Beibu Gulf. V. campbellii and V. hangzhouensis were the dominant groups in seawater and sediments, respectively, in the Beibu Gulf. Higher alpha diversity was observed in the sediments than in the seawater. Marine Vibrio community assembly was dominated by deterministic processes. Pearson's correlation analysis showed that nitrite (NO2--N), dissolved inorganic nitrogen (DIN), ammonium (NH4+-N), and pH were the main factors affecting marine Vibrio community stability in the surface, middle, and bottom layers of seawater and sediment, respectively. Partial least-squares path models (PLS-PM) demonstrated that resource diversity, water properties, nutrients, and geographical distance had important impacts on phylogenetic and taxonomic diversity. Regression analysis revealed that the impact of resource diversity on marine Vibrio diversity and community stability varied across different habitats, but loss of Vibrio diversity increases community stability. Overall, this study provided insights into the mechanisms underlying the maintenance of Vibrio diversity and community stability in marine environments.

PFAS promotes disinfection byproduct formation through triggering particle-bound organic matter release in drinking water pipes.

Loose deposit particles in drinking water distribution system commonly exist as mixtures of metal oxides, organic materials, bacteria, and extracellular secretions. In addition to their turbidity-causing effects, the hazards of such particles in drinking water are rarely recognized. In this study, we found that trace per- and polyfluoroalkyl substances (PFASs) could dramatically promote the formation of disinfection byproducts (DBPs) by triggering the release of particle-bound organic matter. Carboxylic PFASs have a greater ability to increase chloroacetic acid than sulfonic PFASs, and PFASs with longer chains have a greater ability to increase trichloromethane release than shorter-chain PFASs. Characterization by organic carbon and organic nitrogen detectors and Fourier transform ion cyclotron resonance mass spectrometry revealed that the released organic matter was mainly composed of proteins, carbohydrates, lignin, and condensed aromatic structures, which are the main precursors for the formation of DBPs, particularly highly toxic aromatic DBPs. After the release of organic matter, the particles exhibit a decrease in surface functional groups, an increase in surface roughness, and a decrease in particle size. The findings provide new insights into the risks of loose deposits and PFASs in drinking water, not only on PFASs per se but also on its effect of increasing toxic DBPs.

ResCNNT-fold: Combining residual convolutional neural network and Transformer for protein fold recognition from language model embeddings.

A comprehensive understanding of protein functions holds significant promise for disease research and drug development, and proteins with analogous tertiary structures tend to exhibit similar functions. Protein fold recognition stands as a classical approach in the realm of protein structure investigation. Despite significant advancements made by researchers in this field, the continuous updating of protein databases presents an ongoing challenge in accurately identifying protein fold types. In this study, we introduce a predictor, ResCNNT-fold, for protein fold recognition and employ the LE dataset for testing purpose. ResCNNT-fold leverages a pre-trained language model to obtain embedding representations for protein sequences, which are then processed by the ResCNNT feature extractor, a combination of residual convolutional neural network and Transformer, to derive fold-specific features. Subsequently, the query protein is paired with each protein whose structure is known in the template dataset. For each pair, the similarity score of their fold-specific features is calculated. Ultimately, the query protein is identified as the fold type of the template protein in the pair with the highest similarity score. To further validate the utility and efficacy of the proposed ResCNNT-fold predictor, we conduct a 2-fold cross-validation experiment on the fold level of the LE dataset. Remarkably, this rigorous evaluation yields an exceptional accuracy of 91.57%, which surpasses the best result among other state-of-the-art protein fold recognition methods by an approximate margin of 10%. The excellent performance unequivocally underscores the compelling advantages inherent to our proposed ResCNNT-fold predictor in the realm of protein fold recognition. The source code and data of ResCNNT-fold can be downloaded from https://github.com/Bioinformatics-Laboratory/ResCNNT-fold.

Disentangling the Ecological Processes and Driving Forces Shaping the Seasonal Pattern of Halobacteriovorax Communities in a Subtropical Estuary.

Halobacteriovorax are predatory bacteria that have a significant ecological role in marine environments. However, understanding of dynamics of populations, driving forces, and community composition of Halobacteriovorax groups in natural marine environments is still limited. Here, we used high-throughput sequencing to study the underlying mechanisms governing the diversity and assembly of the Halobacteriovorax community at spatiotemporal scales in a subtropical estuary. Phylogenetic analysis showed that 10 of 15 known Halobacteriovorax clusters were found in the studied estuary. Halobacteriovorax α-diversity and ß-diversity exhibited significant seasonal variation. Variation partitioning analysis showed that the effect of nutrients was greater than that of other measured water properties on Halobacteriovorax community distribution. The results of Spearman's and Mantel's tests indicated that the trophic pollutants dissolved inorganic phosphorus (DIP) and NH4+-N in the estuary were the key factors that significantly affected Halobacteriovorax species and community diversity. In addition, this work indicated that the biological stoichiometry (especially N/P) of nutrients exerted a significant influence on the Halobacteriovorax community. Furthermore, we found that both deterministic and stochastic processes contributed to the turnover of Halobacteriovorax communities, and environmental filtering dominated the assembly of estuarine Halobacteriovorax communities. Overall, we provide new insights into the mechanisms in the generation and maintenance of the Halobacteriovorax community in marine environments.

Interface hydrogen bonding dominated perfluorooctanoic acid (PFOA) accumulation by iron particles in drinking water pipes.

Iron particle is one of the key factors inducing discoloration in drinking water distribution system (DWDS), but the mechanism of iron particles on the accumulation of trace organic pollutants in DWDS is not clear. Here, iron-based pipes from real DWDS were used to investigate the perfluorooctanoic acid (PFOA) accumulation mechanisms in DWDS. Results showed that old unlined pipes had a much higher accumulation capacity for PFOA than new pipes. Among the corrosion products in old pipes, Fe2O3 and Fe3O4 did not have obvious accumulation for PFOA, while FeOOH exhibited a strong accumulation effect for PFOA. Furthermore, the in-situ formed iron particles contributed more to PFOA accumulation than pre-formed iron particles. Interestingly, PFOA caused an increase in turbidity and particle size of in-situ formed iron particles. Mulliken charge of F-bonded Fe increased from +1.28 e to +1.30 e, which indicated that the oxidation state of Fe-center was strengthened by PFOA. When dissolved oxygen existed, a PFOA-FeOOH-O2 linkage could form through COO-Fe coordination and O2 interface adsorption, which enhanced cytotoxicity due to the generation of •OH radicals. These findings implied that interface hydrogen bonding dominated PFOA accumulation by iron particles in DWDS, which would increase the risks of discoloration.

Integrative transcriptomic, proteomic and metabolomic analysis reveals the dynamic regulation of secondary metabolism upon development of Prunella vulgaris L.

Prunella vulgaris L. (P. vulgaris, Labiatae) is a perennial medicinal and edible plant widely used in China, Korea, Japan and Europe. The reddish brown spica of P. vulgaris (Prunellae Spica), which is collected in summer, has been commonly used in traditional medicine and food industry, while it is also used with whole grass in Europe and Taiwan. To clarify the regulatory pathways and mechanism of quality formation in P. vulgaris, targeted metabolomic, transcriptomic, and proteomic analyses of Prunellae Spica samples from five consecutive developmental stages were carried out. The results showed that terpenoids were mainly synthesized in the maturity stage of Prunellae Spica, with the key enzymes and coding genes in downstream pathways being mainly expressed during ripening, while related enzymes in the upstream pathway showed the opposite pattern. Flavonoids mainly accumulated before ripening, with highly expressed pathway enzymes and coding genes. The accumulation of phenylpropanoids was relatively active throughout the development process. Rosmarinic acid (RA) and its synthetic intermediate products mainly accumulated via more active pathway enzymes and coding genes before ripening. The regulatory factors and metabolites related to RA synthesis were mainly enriched in phenylpropanoid biosynthesis, plant hormone signal transduction, plant pathogen interaction, oxidative phosphorylation, and endoplasmic reticulum protein processing pathways.

Enhanced toxicity effects of iron particles together with PFOA in drinking water.

Iron particles present in drinking water distribution systems (DWDSs) could cause discoloration, while organic pollutants in DWDSs, such as perfluorooctanoic acid (PFOA), could be enriched by iron particles. However, little is known about the enhanced effects of PFOA and iron particles in DWDSs. To fill in these knowledge gaps, herein, iron-PFOA (FEP) particles were generated using residual chlorine as an oxidant in drinking water conditions and then separated into different sizes (ranging from small to large: FEP-S, FEP-M ，and FEP-L). FEP-S harbored the greatest cytotoxicity among the sizes. Interestingly, our data revealed that the PFOA released from FEP particles transformed into PFOS (perfluorooctane sulfonate) upon digestion in the gastrointestinal environment (GI), and FEP-L bored the strongest transformation, showing a toxicity profile that was distinct from that of FEP-S. Furthermore, mechanistic studies revealed that FEP per se should be accountable for the conversion of PFOA to PFOS dependent on the generation of hydroxyl radicals (·OH) in GI, and that FEP-L revealed the greatest production of ·OH. Collectively, these results showed how iron particles and PFOA could result in enhanced toxicity effects in drinking water: (i) PFOA could increase the toxicity of iron particles by reducing particle size and inducing higher generation of ·OH; (ii) iron particles could induce the transformation of PFOA into more toxic PFOS through digestion.

The Comparison of Life Quality between Ultrasound-Guided High-Intensity Focused Ultrasound and Laparoscopic Myomectomy for the Treatment of Uterine Fibroids.

Objective: This study is aimed at comparing the uterine fibroids patients' postoperative living quality between ultrasound-guided high-intensity focused ultrasound (HIFU) and laparoscopic myomectomy. Materials and Methods: A total of 164 patients were included with uterine fibroids who underwent laparoscopic myomectomy and HIFU in Cangzhou Central Hospital from September 2020 to November 2021. This study divided these objects into HIFU group and laparoscopic group, and both groups were followed up 6 months after surgery. After obtaining the results, Uterine Fibroid Symptom and health-related Quality Of Life questionnaire (UFS-QOL) and 36-Item Short Form Health Survey (SF-36) were performed before and after treatment to assess patient outcome. Results: After treatments, the living quality in both groups was significantly improved compared with that before surgery, which had statistical significant (P < 0.05). After treatment, the scores of the two scales in HIFU group were significantly better than those in the laparoscopic group (P < 0.05). Conclusion: In comparison with laparoscopic myomectomy, ultrasound-guided high-intensity focused ultrasound could improve the life quality of patients more effectively than traditional laparoscopic myomectomy and was helpful to the recovery and prognosis of uterine fibroids after treatment. The outcomes will provide a reference for clinicians to select a more appropriate treatment for uterine fibroids.

ASFold-DNN: Protein Fold Recognition Based on Evolutionary Features With Variable Parameters Using Full Connected Neural Network.

Protein fold recognition contribute to comprehend the function of proteins, which is of great help to the gene therapy of diseases and the development of new drugs. Researchers have been working in this direction and have made considerable achievements, but challenges still exist on low sequence similarity datasets. In this study, we propose the ASFold-DNN framework for protein fold recognition research. Above all, four groups of evolutionary features are extracted from the primary structures of proteins, and a preliminary selection of variable parameter is made for two groups of features including ACC _HMM and SXG _HMM, respectively. Then several feature selection algorithms are selected for comparison and the best feature selection scheme is obtained by changing their internal threshold values. Finally, multiple hyper-parameters of Full Connected Neural Network are fully optimized to construct the best model. DD, EDD and TG datasets with low sequence similarities are chosen to evaluate the performance of the models constructed by the framework, and the final prediction accuracy are 85.28, 95.00 and 88.84 percent, respectively. Furthermore, the ASTRAL186 and LE datasets are introduced to further verify the generalization ability of our proposed framework. Comprehensive experimental results prove that the ASFold-DNN framework is more prominent than the state-of-the-art studies on protein fold recognition. The source code and data of ASFold-DNN can be downloaded from https://github.com/Bioinformatics-Laboratory/project/tree/master/ASFold.

BERT-m7G: A Transformer Architecture Based on BERT and Stacking Ensemble to Identify RNA N7-Methylguanosine Sites from Sequence Information.

As one of the most prevalent posttranscriptional modifications of RNA, N7-methylguanosine (m7G) plays an essential role in the regulation of gene expression. Accurate identification of m7G sites in the transcriptome is invaluable for better revealing their potential functional mechanisms. Although high-throughput experimental methods can locate m7G sites precisely, they are overpriced and time-consuming. Hence, it is imperative to design an efficient computational method that can accurately identify the m7G sites. In this study, we propose a novel method via incorporating BERT-based multilingual model in bioinformatics to represent the information of RNA sequences. Firstly, we treat RNA sequences as natural sentences and then employ bidirectional encoder representations from transformers (BERT) model to transform them into fixed-length numerical matrices. Secondly, a feature selection scheme based on the elastic net method is constructed to eliminate redundant features and retain important features. Finally, the selected feature subset is input into a stacking ensemble classifier to predict m7G sites, and the hyperparameters of the classifier are tuned with tree-structured Parzen estimator (TPE) approach. By 10-fold cross-validation, the performance of BERT-m7G is measured with an ACC of 95.48% and an MCC of 0.9100. The experimental results indicate that the proposed method significantly outperforms state-of-the-art prediction methods in the identification of m7G modifications.

Gene expression profiles for an immunoscore model in bone and soft tissue sarcoma.

BACKGROUND: Immune infiltration is a prognostic marker to clinical outcomes in various solid tumors. However, reports that focus on bone and soft tissue sarcoma are rare. The study aimed to analyze and identify how immune components influence prognosis and develop a novel prognostic system for sarcomas. METHODS: We retrieved the gene expression data from 3 online databases (GEO, TCGA, and TARGET). The immune fraction was estimated using the CIBERSORT algorithm. After that, we re-clustered samples by K-means and constructed immunoscore by the least absolute shrinkage and selection operator (LASSO) Cox regression model. Next, to confirm the prognostic value, nomograms were constructed. RESULTS: 334 samples diagnosed with 8 tumor types (including osteosarcoma) were involved in our analysis. Patients were next re-clustered into three subgroups (OS, SAR1, and SAR2) through immune composition. Survival analysis showed a significant difference between the two soft tissue groups: patients with a higher proportion of CD8+ T cells, macrophages M1, and mast cells had favorable outcomes (p=0.0018). Immunoscore models were successfully established in OS and SAR2 groups consisting of 12 and 9 cell fractions, respectively. We found immunosocre was an independent factor for overall survival time. Patients with higher immunoscore had poor prognosis (p<0.0001). Patients with metastatic lesions scored higher than those counterparts with localized tumors (p<0.05). CONCLUSIONS: Immune fractions could be a useful tool for the classification and prognosis of bone and soft tissue sarcoma patients. This proposed immunoscore showed a promising impact on survival prediction.

DNN-m6A: A Cross-Species Method for Identifying RNA N6-Methyladenosine Sites Based on Deep Neural Network with Multi-Information Fusion.

As a prevalent existing post-transcriptional modification of RNA, N6-methyladenosine (m6A) plays a crucial role in various biological processes. To better radically reveal its regulatory mechanism and provide new insights for drug design, the accurate identification of m6A sites in genome-wide is vital. As the traditional experimental methods are time-consuming and cost-prohibitive, it is necessary to design a more efficient computational method to detect the m6A sites. In this study, we propose a novel cross-species computational method DNN-m6A based on the deep neural network (DNN) to identify m6A sites in multiple tissues of human, mouse and rat. Firstly, binary encoding (BE), tri-nucleotide composition (TNC), enhanced nucleic acid composition (ENAC), K-spaced nucleotide pair frequencies (KSNPFs), nucleotide chemical property (NCP), pseudo dinucleotide composition (PseDNC), position-specific nucleotide propensity (PSNP) and position-specific dinucleotide propensity (PSDP) are employed to extract RNA sequence features which are subsequently fused to construct the initial feature vector set. Secondly, we use elastic net to eliminate redundant features while building the optimal feature subset. Finally, the hyper-parameters of DNN are tuned with Bayesian hyper-parameter optimization based on the selected feature subset. The five-fold cross-validation test on training datasets show that the proposed DNN-m6A method outperformed the state-of-the-art method for predicting m6A sites, with an accuracy (ACC) of 73.58%-83.38% and an area under the curve (AUC) of 81.39%-91.04%. Furthermore, the independent datasets achieved an ACC of 72.95%-83.04% and an AUC of 80.79%-91.09%, which shows an excellent generalization ability of our proposed method.

Structural protein fold recognition based on secondary structure and evolutionary information using machine learning algorithms.

Understanding the function of protein is conducive to research in advanced fields such as gene therapy of diseases, the development and design of new drugs, etc. The prerequisite for understanding the function of a protein is to determine its tertiary structure. The realization of protein structure classification is indispensable for this problem and fold recognition is a commonly used method of protein structure classification. Protein sequences of 40% identity in the ASTRAL protein classification database are used for fold recognition research in current work to predict 27 folding types which mostly belong to four protein structural classes: α, ß, α/ß and α + ß. We extract features from primary structure of protein using methods covering DSSP, PSSM and HMM which are based on secondary structure and evolutionary information to convert protein sequences into feature vectors that can be recognized by machine learning algorithm and utilize the combination of LightGBM feature selection algorithm and incremental feature selection method (IFS) to find the optimal classifiers respectively constructed by machine learning algorithms on the basis of tree structure including Random Forest, XGBoost and LightGBM. Bayesian optimization method is used for hyper-parameter adjustment of machine learning algorithms to make the accuracy of fold recognition reach as high as 93.45% at last. The result obtained by the model we propose is outstanding in the study of protein fold recognition.

Novel therapeutic compounds for prostate adenocarcinoma treatment: An analysis using bioinformatic approaches and the CMap database.

INTRODUCTION: Prostate adenocarcinoma is the most frequently diagnosed malignancy, particularly for people >70 years old. The main challenge in the treatment of advanced neoplasm is bone metastasis and therapeutic resistance for known oncology drugs. Novel treatment methods to prolong the survival time and improve the life quality of these specific patients are required. The present study attempted to screen potential therapeutic compounds for the tumor through bioinformatics approaches, in order to provide conceptual treatment for this malignant disease. METHODS: Differentially expressed genes were obtained from the Gene Expression Omnibus database and submitted into the Connectivity Map database for the detection of potentially associated compounds. Target genes were extracted from the search results. Functional annotation and pathway enrichment were performed for the confirmation. Survival analysis was used to measure potential therapeutic effects. RESULTS: It was revealed that 3 compounds (vanoxerine, tolnaftate, and gabexate) may help to prolong the disease-free survival time from tumor metastasis of patients with the tumor. A total of 6 genes [also-keto reductase family 1 member C3 (AKR1C3), collagen type III α 1 chain (COL3A1), lipoprotein lipase (LPL), glucuronidase, ß pseudogene 11 (GUSBP11), apolipoprotein E (APOE), and collagen type I α 1 chain (COL1A1)] were identified to be the potential therapeutic targets for the aforementioned compounds. CONCLUSION: In the present study, it was speculated that 3 compounds may function as the potential therapeutic drugs of bone metastatic prostate adenocarcinoma; however, further studies verifying vitro and in vivo are necessary.

Succinylation Site Prediction Based on Protein Sequences Using the IFS-LightGBM (BO) Model.

Succinylation is an important posttranslational modification of proteins, which plays a key role in protein conformation regulation and cellular function control. Many studies have shown that succinylation modification on protein lysine residue is closely related to the occurrence of many diseases. To understand the mechanism of succinylation profoundly, it is necessary to identify succinylation sites in proteins accurately. In this study, we develop a new model, IFS-LightGBM (BO), which utilizes the incremental feature selection (IFS) method, the LightGBM feature selection method, the Bayesian optimization algorithm, and the LightGBM classifier, to predict succinylation sites in proteins. Specifically, pseudo amino acid composition (PseAAC), position-specific scoring matrix (PSSM), disorder status, and Composition of k-spaced Amino Acid Pairs (CKSAAP) are firstly employed to extract feature information. Then, utilizing the combination of the LightGBM feature selection method and the incremental feature selection (IFS) method selects the optimal feature subset for the LightGBM classifier. Finally, to increase prediction accuracy and reduce the computation load, the Bayesian optimization algorithm is used to optimize the parameters of the LightGBM classifier. The results reveal that the IFS-LightGBM (BO)-based prediction model performs better when it is evaluated by some common metrics, such as accuracy, recall, precision, Matthews Correlation Coefficient (MCC), and F-measure.