Journal of Microbiology

Journal Articles

NEDD4 Regulated Pyroptosis Occurred from Co‑infection between Influenza A Virus and Streptococcus pneumoniae: Jiangzhou You , Linlin Zhou , Xudong San , Hailing Li , Mingyuan Li , Baoning Wang; J. Microbiol. 2023;61(8):777-789. Published online October 4, 2023; DOI: https://doi.org/10.1007/s12275-023-00076-y

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4 Citations

Abstract: Co-infection of respiratory tract viruses and bacteria often result in excess mortality, especially pneumonia caused by influenza viruses and Streptococcus pneumoniae. However, the synergistic mechanisms remain poorly understood. Therefore, it is necessary to develop a clearer understanding of the molecular basis of the interaction between influenza virus and Streptococcus pneumonia. Here, we developed the BALB/c mouse model and the A549 cell model to investigate inflammation and pyroptotic cell death during co-infection. Co-infection significantly activated the NLRP3 inflammasome and induced pyroptotic cell death, correlated with excess mortality. The E3 ubiquitin ligase NEDD4 interacted with both NLRP3 and GSDMD, the executor of pyroptosis. NEDD4 negatively regulated NLRP3 while positively regulating GSDMD, thereby modulating inflammation and pyroptotic cell death. Our findings suggest that NEDD4 may play a crucial role in regulating the GSDMD-mediated pyroptosis signaling pathway. Targeting NEDD4 represents a promising approach to mitigate excess mortality during influenza pandemics by suppressing synergistic inflammation during co-infection of influenza A virus and Streptococcus pneumoniae.

[PROTOCOL]Analyzing viral epitranscriptomes using nanopore direct RNA sequencing: Ari Hong , Dongwan Kim , V. Narry Kim , Hyeshik Chang; J. Microbiol. 2022;60(9):867-876. Published online August 24, 2022; DOI: https://doi.org/10.1007/s12275-022-2324-4

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6 Citations

Abstract: RNA modifications are a common occurrence across all domains of life. Several chemical modifications, including N6- methyladenosine, have also been found in viral transcripts and viral RNA genomes. Some of the modifications increase the viral replication efficiency while also helping the virus to evade the host immune system. Nonetheless, there are numerous examples in which the host's RNA modification enzymes function as antiviral factors. Although established methods like MeRIP-seq and miCLIP can provide a transcriptome- wide overview of how viral RNA is modified, it is difficult to distinguish between the complex overlapping viral transcript isoforms using the short read-based techniques. Nanopore direct RNA sequencing (DRS) provides both long reads and direct signal readings, which may carry information about the modifications. Here, we describe a refined protocol for analyzing the RNA modifications in viral transcriptomes using nanopore technology.

Reviews

Microbial source tracking using metagenomics and other new technologies: Shahbaz Raza , Jungman Kim , Michael J. Sadowsky , Tatsuya Unno; J. Microbiol. 2021;59(3):259-269. Published online February 10, 2021; DOI: https://doi.org/10.1007/s12275-021-0668-9

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9 Citations

Abstract: The environment is under siege from a variety of pollution sources. Fecal pollution is especially harmful as it disperses pathogenic bacteria into waterways. Unraveling origins of mixed sources of fecal bacteria is difficult and microbial source tracking (MST) in complex environments is still a daunting task. Despite the challenges, the need for answers far outweighs the difficulties experienced. Advancements in qPCR and next generation sequencing (NGS) technologies have shifted the traditional culture-based MST approaches towards culture independent technologies, where communitybased MST is becoming a method of choice. Metagenomic tools may be useful to overcome some of the limitations of community-based MST methods as they can give deep insight into identifying host specific fecal markers and their association with different environments. Adoption of machine learning (ML) algorithms, along with the metagenomic based MST approaches, will also provide a statistically robust and automated platform. To compliment that, ML-based approaches provide accurate optimization of resources. With the successful application of ML based models in disease prediction, outbreak investigation and medicine prescription, it would be possible that these methods would serve as a better surrogate of traditional MST approaches in future.

Recent advances in the development of β-lactamase inhibitors: Shivakumar S. Jalde , Hyun Kyung Choi; J. Microbiol. 2020;58(8):633-647. Published online July 27, 2020; DOI: https://doi.org/10.1007/s12275-020-0285-z

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20 Citations

Abstract: β-Lactam antibiotics are the most commonly prescribed antibiotics worldwide; however, antimicrobial resistance (AMR) is a global challenge. The β-lactam resistance in Gram-negative bacteria is due to the production of β-lactamases, including extended-spectrum β-lactamases, metallo-β-lactamases, and carbapenem-hydrolyzing class D β-lactamases. To restore the efficacy of BLAs, the most successful strategy is to use them in combination with β-lactamase inhibitors (BLI). Here we review the medically relevant β-lactamase families and penicillins, diazabicyclooctanes, boronic acids, and novel chemical scaffold-based BLIs, in particular approved and under clinical development.

Journal Articles

Short-term effects of returning granulated straw on soil microbial community and organic carbon fractions in dryland farming: Wei Fan , Jinggui Wu; J. Microbiol. 2020;58(8):657-667. Published online June 25, 2020; DOI: https://doi.org/10.1007/s12275-020-9266-5

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28 Citations

Abstract: We conducted a 2-year field experiment which was comprised of five treatments, namely no straw returning (CK), straw mulching (SM), straw plowed into the soil (SP), and straw returned in granulated form (SG). The aim of this study was to investigate the effects of different straw returning modes on soil bacterial and fungal community structure and their relationships to soil organic carbon (SOC) fractions at three different soil depths (0–20, 20–40, and 40–60 cm) in a dryland under maize cultivation in Northeast (NE) China. SM, SP, and SG treatments significantly increased SOC content. Compared with SM and SP treatments, SG treatment significantly increased the content of SOC and easily oxidizable carbon (EOC) in the topsoil (0–20 cm depth), and increased dissolved organic carbon (DOC) and SOC content of the light fraction (LFOC) in the 20–40 cm layer. Meanwhile, SG treatment exhibited the highest microbial biomass C (MBC) content in all of the three soil depths. SG treatment also enhanced bacterial richness as well as fungal richness and diversity in the upper 40 cm of soil. In addition, SG treatment increased the relative abundance of Proteobacteria in all depths, and had the highest relative abundance of Basidiomycota in the first 20 cm of soil. SP treatment showed the lowest soil organic carbon content in all fractions and soil microbial community composition. SM treatment exhibited similar results to SG treatment in SOC, DOC, and LFOC contents, and bacterial diversity in the topsoil and subsoil. As a whole, treatment SG improved soil quality and maize yield, hence we recommend returning granulated straw as the most effective practice for enhancing labile SOC fractions as well as maintaining soil diversity and microbial richness of arid farmlands in NE China.

Setup of a scientific computing environment for computational biology: Simulation of a genome-scale metabolic model of Escherichia coli as an example: Junhyeok Jeon , Hyun Uk Kim; J. Microbiol. 2020;58(3):227-234. Published online February 27, 2020; DOI: https://doi.org/10.1007/s12275-020-9516-6

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7 Citations

Abstract: Computational analysis of biological data is becoming increasingly important, especially in this era of big data. Computational analysis of biological data allows efficiently deriving biological insights for given data, and sometimes even counterintuitive ones that may challenge the existing knowledge. Among experimental researchers without any prior exposure to computer programming, computational analysis of biological data has often been considered to be a task reserved for computational biologists. However, thanks to the increasing availability of user-friendly computational resources, experimental researchers can now easily access computational resources, including a scientific computing environment and packages necessary for data analysis. In this regard, we here describe the process of accessing Jupyter Notebook, the most popular Python coding environment, to conduct computational biology. Python is currently a mainstream programming language for biology and biotechnology. In particular, Anaconda and Google Colaboratory are introduced as two representative options to easily launch Jupyter Notebook. Finally, a Python package COBRApy is demonstrated as an example to simulate 1) specific growth rate of Escherichia coli as well as compounds consumed or generated under a minimal medium with glucose as a sole carbon source, and 2) theoretical production yield of succinic acid, an industrially important chemical, using E. coli. This protocol should serve as a guide for further extended computational analyses of biological data for experimental researchers without computational background.

Increased susceptibility against Cryptococcus neoformans of lupus mouse models (pristane-induction and FcGRIIb deficiency) is associated with activated macrophage, regardless of genetic background: Saowapha Surawut , Jiradej Makjaroen , Arthid Thim-uam , Jutamas Wongphoom , Tanapat Palaga , Prapaporn Pisitkun , Ariya Chindamporn , Asada Leelahavanichkul; J. Microbiol. 2019;57(1):45-53. Published online November 19, 2018; DOI: https://doi.org/10.1007/s12275-019-8311-8

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13 Citations

Abstract: The severity of cryptococcosis in lupus from varying geneticbackgrounds might be different due to the heterogeneity of lupus-pathogenesis. This study explored cryptococcosis in lupus mouse models of pristane-induction (normal geneticbackground) and FcGRIIb deficiency (genetic defect). Because the severity of lupus nephritis, as determined by proteinuria and serum creatinine, between pristane and FcGRIIb-/- mice were similar at 6-month-old, Cryptococcus neoformans was intravenously administered in 6-month-old mice and were age-matched with wild-type. Indeed, the cryptococcosis disease severity, as evaluated by mortality rate, internal-organ fungal burdens and serum cytokines, between pristane and FcGRIIb-/- mice was not different. However, the severity of cryptococcosis in wild-type was less severe than the lupus mice. On the other hand, phagocytosis activity of peritoneal macrophages from lupus mice (pristane and FcGRIIb-/-) was more predominant than the wild-type without the difference in macrophage killing-activity among these groups. In addition, the number of active T helper cells (Th-cell) in the spleen, including Th-cells with intracellular IFN-γ, from lupus mice (pristane and FcGRIIb-/-) was higher than wildtype. Moreover, these active Th-cells were even higher after 2 weeks of cryptococcal infection. These data support enhanced macrophage activation through prominent Th-cells in both lupus models. In conclusion, an increased susceptibility of cryptococcosis in both lupus models was independent to genetic background. This might due to Th-cell enhanced macrophage phagocytosis with the interference of macrophage killing activity from Cryptococcal immune-evasion properties.

Reviews

[Minireview] Primary lymphocyte infection models for KSHV and its putative tumorigenesis mechanisms in B cell lymphomas: Sangmin Kang , Jinjong Myoung; J. Microbiol. 2017;55(5):319-329. Published online April 29, 2017; DOI: https://doi.org/10.1007/s12275-017-7075-2

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22 Citations

Abstract: Kaposi’s sarcoma-associated herpesvirus (KSHV) is the latest addition to the human herpesvirus family. Unlike alpha- and beta-herpesvirus subfamily members, gamma-herpesviruses, including Epstein-Barr virus (EBV) and KSHV, cause vari-ous tumors in humans. KSHV primarily infects endothelial and B cells in vivo, and is associated with at least three malig-nancies: Kaposi’s sarcoma and two B cell lymphomas, res-pectively. Although KSHV readily infects endothelial cells in vitro and thus its pathogenic mechanisms have been exten-sively studied, B cells had been refractory to KSHV infection. As such, functions of KSHV genes have mostly been eluci-dated in endothelial cells in the context of viral infection but not in B cells. Whether KSHV oncogenes, defined in endo-thelial cells, play the same roles in the tumorigenesis of B cells remains an open question. Only recently, through a few ground-breaking studies, B cell infection models have been established. In this review, those models will be compared and contrasted and putative mechanisms of KSHV-induced B cell transformation will be discussed.

MINIREVIEW] Unraveling interactions in microbial communities - from co-cultures to microbiomes: Justin Tan , Cristal Zuniga , Karsten Zengler; J. Microbiol. 2015;53(5):295-305. Published online May 3, 2015; DOI: https://doi.org/10.1007/s12275-015-5060-1

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53 Citations

Abstract: Microorganisms do not exist in isolation in the environment. Instead, they form complex communities among themselves as well as with their hosts. Different forms of interactions not only shape the composition of these communities but also define how these communities are established and maintained. The kinds of interaction a bacterium can employ are largely encoded in its genome. This allows us to deploy a genomescale modeling approach to understand, and ultimately predict, the complex and intertwined relationships in which microorganisms engage. So far, most studies on microbial communities have been focused on synthetic co-cultures and simple communities. However, recent advances in molecular and computational biology now enable bottom up methods to be deployed for complex microbial communities from the environment to provide insight into the intricate and dynamic interactions in which microorganisms are engaged. These methods will be applicable for a wide range of microbial communities involved in industrial processes, as well as understanding, preserving and reconditioning natural microbial communities present in soil, water, and the human microbiome.

Research Support, Non-U.S. Gov't

Novel Mutations in CYP51B from Penicillium digitatum Involved in Prochloraz Resistance: Jinlong Wang , Jinhui Yu , Jing Liu , Yongze Yuan , Na Li , Muqing He , Ting Qi , Geng Hui , Li Xiong , Deli Liu; J. Microbiol. 2014;52(9):762-770. Published online August 2, 2014; DOI: https://doi.org/10.1007/s12275-014-4112-2

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46 Citations

Abstract: Green mold caused by Penicillium digitatum is one of the most serious postharvest diseases of citrus fruit, and it is ubiquitous in all citrus growing regions in the world. Sterol 14α-demethylase (CYP51) is one of the key enzymes of sterol biosynthesis in the biological kingdom and a prime target of antifungal drugs. Mutations in CYP51s have been found to be correlated with resistance to azole fungicides in many fungal species. To investigate the mechanism of resistance to prochloraz (PRC) in P. digitatum, the PRC sensitivity was determined in vitro in this study to assess the sensitivity of 78 P. digitatum isolates collected in Hubei province. The results showed that 25 isolates were prochloraz-resistant (PRC-R), including six high-resistant (HR) strains, twelve medium-resistant (MR) and seven low-resistant (LR) strains. A sequence analysis showed no consistent point mutations of PdCYP51A in the PRC-R strains, but four substitutions of CYP51B were found, Q309H in LR strains, Y136H and Q309H in HR strains, and G459S and F506I in MR strains, which corresponded to the four sensitivity levels. Based on the sequence alignment analysis and homology modeling followed by the molecular docking of the PdCYP51B protein, the potential correlation between the mutations and PRC resistance is proposed.

Journal Article

Predictive Modelling of Lactobacillus casei KN291 Survival in Fermented Soy Beverage: Zieli&# , Koło&# , Goryl Antoni , Ilona Motyl; J. Microbiol. 2014;52(2):169-178. Published online February 1, 2014; DOI: https://doi.org/10.1007/s12275-014-3045-0

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10 Citations

Abstract: The aim of the study was to construct and verify predictive growth and survival models of a potentially probiotic bacteria in fermented soy beverage. The research material included natural soy beverage (Polgrunt, Poland) and the strain of lactic acid bacteria (LAB) – Lactobacillus casei KN291. To construct predictive models for the growth and survival of L. casei KN291 bacteria in the fermented soy beverage we design an experiment which allowed the collection of CFU data. Fermented soy beverage samples were stored at various temperature conditions (5, 10, 15, and 20°C) for 28 days. On the basis of obtained data concerning the survival of L. casei KN291 bacteria in soy beverage at different temperature and time conditions, two non-linear models (r2= 0.68–0.93) and two surface models (r2=0.76–0.79) were constructed; these models described the behaviour of the bacteria in the product to a satisfactory extent. Verification of the surface models was carried out utilizing the validation data - at 7°C during 28 days. It was found that applied models were well fitted and charged with small systematic errors, which is evidenced by accuracy factor - Af, bias factor - Bf and mean squared error - MSE. The constructed microbiological growth and survival models of L. casei KN291 in fermented soy beverage enable the estimation of products shelf life period, which in this case is defined by the requirement for the level of the bacteria to be above 106 CFU/cm3. The constructed models may be useful as a tool for the manufacture of probiotic foods to estimate of their shelf life period.

Review

MINIREVIEW] Shiga Toxins Expressed by Human Pathogenic Bacteria Induce Immune Responses in Host Cells: Moo-Seung Lee , Myung Hee Kim , Vernon L. Tesh; J. Microbiol. 2013;51(6):724-730. Published online December 19, 2013; DOI: https://doi.org/10.1007/s12275-013-3429-6

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29 Citations

Abstract: Shiga toxins are a family of genetically and structurally related toxins that are the primary virulence factors produced by the bacterial pathogens Shigella dysenteriae serotype 1 and certain Escherichia coli strains. The toxins are multifunctional proteins inducing protein biosynthesis inhibition, ribotoxic and ER stress responses, apoptosis, autophagy, and inflammatory cytokine and chemokine production. The regulated induction of inflammatory responses is key to minimizing damage upon injury or pathogen-mediated infections, requiring the concerted activation of multiple signaling pathways to control cytokine/chemokine expression. Activation of host cell signaling cascades is essential for Shiga toxinmediated proinflammatory responses and the contribution of the toxins to virulence. Many studies have been reported defining the inflammatory response to Shiga toxins in vivo and in vitro, including production and secretion of tumor necrosis factor alpha (TNF-α), interleukin-1β (IL-1β), macrophage inflammatory protein-1α/β (MIP-1α/β), macrophage chemoattractant monocyte chemoattractant protein 1 (MCP-1), interleukin 8 (IL-8), interleukin 6 (IL-6), and Groβ. These cytokines and chemokines may contribute to damage in the colon and development of life threatening conditions such as acute renal failure (hemolytic uremic syndrome) and neurological abnormalities. In this review, we summarize recent findings in Shiga toxin-mediated inflammatory responses by different types of cells in vitro and in animal models. Signaling pathways involved in the inflammatory responses are briefly reviewed.

Research Support, Non-U.S. Gov'ts

DBA/2 Mouse as an Animal Model for Anti-influenza Drug Efficacy Evaluation: Jin Il Kim , Sehee Park , Sangmoo Lee , Ilseob Lee , Jun Heo , Min-Woong Hwang , Joon-Yong Bae , Donghwan Kim , Seok-Il Jang , Mee Sook Park , Man-Seong Park; J. Microbiol. 2013;51(6):866-871. Published online December 19, 2013; DOI: https://doi.org/10.1007/s12275-013-3428-7

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17 Citations

Abstract: Influenza viruses are seasonally recurring human pathogens. Vaccines and antiviral drugs are available for influenza. However, the viruses, which often change themselves via antigenic drift and shift, demand constant efforts to update vaccine antigens every year and develop new agents with broad-spectrum antiviral efficacy. An animal model is critical for such efforts. While most human influenza viruses are unable to kill BALB/c mice, some strains have been shown to kill DBA/2 mice without prior adaptation. Therefore, in this study, we explored the feasibility of employing DBA/2 mice as a model in the development of anti-influenza drugs. Unlike the BALB/c strain, DBA/2 mice were highly susceptible and could be killed with a relatively low titer (50% DBA/2 lethal dose = 102.83 plaque-forming units) of the A/ Korea/01/2009 virus (2009 pandemic H1N1 virus). When treated with a neuraminidase inhibitor, oseltamivir phosphate, infected DBA/2 mice survived until 14 days postinfection. The reduced morbidity of the infected DBA/2 mice was also consistent with the oseltamivir treatment. Taking these data into consideration, we propose that the DBA/2 mouse is an excellent animal model to evaluate antiviral efficacy against influenza infection and can be further utilized for combination therapies or bioactivity models of existing and newly developed anti-influenza drugs.

Identification and Characterization of an Autolysin Gene, atlA, from Streptococcus criceti: Haruki Tamura , Arisa Yamada , Hirohisa Kato; J. Microbiol. 2012;50(5):777-784. Published online November 4, 2012; DOI: https://doi.org/10.1007/s12275-012-2187-1

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Abstract: AtlA of Streptococcus mutans is a major autolysin and belongs to glycoside hydrolase family 25 with cellosyl of Streptomyces coelicolor. The autolysin gene (atlA) encoding AtlA was identified from S. criceti. AtlA of S. criceti comprises the signal sequence in the N-terminus, the putative cell-wallbinding domain in the middle, and the catalytic domain in the C-terminus. Homology modeling analysis of the catalytic domain of AtlA showed the resemblance of the spatial arrangement of five amino acids around the predicted catalytic cavity to that of cellosyl. Recombinant AtlA and its four point mutants, D655A, D747A, W831A, and D849A, were evaluated on zymogram of S. criceti cells. Lytic activity was destroyed in the mutants D655A and D747A and diminished in the mutants W831A and D849A. These results suggest that Asp655 and Asp747 residues are critical for lytic activity and Trp831 and Asp849 residues are also associated with enzymatic activity.

Interaction of Acinetobacter baumannii 19606 and 1656-2 with Acanthamoeba castellanii: Migma Dorji Tamang , Shukho Kim , Sung-Min Kim , Hyun-Hee Kong , Jungmin Kim; J. Microbiol. 2011;49(5):841-846. Published online November 9, 2011; DOI: https://doi.org/10.1007/s12275-011-1063-8

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9 Citations

Abstract: Acinetobacter baumannii is virtually avirulent for healthy people but maintains a high virulence among critically ill patients or immuno-compromised individuals. The ability of A. baumannii to adhere to cells and persist on surfaces as biofilms could be central to its pathogenicity. In the present study, we compared the virulence of the A. baumannii 1656-2 clinical strain, which is able to form a thick biofilm, with the virulence of the A. baumannii type strain (ATCC 19606T). Acanthamoeba castellanii, a single-celled organism, was used as the host model system to study the virulence of A. baumannii. Compared to A. baumannii ATCC 19606T, A. baumannii 1656-2 exhibited a higher ability to adhere and invade A. castellanii cells and had a higher killing rate of A. castellanii cells. Furthermore, co-incubation of the amoeba cells and the cell-free supernatant of A. baumannii resulted in the cell death of the amoebae. Heat inactivation or proteinase K treatment of the supernatant did not eliminate its cytotoxicity, suggesting heat stable non-protein factors are responsible for its cytotoxicity to A. castellanii cells. In conclusion, this study for the first time has revealed the capacity of the A. baumannii strain and/or its metabolic products to induce cytotoxicity in A. castellanii cells.

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