Frontiers in Cellular and Infection Microbiology最新文献

The infective stages of apicomplexan protozoans, such as the malaria parasite Plasmodium, possess apical organelles rhoptries and micronemes, which contain secretory proteins required for host cell invasion. The mechanisms mediating invasion are largely conserved among apicomplexan parasites; for example, rhoptry proteins are secreted to form a tight junction prior to invasion, which facilitates parasite entry into target cells. Stage-specific invasion mechanisms have also been described; such as those differentially mediating Plasmodium merozoite infection of erythrocytes versus sporozoite stage invasion of mosquito salivary glands and mammalian hepatocytes. Sporozoites are the transmission stage present within the salivary glands of infected mosquitoes, and can efficiently infect the mammalian liver after being deposited in the skin during a blood meal. While some sporozoite rhoptry proteins have been demonstrated to be critical for invasion of mosquito salivary glands and mammalian hepatocytes, their comprehensive molecular mechanisms have not been elucidated due to the limited availability of material. To screen for Plasmodium sporozoite-specific rhoptry proteins in the rodent malaria parasite, Plasmodium berghei, a proximity-dependent biotin identification method was employed combined with a genome editing strategy. Rhoptry neck protein 12 (RON12) was identified as a rhoptry molecule with the highest transcript levels in sporozoites; and was selected for use as a bait following tagging with UltraID. In RON12::ultraID expressing transgenic sporozoites, several secretory proteins were successfully biotinylated during parasite maturation in mosquitoes, including known rhoptry proteins. A novel rhoptry molecule was identified, PBANKA_1363400, which was localized to sporozoite rhoptries and was predominantly expressed in sporozoites rather than merozoites. This study demonstrates that the UltraID strategy enables highly sensitive and comprehensive protein identification in a species- or stage-specific manner in Plasmodium sporozoites.

Objective: This study aimed to construct a predictive model for the early onset of atherosclerotic cardiovascular disease (ASCVD) by integrating oral microbiome data with traditional clinical risk factors.

Methods: A retrospective study was conducted involving participants aged 50-70 years without pre-existing ASCVD. The patients were divided into a training set and a validation set at a ratio of 7:3 by the complete randomization method. The characteristics of the oral microbiome were characterized by 16S rRNA/metagenomic sequencing. In the training set, univariate analysis and multivariate Logistic regression analysis were applied to screen predictive variables, and Random Forest (RF), Gradient Boosting (GB), and K-nearest Neighbor (KNN) were constructed. The receiver operating characteristic (ROC) curve was validated. The model performance was evaluated by net reclassification improvement (NRI) and integrated discrimination improvement (IDI).

Results: A total of 331 patients were enrolled and randomly divided into a training set (n=231) and a validation set (n=100). 40 out of 331 participants experienced major adverse cardiovascular events (MACE). Multivariate Logistic regression analysis confirmed that age, relative abundance of Fusobacterium nucleatum, Prevotella, Porphyromonas, Leptotrichia, Streptococcus and Actinomyces were significantly associated with ASCVD event risk (all P < 0.05). Three machine learning models (RF, GB, and KNN) were constructed, with the RF model achieving the highest predictive performance. The AUC values of the RF, GB, and KNN models in the training set were 0.888 (95% CI: 0.818-0.958), 0.823 (95% CI: 0.745-0.901), and 0.812 (95% CI: 0.727-0.898) respectively, and in the validation set were 0.845 (95% CI: 0.740-0.951), 0.746 (95% CI: 0.621-0.871), and 0.767 (95% CI: 0.647-0.887) respectively. Additionally, the integrated model showed significant improvements in net reclassification improvement (NRI = 0.315, P < 0.05) and integrated discrimination improvement (IDI = 0.227, P < 0.05) compared to traditional clinical models.

Conclusion: The integration of the oral microbiome and clinical data can improve the accuracy of the ASCVD risk prediction model, providing a novel biomarker strategy for primary cardiovascular prevention.

Background: Sepsis carries high ICU mortality globally, often requiring sedated mechanical ventilation. While some studies suggest dexmedetomidine improves survival in these patients, others contradict this finding. This study evaluates dexmedetomidine's survival benefit and sedation value for ventilated sepsis cases.

Methods: This retrospective cohort study utilized the MIMIC-IV database and eICU-CRD to analyze mechanically ventilated septic patients. Propensity score matching was employed to balance covariates. Machine learning algorithms were applied to validate dexmedetomidine's role in predicting mortality.

Results: A propensity score matching analysis was performed for 5176 pairs of patients. The use of dexmedetomidine was associated with a reduced risk of 28-day mortality (13.39% vs. 19.84%, HR: 0.595, P < 0.001) and of 180-day all-cause mortality (17.45% vs. 23.18%, HR: 0.632, P < 0.001). However, dexmedetomidine use was also associated with longer hospital (median 15.08 days vs. 10.2 days, P < 0.001) and ICU stays (median 6.81 days vs. 4.0 days, P < 0.001). Moreover, the duration of mechanical ventilation was significantly longer in the dexmedetomidine group (median 78 h vs. 51.00 h, P < 0.001). Dexmedetomidine was included among the significant features identified with the Boruta algorithm, and of the five machine learning models built using the 20 most important features (including dexmedetomidine), the model constructed on the basis of the Random Forest algorithm performed the best (training set: AUC = 0.781; test set: AUC = 0.811; eICU-CRD set: AUC = 0.820). SHapley Additive exPlanations (SHAP) revealed that comorbid acute kidney injury (AKI) was the most important predictor of mortality among mechanically ventilated septic patients. This was followed by the use of opioids, PaO₂, and the SOFA score, with the use of dexmedetomidine relatively closely behind.

Conclusions: Dexmedetomidine use significantly reduces short-term mortality in mechanically ventilated patients with sepsis but prolongs the hospital and ICU length of stay (LOS) and duration of mechanical ventilation. Administering dexmedetomidine within 48 hours and maintaining an infusion rate at or below 0.6 μg/kg/h appears to be more beneficial. Moreover, dexmedetomidine use strongly influences mortality in these patients.

Background: Pathogenic strains of Escherichia coli (E. coli) cause colibacillosis in pre- and post-weaning piglets. Fimbrial and non-fimbrial adhesins, as well as heat-labile and heat-stable enterotoxins, are main virulence factors in enterotoxigenic (ETEC), enteroaggregative (EAEC), enteropathogenic (EPEC) and shigatoxigenic (STEC) pathotypes which cause colidiarrhea or colitoxemia in piglets.

Methods: Fifty-five piglets submitted for necropsy were examined for gross and histological lesions. E. coli strains were isolated, biochemically confirmed, and tested by PCR for 15 virulence genes (VGs). Statistical analyses used appropriate parametric or non-parametric tests, depending on the distribution. The results with p values less than or equal to 0.05 (p ≤ 0.05) were considered statistically significant.

Results: Overall, 84.48% of strains carried at least one VG. The occurrence of six VGs - astA, estII, faeG, estI, elt, and paa - was high, with frequencies of 67.24%, 63.97%, 55.18%, 50.00%, 48.27%, and 44.82%, respectively. ETEC predominated (63.79%), while 5.17% of strains carried EPEC or STEC genes; 15.52% were non-specific virotypes, and 15.52% were apathogenic. Lesions were most prominent in the small intestine. The virotype LT:STa:STb:EAST1:PAA:F4 was most common, whereas STa:Stx2:Stx2e was linked to the most severe lesions. Lesions varied depending on the pathotype involved and the VGs expressed. Severity of lesions differed significantly between suckling and weaned piglets (p = 0.0091) and between piglets with and without diarrhea (p = 0.0223), with suckling and diarrheic piglets showing more pronounced pathological changes. More extensive lesions in ETEC were associated with the acquired astA and paa genes. Pathoscores were significantly associated with faeG/F4 (p = 0.0001), eltA/LT (p = 0.0001), estII/STb (p = 0.0001), paa/PAA (p = 0.0002), and astA/EAST1 (p = 0.0029).

Discussion and conclusions: Strong associations between specific VGs - particularly faeG, eltA, estII, paa, and astA - and higher lesion scores show that VG detection can help predict disease severity and guide interventions. Age-specific interpretation is crucial, as isolates from pre-weaned piglets often carried more VGs and were associated with more severe lesions. This study underscores the value of integrating bacteriological, molecular and histopathological data for accurate diagnosis, especially given the high prevalence of VG-positive and recombinant ETEC strains.

Background: Due to the high sensitivity of metagenomic next-generation sequencing (mNGS), trace amounts of nucleic acid contamination can lead to false positives, posing challenges for result interpretation. This study is the first to experimentally identify and establish background microbial libraries (BML) related to periprosthetic joint infection (PJI) across different medical institutions, aiming to demonstrate the necessity of institution-specific BMLs to improve mNGS diagnostic accuracy.

Methods: Samples were taken from 3 different acetabular reamer for hip arthroplasty in 7 different hospitals. The whole process was strictly aseptic, mNGS was performed according to standard operating procedures. The sterility of instruments was confirmed by culture method. The sequencing results of specimens from different hospitals were compared to analyze the difference of background bacteria. Bioinformatics analysis and visualization were presented through R language.

Results: A total of 26 samples (24 instrument swabs and 2 negative controls) generated 254 million reads, of which 1.13% matched microbial genomes. The proportion of microbial reads (1.13%) falls within ranges typically observed for contamination in low-biomass metagenomic sequencing studies. Among these, bacteria accounted for 87.48%, fungi 11.18%, parasites 1.26%, and viruses 0.06%. The most abundant bacterial genera included Cutibacterium, Staphylococcus, and Acinetobacter. Principal component analysis revealed distinct bacterial compositions among the seven hospitals, and clustering analysis showed significant inter-hospital variation (p < 0.05). Liaocheng People's Hospital exhibited the highest species richness (340 species), followed by Guanxian County People's Hospital (169 species).

Conclusions: The composition and abundance of residual bacterial DNA vary markedly among institutions, underscoring the necessity of establishing hospital-specific BMLs. Incorporating such libraries into clinical mNGS interpretation can effectively reduce false positives and enhance the diagnostic accuracy of PJI. arthroplasty, bacterial culture, next-generation sequencing, joint replacement, periprosthetic joint infection, background microbial libraries.

In the tumor immune microenvironment, microbes promote tumor progression and metastasis by invading host cancer cells. Blocking these interactions is expected to provide new strategies for inhibiting tumor progression and metastasis, as well as opening up new avenues for immunotherapy. However, technological means of studying the interaction between microorganisms and host cancer cells are still limited. Proximity labeling, a widely used method for analyzing biomolecular and cellular interactions, has the potential to analyze microbe-host cell interactions quantitatively, uncovering the key factors that influence these interactions within the tumor immune microenvironment in order to control tumor initiation and progression. Furthermore, proximity labeling based strategies can be applied to high-throughput drug screening aimed at disrupting pathogenic microbe-host interactions, contributing to the development of therapeutics against advanced and metastatic tumors. This paper provides a systematic review of the topic, introducing cutting-edge microbiological mechanisms that have attracted the attention of oncologists.

Objective: Chimeric antigen receptor (CAR) T-cell therapy has demonstrated remarkable efficacy in hematological malignancies. However, it can also cause severe systemic toxicity, known as cytokine release syndrome (CRS). Therefore, the potential of CAR-T cells to cause toxicity in vivo should be evaluated in preclinical models prior to first-in-human trials. Although murine models exist for this purpose, they are typically complex xenograft systems available only to a limited number of laboratories. Therefore, development of an in vitro assay to assess CRS elicited by CAR-T cells is warranted.

Methods: CAR-T cells, macrophages, or immature dendritic cells (iDCs), along with tumor target cells, were co-cultured under different conditions. The release of CRS-related cytokines, IFN-γ and IL-6, was measured to simulate cytokine release during CAR-T-induced CRS. Additionally, the cellular source of the key CRS cytokine IL-6 was investigated.

Results: A co-culture system containing only CAR-T cells and tumor cells failed to recapitulate the key feature of CRS, specifically a significant elevation of IL-6. However, when CAR-T cells were co-cultured with antigen-presenting cells (macrophages or iDCs) and tumor cells, the core CRS cytokine IL-6 was significantly elevated in an in vitro cell culture model, indicating that this system effectively mimics cytokine release during CAR-T-induced CRS. Furthermore, macrophages and iDCs are the primary cellular sources of IL-6 during CRS, with macrophages playing a central role in the development of CRS. Additionally, a co-culture system involving CAR-T cells, tumor cells, and macrophages under these conditions can indicate the occurrence of clinically severe-grade CRS.

Conclusion: Macrophages and iDCs play a critical role in the development of CAR-T therapy-induced CRS. The triple-cell co-culture system, comprising CAR-T cells, macrophages or iDCs, and tumor cells, provides a viable in vitro model for assessing CAR-T cell-induced CRS.

Background: The specific gut microbial signatures and their correlation with immune-inflammatory markers in infertile women with endometriosis remain underexplored.To investigate the differences in gut microbiota and their associations with biochemical immune markers in infertile women with endometriosis compared to controls.

Methods: This case-control study enrolled 32 infertile women with endometriosis and 13 control women with male-factor infertility. Fecal samples were collected for 16S rRNA sequencing to profile the gut microbiota, and serum samples were obtained to measure inflammation-related biomarkers. Bioinformatics analyses were applied to compare gut microbial community structures and to examine correlations between differentially abundant bacteria and immune markers.

Results: The endometriosis group exhibited significant enrichment of Lachnospira, Bacilli, Lactobacillales, Parasutterella, Enterococcus, and Veillonella. Comparative analysis revealed significantly altered abundances of multiple taxa, including Lachnospira, Parasutterella, Alistipes, Enterococcus, Veillonella, Streptococcus, Desulfovibrionaceae, Ruminococcaceae, Bilophila, and Peptoniphilus (all P < 0.05). Several inter-species correlations were identified among these bacteria. Importantly, specific microbiota were correlated with immune markers: Streptococcus and Veillonella were positively correlated with macrophage migration inhibitory factor (MIF); Bilophila and Enterococcus were positively correlated with TNF-α and IL-6; Veillonella was positively correlated with TNF-α; Desulfovibrionaceae was negatively correlated with TNF-α and IL-6; and Parasutterella was negatively correlated with CA125.

Conclusion: In this exploratory investigation, specific gut microbial signatures were observed in infertile patients with endometriosis, showing correlations with select systemic immune-inflammatory biomarkers. These initial observations point to a possible association between gut microbiota imbalance and the inflammatory aspects of endometriosis-associated infertility. Consequently, microbial modulation merits further investigation as a potential strategy to alleviate inflammation and potentially enhance reproductive outcomes.

Background: Chikungunya fever (CHIKF) is a mosquito-borne viral disease characterized by fever, rash, and severe joint pain. However, these classical descriptions are based overwhelmingly on the Indian Ocean and Caribbean lineages. With the recent introduction and spread of the Middle Africa lineage (MAL) into Asia, understanding its clinical presentation in new populations, such as Chinese, has become a public health priority. Whether the recently introduced MAL causes comparably severe disease in China remains unknown.

Methods: We enrolled 415 laboratory-confirmed cases of Chikungunya virus (CHIKV) infection during an outbreak in Foshan, China. Clinical manifestations, laboratory parameters, and whole-genome sequencing data were integrated to quantify the symptom burden from three different perspectives using multivariate logistic regression, and to trace the viral source via maximum-likelihood phylogenetic analysis.

Results: Compared with the classical phenotype, the MAL outbreak in China was appreciably milder. The most common clinical manifestations were arthralgia (83.61%), fever (74.46%), and rash (61.93%). Multivariate logistic regression showed that older age (OR = 0.979, P = 0.029) and male sex (OR = 0.528, P = 0.038) were negatively correlated with the occurrence of higher symptom burden, while prolonged fever (OR = 8.156, P < 0.001) was a significant risk factor. Reduced estimated glomerular filtration rate and thrombocytopenia were associated with longer disease duration. Phylogenetic analysis revealed that the outbreak-associated CHIKV strains belonged to MAL and harbored the E1-A226V and E2-I211T mutations.

Conclusion: These findings provide an evidence base for clinical management and prognostic assessment during CHIKF outbreaks and underscore the importance of monitoring laboratory parameters alongside molecular surveillance.

Global public health is formidably threatened by antimicrobial resistance (AMR). Antimicrobial susceptibility testing (AST) is characterized by its long duration. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is notable for its rapid analysis and cost-effectiveness. However, its role in AST has not been fully explored. In recent years, new opportunities for predicting AMR using MALDI-TOF MS data have been provided by the development of machine learning (ML) technologies. The research progress in using MALDI-TOF MS combined with ML for AMR testing is surveyed by this review, and critical steps including raw MALDI-TOF MS data acquisition, raw data preprocessing, algorithm selection, hyperparameter optimization, among others. It was found by us that the true resistance status can be comprehensively reflected by large-scale datasets, but effective management of high-dimensional data challenges is required. Algorithm performance can be enhanced by identifying the optimal combination of hyperparameters. Better predictive performance than individual models can be achieved by stacking ensemble learning methods. Model performance and generalizability can be more effectively assessed by metrics such as the Area Under the Receiver Operating Characteristic Curve (AUROC). The decision-making process can be understood by users with the help of model interpretation, thereby increasing model transparency and acceptability. Insufficient sample size, inadequate data standardization, and limited model generalizability are included in the current challenges. Continuously optimized, the integration of MALDI-TOF MS and ML is poised to open future avenues for rapid and accurate AMR prediction.