Unique miRomics Expression Profiles in Tannerella forsythia-Infected Mandibles during Periodontitis Using Machine Learning

T. forsythia is a subgingival periodontal bacterium constituting the subgingival pathogenic polymicrobial milieu during periodontitis (PD). miRNAs play a pivotal role in maintaining periodontal tissue homeostasis at the transcriptional, post-transcriptional, and epigenetic levels. The aim of this study was to characterize the global microRNAs (miRNA, miR) expression kinetics in 8- and 16-week-old T. forsythia-infected C57BL/6J mouse mandibles and to identify the miRNA bacterial biomarkers of disease process at specific time points. We examined the differential expression (DE) of miRNAs in mouse mandibles (n = 10) using high-throughput NanoString nCounter® miRNA expression panels, which provided significant advantages over specific candidate miRNA or pathway analyses. All the T. forsythia-infected mice at two specific time points showed bacterial colonization (100%) in the gingival surface, along with a significant increase in alveolar bone resorption (ABR) (p < 0.0001). We performed a NanoString analysis of specific miRNA signatures, miRNA target pathways, and gene network analysis. A total of 115 miRNAs were DE in the mandible tissue during 8 and 16 weeks The T. forsythia infection, compared with sham infection, and the majority (99) of DE miRNAs were downregulated. nCounter miRNA expression kinetics identified 67 downregulated miRNAs (e.g., miR-375, miR-200c, miR-200b, miR-34b-5p, miR-141) during an 8-week infection, whereas 16 upregulated miRNAs (e.g., miR-1902, miR-let-7c, miR-146a) and 32 downregulated miRNAs (e.g., miR-2135, miR-720, miR-376c) were identified during a 16-week infection. Two miRNAs, miR-375 and miR-200c, were highly downregulated with >twofold change during an 8-week infection. Six miRNAs in the 8-week infection (miR-200b, miR-141, miR-205, miR-423-3p, miR-141-3p, miR-34a-5p) and two miRNAs in the 16-week infection (miR-27a-3p, miR-15a-5p) that were downregulated have also been reported in the gingival tissue and saliva of periodontitis patients. This preclinical in vivo study identified T. forsythia-specific miRNAs (miR-let-7c, miR-210, miR-146a, miR-423-5p, miR-24, miR-218, miR-26b, miR-23a-3p) and these miRs have also been reported in the gingival tissues and saliva of periodontitis patients. Further, several DE miRNAs that are significantly upregulated (e.g., miR-101b, miR-218, miR-127, miR-24) are also associated with many systemic diseases such as atherosclerosis, Alzheimer's disease, rheumatoid arthritis, osteoarthritis, diabetes, obesity, and several cancers. In addition to DE analysis, we utilized the XGBoost (eXtreme Gradient boost) and Random Forest machine learning (ML) algorithms to assess the impact that the number of miRNA copies has on predicting whether a mouse is infected. XGBoost found that miR-339-5p was most predictive for mice infection at 16 weeks. miR-592-5p was most predictive for mice infection at 8 weeks and also when the 8-week and 16-week results were grouped together. Random Forest predicted miR-592 as most predictive at 8 weeks as well as the combined 8-week and 16-week results, but miR-423-5p was most predictive at 16 weeks. In conclusion, the expression levels of miR-375 and miR-200c family differed significantly during disease process, and these miRNAs establishes a link between T. forsythia and development of periodontitis genesis, offering new insights regarding the pathobiology of this bacterium.

Oral Spirochete Treponema denticola Intraoral Infection Reveals Unique miR-133a, miR-486, miR-126-3p, miR-126-5p miRNA Expression Kinetics during Periodontitis

miRNAs are major regulators of eukaryotic gene expression and host immunity, and play an important role in the inflammation-mediated pathways in periodontal disease (PD) pathogenesis. Expanding our previous observation with the global miRNA profiling using partial human mouth microbes, and lack of in vivo studies involving oral spirochete Treponema denticola-induced miRNAs, this study was designed to delineate the global miRNA expression kinetics during progression of periodontitis in mice infected with T. denticola by using NanoString nCounter® miRNA panels. All of the T. denticola-infected male and female mice at 8 and 16 weeks demonstrated bacterial colonization (100%) on the gingival surface, and an increase in alveolar bone resorption (p < 0.0001). A total of 70 miRNAs with at least 1.0-fold differential expression/regulation (DE) (26 upregulated and 44 downregulated) were identified. nCounter miRNA expression profiling identified 13 upregulated miRNAs (e.g., miR-133a, miR-378) and 25 downregulated miRNAs (e.g., miR-375, miR-34b-5p) in T. denticola-infected mouse mandibles during 8 weeks of infection, whereas 13 upregulated miRNAs (e.g., miR-486, miR-126-5p) and 19 downregulated miRNAs (miR-2135, miR-142-3p) were observed during 16 weeks of infection. One miRNA (miR-126-5p) showed significant difference between 8 and 16 weeks of infection. Interestingly, miR-126-5p has been presented as a potential biomarker in patients with periodontitis and coronary artery disease. Among the upregulated miRNAs, miR-486, miR-126-3p, miR-126-5p, miR-378a-3p, miR-22-3p, miR-151a-3p, miR-423-5p, and miR-221 were reported in human gingival plaques and saliva samples from periodontitis and with diabetes. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed various functional pathways of DE miRNAs, such as bacterial invasion of epithelial cells, Ras signaling, Fc gamma R-mediated phagocytosis, osteoclast differentiation, adherens signaling, and ubiquitin mediated proteolysis. This is the first study of DE miRNAs in mouse mandibles at different time-points of T. denticola infection; the combination of three specific miRNAs, miR-486, miR-126-3p, and miR-126-5p, may serve as an invasive biomarker of T. denticola in PD. These miRNAs may have a significant role in PD pathogenesis, and this research establishes a link between miRNA, periodontitis, and systemic diseases.

Global Noncoding microRNA Profiling in Mice Infected with Partial Human Mouth Microbes (PAHMM) Using an Ecological Time-Sequential Polybacterial Periodontal Infection (ETSPPI) Model Reveals Sex-Specific Differential microRNA Expression

Periodontitis (PD) is a polymicrobial dysbiotic immuno-inflammatory disease. It is more prevalent in males and has poorly understood pathogenic molecular mechanisms. Our primary objective was to characterize alterations in sex-specific microRNA (miRNA, miR) after periodontal bacterial infection. Using partial human mouth microbes (PAHMM) (Streptococcus gordonii, Fusobacterium nucleatum, Porphyromonas gingivalis, Treponema denticola, and Tannerella forsythia) in an ecological time-sequential polybacterial periodontal infection (ETSPPI) mouse model, we evaluated differential mandibular miRNA profiles by using high-throughput Nanostring nCounter® miRNA expression panels. All PAHMM mice showed bacterial colonization (100%) in the gingival surface, an increase in alveolar bone resorption (p < 0.0001), and the induction of a specific immunoglobin G antibody immune response (p < 0.001). Sex-specific differences in distal organ bacterial dissemination were observed in the heart (82% male vs. 28% female) and lungs (2% male vs. 68% female). Moreover, sex-specific differential expression (DE) of miRNA was identified in PAHMM mice. Out of 378 differentially expressed miRNAs, we identified seven miRNAs (miR-9, miR-148a, miR-669a, miR-199a-3p, miR-1274a, miR-377, and miR-690) in both sexes that may be implicated in the pathogenesis of periodontitis. A strong relationship was found between male-specific miR-377 upregulation and bacterial dissemination to the heart. This study demonstrates sex-specific differences in bacterial dissemination and in miRNA differential expression. A novel PAHMM mouse and ETSPPI model that replicates human pathobiology can be used to identify miRNA biomarkers in periodontitis.

Expression of immune response genes in human corneal epithelial cells interacting with Aspergillus flavus conidia

Background

Aspergillus flavus, one of the causative agents of human fungal keratitis, can be phagocytosed by human corneal epithelial (HCE) cells and the conidia containing phagosomes mature into phagolysosomes. But the immunological responses of human corneal epithelial cells interacting with A. flavus are not clear. In this study, we report the expression of immune response related genes of HCE cells exposed to A. flavus spores using targeted transcriptomics.

Methods

Human corneal epithelial cell line and primary cultures were grown in a six-well plate and used for coculture experiments. Internalization of the conidia was confirmed by immunofluorescence microscopy of the colocalized endosomal markers CD71 and LAMP1. Total RNA was isolated, and the quantity and quality of the isolated RNA were assessed using Qubit and Bioanalyzer. NanoString nCounter platform was used for the analysis of mRNA abundance using the Human Immunology panel. R-package and nSolver software were used for data analysis. KEGG and FunRich 3.1.3 tools were used to analyze the differentially expressed genes.

Results

Different morphotypes of conidia were observed after 6 h of coculture with human corneal epithelial cells and found to be internalized by epithelial cells. NanoString profiling showed more than 20 differentially expressed genes in immortalized human corneal epithelial cell line and more than ten differentially expressed genes in primary corneal epithelial cells. Distinct set of genes were altered in their expression in cell line and primary corneal epithelial cells. KEGG pathway analysis revealed that genes associated with TNF signaling, NF-KB signaling, and Th17 signaling were up-regulated, and genes associated with chemokine signaling and B cell receptor signaling were down regulated. FunRich pathway analysis showed that pathways such as CDC42 signaling, PI3K signaling, and Arf6 trafficking events were activated by the clinical isolates CI1123 and CI1698 in both type of cells.

Conclusions

Combining the transcript analysis data from cell lines and primary cultures, we showed the up regulation of immune defense genes in A. flavus infected cells. At the same time, chemokine signaling and B cell signaling pathways are downregulated. The variability in the expression levels in the immortalized cell line and the primary cultures is likely due to the variable epigenetic reprogramming in the immortalized cells and primary cultures in the absence of any changes in the genome. It highlights the importance of using both cell types in host-pathogen interaction studies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-08218-5.

A Nine-Gene Signature for Predicting the Response to Preoperative Chemoradiotherapy in Patients with Locally Advanced Rectal Cancer

Preoperative chemoradiotherapy (PCRT) and subsequent surgery is the standard multimodal treatment for locally advanced rectal cancer (LARC), albeit PCRT response varies among the individuals. This creates a dire necessity to identify a predictive model to forecast treatment response outcomes and identify patients who would benefit from PCRT. In this study, we performed a gene expression study using formalin-fixed paraffin-embedded (FFPE) tumor biopsy samples from 156 LARC patients (training cohort n = 60; validation cohort n = 96); we identified the nine-gene signature (FGFR3, GNA11, H3F3A, IL12A, IL1R1, IL2RB, NKD1, SGK2, and SPRY2) that distinctively differentiated responders from non-responders in the training cohort (accuracy = 86.9%, specificity = 84.8%, sensitivity = 81.5%) as well as in an independent validation cohort (accuracy = 81.0%, specificity = 79.4%, sensitivity = 82.3%). The signature was independent of all pathological and clinical features and was robust in predicting PCRT response. It is readily applicable to the clinical setting using FFPE samples and Food and Drug Administration (FDA) approved hardware and reagents. Predicting the response to PCRT may aid in tailored therapies for respective responders to PCRT and improve the oncologic outcomes for LARC patients.

Identification of CHEK1, SLC26A4, c-KIT, TPO and TG as new biomarkers for human follicular thyroid carcinoma

The search for preoperative biomarkers for thyroid malignancies, in particular for follicular thyroid carcinoma (FTC) diagnostics, is of utmost clinical importance. We thus aimed at screening for potential biomarker candidates for FTC. To evaluate dynamic alterations in molecular patterns as a function of thyroid malignancy progression, a comparative analysis was conducted in clinically distinct subgroups of FTC and poorly differentiated thyroid carcinoma (PDTC) nodules. NanoString analysis of FFPE samples was performed in 22 follicular adenomas, 56 FTC and 25 PDTC nodules, including oncocytic and non-oncocytic subgroups. The expression levels of CHEK1, c-KIT, SLC26A4, TG and TPO were significantly altered in all types of thyroid carcinomas. Based on collective changes of these biomarkers which correlating among each other, a predictive score has been established, allowing for discrimination between benign and FTC samples with high sensitivity and specificity. Additional transcripts related to thyroid function, cell cycle, circadian clock, and apoptosis regulation were altered in the more aggressive oncocytic subgroups only, with expression levels correlating with disease progression. Distinct molecular patterns were observed for oncocytic and non-oncocytic FTCs and PDTCs. A predictive score correlation coefficient based on collective alterations of identified here biomarkers might help to improve the preoperative diagnosis of FTC nodules.

Adaptation of Laser Microdissection Technique to Nanostring RNA Analysis in the Study of a Spontaneous Metastatic Mammary Carcinoma Mouse Model

The mouse model characterized by spontaneous lung metastasis from JygMC (A) cells closely resembles the human triple negative breast cancer (TNBC) subtype. The primary tumors morphologically present both epithelial and spindle-like cells, but metastases in lung parenchyma display only adenocarcinoma properties. In the study of molecular signatures, laser capture microdissection (LCM) on frozen tissue sections was used to separate the following regions of interest: the epithelial-mesenchymal transition (EMT), mesenchymal-epithelial transition (MET), carcinoma, lung metastases, normal mammary gland and normal lung parenchyma. NanoString was selected for the study of molecular signatures in LCM targets as a reliable downstream gene expression platform allowing analysis of tissue lysates without RNA extraction and amplification. This chapter provides detailed protocols for the collection of tissue, LCM sample preparation and dissection, production of lysates, extraction, and quality control of RNA for NanoString analysis, as well as the methodology of Nanostring gene expression profiling experiment.

Identification of new biomarkers for human papillary thyroid carcinoma employing NanoString analysis

We previously reported an upregulation of the clock transcript BMAL1, correlating with TIMP1 expression in fresh-frozen samples from papillary thyroid carcinoma (PTC). Since frozen postoperative biopsy samples are difficult to obtain, we aimed to validate the application of high-precision NanoString analysis for formalin-fixed paraffin-embedded (FFPE) thyroid nodule samples and to screen for potential biomarkers associated with PTC. No significant differences were detected between fresh-frozen and FFPE samples. NanoString analysis of 51 transcripts in 17 PTC and 17 benign nodule samples obtained from different donors and in 24 pairs of benign and PTC nodules, obtained from the same donor (multinodular goiters), confirmed significant alterations in the levels of BMAL1, c-MET, c-KIT, TIMP1, and other transcripts. Moreover, we identified for the first time alterations in CHEK1 and BCL2 levels in PTC. A predictive score was established for each sample, based on the combined expression levels of BMAL1, CHEK1, c-MET, c-KIT and TIMP1. In combination with BRAF mutation analysis, this predictive score closely correlated with the clinicopathological characteristics of the analyzed thyroid nodules. Our study identified new thyroid transcripts with altered levels in PTC using the NanoString approach. A predictive score correlation coefficient might contribute to improve the preoperative diagnosis of thyroid nodules.