Microorganisms最新文献

Simultaneous conversion of syngas and sugars is a promising approach to overcome limitations of syngas fermentation. Clostridium autoethanogenum LAbrini, obtained by adaptive laboratory evolution, is known to show improved autotrophic process performance. Under purely autotrophic conditions, C. autoethanogenum LAbrini exhibits substantially faster growth and biomass formation compared to the wild-type in fully controlled, stirred-tank bioreactors with a continuous gas supply. In mixotrophic processes, the pre-culture strategy has a significant impact on the growth and metabolic activity of C. autoethanogenum LAbrini. C. autoethanogenum LAbrini can metabolize sugars (D-fructose, D-xylose, or L-arabinose) and CO simultaneously. All mixotrophic batch processes showed increased growth and product formation compared to the autotrophic process. The mixotrophic batch process with D-fructose enabled superior production of alcohols (10.7 g L^-1 ethanol and 3.2 g L^-1 D-2,3-butanediol) with a heterotrophic pre-culture. Using an autotrophic pre-culture and L-arabinose resulted in a total alcohol formation of more than 13 g L^-1. The formation of meso-2,3-butanediol (>0.50 g L^-1) occurred exclusively under mixotrophic conditions. Thus, C. autoethanogenum LAbrini, clearly representing notable improvements over the wild-type strain in mixotrophic batch processes, offers a good basis for further strain improvements to shift the product range even further towards more reduced products.

This study successfully developed an oral vaccine for Type 1 Diabetes utilizing recombinant Lactococcus lactis expressing the GAD65 autoantigen. We conducted an in-depth investigation into its protective mechanisms in NOD mice, with a particular focus on its effects on the gut microbiota and metabolome. The administration of the GAD65-L. lactis vaccine resulted in a significant delay in diabetes onset and the preservation of pancreatic function. Our analyses revealed notable alterations in the gut microbial ecosystem, enhancing its diversity and the abundance of beneficial bacteria. Metabolomic profiling indicated time-dependent changes in metabolic pathways, with a marked enrichment of pyrimidine metabolism at 16 weeks and arachidonic acid metabolism at 24 weeks after vaccination by both GAD65-L. lactis and NZ9000-L. lactis. Integrated correlation analysis identified specific microbiota-metabolite interactions, including associations between Ruminiclostridium and lipid species in the GAD65-L. lactis group. These modifications in the microbial community and metabolic landscape were accompanied by enhanced immunoregulatory responses in intestinal LPLs, including expanded Treg populations and suppressed CD8⁺ T cells, a rising trend in IL-10-producing naive dendritic cells, and increased concentrations of TGF-β.

Rapid antimicrobial resistance (AMR) prediction from MALDI-TOF mass spectrometry (MS) remains challenging, particularly when training artificial intelligence (AI) models under small-sample constraints. Performance is often hampered by the high dimensionality of spectral data and the subtle nature of resistance-related signals: full-spectrum approaches risk overfitting to high-dimensional noise, whereas peak-selection strategies risk discarding structurally informative, low-intensity signals. Here, we propose ReShuffle-MS, a region-guided data augmentation framework for MS data. Each spectrum is partitioned into a Main Discriminative Region (MDR) and a Peripheral Peak Region (PPR). By recombining signals within the PPR across samples of the same class while keeping the MDR intact, ReShuffle-MS generates structure-preserving augmented samples. On a clinical dataset for Escherichia coli (E. coli) levofloxacin resistance prediction, ReShuffle-MS delivered significant and consistent performance gains. It improved the average accuracy of classical machine learning models by 3.7% and enabled a one-dimensional convolutional neural network (CNN) to achieve 83.25% accuracy and 97.28% recall. Visualization using Grad-CAM revealed a shift from sparse, peak-dependent attention toward broader and more meaningful spectral patterns. Validation on the external DRIAMS-C dataset for ceftriaxone resistance further demonstrated that the method generalizes to a distinct laboratory setting and a different antibiotic target. These findings suggest that ReShuffle-MS can enhance the robustness and clinical utility of AI-based AMR prediction from routinely acquired MALDI-TOF spectra.

The MALDI-TOF MS Bruker Biotyper MBT subtyping IVD module enables the early detection of cfiA-positive Bacteroides fragilis (cfiA+ BF) during bacterial identification. However, the relationship between genetic positivity, phenotypic resistance, and clinical outcomes has not been fully elucidated. This retrospective study analyzed B. fragilis isolates from three Hong Kong hospitals between 2021 and 2025 to examine their prevalence and the clinical utility of MALDI-TOF MS in rapid cfiA detection. Antibiotic susceptibility testing, cfiA gene detection using MALDI-TOF MS, and Oxford Nanopore sequencing were performed. Medical records were reviewed, and univariate analyses and multivariate logistic regression were used to identify factors associated with cfiA positivity and 30-day all-cause mortality. Overall, B. fragilis exhibited a high rate of antibiotic resistance. Concomitant resistance to carbapenems and metronidazole was identified in three isolates. Among the 166 isolates, 40 (24.1%) were cfiA-positive. cfiA detection by MALDI-TOF MS showed 100% concordance with the gene sequencing results and correlated strongly with phenotypic carbapenem resistance (Φ = 0.82, p < 0.001 for meropenem; Φ = 0.70, p < 0.001 for ertapenem; Φ = 0.63, p < 0.001 for imipenem). Phylogenetic analysis revealed two distinct clusters corresponding to cfiA status, each exhibiting genetic diversity based on multi-locus sequence typing (MLST). The cfiA+ BF isolates demonstrated high-level phenotypic carbapenem resistance in the presence of upstream insertion sequences. The predominant sequence type (ST) among cfiA+ BF isolates was ST157, and 70% of ST157 isolates harbored IS1187 in the upstream region of cfiA. Gene sequencing also identified other emerging beta-lactamase genes bla_OXA-347 and bla_MUN. The 30-day all-cause mortality following B. fragilis infection was 13.3%, with independent predictors including a high Charlson Comorbidity Index (OR = 1.30; p = 0.02) and the absence of early source control (OR = 4.84; p = 0.03). This study highlights the widespread occurrence of cfiA+ BF in Hong Kong and the clinical significance of rapid cfiA detection. Continuous surveillance is essential to monitor the ongoing threat of antibiotic resistance in B. fragilis.

C. perfringens is a Gram-positive, anaerobic, spore-forming bacterium that causes serious diseases in humans and animals. In this study, C. perfringens was isolated from the intestinal content of an alpaca, cultured, and then identified using laboratory methods including Gram staining, biochemical tests, and PCR for 16S rRNA of six toxins. Furthermore, the pathogenicity of different strains was analyzed in mice. The results showed that C. perfringens was identified as type A and caused severe pathology of the spleen, lungs, and duodenum in mice through CD4⁺ and CD8⁺ T cells. Also, the mRNA expression levels of ZO-1 and Occludin were further quantified by qRT-PCR with normalization to β-actin, which showed decreased expression levels in the duodenum of mice in the gavage group compared to those in the NC groups, with significant differences (n = 3; * p < 0.05, ** p < 0.01). The results could inform the development of drugs and vaccines resistant to C. perfringens in alpaca.

Tuberculosis (TB) remains a global health challenge, partly due to difficulties in diagnosis and the prolonged duration and toxicity of standard antibiotic regimens [...].

Background and objectives: Secondary bacterial infection drives poor outcomes in older adults with COVID-19, but age-specific microbiology and its interaction with severity scores are not well defined. We characterized respiratory and pleural pathogens, resistance profiles, and their impact on day-5 SOFA/APACHE II in octogenarians versus younger adults.

Methods: We performed a retrospective cohort study of adults with RT-PCR-confirmed coronavirus disease 2019 (COVID-19) at a tertiary infectious diseases center (≥80 years, n = 152; <65 years, n = 327). Respiratory and pleural samples were processed according to EUCAST standards. Identification employed matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). Pathogen distributions, susceptibilities, and rates of superimposed pneumonia, empyema, and bacteremia were compared by age, and associations between secondary pneumonia, day-5 SOFA/APACHE II, and 28-day mortality were analyzed.

Results: Sputum was obtained in 67.1% of older and 65.7% of younger adults, with numerically higher culture positivity in older patients (73.5% vs. 65.1%). Pathogen spectra were similar, dominated by Streptococcus pneumoniae (24.0% vs. 24.3%), methicillin-susceptible Staphylococcus aureus (MSSA) (18.7% vs. 20.7%), methicillin-resistant Staphylococcus aureus (MRSA) (9.3% vs. 6.4%), and Klebsiella pneumoniae, including extended-spectrum β-lactamase (ESBL)-producing strains. Empyema was more frequent in octogenarians (7.9% vs. 3.1%), and pleural cultures were usually positive. Meropenem retained 100% activity against ESBL-producing K. pneumoniae and Pseudomonas in both strata. In ≥80-year-olds, superimposed pneumonia was associated with higher day-5 SOFA (6.6 vs. 5.5) and APACHE II (24.3 vs. 21.0) scores and markedly increased 28-day mortality (37.5% vs. 9.8%).

Conclusions: In octogenarians with COVID-19, secondary bacterial pneumonia and empyema are frequent, microbiologically similar to younger adults, and strongly amplify organ dysfunction and mortality even with largely preserved carbapenem susceptibility.

The overlapping circulation of influenza (Flu), severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; SC2), and respiratory syncytial virus (RSV) continues to challenge clinical laboratories, particularly in settings with limited automation and fragmented healthcare coverage. This study expanded the CDC Flu-SC2 assay by incorporating a laboratory-developed test (LDT) for RSV A/B detection into a fully automated quadruplex RT-qPCR (LDRA) on the Panther Fusion^® Open Access™ system. The design, based on more than 8000 RSV genomic sequences targeting the conserved M gene, achieved optimal amplification efficiencies (97-105%) and full multiplex compatibility. Analytical assessment established limits of detection between 9.6 and 37.8 copies per reaction, absence of cross-reactivity with 30 respiratory pathogens, and inclusivity for 32 viral variants. Commutability and diagnostic performance among the LDRA, CE IVD-marked Allplex™ SARS-CoV-2/FluA/FluB/RSV, and US IVD-marked Panther Fusion^® SARS-CoV-2/Flu A/B/RSV Assays were evaluated using 405 nasopharyngeal UTM-preserved swabs. The LDRA demonstrated excellent concordance (overall agreement ≥ 98%, κ > 0.95), strong diagnostic accuracy, and reliable detection of mixed infections. This quadruplex provides a fully automated, rapid, and accurate solution for the simultaneous detection of influenza A, influenza B, SARS-CoV-2, and RSV viruses, enhancing molecular diagnostic capacity and supporting equitable, timely clinical decision-making in middle-income healthcare systems such as that of the Dominican Republic.

Bacterial communities in the rhizosphere and endorhizosphere of plants show distinct composition, function, and ecological roles during adaptation to diverse habitats. This study examines how rhizosphere and endophytic microbes associated with Aeluropus sinensis-a salt-excreting halophyte-contribute to its salt tolerance across saline-alkali environments. Microbial diversity and composition were analyzed via 16S rRNA gene amplicon sequencing. Soil physicochemical properties were measured to evaluate environmental effects. Linear regression assessed microbial-environment relationships, and co-occurrence networks identified key taxa and their adaptive strategies along environmental gradients. Soil salinity significantly affected rhizosphere bacterial diversity, with moderate levels increasing richness. Proteobacteria dominated both root and rhizosphere microbiomes across habitats. The endorhizosphere community strongly correlated with soil nutrients such as available phosphorus (AP) and total nitrogen (TN). Co-occurrence analysis reveals that chemoheterotrophic microbes in the A. sinensis rhizosphere employ distinct adaptive strategies across gradients, and ammonia-oxidizing bacteria (AOB) may support nitrogen cycling in the Yellow River Delta saline-alkaline ecosystem. This study underscores microbial adaptability in salt-tolerant grasses, demonstrating that comparing rhizosphere and endorhizosphere microbiomes in Poaceae under stress improves understanding of microbial functions in harsh environments.

Vegetation restoration is an effective strategy to improve the ecosystem function of the Loess Plateau. Soil microbiomes play a critical role in maintaining soil multifunctionality (SMF). However, the role of aggregate-scale microbial communities and interactions in regulating SMF during vegetation restoration remains poorly understood. Here, we selected six types of vegetation restoration measures in the Loess Plateau, including natural grassland (NL), Medicago sativa (MS), Hippophae rhamnoides (HR), Caragana korshinskii (CK), Armeniaca vulgaris (AV), and Populus alba (PA), and used abandoned land (AL) as a control to identify key microbial mechanisms driving SMF at the aggregate scale. The results show that vegetation restoration increased bacterial diversity, fungal network complexity, and SMF, especially in AV. In contrast, fungal diversity and bacterial network complexity exhibited asynchronous dynamics across different-sized aggregates. Soil microbial diversity peaked at micro-aggregates (0.053-0.25 mm), while fungal network complexity increased with decreasing aggregate size. The structural equation model confirmed that fungal community composition in large macro-aggregates (>2 mm) and fungal network complexity in <2 mm aggregates were the key drivers of SMF. Our results emphasize the divergent mechanisms by which microbial properties influence SMF across aggregate sizes, highlighting the importance of fungal communities in maintaining soil ecosystem functions.