Journal of Microbiology最新文献

Tella is a traditional beverage widely accepted by consumers, despite the lack of product consistency owing to its reliance on natural fermentation. This study aimed to identify potential industrial lactic acid bacteria (LAB) starter cultures based on their technological properties. Seven LAB strains isolated from Tella were characterized for their carbohydrate utilization, salt content, temperature, and acid tolerances, growth and acidification rates, and metabolite profiles. Most strains efficiently utilized various carbohydrates, with Lactiplantibacillus plantarum TDM41 showing exceptional versatility. The strains exhibited similar growth characteristics. Principal component analysis of stress tolerance properties revealed that L. plantarum TDM41, Pediococcus pentosaceus TAA01, and Leuconostoc mesenteroides TDB22 exhibited superior tolerance ability. Strong acidification properties were detected in the L. plantarum TDM41, P. pentosaceus TAA01, and Leuconostoc mesenteroides TDB22 strains after 24 h incubation at 30°C. L. plantarum TDM41 displayed the fastest acidification rate throughout the analysis period. All LAB strains produced significant amounts of diverse organic acids, including lactic acid, citric acid, acetic acid, malic acid, and succinic acid, with lactic acid being the primary acid produced by each strain. Overall, strains L. plantarum TDM41 and P. pentosaceus TAA01 prove to be potential candidates for Tella industrial starter cultures and similar cereal products owing to their robust technological properties.

The presence of multiple nuclei in a common cytoplasm poses a significant challenge to genetic modification in mushrooms. Here, we demonstrate successful gene editing in both nuclei of a dikaryotic strain of Ganoderma lucidum using the Cas9-gRNA ribonucleoprotein complex (RNP). The RNP targeting the pyrG gene was introduced into dikaryotic protoplasts of G. lucidum, resulting in the isolation of 31 mycelial colonies resistant to 5-fluoroorotic acid (5-FOA). Twenty-six of these isolates were confirmed as dikaryotic strains by the presence of two distinct A mating type markers, denoted as A1 and A2. All dikaryons exhibited clamp connections on their mycelial hyphae, while the remaining 5 transformants were monokaryotic. Subsequent sequence analysis of PCR amplicons targeting pyrG revealed that two dikaryons harbored disrupted pyrG in both nuclei (pyrG-/pyrG-), while 10 and 14 displayed pyrG+/pyrG- (A1/A2) and pyrG-/pyrG+ (A1/A2) configurations, respectively. The disruption was achieved through non-homologous end joining repair, involving deletion or insertion of DNA fragments at the site of the double-strand break induced by RNP. Importantly, the nuclei were stable throughout 10 serial transfers over a period of 6 months. These findings highlight the capability of RNP to target genes across multiple nuclei within the same cytoplasm.

This study aimed to provide a taxonomic description of two bacterial strains, NKC19-3T and NKC19-16T, isolated from commercially produced kimchi obtained from various regions within the Republic of Korea. Both strains were rod-shaped, gram-stain-positive, facultatively anaerobic, and displayed positive reactions for oxidase and catalase. Additionally, these bacteria were motile, halophilic (salt-tolerant), and proliferated under alkaline conditions. Genetically, both strains showed 98.0% similarity in their 16S rRNA gene sequences and were most closely related to Virgibacillus natechei FarDT, with 96.5 and 96.8% sequence similarity, respectively. ANI values indicated that the two novel strains were distinct from V. natechei FarDT, as they were below the species demarcation threshold. The ANI value between strains NKC19-3ᵀ and NKC19-16ᵀ was 84.64-84.75%, and the values between these strains and other related strains did not exceed 80.0%, further supporting their classification as novel species. Phylogenetic analysis revealed that strains NKC19-3T and NKC19-16T formed a distinct branch within the genus Virgibacillus, clearly distinguishing them from other species in the same genus. Regarding genomic characteristics, the GC content was 38.9% for strain NKC19-3T and 39.5% for strain NKC19-16T. The genome of strain NKC19-3T had a size of approximately 4.1 Mb and contained 3,785 protein-coding genes (CDSs). Strain NKC19-16T had a slightly smaller genome, approximately 3.9 Mb in size and harbored 3,726 CDSs. The polar lipid profiles of strains NKC19-3ᵀ and NKC19-16ᵀ included diphosphatidylglycerol (DPG), phosphatidylglycerol (PG), glycolipids (GL), and an unidentified lipid (L). The predominant fatty acids of both strains were anteiso-C15:0 and anteiso-C17:0. Considering the comprehensive analysis encompassing phenotypic, genomic, phylogenetic, and chemotaxonomic data, strains NKC19-3T and NKC19-16T are proposed to represent two novel species within the genus Virgibacillus. The suggested names for these species are Virgibacillus saliphilus sp. nov. (type strain NKC19-3T, also referred to as KACC 22326T and DSM 112707T) and Virgibacillus salidurans sp. nov. (type strain NKC19-16T, also referred to as KACC 22327T and DSM 112708T).

This review explores current advancements in microbiome functional analysis enabled by next-generation sequencing technologies, which have transformed our understanding of microbial communities from mere taxonomic composition to their functional potential. We examine approaches that move beyond species identification to characterize microbial activities, interactions, and their roles in host health and disease. Genome-scale metabolic models allow for in-depth simulations of metabolic networks, enabling researchers to predict microbial metabolism, growth, and interspecies interactions in diverse environments. Additionally, computational methods for predicting metabolite profiles offer indirect insights into microbial metabolic outputs, which is crucial for identifying biomarkers and potential therapeutic targets. Functional pathway analysis tools further reveal microbial contributions to metabolic pathways, highlighting alterations in response to environmental changes and disease states. Together, these methods offer a powerful framework for understanding the complex metabolic interactions within microbial communities and their impact on host physiology. While significant progress has been made, challenges remain in the accuracy of predictive models and the completeness of reference databases, which limit the applicability of these methods in under-characterized ecosystems. The integration of these computational tools with multi-omic data holds promise for personalized approaches in precision medicine, allowing for targeted interventions that modulate the microbiome to improve health outcomes. This review highlights recent advances in microbiome functional analysis, providing a roadmap for future research and translational applications in human health and environmental microbiology.

With the advent of whole-genome sequencing, opportunities to investigate the population structure, transmission patterns, antimicrobial resistance profiles, and virulence determinants of Streptococcus pneumoniae at high resolution have been increasingly expanding. Consequently, a user-friendly bioinformatics tool is needed to automate the analysis of Streptococcus pneumoniae whole-genome sequencing data, summarize clinically relevant genomic features, and further guide treatment options. Here, we developed PneusPage, a web-based tool that integrates functions for species prediction, molecular typing, drug resistance determination, and data visualization of Streptococcus pneumoniae. To evaluate the performance of PneusPage, we analyzed 80 pneumococcal genomes with different serotypes from the Global Pneumococcal Sequencing Project and compared the results with those from another platform, PathogenWatch. We observed a high concordance between the two platforms in terms of serotypes (100% concordance rate), multilocus sequence typing (100% concordance rate), penicillin-binding protein typing (88.8% concordance rate), and the Global Pneumococcal Sequencing Clusters (98.8% concordance rate). In addition, PneusPage offers integrated analysis functions for the detection of virulence and mobile genetic elements that are not provided by previous platforms. By automating the analysis pipeline, PneusPage makes whole-genome sequencing data more accessible to non-specialist users, including microbiologists, epidemiologists, and clinicians, thereby enhancing the utility of whole-genome sequencing in both research and clinical settings. PneusPage is available at https://pneuspage.minholee.net/.

Protein solubility is a critical factor in the production of recombinant proteins, which are widely used in various industries, including pharmaceuticals, diagnostics, and biotechnology. Predicting protein solubility remains a challenging task due to the complexity of protein structures and the multitude of factors influencing solubility. Recent advances in computational methods, particularly those based on machine learning, have provided powerful tools for predicting protein solubility, thereby reducing the need for extensive experimental trials. This review provides an overview of current computational approaches to predict protein solubility. We discuss the datasets, features, and algorithms employed in these models. The review aims to bridge the gap between computational predictions and experimental validations, fostering the development of more accurate and reliable solubility prediction models that can significantly enhance recombinant protein production.

Systemic sclerosis (SSc) is a chronic autoimmune disorder characterised by skin fibrosis and internal organ involvement. Disruptions in the microbial communities on the skin may contribute to the onset of autoimmune diseases that affect the skin. However, current research on the skin microbiome in SSc is lacking. This study aimed to investigate skin microbiome associated with disease severity in SSc. Skin swabs were collected from the upper limbs of 46 healthy controls (HCs) and 36 patients with SSc. Metagenomic analysis based on the 16S rRNA gene was conducted and stratified by cutaneous subtype and modified Rodnan skin score (mRSS) severity. Significant differences in skin bacterial communities were observed between the HCs and patients with SSc, with further significant variations based on subtype and mRSS severity. The identified biomarkers were Bacteroides and Faecalibacterium for patients with diffuse cutaneous SSc with high mRSS (≥ 10) and Mycobacterium and Parabacteroides for those with low mRSS (< 10). Gardnerella, Abies, Lactobacillus, and Roseburia were the biomarkers in patients with limited cutaneous SSc (lcSS) and high mRSS, whereas Coprococcus predominated in patients with lcSS and low mRSS. Cutaneous subtype analysis identified Pediococcus as a biomarker in the HCs, whereas mRSS analysis revealed the presence of Pseudomonas in conjunction with Pediococcus. In conclusion, patients with SSc exhibit distinct skin microbiota compared with healthy controls. Bacterial composition varies by systemic sclerosis cutaneous subtype and skin thickness.

Korean Red ginseng has emerged as a potent candidate in the fight against various viral infections, demonstrating significant efficacy both in vitro and in vivo, particularly against influenza A viruses. Despite substantial evidence of its antiviral properties, the detailed molecular mechanisms through which it reduces viral lethality remain insufficiently understood. Our investigations have highlighted the superior effectiveness of Korean Red ginseng against influenza viruses, outperforming its effects on numerous other viral strains. We aim to uncover the specific mechanisms by which Korean Red ginseng exerts its antiviral effects, focusing on influenza A viruses. Our prior studies have identified the role of Z-DNA-binding protein 1 (ZBP1), a signaling complex involved in inducing programmed cell death in response to influenza virus infection. Given the critical role of ZBP1 as a sensor for viral nucleic acid, we hypothesize that Korean Red ginseng may modulate the ZBP1-derived cell death pathway. This interaction is anticipated to enhance cell death while concurrently suppressing viral protein expression, offering novel insights into the antiviral mechanism of Korean Red ginseng against influenza A viruses.

Transcriptional regulatory networks (TRNs) in bacteria are crucial for elucidating the mechanisms that regulate gene expression and cellular responses to environmental stimuli. These networks delineate the interactions between transcription factors (TFs) and their target genes, thereby uncovering the regulatory processes that modulate gene expression under varying environmental conditions. Analyzing TRNs offers valuable insights into bacterial adaptation, stress responses, and metabolic optimization from an evolutionary standpoint. Additionally, understanding TRNs can drive the development of novel antimicrobial therapies and the engineering of microbial strains for biofuel and bioproduct production. This protocol integrates advanced data analysis pipelines, including ChEAP, DEOCSU, and DESeq2, to analyze omics datasets that encompass genome-wide TF binding sites and transcriptome profiles derived from ChIP-exo and RNA-seq experiments. This approach minimizes both the time required and the risk of bias, making it accessible to non-expert users. Key steps in the protocol include preprocessing and peak calling from ChIP-exo data, differential expression analysis of RNA-seq data, and motif and regulon analysis. This method offers a comprehensive and efficient framework for TRN reconstruction across various bacterial strains, enhancing both the accuracy and reliability of the analysis while providing valuable insights for basic and applied research.

To study the role of intestinal flora in the development of bloodstream infections (BSIs). 42 patients and 19 healthy controls (HCs) were screened into the study and their intestinal flora was measured by 16S rRNA gene sequencing. The bacterial diversity was significantly lower in the BSI group compared with that in the HCs (P < 0.001), and beta diversity was significantly differentiated between the two groups (PERMANOVA, P = 0.001). The four keystone species [Roseburia, Faecalibacterium, Prevotella, and Enterococcus (LDA > 4)] differed significantly between the two groups. Dysbiosis of fecal microbial ecology is a common condition present in patients with BSI. The proliferation of certain pathogens or reduction of SCFA-producing bacteria would cause susceptibility to BSI.