Co-occurrence network analysis is an effective tool for predicting complex networks of microbial interactions in the natural environment. Using isolates from a rhizosphere, we constructed multi-species biofilm communities and investigated co-occurrence patterns between microbial species in genome-scale metabolic models and in vitro experiments. According to our results, metabolic exchanges and resource competition may partially explain the co-occurrence network analysis results found in synthetic bacterial biofilm communities.

Enterobacter sp. FY-07 can produce bacterial cellulose (BC) under aerobic and anaerobic conditions. Three potential BC synthesis gene clusters (bcsI, bcsII and bcsIII) of Enterobacter sp. FY-07 have been predicted using genome sequencing and comparative genome analysis, in which bcsIII was confirmed as the main contributor to BC synthesis by gene knockout and functional reconstitution methods. Protein homology, gene arrangement and gene constitution analysis indicated that bcsIII had high identity to the bcsI operon of Enterobacter sp. 638; however, its arrangement and composition were same as those of BC synthesizing operon of G. xylinum ATCC53582 except for the flanking sequences. According to the BC biosynthesizing process, oxygen is not directly involved in the reactions of BC synthesis, however, energy is required to activate intermediate metabolites and synthesize the activator, c-di-GMP. Comparative transcriptome and metabolite quantitative analysis demonstrated that under anaerobic conditions genes involved in the TCA cycle were downregulated, however, genes in the nitrate reduction and gluconeogenesis pathways were upregulated, especially, genes in three pyruvate metabolism pathways. These results suggested that Enterobacter sp. FY-07 could produce energy efficiently under anaerobic conditions to meet the requirement of BC biosynthesis.

The associated microbiota plays an essential role in the life process of jellyfish. The endobiotic bacterial communities from four common jellyfish Phyllorhiza punctata, Cyanea capillata, Chrysaora melanaster, and Aurelia coerulea were comparatively analyzed by 16S rDNA sequencing in this study. Several 1049 OTUs were harvested from a total of 130 183 reads. Tenericutes (68.4%) and Firmicutes (82.1%) are the most abundant phyla in P. punctata and C. melanaster, whereas C. capillata and A. coerulea share the same top phylum Proteobacteria (76.9% vs. 78.3%). The classified OTUs and bacterial abundance greatly decrease from the phylum to genus level. The top 20 matched genera only account for 9.03% of the total community in P. punctata, 48.9% in C. capillata, 83.05% in C. melanaster, and 58.1% in A. coerulea, respectively. The heatmap of the top 50 genera shows that the relative abundances in A. coerulea and C. capillata are far richer than that in P. punctata and C. melanaster. Moreover, a total of 41 predictive functional categories at KEGG level 2 were identified. Our study indicates the independent diversity of the bacterial communities in the four common Scyphomedusae, which might involve in the metabolism and environmental information processing of the hosts. The associated microbiota plays an essential role in the life process of jellyfish. The endobiotic bacterial communities from four common jellyfish Phyllorhiza punctata, Cyanea capillata, Chrysaora melanaster, and Aurelia coerulea were comparatively analyzed by 16S rDNA sequencing in this study. Several 1049 OTUs were harvested from a total of 130 183 reads. Tenericutes (68.4%) and Firmicutes (82.1%) are the most abundant phyla in P. punctata and C. melanaster, whereas C. capillata and A. coerulea share the same top phylum Proteobacteria (76.9% vs. 78.3%). The classified OTUs and bacterial abundance greatly decrease from the phylum to genus level. The top 20 matched genera only account for 9.03% of the total community in P. punctata, 48.9% in C. capillata, 83.05% in C. melanaster, and 58.1% in A. coerulea, respectively. The heatmap of the top 50 genera shows that the relative abundances in A. coerulea and C. capillata are far richer than that in P. punctata and C. melanaster. Moreover, a total of 41 predictive functional categories at KEGG level 2 were identified. Our study indicates the independent diversity of the bacterial communities in the four common Scyphomedusae, which might involve in the metabolism and environmental information processing of the hosts.

Crude oil contamination greatly influence soil bacterial community. Proliferative microbes in the crude oil-contaminated soil are closely related to the living conditions. Oil wells in the Yellow River Delta Natural Reserve (YRDNR) region is an ideal site for investigating the bacterial community of crude oil-contaminated saline soil. In the present study, 18 soil samples were collected from the depths of 0-20 cm and 20-40 cm around the oil wells in the YRDNR. The bacterial community profile was analyzed through high-throughput sequencing to trace the oil-degrading aerobic and anaerobic bacteria. The results indicated that C15-C28 and C29-C38 were the main fractions of total petroleum hydrocarbon (TPH) in the sampled soil. These TPH fractions had a significant negative effect on bacterial biodiversity (Shannon, Simpson, and Chao1 indices), which led to the proliferation of hydrocarbon-degrading bacteria. A comprehensive analysis between the environmental factors and soil microbial community structure showed that Streptococcus, Bacillus, Sphingomonas, and Arthrobacter were the aerobic hydrocarbon-degrading bacteria; unidentified Rhodobacteraceae and Porticoccus were considered to be the possible facultative anaerobic bacteria with hydrocarbon biodegradation ability; Acidithiobacillus, SAR324 clade, and Nitrosarchaeum were predicted to be the anaerobic hydrocarbon-degrading bacteria in the sub-surface soil. Furthermore, large amount of carbon sources derived from TPH was found to cause depletion of bioavailable nitrogen in the soil. The bacteria associated with nitrogen transformation, such as Solirubrobacter, Candidatus Udaeobacter, Lysinibacillus, Bradyrhizobium, Sphingomonas, Mycobacterium, and Acidithiobacillus, were highly abundant; these bacteria may possess the ability to increase nitrogen availability in the crude oil-contaminated soil. The bacterial community functions were significantly different between the surface and the sub-surface soil, and the dissolved oxygen concentration in soil was considered to be potential influencing factor. Our results could provide useful information for the bioremediation of crude oil-contaminated saline soil.

Mutans streptococci are a group of bacteria significantly contributing to tooth decay. Their genetic variability is however still not well understood.

Pseudomonas chlororaphis is a non-pathogenic, plant growth-promoting rhizobacterium that secretes phenazine compounds with broad-spectrum antibiotic activity. Currently available genome-editing methods for P. chlororaphis are based on homologous recombination (HR)-dependent allelic exchange, which requires both exogenous DNA repair proteins (e.g. λ-Red-like systems) and endogenous functions (e.g. RecA) for HR and/or providing donor DNA templates. In general, these procedures are time-consuming, laborious and inefficient. Here, we established a CRISPR-assisted base-editing (CBE) system based on the fusion of a rat cytidine deaminase (rAPOBEC1), enhanced-specificity Cas9 nickase (eSpCas9ppD10A ) and uracil DNA glycosylase inhibitor (UGI). This CBE system converts C:G into T:A without DNA strands breaks or any donor DNA template. By engineering a premature STOP codon in target spacers, the hmgA and phzO genes of P. chlororaphis were successfully interrupted at high efficiency. The phzO-inactivated strain obtained by base editing exhibited identical phenotypic features as compared with a mutant obtained by HR-based allelic exchange. The use of this CBE system was extended to other P. chlororaphis strains (subspecies LX24 and HT66) and also to P. fluorescens 10586, with an equally high editing efficiency. The wide applicability of this CBE method will accelerate bacterial physiology research and metabolic engineering of non-traditional bacterial hosts.

The dairy cow's postpartum period is characterized by dramatic physiological changes, therefore imposing severe challenges on the animal for maintaining health and milk output. The dynamics of the ruminal microbiota are also tremendous and may play a crucial role in lactation launch. We aim to investigate the potential benefits of early microbial intervention by fresh rumen microbiota transplantation (RMT) and sterile RMT in postpartum dairy cows. Twelve fistulated peak-lactation dairy cows were selected to be the donors for rumen fluid collection. Thirty postpartum cows were divided into 3 groups as the transplantation receptors respectively receiving 10 L fresh rumen fluid (FR), 10 L sterile rumen fluid (SR), or 10 L saline (CON) during 3 d after calving.

AOAN may provide enzymes to improve the digestibility of feeds and enhance rumen fermentation. This study determined the effects of AOAN on digestibility, fermentation characteristics, and bacterial composition using in vitro gas recording fermentation system. A total of 30 mg of AOAN was supplemented into 500 mg of TMR, corn silage, oat hay, and alfalfa hay. Fermentation parameters and bacterial communities were determined after 48 h fermentation, and digestibility was determined after 7, 24, 30, and 48 h fermentation. Gas production and dry matter (DM), crude protein (CP), neutral detergent fiber (NDF), and acid detergent fiber (ADF) digestibility were significantly increased by AOAN supplementation at 48 h (p < 0.05), except for digestibility of CP of the TMR (p > 0.05). AOAN increased starch digestibility in corn silage (p < 0.05) and tended to increase that in TMR (0.05 < p < 0.10). AOAN supplementation increased total volatile fatty acid production (p < 0.05). The molar proportions of acetate and acetate to propionate ratio of oat hay and alfalfa hay were increased (p < 0.05). The 16S rRNA analysis revealed that the microbial richness of TMR and oat hay, and microbial evenness of TMR were increased (p < 0.05). AOAN did not affect the α diversity, β diversity, and bacterial composition of the corn silage. The relative abundance of Prevotella was increased and Ruminococcus was decreased in TMR, oat hay, and alfalfa hay. In conclusion, results suggest that AOAN has the potential to improve the utilization of diets differently, including providing enzymes with changing microbiota (TMR, oat hay, and alfalfa hay) or providing enzymes alone (corn silage).

Mutans streptococci are a group of gram-positive bacteria including the primary cariogenic dental pathogen Streptococcus mutans and closely related species. Two component systems (TCSs) composed of a signal sensing histidine kinase (HK) and a response regulator (RR) play key roles in pathogenicity, but have not been comparatively studied for these oral bacterial pathogens.

Staphylococcus aureus is a worldwide leading cause of numerous diseases ranging from food-poisoning to lethal infections. Methicillin-resistant S. aureus (MRSA) has been found capable of acquiring resistance to most antimicrobials. MRSA is ubiquitous and diverse even in terms of antimicrobial resistance (AMR) profiles, posing a challenge for treatment. Here, we present a comprehensive study of S. aureus in China, addressing epidemiology, phylogenetic reconstruction, genomic characterization, and identification of AMR profiles. The study analyzes 673 S. aureus isolates from food as well as from hospitalized and healthy individuals. The isolates have been collected over a 9-year period, between 2010 and 2018, from 27 provinces across China. By whole-genome sequencing, Bayesian divergence analysis, and supervised machine learning, we reconstructed the phylogeny of the isolates and compared them to references from other countries. We identified 72 sequence types (STs), of which, 29 were novel. We found 81 MRSA lineages by multilocus sequence type (MLST), spa, staphylococcal cassette chromosome mec element (SCCmec), and Panton-Valentine leukocidin (PVL) typing. In addition, novel variants of SCCmec type IV hosting extra metal and antimicrobial resistance genes, as well as a new SCCmec type, were found. New Bayesian dating of the split times of major clades showed that ST9, ST59, and ST239 in China and European countries fell in different branches, whereas this pattern was not observed for the ST398 clone. On the contrary, the clonal transmission of ST398 was more intermixed in regard to geographic origin. Finally, we identified genetic determinants of resistance to 10 antimicrobials, discriminating drug-resistant bacteria from susceptible strains in the cohort. Our results reveal the emergence of Chinese MRSA lineages enriched of AMR determinants that share similar genetic traits of antimicrobial resistance across human and food, hinting at a complex scenario of evolving transmission routes. IMPORTANCE Little information is available on the epidemiology and characterization of Staphylococcus aureus in China. The role of food is a cause of major concern: staphylococcal foodborne diseases affect thousands every year, and the presence of resistant Staphylococcus strains on raw retail meat products is well documented. We studied a large heterogeneous data set of S. aureus isolates from many provinces of China, isolated from food as well as from individuals. Our large whole-genome collection represents a unique catalogue that can be easily meta-analyzed and integrated with further studies and adds to the library of S. aureus sequences in the public domain in a currently underrepresented geographical region. The new Bayesian dating of the split times of major drug-resistant enriched clones is relevant in showing that Chinese and European methicillin-resistant S. aureus (MRSA) have evolved differently. Our machine learning approach, across a large number of antibiotics, shows novel determinants underlying resistance and reveals frequent resistant traits in specific clonal complexes, highlighting the importance of particular clonal complexes in China. Our findings substantially expand what is known of the evolution and genetic determinants of resistance in food-associated S. aureus in China and add crucial information for whole-genome sequencing (WGS)-based surveillance of S. aureus.

Antimicrobial resistance (AMR) is becoming one of the largest threats to public health worldwide, with the opportunistic pathogen Escherichia coli playing a major role in the AMR global health crisis. Unravelling the complex interplay between drug resistance and metabolic rewiring is key to understand the ability of bacteria to adapt to new treatments and to the development of new effective solutions to combat resistant infections. We developed a computational pipeline that combines machine learning with genome-scale metabolic models (GSMs) to elucidate the systemic relationships between genetic determinants of resistance and metabolism beyond annotated drug resistance genes. Our approach was used to identify genetic determinants of 12 AMR profiles for the opportunistic pathogenic bacterium E. coli. Then, to interpret the large number of identified genetic determinants, we applied a constraint-based approach using the GSM to predict the effects of genetic changes on growth, metabolite yields, and reaction fluxes. Our computational platform leads to multiple results. First, our approach corroborates 225 known AMR-conferring genes, 35 of which are known for the specific antibiotic. Second, integration with the GSM predicted 20 top-ranked genetic determinants (including accA, metK, fabD, fabG, murG, lptG, mraY, folP, and glmM) essential for growth, while a further 17 top-ranked genetic determinants linked AMR to auxotrophic behavior. Third, clusters of AMR-conferring genes affecting similar metabolic processes are revealed, which strongly suggested that metabolic adaptations in cell wall, energy, iron and nucleotide metabolism are associated with AMR. The computational solution can be used to study other human and animal pathogens. IMPORTANCE Escherichia coli is a major public health concern given its increasing level of antibiotic resistance worldwide and extraordinary capacity to acquire and spread resistance via horizontal gene transfer with surrounding species and via mutations in its existing genome. E. coli also exhibits a large amount of metabolic pathway redundancy, which promotes resistance via metabolic adaptability. In this study, we developed a computational approach that integrates machine learning with metabolic modeling to understand the correlation between AMR and metabolic adaptation mechanisms in this model bacterium. Using our approach, we identified AMR genetic determinants associated with cell wall modifications for increased permeability, virulence factor manipulation of host immunity, reduction of oxidative stress toxicity, and changes to energy metabolism. Unravelling the complex interplay between antibiotic resistance and metabolic rewiring may open new opportunities to understand the ability of E. coli, and potentially of other human and animal pathogens, to adapt to new treatments.

Anthropogenic environments such as those created by intensive farming of livestock, have been proposed to provide ideal selection pressure for the emergence of antimicrobial-resistant Escherichia coli bacteria and antimicrobial resistance genes (ARGs) and spread to humans. Here, we performed a longitudinal study in a large-scale commercial poultry farm in China, collecting E. coli isolates from both farm and slaughterhouse; targeting animals, carcasses, workers and their households and environment. By using whole-genome phylogenetic analysis and network analysis based on single nucleotide polymorphisms (SNPs), we found highly interrelated non-pathogenic and pathogenic E. coli strains with phylogenetic intermixing, and a high prevalence of shared multidrug resistance profiles amongst livestock, human and environment. Through an original data processing pipeline which combines omics, machine learning, gene sharing network and mobile genetic elements analysis, we investigated the resistance to 26 different antimicrobials and identified 361 genes associated to antimicrobial resistance (AMR) phenotypes; 58 of these were known AMR-associated genes and 35 were associated to multidrug resistance. We uncovered an extensive network of genes, correlated to AMR phenotypes, shared among livestock, humans, farm and slaughterhouse environments. We also found several human, livestock and environmental isolates sharing closely related mobile genetic elements carrying ARGs across host species and environments. In a scenario where no consensus exists on how antibiotic use in the livestock may affect antibiotic resistance in the human population, our findings provide novel insights into the broader epidemiology of antimicrobial resistance in livestock farming. Moreover, our original data analysis method has the potential to uncover AMR transmission pathways when applied to the study of other pathogens active in other anthropogenic environments characterised by complex interconnections between host species.

Identification of specific genes and gene expression patterns important for bacterial survival, transmission and pathogenesis is critically needed to enable development of more effective pathogen control strategies. The stationary phase stress response transcriptome, including many sigmaB-dependent genes, was defined for the human bacterial pathogen Listeria monocytogenes using RNA sequencing (RNA-Seq) with the Illumina Genome Analyzer. Specifically, bacterial transcriptomes were compared between stationary phase cells of L. monocytogenes 10403S and an otherwise isogenic DeltasigB mutant, which does not express the alternative sigma factor sigmaB, a major regulator of genes contributing to stress response, including stresses encountered upon entry into stationary phase.

The expression and localization of different isoforms of creatine kinase in Pelodiscus sinensis (PSCK) were studied to reveal the role of PSCK isozymes (PSCK-B, PSCK-M, PSCK-S) under bacterial infection-induced immunologic stress. The computational molecular dynamics simulations predicted that PSCK-S would mostly possess a kinase function in a structural aspect when compared to PSCK-B and PSCK-M. The assay of biochemical parameters such as total superoxide dismutase (T-SOD), lactate dehydrogenase (LDH), malondialdehyde (MDA), catalase (CAT), and the content of ATP were measured along with total PSCK activity in different tissue samples under bacterial infection. The expression detections of PSCK isozymes in vitro and in vivo were overall well-matched where PSCK isozymes were expressed differently in P. sinensis tissues. The results showed that PSCK-B mostly contributes to the spleen, followed by the liver and myocardium; PSCK-M mostly contributes to the liver, followed by the myocardium and skeletal muscle, while PSCK-S contributes to the spleen and is uniquely expressed in skeletal muscle. Our study suggests that the various alterations of PSCK isozymes in tissues of P. sinensis are prone to defense the bacterial infection and blocking energetic imbalance before severe pathogenesis turned on in P. sinensis.

Co-occurrence of hypervirulence and KPC-2 carbapenem resistant phenotypes in a highly-transmissible ST11 clone ofKlebsiella pneumoniae has elicited deep concerns from public health stand point. To address this puzzle, we conducted a large-scale epidemiological, clinical and genomic study of K. pneumonia ST11 clones with both hypervirulence and carbapenem resistance in two tertiary hospitals in Zhejiang province. Most of the patients (15/23) were diagnosed with exclusively carbapenem-resistant K. pneumoniae (CRKP) infections. Ten death cases were reported, some of which are due to the failure of antibiotic therapies. As a result, we identified one new rare sequence types (ST449) to KPC-2-producing CRKP, in addition to the dominant ST11. These clinical isolates of K. pneumoniae are multi-drug resistant and possess a number of virulence factors. Experimental infections of wax moth larvae revealed the presence of hypervirulence at varied level, suggesting the complexity in bacterial virulence factors. However, plasmid curing assays further suggested that the rmpA2-virulence plasmid is associated with, but not sufficient for neither phenotypic hypermucoviscosity nor virulence of K. pneumoniae. Intriguingly, all the rmpA2 genes were found to be inactive due to genetic deletion. In total, we reported 21 complete plasmid sequences comprising 13 rmpA2-positive virulence plasmids and 8 blaKPC-2-harboring resistance plasmids. In addition to the prevalent pLVKP-like virulence plasmid variants (~178kb), we found an unexpected diversity among KPC-2-producing plasmids whose dominant form is IncFII-IncR type (~120kb), rather than the previously anticipated version of ~170kb. These findings provide an updated snapshot of convergence of hypervirulence and carbapenem resistance in ST11 K. pneumoniae.

Bacterial plant pathogens are very harmful to their host plants, which can cause devastating agricultural losses in the world. With the development of microbial genome sequencing, many strains of phytopathogens have been sequenced. However, some misannotations exist in these phytopathogen genomes. Our objective is to improve these annotations and store them in a central database DIGAP.

In the absence of practical and reliable predictors for whether the endocervical curettage (ECC) procedure should be performed, decisions regarding patient selection are usually based on the colposcopists' clinical judgment instead of evidence. We aimed to develop and validate a practical prediction model that uses available information to reliably estimate the need to perform ECC in patients suspected of having cervical lesions.

Calf diarrhea is one of the common diseases involved in the process of calf feeding. In this study, a sample of calf diarrhea that tested positive for bovine coronavirus and bovine astrovirus was subjected to high-throughput sequencing. The reassembly revealed the complete genomes of bovine norovirus, bovine astrovirus, bovine kobuvirus, and the S gene of bovine coronavirus. Phylogenetic analysis showed that the ORF2 region of bovine astrovirus had the lowest similarity with other strains and gathered in the Mamastrovirus unclassified genogroup, suggesting a new serotype/genotype could appear. Compared with the most closely related strain, there are six amino acid mutation sites in the S gene of bovine coronavirus, most of which are located in the S1 subunit region. The bovine norovirus identified in our study was BNoV-GIII 2, based on the VP1 sequences. The bovine kobuvirus is distributed in the Aichi virus B genus; the P1 gene shows as highly variable, while the 3D gene is highly conserved. These findings enriched our knowledge of the viruses in the role of calf diarrhea, and help to develop an effective strategy for disease prevention and control.

AI, or artificial intelligence, is a technology of creating algorithms and computer systems that mimic human cognitive abilities to perform tasks. Many industries are undergoing revolutions due to the advances and applications of AI technology. The current study explored a burgeoning field-Psychometric AI, which integrates AI methodologies and psychological measurement to not only improve measurement accuracy, efficiency, and effectiveness but also help reduce human bias and increase objectivity in measurement. Specifically, by leveraging unobtrusive eye-tracking sensing techniques and performing 1470 runs with seven different machine-learning classifiers, the current study systematically examined the efficacy of various (ML) models in measuring different facets and measures of the emotional intelligence (EI) construct. Our results revealed an average accuracy ranging from 50-90%, largely depending on the percentile to dichotomize the EI scores. More importantly, our study found that AI algorithms were powerful enough to achieve high accuracy with as little as 5 or 2 s of eye-tracking data. The research also explored the effects of EI facets/measures on ML measurement accuracy and identified many eye-tracking features most predictive of EI scores. Both theoretical and practical implications are discussed.

A scarcity of functionally validated enhancers in the human genome presents a significant hurdle to understanding how these cis-regulatory elements contribute to human diseases. We carry out highly multiplexed CRISPR-based perturbation and sequencing to identify enhancers required for cell proliferation and fitness in 10 human cancer cell lines. Our results suggest that the cell fitness enhancers, unlike their target genes, display high cell-type specificity of chromatin features. They typically adopt a modular structure, comprised of activating elements enriched for motifs of oncogenic transcription factors, surrounded by repressive elements enriched for motifs recognized by transcription factors with tumor suppressor functions. We further identify cell fitness enhancers that are selectively accessible in clinical tumor samples, and the levels of chromatin accessibility are associated with patient survival. These results reveal functional enhancers across multiple cancer cell lines, characterize their context-dependent chromatin organization, and yield insights into altered transcription programs in cancer cells.