TGF-β1 inhibition of ACE2 mediated by miRNA uncovers novel mechanism of SARS-CoV-2 pathogenesis.

SARS-CoV-2 utilizes receptor binding domain (RBD) of spike glycoprotein to interact with angiotensin-converting enzyme 2 (ACE2). Decreased cell surface density of ACE2 contributes to mortality during COVID-19. Studies published early during the pandemic reported that people with cystic fibrosis (PwCF) treated with the high efficiency CFTR modulators ETI (elexacaftor-tezacaftor-ivacaftor) had higher ACE2 levels and milder COVID-19 symptoms, compared to people without CF. Subsequent studies did not confirm these findings. TGF-β1 gene polymorphisms associated with higher TGF-β1 levels, present in approximately 40% of CF patients, lead to more severe CF lung disease. To understand whether TGF-β1 modulates COVID-19 severity by affecting ACE2 levels in the airway, we performed small RNAseq and microRNA profiling and identified pathways uniquely affected by TGF-β1, including those associated with SARS-CoV-2 invasion. TGF-β1 inhibited ACE2 expression by miR-136-3p and miR-369-5p. ACE2 levels were higher in CF bronchial epithelial cell models. ETI did not prevent TGF-β1 inhibition of ACE2. Finally, TGF-β1 reduced the interaction between ACE2 and RBD by lowering ACE2 levels and its binding to RBD. Our data demonstrate novel mechanism whereby TGF-β1 inhibition of ACE2 in CF and non-CF bronchial epithelia may modulate SARS-CoV-2 pathogenicity and COVID-19 severity. By reducing ACE2 levels, TGF-β1 may decrease entry of SARS-CoV-2 into host cells while hindering recovery from COVID-19 due to loss of anti-inflammatory effects of ACE2

Multivariate statistical process monitoring and its integration with HAZOP analysis for abnormal event management

Abnormal event management (AEM) is an important problem in industrial chemical process operations. Principal component analysis (PCA) is widely used for process monitoring, which is the first step of AEM. The PCA method is sensitive to outliers in the data used to build the model. Robust PCA is first proposed using robust estimator as a diagnostic tool. Minimum covariance determinant (MCD) estimator is used as a diagnostic tool for robust PCA. A hybrid genetic algorithm is proposed for MCD estimation. After outliers are removed, PCA is built based on the remaining ‘good’ part of the data. To provide a tradeoff between false alarm and quick detection, a novel statistical testing algorithm is integrated with PCA to improve the fault detection and identification performance. Scores space and residuals space generated by PCA is decomposed into several subsets so chosen that in each subset the detection problem can be solved with an efficient recursive change detection algorithm based on χ2-generalized likelihood ratio (GLR) test. PCA is a global linear model. For a highly nonlinear chemical process, nonlinear PCA based on local linear approximation is proposed for process monitoring. Integration of Isomap, an efficient algorithm to generate the intrinsic dimensionality of the process data, with mixture of probabilistic principal component analyzers, is proposed to generate nonlinear PCA model, which can then be used for process monitoring. HAZOP analysis is a systematic proactive identification, evaluation and mitigation of process hazards during the design stage of a process. PHASuite, a knowledge based system for automated HAZOP analysis, is overviewed. A framework to integrate PCA and PHASuite for online abnormal event management is then proposed. The framework includes three major parts: process monitoring, automated HAZOP analysis module and a coordinator. Multiblock PCA is used for process monitoring of continuous process, while multiway PCA is used for batch process. When an abnormal event is detected, quantitative to qualitative measurements transformation based on contribution plots is then proposed. Based on the identified qualitative states, HAZOP analysis is performed using PHASuite based on digraphs to locate the original deviation, potential causes, consequences and recommendations to mitigate the consequences

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

Transforming Growth Factor-β1 Selectively Recruits microRNAs to the RNA-Induced Silencing Complex and Degrades CFTR mRNA under Permissive Conditions in Human Bronchial Epithelial Cells

<p>Mutations in the Cystic Fibrosis Transmembrane Conductance Regulator (<italic>CFTR</italic>) 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-β1), overexpressed in many CF patients, blocks corrector/potentiator rescue by inhibiting CFTR mRNA in vitro. Increased TGF-β1 signaling and acquired CFTR dysfunction are present in other lung diseases. To study the mechanism of TGF-β1 repression of CFTR, we used molecular, biochemical, and functional approaches in primary human bronchial epithelial cells from over 50 donors. TGF-β1 destabilized CFTR mRNA in cells from lungs with chronic disease, including CF, and impaired F508del-CFTR rescue by new-generation correctors. TGF-β1 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-β1-induced changes in the miRNA landscape, creating a permissive environment required for degradation of F508del-CFTR mRNA. In conclusion, TGF-β1 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-β1 to destabilize CFTR mRNA

Ovarian Transcriptome Analysis of Portunus trituberculatus Provides Insights into Genes Expressed during Phase III and IV Development.

Enhancing the production of aquatic animals is crucial for fishery management and aquaculture applications. Ovaries are specialized tissues that play critical roles in producing oocytes and hormones. Significant biochemical changes take place during the sexual maturation of Portunus trituberculatus, but the genetics of this process has not been extensively studied. Transcriptome sequencing can be used to determine gene expression changes within specific periods. In the current study, we used transcriptome sequencing to produce a comprehensive transcript dataset for the ovarian development of P. trituberculatus. Approximately 100 million sequencing reads were generated, and 126,075 transcripts were assembled. Functional annotation of the obtained transcripts revealed important pathways in ovarian development, such as those involving the vitellogenin gene. Also, we performed deep sequencing of ovaries in phases III and IV of sexual maturation in P. trituberculatus. Differential analysis of gene expression identified 506 significantly differentially expressed genes, which belong to 20 pathway, transporters, development, transcription factors, metabolism of other amino acids, carbohydrate and lipid, solute carrier family members, and enzymes. Taken together, our study provides the first comprehensive transcriptomic resource for P. trituberculatus ovaries, which will strengthen understanding of the molecular mechanisms underlying the sexual maturation process and advance molecular nutritional studies of P. trituberculatus