Swing Trend Prediction of Main Guide Bearing in Hydropower Units Based on MFS-DCGNN.

Hydropower units are the core equipment of hydropower stations, and research on the fault prediction and health management of these units can help improve their safety, stability, and the level of reliable operation and can effectively reduce costs. Therefore, it is necessary to predict the swing trend of these units. Firstly, this study considers the influence of various factors, such as electrical, mechanical, and hydraulic swing factors, on the swing signal of the main guide bearing y-axis. Before swing trend prediction, the multi-index feature selection algorithm is used to obtain suitable state variables, and the low-dimensional effective feature subset is obtained using the Pearson correlation coefficient and distance correlation coefficient algorithms. Secondly, the dilated convolution graph neural network (DCGNN) algorithm, with a dilated convolution graph, is used to predict the swing trend of the main guide bearing. Existing GNN methods rely heavily on predefined graph structures for prediction. The DCGNN algorithm can solve the problem of spatial dependence between variables without defining the graph structure and provides the adjacency matrix of the graph learning layer simulation, avoiding the over-smoothing problem often seen in graph convolutional networks; furthermore, it effectively improves the prediction accuracy. The experimental results showed that, compared with the RNN-GRU, LSTNet, and TAP-LSTM algorithms, the MAEs of the DCGNN algorithm decreased by 6.05%, 6.32%, and 3.04%; the RMSEs decreased by 9.21%, 9.01%, and 2.83%; and the CORR values increased by 0.63%, 1.05%, and 0.37%, respectively. Thus, the prediction accuracy was effectively improved.

PSO-MCKD-MFFResnet based fault diagnosis algorithm for hydropower units.

Due to the coupling effect of external environmental noise and vibration noise, the feature rate of the original hydroelectric unit fault signal is not prominent, which will affect the performance of fault diagnosis algorithms. To solve the above problems, this paper proposes a PSO-MCKD-MFFResnet algorithm for fault diagnosis of hydropower units (Particle swarm optimization, PSO; Maximum correlation kurtosis deconvolution, MCKD; Multi-scale feature fusion residual network, MFFResnet). In practical applications, the selection of key parameters in the traditional MCKD method is heavily dependent on prior knowledge. First, this paper proposes a PSO-MCKD enhancement algorithm for fault features, which uses the PSO algorithm to search for the influencing parameters of MCKD to enhance the features from the original fault signal. Second, a fault feature diagnosis algorithm based on MFFResnet is proposed to improve the utilization of local features. The multi-scale residual module is used to extract features at different scales and then put the enhanced signal into MFFResnet for training and classification. The experimental results show that our approach can accurately and effectively classify the fault types of hydropower units, with an accuracy rate of 98.85. It is superior to other representative algorithms in different indicators and has a good stability.

Effect of an Adaptive-Density Filling Structure on the Mechanical Properties of FDM Parts with a Variable Cross-Section.

Fused deposition modeling (FDM) technique is one of the most popular additive manufacturing techniques. Infill density is a critical factor influencing the mechanical properties of 3D-printed components using the FDM technique. For irregular components with variable cross-sections, to increase their overall mechanical properties while maintaining a lightweight, it is necessary to enhance the local infill density of the thin part while decreasing the infill density of the thick part. However, most current slicing software can only generate a uniform infill throughout one model to be printed and cannot adaptively create a filling structure with a varying infill density according to the dimensional variation of the cross-section. In the present study, to improve the mechanical properties of irregular components with variable cross-sections, an adaptive-density filling structure was proposed, in which Hilbert curve with the same order was used to fill each slice, i.e., the level of the Hilbert curves in each slice is the same, but the side length of the Hilbert curve decreases with the decreasing size of each slice; hence, the infill density of the smaller cross-section is greater than that of the larger cross-section. The ultimate bearing capacity of printed specimens with the adaptive-density filling structure was evaluated by quasi-static compression, three-point bending, and dynamic compression tests, and the printed specimens with uniform filling structure and the same overall infill density were tested for comparison. The results show that the maximum flexural load, the ultimate compression load, and the maximum impact resistance of the printed specimens with the adaptive-density filling structure were increased by 140%, 47%, and 82%, respectively, compared with their counterparts using the uniform filling structure.

Metagenomic Insights into Pathogenic Characterization of ST410 Acinetobacter nosocomialis Prevalent in China.

Acinetobacter nosocomialis is a prevalent opportunistic pathogen that causes hospital-acquired infections. The increasing threats from A. nosocomialis infections have led to attention from the scientific and medical communities. Metagenomic next-generation sequencing (mNGS) was performed for an exudate specimen collected from an ICU patient with wound infection, followed by sepsis, in Tongji Hospital. Three assembly strategies were employed to recover the genome of A. nosocomialis in the metagenomic sample. Together with publicly available genomes of A. nosocomialis, the features of population genetics and molecular epidemiology were deeply analyzed. A draft genome was reconstructed for the metagenomic strain WHM01, derived from the ST410 A. nosocomialis dominating the microbial community, thereby prompting its highly pathogenic risk, which is associated with infection and persistence. The structure of the bacterial pangenome was characterized, including the 1862 core and 11,815 accessory genes present in the 157 strains. The genetic diversity of the genes coding for the 128 virulence factors assigned to 14 functional categories was uncovered in this nosocomial pathogen, such as the lipooligosaccharide, capsule, type IV pilus, and outer membrane proteins. Our work revealed genomic properties of ST410 A. nosocomialis, which is prevalent in China, and further highlighted that metagenomic surveillance may be a prospective application for evaluating the pathogenic characteristics of the nosocomial opportunistic pathogens.

Whole Genome Profiling of Lung Microbiome in Solid Organ Transplant Recipients Reveals Virus Involved Microecology May Worsen Prognosis.

Solid organ transplantation (SOT) is the final therapeutic option for recipients with end-stage organ failure, and its long-term success is limited by infections and chronic allograft dysfunction. Viral infection in SOT recipients is considered an important factor affecting prognosis. In this study, we retrospectively analyzed 43 cases of respiratory infections in SOT recipients using metagenomic next-generation sequencing (mNGS) for bronchoalveolar lavage fluid (BALF). At least one virus was detected in 26 (60.5%) recipients, while 17 (39.5%) were virus-negative. Among virus-positive recipients, cytomegalovirus (CMV) was detected in 14 (32.6%), Torque teno virus (TTV) was detected in 9 (20.9%), and other viruses were detected in 6 (14.0%). Prognostic analysis showed that the mortality of the virus-positive group was higher than that of the virus-negative group regardless whether it is the main cause of infection. Analysis of different types of viruses showed that the mortality of the CMV-positive group was significantly higher than that of the CMV-negative group, but no significant difference was observed in other type of virus groups. The diversity analysis of the lung microbiome showed that there was a significant difference between the virus-positive group and the negative group, in particular, the significant differences in microorganisms such as Pneumocystis jirovecii (PJP) and Moraxella osloensiswere detected. Moreover, in the presence of CMV, Pneumocystis jirovecii, Veillonella parvula, and other species showed dramatic changes in the lung of SOT patients, implying that high degree of co-infection between CMV and Pneumocystis jirovecii may occur. Taken together, our study shows that the presence of virus is associated with worse prognosis and dramatically altered lung microbiota in SOT recipients.

Antimicrobial resistance and population genomics of multidrug-resistant Escherichia coli in pig farms in mainland China.

The expanding use of antimicrobials in livestock is an important contributor to the worldwide rapid increase in antimicrobial resistance (AMR). However, large-scale studies on AMR in livestock remain scarce. Here, we report findings from surveillance of E. coli AMR in pig farms in China in 2018-2019. We isolated E. coli in 1,871 samples from pigs and their breeding environments, and found AMR in E. coli in all provinces in mainland China. We detected multidrug-resistance in 91% isolates and found resistance to last-resort drugs including colistin, carbapenems and tigecycline. We also identified a heterogeneous group of O-serogroups and sequence types among the multidrug-resistant isolates. These isolates harbored multiple resistance genes, virulence factor-encoding genes, and putative plasmids. Our data will help to understand the current AMR profiles of pigs and provide a reference for AMR control policy formulation for livestock in China.

Metagenomic Approaches Reveal Strain Profiling and Genotyping of Klebsiella pneumoniae from Hospitalized Patients in China.

Klebsiella pneumoniae is a leading cause of highly drug-resistant infections in hospitals worldwide. Strain-level bacterial identification on the genetic determinants of multidrug resistance and high pathogenicity is critical for the surveillance and treatment of this clinically relevant pathogen. In this study, metagenomic next-generation sequencing was performed for specimens collected from August 2020 to May 2021 in Ruijin Hospital, Ningbo Women and Children's Hospital, and the Second Affiliated Hospital of Harbin Medical University. Genome biology of K. pneumoniae prevalent in China was characterized based on metagenomic data. Thirty K. pneumoniae strains derived from 14 sequence types were identified by multilocus sequence typing. The hypervirulent ST11 K. pneumoniae strains carrying the KL64 capsular locus were the most prevalent in the hospital population. The phylogenomic analyses revealed that the metagenome-reconstructed strains and public isolate genomes belonging to the same STs were closely related in the phylogenetic tree. Furthermore, the pangenome structure of the detected K. pneumoniae strains was analyzed, particularly focusing on the distribution of antimicrobial resistance genes and virulence genes across the strains. The genes encoding carbapenemases and extended-spectrum beta-lactamases were frequently detected in the strains of ST11 and ST15. The highest numbers of virulence genes were identified in the well-known hypervirulent strains affiliated to ST23 bearing the K1 capsule. In comparison to traditional cultivation and identification, strain-level metagenomics is advantageous to understand the mechanisms underlying resistance and virulence of K. pneumoniae directly from clinical specimens. Our findings should provide novel clues for future research into culture-independent metagenomic surveillance for bacterial pathogens. IMPORTANCE Routine culture and PCR-based molecular testing in the clinical microbiology laboratory are unable to recognize pathogens at the strain level and to detect strain-specific genetic determinants involved in virulence and resistance. To address this issue, we explored the strain-level profiling of K. pneumoniae prevalent in China based on metagenome-sequenced patient materials. Genome biology of the targeted bacterium can be well characterized through decoding sequence signatures and functional gene profiles at the single-strain resolution. The in-depth metagenomic analysis on strain profiling presented here shall provide a promising perspective for culture-free pathogen surveillance and molecular epidemiology of nosocomial infections.

Aspergillus fumigatus versus Genus Aspergillus: Conservation, Adaptive Evolution and Specific Virulence Genes.

Aspergillus is an important fungal genus containing economically important species, as well as pathogenic species of animals and plants. Using eighteen fungal species of the genus Aspergillus, we conducted a comprehensive investigation of conserved genes and their evolution. This also allows us to investigate the selection pressure driving the adaptive evolution in the pathogenic species A. fumigatus. Among single-copy orthologs (SCOs) for A. fumigatus and the closely related species A. fischeri, we identified 122 versus 50 positively selected genes (PSGs), respectively. Moreover, twenty conserved genes of unknown function were established to be positively selected and thus important for adaption. A. fumigatus PSGs interacting with human host proteins show over-representation of adaptive, symbiosis-related, immunomodulatory and virulence-related pathways, such as the TGF-ß pathway, insulin receptor signaling, IL1 pathway and interfering with phagosomal GTPase signaling. Additionally, among the virulence factor coding genes, secretory and membrane protein-coding genes in multi-copy gene families, 212 genes underwent positive selection and also suggest increased adaptation, such as fungal immune evasion mechanisms (aspf2), siderophore biosynthesis (sidD), fumarylalanine production (sidE), stress tolerance (atfA) and thermotolerance (sodA). These genes presumably contribute to host adaptation strategies. Genes for the biosynthesis of gliotoxin are shared among all the close relatives of A. fumigatus as an ancient defense mechanism. Positive selection plays a crucial role in the adaptive evolution of A. fumigatus. The genome-wide profile of PSGs provides valuable targets for further research on the mechanisms of immune evasion, antimycotic targeting and understanding fundamental virulence processes.

Characterization of Metagenome-Assembled Genomes and Carbohydrate-Degrading Genes in the Gut Microbiota of Tibetan Pig.

Tibetan pig is an important domestic mammal, providing products of high nutritional value for millions of people living in the Qinghai-Tibet Plateau. The genomes of mammalian gut microbiota encode a large number of carbohydrate-active enzymes, which are essential for the digestion of complex polysaccharides through fermentation. However, the current understanding of microbial degradation of dietary carbohydrates in the Tibetan pig gut is limited. In this study, we produced approximately 145 gigabases of metagenomic sequence data for the fecal samples from 11 Tibetan pigs. De novo assembly and binning recovered 322 metagenome-assembled genomes taxonomically assigned to 11 bacterial phyla and two archaeal phyla. Of these genomes, 191 represented the uncultivated microbes derived from novel prokaryotic taxa. Twenty-three genomes were identified as metagenomic biomarkers that were significantly abundant in the gut ecosystem of Tibetan pigs compared to the other low-altitude relatives. Further, over 13,000 carbohydrate-degrading genes were identified, and these genes were more abundant in some of the genomes within the five principal phyla: Firmicutes, Bacteroidetes, Spirochaetota, Verrucomicrobiota, and Fibrobacterota. Particularly, three genomes representing the uncultivated Verrucomicrobiota encode the most abundant degradative enzymes in the fecal microbiota of Tibetan pigs. These findings should substantially increase the phylogenetic diversity of specific taxonomic clades in the microbial tree of life and provide an expanded repertoire of biomass-degrading genes for future application to microbial production of industrial enzymes.

Extreme Interval Entropy Based on Symbolic Analysis and a Self-Adaptive Method.

Self-adaptive methods are recognized as important tools in signal process and analysis. A signal can be decomposed into a serious of new components with these mentioned methods, thus the amount of information is also increased. In order to use these components effectively, a feature set is used to describe them. With the development of pattern recognition, the analysis of self-adaptive components is becoming more intelligent and depend on feature sets. Thus, a new feature is proposed to express the signal based on the hidden property between extreme values. In this investigation, the components are first simplified through a symbolization method. The entropy analysis is incorporated into the establishment of the characteristics to describe those self-adaptive decomposition components according to the relationship between extreme values. Subsequently, Extreme Interval Entropy is proposed and used to realize the pattern recognition, with two typical self-adaptive methods, based on both Empirical Mode Decomposition (EMD) and Empirical Wavelet Transform (EWT). Later, extreme interval entropy is applied in two fault diagnosis experiments. One experiment is the fault diagnosis for rolling bearings with both different faults and damage degrees, the other experiment is about rolling bearing in a printing press. The effectiveness of the proposed method is evaluated in both experiments with K-means cluster. The accuracy rate of the fault diagnosis in rolling bearing is in the range of 75% through 100% using EMD, 95% through 100% using EWT. In the printing press experiment, the proposed method can reach 100% using EWT to distinguish the normal bearing (but cannot distinguish normal samples at different speeds), with fault bearing in 4 r/s and in 8 r/s. The fault samples are identified only according to a single proposed feature with EMD and EWT. Therefore, the extreme interval entropy is proved to be a reliable and effective tool for fault diagnosis and other similar applications.

Genetic and Phylogenetic Characteristics of Pasteurella multocida Isolates From Different Host Species.

Pasteurella multocida is a leading cause of respiratory diseases in many host species. To understand the genetic characteristics of P. multocida strains isolated from different host species, we sequenced the genomic DNA of P. multocida isolated from pigs and analyzed the genetic characteristics of strains from avian species, bovine species, pigs, and rabbits using whole genome sequence (WGS) data. Our results found that a capsular: lipopolysaccharide (LPS): multilocus sequence typing (MLST) genotype A: L1: ST129 (43.75%) was predominant in avian P. multocida; while genotypes B: L2: ST122 (60.00%) and A: L3: ST79 (30.00%) were predominate in bovine P. multocida; genotype D: L6: ST50 (37.50%) in porcine P. multocida; and genotype A: L3: ST9 (76.47%) in rabbit P. multocida. Comparative genomic analysis of P. multocida from different host species found that there are no genes in the P. multocida genome that are specific to any type of host. Phylogenetic analysis using either whole-genome single nucleotide polymorphisms (SNPs) or the set of SNPs present in all single-copy core genes across genomes showed that P. multocida strains with the same LPS genotype and MLST genotype were clustered together, suggesting the combining both the LPS and MLST typing schemes better explained the topology seen in the P. multocida phylogeny.

Identification of drug target candidates of the swine pathogen Actinobacillus pleuropneumoniae by construction of protein-protein interaction network.

Porcine pleuropneumonia caused by Actinobacillus pleuropneumoniae has led to severe economic losses in the pig industry worldwide. A. pleuropneumoniae displays various levels of antimicrobial resistance, leading to the dire need to identify new drug targets. Protein-protein interaction (PPI) network can aid the identification of drug targets by discovering essential proteins during the life of bacteria. The aim of this study is to identify drug target candidates of A. pleuropneumoniae from essential proteins in PPI network. The homologous protein mapping method (HPM) was utilized to construct A. pleuropneumoniae PPI network. Afterwards, the subnetwork centered with H-NS was selected to verify the PPI network using bacterial two-hybrid assays. Drug target candidates were identified from the hub proteins by analyzing the topology of the network using interaction degree and homologous comparison with the pig proteome. An A. pleuropneumoniae PPI network containing 2737 non-redundant interaction pairs among 533 proteins was constructed. These proteins were distributed in 21 COG functional categories and 28 KEGG metabolic pathways. The A. pleuropneumoniae PPI network was scale free and the similar topological tendencies were found when compared with other bacteria PPI network. Furthermore, 56.3% of the H-NS subnetwork interactions were validated. 57 highly connected proteins (hub proteins) were identified from the A. pleuropneumoniae PPI network. Finally, 9 potential drug targets were identified from the hub proteins, with no homologs in swine. This study provides drug target candidates, which are promising for further investigations to explore lead compounds against A. pleuropneumoniae.

Functional characterization of murB-potABCD operon for polyamine uptake and peptidoglycan synthesis in Streptococcus suis.

Spermidine (Spd), spermine (Spm), and putrescine (Put), which are the most widely distributed cellular polyamines, are essential for normal growth and multiplication of both eukaryotic and prokaryotic cells. In this study, we identified the only putative polyamine transport system PotABCD in Streptococcus suis, a worldwide zoonotic Gram-positive pathogen causing lethal infections in humans and pigs. It was discovered that S. suis could uptake polyamines preferably Spd and Spm. By constructing a potA deleted mutant, we confirmed that PotABCD was responsible for polyamine uptake, and PotD bound to the protein of polyamines. The four PotABCD genes were co-transcribed with murB, a gene involved in peptidoglycan (PG) synthesis. Furthermore the roles of polyamine transport system in maintaining the PG structure were detected to understand the biological significance of this co-transcription. In contrast to the wild type, the mutant ΔpotA exhibited elongated chain length and abnormal cell division morphology. Phenotypic changes were attributed to be the up-regulation of genes involved in PG synthesis and hydrolysis in ΔpotA. Additionally, polyamines functioned not only as feedback regulators of PotA by inhibiting PotA activity but also as regulators on potABCD and genes involved in PG synthesis. This study reveals the functions of PotABCD in polyamine transport and the regulatory roles of polyamines in PG synthesis. Results provide new insights into the machineries contributing to normal growth and cell division of S. suis.

Dynamic regulation of EZH2 from HPSc to hepatocyte-like cell fate.

Currently, drug metabolization and toxicity studies rely on the use of primary human hepatocytes and hepatoma cell lines, which both have conceivable limitations. Human pluripotent stem cell (hPSC)-derived hepatocyte-like cells (HLCs) are an alternative and valuable source of hepatocytes that can overcome these limitations. EZH2 (enhancer of zeste homolog 2), a transcriptional repressor of the polycomb repressive complex 2 (PRC2), may play an important role in hepatocyte development, but its role during in vitro hPSC-HLC differentiation has not yet been assessed. We here demonstrate dynamic regulation of EZH2 during hepatic differentiation of hPSC. To enhance EZH2 expression, we inducibly overexpressed EZH2 between d0 and d8, demonstrating a significant improvement in definitive endoderm formation, and improved generation of HLCs. Despite induction of EZH2 overexpression until d8, EZH2 transcript and protein levels decreased from d4 onwards, which might be caused by expression of microRNAs predicted to inhibit EZH2 expression. In conclusion, our studies demonstrate that EZH2 plays a role in endoderm formation and hepatocyte differentiation, but its expression is tightly post-transcriptionally regulated during this process.

Genome characterization of a novel binary toxin-positive strain of Clostridium difficile and comparison with the epidemic 027 and 078 strains.

BACKGROUND: Clostridium difficile is an anaerobic Gram-positive spore-forming gut pathogen that causes antibiotic-associated diarrhea worldwide. A small number of C. difficile strains express the binary toxin (CDT), which is generally found in C. difficile 027 (ST1) and/or 078 (ST11) in clinic. However, we isolated a binary toxin-positive non-027, non-078 C. difficile LC693 that is associated with severe diarrhea in China. The genotype of this strain was determined as ST201. To understand the pathogenesis-basis of C. difficile ST201, the strain LC693 was chosen for whole genome sequencing, and its genome sequence was analyzed together with the other two ST201 strains VL-0104 and VL-0391 and compared to the epidemic 027/ST1 and 078/ST11 strains. RESULTS: The project finally generated an estimated genome size of approximately 4.07 Mbp for strain LC693. Genome size of the three ST201 strains ranged from 4.07 to 4.16 Mb, with an average GC content between 28.5 and 28.9%. Phylogenetic analysis demonstrated that the ST201 strains belonged to clade 3. The ST201 genomes contained more than 40 antibiotic resistance genes and 15 of them were predicted to be associated with vancomycin-resistance. The ST201 strains contained a larger PaLoc with a Tn6218 element inserted than the 027/ST1 and 078/ST11 strains, and encoded a truncated TcdC. In addition, the ST201 strains contained intact binary toxin coding and regulation genes which are highly homologous to the 027/ST1 strain. Genome comparison of the ST201 strains with the epidemic 027 and 078 strain identified 641 genes specific for C. difficile ST201, and a number of them were predicted as fitness and virulence associated genes. The presence of those genes also contributes to the pathogenesis of the ST201 strains. CONCLUSIONS: In this study, the genomic characterization of three binary toxin-positive C. difficile ST201 strains in clade 3 was discussed and compared to the genomes of the epidemic 027 and the 078 strains. Our analysis identified a number fitness and virulence associated genes/loci in the ST201 genomes that contribute to the pathogenesis of C. difficile ST201.

Genome-Wide Analyses Reveal Genes Subject to Positive Selection in Pasteurella multocida.

Pasteurella multocida, a Gram-negative opportunistic pathogen, has led to a broad range of diseases in mammals and birds, including fowl cholera in poultry, pneumonia and atrophic rhinitis in swine and rabbit, hemorrhagic septicemia in cattle, and bite infections in humans. In order to better interpret the genetic diversity and adaptation evolution of this pathogen, seven genomes of P. multocida strains isolated from fowls, rabbit and pigs were determined by using high-throughput sequencing approach. Together with publicly available P. multocida genomes, evolutionary features were systematically analyzed in this study. Clustering of 70,565 protein-coding genes showed that the pangenome of 33 P. multocida strains was composed of 1,602 core genes, 1,364 dispensable genes, and 1,070 strain-specific genes. Of these, we identified a full spectrum of genes related to virulence factors and revealed genetic diversity of these potential virulence markers across P. multocida strains, e.g., bcbAB, fcbC, lipA, bexDCA, ctrCD, lgtA, lgtC, lic2A involved in biogenesis of surface polysaccharides, hsf encoding autotransporter adhesin, and fhaB encoding filamentous haemagglutinin. Furthermore, based on genome-wide positive selection scanning, a total of 35 genes were subject to strong selection pressure. Extensive analyses of protein subcellular location indicated that membrane-associated genes were highly abundant among all positively selected genes. The detected amino acid sites undergoing adaptive selection were preferably located in extracellular space, perhaps associated with bacterial evasion of host immune responses. Our findings shed more light on conservation and distribution of virulence-associated genes across P. multocida strains. Meanwhile, this study provides a genetic context for future researches on the mechanism of adaptive evolution in P. multocida.

The Eukaryote-Like Serine/Threonine Kinase STK Regulates the Growth and Metabolism of Zoonotic Streptococcus suis.

Like eukaryotes, bacteria express one or more serine/threonine kinases (STKs) that initiate diverse signaling networks. The STK from Streptococcus suis is encoded by a single-copy stk gene, which is crucial in stress response and virulence. To further understand the regulatory mechanism of STK in S. suis, a stk deletion strain (Δstk) and its complementary strain (CΔstk) were constructed to systematically decode STK characteristics by applying whole transcriptome RNA sequencing (RNA-Seq) and phosphoproteomic analysis. Numerous genes were differentially expressed in Δstk compared with the wild-type parental strain SC-19, including 320 up-regulated and 219 down-regulated genes. Particularly, 32 virulence-associated genes (VAGs) were significantly down-regulated in Δstk. Seven metabolic pathways relevant to bacterial central metabolism and translation are significantly repressed in Δstk. Phosphoproteomic analysis further identified 12 phosphoproteins that exhibit differential phosphorylation in Δstk. These proteins are associated with cell growth and division, glycolysis, and translation. Consistently, phenotypic assays confirmed that the Δstk strain displayed deficient growth and attenuated pathogenicity. Thus, STK is a central regulator that plays an important role in cell growth and division, as well as S. suis metabolism.

Distinct Molecular Signature of Murine Fetal Liver and Adult Hematopoietic Stem Cells Identify Novel Regulators of Hematopoietic Stem Cell Function.

During ontogeny, fetal liver (FL) acts as a major site for hematopoietic stem cell (HSC) maturation and expansion, whereas HSCs in the adult bone marrow (ABM) are largely quiescent. HSCs in the FL possess faster repopulation capacity as compared with ABM HSCs. However, the molecular mechanism regulating the greater self-renewal potential of FL HSCs has not yet extensively been assessed. Recently, we published RNA sequencing-based gene expression analysis on FL HSCs from 14.5-day mouse embryo (E14.5) in comparison to the ABM HSCs. We reanalyzed these data to identify key transcriptional regulators that play important roles in the expansion of HSCs during development. The comparison of FL E14.5 with ABM HSCs identified more than 1,400 differentially expressed genes. More than 200 genes were shortlisted based on the gene ontology (GO) annotation term "transcription." By morpholino-based knockdown studies in zebrafish, we assessed the function of 18 of these regulators, previously not associated with HSC proliferation. Our studies identified a previously unknown role for tdg, uhrf1, uchl5, and ncoa1 in the emergence of definitive hematopoiesis in zebrafish. In conclusion, we demonstrate that identification of genes involved in transcriptional regulation differentially expressed between expanding FL HSCs and quiescent ABM HSCs, uncovers novel regulators of HSC function.

A Transcriptome Map of Actinobacillus pleuropneumoniae at Single-Nucleotide Resolution Using Deep RNA-Seq.

Actinobacillus pleuropneumoniae is the pathogen of porcine contagious pleuropneumoniae, a highly contagious respiratory disease of swine. Although the genome of A. pleuropneumoniae was sequenced several years ago, limited information is available on the genome-wide transcriptional analysis to accurately annotate the gene structures and regulatory elements. High-throughput RNA sequencing (RNA-seq) has been applied to study the transcriptional landscape of bacteria, which can efficiently and accurately identify gene expression regions and unknown transcriptional units, especially small non-coding RNAs (sRNAs), UTRs and regulatory regions. The aim of this study is to comprehensively analyze the transcriptome of A. pleuropneumoniae by RNA-seq in order to improve the existing genome annotation and promote our understanding of A. pleuropneumoniae gene structures and RNA-based regulation. In this study, we utilized RNA-seq to construct a single nucleotide resolution transcriptome map of A. pleuropneumoniae. More than 3.8 million high-quality reads (average length ~90 bp) from a cDNA library were generated and aligned to the reference genome. We identified 32 open reading frames encoding novel proteins that were mis-annotated in the previous genome annotations. The start sites for 35 genes based on the current genome annotation were corrected. Furthermore, 51 sRNAs in the A. pleuropneumoniae genome were discovered, of which 40 sRNAs were never reported in previous studies. The transcriptome map also enabled visualization of 5'- and 3'-UTR regions, in which contained 11 sRNAs. In addition, 351 operons covering 1230 genes throughout the whole genome were identified. The RNA-Seq based transcriptome map validated annotated genes and corrected annotations of open reading frames in the genome, and led to the identification of many functional elements (e.g. regions encoding novel proteins, non-coding sRNAs and operon structures). The transcriptional units described in this study provide a foundation for future studies concerning the gene functions and the transcriptional regulatory architectures of this pathogen.

Highly proliferative primitive fetal liver hematopoietic stem cells are fueled by oxidative metabolic pathways.

Hematopoietic stem cells (HSCs) in the fetal liver (FL) unlike adult bone marrow (BM) proliferate extensively, posing different metabolic demands. However, metabolic pathways responsible for the production of energy and cellular building blocks in FL HSCs have not been described. Here, we report that FL HSCs use oxygen dependent energy generating pathways significantly more than their BM counterparts. RNA-Seq analysis of E14.5 FL versus BM derived HSCs identified increased expression levels of genes involved in oxidative phosphorylation (OxPhos) and the citric acid cycle (TCA). We demonstrated that FL HSCs contain more mitochondria than BM HSCs, which resulted in increased levels of oxygen consumption and reactive oxygen species (ROS) production. Higher levels of DNA repair and antioxidant pathway gene expression may prevent ROS-mediated (geno)toxicity in FL HSCs. Thus, we here for the first time highlight the underestimated importance of oxygen dependent pathways for generating energy and building blocks in FL HSCs.