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The identification of immune environments and cellular interactions in the colon microenvironment is essential for understanding the mechanisms of chronic inflammatory disease. Despite occurring in the same organ, there is a significant gap in understanding the pathophysiology of ulcerative colitis (UC) and colorectal cancer (CRC). Our study aims to address the distinct immunopathological response of UC and CRC. Using single-cell RNA sequencing datasets, we analyzed the profiles of immune cells in colorectal tissues obtained from healthy donors, UC patients, and CRC patients. The colon tissues from patients and healthy participants were visualized by immunostaining followed by laser confocal microscopy for select targets. Natural killer (NK) cells from UC patients on medication showed reduced cytotoxicity compared to those from healthy individuals. Nonetheless, a UC-specific pathway called the BAG6-NCR3 axis led to higher levels of inflammatory cytokines and increased the cytotoxicity of NCR3+ NK cells, thereby contributing to the persistence of colitis. In the context of colorectal cancer (CRC), both NK cells and CD8+ T cells exhibited significant changes in cytotoxicity and exhaustion. The GALECTIN-9 (LGALS9)-HAVCR2 axis was identified as one of the CRC-specific pathways. Within this pathway, NK cells solely communicated with myeloid cells under CRC conditions. HAVCR2+ NK cells from CRC patients suppressed NK cell-mediated cytotoxicity, indicating a reduction in immune surveillance. Overall, we elucidated the comprehensive UC and CRC immune microenvironments and NK cell-mediated immune responses. Our findings can aid in selecting therapeutic targets that increase the efficacy of immunotherapy.

Abstract Background Accurate estimation of implant size before surgery is crucial in preparing for total knee arthroplasty. However, this task is time-consuming and labor-intensive. To alleviate this burden on surgeons, we developed a reliable artificial intelligence (AI) model to predict implant size. Methods We enrolled 714 patients with knee osteoarthritis who underwent total knee arthroplasty from March 2010 to February 2014. All surgeries were performed by the same surgeon using implants from the same manufacturer. We collected 1412 knee anteroposterior (AP) and lateral view x-ray images and retrospectively investigated the implant size. We trained the AI model using both AP and lateral images without any clinical or demographic information and performed data augmentation to resolve issues of uneven distribution and insufficient data. Using data augmentation techniques, we generated 500 images for each size of the femur and tibia, which were then used to train the model. Using data augmentation techniques, we generated 500 images for each size of the femur and tibia, which were then used to train the model. We used ResNet-101 and optimized the model with the aim of minimizing the cross-entropy loss function using both the Stochastic Gradient Descent (SGD) and Adam optimizer. Results The SGD optimizer achieved the best performance in internal validation. The model showed micro F1-score 0.91 for femur and 0.87 for tibia. For predicting within ± one size, micro F1-score was 0.99 for femur and 0.98 for tibia. Conclusion We developed a deep learning model with high predictive power for implant size using only simple x-ray images. This could help surgeons reduce the time and labor required for preoperative preparation in total knee arthroplasty. While similar studies have been conducted, our work is unique in its use of simple x-ray images without any other data, like demographic features, to achieve a model with strong predictive power.

Highlights The aligned ternary nanofibrous matrices composed of poly(lactide-co-ε-caprolactone), collagen, and Ti3C2Tx MXene nanoparticles were fabricated (referred as PCM matrices). The aligned PCM matrices exhibited favorable physicochemical properties and excellent cytocompatibility and myogenic properties, which in turn promoted fast regeneration of volumetric muscle loss in vivo. The Ca2+ binding of MXene nanoparticles activated inducible nitric oxide synthase and serum/glucocorticoid regulated kinase 1-mediated mTOR-AKT pathway to promote myoblast differentiation and maturation.

[This corrects the article DOI: 10.1371/journal.pone.0257449.].

Abstract Background Periodontitis is a major inflammatory disease of the oral mucosa that is not limited to the oral cavity but also has systemic consequences. Although the importance of chronic periodontitis has been emphasized, the systemic immune response induced by periodontitis and its therapeutic effects remain elusive. Here, we report the transcriptomes of peripheral blood mononuclear cells (PBMCs) from patients with periodontitis. Methods Using single-cell RNA sequencing, we profiled PBMCs from healthy controls and paired pre- and post-treatment patients with periodontitis. We extracted differentially expressed genes and biological pathways for each cell type and calculated activity scores reflecting cellular characteristics. Intercellular crosstalk was classified into therapy-responsive and -nonresponsive pathways. Results We analyzed pan-cellular differentially expressed genes caused by periodontitis and found that most cell types showed a significant increase in CRIP1, which was further supported by the increased levels of plasma CRIP1 observed in patients with periodontitis. In addition, activated cell type-specific ligand-receptor interactions, including the BTLA, IFN-γ, and RESISTIN pathways, were prominent in patients with periodontitis. Both the BTLA and IFN-γ pathways returned to similar levels in healthy controls after periodontal therapy, whereas the RESISTIN pathway was still activated even after therapy. Conclusion These data collectively provide insights into the transcriptome changes and molecular interactions that are responsive to periodontal treatment. We identified periodontitis-specific systemic inflammatory indicators and suggest unresolved signals of non-surgical therapy as future therapeutic targets.

The importance of lung microbiome changes in developing chronic lung allograft dysfunction (CLAD) after lung transplantation is poorly understood. The lung microbiome–immune interaction may be critical in developing CLAD. In this context, examining alterations in the microbiome and immune cells of the lungs following CLAD, in comparison to the lung condition immediately after transplantation, can offer valuable insights. Four adult patients who underwent lung retransplantation between January 2019 and June 2020 were included in this study. Lung tissues were collected from the same four individuals at two different time points: at the time of the first transplant and at the time of the explantation of CLAD lungs at retransplantation due to CLAD. We analyzed whole-genome sequencing using the Kraken2 algorithm and quantified the cell fractionation from the bulk tissue gene expression profile for each lung tissue. Finally, we compared the differences in lung microbiome and immune cells between the lung tissues of these two time points. The median age of the recipients was 57 years, and most (75%) had undergone lung transplants for idiopathic pulmonary fibrosis. All patients were administered basiliximab for induction therapy and were maintained on three immunosuppressants. The median CLAD-free survival term was 693.5 days, and the median time to redo the lung transplant was 843.5 days. Bacterial diversity was significantly lower in the CLAD lungs than at transplantation. Bacterial diversity tended to decrease according to the severity of the CLAD. Aerococcus, Caldiericum, Croceibacter, Leptolyngbya, and Pulveribacter genera were uniquely identified in CLAD, whereas no taxa were identified in lungs at transplantation. In particular, six taxa, including Croceibacter atlanticus, Caldiserium exile, Dolichospermum compactum, Stappia sp. ES.058, Kinetoplastibacterium sorsogonicusi, and Pulveribacter suum were uniquely detected in CLAD. Among immune cells, CD8+ T cells were significantly increased, while neutrophils were decreased in the CLAD lung. In conclusion, unique changes in lung microbiome and immune cell composition were confirmed in lung tissue after CLAD compared to at transplantation.

Abstract Background Microbiome has been shown to substantially contribute to some cancers. However, the diagnostic implications of microbiome in head and neck squamous cell carcinoma (HNSCC) remain unknown. Methods To identify the molecular difference in the microbiome of oral and non-oral HNSCC, primary data was downloaded from the Kraken-TCGA dataset. The molecular differences in the microbiome of oral and non-oral HNSCC were identified using the linear discriminant analysis effect size method. Results In the study, the common microbiomes in oral and non-oral cancers were Fusobacterium, Leptotrichia, Selenomonas and Treponema and Clostridium and Pseudoalteromonas, respectively. We found unique microbial signatures that positively correlated with Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways in oral cancer and positively and negatively correlated KEGG pathways in non-oral cancer. In oral cancer, positively correlated genes were mostly found in prion diseases, Alzheimer disease, Parkinson disease, Salmonella infection, and Pathogenic Escherichia coli infection. In non-oral cancer, positively correlated genes showed Herpes simplex virus 1 infection and Spliceosome and negatively correlated genes showed results from PI3K-Akt signaling pathway, Focal adhesion, Regulation of actin cytoskeleton, ECM-receptor interaction and Dilated cardiomyopathy. Conclusions These results could help in understanding the underlying biological mechanisms of the microbiome of oral and non-oral HNSCC. Microbiome-based oncology diagnostic tool warrants further exploration.

Periodontitis and diabetes mellitus (DM) have a bidirectional relationship. Periodontitis is initiated by dysbiosis of oral microorganisms, and in particular, the characteristics of the microorganisms that have penetrated the tissue are directly related to the disease; therefore, we investigated the effect of DM on intragingival microbial profiling of patients with periodontitis. A total of 39 subjects were recruited and divided into three groups in this case control study as follows: healthy (NA, 10), periodontitis only (PD, 18), and periodontitis with DM (PD_DM, 11). Gingival tissue was collected, DNA was extracted, and whole-genome sequencing was performed. PD and PD_DM showed different characteristics from NA in diversity and composition of the microbial community; however, no difference was found between the PD nad PD_DM. PD_DM showed discriminatory characteristics for PD in the network analysis. PD showed a network structure in which six species were connected, including three red complex species, and PD_DM’s network was more closely connected and expanded, with six additional species added to the PD network. Although DM did not significantly affect α- and β-diversity or abundance of phyla and genera of microbiota that invaded the gingival tissue of patients with periodontitis, DM will affect the progression of periodontitis by strengthening the bacterial network in the gingival tissue.

Introduction: Debate on the association between the use of angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) and the risk of developing cancer has been ongoing for decades. This study aimed to generate reliable results by analysing observational studies published in the decade after our last meta-analysis was conducted. Methods: We searched Embase and Medline databases on 21 January 2021 for cohort and case-control studies. Two researchers independently reviewed the literature and assessed the title and abstract of each publication. The I2 statistic used to evaluate the heterogeneity of the effect measures. Risk of bias was qualitatively assessed using the Newcastle–Ottawa scale. Results and discussion: We included an additional 16 cohort, 6 nested case-control, and 9 conventional case-control studies in the updated analysis. Overall HRs decreased, while overall relative risks increased. Conclusion: Our results show some protective effects through the hazard ratio and some detrimental effects through the relative risk. Large-scale investigations of cohorts followed up for decades are needed to clarify association. Plain Language Summary Introduction: Two types of drug, angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs), have been linked to the risk of developing cancer. We performed a meta-analysis by aggregating individual studies looking into the cancer risk of ACEIs and ARBs. Methods: We searched for articles on Embase and Medline databases until 21 January, 2021. Two researchers independently reviewed the literature and assessed the title and abstract of each publication. Results: Overall, the hazard ratio showed less than 1, while the relative risks showed higher than 1. Conclusion: Our results show some protective effects through the hazard ratio and some detrimental effects through the relative risk. Evidence supporting the risk of developing cancer is insufficient to prevent prescribing ACEIs or ARBs for patients with high blood pressure.

Recent advances in genomic technologies have enabled more in-depth study of the oral microbiome. In this study, we compared the amplicons generated by primers targeting different sites of the 16S rRNA gene found in the Human Oral Microbiome Database (HOMD). Six sets of primer targeting V1–V2, V1–V3, V3–V4, V4–V5, V5–V7 and V6–V8 regions of 16S rRNA were tested via in silico simulation. Primers targeting the V1–V2, V3–V4, and V4–V5 regions generated more than 90% of the original input sequences. Primers targeting the V1–V2 and V1–V3 regions exhibited a low number of mismatches and unclassified sequences at the taxonomic level, but there were notable discrepancies at the species level. Phylogenetic tree comparisons showed primers targeting the V1–V2 and V3–V4 regions showed performances similar to primers targeting the whole 16s RNA region in terms of separating total oral microbiomes and periodontopathogens. In an analysis of clinical oral samples, V1–V2 primers showed superior performance for identifying more taxa and had better resolution sensitivity for Streptococcus than V3–V4 primers. In conclusion, primers targeting the V1–V2 region of 16S rRNA showed the best performance for oral microbiome studies. In addition, the study demonstrates the need for careful PCR primer selections.

IntroductionIt is well known that the presence of diabetes significantly affects the progression of periodontitis and that periodontitis has negative effects on diabetes and diabetes-related complications. Although this two-way relationship between type 2 diabetes and periodontitis could be understood through experimental and clinical studies, information on common genetic factors would be more useful for the understanding of both diseases and the development of treatment strategies.Materials and MethodsGene expression data for periodontitis and type 2 diabetes were obtained from the Gene Expression Omnibus database. After preprocessing of data to reduce heterogeneity, differentially expressed genes (DEGs) between disease and normal tissue were identified using a linear regression model package. Gene ontology and Kyoto encyclopedia of genes and genome pathway enrichment analyses were conducted using R package ‘vsn’. A protein-protein interaction network was constructed using the search tool for the retrieval of the interacting genes database. We used molecular complex detection for optimal module selection. CytoHubba was used to identify the highest linkage hub gene in the network.ResultsWe identified 152 commonly DEGs, including 125 upregulated and 27 downregulated genes. Through common DEGs, we constructed a protein-protein interaction and identified highly connected hub genes. The hub genes were up-regulated in both diseases and were most significantly enriched in the Fc gamma R-mediated phagocytosis pathway.DiscussionWe have identified three up-regulated genes involved in Fc gamma receptor-mediated phagocytosis, and these genes could be potential therapeutic targets in patients with periodontitis and type 2 diabetes.

Glioma is the most common primary malignant tumor that occurs in the central nervous system. Gliomas are subdivided according to a combination of microscopic morphological, molecular, and genetic factors. Glioblastoma (GBM) is the most aggressive malignant tumor; however, efficient therapies or specific target molecules for GBM have not been developed. We accessed RNA-seq and clinical data from The Cancer Genome Atlas, the Chinese Glioma Genome Atlas, and the GSE16011 dataset, and identified differentially expressed genes (DEGs) that were common to both GBM and lower-grade glioma (LGG) in three independent cohorts. The biological functions of common DEGs were examined using NetworkAnalyst. To evaluate the prognostic performance of common DEGs, we performed Kaplan-Meier and Cox regression analyses. We investigated the function of SOCS3 in the central nervous system using three GBM cell lines as well as zebrafish embryos. There were 168 upregulated genes and 50 downregulated genes that were commom to both GBM and LGG. Through survival analyses, we found that SOCS3 was the only prognostic gene in all cohorts. Inhibition of SOCS3 using siRNA decreased the proliferation of GBM cell lines. We also found that the zebrafish ortholog, socs3b, was associated with brain development through the regulation of cell proliferation in neuronal tissue. While additional mechanistic studies are necessary, our results suggest that SOCS3 is an important biomarker for glioma and that SOCS3 is related to the proliferation of neuronal tissue.

Background: Diabetic peripheral neuropathy (DPN), the most common microvascular complication of type-2 diabetes mellitus (T2DM), results in nontraumatic lower-limb amputations. When DPN is not detected early, disease progression is irreversible. Thus, biomarkers for diagnosing DPN are needed. Methods: We analyzed three data sets of T2DM DPN: two for mouse models (GSE70852 and GSE34889) and one for a human model (GSE24290). We found common differentially expressed genes (DEGs) in the two mouse data sets and validated them in the human data set. To identify the phenotypic function of the DEGs, we overexpressed them in zebrafish embryos. Clinical information and serum samples of T2DM patients with and without DPN were obtained from the Korea Biobank Network. To assess the plausibility of DEGs as biomarkers of DPN, we performed an enzyme-linked immunosorbent assay. Results: Among the DEGs, only NPY and SLPI were validated in the human data set. As npy is conserved in zebrafish, its mRNA was injected into zebrafish embryos, and it was observed that the branches of the central nervous system became thicker and the number of dendritic branches increased. Baseline characteristics between T2DM patients with and without DPN did not differ, except for the sex ratio. The mean serum NPY level was higher in T2DM patients with DPN than in those without DPN ( p = 0.0328), whereas serum SLPI levels did not differ ( p = 0.9651). Conclusion: In the pathogenesis of DPN, NPY may play a protective role in the peripheral nervous system and may be useful as a biomarker for detecting T2DM DPN.

Background: Periodontitis (PT) is a multifactorial, chronic inflammatory disease that can have heterogeneous clinical presentations. The oral microbiome and its metabolites have been implicated as the causes and regulators of PT pathogenesis. In this study, we assessed the oral microbiome and its metabolome in PT patients to clarify the interactions between the microbiome and its metabolites.Methods: A total of 112 subjects were recruited. Buccal and supragingival samples were collected for microbiome analysis. Saliva samples were collected for metabolomic analyses. Microbiome and metabolome data were analyzed and further integrated for combined analysis using various bioinformatics approaches.Results: Oral metabolomic analysis identified 28 metabolites distinguishing the healthy (H) and PT groups. PT group were further clustered into two subgroups (PT_G1 and PT_G2) depending on metabolite profiles. Oral microbiome analysis revealed discriminatory bacterial species in the H, PT_G1, and PT_G2 microbiota. Interestingly, PT_G2 had significantly higher concentration of short chain fatty acids and higher abundance of pathogenic bacteria. Integrated analysis of the microbiome and metabolome showed close association.Conclusion: Our results provide evidence of a close interplay between the oral microbiome and metabolome. Multi-omics approach including microbiome and microbe-associated metabolites may serve as diagnostic biomarkers and enhance treatment prediction in periodontal disease.

Despite the formation of biofilms on catheters for extracorporeal membrane oxygenation (ECMO), some patients do not show bacteremia. To elucidate the specific linkage between biofilms and bacteremia in patients with ECMO, an improved understanding of the microbial community within catheter biofilms is necessary. Hence, we aimed to evaluate the biofilm microbiome of ECMO catheters from adults with (n = 6) and without (n = 15) bacteremia. The microbiomes of the catheter biofilms were evaluated by profiling the V3 and V4 regions of bacterial 16s rRNA genes using the Illumina MiSeq sequencing platform. In total, 2,548,172 reads, with an average of 121,341 reads per sample, were generated. Although alpha diversity was slightly higher in the non-bacteremic group, the difference was not statistically significant. In addition, there was no difference in beta diversity between the two groups. We found 367 different genera, of which 8 were present in all samples regardless of group; Limnohabitans, Flavobacterium, Delftia, Massilia, Bacillus, Candidatus, Xiphinematobacter, and CL0-1 showed an abundance of more than 1% in the sample. In particular, Arthrobacter, SMB53, Neisseria, Ortrobactrum, Candidatus Rhabdochlamydia, Deefgae, Dyella, Paracoccus, and Pedobacter were highly abundant in the bacteremic group. Network analysis indicated that the microbiome of the bacteremic group was more complex than that of the non-bacteremic group. Flavobacterium and CL0.1, which were abundant in the bacteremic group, were considered important genera because they connected different subnetworks. Biofilm characteristics in ECMO catheters varied according to the presence or absence of bacteremia. There were no significant differences in diversity between the two groups, but there were significant differences in the community composition of the biofilms. The biofilm-associated community was dynamic, with the bacteremic group showing very complex network connections within the microbiome.

Background: High cathepsin D has been associated with poor prognosis in breast cancer; however, the results of many studies are controversial. Here, we assessed the association between high cathepsin D levels and worse breast cancer prognosis by conducting a meta-analysis. Methods: A comprehensive search strategy was used to search relevant literature in PUBMED and EMBASE by September 2018. The meta-analysis was performed in Review Manager 5.3 using hazard ratios (HRs) with 95% confidence intervals (CIs). Results: A total of 15,355 breast cancer patients from 26 eligible studies were included in this meta-analysis. Significant associations between elevated high cathepsin D and poor overall survival (OS) (HR = 1.61, 95% CI: 1.35–1.92, p

Purpose Triple-negative breast cancer (TNBC) is a breast cancer subtype that has poor prognosis and exhibits a unique tumor microenvironment. Analysis of the tumor microbiome has indicated a relationship between the tumor microenvironment and treatment response. Therefore, we attempted to reveal the role of the tumor microbiome in patients with TNBC receiving neoadjuvant chemotherapy. Materials and Methods We collected TNBC patient RNA-sequencing samples from the Gene Expression Omnibus and extracted microbiome count data. Differential and relative abundance were estimated with linear discriminant analysis effect size. We calculated the immune cell fraction with CIBERSORTx and conducted survival analysis using the Cancer Genome Atlas patient data. Correlations between the microbiome and immune cell compositions were analyzed and a prediction model was constructed to estimate drug response. Results Among the pathological complete response group (pCR), the beta diversity varied considerably; consequently, 20 genera and 24 species were observed to express a significant differential and relative abundance. Pandoraea pulmonicola and Brucella melitensis were found to be important features in determining drug response. In correlation analysis, Geosporobacter ferrireducens, Streptococcus sanguinis, and resting natural killer cells were the most correlated factors in the pCR, whereas Nitrosospira briensis, Plantactinospora sp. BC1, and regulatory T cells were key features in the residual disease group. Conclusion Our study demonstrated that the microbiome analysis of tumor tissue can predict chemotherapy response of patients with TNBC. Further, the immunological tumor microenvironment may be impacted by the tumor microbiome, thereby affecting the corresponding survival and treatment response. [ABSTRACT FROM AUTHOR]

Background Pheochromocytoma and paraganglioma (PPGL) are tumours that arise from chromaffin cells. Some genetic mutations influence PPGL, among which, those in genes encoding subunits of succinate dehydrogenase (SDHA, SDHB, SDHC and SDHD) and assembly factor (SDHAF2) are the most relevant. However, the risk of metastasis posed by these mutations is not reported except for SDHB and SDHD mutations. This study aimed to update the metastatic risks, considering prevalence and incidence of each SDHx mutation, which were dealt formerly all together. Methods We searched EMBASE and MEDLINE and selected 27 articles. The patients included in the studies were divided into three groups depending on the presence of PPGL. We checked the heterogeneity between studies and performed a meta-analysis using Hartung-Knapp-Sidik-Jonkman method based on a random effect model. Results The highest PPGL prevalence was for SDHB mutation, ranging from 23% to 31%, and for SDHC mutation (23%), followed by that for SDHA mutation (16%). The lowest prevalence was for SDHD mutation, ranging from 6% to 8%. SDHAF2 mutation showed no metastatic events. The PPGL incidence showed a tendency similar to that of its prevalence with the highest risk of metastasis posed by SDHB mutation (12%-41%) and the lowest risk by SDHD mutation (~4%). Conclusion There was no integrated evidence of how SDHx mutations are related to metastatic PPGL. However, these findings suggest that SDHA, SDHB and SDHC mutations are highly associated and should be tested as indicators of metastasis in patients with PPGL. [ABSTRACT FROM AUTHOR]