Human Amniotic MSC Response in LPS-Stimulated Ascites from Patients with Cirrhosis: FOXO1 Gene and Th17 Activation in Enhanced Antibacterial Activation

Spontaneous bacterial peritonitis (SBP) is a severe complication in patients with decompensated liver cirrhosis and is commonly treated with broad spectrum antibiotics. However, the rise of antibiotic resistance requires alternative therapeutic strategies. As recently shown, human amnion-derived mesenchymal stem cells (hA-MSCs) are able, in vitro, to promote bacterial clearance and modulate the immune and inflammatory response in SBP. Our results highlight the upregulation of FOXO1, CXCL5, CXCL6, CCL20, and MAPK13 in hA-MSCs as well as the promotion of bacterial clearance, prompting a shift in the immune response toward a Th17 lymphocyte phenotype after 72 h treatment. In this study, we used an in vitro SBP model and employed omics techniques (next-generation sequencing) to investigate the mechanisms by which hA-MSCs modify the crosstalk between immune cells in LPS-stimulated ascitic fluid. We also validated the data obtained via qRT-PCR, cytofluorimetric analysis, and Luminex assay. These findings provide further support to the hope of using hA-MSCs for the prevention and treatment of infective diseases, such as SBP, offering a viable alternative to antibiotic therapy.

In-silico guided chemical exploration of KDM4A fragments hits

BACKGROUND

Lysine demethylase enzymes (KDMs) are an emerging class of therapeutic targets, that catalyse the removal of methyl marks from histone lysine residues regulating chromatin structure and gene expression. KDM4A isoform plays an important role in the epigenetic dysregulation in various cancers and is linked to aggressive disease and poor clinical outcomes. Despite several efforts, the KDM4 family lacks successful specific molecular inhibitors.

RESULTS

Herein, starting from a structure-based fragments virtual screening campaign we developed a synergic framework as a guide to rationally design efficient KDM4A inhibitors. Commercial libraries were used to create a fragments collection and perform a virtual screening campaign combining docking and pharmacophore approaches. The most promising compounds were tested in-vitro by a Homogeneous Time-Resolved Fluorescence-based assay developed for identifying selective substrate-competitive inhibitors by means of inhibition of H3K9me3 peptide demethylation. 2-(methylcarbamoyl)isonicotinic acid was identified as a preliminary active fragment, displaying inhibition of KDM4A enzymatic activity. Its chemical exploration was deeply investigated by computational and experimental approaches which allowed a rational fragment growing process. The in-silico studies guided the development of derivatives designed as expansion of the primary fragment hit and provided further knowledge on the structure-activity relationship.

CONCLUSIONS

Our study describes useful insights into key ligand-KDM4A protein interaction and provides structural features for the development of successful selective KDM4A inhibitors.

Caspase-8 activation by cigarette smoke induces pro-inflammatory cell death of human macrophages exposed to lipopolysaccharide

Cigarette smoking impairs the lung innate immune response making smokers more susceptible to infections and severe symptoms. Dysregulation of cell death is emerging as a key player in chronic inflammatory conditions. We have recently reported that short exposure of human monocyte-derived macrophages (hMDMs) to cigarette smoke extract (CSE) altered the TLR4-dependent response to lipopolysaccharide (LPS). CSE caused inhibition of the MyD88-dependent inflammatory response and activation of TRIF/caspase-8/caspase-1 pathway leading to Gasdermin D (GSDMD) cleavage and increased cell permeability. Herein, we tested the hypothesis that activation of caspase-8 by CSE increased pro-inflammatory cell death of LPS-stimulated macrophages. To this purpose, we measured apoptotic and pyroptotic markers as well as the expression/release of pro-inflammatory mediators in hMDMs exposed to LPS and CSE, alone or in combination, for 6 and 24 h. We show that LPS/CSE-treated hMDMs, but not cells treated with CSE or LPS alone, underwent lytic cell death (LDH release) and displayed apoptotic features (activation of caspase-8 and -3/7, nuclear condensation, and mitochondrial membrane depolarization). Moreover, the negative regulator of caspase-8, coded by CFLAR gene, was downregulated by CSE. Activation of caspase-3 led to Gasdermin E (GSDME) cleavage. Notably, lytic cell death caused the release of the damage-associated molecular patterns (DAMPs) heat shock protein-60 (HSP60) and S100A8/A9. This was accompanied by an impaired inflammatory response resulting in inhibited and delayed release of IL6 and TNF. Of note, increased cleaved caspase-3, higher levels of GSDME and altered expression of cell death-associated genes were found in alveolar macrophages of smoker subjects compared to non-smoking controls. Overall, our findings show that CSE sensitizes human macrophages to cell death by promoting pyroptotic and apoptotic pathways upon encountering LPS. We propose that while the delayed inflammatory response may result in ineffective defenses against infections, the observed cell death associated with DAMP release may contribute to establish chronic inflammation. CS exposure sensitizes human macrophages to pro-inflammatory cell death. Upon exposure to LPS, CS inhibits the TLR4/MyD88 inflammatory response, downregulating the pro-inflammatory genes TNF and IL6 and the anti-apoptotic gene CFLAR, known to counteract caspase-8 activity. CS enhances caspase-8 activation through TLR4/TRIF, with a partial involvement of RIPK1, resulting on the activation of caspase-1/GSDMD axis leading to increased cell permeability and DAMP release through gasdermin pores [19]. At later timepoints caspase-3 becomes strongly activated by caspase-8 triggering apoptotic events which are associated with mitochondrial membrane depolarization, gasdermin E cleavage and secondary necrosis with consequent massive DAMP release.

MBS: a genome browser annotation track for high-confident microRNA binding sites in whole human transcriptome

MicroRNAs (miRNAs) are small non-coding ribonucleic acids (RNAs) that play a role in many regulatory pathways in eukaryotes. They usually exert their functions by binding mature messenger RNAs. The prediction of the binding targets of the endogenous miRNAs is crucial to unravel the processes they are involved in. In this work, we performed an extensive miRNA binding sites (MBS) prediction over all the annotated transcript sequences and made them available through an UCSC track. MBS annotation track allows to study and visualize the human miRNA binding sites transcriptome-wide in a genome browser, together with any other available information the user is interested in. In the creation of the database that underlies the MBS track, three consolidated algorithms of miRNA binding prediction have been used: PITA, miRanda and TargetScan, and information about the binding sites predicted by all of them has been collected. MBS track displays high-confident miRNA binding sites for the whole length of each human transcript, both coding and non-coding ones. Each annotation can redirect to a web page with the details of the miRNA binding and the involved transcripts. MBS can be easily applied to retrieve specific information such as the effects of alternative splicing on miRNA binding or when a specific miRNA binds an exon-exon junction in the mature RNA. Overall, MBS will be of great help for studying and visualizing, in a user-friendly mode, the predicted miRNA binding sites on all the transcripts arising from a gene or a region of interest. Database URL https://datasharingada.fondazionerimed.com:8080/MBS.

An Overview of In Vitro Assays of 64Cu-, 68Ga-, 125I-, and 99mTc-Labelled Radiopharmaceuticals Using Radiometric Counters in the Era of Radiotheranostics

Radionuclides are unstable isotopes that mainly emit alpha (α), beta (β) or gamma (γ) radiation through radiation decay. Therefore, they are used in the biomedical field to label biomolecules or drugs for diagnostic imaging applications, such as positron emission tomography (PET) and/or single-photon emission computed tomography (SPECT). A growing field of research is the development of new radiopharmaceuticals for use in cancer treatments. Preclinical studies are the gold standard for translational research. Specifically, in vitro radiopharmaceutical studies are based on the use of radiopharmaceuticals directly on cells. To date, radiometric β- and γ-counters are the only tools able to assess a preclinical in vitro assay with the aim of estimating uptake, retention, and release parameters, including time- and dose-dependent cytotoxicity and kinetic parameters. This review has been designed for researchers, such as biologists and biotechnologists, who would like to approach the radiobiology field and conduct in vitro assays for cellular radioactivity evaluations using radiometric counters. To demonstrate the importance of in vitro radiopharmaceutical assays using radiometric counters with a view to radiogenomics, many studies based on ⁶⁴Cu-, ⁶⁸Ga-, ¹²⁵I-, and ^99mTc-labeled radiopharmaceuticals have been revised and summarized in this manuscript.

Abstract 3146: EGFL6 induces immunosuppressive functions of tumor-associated myeloid cells and mediates resistance to anti-PDL1 therapy

Myeloid-derived suppressor cells (MDSCs) and tumor-associated macrophages (TAMs) are critical negative regulators of immunity in cancer. Understanding factors which regulate these cells could result in the identification of new approaches to enhance anti-tumor immunotherapy. One such factor is Epidermal growth factor-like 6 (EGFL6). EGFL6 is a secreted factor known to promote cancer stem like cell migration and regulate cancer cell differentiation. Similarly, EGFL6 promotes endothelial cell migration and proliferation. Indicating a potential role for EGFL6 as a myeloid cell regulatory factor, we found that mice which overexpress Egfl6 have an increased numbers of granulocytes and monocytes in both the bone marrow and spleen. In vitro and ex-vivo analysis indicated that EGFL6, via binding with beta integrins and activation of Syk/ERK signaling, (i) acts as a chemotactic factor for human myeloid cells migration and (ii) promotes their differentiation toward a suppressive state. Suggesting an important role in promoting an immunosuppressive tumor microenvironment (TME), using two syngeneic mouse models of ovarian cancer, we found that expression of Egfl6 in tumor cells resulted in increased accumulation of intra-tumoral MDSCs and TAMs and fewer cytotoxic CD8+ T cells. This was associated with increased tumor growth and shortened animal survival. Gene expression profiling and flow cytometry analysis of tumor infiltrating myeloid cells indicated that Egfl6 induced the expression of immunosuppressive factors, including CXCL2, IL-10 and PD-L1. Consistent with Egfl6 driving an immune suppressive TME, EGFL6 expression in an otherwise immune ‘hot’/anti-PD-L1 responsive tumor model completely inhibited response to anti-PD-L-1 therapy. We are currently evaluating the impact of Egfl6 neutralizing antibody on the efficacy of ICI therapy and anti-tumor immunity. Combined our data show that EGFL6 acts as a chemotactic factor to both recruit myeloid cells to the ovarian TME and subsequently promotes their differentiation to an immunosuppressive phenotype. This suggests EGFL6 is a potential novel therapeutic target to ovarian tumor mediated immunosuppression and enhance response to immune therapy in ovarian cancer patients.

Citation Format: Sarah Sinno, Shoumei Bai, Claudia Coronnello, Anda Vlad, Ronald J. Buckanovich, Sandra Cascio. EGFL6 induces immunosuppressive functions of tumor-associated myeloid cells and mediates resistance to anti-PDL1 therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3146.

A multivariate statistical test for differential expression analysis

Statistical tests of differential expression usually suffer from two problems. Firstly, their statistical power is often limited when applied to small and skewed data sets. Secondly, gene expression data are usually discretized by applying arbitrary criteria to limit the number of false positives. In this work, a new statistical test obtained from a convolution of multivariate hypergeometric distributions, the Hy-test, is proposed to address these issues. Hy-test has been carried out on transcriptomic data from breast and kidney cancer tissues, and it has been compared with other differential expression analysis methods. Hy-test allows implicit discretization of the expression profiles and is more selective in retrieving both differential expressed genes and terms of Gene Ontology. Hy-test can be adopted together with other tests to retrieve information that would remain hidden otherwise, e.g., terms of (1) cell cycle deregulation for breast cancer and (2) “programmed cell death” for kidney cancer.

Egfl6 promotes ovarian cancer progression by inducing the immunosuppressive functions of tumor-infiltrating myeloid cells

Myeloid-derived suppressor cells (MDSCs) and tumor-associated macrophages (TAMs) are critical negative regulators of immunity in cancer. Understanding factors which regulate these cells could result in the identification of new approaches to enhance anti-tumor immunotherapy. One such factor is epidermal growth factor-like 6 (EGFL6), a secreted factor known to promote cancer stem like cell migration and regulate cancer cell differentiation. We found that mice which overexpress Egfl6 have an increased numbers of granulocytes and monocytes in both the bone marrow and spleen. In vitro and ex-vivo analysis indicated that EGFL6, via binding with beta integrins and activation of SYK and ERK signaling, (i) acts as a chemotactic factor for myeloid cells migration and (ii) promotes their differentiation toward a suppressive state. Suggesting an important role in promoting an immunosuppressive tumor microenvironment (TME), using two syngeneic mouse models of ovarian cancer, we found that expression of Egfl6 in tumor cells resulted in increased accumulation of intra-tumoral MDSCs and TAMs and fewer cytotoxic CD8+ T cells. This was associated with increased tumor growth and shortened animal survival. Gene expression profiling of tumor infiltrating myeloid cells indicated that Egfl6 induced the expression of immunosuppressive factors, including CXCL2, IL-10 and PD-L1. Moreover, in an immune ‘hot’ tumor model, EGFL6 completely inhibited response to a-PD-L-1 therapy. Combined our data show that EGFL6 induces the recruitment of myeloid cells into the ovarian TME and subsequently promotes their immunosuppressive functions. This suggest EGFL6 is a potential novel therapeutic target to enhance response to immune therapy in OvCa patients.

Supported by Ovarian Cancer Research Alliance

miRNA expression analysis in the human heart: Undifferentiated progenitors vs. bioptic tissues-Implications for proliferation and ageing

In developed countries, cardiovascular diseases are currently the first cause of death. Cardiospheres (CSs) and cardiosphere-derived cells (CDCs) have been found to have the ability to regenerate the myocardium after myocardial infarction (MI). In recent years, much effort has been made to gain insight into the human heart repair mechanisms, in which miRNAs have been shown to play an important role. In this regard, to elucidate the involvement of miRNAs, we evaluated the miRNA expression profile across human heart biopsy, CSs and CDCs using microarray and next-generation sequencing (NGS) technologies. We identified several miRNAs more represented in the progenitors, where some of them can be responsible for the proliferation or the maintenance of an undifferentiated state, while others have been found to be downregulated in the undifferentiated progenitors compared with the biopsies. Moreover, we also found a correlation between downregulated miRNAs in CSs/CDCs and patient age (eg miR-490) and an inverse correlation among miRNAs upregulated in CSs/CDCs (eg miR-31).

Support Vector Machine as a Supervised Learning for the Prioritization of Novel Potential SARS-CoV-2 Main Protease Inhibitors

In the last year, the COVID-19 pandemic has highly affected the lifestyle of the world population, encouraging the scientific community towards a great effort on studying the infection molecular mechanisms. Several vaccine formulations are nowadays available and helping to reach immunity. Nevertheless, there is a growing interest towards the development of novel anti-covid drugs. In this scenario, the main protease (Mpro) represents an appealing target, being the enzyme responsible for the cleavage of polypeptides during the viral genome transcription. With the aim of sharing new insights for the design of novel Mpro inhibitors, our research group developed a machine learning approach using the support vector machine (SVM) classification. Starting from a dataset of two million commercially available compounds, the model was able to classify two hundred novel chemo-types as potentially active against the viral protease. The compounds labelled as actives by SVM were next evaluated through consensus docking studies on two PDB structures and their binding mode was compared to well-known protease inhibitors. The best five compounds selected by consensus docking were then submitted to molecular dynamics to deepen binding interactions stability. Of note, the compounds selected via SVM retrieved all the most important interactions known in the literature.

Lung Segmentation on High-Resolution Computerized Tomography Images Using Deep Learning: A Preliminary Step for Radiomics Studies

BACKGROUND

The aim of this work is to identify an automatic, accurate, and fast deep learning segmentation approach, applied to the parenchyma, using a very small dataset of high-resolution computed tomography images of patients with idiopathic pulmonary fibrosis. In this way, we aim to enhance the methodology performed by healthcare operators in radiomics studies where operator-independent segmentation methods must be used to correctly identify the target and, consequently, the texture-based prediction model.

METHODS

Two deep learning models were investigated: (i) U-Net, already used in many biomedical image segmentation tasks, and (ii) E-Net, used for image segmentation tasks in self-driving cars, where hardware availability is limited and accurate segmentation is critical for user safety. Our small image dataset is composed of 42 studies of patients with idiopathic pulmonary fibrosis, of which only 32 were used for the training phase. We compared the performance of the two models in terms of the similarity of their segmentation outcome with the gold standard and in terms of their resources' requirements.

RESULTS

E-Net can be used to obtain accurate (dice similarity coefficient = 95.90%), fast (20.32 s), and clinically acceptable segmentation of the lung region.

CONCLUSIONS

We demonstrated that deep learning models can be efficiently applied to rapidly segment and quantify the parenchyma of patients with pulmonary fibrosis, without any radiologist supervision, in order to produce user-independent results.

miR-1207-5p Can Contribute to Dysregulation of Inflammatory Response in COVID-19 via Targeting SARS-CoV-2 RNA

The present study focuses on the role of human miRNAs in SARS-CoV-2 infection. An extensive analysis of human miRNA binding sites on the viral genome led to the identification of miR-1207-5p as potential regulator of the viral Spike protein. It is known that exogenous RNA can compete for miRNA targets of endogenous mRNAs leading to their overexpression. Our results suggest that SARS-CoV-2 virus can act as an exogenous competing RNA, facilitating the over-expression of its endogenous targets. Transcriptomic analysis of human alveolar and bronchial epithelial cells confirmed that the CSF1 gene, a known target of miR-1207-5p, is over-expressed following SARS-CoV-2 infection. CSF1 enhances macrophage recruitment and activation and its overexpression may contribute to the acute inflammatory response observed in severe COVID-19. In summary, our results indicate that dysregulation of miR-1207-5p-target genes during SARS-CoV-2 infection may contribute to uncontrolled inflammation in most severe COVID-19 cases.

An improvement of ComiR algorithm for microRNA target prediction by exploiting coding region sequences of mRNAs

MicroRNA are small non-coding RNAs that post-transcriptionally regulate the expression levels of messenger RNAs. MicroRNA regulation activity depends on the recognition of binding sites located on mRNA molecules. ComiR is a web tool realized to predict the targets of a set of microRNAs, starting from their expression profile. ComiR was trained with the information regarding binding sites in the 3’utr region, by using a reliable dataset containing the targets of endogenously expressed microRNA in D. melanogaster S2 cells. This dataset was obtained by comparing the results from two different experimental approaches, i.e., inhibition, and immunoprecipitation of the AGO1 protein--a component of the microRNA induced silencing complex.

In this work, we tested whether including coding region binding sites in ComiR algorithm improves the performance of the tool in predicting microRNA targets. We focused the analysis on the D. melanogaster species and updated the ComiR underlying database with the currently available releases of mRNA and microRNA sequences. As a result, we find that ComiR algorithm trained with the information related to the coding regions is more efficient in predicting the microRNA targets, with respect to the algorithm trained with 3’utr information. On the other hand, we show that 3’utr based predictions can be seen as complementary to the coding region based predictions, which suggests that both predictions, from 3’utr and coding regions, should be considered in comprehensive analysis.

Furthermore, we observed that the lists of targets obtained by analyzing data from one experimental approach only, that is, inhibition or immunoprecipitation of AGO1, are not reliable enough to test the performance of our microRNA target prediction algorithm. Further analysis will be conducted to investigate the effectiveness of the tool with data from other species, provided that validated datasets, as obtained from the comparison of RISC proteins inhibition and immunoprecipitation experiments, will be available for the same samples. Finally, we propose to upgrade the existing ComiR web-tool by including the coding region based trained model, available together with the 3’utr based one.

Residue analysis of a synthetic glucocorticoid in liver samples by a 1HMR spectroscopy approach: An exploratory study on animal model

Betamethasone is a glucocorticoid authorised in cattle for the treatment of metabolic and inflammatory diseases, but, in Europe, it is illegally employed to improve productive performances. LC-MS/MS is the official control method of veterinary drugs residues in food of animal origin. An experimental study was developed to evaluate the feasibility of proton magnetic resonance spectroscopy (1H-MRS) as a potential alternative approach to detect the presence of betamethasone residues. Eight rat liver samples were collected 24 h post-betamethasone-treatment from experimental and control animals and were analysed by 1H-MRS using a 7-Tesla MRI scanner. 1H-MR reference spectra both of the Bentelan formulation used for treatment, and of three solutions of betamethasone in dimethyl sulphoxide (DMSO) at 5, 10 and 100 mM, respectively, were acquired to fit analyte-peaks in the liver samples spectra. Betamethasone-peaks were found only in the 100 mM betamethasone in DMSO solution spectrum. Betamethasone residues were not detected in any of the tissue samples analysed, probably related to the low concentration of injected drug. These findings allow us to establish, for the first time in the literature, the detection limit (in the range 10-100 mM) of betamethasone for the 7-Tesla MRI scanner used here. Given this very-low sensitivity, we conclude that the evaluated 1H-MR spectroscopy approach is not suitable for the detection of betamethasone residues in edible tissues, since the maximum residue limit imposed by Commission Regulation (EC) 37/2010 for betamethasone in the liver, and metabolic concentrations required to be detected in animal samples from livestock, are far below the detection limit we found.

Aneuploid IMR90 cells induced by depletion of pRB, DNMT1 and MAD2 show a common gene expression signature

Chromosome segregation defects lead to aneuploidy which is a major feature of solid tumors. How diploid cells face chromosome mis-segregation and how aneuploidy is tolerated in tumor cells are not completely defined yet. Thus, an important goal of cancer genetics is to identify gene networks that underlie aneuploidy and are involved in its tolerance. To this aim, we induced aneuploidy in IMR90 human primary cells by depleting pRB, DNMT1 and MAD2 and analyzed their gene expression profiles by microarray analysis. Bioinformatic analysis revealed a common gene expression profile of IMR90 cells that became aneuploid. Gene Set Enrichment Analysis (GSEA) also revealed gene-sets/pathways that are shared by aneuploid IMR90 cells that may be exploited for novel therapeutic approaches in cancer. Furthermore, Protein-Protein Interaction (PPI) network analysis identified TOP2A and KIF4A as hub genes that may be important for aneuploidy establishment.

Cross-talk between Colon Cells and Macrophages Increases ST6GALNAC1 and MUC1-sTn Expression in Ulcerative Colitis and Colitis-Associated Colon Cancer

Patients with ulcerative colitis have an increased risk of developing colitis-associated colon cancer (CACC). Changes in glycosylation of the oncoprotein MUC1 commonly occur in chronic inflammation, including ulcerative colitis, and this abnormally glycosylated MUC1 promotes cancer development and progression. It is not known what causes changes in glycosylation of MUC1. Gene expression profiling of myeloid cells in inflamed and malignant colon tissues showed increased expression levels of inflammatory macrophage-associated cytokines compared with normal tissues. We analyzed the involvement of macrophage-associated cytokines in the induction of aberrant MUC1 glycoforms. A coculture system was used to examine the effects of M1 and M2 macrophages on glycosylation-related enzymes in colon cancer cells. M2-like macrophages induced the expression of the glycosyltransferase ST6GALNAC1, an enzyme that adds sialic acid to O-linked GalNAc residues, promoting the formation of tumor-associated sialyl-Tn (sTn) O-glycans. Immunostaining of ulcerative colitis and CACC tissue samples confirmed the elevated number of M2-like macrophages as well as high expression of ST6GALNAC1 and the altered MUC1-sTn glycoform on colon cells. Cytokine arrays and blocking antibody experiments indicated that the macrophage-dependent ST6GALNAC1 activation was mediated by IL13 and CCL17. We demonstrated that IL13 promoted phosphorylation of STAT6 to activate transcription of ST6GALNAC1. A computational model of signaling pathways was assembled and used to test IL13 inhibition as a possible therapy. Our findings revealed a novel cellular cross-talk between colon cells and macrophages within the inflamed and malignant colon that contributes to the pathogenesis of ulcerative colitis and CACC.See related Spotlight on p. 160.

Usefulness of regional right ventricular and right atrial strain for prediction of early and late right ventricular failure following a left ventricular assist device implant: A machine learning approach

BACKGROUND

Identifying candidates for left ventricular assist device surgery at risk of right ventricular failure remains difficult. The aim was to identify the most accurate predictors of right ventricular failure among clinical, biological, and imaging markers, assessed by agreement of different supervised machine learning algorithms.

METHODS

Seventy-four patients, referred to HeartWare left ventricular assist device since 2010 in two Italian centers, were recruited. Biomarkers, right ventricular standard, and strain echocardiography, as well as cath-lab measures, were compared among patients who did not develop right ventricular failure (N = 56), those with acute-right ventricular failure (N = 8, 11%) or chronic-right ventricular failure (N = 10, 14%). Logistic regression, penalized logistic regression, linear support vector machines, and naïve Bayes algorithms with leave-one-out validation were used to evaluate the efficiency of any combination of three collected variables in an "all-subsets" approach.

RESULTS

Michigan risk score combined with central venous pressure assessed invasively and apical longitudinal systolic strain of the right ventricular-free wall were the most significant predictors of acute-right ventricular failure (maximum receiver operating characteristic-area under the curve = 0.95, 95% confidence interval = 0.91-1.00, by the naïve Bayes), while the right ventricular-free wall systolic strain of the middle segment, right atrial strain (QRS-synced), and tricuspid annular plane systolic excursion were the most significant predictors of Chronic-RVF (receiver operating characteristic-area under the curve = 0.97, 95% confidence interval = 0.91-1.00, according to naïve Bayes).

CONCLUSION

Apical right ventricular strain as well as right atrial strain provides complementary information, both critical to predict acute-right ventricular failure and chronic-right ventricular failure, respectively.

MTGO-SC, A Tool to Explore Gene Modules in Single-Cell RNA Sequencing Data

The identification of functional modules in gene interaction networks is a key step in understanding biological processes. Network interpretation is essential for unveiling biological mechanisms, candidate biomarkers, or potential targets for drug discovery/repositioning. Plenty of biological module identification algorithms are available, although none is explicitly designed to perform the task on single-cell RNA sequencing (scRNA-seq) data. Here, we introduce MTGO-SC, an adaptation for scRNA-seq of our biological network module detection algorithm MTGO. MTGO-SC isolates gene functional modules by leveraging on both the network topological structure and the annotations characterizing the nodes (genes). These annotations are provided by an external source, such as databases and literature repositories (e.g., the Gene Ontology, Reactome). Thanks to the depth of single-cell data, it is possible to define one network for each cell cluster (typically, cell type or state) composing each sample, as opposed to traditional bulk RNA-seq, where the emerging gene network is averaged over the whole sample. MTGO-SC provides two complexity levels for interpretation: the gene-gene interaction and the intermodule interaction networks. MTGO-SC is versatile in letting the users define the rules to extract the gene network and integrated with the Seurat scRNA-seq analysis pipeline. MTGO-SC is available at https://github.com/ne1s0n/MTGOsc.

On the prospect of serum exosomal miRNA profiling and protein biomarkers for the diagnosis of ascending aortic dilatation in patients with bicuspid and tricuspid aortic valve

BACKGROUND

To determine the impact of circulating miRNA and protein activity on the severity of ascending aortic dilatation in patients with bicuspid (BAV) and tricuspid aortic valve (TAV).

METHODS

By reverse transcription polymerase chain reaction, exosomal circulating expression levels (versus healthy aorta) of miRNAs and absolute levels of transforming growth factor β (TGF-β), matrix metalloproteinases (MMP-2, -3 and -9), tissue inhibitors (TIMP-1, -2, -3 and -4), and soluble receptors for advanced glycation end products AGEs (sRAGE) were evaluated in ascending dilated aortas of 71 patients with different valve morphotype.

RESULTS

Less-dilated ascending aorta exhibited a specific miRNA signature (i.e., miR-126 miR-15b, miR-195, miR-221, miR24, miR-30b and miR-320a), which was statistically different from that of severely-dilated ascending aorta. Among these analytes, miR-15b was the most significant (p < 0.001) and resulted as an independent predictor of aortic dilatation (β = -1.099, p = 0.041). When patients were grouped according to aortic valve morphology, miRNAs and protein proteolytic activity were different between BAV and TAV in the expression level of miR-133a, miR-155, miR-320a, miR-34a_(#000425), miR-34a_(#000426), miR-494 and measurements of TGF-β and MMP-3, MMP-9, TIMP-4. The circulating level of miR-34a_(#000426) was negatively correlated to the aortic wall elasticity of bicuspid patients (R = -0.653 and p = 0.011), suggesting an apparent different mechanism of aortic wall degeneration specific for BAV.

CONCLUSIONS

Taken these biomarkers together, we demonstrated that the severity of aortic size and valve morphology differently modulates miRNA analytes and protein proteolytic activity in patients with ascending aortic dilatation, and this may be useful to design new therapies that inhibit miRNAs.

Aldosterone regulates microRNAs in the cortical collecting duct to alter sodium transport

A role for microRNAs (miRs) in the physiologic regulation of sodium transport in the kidney has not been established. In this study, we investigated the potential of aldosterone to alter miR expression in mouse cortical collecting duct (mCCD) epithelial cells. Microarray studies demonstrated the regulation of miR expression by aldosterone in both cultured mCCD and isolated primary distal nephron principal cells. Aldosterone regulation of the most significantly downregulated miRs, mmu-miR-335-3p, mmu-miR-290-5p, and mmu-miR-1983 was confirmed by quantitative RT-PCR. Reducing the expression of these miRs separately or in combination increased epithelial sodium channel (ENaC)-mediated sodium transport in mCCD cells, without mineralocorticoid supplementation. Artificially increasing the expression of these miRs by transfection with plasmid precursors or miR mimic constructs blunted aldosterone stimulation of ENaC transport. Using a newly developed computational approach, termed ComiR, we predicted potential gene targets for the aldosterone-regulated miRs and confirmed ankyrin 3 (Ank3) as a novel aldosterone and miR-regulated protein. A dual-luciferase assay demonstrated direct binding of the miRs with the Ank3-3' untranslated region. Overexpression of Ank3 increased and depletion of Ank3 decreased ENaC-mediated sodium transport in mCCD cells. These findings implicate miRs as intermediaries in aldosterone signaling in principal cells of the distal kidney nephron.

Overexpression of microRNA-1 promotes cardiomyocyte commitment from human cardiovascular progenitors via suppressing WNT and FGF signaling pathways

Early heart development takes place through a complex series of steps, including the induction of cardiac mesoderm, formation of the cardiovascular progenitor cells and the commitment of cardiovascular lineage cells, such as cardiomyocytes (CMs), smooth muscle cells (SMCs) and endothelial cells (ECs). Recently, microRNAs, a family of endogenous, small non-coding RNAs, have been identified as critical regulators in cardiogenesis and cardiovascular disease. Previous studies demonstrated that microRNA-1 (miR-1) could promote cardiac differentiation from mouse and human embryonic stem (ES) cells. However, the underlying mechanism remained largely unclear. We performed microRNA deep sequencing among human ES cells, ES cell derived-multipotent cardiovascular progenitors (MCPs), and MCP-specified CMs, ECs, and SMCs. A specific enrichment of miR-1 was found in CMs, not in SMCs or ECs, implying a key role of miR-1 in determining cardiovascular commitment from MCPs. When overexpressed in human induced pluripotent stem cells, miR-1 enhanced the expression of key cardiac transcriptional factors and sarcomeric genes. Importantly, we found miR-1 promoted CM differentiation and suppressed EC commitment from MCPs by modulating the activities of WNT and FGF signaling pathways. FZD7 and FRS2 were confirmed as miR-1 targets using luciferase reporter assay and western blot. Overall, this study reveals a fate-switching role of miR-1 at early human cardiovascular commitment stage via suppressing both WNT and FGF signaling pathways.

Estrogen represses gene expression through reconfiguring chromatin structures

Estrogen regulates over a thousand genes, with an equal number of them being induced or repressed. The distinct mechanisms underlying these dual transcriptional effects remain largely unknown. We derived comprehensive views of the transcription machineries assembled at estrogen-responsive genes through integrating multiple types of genomic data. In the absence of estrogen, the majority of genes formed higher-order chromatin structures, including DNA loops tethered to protein complexes involving RNA polymerase II (Pol II), estrogen receptor alpha (ERα) and ERα-pioneer factors. Genes to be 'repressed' by estrogen showed active transcription at promoters and throughout the gene bodies; genes to be 'induced' exhibited active transcription initiation at promoters, but with transcription paused in gene bodies. In the presence of estrogen, the majority of estrogen-induced genes retained the original higher-order chromatin structures, whereas most estrogen-repressed genes underwent a chromatin reconfiguration. For estrogen-induced genes, estrogen enhances transcription elongation, potentially through recruitment of co-activators or release of co-repressors with unique roles in elongation. For estrogen-repressed genes, estrogen treatment leads to chromatin structure reconfiguration, thereby disrupting the originally transcription-efficient chromatin structures. Our in silico studies have shown that estrogen regulates gene expression, at least in part, through modifying previously assembled higher-order complexes, rather than by facilitating de novo assembly of machineries.

ComiR: Combinatorial microRNA target prediction tool

ComiR is a web tool for combinatorial microRNA (miRNA) target prediction. Given an messenger RNA (mRNA) in human, mouse, fly or worm genomes, ComiR computes the potential of being targeted by a set of miRNAs, each of which can have zero, one or more targets on its 3′untranslated region. In determining the regulatory potential of an mRNA from a set of miRNAs, ComiR uses user-provided miRNA expression levels in a combination of appropriate thermodynamic modeling and machine learning techniques to make more accurate predictions. For each gene, ComiR returns the probability of being a functional target of a set of miRNAs, which depends on the relative miRNA expression levels. The tool provides a user-friendly interface to input a miRNA expression table containing many sample information and filter out the most relevant miRNAs. ComiR results can be downloaded or visualized on a table, which can then be used to select the most relevant targets and to compare the results obtained with different miRNA expression input. ComiR is freely available for academic use at http://www.benoslab.pitt.edu/comir/.

Novel modeling of combinatorial miRNA targeting identifies SNP with potential role in bone density

MicroRNAs (miRNAs) are post-transcriptional regulators that bind to their target mRNAs through base complementarity. Predicting miRNA targets is a challenging task and various studies showed that existing algorithms suffer from high number of false predictions and low to moderate overlap in their predictions. Until recently, very few algorithms considered the dynamic nature of the interactions, including the effect of less specific interactions, the miRNA expression level, and the effect of combinatorial miRNA binding. Addressing these issues can result in a more accurate miRNA:mRNA modeling with many applications, including efficient miRNA-related SNP evaluation. We present a novel thermodynamic model based on the Fermi-Dirac equation that incorporates miRNA expression in the prediction of target occupancy and we show that it improves the performance of two popular single miRNA target finders. Modeling combinatorial miRNA targeting is a natural extension of this model. Two other algorithms show improved prediction efficiency when combinatorial binding models were considered. ComiR (Combinatorial miRNA targeting), a novel algorithm we developed, incorporates the improved predictions of the four target finders into a single probabilistic score using ensemble learning. Combining target scores of multiple miRNAs using ComiR improves predictions over the naïve method for target combination. ComiR scoring scheme can be used for identification of SNPs affecting miRNA binding. As proof of principle, ComiR identified rs17737058 as disruptive to the miR-488-5p:NCOA1 interaction, which we confirmed in vitro. We also found rs17737058 to be significantly associated with decreased bone mineral density (BMD) in two independent cohorts indicating that the miR-488-5p/NCOA1 regulatory axis is likely critical in maintaining BMD in women. With increasing availability of comprehensive high-throughput datasets from patients ComiR is expected to become an essential tool for miRNA-related studies.