Pdif-mediated antibiotic resistance genes transfer in bacteria identified by pdifFinder.

Modules consisting of antibiotic resistance genes (ARGs) flanked by inverted repeat Xer-specific recombination sites were thought to be mobile genetic elements that promote horizontal transmission. Less frequently, the presence of mobile modules in plasmids, which facilitate a pdif-mediated ARGs transfer, has been reported. Here, numerous ARGs and toxin-antitoxin genes have been found in pdif site pairs. However, the mechanisms underlying this apparent genetic mobility is currently not understood, and the studies relating to pdif-mediated ARGs transfer onto most bacterial genera are lacking. We developed the web server pdifFinder based on an algorithm called PdifSM that allows the prediction of diverse pdif-ARGs modules in bacterial genomes. Using test set consisting of almost 32 thousand plasmids from 717 species, PdifSM identified 481 plasmids from various bacteria containing pdif sites with ARGs. We found 28-bp-long elements from different genera with clear base preferences. The data we obtained indicate that XerCD-dif site-specific recombination mechanism may have evolutionary adapted to facilitate the pdif-mediated ARGs transfer. Through multiple sequence alignment and evolutionary analyses of duplicated pdif-ARGs modules, we discovered that pdif sites allow an interspecies transfer of ARGs but also across different genera. Mutations in pdif sites generate diverse arrays of modules which mediate multidrug-resistance, as these contain variable numbers of diverse ARGs, insertion sequences and other functional genes. The identification of pdif-ARGs modules and studies focused on the mechanism of ARGs co-transfer will help us to understand and possibly allow controlling the spread of MDR bacteria in clinical settings. The pdifFinder code, standalone software package and description with tutorials are available at https://github.com/mjshao06/pdifFinder.

MiR-942-3p as a Potential Prognostic Marker of Gastric Cancer Associated with AR and MAPK/ERK Signaling Pathway.

Gastric cancer is a common tumor with high morbidity and mortality. MicroRNA (miRNA) can regulate gene expression at the translation level and various tumorigenesis processes, playing an important role in tumor occurrence and prognosis. This study aims to screen miRNA associated with gastric cancer prognosis as biomarkers and explore the regulatory genes and related signaling pathways. In this work, R language was used for the standardization and differential analysis of miRNA and mRNA expression profiles. Samples were randomly divided into a testing group and a training group. Subsequently, we built the five miRNAs (has-miR-9-3p, has-miR-135b-3p, has-miR-143-5p, has-miR-942-3p, has-miR-196-3p) prognostic modules, verified and evaluated their prediction ability by the Cox regression analysis. They can be used as an independent factor in the prognosis of gastric cancer. By predicting and analyzing potential biological functions of the miRNA target genes, this study found that the AR gene was not only a hub gene in the PPI network, but also associated with excessive survival of patients. In conclusion, this study demonstrated that hsa-miR-942-3p could be a potential prognostic marker of gastric cancer associated with the AR and MAPK/ERK signaling pathways. The results of this study provide insights into the occurrence and development of gastric cancer.

Discovery and validation of vascular endothelial growth factor (VEGF) pathway polymorphisms in esophageal adenocarcinoma outcome.

Polymorphisms in the vascular endothelial growth factor (VEGF)/angiogenesis pathway have been implicated previously in cancer risk, prognosis and response to therapy including in esophageal adenocarcinoma. Prior esophageal adenocarcinoma studies focused on using candidate polymorphisms, limiting the discovery of novel polymorphisms. Here, we applied the tagSNP (single nucleotide polymorphism) approach to identify new VEGF pathway polymorphisms associated with esophageal adenocarcinoma prognosis and validated them in an independent cohort of esophageal adenocarcinoma patients. In 231 esophageal adenocarcinoma patients of all stages/treatment plans, 58 genetic polymorphisms (18 KDR, 7 VEGFA and 33 FLT1) selected through tagging and assessment of predicted function were genotyped. Cox-proportional hazard models adjusted for important socio-demographic and clinico-pathological factors were applied to assess the association of genetic polymorphisms with overall survival (OS) and progression-free survival (PFS). Significantly associated polymorphisms were then validated in an independent cohort of 137 esophageal adenocarcinoma patients. Among the 231 discovery cohort patients, 86% were male, median diagnosis age was 64 years, 34% were metastatic at diagnosis and median OS and PFS were 20 and 12 months, respectively. KDR rs17709898 was found significantly associated with PFS (adjusted hazard ratio, aHR = 0.69, 95% confidence interval (CI): 0.53-0.90; P = 5.9E-3). FLT1 rs3794405 and rs678714 were significantly associated with OS (aHR = 1.44, 95% CI: 1.04-1.99; P = 0.03 and aHR = 1.50, 95% CI: 1.01-2.24; P = 0.045, respectively). No VEGFA polymorphisms were found significantly associated with either outcome. Upon validation, FLT1 rs3794405 remained strongly associated with OS (aHR = 1.59, 95% CI: 1.04-2.44; P = 0.03). FLT1 rs3794405 is significantly associated with OS in esophageal adenocarcinoma, whereby each variant allele confers a 45-60% increased risk of mortality. Validation and evaluation of this association in other cancer sites are warranted.

Instance-representation transfer method based on joint distribution and deep adaptation for EEG emotion recognition.

Electroencephalogram (EEG) emotion recognition technology is essential for improving human-computer interaction. However, the practical application of emotion recognition technology is limited due to the variety of subjects and sessions. Transfer learning has been applied to address this issue and has received extensive research and application. Studies mainly concentrate on either instance transfer or representation transfer methods. This paper proposes an emotion recognition method called Joint Distributed Instances Represent Transfer (JD-IRT), which includes two core components: Joint Distribution Deep Adaptation (JDDA) and Instance-Representation Transfer (I-RT). Specifically, JDDA is different from common representation transfer methods in transfer learning. It bridges the discrepancies of marginal and conditional distributions simultaneously and combines multiple adaptive layers and kernels for deep domain adaptation. On the other hand, I-RT utilizes instance transfer to select source domain data for better representation transfer. We performed experiments and compared them with other representative methods in the SEED, SEED-IV, and SEED-V datasets. In cross-subject experiments, our approach achieved an average accuracy of 83.21% in SEED, 52.12% in SEED-IV, and 60.17% in SEED-V. Similarly, in cross-session experiments, the accuracy was 91.29% in SEED, 59.02% in SEED-IV, and 65.91% in SEED-V. These results demonstrate the improvement in the accuracy of EEG emotion recognition using the proposed approach.

Transfer learning with data alignment and optimal transport for EEG based motor imagery classification.

Objective. The non-stationarity of electroencephalogram (EEG) signals and the variability among different subjects present significant challenges in current Brain-Computer Interfaces (BCI) research, which requires a time-consuming specific calibration procedure to address. Transfer Learning (TL) offers a potential solution by leveraging data or models from one or more source domains to facilitate learning in the target domain, so as to address these challenges.Approach. In this paper, a novel Multi-source domain Transfer Learning Fusion (MTLF) framework is proposed to address the calibration problem. Firstly, the method transforms the source domain data with the resting state segment data, in order to decrease the differences between the source domain and the target domain. Subsequently, feature extraction is performed using common spatial pattern. Finally, an improved TL classifier is employed to classify the target samples. Notably, this method does not require the label information of target domain samples, while concurrently reducing the calibration workload.Main results. The proposed MTLF is assessed on Datasets 2a and 2b from the BCI Competition IV. Compared with other algorithms, our method performed relatively the best and achieved mean classification accuracy of 73.69% and 70.83% on Datasets 2a and 2b respectively.Significance.Experimental results demonstrate that the MTLF framework effectively reduces the discrepancy between the source and target domains and acquires better classification performance on two motor imagery datasets.

Prediction of genome-wide imipenem resistance features in Klebsiella pneumoniae using machine learning.

Introduction. The resistance rate of Klebsiella pneumoniae (K. pneumoniae) to imipenem is increasing year by year, and the imipenem resistance mechanism of K. pneumoniae is complex. Therefore, it is urgent to develop new strategies to explore the resistance mechanism of imipenem for its effective and accurate use in clinical practice.Hypothesis/Gap sStatement. Machine learning could identify resistance features and biological process that influence microbial resistance from whole-genome sequencing (WGS) data.Aims. This work aimed to predict imipenem resistance genetic features in K. pneumoniae from whole-genome k-mer features, and analyse their function for understanding its resistance mechanism.Methods. This study analysed WGS data of K. pneumoniae combined with resistance phenotype for imipenem, and established K. pneumoniae to imipenem genotype-phenotype model to predict resistance features using chi-squared test and random forest. An external clinical dataset was used to verify prediction power of resistance features. The potential genes were identified through alignment the resistance features with the K. pneumoniae reference genome using blastn, the functions of potential genes were further analysed to explore its resistance-related signalling pathways with GO and KEGG analysis, the resistance sequence patterns were screened using streme software. Finally, the resistance features were combined and modelled through four machine-learning algorithms (logistic regression, SVM, GBDT and XGBoost) to evaluate their phenotype prediction ability.Results. A total of 16 670 imipenem resistance features were predicted from genotype-phenotype model. The 30 potential genes were identified by annotating the resistance features and corresponded to known antibiotic-related genes (mdtM, dedA, rne, etc.). GO and KEGG pathway analyses indicated the possible association of imipenem resistance with metabolism process and cell membrane. CRYCAGCDN and CGRDAAAN were found from the imipenem resistance features, which were widely presented in the reported ß-lactam resistance genes (bla SHV, bla CTX-M, bla TEM, etc.), and YCYAGCMCAST with metabolic functions (organic substance metabolic process, nitrogen compound metabolic process and cellular metabolic process) was identified from the top 50 resistance features. The 25 resistance genes in the training dataset included 19 genes in the external dataset, which verified the accuracy of prediction. The area under curve values of logistics regression, SVM, GBDT and XGBoost were 0.965, 0.966, 0.969 and 0.969, respectively, indicating that the imipenem resistance features have a strong prediction power.Conclusion. Machine-learning methods could effectively predict the imipenem resistance feature in K. pneumoniae, and provide resistance sequence profiles for predicting resistance phenotype and exploring potential resistance mechanisms. It provides an important insight into the potential therapeutic strategies of K. pneumoniae resistance to imipenem, and speed up the application of machine learning in routine diagnosis.

EEG Signal Classification Using Manifold Learning and Matrix-Variate Gaussian Model.

In brain-computer interface (BCI), feature extraction is the key to the accuracy of recognition. There is important local structural information in the EEG signals, which is effective for classification; and this locality of EEG features not only exists in the spatial channel position but also exists in the frequency domain. In order to retain sufficient spatial structure and frequency information, we use one-versus-rest filter bank common spatial patterns (OVR-FBCSP) to preprocess the data and extract preliminary features. On this basis, we conduct research and discussion on feature extraction methods. One-dimensional feature extraction methods like linear discriminant analysis (LDA) may destroy this kind of structural information. Traditional manifold learning methods or two-dimensional feature extraction methods cannot extract both types of information at the same time. We introduced the bilinear structure and matrix-variate Gaussian model into two-dimensional discriminant locality preserving projection (2DDLPP) algorithm and decompose EEG signals into spatial and spectral parts. Afterwards, the most discriminative features were selected through a weight calculation method. We tested the method on BCI competition data sets 2a, data sets IIIa, and data sets collected by our laboratory, and the results were expressed in terms of recognition accuracy. The cross-validation results were 75.69%, 70.46%, and 54.49%, respectively. The average recognition accuracy of new method is improved by 7.14%, 7.38%, 4.86%, and 3.8% compared to those of LDA, two-dimensional linear discriminant analysis (2DLDA), discriminant locality property projections (DLPP), and 2DDLPP, respectively. Therefore, we consider that the proposed method is effective for EEG classification.

Machine learning for identifying resistance features of Klebsiella pneumoniae using whole-genome sequence single nucleotide polymorphisms.

Introduction. Klebsiella pneumoniae, a gram-negative bacterium, is a common pathogen causing nosocomial infection. The drug-resistance rate of K. pneumoniae is increasing year by year, posing a severe threat to public health worldwide. K. pneumoniae has been listed as one of the pathogens causing the global crisis of antimicrobial resistance in nosocomial infections. We need to explore the drug resistance of K. pneumoniae for clinical diagnosis. Single nucleotide polymorphisms (SNPs) are of high density and have rich genetic information in whole-genome sequencing (WGS), which can affect the structure or expression of proteins. SNPs can be used to explore mutation sites associated with bacterial resistance.Hypothesis/Gap Statement. Machine learning methods can detect genetic features associated with the drug resistance of K. pneumoniae from whole-genome SNP data.Aims. This work used Fast Feature Selection (FFS) and Codon Mutation Detection (CMD) machine learning methods to detect genetic features related to drug resistance of K. pneumoniae from whole-genome SNP data.Methods. WGS data on resistance of K. pneumoniae strains to four antibiotics (tetracycline, gentamicin, imipenem, amikacin) were downloaded from the European Nucleotide Archive (ENA). Sequence alignments were performed with MUMmer 3 to complete SNP calling using K. pneumoniae HS11286 chromosome as the reference genome. The FFS algorithm was applied to feature selection of the SNP dataset. The training set was constructed based on mutation sites with mutation frequency >0.995. Based on the original SNP training set, 70% of SNPs were randomly selected from each dataset as the test set to verify the accuracy of the training results. Finally, the resistance genes were obtained by the CMD algorithm and Venny.Results. The number of strains resistant to tetracycline, gentamicin, imipenem and amikacin was 931, 1048, 789 and 203, respectively. Machine learning algorithms were applied to the SNP training set and test set, and 28 and 23 resistance genes were predicted, respectively. The 28 resistance genes in the training set included 22 genes in the test set, which verified the accuracy of gene prediction. Among them, some genes (KPHS_35310, KPHS_18220, KPHS_35880, etc.) corresponded to known resistance genes (Eef2, lpxK, MdtC, etc). Logistic regression classifiers were established based on the identified SNPs in the training set. The area under the curves (AUCs) of the four antibiotics was 0.939, 0.950, 0.912 and 0.935, showing a strong ability to predict bacterial resistance.Conclusion. Machine learning methods can effectively be used to predict resistance genes and associated SNPs. The FFS and CMD algorithms have wide applicability. They can be used for the drug-resistance analysis of any microorganism with genomic variation and phenotypic data. This work lays a foundation for resistance research in clinical applications.

RecO is essential for DNA damage repair in Deinococcus radiodurans.

Here we present direct evidence for the vital role of RecO in Deinococcus radiodurans's radioresistance. A recO null mutant was constructed using a deletion replacement method. The mutant exhibited a growth defect and extreme sensitivity to irradiation with gamma rays and UV light. These results suggest that DNA repair in this organism occurs mainly via the RecF pathway.

Extracellular proteome changes of Deinococcus radiodurans under gamma-irradiation stress conditions.

To analysis the change of Deinococcus radiodurans extracellular proteins recovering from gamma-irradiation, we examined extracellular proteome changes using two-dimensional polyacrylamide gel electrophoresis. Twenty-six spots on the gel of irradiated sample were showed significant changes compared with spots on the control gel. Using peptide mass fingerprinting via matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF-MS), 21 different proteins could be distinguished. Among the identified proteins, seven are classified in transport and metabolism, and one is involved in intracellular trafficking and secretion. The other proteins are known to several functions in the cytosol. Most of the proteins have not previously been reported to be relevant to radioresistance. These results imply that the transmembrane transportation is involved in and contributes to the radioresistance in this organism.

Mass spectrometry-based quantification of the cellular response to ultraviolet radiation in HeLa cells.

Ultraviolet (UV) irradiation is a common form of DNA damage that can cause pyrimidine dimers between DNA, which can cause gene mutations, even double-strand breaks and threaten genome stability. If DNA repair systems default their roles at this stage, the organism can be damaged and result in disease, especially cancer. To better understand the cellular response to this form of damage, we applied highly sensitive mass spectrometry to perform comparative proteomics of phosphorylation in HeLa cells. A total of 4367 phosphorylation sites in 2100 proteins were identified, many of which had not been reported previously. Comprehensive bioinformatics analysis revealed that these proteins were involved in many important biological processes, including signaling, localization and cell cycle regulation. The nuclear pore complex, which is very important for RNA transport, was changed significantly at phosphorylation level, indicating its important role in response to UV-induced cellular stress. Protein-protein interaction network analysis and DNA repair pathways crosstalk were also examined in this study. Proteins involved in base excision repair, nucleotide repair and mismatch repair changed their phosphorylation pattern in response to UV treatment, indicating the complexity of cellular events and the coordination of these pathways. These systematic analyses provided new clues of protein phosphorylation in response to specific DNA damage, which is very important for further investigation. And give macroscopic view on an overall phosphorylation situation under UV radiation.

Validation of microRNA pathway polymorphisms in esophageal adenocarcinoma survival.

Polymorphisms in miRNA and miRNA pathway genes have been previously associated with cancer risk and outcome, but have not been studied in esophageal adenocarcinoma outcomes. Here, we evaluate candidate miRNA pathway polymorphisms in esophageal adenocarcinoma prognosis and attempt to validate them in an independent cohort of esophageal adenocarcinoma patients. Among 231 esophageal adenocarcinoma patients of all stages/treatment plans, 38 candidate genetic polymorphisms (17 biogenesis, 9 miRNA targets, 5 pri-miRNA, 7 pre-miRNA) were genotyped and analyzed. Cox proportional hazard models adjusted for sociodemographic and clinicopathological covariates helped assess the association of genetic polymorphisms with overall survival (OS) and progression-free survival (PFS). Significantly associated polymorphisms were then evaluated in an independent cohort of 137 esophageal adenocarcinoma patients. Among the 231 discovery cohort patients, 86% were male, median diagnosis age was 64 years, 34% were metastatic at diagnosis, and median OS and PFS were 20 and 12 months, respectively. GEMIN3 rs197412 (aHR = 1.37, 95%CI: [1.04-1.80]; P = 0.02), hsa-mir-124-1 rs531564 (aHR = 0.60, 95% CI: [0.53-0.90]; P = 0.05), and KIAA0423 rs1053667 (aHR = 0.51, 95% CI: [0.28-0.96]; P = 0.04) were found associated with OS. Furthermore, GEMIN3 rs197412 (aHR = 1.33, 95% CI: [1.03-1.74]; P = 0.03) and KRT81 rs3660 (aHR = 1.29, 95% CI: [1.01-1.64]; P = 0.04) were found associated with PFS. Although none of these polymorphisms were significant in the second cohort, hsa-mir-124-1 rs531564 and KIAA0423 rs1053667 had trends in the same direction; when both cohorts were combined together, GEMIN3 rs197412, hsa-mir-124-1 rs531564, and KIAA0423 rs1053667 remained significantly associated with OS. We demonstrate the association of multiple miRNA pathway polymorphisms with esophageal adenocarcinoma prognosis in a discovery cohort of patients, which did not validate in a separate cohort but had consistent associations in the pooled cohort. Larger studies are required to confirm/validate the prognostic value of these polymorphisms in esophageal adenocarcinoma.

Association between FEN1 Polymorphisms -69G>A and 4150G>T with Susceptibility in Human Disease: A Meta-Analysis.

BACKGROUND: As a DNA repair protein, flap endonuclease 1 is a key enzyme in maintaining genomic instability and preventing carcinogenesis. Two single nucleotide polymorphisms (SNPs), -69G>A and 4150G>T are associated with DNA damage. This meta-analysis is to evaluate the genetic effects of FEN1 gene SNPs (-69G/A and 4150G/T) and the susceptibility to diseases, including glioma risk, breast cancer, lung cancer, keratoconus (KC) and fuchs' endothelial corneal dystrophy (FECD). METHODS: A literature search of PubMed and Embase was conducted to identify all eligible published studies. Five case-control studies were included with a total of 5612 cases and 6703 controls in this meta-analysis. Crude odds ratios (ORs) with their corresponding confidence intervals (95%CI) were used to assess the strength of the association. RESULTS: The FEN1 -69G/A and 4150G/T polymorphisms were significantly associated with the disease risk. Our meta-analysis showed the FEN1 -69GG genotype was correlated to increase risk for the contained diseases compared with the -69AG genotype (OR=0.77, 95%CI=0.71∼0.83). Moreover, the FEN1 4150GG genotype could increase diseases risk compared with the 4150TG genotype (OR=0.81, 95%CI=0.75∼0.87). CONCLUSION: The variant genotypes of the FEN1 -69G/A and FEN1 4150G/T polymorphisms may be associated with diseases susceptibility. However, more studies are needed to detect the disease risk in different ethnic populations.

A novel OxyR sensor and regulator of hydrogen peroxide stress with one cysteine residue in Deinococcus radiodurans.

In bacteria, OxyR is a peroxide sensor and transcription regulator, which can sense the presence of reactive oxygen species and induce antioxidant system. When the cells are exposed to H(2)O(2), OxyR protein is activated via the formation of a disulfide bond between the two conserved cysteine residues (C199 and C208). In Deinococcus radiodurans, a previously unreported special characteristic of DrOxyR (DR0615) is found with only one conserved cysteine. dr0615 gene mutant is hypersensitive to H(2)O(2), but only a little to ionizing radiation. Site-directed mutagenesis and subsequent in vivo functional analyses revealed that the conserved cysteine (C210) is necessary for sensing H(2)O(2), but its mutation did not alter the binding characteristics of OxyR on DNA. Under oxidant stress, DrOxyR is oxidized to sulfenic acid form, which can be reduced by reducing reagents. In addition, quantitative real-time PCR and global transcription profile results showed that OxyR is not only a transcriptional activator (e.g., katE, drb0125), but also a transcriptional repressor (e.g., dps, mntH). Because OxyR regulates Mn and Fe ion transporter genes, Mn/Fe ion ratio is changed in dr0615 mutant, suggesting that the genes involved in Mn/Fe ion homeostasis, and the genes involved in antioxidant mechanism are highly cooperative under extremely oxidant stress. In conclusion, these findings expand the OxyR family, which could be divided into two classes: typical 2-Cys OxyR and 1-Cys OxyR.

Proteomic analysis of Deinococcus radiodurans recovering from gamma-irradiation.

In order to reveal the mechanisms of the extreme radioresistance and DNA repair in Deinococcus radiodurans, we examined proteome changes in a wild-type strain following gamma-irradiation using two-dimensional polyacrylamide gel electrophoresis and Silver-staining. The expression levels of 26 protein spots showed significant changes under radiation stress. Of these spots, 21 were identified with peptide mass fingerprinting using matrix-assisted laser desorption/ionization-time of flight mass spectrometry after tryptic in-gel digestion. These proteins exhibited various cellular functions, including (i) translation; (ii) transcription; (iii) signal transduction; (iv) post-translational modification, protein turnover, chaperones; (v) carbohydrate transport and metabolism; (vi) energy production and conversion; (vii) nucleotide transport and metabolism; (viii) inorganic ion transport and metabolism; (ix) DNA replication, recombination and repair; and (x) yet unknown. Most of the proteins have not previously been reported to be relevant to radioresistance.