Downregulation of monocyte miRNAs: implications for immune dysfunction and disease severity in drug-resistant tuberculosis.

Background: Monocyte miRNAs govern both protective and pathological responses during tuberculosis (TB) through their differential expression and emerged as potent targets for biomarker discovery and host-directed therapeutics. Thus, this study examined the miRNA profile of sorted monocytes across the TB disease spectrum [drug-resistant TB (DR-TB), drug-sensitive TB (DS-TB), and latent TB] and in healthy individuals (HC) to understand the underlying pathophysiology and their regulatory mechanism. Methods: We sorted total monocytes including three subsets (HLA-DR+CD14+, HLA-DR+CD14+CD16+, and HLA-DR+CD16+cells) from peripheral blood mononuclear cells (PBMCs) of healthy and TB-infected individuals through flow cytometry and subjected them to NanoString-based miRNA profiling. Results: The outcome was the differential expression of 107 miRNAs particularly the downregulation of miRNAs in the active TB groups (both drug-resistant and drug-sensitive). The miRNA profile revealed differential expression signatures: i) decline of miR-548m in DR-TB alone, ii) decline of miR-486-3p in active TB but significant elevation only in LTB iii) elevation of miR-132-3p only in active TB (DR-TB and DS-TB) and iv) elevation of miR-150-5p in DR-TB alone. The directionality of functions mediated by monocyte miRNAs from Gene Set Enrichment Analysis (GSEA) facilitated two phenomenal findings: i) a bidirectional response between active disease (activation profile in DR-TB and DS-TB compared to LTB and HC) and latent infection (suppression profile in LTB vs HC) and ii) hyper immune activation in the DR-TB group compared to DS-TB. Conclusion: Thus, monocyte miRNA signatures provide pathological clues for altered monocyte function, drug resistance, and disease severity. Further studies on monocyte miRNAs may shed light on the immune regulatory mechanism for tuberculosis.

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.

Analytical validation of CanAssist-Breast: an immunohistochemistry based prognostic test for hormone receptor positive breast cancer patients.

BACKGROUND: CanAssist-Breast is an immunohistochemistry based test that predicts risk of distant recurrence in early-stage hormone receptor positive breast cancer patients within first five years of diagnosis. Immunohistochemistry gradings for 5 biomarkers (CD44, ABCC4, ABCC11, N-Cadherin and pan-Cadherins) and 3 clinical parameters (tumor size, tumor grade and node status) of 298 patient cohort were used to develop a machine learning based statistical algorithm. The algorithm generates a risk score based on which patients are stratified into two groups, low- or high-risk for recurrence. The aim of the current study is to demonstrate the analytical performance with respect to repeatability and reproducibility of CanAssist-Breast. METHODS: All potential sources of variation in CanAssist-Breast testing involving operator, run and observer that could affect the immunohistochemistry performance were tested using appropriate statistical analysis methods for each of the CanAssist-Breast biomarkers using a total 309 samples. The cumulative effect of these variations in the immunohistochemistry gradings on the generation of CanAssist-Breast risk score and risk category were also evaluated. Intra-class Correlation Coefficient, Bland Altman plots and pair-wise agreement were performed to establish concordance on IHC gradings, risk score and risk categorization respectively. RESULTS: CanAssist-Breast test exhibited high levels of concordance on immunohistochemistry gradings for all biomarkers with Intra-class Correlation Coefficient of ≥0.75 across all reproducibility and repeatability experiments. Bland-Altman plots demonstrated that agreement on risk scores between the comparators was within acceptable limits. We also observed > 90% agreement on risk categorization (low- or high-risk) across all variables tested. CONCLUSIONS: The extensive analytical validation data for the CanAssist-Breast test, evaluating immunohistochemistry performance, risk score generation and risk categorization showed excellent agreement across variables, demonstrating that the test is robust.

Clinical validation of an immunohistochemistry-based CanAssist-Breast test for distant recurrence prediction in hormone receptor-positive breast cancer patients.

CanAssist-Breast (CAB) is an immunohistochemistry (IHC)-based prognostic test for early-stage Hormone Receptor (HR+)-positive breast cancer patients. CAB uses a Support Vector Machine (SVM) trained algorithm which utilizes expression levels of five biomarkers (CD44, ABCC4, ABCC11, N-Cadherin, and Pan-Cadherin) and three clinical parameters such as tumor size, grade, and node status as inputs to generate a risk score and categorizes patients as low- or high-risk for distant recurrence within 5 years of diagnosis. In this study, we present clinical validation of CAB. CAB was validated using a retrospective cohort of 857 patients. All patients were treated either with endocrine therapy or chemoendocrine therapy. Risk categorization by CAB was analyzed by calculating Distant Metastasis-Free Survival (DMFS) and recurrence rates using Kaplan-Meier survival curves. Multivariate analysis was performed to calculate Hazard ratios (HR) for CAB high-risk vs low-risk patients. The results showed that Distant Metastasis-Free Survival (DMFS) was significantly different (P-0.002) between low- (DMFS: 95%) and high-risk (DMFS: 80%) categories in the endocrine therapy treated alone subgroup (n = 195) as well as in the total cohort (n = 857, low-risk DMFS: 95%, high-risk DMFS: 84%, P < 0.0001). In addition, the segregation of the risk categories was significant (P = 0.0005) in node-positive patients, with a difference in DMFS of 12%. In multivariate analysis, CAB risk score was the most significant predictor of distant recurrence with hazard ratio of 3.2048 (P < 0.0001). CAB stratified patients into discrete risk categories with high statistical significance compared to Ki-67 and IHC4 score-based stratification. CAB stratified a higher percentage of the cohort (82%) as low-risk than IHC4 score (41.6%) and could re-stratify >74% of high Ki-67 and IHC4 score intermediate-risk zone patients into low-risk category. Overall the data suggest that CAB can effectively predict risk of distant recurrence with clear dichotomous high- or low-risk categorization.

Development of a Novel Proteomic Risk-Classifier for Prognostication of Patients With Early-Stage Hormone Receptor-Positive Breast Cancer.

Use of proteomic strategies to identify a risk classifier that estimates probability of distant recurrence in early-stage hormone receptor (HR)-positive breast cancer is relevant to physiological cellular function and therefore to intrinsic tumor biology. We used a 298-sample retrospective training set to develop an immunohistochemistry-based novel risk classifier called CanAssist-Breast (CAB) which combines 5 prognostically relevant biomarkers and 3 clinico-pathological parameters to arrive at probability of distant recurrence within 5 years from diagnosis. Five selected biomarkers, namely, CD44, ABCC4, ABCC11, N-cadherin, and pan-cadherin, were chosen based on their role in tumor metastasis. The chosen biomarkers represent the hallmarks of cancer and are distinct from other proliferation and gene expression-based prognostic signatures. The 3 clinico-pathological parameters integrated into the machine learning-based CAB algorithm are tumor size, tumor grade, and node status. These features are used to calculate a "CAB risk score" that classifies patients into low- or high-risk groups and predicts probability of distant recurrence in 5 years. Independent clinical validation of CAB in a retrospective study comprising 196 patients indicated that distant metastasis-free survival (DMFS) was significantly different in the 2 risk groups. The difference in DMFS between the low- and high-risk categories was 19% in the validation cohort (P = .0002). In multivariate analysis, CAB risk score was the most significant independent predictor of distant recurrence with a hazard ratio of 4.3 (P = .0003). CanAssist-Breast is a precise and unique machine learning-based proteomic risk-classifier that can assist in risk stratification of patients with early-stage HR+ breast cancer.