Phycosphere bacteria can regulate the dynamics of different algal blooms that impact marine ecosystems. Phaeocystis globosa can alternate between solitary free-living cells and colonies and the latter morphotype is dominate during blooms. The mechanisms underlying the formation of these blooms have received much attention. High throughput sequencing results showed that the bacterial community composition differed significantly between colony and solitary strains in bacterial composition and function. It was found that the genera SM1A02 and Haliea were detected only among the colony strains and contribute to ammonium accumulation in colonies, and the genus Sulfitobacter was abundant among the colony strains that were excellent at producing DMS. In addition, the bacterial communities of the two colony strains exhibited stronger abilities for carbon and sulfur metabolism, energy metabolism, vitamin B synthesis, and signal transduction, providing inorganic and organic nutrients and facilitating tight communication with the host algae, thereby promoting growth and bloom development.

BACKGROUND: Helicobacter pylori (H. pylori) infection is closely associated with gastrointestinal diseases. Our preliminary studies have indicated that H. pylori infection had a significant impact on the mucosal microbiome structure in patients with gastric ulcer (GU) or duodenal ulcer (DU).
OBJECTIVE: To investigate the contributions of H. pylori infection and the mucosal microbiome to the pathogenesis and progression of ulcerative diseases.
METHODS: Patients with H. pylori infection and either GU or DU, and healthy individuals without H. pylori infection were included. Gastric or duodenal mucosal samples was obtained and subjected to metagenomic sequencing. The compositions of the microbial communities and their metabolic functions in the mucosal tissues were analyzed.
RESULTS: Compared with that in the healthy individuals, the gastric mucosal microbiota in the H. pylori-positive patients with GU was dominated by H. pylori, with significantly reduced biodiversity. The intergroup differential functions, which were enriched in the H. pylori-positive GU patients, were all derived from H. pylori, particularly those concerning transfer RNA queuosine-modification and the synthesis of demethylmenaquinones or menaquinones. A significant enrichment of the uibE gene was detected in the synthesis pathway. There was no significant difference in microbial diversity between the H. pylori-positive DU patients and healthy controls.
CONCLUSIONS: H. pylori infection significantly alters the gastric microbiota structure, diversity, and biological functions, which may be important contributing factors for GU.

The impact of gestational diabetes mellitus (GDM) on maternal or infant microbiome trajectory remains poorly understood. Utilizing large-scale longitudinal fecal samples from 264 mother-baby dyads, we present the gut microbiome trajectory of the mothers throughout pregnancy and infants during the first year of life. GDM mothers had a distinct microbiome diversity and composition during the gestation period. GDM leaves fingerprints on the infant\'s gut microbiome, which are confounded by delivery mode. Further, Clostridium species positively correlate with a larger head circumference at month 12 in male offspring but not females. The gut microbiome of GDM mothers with male fetuses displays depleted gut-brain modules, including acetate synthesis I and degradation and glutamate synthesis II. The gut microbiome of female infants of GDM mothers has higher histamine degradation and dopamine degradation. Together, our integrative analysis indicates that GDM affects maternal and infant gut composition, which is associated with sexually dimorphic infant head growth.

In recent years, most studies on the gut microbiome have primarily focused on feces samples, leaving the microbial communities in the intestinal mucosa relatively unexplored. To address this gap, our study employed shotgun metagenomics to analyze the microbial compositions in normal rectal mucosa and matched feces from 20 patients with colonic polyps. Our findings revealed a pronounced distinction of the microbial communities between these two sample sets. Compared with feces, the mucosal microbiome contains fewer genera, with Burkholderia being the most discriminating genus between feces and mucosa, highlighting its significant influence on the mucosa. Furthermore, based on the microbial classification and KEGG Orthology (KO) annotation results, we explored the association between rectal mucosal microbiota and factors such as age, gender, BMI, and polyp risk level. Notably, we identified novel biomarkers for these phenotypes, such as Clostridium ramosum and Enterobacter cloacae in age. The mucosal microbiota showed an enrichment of KO pathways related to sugar transport and short chain fatty acid metabolism. Our comprehensive approach not only bridges the knowledge gap regarding the microbial community in the rectal mucosa but also underscores the complexity and specificity of microbial interactions within the human gut, particularly in the Chinese population.
OBJECTIVE: This study presents a system-level map of the differences between feces and rectal mucosal microbial communities in samples with colorectal cancer risk. It reveals the unique microecological characteristics of rectal mucosa and its potential influence on health. Additionally, it provides novel insights into the role of the gut microbiome in the pathogenesis of colorectal cancer and paves the way for the development of new prevention and treatment strategies.

OBJECTIVE: The study aimed to develop a genotypic antimicrobial resistance testing method for Klebsiella pneumoniae using metagenomic sequencing data.
METHODS: We utilized Lasso regression on assembled genomes to identify genetic resistance determinants for six antibiotics (Gentamicin, Tobramycin, Imipenem, Meropenem, Ceftazidime, Trimethoprim/Sulfamethoxazole). The genetic features were weighted, grouped into clusters to establish classifier models. Origin species of detected antibiotic resistant gene (ARG) was determined by novel strategy integrating \"possible species,\" \"gene copy number calculation\" and \"species-specific kmers.\" The performance of the method was evaluated on retrospective case studies.
RESULTS: Our study employed machine learning on 3928 K. pneumoniae isolates, yielding stable models with AUCs > 0.9 for various antibiotics. GenseqAMR, a read-based software, exhibited high accuracy (AUC 0.926-0.956) for short-read datasets. The integration of a species-specific kmer strategy significantly improved ARG-species attribution to an average accuracy of 96.67%. In a retrospective study of 191 K. pneumoniae-positive clinical specimens (0.68-93.39% genome coverage), GenseqAMR predicted 84.23% of AST results on average. It demonstrated 88.76-96.26% accuracy for resistance prediction, offering genotypic AST results with a shorter turnaround time (mean ± SD: 18.34 ± 0.87 hours) than traditional culture-based AST (60.15 ± 21.58 hours). Furthermore, a retrospective clinical case study involving 63 cases showed that GenseqAMR could lead to changes in clinical treatment for 24 (38.10%) cases, with 95.83% (23/24) of these changes deemed beneficial.
CONCLUSIONS: In conclusion, GenseqAMR is a promising tool for quick and accurate AMR prediction in Klebsiella pneumoniae, with the potential to improve patient outcomes through timely adjustments in antibiotic treatment.

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Nocardiosis demonstrates a temporal categorization that includes acute, subacute, and chronic stages alongside distinct typical localizations such as pulmonary, cutaneous, and disseminated forms. Disseminated nocardiosis, commonly caused by Nocardia asteroides, N. brasiliensis, and N. farcinica, continues to result in substantial morbidity and mortality. Herein, we report a life-threatening disseminated nocardiosis caused by Nocardia otitidiscaviarum in a patient with minimal change disease. This study emphasizes the difficulty in the diagnosis and treatment of unknown infections in clinical settings and highlights the important role played by laboratories in solving infectious diseases caused by rare pathogens.

Antimicrobial resistance (AMR) is a major threat to global public health, and antibiotic resistance genes (ARGs) are widely distributed across humans, animals, and environment. Farming environments are emerging as a key research area for ARGs and antibiotic resistant bacteria (ARB). While the skin is an important reservoir of ARGs and ARB, transmission mechanisms between farming environments and human skin remain unclear. Previous studies confirmed that swine farm environmental exposures alter skin microbiome, but the timeline of these changes is ill defined. To improve understanding of these changes and to determine the specific time, we designed a cohort study of swine farm workers and students through collected skin and environmental samples to explore the impact of daily occupational exposure in swine farm on human skin microbiome. Results indicated that exposure to livestock-associated environments where microorganisms are richer than school environment can reshape the human skin microbiome and antibiotic resistome. Exposure of 5 h was sufficient to modify the microbiome and ARG structure in workers\' skin by enriching microorganisms and ARGs. These changes were preserved once formed. Further analysis indicated that ARGs carried by host microorganisms may transfer between the environment with workers\' skin and have the potential to expand to the general population using farm workers as an ARG vector. These results raised concerns about potential transmission of ARGs to the broader community. Therefore, it is necessary to take corresponding intervention measures in the production process to reduce the possibility of ARGs and ARB transmission.

Gut microbiota dysbiosis increases the susceptibility to Clostridioides difficile infection (CDI). In this study, we monitored C. difficile colonization (CDC) patients from no CDC status (CDN) to CDC status (CDCp) and CDI patients from asymptomatic status before CDI (PRECDI), CDI status (ONCDI), to asymptomatic status after CDI (POSTCDI). Based on metagenomic sequencing, we aimed to investigate the interaction pattern between gut microbiota and C. difficile. There was no significant difference of microbiota diversity between CDN and CDCp. In CDCp, Bacteroidetes and short-chain fatty acid (SCFA)-producing bacteria increased, with a positive correlation between SCFA-producing bacteria and C. difficile colonization. Compared with PRECDI, ONCDI and POSTCDI showed a significant decrease in microbiota diversity, particularly in Bacteroidetes and SCFA-producing bacteria, with a positive correlation between opportunistic pathogen and C. difficile. Fatty acid metabolism, and amino acid biosynthesis were enriched in CDN, CDCp, and PRECDI, while bile secretion was enriched in ONCDI and POSTCDI. Microbiota and metabolic pathways interaction networks in CDN and CDCp were more complex, particularly pathways in fatty acid and bile acid metabolism. Increasing of Bacteroidetes and SCFA-producing bacteria, affecting amino acid and fatty acid metabolism, is associated with colonization resistance to C. difficile and inhibiting the development of CDI.

BACKGROUND: Microbiota may be associated with esophageal squamous cell carcinoma (ESCC) development. However, it is not known the predictive value of microbial biomarkers combining epidemiological factors for the early detection of ESCC and precancerous lesions.
METHODS: A total of 449 specimens (esophageal swabs and saliva) were collected from 349 participants with different esophageal statuses in China to explore and validate ESCC-associated microbial biomarkers from genes level to species level by 16S rRNA sequencing, metagenomic sequencing and real-time quantitative polymerase chain reaction.
RESULTS: A bacterial biomarker panel including Actinomyces graevenitzii (A.g_1, A.g_2, A.g_3, A.g_4), Fusobacteria nucleatum (F.n_1, F.n_2, F.n_3), Haemophilus haemolyticus (H.h_1), Porphyromonas gingivalis (P.g_1, P.g_2, P.g_3) and Streptococcus australis (S.a_1) was explored by metagenomic sequencing to early detect the participants in Need group (low-grade intraepithelial neoplasia, high-grade intraepithelial neoplasia and ESCC) vs participants without these lesions as the Noneed group. Significant quantitative differences existed for each microbial target in which the detection efficiency rate was higher in saliva than esophageal swab. In saliva, the area under the curve (AUC) based on the microbial biomarkers (A.g_4 ∩ P.g_3 ∩ H.h_1 ∩ S.a_1 ∩ F.n_2) was 0.722 (95% CI 0.621-0.823) in the exploration cohort. Combining epidemiological factors (age, smoking, drinking, intake of high-temperature food and toothache), the AUC improved to 0.869 (95% CI 0.802-0.937) in the exploration cohort, which was validated with AUC of 0.757 (95% CI 0.663-0.852) in the validation cohort.
CONCLUSIONS: It is feasible to combine microbial biomarkers in saliva and epidemiological factors to early detect ESCC and precancerous lesions in China.