a-non-degradative-extraction-method-for-molecular-structure

A Non-Degradative Extraction Method for Molecular Structure Characterization of Bacterial Glycogen Particles

Currently, there exist a variety of glycogen extraction methods, which either damage glycogen spatial structure or only partially extract glycogen, ...

Macau University of Science and Technology

Glycogen is a highly-branched polysaccharide that is widely distributed across the three life domains. It has versatile functions in physiological activities such as energy reserve, osmotic regulation, blood glucose homeostasis, and pH maintenance. Recent research also confirms that glycogen plays important roles in longevity and cognition. Intrinsically, glycogen function is determined by its structure that has been intensively studied for many years. The recent association of glycogen α-particle fragility with diabetic conditions further strengthens the importance of glycogen structure in its function. By using improved glycogen extraction procedures and a series of advanced analytical techniques, the fine molecular structure of glycogen particles in human beings and several model organisms such as , , , and  have been characterized. However, there are still many unknowns about the assembly mechanisms of glycogen particles, the dynamic changes of glycogen structures, and the composition of glycogen associated proteins (glycogen proteome). In this review, we explored the recent progresses in glycogen studies with a focus on the structure of glycogen particles, which may not only provide insights into glycogen functions, but also facilitate the discovery of novel drug targets for the treatment of diabetes mellitus.

From Prokaryotes to Eukaryotes: Insights Into the Molecular Structure of Glycogen Particles.

Mulberry leaf as a traditional Chinese medicine is able to treat obesity, diabetes, and dyslipidemia. It is well known that diabetes leads to intestinal microbiota dysbiosis. It is also recently discovered that liver glycogen structure is impaired in diabetic animals. Since mulberry leaves are able to improve the diabetic conditions through reducing blood glucose level, it would be interesting to investigate whether they have any positive effects on intestinal microbiota and liver glycogen structure.

Ethanol extract of mulberry leaves partially restores the composition of intestinal microbiota and strengthens liver glycogen fragility in type 2 diabetic rats.

Infectious diseases caused by bacterial pathogens are important public issues. In addition, due to the overuse of antibiotics, many multidrug-resistant bacterial pathogens have been widely encountered in clinical settings. Thus, the fast identification of bacteria pathogens and profiling of antibiotic resistance could greatly facilitate the precise treatment strategy of infectious diseases. So far, many conventional and molecular methods, both manual or automatized, have been developed for  diagnostics, which have been proven to be accurate, reliable, and time efficient. Although Raman spectroscopy (RS) is an established technique in various fields such as geochemistry and material science, it is still considered as an emerging tool in research and diagnosis of infectious diseases. Based on current studies, it is too early to claim that RS may provide practical guidelines for microbiologists and clinicians because there is still a gap between basic research and clinical implementation. However, due to the promising prospects of label-free detection and noninvasive identification of bacterial infections and antibiotic resistance in several single steps, it is necessary to have an overview of the technique in terms of its strong points and shortcomings. Thus, in this review, we went through recent studies of RS in the field of infectious diseases, highlighting the application potentials of the technique and also current challenges that prevent its real-world applications.

Applications of Raman Spectroscopy in Bacterial Infections: Principles, Advantages, and Shortcomings.

According to the sit-and-wait hypothesis, long-term environmental survival is positively correlated with increased bacterial pathogenicity because high durability reduces the dependence of transmission on host mobility. Many indirectly transmitted bacterial pathogens, such as  and , have high durability in the external environment and are highly virulent. It is possible that abiotic stresses may activate certain pathways or the expressions of certain genes, which might contribute to bacterial durability and virulence, synergistically. Therefore, exploring how bacterial phenotypes change in response to environmental stresses is important for understanding their potentials in host infections. In this study, we investigated the effects of different concentrations of salt (sodium chloride, NaCl), on survival ability, phenotypes associated with virulence, and energy metabolism of the lab strain  BW25113. In particular, we investigated how NaCl concentrations influenced growth patterns, biofilm formation, oxidative stress resistance, and motile ability. In terms of energy metabolism that is central to bacterial survival, glucose consumption, glycogen accumulation, and trehalose content were measured in order to understand their roles in dealing with the fluctuation of osmolarity. According to the results, trehalose is preferred than glycogen at high NaCl concentration. In order to dissect the molecular mechanisms of NaCl effects on trehalose metabolism, we further checked how the impairment of trehalose synthesis pathway ( operon) via single-gene mutants influenced  durability and virulence under salt stress. After that, we compared the transcriptomes of  cultured at different NaCl concentrations, through which differentially expressed genes (DEGs) and differential pathways with statistical significance were identified, which provided molecular insights into  responses to NaCl concentrations. In sum, this study explored the in vitro effects of NaCl concentrations on  from a variety of aspects and aimed to facilitate our understanding of bacterial physiological changes under salt stress, which might help clarify the linkages between bacterial durability and virulence outside hosts under environmental stresses.

Effects of NaCl Concentrations on Growth Patterns, Phenotypes Associated With Virulence, and Energy Metabolism in  BW25113.

Raman spectroscopy (RS) is a widely used analytical technique based on the detection of molecular vibrations in a defined system, which generates Raman spectra that contain unique and highly resolved fingerprints of the system. However, the low intensity of normal Raman scattering effect greatly hinders its application. Recently, the newly emerged surface enhanced Raman spectroscopy (SERS) technique overcomes the problem by mixing metal nanoparticles such as gold and silver with samples, which greatly enhances signal intensity of Raman effects by orders of magnitudes when compared with regular RS. In clinical and research laboratories, SERS provides a great potential for fast, sensitive, label-free, and non-destructive microbial detection and identification with the assistance of appropriate machine learning (ML) algorithms. However, choosing an appropriate algorithm for a specific group of bacterial species remains challenging, because with the large volumes of data generated during SERS analysis not all algorithms could achieve a relatively high accuracy. In this study, we compared three unsupervised machine learning methods and 10 supervised machine learning methods, respectively, on 2,752 SERS spectra from 117  strains belonging to nine clinically important  species in order to test the capacity of different machine learning methods for bacterial rapid differentiation and accurate prediction. According to the results, density-based spatial clustering of applications with noise (DBSCAN) showed the best clustering capacity (Rand index 0.9733) while convolutional neural network (CNN) topped all other supervised machine learning methods as the best model for predicting  species  SERS spectra (ACC 98.21%, AUC 99.93%). Taken together, this study shows that machine learning methods are capable of distinguishing closely related  species and therefore have great application potentials for bacterial pathogen diagnosis in clinical settings.

Comparative Analysis of Machine Learning Algorithms on Surface Enhanced Raman Spectra of Clinical  Species.

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.  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.

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

Qing-Hua Liu

State Key Laboratory of Quality Research in Chinese Medicines

Macau University of Science and Technology

A Non-Degradative Extraction Method for Molecular Structure Characterization of Bacterial Glycogen Particles

Qing-Hua Liu

.css-o250i3{font-size:18px;font-family:Open Sans,sans-serif;line-height:21.6px;}State Key Laboratory of Quality Research in Chinese Medicines

Macau University of Science and Technology

A Non-Degradative Extraction Method for Molecular Structure Characterization of Bacterial Glycogen Particles

State Key Laboratory of Quality Research in Chinese Medicines