Molecular mechanisms of glycogen particle assembly in Escherichia coli.

It has been reported that glycogen in Escherichia coli has two structural states, that is, fragility and stability, which alters dynamically. However, molecular mechanisms behind the structural alterations are not fully understood. In this study, we focused on the potential roles of two important glycogen degradation enzymes, glycogen phosphorylase (glgP) and glycogen debranching enzyme (glgX), in glycogen structural alterations. The fine molecular structure of glycogen particles in Escherichia coli and three mutants (ΔglgP, ΔglgX and ΔglgP/ΔglgX) were examined, which showed that glycogen in E. coli ΔglgP and E. coli ΔglgP/ΔglgX were consistently fragile while being consistently stable in E. coli ΔglgX, indicating the dominant role of GP in glycogen structural stability control. In sum, our study concludes that glycogen phosphorylase is essential in glycogen structural stability, leading to molecular insights into structural assembly of glycogen particles in E. coli.

Rapid Prediction of Multidrug-Resistant Klebsiella pneumoniae through Deep Learning Analysis of SERS Spectra.

Klebsiella pneumoniae is listed by the WHO as a priority pathogen of extreme importance that can cause serious consequences in clinical settings. Due to its increasing multidrug resistance all over the world, K. pneumoniae has the potential to cause extremely difficult-to-treat infections. Therefore, rapid and accurate identification of multidrug-resistant K. pneumoniae in clinical diagnosis is important for its prevention and infection control. However, the limitations of conventional and molecular methods significantly hindered the timely diagnosis of the pathogen. As a label-free, noninvasive, and low-cost method, surface-enhanced Raman scattering (SERS) spectroscopy has been extensively studied for its application potentials in the diagnosis of microbial pathogens. In this study, we isolated and cultured 121 K. pneumoniae strains from clinical samples with different drug resistance profiles, which included polymyxin-resistant K. pneumoniae (PRKP; n = 21), carbapenem-resistant K. pneumoniae, (CRKP; n = 50), and carbapenem-sensitive K. pneumoniae (CSKP; n = 50). For each strain, a total of 64 SERS spectra were generated for the enhancement of data reproducibility, which were then computationally analyzed via the convolutional neural network (CNN). According to the results, the deep learning model CNN plus attention mechanism could achieve a prediction accuracy as high as 99.46%, with robustness score of 5-fold cross-validation at 98.87%. Taken together, our results confirmed the accuracy and robustness of SERS spectroscopy in the prediction of drug resistance of K. pneumoniae strains with the assistance of deep learning algorithms, which successfully discriminated and predicted PRKP, CRKP, and CSKP strains. IMPORTANCE This study focuses on the simultaneous discrimination and prediction of Klebsiella pneumoniae strains with carbapenem-sensitive, carbapenem-resistant, and polymyxin-resistant phenotypes. The implementation of CNN plus an attention mechanism makes the highest prediction accuracy at 99.46%, which confirms the diagnostic potential of the combination of SERS spectroscopy with the deep learning algorithm for antibacterial susceptibility testing in clinical settings.

Rapid discrimination of Shigella spp. and Escherichia coli via label-free surface enhanced Raman spectroscopy coupled with machine learning algorithms.

Shigella and enterotoxigenic Escherichia coli (ETEC) are major bacterial pathogens of diarrheal disease that is the second leading cause of childhood mortality globally. Currently, it is well known that Shigella spp., and E. coli are very closely related with many common characteristics. Evolutionarily speaking, Shigella spp., are positioned within the phylogenetic tree of E. coli. Therefore, discrimination of Shigella spp., from E. coli is very difficult. Many methods have been developed with the aim of differentiating the two species, which include but not limited to biochemical tests, nucleic acids amplification, and mass spectrometry, etc. However, these methods suffer from high false positive rates and complicated operation procedures, which requires the development of novel methods for accurate and rapid identification of Shigella spp., and E. coli. As a low-cost and non-invasive method, surface enhanced Raman spectroscopy (SERS) is currently under intensive study for its diagnostic potential in bacterial pathogens, which is worthy of further investigation for its application in bacterial discrimination. In this study, we focused on clinically isolated E. coli strains and Shigella species (spp.), that is, S. dysenteriae, S. boydii, S. flexneri, and S. sonnei, based on which SERS spectra were generated and characteristic peaks for Shigella spp., and E. coli were identified, revealing unique molecular components in the two bacterial groups. Further comparative analysis of machine learning algorithms showed that, the Convolutional Neural Network (CNN) achieved the best performance and robustness in bacterial discrimination capacity when compared with Random Forest (RF) and Support Vector Machine (SVM) algorithms. Taken together, this study confirmed that SERS paired with machine learning could achieve high accuracy in discriminating Shigella spp., from E. coli, which facilitated its application potential for diarrheal prevention and control in clinical settings. Graphical abstract.

Clinical Distribution and Drug Resistance of Pseudomonas aeruginosa in Guangzhou, China from 2017 to 2021.

The aim of the current study was to analyse the distribution of antimicrobial drug resistance (AMR) among Pseudomonas aeruginosa (P. aeruginosa, PA) isolates from Guangdong Provincial People's Hospital (GDPH) from 2017 to 2021, and the impact of the COVID-19 outbreak on changes in the clinical distribution and drug resistance rate of P. aeruginosa to establish guidelines for empiric therapy. Electronic clinical data registry records from 2017 to 2021 were retrospectively analysed to study the AMR among P. aeruginosa strains from GDPH. The strains were identified by VITEK 2 Compact and MALDI-TOF MS, MIC method or Kirby-Bauer method for antibiotic susceptibility testing. The results were interpreted according to the CLSI 2020 standard, and the data were analysed using WHONET 5.6 and SPSS 23.0 software. A total of 3036 P. aeruginosa strains were detected in the hospital from 2017 to 2021, and they were primarily distributed in the ICU (n = 1207, 39.8%). The most frequent specimens were respiratory tract samples (59.6%). The detection rate for P. aeruginosa in 5 years was highest in September, and the population distribution was primarily male(68.2%). For the trend in the drug resistance rate, the 5-year drug resistance rate of imipenem (22.4%), aztreonam (21.5%) and meropenem (19.3%) remained at high levels. The resistance rate of cefepime decreased from 9.4% to 4.8%, showing a decreasing trend year by year (p < 0.001). The antibiotics with low resistance rates were aminoglycoside antibiotics, which were gentamicin (4.4%), tobramycin (4.3%), and amikacin (1.4%), but amikacin showed an increasing trend year by year (p = 0.008). Our analysis indicated that the detection rate of clinically resistant P. aeruginosa strains showed an upwards trend, and the number of multidrug-resistant (MDR) strains increased year by year, which will lead to stronger pathogenicity and mortality. However, after the outbreak of COVID-19 in 2020, the growth trend in the number of MDR bacteria slowed, presumably due to the strict epidemic prevention and control measures in China. This observation suggests that we should reasonably use antibiotics and treatment programs in the prevention and control of P. aeruginosa infection. Additionally, health prevention and control after the outbreak of the COVID-19 epidemic (such as wearing masks, washing hands with disinfectant, etc., which reduced the prevalence of drug resistance) led to a slowdown in the growth of the drug resistance rate of P. aeruginosa in hospitals, effectively reducing the occurrence and development of drug resistance, and saving patient's treatment costs and time.

Clonal Spread of Carbapenem-Resistant Klebsiella pneumoniae Sequence Type 11 in Chinese Pediatric Patients.

Klebsiella pneumoniae often causes life-threatening infections in patients globally. Despite its notability, little is known about potential nosocomial outbreak and spread of K. pneumoniae among pediatric patients in low- and middle-income countries. Ninety-eight K. pneumoniae strains isolated from pediatric patients in a large general hospital in China between February 2018 and May 2019 were subjected to nanopore and Illumina sequencing and genomic analysis to elucidate transmission and genetic diversity. The temporal distribution patterns of K. pneumoniae revealed a cluster of sequence type 11 (ST11) strains comprising two clades. Most inferred transmissions were of clade 1, which could be traced to a common ancestor dating to mid-2017. An infant in the coronary care unit played a central role, potentially seeding transmission clusters in other wards. Major genomic changes during the outbreak included chromosomal mutations associated with virulence and gains and losses of plasmids encoding resistance. In summary, we report a nosocomial outbreak among pediatric patients caused by clonal dissemination of KPC-2-producing ST11 K. pneumoniae. Our findings highlight the value of whole-genome sequencing during outbreak investigations and illustrate that transmission chains can be identified during hospital stays. IMPORTANCE We report a nosocomial outbreak among pediatric patients caused by clonal dissemination of blaKPC-2-carrying ST11 K. pneumoniae. Strains of various sequence types coexist in the complex hospital environment; the quick emergence and spread of ST11 strains were mainly due to the plasmid-mediated acquisition of resistance genes. The spread of hospital infection was highly associated with several specific wards, suggesting the importance of genomic surveillance on wards at high risk of infection.

Pneumonia Caused by Coinfection with Cytomegalovirus and Pneumocystis jirovecii in an HIV-Negative Infant Diagnosed by Metagenomic Next-Generation Sequencing.

Background: Pneumonia produced by coinfection with Pneumocystis jirovecii (PJ) and cytomegalovirus (CMV) in infants and young children without timely diagnosis and treatment is often fatal due to the limitations of traditional tests. More accurate and rapid diagnostic methods for multiple infections are urgently needed. Case Presentation: Here, we report a case of a 2-month-old boy with pneumonia caused by Pneumocystis jirovecii (PJ) and cytomegalovirus (CMV) without HIV infection. Chest computed tomography (CT) showed massive exudative consolidation in both lungs. Microscopic examination of stained sputum and smear specimens and bacterial and fungal culture tests were all negative, and CMV nucleic acid and antibody tests were positive. After a period of antiviral and anti-infective therapy, pulmonary inflammation was not relieved. Subsequently, sputum and venous blood samples were analysed by metagenomic next-generation sequencing (mNGS), and the sequences of PJ and CMV were acquired. The patient was finally diagnosed with pneumonia caused by PJ and CMV coinfection. Anti-fungal combined with anti-viral therapy was given immediately. mNGS re-examination of bronchoalveolar lavage fluid (BALF) also revealed the same primary pathogen. Therapy was stopped due to the request of the patient's guardian. Hence, the child was discharged from the hospital and eventually died. Conclusion: This case emphasizes the combined use of mNGS and traditional tests in the clinical diagnosis of mixed lung infections in infants without HIV infection. mNGS is a new adjunctive diagnostic method that can rapidly discriminate multiple causes of pneumonia.

Prevalence and identification of antibiotic-resistant scarlet fever group A Streptococcus strains in some paediatric cases at Shenzhen, China.

OBJECTIVES: This study aimed to investigate the annual incidence, molecular epidemiological characteristics, and antimicrobial resistance of group A Streptococcus (GAS) clinical isolates from paediatric patients at Shenzhen Children's Hospital during 2016-2020. METHODS: Clinical samples were collected from paediatric patients with a suspected diagnosis of GAS infections. We studied the annual incidence and characteristics of GAS infections using the GAS antigen detection method. Additionally, 250 GAS isolates were randomly selected for genotyping of the emm gene, and antimicrobial susceptibility assay was performed using the Kirby-Bauer paper dispersion strategy. RESULTS: Among 43 593 collected samples, 9313 were positive for the GAS antigen. The main emm type was emm12, followed by emm1, emm6, and emm 4, which were used for distinguishing 90% of the scarlet fever isolated strains. The percentage of emm1 increased from 36% in 2016 to 44% in 2019, whereas the percentage of emm12 decreased from 62% to 50%. Several unusual emm types isolated from scarlet fever patients showed an increase in proportions from 2016 to 2020. These GAS isolates were sensitive to penicillin, ceftriaxone, and vancomycin and were highly resistant to erythromycin and clindamycin. CONCLUSION: There was a high incidence of GAS infections during 2016-2020 in Shenzhen, China. The GAS isolates had a high resistance rate to erythromycin and clindamycin; penicillin was the antibiotic of choice for GAS infections. The common emm types were emm12 and emm1. Future studies should investigate the clonal structure and superantigen profiles of the population of GAS isolates associated with scarlet fever.

Discrimination between Carbapenem-Resistant and Carbapenem-Sensitive Klebsiella pneumoniae Strains through Computational Analysis of Surface-Enhanced Raman Spectra: a Pilot Study.

In clinical settings, rapid and accurate diagnosis of antibiotic resistance is essential for the efficient treatment of bacterial infections. Conventional methods for antibiotic resistance testing are time consuming, while molecular methods such as PCR-based testing might not accurately reflect phenotypic resistance. Thus, fast and accurate methods for the analysis of bacterial antibiotic resistance are in high demand for clinical applications. In this pilot study, we isolated 7 carbapenem-sensitive Klebsiella pneumoniae (CSKP) strains and 8 carbapenem-resistant Klebsiella pneumoniae (CRKP) strains from clinical samples. Surface-enhanced Raman spectroscopy (SERS) as a label-free and noninvasive method was employed for discriminating CSKP strains from CRKP strains through computational analysis. Eight supervised machine learning algorithms were applied for sample analysis. According to the results, all supervised machine learning methods could successfully predict carbapenem sensitivity and resistance in K. pneumoniae, with a convolutional neural network (CNN) algorithm on top of all other methods. Taken together, this pilot study confirmed the application potentials of surface-enhanced Raman spectroscopy in fast and accurate discrimination of Klebsiella pneumoniae strains with different antibiotic resistance profiles. IMPORTANCE With the low-cost, label-free, and nondestructive features, Raman spectroscopy is becoming an attractive technique with great potential to discriminate bacterial infections. In this pilot study, we analyzed surfaced-enhanced Raman spectroscopy (SERS) spectra via supervised machine learning algorithms, through which we confirmed the application potentials of the SERS technique in rapid and accurate discrimination of Klebsiella pneumoniae strains with different antibiotic resistance profiles.

Accumulated quiescent neural stem cells in adult hippocampus of the mouse model for the MECP2 duplication syndrome.

Duplications of Methyl CpG binding protein 2 (MECP2) -containing segments lead to the MECP2 duplication syndrome, in which severe autistic symptoms were identified. Whether adult neurogenesis may play a role in pathogenesis of autism and the role of MECP2 on state determination of adult neural stem cells (NSCs) remain largely unclear. Using a MECP2 transgenic (TG) mouse model for the MECP2 duplication syndrome, we found that adult hippocampal quiescent NSCs were significantly accumulated in TG mice comparing to wild type (WT) mice, the neural progenitor cells (NPCs) were reduced and the neuroblasts were increased in adult hippocampi of MECP2 TG mice. Interestingly, we found that parvalbumin (PV) positive interneurons were significantly decreased in MECP2 TG mice, which were critical for determining fates of adult hippocampal NSCs between the quiescence and activation. In summary, we found that MeCP2 plays a critical role in regulating fate determination of adult NSCs. These evidences further suggest that abnormal development of NSCs may play a role in the pathogenesis of the MECP2 duplication syndrome.

Reciprocal regulation of autism-related genes MeCP2 and PTEN via microRNAs.

MeCP2 encodes a methyl-CpG-binding protein that plays a critical role in repressing gene expression, mutations of which lead to Rett syndrome and autism. PTEN is a critical tumor suppressor gene that is frequently mutated in human cancers and autism spectrum disorders. Various studies have shown that both MeCP2 and PTEN proteins play important roles in brain development. Here we find that MeCP2 and PTEN reciprocally regulate expression of each other via microRNAs. Knockdown of MeCP2 leads to upregulation of microRNA-137, which in turn represses expression of PTEN, thus PTEN would be down-regulated when MeCP2 is knockdown. Furthermore, we find that deletion of PTEN leads to phosphorylation of Serine 133 of CREB, then increases the expression of microRNA-132. miR-132 inhibits the expression of MeCP2 by targeting on the 3'UTR of MeCP2 mRNA. Our work shows that two critical disorders-related gene MeCP2 and PTEN reciprocally regulate expression of each other by distinct mechanisms, suggesting that rare mutations in various disorders may lead to dysregulation of other critical genes and yield unexpected consequences.

The protein phosphatase activity of PTEN is essential for regulating neural stem cell differentiation.

BACKGROUND: The tumor suppressor gene Phosphatase and tensin homolog (PTEN) is highly expressed in neural progenitor cells (NPCs) and plays an important role in development of the central nervous system. As a dual-specificity phosphatase, the loss of PTEN phosphatase activity has been linked to various diseases. RESULTS: Here we report that the protein phosphatase activity of Pten is critical for regulating differentiation of neural progenitor cells. First we found that deletion of Pten promotes neuronal differentiation. To determine whether the protein or lipid phosphatase activity is required for regulating neuronal differentiation, we generated phosphatase domain-specific Pten mutations. Interestingly, only expression of protein phosphatase-deficient mutant Y138L could mimic the effect of knocking down Pten, suggesting the protein phosphatase of Pten is critical for regulating NPC differentiation. Importantly, we showed that the wild-type and lipid phosphatase mutant (G129E) forms of Pten are able to rescue neuronal differentiation in Pten knockout NPCs, but mutants containing protein phosphatase mutant cannot. We further found that Pten-dependent dephosphorylation of CREB is critical for neuronal differentiation. CONCLUSION: Our data indicate that the protein phosphatase activity of PTEN is critical for regulating differentiation of NSCs during cortical development.