Precise Targeting of miRNA Sites Restores CFTR Activity in CF Bronchial Epithelial Cells.

MicroRNAs that are overexpressed in cystic fibrosis (CF) bronchial epithelial cells (BEC) negatively regulate CFTR and nullify the beneficial effects of CFTR modulators. We hypothesized that it is possible to reverse microRNA-mediated inhibition of CFTR using CFTR-specific target site blockers (TSBs) and to develop a drug-device combination inhalation therapy for CF. Lead microRNA expression was quantified in a series of human CF and non-CF samples and in vitro models. A panel of CFTR 3' untranslated region (UTR)-specific locked nucleic acid antisense oligonucleotide TSBs was assessed for their ability to increase CFTR expression. Their effects on CFTR activity alone or in combination with CFTR modulators were measured in CF BEC models. TSB encapsulation in poly-lactic-co-glycolic acid (PLGA) nanoparticles was assessed as a proof of principle of delivery into CF BECs. TSBs targeting the CFTR 3' UTR 298-305:miR-145-5p or 166-173:miR-223-3p sites increased CFTR expression and anion channel activity and enhanced the effects of ivacaftor/lumacaftor or ivacaftor/tezacaftor in CF BECs. Biocompatible PLGA-TSB nanoparticles promoted CFTR expression in primary BECs and retained desirable biophysical characteristics following nebulization. Alone or in combination with CFTR modulators, aerosolized CFTR-targeting TSBs encapsulated in PLGA nanoparticles could represent a promising drug-device combination therapy for the treatment for CFTR dysfunction in the lung.

Transforming Growth Factor-ß1 Selectively Recruits microRNAs to the RNA-Induced Silencing Complex and Degrades CFTR mRNA under Permissive Conditions in Human Bronchial Epithelial Cells.

Mutations in the Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) gene lead to cystic fibrosis (CF). The most common mutation F508del inhibits folding and processing of CFTR protein. FDA-approved correctors rescue the biosynthetic processing of F508del-CFTR protein, while potentiators improve the rescued CFTR channel function. Transforming growth factor (TGF-ß1), overexpressed in many CF patients, blocks corrector/potentiator rescue by inhibiting CFTR mRNA in vitro. Increased TGF-ß1 signaling and acquired CFTR dysfunction are present in other lung diseases. To study the mechanism of TGF-ß1 repression of CFTR, we used molecular, biochemical, and functional approaches in primary human bronchial epithelial cells from over 50 donors. TGF-ß1 destabilized CFTR mRNA in cells from lungs with chronic disease, including CF, and impaired F508del-CFTR rescue by new-generation correctors. TGF-ß1 increased the active pool of selected micro(mi)RNAs validated as CFTR inhibitors, recruiting them to the RNA-induced silencing complex (RISC). Expression of F508del-CFTR globally modulated TGF-ß1-induced changes in the miRNA landscape, creating a permissive environment required for degradation of F508del-CFTR mRNA. In conclusion, TGF-ß1 may impede the full benefit of corrector/potentiator therapy in CF patients. Studying miRNA recruitment to RISC under disease-specific conditions may help to better characterize the miRNAs utilized by TGF-ß1 to destabilize CFTR mRNA.

Automated genomic context analysis and experimental validation platform for discovery of prokaryote transcriptional regulator functions.

BACKGROUND: The clustering of genes in a pathway and the co-location of functionally related genes is widely recognized in prokaryotes. We used these characteristics to predict the metabolic involvement for a Transcriptional Regulator (TR) of unknown function, identified and confirmed its biological activity. RESULTS: A software tool that identifies the genes encoded within a defined genomic neighborhood for the subject TR and its homologs was developed. The output lists of genes in the genetic neighborhoods, their annotated functions, the reactants/products, and identifies the metabolic pathway in which the encoded-proteins function. When a set of TRs of known function was analyzed, we observed that their homologs frequently had conserved genomic neighborhoods that co-located the metabolically related genes regulated by the subject TR. We postulate that TR effectors are metabolites in the identified pathways; indeed the known effectors were present. We analyzed Bxe_B3018 from Burkholderia xenovorans, a TR of unknown function and predicted that this TR was related to the glycine, threonine and serine degradation. We tested the binding of metabolites in these pathways and for those that bound, their ability to modulate TR binding to its specific DNA operator sequence. Using rtPCR, we confirmed that methylglyoxal was an effector of Bxe_3018. CONCLUSION: These studies provide the proof of concept and validation of a systematic approach to the discovery of the biological activity for proteins of unknown function, in this case a TR. Bxe_B3018 is a methylglyoxal responsive TR that controls the expression of an operon composed of a putative efflux system.

Binding of nucleoid-associated protein fis to DNA is regulated by DNA breathing dynamics.

Physicochemical properties of DNA, such as shape, affect protein-DNA recognition. However, the properties of DNA that are most relevant for predicting the binding sites of particular transcription factors (TFs) or classes of TFs have yet to be fully understood. Here, using a model that accurately captures the melting behavior and breathing dynamics (spontaneous local openings of the double helix) of double-stranded DNA, we simulated the dynamics of known binding sites of the TF and nucleoid-associated protein Fis in Escherichia coli. Our study involves simulations of breathing dynamics, analysis of large published in vitro and genomic datasets, and targeted experimental tests of our predictions. Our simulation results and available in vitro binding data indicate a strong correlation between DNA breathing dynamics and Fis binding. Indeed, we can define an average DNA breathing profile that is characteristic of Fis binding sites. This profile is significantly enriched among the identified in vivo E. coli Fis binding sites. To test our understanding of how Fis binding is influenced by DNA breathing dynamics, we designed base-pair substitutions, mismatch, and methylation modifications of DNA regions that are known to interact (or not interact) with Fis. The goal in each case was to make the local DNA breathing dynamics either closer to or farther from the breathing profile characteristic of a strong Fis binding site. For the modified DNA segments, we found that Fis-DNA binding, as assessed by gel-shift assay, changed in accordance with our expectations. We conclude that Fis binding is associated with DNA breathing dynamics, which in turn may be regulated by various nucleotide modifications.

Improved predictions of transcription factor binding sites using physicochemical features of DNA.

Typical approaches for predicting transcription factor binding sites (TFBSs) involve use of a position-specific weight matrix (PWM) to statistically characterize the sequences of the known sites. Recently, an alternative physicochemical approach, called SiteSleuth, was proposed. In this approach, a linear support vector machine (SVM) classifier is trained to distinguish TFBSs from background sequences based on local chemical and structural features of DNA. SiteSleuth appears to generally perform better than PWM-based methods. Here, we improve the SiteSleuth approach by considering both new physicochemical features and algorithmic modifications. New features are derived from Gibbs energies of amino acid-DNA interactions and hydroxyl radical cleavage profiles of DNA. Algorithmic modifications consist of inclusion of a feature selection step, use of a nonlinear kernel in the SVM classifier, and use of a consensus-based post-processing step for predictions. We also considered SVM classification based on letter features alone to distinguish performance gains from use of SVM-based models versus use of physicochemical features. The accuracy of each of the variant methods considered was assessed by cross validation using data available in the RegulonDB database for 54 Escherichia coli TFs, as well as by experimental validation using published ChIP-chip data available for Fis and Lrp.

TGF-ß1 Inhibition of ACE2 Mediated by miRNA Uncovers Novel Mechanism of SARS-CoV-2 Pathogenesis.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), responsible for COVID-19, utilizes receptor binding domain (RBD) of spike glycoprotein to interact with angiotensin (Ang)-converting enzyme 2 (ACE2). Altering ACE2 levels may affect entry of SARS-CoV-2 and recovery from COVID-19. Decreased cell surface density of ACE2 leads to increased local levels of Ang II and may contribute to mortality resulting from acute lung injury and fibrosis during COVID-19. Studies published early during the COVID-19 pandemic reported that people with cystic fibrosis (PwCF) had milder symptoms, compared to people without CF. This finding was attributed to elevated ACE2 levels and/or treatment with the high efficiency CFTR modulators. Subsequent studies did not confirm these findings reporting variable effects of CFTR gene mutations on ACE2 levels. Transforming growth factor (TGF)-ß signaling is essential during SARS-CoV-2 infection and dominates the chronic immune response in severe COVID-19, leading to pulmonary fibrosis. TGF-ß1 is a gene modifier associated with more severe lung disease in PwCF but its effects on the COVID-19 course in PwCF is unknown. To understand whether TGF-ß1 affects ACE2 levels in the airway, we examined miRNAs and their gene targets affecting SARS-CoV-2 pathogenesis in response to TGF-ß1. Small RNAseq and micro(mi)RNA profiling identified pathways uniquely affected by TGF-ß1, including those associated with SARS-CoV-2 invasion, replication, and the host immune responses. TGF-ß1 inhibited ACE2 expression by miR-136-3p and miR-369-5p mediated mechanism in CF and non-CF bronchial epithelial cells. ACE2 levels were higher in two bronchial epithelial cell models expressing the most common CF-causing mutation in CFTR gene F508del, compared to controls without the mutation. After TGF-ß1 treatment, ACE2 protein levels were still higher in CF, compared to non-CF cells. TGF-ß1 prevented the modulator-mediated rescue of F508del-CFTR function while the modulators did not prevent the TGF-ß1 inhibition of ACE2 levels. Finally, TGF-ß1 reduced the interaction between ACE2 and the recombinant spike RBD by lowering ACE2 levels and its binding to RBD. Our data demonstrate novel mechanism whereby TGF-ß1 inhibition of ACE2 in CF and non-CF bronchial epithelial cells may modulate SARS-CoV-2 pathogenicity and COVID-19 severity. By reducing ACE2 levels, TGF-ß1 may decrease entry of SARS-CoV-2 into the host cells while hindering the recovery from COVID-19 due to loss of the anti-inflammatory and regenerative effects of ACE2. The above outcomes may be modulated by other, miRNA-mediated effects exerted by TGF-ß1 on the host immune responses, leading to a complex and yet incompletely understood circuitry.

Genomic glucocorticoid action in embryonic mouse neural stem cells.

Prenatal exposure to synthetic glucocorticoids (sGCs) reprograms brain development and predisposes the developing fetus towards potential adverse neurodevelopmental outcomes. Using a mouse model of sGC administration, previous studies show that these changes are accompanied by sexually dimorphic alterations in the transcriptome of neural stem and progenitor cells (NSPCs) derived from the embryonic telencephalon. Because cell type-specific gene expression profiles tightly regulate cell fate decisions and are controlled by a flexible landscape of chromatin domains upon which transcription factors and enhancer elements act, we multiplexed data from four genome-wide assays: RNA-seq, ATAC-seq (assay for transposase accessible chromatin followed by genome wide sequencing), dual cross-linking ChIP-seq (chromatin immunoprecipitation followed by genome wide sequencing), and microarray gene expression to identify novel relationships between gene regulation, chromatin structure, and genomic glucocorticoid receptor (GR) action in NSPCs. These data reveal that GR binds preferentially to predetermined regions of accessible chromatin to influence gene programming and cell fate decisions. In addition, we identify SOX2 as a transcription factor that impacts the genomic response of select GR target genes to sGCs (i.e., dexamethasone) in NSPCs.

Prediction of metabolic reactions based on atomic and molecular properties of small-molecule compounds.

MOTIVATION: Our knowledge of the metabolites in cells and their reactions is far from complete as revealed by metabolomic measurements that detect many more small molecules than are documented in metabolic databases. Here, we develop an approach for predicting the reactivity of small-molecule metabolites in enzyme-catalyzed reactions that combines expert knowledge, computational chemistry and machine learning. RESULTS: We classified 4843 reactions documented in the KEGG database, from all six Enzyme Commission classes (EC 1-6), into 80 reaction classes, each of which is marked by a characteristic functional group transformation. Reaction centers and surrounding local structures in substrates and products of these reactions were represented using SMARTS. We found that each of the SMARTS-defined chemical substructures is widely distributed among metabolites, but only a fraction of the functional groups in these substructures are reactive. Using atomic properties of atoms in a putative reaction center and molecular properties as features, we trained support vector machine (SVM) classifiers to discriminate between functional groups that are reactive and non-reactive. Classifier accuracy was assessed by cross-validation analysis. A typical sensitivity [TP/(TP+FN)] or specificity [TN/(TN+FP)] is ≈0.8. Our results suggest that metabolic reactivity of small-molecule compounds can be predicted with reasonable accuracy based on the presence of a potentially reactive functional group and the chemical features of its local environment. AVAILABILITY: The classifiers presented here can be used to predict reactions via a web site (http://cellsignaling.lanl.gov/Reactivity/). The web site is freely available.

Using sequence-specific chemical and structural properties of DNA to predict transcription factor binding sites.

An important step in understanding gene regulation is to identify the DNA binding sites recognized by each transcription factor (TF). Conventional approaches to prediction of TF binding sites involve the definition of consensus sequences or position-specific weight matrices and rely on statistical analysis of DNA sequences of known binding sites. Here, we present a method called SiteSleuth in which DNA structure prediction, computational chemistry, and machine learning are applied to develop models for TF binding sites. In this approach, binary classifiers are trained to discriminate between true and false binding sites based on the sequence-specific chemical and structural features of DNA. These features are determined via molecular dynamics calculations in which we consider each base in different local neighborhoods. For each of 54 TFs in Escherichia coli, for which at least five DNA binding sites are documented in RegulonDB, the TF binding sites and portions of the non-coding genome sequence are mapped to feature vectors and used in training. According to cross-validation analysis and a comparison of computational predictions against ChIP-chip data available for the TF Fis, SiteSleuth outperforms three conventional approaches: Match, MATRIX SEARCH, and the method of Berg and von Hippel. SiteSleuth also outperforms QPMEME, a method similar to SiteSleuth in that it involves a learning algorithm. The main advantage of SiteSleuth is a lower false positive rate.

Differential Gene Expression Analysis Reveals Global LMTK2 Regulatory Network and Its Role in TGF-ß1 Signaling.

Lemur tyrosine kinase 2 (LMTK2) is a transmembrane Ser/Thr kinase whose role has been increasingly recognized; however, when compared to other kinases, understanding of the LMTK2 networks and biological functions is still limited. Recent data have shown that transforming growth factor (TGF)-ß1 plays a role in modulating LMTK2 function by controlling its endocytic trafficking in human bronchial epithelial cells. Here, we aimed to unveil the LMTK2 regulatory network and elucidate how it affects cellular functions and disease pathways in either TGF-ß1 dependent or independent manner. To understand how the LMTK2 and TGF-ß1 pathways interconnect, we knocked down (KD) LMTK2 using small(si)RNA-mediated silencing in human bronchial epithelial CFBE41o- cells, treated cells with TGF-ß1 or vehicle control, and performed differential gene expression analysis by RNA sequencing (RNAseq). In vehicle-treated cells, LMTK2 KD affected expression of 2,506 genes while it affected 4,162 genes after TGF-ß1 stimulation. Bioinformatics analysis shows that LMTK2 is involved in diverse cellular functions and disease pathways, such as cell death and survival, cellular development, and cancer susceptibility. In summary, our study increases current knowledge about the LMTK2 network and its intersection with the TGF-ß1 signaling pathway. These findings will serve as basis for future exploration of the predicted LMTK2 interactions and signaling pathways.

Carbon-fate maps for metabolic reactions.

MOTIVATION: Stable isotope labeling of small-molecule metabolites (e.g. (13)C-labeling of glucose) is a powerful tool for characterizing pathways and reaction fluxes in a metabolic network. Analysis of isotope labeling patterns requires knowledge of the fates of individual atoms and moieties in reactions, which can be difficult to collect in a useful form when considering a large number of enzymatic reactions. RESULTS: We report carbon-fate maps for 4605 enzyme-catalyzed reactions documented in the KEGG database. Every fate map has been manually checked for consistency with known reaction mechanisms. A map includes a standardized structure-based identifier for each reactant (namely, an InChI string); indices for carbon atoms that are uniquely derived from the metabolite identifiers; structural data, including an identification of homotopic and prochiral carbon atoms; and a bijective map relating the corresponding carbon atoms in substrates and products. Fate maps are defined using the BioNetGen language (BNGL), a formal model-specification language, which allows a set of maps to be automatically translated into isotopomer mass-balance equations. AVAILABILITY: The carbon-fate maps and software for visualizing the maps are freely available (http://cellsignaling.lanl.gov/FateMaps/).

Prediction of oxidoreductase-catalyzed reactions based on atomic properties of metabolites.

MOTIVATION: Our knowledge of metabolism is far from complete, and the gaps in our knowledge are being revealed by metabolomic detection of small-molecules not previously known to exist in cells. An important challenge is to determine the reactions in which these compounds participate, which can lead to the identification of gene products responsible for novel metabolic pathways. To address this challenge, we investigate how machine learning can be used to predict potential substrates and products of oxidoreductase-catalyzed reactions. RESULTS: We examined 1956 oxidation/reduction reactions in the KEGG database. The vast majority of these reactions (1626) can be divided into 12 subclasses, each of which is marked by a particular type of functional group transformation. For a given transformation, the local structures of reaction centers in substrates and products can be characterized by patterns. These patterns are not unique to reactants but are widely distributed among KEGG metabolites. To distinguish reactants from non-reactants, we trained classifiers (linear-kernel Support Vector Machines) using negative and positive examples. The input to a classifier is a set of atomic features that can be determined from the 2D chemical structure of a compound. Depending on the subclass of reaction, the accuracy of prediction for positives (negatives) is 64 to 93% (44 to 92%) when asking if a compound is a substrate and 71 to 98% (50 to 92%) when asking if a compound is a product. Sensitivity analysis reveals that this performance is robust to variations of the training data. Our results suggest that metabolic connectivity can be predicted with reasonable accuracy from the presence or absence of local structural motifs in compounds and their readily calculated atomic features. AVAILABILITY: Classifiers reported here can be used freely for noncommercial purposes via a Java program available upon request.