The gut microbiota and immune system interaction play a crucial role in maintaining overall health. Probiotics, prebiotics, and postbiotics have emerged as promising therapeutic approaches to positively influence this complex axis and enhance health outcomes. Probiotics, as live bacteria, promote the growth of immune cells, shape immune responses, and maintain gut barrier integrity. They modify the gut microbiota by fostering beneficial bacteria while suppressing harmful ones. Additionally, probiotics interact with the immune system, increasing immune cell activity and anti-inflammatory cytokine production. Prebiotics, as indigestible fibers, selectively nourish beneficial microorganisms in the gut, enhancing gut microbial diversity and activity. This, in turn, improves gut health and boosts immune responses while controlling inflammation through its immunomodulatory properties. Postbiotics, produced during probiotic fermentation, such as short-chain fatty acids and antimicrobial peptides, positively impact gut health and modulate immune responses. Ensuring quality control and standardization will be essential for successful clinical implementation of these interventions. Overall, understanding and harnessing the gut microbiota-immune system interplay offer promising avenues for improving digestive and immunological health.

Determining the complete Arabidopsis (Arabidopsis thaliana) protein-protein interaction (PPI) network is essential for understanding the functional organization of the proteome. Numerous small-scale studies and a couple of large-scale ones have elucidated a fraction of the estimated 300,000 binary PPIs in Arabidopsis. In this study, we provide evidence that a docking algorithm has the ability to identify real interactions using both experimentally determined and predicted protein structures. We ranked 0.91 million interactions generated by all possible pairwise combinations of 1,346 predicted structure models from an Arabidopsis predicted "structure-ome" and found a significant enrichment of real interactions for the top-ranking predicted interactions, as shown by co-subcellular enrichment analysis and yeast two-hybrid validation. Our success rate for computationally predicted, structure-based interactions was 63% of the success rate for published interactions naïvely tested using the yeast two-hybrid system and 2.7 times better than for randomly picked pairs of proteins. This study provides another perspective in interactome exploration and biological network reconstruction using protein structural information. We have made these interactions freely accessible through an improved Arabidopsis Interactions Viewer and have created community tools for accessing these and ~2.8 million other protein-protein and protein-DNA interactions for hypothesis generation by researchers worldwide. The Arabidopsis Interactions Viewer is freely available at http://bar.utoronto.ca/interactions2/.

Castration-resistant prostate cancer (CRPC) represents one type of advanced prostate cancer (PCa) with a median survival time of 1-2 years. Currently, there is a lack of reliable gene panels in predicting hormone treatment (HT) responses due to limited knowledge of CRPC-specific tumor-microenvironment (TME) characteristics. In this study, we first screened for up-regulated genes in CRPC samples using bulk-sequencing data retrieved from TCGA online database, and further investigated the expression status of these genes in four sets of downloaded single-cell RNA sequencing (scRNAseq) data: GSE117403 containing 16 normal human prostate samples; GSE141445 containing 13 PCa samples; GSE176031 containing 11 PCa samples and GSE137829 containing 6 CRPC samples. We identified a series of CRPC-specific TME characteristics including an enriched number of PEG10⁺ neuroendocrine cells, elevated expression of PPIB/CCDC74A/GAPDH/AR genes in tumor cells, increased expression of FAP/TGFB1 in cancer-associated fibroblasts (CAFs), suppressed immune environment featured by enhanced M2 macrophage polarization, T cell exhaustion and increased number of regulatory B cells. We further established a 12-gene panel using these characteristics and showed that this panel could separate CRPC samples from PCa samples (AUC of 0.78), and CRPC patients with higher panel scores tended to have treatment failure or progression (R = -0.47, p = 0.019). Based on these unique TME characteristics of CRPC, we established a prediction tool for estimating the duration of HT responses in PCa treatment. Our results suggest mechanisms by which prostate cancer becomes castrate resistant. Further study of PEG10 (and/or others) to evaluate therapeutic efficacy should be considered.

Background: PCV13 introduction in China has led to a significant reduction of vaccine serotype Streptococcus pneumoniae. However, non-vaccine serotypes with highly resistance and invasiveness were often reported in the post-pneumococcal conjugate vaccine era and there was regional differences.Methods: A total of 669 S. pneumoniae strains were collected from the respiratory tracts of hospitalized children at Shenzhen Children’s Hospital in 2021 and 2022. Antimicrobial resistance (AMR) characteristics were assessed through antibiotic susceptibility testing performed with the VITEK 2 compact system. AMR genes and single nucleotide polymorphisms (SNPs) in pbp1a, pbp2b, and pbp2x were identified via analysis of whole genome sequencing data. Statistical examination of the data was conducted employing chi-square and Fisher’s exact tests.Results: We found that non-vaccine serotypes strains had accounted for 46.6% of all the pneumococcal isolated strains. The most common non-vaccine serotype is 23A, with a prevalence rate of 8.9%, followed by 15A (6.6%), 6E (5.7%), 34 (3.2%), and 15B (2.9%). The multidrug resistance rates (MDR) of vaccine serotypes were 19F (99.36%), 19A (100%), 23F (98.08%), 6B (100%), and 6C (100%). Meanwhile, the MDR of non-vaccine serotypes were 15B (100.00%), 6E (100%), 15C (100%), 34 (95.24%), and 23A (98.31%). Resistance rates of 6E to more than six antibiotic classes reached 89.47%, which is similar to 19F (83.33%) and 19A (90%). Unique resistance profiles were also identified for non-vaccine serotypes, including significantly higher resistance to chloramphenicol in 6E, 15B, and 15C than in 19F and 19A. Furthermore, through genome sequencing, we revealed strong correlation of cat-TC with chloramphenicol resistance, patA/patB with tetracycline resistance, ermB and pmrA with erythromycin resistance.Conclusion: The introduction of PCV13 into China from 2017 has led to a shift in the dominant composition of pneumococcal strains. There has been a notable rise and spread of multidrug-resistant non-vaccine serotypes among children. Specifically, the non-vaccine serotype 6E, which was not widely reported in China previously, has emerged. To comprehend the resistance mechanisms, it is crucial to further investigate the molecular and genetic characteristics of these non-vaccine serotypes.

Introduction: Bone metastasis (BoM) occurs when cancer cells spread from their primary sites to a bone. Currently, the mechanism underlying this metastasis process remains unclear.Methods: In this project, through an integrated analysis of bulk-sequencing and single-cell RNA transcriptomic data, we explored the BoM-related features in tumor microenvironments of different tumors.Results: We first identified 34 up-regulated genes during the BoM process in breast cancer, and further explored their expression status among different components in the tumor microenvironment (TME) of BoM samples. Enriched EMP1+ fibroblasts were found in BoM samples, and a COL3A1-ADGRG1 communication between these fibroblasts and cancer cells was identified which might facilitate the BoM process. Moreover, a significant correlation between EMP1 and COL3A1 was identified in these fibroblasts, confirming the potential connection of these genes during the BoM process. Furthermore, the existence of these EMP1+/COL3A1+ fibroblasts was also verified in prostate cancer and renal cancer BoM samples, suggesting the importance of these fibroblasts from a pan-cancer perspective.Discussion: This study is the first attempt to investigate the relationship between fibroblasts and BoM process across multi-tumor TMEs. Our findings contribute another perspective in the exploration of BoM mechanism while providing some potential targets for future treatments of tumor metastasis.