Prediction of DNA-binding proteins (DBPs) has become a popular research topic in protein science due to its crucial role in all aspects of biological activities. Even though considerable efforts have been devoted to developing powerful computational methods to solve this problem, it is still a challenging task in the field of bioinformatics. A hidden Markov model (HMM) profile has been proved to provide important clues for improving the prediction performance of DBPs. In this paper, we propose a method, called HMMPred, which extracts the features of amino acid composition and auto- and cross-covariance transformation from the HMM profiles, to help train a machine learning model for identification of DBPs. Then, a feature selection technique is performed based on the extreme gradient boosting (XGBoost) algorithm. Finally, the selected optimal features are fed into a support vector machine (SVM) classifier to predict DBPs. The experimental results tested on two benchmark datasets show that the proposed method is superior to most of the existing methods and could serve as an alternative tool to identify DBPs.

An important reason of cancer proliferation is the change in DNA methylation patterns, characterized by the localized hypermethylation of the promoters of tumor-suppressor genes together with an overall decrease in the level of 5-methylcytosine (5mC). Therefore, identifying the 5mC sites in the promoters is a critical step towards further understanding the diverse functions of DNA methylation in genetic diseases such as cancers and aging. However, most wet-lab experimental techniques are often time consuming and laborious for detecting 5mC sites. In this study, we proposed a deep learning-based approach, called BiLSTM-5mC, for accurately identifying 5mC sites in genome-wide DNA promoters. First, we randomly divided the negative samples into 11 subsets of equal size, one of which can form the balance subset by combining with the positive samples in the same amount. Then, two types of feature vectors encoded by the one-hot method, and the nucleotide property and frequency (NPF) methods were fed into a bidirectional long short-term memory (BiLSTM) network and a full connection layer to train the 22 submodels. Finally, the outputs of these models were integrated to predict 5mC sites by using the majority vote strategy. Our experimental results demonstrated that BiLSTM-5mC outperformed existing methods based on the same independent dataset.

Vibrio parahaemolyticus causes serious seafood-borne gastroenteritis and death in humans. Raw seafood is often subjected to post-harvest processing and low-temperature storage. To date, very little information is available regarding the biological functions of cold shock proteins (CSPs) in the low-temperature survival of the bacterium. In this study, we determined the complete genome sequence of V. parahaemolyticus CHN25 (serotype: O5:KUT). The two main CSP-encoding genes (VpacspA and VpacspD) were deleted from the bacterial genome, and comparative transcriptomic analysis between the mutant and wild-type strains was performed to dissect the possible molecular mechanisms that underlie low-temperature adaptation by V. parahaemolyticus. The 5,443,401-bp V. parahaemolyticus CHN25 genome (45.2% G + C) consisted of two circular chromosomes and three plasmids with 4,724 predicted protein-encoding genes. One dual-gene and two single-gene deletion mutants were generated for VpacspA and VpacspD by homologous recombination. The growth of the ΔVpacspA mutant was strongly inhibited at 10 °C, whereas the VpacspD gene deletion strongly stimulated bacterial growth at this low temperature compared with the wild-type strain. The complementary phenotypes were observed in the reverse mutants (ΔVpacspA-com, and ΔVpacspD-com). The transcriptome data revealed that 12.4% of the expressed genes in V. parahaemolyticus CHN25 were significantly altered in the ΔVpacspA mutant when it was grown at 10 °C. These included genes that were involved in amino acid degradation, secretion systems, sulphur metabolism and glycerophospholipid metabolism along with ATP-binding cassette transporters. However, a low temperature elicited significant expression changes for 10.0% of the genes in the ΔVpacspD mutant, including those involved in the phosphotransferase system and in the metabolism of nitrogen and amino acids. The major metabolic pathways that were altered by the dual-gene deletion mutant (ΔVpacspAD) radically differed from those that were altered by single-gene mutants. Comparison of the transcriptome profiles further revealed numerous differentially expressed genes that were shared among the three mutants and regulators that were specifically, coordinately or antagonistically modulated by VpaCspA and VpaCspD. Our data also revealed several possible molecular coping strategies for low-temperature adaptation by the bacterium. This study is the first to describe the complete genome sequence of V. parahaemolyticus (serotype: O5:KUT). The gene deletions, complementary insertions, and comparative transcriptomics demonstrate that VpaCspA is a primary CSP in the bacterium, while VpaCspD functions as a growth inhibitor at 10 °C. These results have improved our understanding of the genetic basis for low-temperature survival by the most common seafood-borne pathogen worldwide.

Vibrio parahaemolyticus is the causative agent of food-borne gastroenteritis disease. Once consumed, human acid gastric fluid is perhaps one of the most important environmental stresses imposed on the bacterium. Herein, for the first time, we investigated Vibrio parahaemolyticus CHN25 response to artificial gastric fluid (AGF) stress by transcriptomic analysis. The bacterium at logarithmic growth phase (LGP) displayed lower survival rates than that at stationary growth phase (SGP) under a sub-lethal acid condition (pH 4.9). Transcriptome data revealed that 11.6% of the expressed genes in Vibrio parahaemolyticus CHN25 was up-regulated in LGP cells after exposed to AGF (pH 4.9) for 30 min, including those involved in sugar transport, nitrogen metabolism, energy production and protein biosynthesis, whereas 14.0% of the genes was down-regulated, such as ATP-binding cassette (ABC) transporter and flagellar biosynthesis genes. In contrast, the AGF stress only elicited 3.4% of the genes from SGP cells, the majority of which were attenuated in expression. Moreover, the number of expressed regulator genes was also substantially reduced in SGP cells. Comparison of transcriptome profiles further revealed forty-one growth-phase independent genes in the AGF stress, however, half of which displayed distinct expression features between the two growth phases. Vibrio parahaemolyticus seemed to have evolved a number of molecular strategies for coping with the acid stress. The data here will facilitate future studies for environmental stresses and pathogenicity of the leading seafood-borne pathogen worldwide.

As the most abundant RNA methylation modification, N6-methyladenosine (m6A) could regulate asymmetric and symmetric division of hematopoietic stem cells and play an important role in various diseases. Therefore, the precise identification of m6A sites around the genomes of different species is a critical step to further revealing their biological functions and influence on these diseases. However, the traditional wet-lab experimental methods for identifying m6A sites are often laborious and expensive. In this study, we proposed an ensemble deep learning model called m6A-BERT-Stacking, a powerful predictor for the detection of m6A sites in various tissues of three species. First, we utilized two encoding methods, i.e., di ribonucleotide index of RNA (DiNUCindex_RNA) and k-mer word segmentation, to extract RNA sequence features. Second, two encoding matrices together with the original sequences were respectively input into three different deep learning models in parallel to train three sub-models, namely residual networks with convolutional block attention module (Resnet-CBAM), bidirectional long short-term memory with attention (BiLSTM-Attention), and pre-trained bidirectional encoder representations from transformers model for DNA-language (DNABERT). Finally, the outputs of all sub-models were ensembled based on the stacking strategy to obtain the final prediction of m6A sites through the fully connected layer. The experimental results demonstrated that m6A-BERT-Stacking outperformed most of the existing methods based on the same independent datasets.

In this study, we analyzed Chinese traditional fermented food to isolate and identify new lactic acid bacteria (LAB) strains with novel functional properties and to evaluate their cellular antioxidant and bile salt hydrolase (BSH) activities in vitro. A sequential screening strategy was developed to efficiently isolate and obtain 261 LAB strains tolerant of bile salt, acid, and H2O2 from nine Chinese traditional fermented foods. Among these strains, 70 were identified as having 2,2-diphenyl-1-picrylhydrazyl radical scavenging and/or BSH activity. These strains belonged to eight species: Enterococcus faecium (33% of the strains), Lactobacillus plantarum (26%), Leuconostoc mesenteroides (14%), Pediococcus pentosaceus (6%), Enterococcus durans (9%), Lactobacillus brevis (9%), Pediococcus ethanolidurans (3%), and Lactobacillus casei (1%). The pulsed-field gel electrophoresis genome fingerprinting profiles of these strains revealed 38 distinct pulsotypes, indicating a high level of genomic diversity a...