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Abstract Optical density (OD) is an important indicator of microbial density, and a commonly used variable in growth curves to express the growth of microbial culture. However, OD values show a linear relationship with bacterial concentration only at low concentrations. When the cell density is high, the relationship loses linearity, and serial dilution is needed to obtain readings of better accuracy. Here, we show that measuring OD values using shorter light paths is in close equivalence to measuring OD values of the cell culture with corresponding dilution. By measuring three different light paths simultaneously, accurate OD values can be easily obtained from low to high cell density. Using this method, growth curves of Escherichia coli, Staphylococcus aureus, and Pichia pastoris are measured with higher accuracy. To further simplify the process, an L‐shaped cuvette and a corresponding turbidimeter are designed specifically for OD value measurement based on the multi‐light path transmission method.

Abstract CircRNAs are a type of circular non-coding RNA whose associations with drug sensitivities have been demonstrated in recent studies. Due to the high cost of biomedical experiments for detecting the associations between circRNAs and drug sensitivities, several computational methods have been developed. However, these methods were evaluated mainly based on 5- or tenfold cross-validation, which are often over-optimistic. Furthermore, there are technique issues with these models, such as over-smoothing and over-squashing. To address these issues, we propose a strategy to evaluate models based on independent test sets for association prediction-related studies. In the light of this effective assessment, we constructed a model, SGTCDA, by integrating structural deep network embedding (SDNE) and a graph transformer to predict the potential associations of circRNA-drug sensitivity, which can efficiently capture long-range dependencies and local structural information of nodes. Our results on the training sets and the independent test sets indicate that SGTCDA outperforms the other state-of-the-art models, demonstrating its capacity for accurate prediction of circRNA-drug sensitivity. Moreover, we leveraged EdgeSHAPer to explain the performance of the proposed SGTCDA model, which illustrates that the edges between drugs are more important than other edges for the performance of the model. The source code and dataset of SGTCDA are available at: https://github.com/hwxia/SGTCDA .

Abstract Survival risk prediction models have become important tools for clinicians to improve cancer treatment decisions. In the medical field, using gene expression data to build deep survival neural network models significantly improves accurate survival prognosis. However, it still poses a challenge in building an efficient method to improve the accuracy of cancer-specific survival risk prediction, such as data noise problem. In order to solve the above problem, we propose a diversity reweighted deep survival neural network method with grid optimization (DRGONet) to improve the accuracy of cancer-specific survival risk prediction. Specifically, reweighting can be employed to adjust the weights assigned to each data point in the dataset based on their importance or relevance, thereby mitigating the impact of noisy or irrelevant data and improving model performance. Incorporating diversity into the goal of multiple learning models can help minimize bias and improve learning outcomes. Furthermore, hyperparameters can be optimized with grid optimization. Experimental results have demonstrated that our proposed approach has significant advantages (improved about 5%) in real-world medical scenarios, outperforming state-of-the-art comparison methods by a large margin. Our study highlights the significance of using DRGONet to overcome the limitations of building accurate survival prediction models. By implementing our technique in cancer research, we hope to reduce the suffering experienced by cancer patients and improve the effectiveness of treatment.

Abstract Overgrowth of Gardnerella vaginalis causes an imbalance in vaginal microecology. The pathogenicity of G. vaginalis is directly regulated by the cAMP receptor protein (CRP). In this study, we resolve the crystal structure of CRPGv at a resolution of 2.22 Å and find some significant differences from homologous proteins. The first 23 amino acids of CRPGv are inserted into the ligand binding pocket, creating a strong steric barrier to ligand entry that has not been seen previously in its homologues. In the absence of ligands, the two α helices used by CRPGv to bind oligonucleotide chains are exposed and can specifically bind TGTGA-N6-TCACA sequences. cAMP and other ligands of CRP homologs are not cofactors of CRPGv. There is no coding gene of the adenylate cyclase, and cAMP could not be identified in G. vaginalis by liquid chromatography tandem mass spectrometry. We speculate that CRPGv may achieve fine regulation through a conformational transformation different from that of its homologous proteins, and this conformational transformation is no longer dependent on small molecules, but may be aided by accessory proteins. CRPGv is the first discovered CRP that is not ligand-regulated, and its active conformation provides a structural basis for drug screening.

Abstract Colonizing in the gastrointestinal tract, Escherichia coli confronts diverse acidic challenges and evolves intricate acid resistance strategies for its survival. The lysine-mediated decarboxylation (Cad) system, featuring lysine decarboxylase CadA, lysine/cadaverine antiporter CadB, and transcriptional activator CadC, plays a crucial role in E. coli’s adaptation to moderate acidic stress. While the activation of the one-component system CadC and subsequent upregulation of cadBA operon in response to acid and lysine presence have been proposed, the molecular mechanisms governing the transition of CadC from an inactive to an active state remain elusive. Under neutral conditions, CadC is inhibited by forming a complex with lysine-specific permease LysP, stabilized in this inactive state by a disulfide bond. Our study unveils that, in an acidic environment, the disulfide bond in CadC is reduced by the disulfide bond isomerase DsbC, exposing R184 to periplasmic proteases, namely DegQ and DegP. Cleavage at R184 by DegQ and DegP generates an active N-terminal DNA-binding domain of CadC, which binds to the cadBA promoter, resulting in the upregulated transcription of the cadA and cadB genes. Upon activation, CadA decarboxylates lysine, producing cadaverine, subsequently transported extracellularly by CadB. We propose that accumulating cadaverine gradually binds to the CadC pH-sensing domain, preventing cleavage and activation of CadC as a feedback mechanism. The identification of DegP, DegQ, and DsbC completes a comprehensive roadmap for the activation and regulation of the Cad system in response to moderate acidic stress in E. coli.

Cysteine S-carboxyethylation, a novel post-translational modification (PTM), plays a critical role in the pathogenesis of autoimmune diseases, particularly ankylosing spondylitis. Accurate identification of S-carboxyethylation modification sites is essential for elucidating their functional mechanisms. Unfortunately, there are currently no computational tools that can accurately predict these sites, posing a significant challenge to this area of research. In this study, we developed a new deep learning model, DLBWE-Cys, which integrates CNN, BiLSTM, Bahdanau attention mechanisms, and a fully connected neural network (FNN), using Binary-Weight encoding specifically designed for the accurate identification of cysteine S-carboxyethylation sites. Our experimental results show that our model architecture outperforms other machine learning and deep learning models in 5-fold cross-validation and independent testing. Feature comparison experiments confirmed the superiority of our proposed Binary-Weight encoding method over other encoding techniques. t-SNE visualization further validated the model’s effective classification capabilities. Additionally, we confirmed the similarity between the distribution of positional weights in our Binary-Weight encoding and the allocation of weights in attentional mechanisms. Further experiments proved the effectiveness of our Binary-Weight encoding approach. Thus, this model paves the way for predicting cysteine S-carboxyethylation modification sites in protein sequences. The source code of DLBWE-Cys and experiments data are available at: https://github.com/ztLuo-bioinfo/DLBWE-Cys.

The coronavirus disease 2019 (COVID-19) pandemic has highlighted the importance of developing novel vaccines. An ideal vaccine should trigger an intense immune reaction without causing significant side effects. In this study we found that substitution of tryptophan located in the cores of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) protein structures with certain smaller amino acids resulted in variants with melting temperatures of 33–37 °C. An enzyme activity assay indicated that the proteolytic activity of the main proteinase (3CLpro) decreased sharply when the environmental temperature exceeded the melting temperature, implying that other protein variants may lose most of their functions under the same conditions. This finding suggests that a virus variant containing engineered proteins with melting temperatures of 33–37 °C may only be functional in the upper respiratory tract where the temperature is about 33 °C, but will be unable to invade internal organs, which maintain temperatures above 37 °C, thus making it possible to construct temperature-sensitive attenuated vaccines.

N6-methyladenosine (m6A) plays a crucial regulatory role in the control of cellular functions and gene expression. Recent advances in sequencing techniques for transcriptome-wide m6A mapping have accelerated the accumulation of m6A site information at a single-nucleotide level, providing more high-confidence training data to develop computational approaches for m6A site prediction. However, it is still a major challenge to precisely predict m6A sites using in silico approaches. To advance the computational support for m6A site identification, here, we curated 13 up-to-date benchmark datasets from nine different species (i.e., H. sapiens, M. musculus, Rat, S. cerevisiae, Zebrafish, A. thaliana, Pig, Rhesus, and Chimpanzee). This will assist the research community in conducting an unbiased evaluation of alternative approaches and support future research on m6A modification. We revisited 52 computational approaches published since 2015 for m6A site identification, including 30 traditional machine learning-based, 14 deep learning-based, and 8 ensemble learning-based methods. We comprehensively reviewed these computational approaches in terms of their training datasets, calculated features, computational methodologies, performance evaluation strategy, and webserver/software usability. Using these benchmark datasets, we benchmarked nine predictors with available online websites or stand-alone software and assessed their prediction performance. We found that deep learning and traditional machine learning approaches generally outperformed scoring function-based approaches. In summary, the curated benchmark dataset repository and the systematic assessment in this study serve to inform the design and implementation of state-of-the-art computational approaches for m6A identification and facilitate more rigorous comparisons of new methods in the future.

Abstract LncRNA–protein interactions are ubiquitous in organisms and play a crucial role in a variety of biological processes and complex diseases. Many computational methods have been reported for lncRNA–protein interaction prediction. However, the experimental techniques to detect lncRNA–protein interactions are laborious and time-consuming. Therefore, to address this challenge, this paper proposes a reweighting boosting feature selection (RBFS) method model to select key features. Specially, a reweighted apporach can adjust the contribution of each observational samples to learning model fitting; let higher weights are given more influence samples than those with lower weights. Feature selection with boosting can efficiently rank to iterate over important features to obtain the optimal feature subset. Besides, in the experiments, the RBFS method is applied to the prediction of lncRNA–protein interactions. The experimental results demonstrate that our method achieves higher accuracy and less redundancy with fewer features.

The accurate identification, detection, and segmentation of tea buds and leaves are important factors for realizing intelligent tea picking. A tea picking point location method based on the region-based convolutional neural network(R-CNN) Mask- RCNN is proposed, and a tea bud and leaf and picking point recognition model is established. First, tea buds and leaf pictures are collected in a complex environment, the Resnet50 residual network and a feature pyramid network (FPN) are used to extract bud and leaf features, and preliminary classification and preselection box regression training-performed on the feature maps through a regional proposal network (RPN). Second, the regional feature aggregation method (RoIAlign) is used to eliminate the quantization error, and the feature map of the preselected region of interest (ROI) is converted into a fixed-size feature map. The output module of the model realizes the functions of classification, regression and segmentation. Finally, through the output mask image and the positioning algorithm the positioning of the picking points of tea buds and leaves is determined. One hundred tea tree bud and leaf pictures in a complex environment are selected for testing. The experimental results show that the average detection accuracy rate reaches 93.95% and that the recall rate reaches 92.48%. The tea picking location method presented in this paper is more versatile and robust in complex environments.

In order to solve the problem of difficult separation of audio signals collected in pig environments, this study proposes an underdetermined blind source separation (UBSS) method based on sparsification theory. The audio signals obtained by mixing the audio signals of pigs in different states with different coefficients are taken as observation signals, and the mixing matrix is first estimated from the observation signals using the improved AP clustering method based on the “two-step method” of sparse component analysis (SCA), and then the audio signals of pigs are reconstructed by L1-paradigm separation. Five different types of pig audio are selected for experiments to explore the effects of duration and mixing matrix on the blind source separation algorithm by controlling the audio duration and mixing matrix, respectively. With three source signals and two observed signals, the reconstructed signal metrics corresponding to different durations and different mixing matrices perform well. The similarity coefficient is above 0.8, the average recovered signal-to-noise ratio is above 8 dB, and the normalized mean square error is below 0.02. The experimental results show that different audio durations and different mixing matrices have certain effects on the UBSS algorithm, so the recording duration and the spatial location of the recording device need to be considered in practical applications. Compared with the classical UBSS algorithm, the proposed algorithm outperforms the classical blind source separation algorithm in estimating the mixing matrix and separating the mixed audio, which improves the reconstruction quality.

Abstract For the past 30 years, in vitro transcription (IVT) technology has been extensively used for RNA production or for basic transcriptional mechanism research. However, methods for mRNA quantification still need to be improved. In this study, we designed a RT-IVT method using binary fluorescence quencher (BFQ) probes and the PBCV-1 DNA ligase to quantify mRNA production in real-time by fluorescence resonance energy transfer (FRET) and RNA-splinted DNA ligation. Compared with existing methods, the RT-IVT method is inexpensive and non-radioactive, and can detect mRNA production in unpurified systems in real-time and shows high sensitivity and selectivity. The activity of T7 RNA polymerase and Escherichia coli RNA polymerase holoenzyme was then characterized with this method. We then multiplexed the real-time mRNA quantification for three T7 promoters on a RT-PCR thermocycler by using BFQ probes with different colored fluorophores that were specific for each target. Ultimately, we created an inexpensive multiplexed method to quantify mRNA production in real-time, and future research could use these methods to measure the affinity of transcriptional repressors to their target DNA sequence.

Abstract Background Target drugs play an important role in the clinical treatment of virus diseases. Virus-encoded proteins are widely used as targets for target drugs. However, they cannot cope with the drug resistance caused by a mutated virus and ignore the importance of host proteins for virus replication. Some methods use interactions between viruses and their host proteins to predict potential virus–target host proteins, which are less susceptible to mutated viruses. However, these methods only consider the network topology between the virus and the host proteins, ignoring the influences of protein complexes. Therefore, we introduce protein complexes that are less susceptible to drug resistance of mutated viruses, which helps recognize the unknown virus–target host proteins and reduce the cost of disease treatment. Results Since protein complexes contain virus–target host proteins, it is reasonable to predict virus–target human proteins from the perspective of the protein complexes. We propose a coverage clustering-core-subsidiary protein complex recognition method named CCA-SE that integrates the known virus–target host proteins, the human protein–protein interaction network, and the known human protein complexes. The proposed method aims to obtain the potential unknown virus–target human host proteins. We list part of the targets after proving our results effectively in enrichment experiments. Conclusions Our proposed CCA-SE method consists of two parts: one is CCA, which is to recognize protein complexes, and the other is SE, which is to select seed nodes as the core of protein complexes by using seed expansion. The experimental results validate that CCA-SE achieves efficient recognition of the virus–target host proteins.

It is vital for the annotation of uncharacterized proteins by protein function prediction. At present, Deep Neural Network based protein function prediction is mainly carried out for dataset of small scale proteins or Gene Ontology, and usually explore the relationships between single protein feature and function tags. The practical methods for large-scale multi-features protein prediction still need to be studied in depth. This paper proposes a DNN based protein function prediction approach IGP-DNN. This method uses Grasshopper Optimization Algorithm (GOA) and Intuitionistic Fuzzy c-Means clustering (IFCM) based protein function modules extracting algorithm to extract the features of protein modules, utilizing Kernel Principal Component Analysis (KPCA) method to reduce the dimensionality of the protein attribute information, and integrating module features and attribute features. Inputting integrated data into DNN through multiple hidden layers to classify proteins and predict protein functions. In the experiments, the F-measure value of IGP-DNN on the DIP dataset reaches 0.4436, which shows better performance.

The existing algorithms for identifying and tracking pigs in barns generally have a large number of parameters, relatively complex networks and a high demand for computational resources, which are not suitable for deployment in embedded-edge nodes on farms. A lightweight multi-objective identification and tracking algorithm based on improved YOLOv5s and DeepSort was developed for group-housed pigs in this study. The identification algorithm was optimized by: (i) using a dilated convolution in the YOLOv5s backbone network to reduce the number of model parameters and computational power requirements; (ii) adding a coordinate attention mechanism to improve the model precision; and (iii) pruning the BN layers to reduce the computational requirements. The optimized identification model was combined with DeepSort to form the final Tracking by Detecting algorithm and ported to a Jetson AGX Xavier edge computing node. The algorithm reduced the model size by 65.3% compared to the original YOLOv5s. The algorithm achieved a recognition precision of 96.6%; a tracking time of 46 ms; and a tracking frame rate of 21.7 FPS, and the precision of the tracking statistics was greater than 90%. The model size and performance met the requirements for stable real-time operation in embedded-edge computing nodes for monitoring group-housed pigs.

Heterologous expression is an indispensable approach to exploiting natural products from phylogenetically diverse microbial communities. In this study, we constructed a heterologous expression system based on strain Burkholderia thailandensis E264 by deleting efflux pump genes and screening constitutive strong promoters. The biosynthetic gene cluster (BGC) of disorazol from Sorangium cellulosum So ce12 was expressed successfully with this host, and the yield of its product, disorazol F2, rather than A1, was improved to 38.3 mg/L by promoter substitution and insertion. In addition to the disorazol gene cluster, the BGC of rhizoxin from Burkholderia rhizoxinica was also expressed efficiently, whereas no specific peak was detected when shuangdaolide BGC from Streptomyces sp. B59 was transformed into the host. This system provides another option to explore natural products from different phylogenetic taxa.

Gardnerella overgrowth is the primary cause of bacterial vaginosis (BV), a common vaginal infection with incidences as high as 23–29% worldwide. Here, we studied the pathogenicity, drug resistance, and prevalence of varying Gardnerella spp. We isolated 20 Gardnerella strains from vaginal samples of 31 women in local China. Ten strains were then selected via phylogenetic analysis of cpn60 and vly gene sequences to carry out genome sequencing and comparative genomic analysis. Biofilm-formation, sialidase, and antibiotic resistance activities of the strains were characterized. All strains showed striking heterogeneity in genomic structure, biofilm formation and drug resistance. Two of the ten strains, JNFY3 and JNFY15, were classified as Gardnerella swidsinskii and Gardnerella piotii, respectively, according to their phenotypic characteristics and genome sequences. In particular, seven out of the ten strains exhibited super resistance (≥ 128 μg/mL) to metronidazole, which is the first line of treatment for BV in China. Based on the biochemical and genomic results of the strains, we proposed a treatment protocol of prevalent Gardnerella strains in local China, which provides the basis for accurate diagnosis and therapy.

Abstract Background The study of protein complexes and protein functional modules has become an important method to further understand the mechanism and organization of life activities. The clustering algorithms used to analyze the information contained in protein-protein interaction network are effective ways to explore the characteristics of protein functional modules. Results This paper conducts an intensive study on the problems of low recognition efficiency and noise in the overlapping structure of protein functional modules, based on topological characteristics of PPI network. Developing a protein function module recognition method ECTG based on Topological Features and Gene expression data for Protein Complex Identification. Conclusions The algorithm can effectively remove the noise data reflected by calculating the topological structure characteristic values in the PPI network through the similarity of gene expression patterns, and also properly use the information hidden in the gene expression data. The experimental results show that the ECTG algorithm can detect protein functional modules better.

The balance between exploration and exploitation is critical to the performance of a Meta-heuristic optimization method. At different stages, a proper tradeoff between exploration and exploitation can drive the search process towards better performance. This paper develops a multi-objective grasshopper optimization algorithm (MOGOA) with a new proposed framework called the Multi-group and Co-evolution Framework which can archive a fine balance between exploration and exploitation. For the purpose, a grouping mechanism and a co-evolution mechanism are designed and integrated into the framework for ameliorating the convergence and the diversity of multi-objective optimization solutions and keeping the exploration and exploitation of swarm intelligence algorithm in balance. The grouping mechanism is employed to improve the diversity of search agents for increasing coverage of search space. The co-evolution mechanism is used to improve the convergence to the true Pareto optimal front by the interaction of search agents. Quantitative and qualitative outcomes prove that the framework prominently ameliorate the convergence accuracy and convergence speed of MOGOA. The performance of the presented algorithm has been benchmarked by several standard test functions, such as CEC2009, ZDT and DTLZ. The diversity and convergence of the obtained multi-objective optimization solutions are quantitatively and qualitatively compared with the original MOGOA by using two performance indicators (GD and IGD). The results on test suits show that the diversity and convergence of the obtained solutions are significantly improved. On several test functions, some statistical indicators are more than doubled. The validity of the results has been verified by the Wilcoxon rank-sum test.

ABSTRACT Iron limitation is a universal strategy of host immunity during bacterial infection. However, the mechanisms by which pathogens antagonize host nutritional immunity have not been fully elucidated. Here, we identified a requirement for the UMPylator YdiU for this process in Salmonella. The expression of YdiU was dramatically induced by the metal starvation signal. The intracellular iron content was much lower in the ΔydiU strain than in wild-type Salmonella, and the ΔydiU strain exhibited severe growth defect under metal deficiency environments. Genome-wide expression analyses revealed significantly decreased expression of iron uptake genes in ΔydiU strain compared with the wild-type strain. Interestingly, YdiU did not affect the expression level of the major iron uptake regulator Fur but directly UMPylated Fur on its H118 residue in vivo and in vitro. UMPylation destroyed the Fur dimer, promoted Fur aggregation, and eliminated the DNA-binding activity of Fur, thus abolishing the ability of Fur to inhibit iron uptake. Restricting Fur to the deUMPylated state dramatically eliminates Salmonella iron uptake in iron deficiency environments. In parallel, YdiU facilitates Salmonella survival within host cells by regulating the iron uptake pathway. IMPORTANCE Salmonella is the major pathogen causing bacterial enteric illness in both humans and animals. Iron availability is strictly controlled upon Salmonella entry into host cells. The mechanisms by which Salmonella balances the acquisition of sufficient iron while preventing a toxic overload has not been fully understood. Here, we reveal a novel regulation process of iron acquisition mediated by the UMPylator YdiU. Fur acts as the central regulator of bacterial iron homeostasis. YdiU UMPylates Fur on H118 and prevents Fur from binding to target DNA, thus activating the expression of iron uptake genes under iron-deficient conditions. We describe the first posttranslational modification-based regulation of Fur and highlight a potential mechanism by which Salmonella can adapt to eliminate host nutritional immunity.

Weed control has always been one of the most important issues in agriculture. The research based on deep learning methods for weed identification and segmentation in the field provides necessary conditions for intelligent point-to-point spraying and intelligent weeding. However, due to limited and difficult-to-obtain agricultural weed datasets, complex changes in field lighting intensity, mutual occlusion between crops and weeds, and uneven size and quantity of crops and weeds, the existing weed segmentation methods are unable to perform effectively. In order to address these issues in weed segmentation, this study proposes a multi-scale convolutional attention network for crop and weed segmentation. In this work, we designed a multi-scale feature convolutional attention network for segmenting crops and weeds in the field called MSFCA-Net using various sizes of strip convolutions. A hybrid loss designed based on the Dice loss and focal loss is used to enhance the model’s sensitivity towards different classes and improve the model’s ability to learn from hard samples, thereby enhancing the segmentation performance of crops and weeds. The proposed method is trained and tested on soybean, sugar beet, carrot, and rice weed datasets. Comparisons with popular semantic segmentation methods show that the proposed MSFCA-Net has higher mean intersection over union (MIoU) on these datasets, with values of 92.64%, 89.58%, 79.34%, and 78.12%, respectively. The results show that under the same experimental conditions and parameter configurations, the proposed method outperforms other methods and has strong robustness and generalization ability.

This paper develops an unmanned aerial vehicle (UAV) deployment scheme in the context of the directional modulation-based secure precise wireless transmissions (SPWTs) to achieve more secure and more energy efficiency transmission, where the optimal UAV position for the SPWT is derived to maximize the secrecy rate (SR) without frequency diverse array (FDA) and injecting any artificial noise (AN) signaling. To be specific, the proposed scheme reveals that the optimal position of UAV for maximizing the SR performance has to be placed at the null space of Eves channel, which impels the received energy of the confidential message at the unintended receiver deteriorating to zero, whilst benefits the one at the intended receiver by achieving its maximum value. Moreover, the highly cost FDA structure is eliminated and transmit power is all allocated for transmitting a useful message which shows its energy efficiency. Finally, simulation results verify the optimality of our proposed scheme in terms of the achievable SR performance.

Gardnerella vaginalis is the main pathogen that causes bacterial vaginosis. In the healthy vaginal microecological environment of a woman, the lactobacilli produce lactate and hydrogen peroxide to inhibit the growth of pathogens such as G. vaginalis. The lack of lactobacilli results in a high pH and low hydrogen peroxide in the vagina which facilitate G. vaginalis growth, leading to the imbalance of the vaginal microecology. In this study, lactate and hydrogen peroxide were added to a G. vaginalis culture medium to simulate the co-culture of the lactobacilli and G. vaginalis, and then the genes related to the stress response of G. vaginalis were identified using transcriptomics and proteomics. It was indicated that, among all the upregulated genes, most of them encoded transporters associated with the efflux of harmful substances, and the majority of the downregulated genes were related to the biofilm formation and epithelial cell adhesion. This study may help find new drug targets for G. vaginalis for the development of novel therapies for bacterial vaginosis.

The solvent-producing bacterium Clostridium acetobutylicum is able to grow on a variety of carbohydrates. The main hexose transport system is the phosphoenolpyruvate-dependent phosphotransferase system (PTS). When the gene glcG that encodes the glucose transporter was inactivated, the resulting mutant glcG::int(1224) grew as well as the wild type, yet its glucose consumption was reduced by 17% in a batch fermentation. Transcriptomics analysis of the phosphate-limited continuous cultures showed that the cellobiose transporter GlcCE was highly up-regulated in the mutant glcG::int(1224). The glcCE mutation did not affect growth and even consumed slightly more glucose during solventogenesis growth compared to wild type, indicating that GlcG is the primary glucose-specific PTS. Poor growth of the double mutant glcG::int(1224)-glcCE::int(193) further revealed that GlcCE was the secondary glucose PTS and that there must be other PTSs capable of glucose uptake. The observations obtained in this study provided a promising foundation to understand glucose transport in C. acetobutylicum.

The coronavirus disease 2019 (COVID-19) has caused and is still causing tremendous damage to the global economy and human health. Qualitative reverse transcription-PCR (RT-qPCR) is the golden standard for COVID-19 test. However, the SARS-CoV-2 variants may not only make vaccine less effective but also evade RT-qPCR test. Here we suggest an innovative primer design strategy for the RT-qPCR test of SARS-CoV-2. The principle is that the primers should be designed based on both the nucleic acid sequence and the structure of the protein encoded. The three nucleotides closest to the 3′ end of the primer should be the codon which encodes the tryptophan in the structure core. Based on this principle, we designed a pair of primers targeting the nucleocapsid (N) gene. Since tryptophan is encoded by only one codon, any mutation that occurs at this position would change the amino acid residue, resulting in an unstable N protein. This means that this kind of SARS-CoV-2 variant could not survive. In addition, both our data and previous reports all indicate that the mutations occurring at other places in the primers do not significantly affect the RT-qPCR result. Consequently, no SARS-CoV-2 variant can escape detection by the RT-qPCR kit containing the primers designed based on our strategy.

Billions of people suffer from dental caries every year in spite of the effort to reduce the prevalence over the past few decades. Streptococcus mutans is the leading member of a specific group of cariogenic bacteria that cause dental caries. S. mutans forms biofilm, which is highly resistant to harsh environment, host immunity, and antimicrobial treatments. In this study, we found that S. mutans biofilm is highly resistant to both antimicrobial agents and lysozyme. DexA70, the truncated form of DexA (amino acids 100–732), a dextranase in S. mutans, prevents S. mutans biofilm formation and disassembles existing biofilms within minutes at nanomolar concentrations when supplied exogenously. DexA70 treatment markedly enhances biofilm sensitivity to antimicrobial agents and lysozyme, indicating its great potential in combating biofilm-related dental caries.

Orphan genes are associated with regulatory patterns, but experimental methods for identifying orphan genes are both time-consuming and expensive. Designing an accurate and robust classification model to detect orphan and non-orphan genes in unbalanced distribution datasets poses a particularly huge challenge. Synthetic minority over-sampling algorithms (SMOTE) are selected in a preliminary step to deal with unbalanced gene datasets. To identify orphan genes in balanced and unbalanced Arabidopsis thaliana gene datasets, SMOTE algorithms were then combined with traditional and advanced ensemble classified algorithms respectively, using Support Vector Machine, Random Forest (RF), AdaBoost (adaptive boosting), GBDT (gradient boosting decision tree), and XGBoost (extreme gradient boosting). After comparing the performance of these ensemble models, SMOTE algorithms with XGBoost achieved an F1 score of 0.94 with the balanced A. thaliana gene datasets, but a lower score with the unbalanced datasets. The proposed ensemble method combines different balanced data algorithms including Borderline SMOTE (BSMOTE), Adaptive Synthetic Sampling (ADSYN), SMOTE-Tomek, and SMOTE-ENN with the XGBoost model separately. The performances of the SMOTE-ENN-XGBoost model, which combined over-sampling and under-sampling algorithms with XGBoost, achieved higher predictive accuracy than the other balanced algorithms with XGBoost models. Thus, SMOTE-ENN-XGBoost provides a theoretical basis for developing evaluation criteria for identifying orphan genes in unbalanced and biological datasets.

Summary: Sensing stressful conditions and adjusting the cellular metabolism to adapt to the environment are essential activities for bacteria to survive in variable situations. Here, we describe a stress-related protein, YdiU, and characterize YdiU as an enzyme that catalyzes the covalent attachment of uridine-5′-monophosphate to a protein tyrosine/histidine residue, an unusual modification defined as UMPylation. Mn2+ serves as an essential co-factor for YdiU-mediated UMPylation. UTP and Mn2+ binding converts YdiU to an aggregate-prone state facilitating the recruitment of chaperones. The UMPylation of chaperones prevents them from binding co-factors or clients, thereby impairing their function. Consistent with the recent finding that YdiU acts as an AMPylator, we further demonstrate that the self-AMPylation of YdiU padlocks its chaperone-UMPylation activity. A detailed mechanism is proposed based on the crystal structures of Apo-YdiU and YdiU-AMPNPP-Mn2+ and on molecular dynamics simulation models of YdiU-UTP-Mn2+ and YdiU-UTP-peptide. In vivo data demonstrate that YdiU effectively protects Salmonella from stress-induced ATP depletion through UMPylation.

A glycosyl hydrolase produced by Pseudomonas aeruginosa, PslG, has become a promising candidate for biofilm treatment because of its ability to inhibit and disperse biofilms by disrupting exopolysaccharide matrix at nanomolar concentrations. However, as a protein, PslG used for treatment may be degraded by the ubiquitous proteases (of which trypsin-like serine proteases are a major group) secreted by human cells. This would lead to an insufficient effective concentration of PslG. Here, based on the result of liquid chromatography–tandem mass spectrometry (LC-MS/MS) and structural analysis, we generate a PslG mutant (K286A/K433S) with greatly enhanced trypsin resistance. This measure raises IC50 (the concentration of trypsin that can degrade 50% of protein in 30 min at 37°C) from 0.028 mg mL–1 of the wild-type PslG to 0.283 mg mL–1 of PslGK286A/K433S. In addition, biofilm inhibition assay shows that PslGK286A/K433S is much more efficient than wild-type PslG in the presence of trypsin. This indicates that PslGK286A/K433S is a better biofilm inhibitor than wild-type PslG in clinical use where trypsin-like proteases widely exist.

The virulence factor pyocyanin and the second messenger c-di-GMP regulate biofilm formation and antibiotic tolerance in Pseudomonas aeruginosa. Here, the authors perform structural and biochemical analyses to show that a transcriptional regulator, BrlR, acts as a receptor for both pyocyanin and c-di-GMP.

Chondroitin sulfate/dermatan sulfate (CS/DS) sulfatases are potential tools for structural and functional studies of CD/DS chains. In our previous study, a CS/DS 4-O-endosulfatase (endoVB4SF) was identified from a marine bacterium (Wang et al., 2015). Herein, another CS/DS 4-O-sulfatase (exoPB4SF) was identified from a Photobacterium sp. ExoPB4SF shares an 83% identity with endoVB4SF but showed strict exolytic activity. Comparative studies were performed for both enzymes on the basis of biochemical features, substrate-degrading patterns and three-dimensional structures. exoPB4SF exhibited a wider temperature and pH adaptability and better thermostability than endoVB4SF. Furthermore, exoPB4SF is a strict exolytic sulfatase that only releases the sulfate group from the GalNAc residue located at the reducing end, whereas endoVB4SF preferentially removed sulfate esters from the reducing end toward the non-reducing end though its directional degradation property was not strict. In addition, the structure of endoVB4SF was determined by X-ray crystallography at 1.95 Å. It adopts a globular conformation with two monomers per asymmetric unit. The exoPB4SF structure was constructed by homology modeling. Molecular docking results showed that although the residues around the catalytic center are conserved, the residues at the active site of endoVB4SF adopted a more favorable conformation for the binding of long CS/DS chains than those of exoPB4SF, which may explain why the two highly homogenous sulfatases possessed different action patterns. The results of this study provide insight into the structure-function relationship of CS/DS endo- and exosulfatases for the first time.

Intelligent detection of imperfect wheat grains based on machine vision is of great significance to correctly and rapidly evaluate wheat quality. There is little difference between the partial characteristics of imperfect and perfect wheat grains, which is a key factor limiting the classification and recognition accuracy of imperfect wheat based on a deep learning network model. In this paper, we propose a method for imperfect wheat grains recognition combined with an attention mechanism and residual network (ResNet), and verify its recognition accuracy by adding an attention mechanism module into different depths of residual network. Five residual networks with different depths (18, 34, 50, 101, and 152) were selected for the experiment, it was found that the recognition accuracy of each network model was improved with the attention mechanism, and the average recognition rate of ResNet-50 with the addition of the attention mechanism reached 96.5%. For ResNet-50 with the attention mechanism, the optimal learning rate was further screened as 0.0003. The average recognition accuracy reached 97.5%, among which the recognition rates of scab wheat grains, insect-damaged wheat grains, sprouted wheat grains, mildew wheat grains, broken wheat grains, and perfect wheat grains reached 97%, 99%, 99%, 95%, 96%, and 99% respectively. This work can provide guidance for the detection and recognition of imperfect wheat grains using machine vision.

NrnC from Agrobacterium tumefaciens (At_NrnC, UniProt accession number A9CG28) is a nuclease containing a single DEDDy domain. Here, we determined the structures of both the apo and metal-ion-bound forms of At_NrnC. Although the overall structure of the At_NrnC protomer is similar to that of the RNase D exonuclease domain, nuclease assays unexpectedly revealed that At_NrnC possesses remarkably different substrate specificity. In contrast to RNase D, which degrades both single-stranded RNA (ssRNA) and double-stranded RNA (dsRNA), At_NrnC hydrolyses ssRNA, single-stranded DNA (ssDNA), and double-stranded DNA (dsDNA) with high efficiency but does not degrade dsRNA. Crystal packing analysis and biochemical data indicated that At_NrnC forms an octameric hollow cylindrical structure that allows ssRNA, ssDNA, and dsDNA, but not dsRNA, to enter the central tunnel where the multiple active sites perform hydrolysis. This novel structural feature confers a high processivity and is responsible for the preference of At_NrnC for longer dsDNA substrates.

Dibenzothiophene (DBT) and their derivatives, accounting for the major part of the sulfur components in crude oil, make one of the most significant pollution sources. The DBT sulfone monooxygenase BdsA, one of the key enzymes in the “4S” desulfurization pathway, catalyzes the oxidation of DBT sulfone to 2′-hydroxybiphenyl 2-sulfonic acid (HBPSi). Here, we determined the crystal structure of BdsA from Bacillus subtilis WU-S2B, at the resolution of 2.2 Å, and the structure of the BdsA-FMN complex at 2.4 Å. BdsA and the BdsA-FMN complex exist as tetramers. DBT sulfone was placed into the active site by molecular docking. Seven residues (Phe12, His20, Phe56, Phe246, Val248, His316, and Val372) are found to be involved in the binding of DBT sulfone. The importance of these residues is supported by the study of the catalytic activity of the active site variants. Structural analysis and enzyme activity assay confirmed the importance of the right position and orientation of FMN and DBT sulfone, as well as the involvement of Ser139 as a nucleophile in catalysis. This work combined with our previous structure of DszC provides a systematic structural basis for the development of engineered desulfurization enzymes with higher efficiency and stability.

The intracellular pathogen Legionella pneumophila hijacks the endoplasmic reticulum (ER)-derived vesicles to create an organelle designated Legionella-containing vacuole (LCV) required for bacterial replication. Maturation of the LCV involved acquisition of Rab1, which is mediated by the bacterial effector protein SidM/DrrA. SidM/DrrA is a bifunctional enzyme having the activity of both Rab1-specific GDP dissociation inhibitor (GDI) displacement factor (GDF) and guanine nucleotide exchange factor (GEF). LidA, another Rab1-interacting bacterial effector protein, was reported to promote SidM/DrrA-mediated recruitment of Rab1 to the LCV as well. Here we report the crystal structures of LidA complexes with GDP- and GTP-bound Rab1 respectively. Structural comparison revealed that GDP-Rab1 bound by LidA exhibits an active and nearly identical conformation with that of GTP-Rab1, suggesting that LidA can disrupt the switch function of Rab1 and render it persistently active. As with GTP, LidA maintains GDP-Rab1 in the active conformation through interaction with its two conserved switch regions. Consistent with the structural observations, biochemical assays showed that LidA binds to GDP- and GTP-Rab1 equally well with an affinity approximately 7.5 nM. We propose that the tight interaction with Rab1 allows LidA to facilitate SidM/DrrA-catalyzed release of Rab1 from GDIs. Taken together, our results support a unique mechanism by which a bacterial effector protein regulates Rab1 recycling.

Herpesvirus-associated ubiquitin-specific protease (HAUSP, also known as USP7), a deubiquitylating enzyme of the ubiquitin-specific processing protease family, specifically deubiquitylates both p53 and MDM2, hence playing an important yet enigmatic role in the p53-MDM2 pathway. Here we demonstrate that both p53 and MDM2 specifically recognize the N-terminal tumor necrosis factor-receptor associated factor (TRAF)-like domain of HAUSP in a mutually exclusive manner. HAUSP preferentially forms a stable HAUSP-MDM2 complex even in the presence of excess p53. The HAUSP-binding elements were mapped to a peptide fragment in the carboxy-terminus of p53 and to a short-peptide region preceding the acidic domain of MDM2. The crystal structures of the HAUSP TRAF-like domain in complex with p53 and MDM2 peptides, determined at 2.3-A and 1.7-A resolutions, respectively, reveal that the MDM2 peptide recognizes the same surface groove in HAUSP as that recognized by p53 but mediates more extensive interactions. Structural comparison led to the identification of a consensus peptide-recognition sequence by HAUSP. These results, together with the structure of a combined substrate-binding-and-deubiquitylation domain of HAUSP, provide important insights into regulation of the p53-MDM2 pathway by HAUSP.