PFKFB3 Inhibits Fructose Metabolism in Pulmonary Microvascular Endothelial Cells.

Pulmonary microvascular endothelial cells contribute to the integrity of the lung gas exchange interface, and they are highly glycolytic. Although glucose and fructose represent discrete substrates available for glycolysis, pulmonary microvascular endothelial cells prefer glucose over fructose, and the mechanisms involved in this selection are unknown. 6-Phosphofructo-2-kinase/fructose-2, 6-bisphosphatase 3 (PFKFB3) is an important glycolytic enzyme that drives glycolytic flux against negative feedback and links glycolytic and fructolytic pathways. We hypothesized that PFKFB3 inhibits fructose metabolism in pulmonary microvascular endothelial cells. We found that PFKFB3 knockout cells survive better than wild-type cells in fructose-rich medium under hypoxia. Seahorse assays, lactate and glucose measurements, and stable isotope tracing showed that PFKFB3 inhibits fructose-hexokinase-mediated glycolysis and oxidative phosphorylation. Microarray analysis revealed that fructose upregulates PFKFB3, and PFKFB3 knockout cells increase fructose-specific GLUT5 (glucose transporter 5) expression. Using conditional endothelial-specific PFKFB3 knockout mice, we demonstrated that endothelial PFKFB3 knockout increases lung tissue lactate production after fructose gavage. Last, we showed that pneumonia increases fructose in BAL fluid in mechanically ventilated ICU patients. Thus, PFKFB3 knockout increases GLUT5 expression and the hexokinase-mediated fructose use in pulmonary microvascular endothelial cells that promotes their survival. Our findings indicate that PFKFB3 is a molecular switch that controls glucose versus fructose use in glycolysis and help better understand lung endothelial cell metabolism during respiratory failure.

Characterization of long G4-rich enhancer-associated genomic regions engaging in a novel loop:loop 'G4 Kissing' interaction.

Mammalian antibody switch regions (∼1500 bp) are composed of a series of closely neighboring G4-capable sequences. Whereas numerous structural and genome-wide analyses of roles for minimal G4s in transcriptional regulation have been reported, Long G4-capable regions (LG4s)-like those at antibody switch regions-remain virtually unexplored. Using a novel computational approach we have identified 301 LG4s in the human genome and find LG4s prone to mutation and significantly associated with chromosomal rearrangements in malignancy. Strikingly, 217 LG4s overlap annotated enhancers, and we find the promoters regulated by these enhancers markedly enriched in G4-capable sequences suggesting G4s facilitate promoter-enhancer interactions. Finally, and much to our surprise, we also find single-stranded loops of minimal G4s within individual LG4 loci are frequently highly complementary to one another with 178 LG4 loci averaging >35 internal loop:loop complements of >8 bp. As such, we hypothesized (then experimentally confirmed) that G4 loops within individual LG4 loci directly basepair with one another (similar to characterized stem-loop kissing interactions) forming a hitherto undescribed, higher-order, G4-based secondary structure we term a 'G4 Kiss or G4K'. In conclusion, LG4s adopt novel, higher-order, composite G4 structures directly contributing to the inherent instability, regulatory capacity, and maintenance of these conspicuous genomic regions.

A partial encryption algorithm for medical images based on quick response code and reversible data hiding technology.

BACKGROUND: Medical image data, like most patient information, have a strong requirement for privacy and confidentiality. This makes transmitting medical image data, within an open network, problematic, due to the aforementioned issues, along with the dangers of data/information leakage. Possible solutions in the past have included the utilization of information-hiding and image-encryption technologies; however, these methods can cause difficulties when attempting to recover the original images. METHODS: In this work, we developed an algorithm for protecting medical image key regions. Coefficient of variation is first employed to identify key regions, a.k.a. image lesion areas; then additional areas are processed as blocks and texture complexity is analyzed. Next, our novel reversible data-hiding algorithm embeds lesion area contents into a high-texture area, after which an Arnold transformation is utilized to protect the original lesion information. After this, we use image basic information ciphertext and decryption parameters to generate a quick response (QR) code used in place of original key regions. RESULTS: The approach presented here allows for the storage (and sending) of medical image data within open network environments, while ensuring only authorized personnel are able to recover sensitive patient information (both image and meta-data) without information loss. DISCUSSION: Peak signal to noise ratio and the Structural Similarity Index measures show that the algorithm presented in this work can encrypt and restore original images without information loss. Moreover, by adjusting the threshold and the Mean Squared Error, we can control the overall quality of the image: the higher the threshold, the better the quality and vice versa. This allows the encryptor to control the amount of degradation as, at appropriate amounts, degradation aids in the protection of the image. CONCLUSIONS: As shown in the experimental results, the proposed method allows for (a) the safe transmission and storage of medical image data, (b) the full recovery (no information loss) of sensitive regions within the medical image following encryption, and (c) meta-data about the patient and image to be stored within and recovered from the public image.

Development of a support vector machine learning and smart phone Internet of Things-based architecture for real-time sleep apnea diagnosis.

BACKGROUND: The breathing disorder obstructive sleep apnea syndrome (OSAS) only occurs while asleep. While polysomnography (PSG) represents the premiere standard for diagnosing OSAS, it is quite costly, complicated to use, and carries a significant delay between testing and diagnosis. METHODS: This work describes a novel architecture and algorithm designed to efficiently diagnose OSAS via the use of smart phones. In our algorithm, features are extracted from the data, specifically blood oxygen saturation as represented by SpO2. These features are used by a support vector machine (SVM) based strategy to create a classification model. The resultant SVM classification model can then be employed to diagnose OSAS. To allow remote diagnosis, we have combined a simple monitoring system with our algorithm. The system allows physiological data to be obtained from a smart phone, the data to be uploaded to the cloud for processing, and finally population of a diagnostic report sent back to the smart phone in real-time. RESULTS: Our initial evaluation of this algorithm utilizing actual patient data finds its sensitivity, accuracy, and specificity to be 87.6%, 90.2%, and 94.1%, respectively. DISCUSSION: Our architecture can monitor human physiological readings in real time and give early warning of abnormal physiological parameters. Moreover, after our evaluation, we find 5G technology offers higher bandwidth with lower delays ensuring more effective monitoring. In addition, we evaluate our algorithm utilizing real-world data; the proposed approach has high accuracy, sensitivity, and specific, demonstrating that our approach is very promising. CONCLUSIONS: Experimental results on the apnea data in University College Dublin (UCD) Database have proven the efficiency and effectiveness of our methodology. This work is a pilot project and still under development. There is no clinical validation and no support. In addition, the Internet of Things (IoT) architecture enables real-time monitoring of human physiological parameters, combined with diagnostic algorithms to provide early warning of abnormal data.

Characterization of novel small RNAs (sRNAs) contributing to the desiccation response of Salmonella enterica serovar Typhimurium.

Noncoding RNA (ncRNA) modulation of gene expression has now been ubiquitously observed across all domains of life. An increasingly apparent role of ncRNAs is to coordinate changes in gene expressions in response to environmental stress. Salmonella enterica, a common food-born pathogen, is known for its striking ability to survive, adapt, and thrive in various unfavourable environments which makes it a particularly difficult pathogen to eliminate as well as an interesting model in which to study ncRNA contributions to cellular stress response. Mounting evidence now suggests that small RNAs (sRNAs) represent key regulators of Salmonella stress adaptation. Approximately 50-500 nucleotides in length, sRNAs regulate gene expression through complementary base pairing with molecular targets and have recently been suggested to outnumber protein-coding genes in bacteria. In this work, we employ small RNA transcriptome sequencing to characterize changes in the sRNA profiles of Salmonella in response to desiccation. In all, we identify 102 previously annotated sRNAs significantly differentially expressed during desiccation; and excitingly, 71 novel sRNAs likewise differentially expressed. Small transcript northern blotting and qRT-PCRs confirm the identities and expressions of several of our novel sRNAs, and computational analyses indicate the majority are highly conserved and structurally related to characterized sRNAs. Predicted sRNA targets include several proteins necessary for desiccation survival and this, in part, suggests a role for desiccation-regulated sRNAs in this stress response. Furthermore, we find individual knock-outs of two of the novel sRNAs identified herein, either sRNA1320429 or sRNA3981754, significantly impairs the ability of Salmonella to survive desiccation, confirming their involvements (and suggesting the potential involvements of other sRNAs we identify in this work) in the Salmonella response to desiccation.

Using an artificial neural network to map cancer common data elements to the biomedical research integrated domain group model in a semi-automated manner.

BACKGROUND: The medical community uses a variety of data standards for both clinical and research reporting needs. ISO 11179 Common Data Elements (CDEs) represent one such standard that provides robust data point definitions. Another standard is the Biomedical Research Integrated Domain Group (BRIDG) model, which is a domain analysis model that provides a contextual framework for biomedical and clinical research data. Mapping the CDEs to the BRIDG model is important; in particular, it can facilitate mapping the CDEs to other standards. Unfortunately, manual mapping, which is the current method for creating the CDE mappings, is error-prone and time-consuming; this creates a significant barrier for researchers who utilize CDEs. METHODS: In this work, we developed a semi-automated algorithm to map CDEs to likely BRIDG classes. First, we extended and improved our previously developed artificial neural network (ANN) alignment algorithm. We then used a collection of 1284 CDEs with robust mappings to BRIDG classes as the gold standard to train and obtain the appropriate weights of six attributes in CDEs. Afterward, we calculated the similarity between a CDE and each BRIDG class. Finally, the algorithm produces a list of candidate BRIDG classes to which the CDE of interest may belong. RESULTS: For CDEs semantically similar to those used in training, a match rate of over 90% was achieved. For those partially similar, a match rate of 80% was obtained and for those with drastically different semantics, a match rate of up to 70% was achieved. DISCUSSION: Our semi-automated mapping process reduces the burden of domain experts. The weights are all significant in six attributes. Experimental results indicate that the availability of training data is more important than the semantic similarity of the testing data to the training data. We address the overfitting problem by selecting CDEs randomly and adjusting the ratio of training and verification samples. CONCLUSIONS: Experimental results on real-world use cases have proven the effectiveness and efficiency of our proposed methodology in mapping CDEs with BRIDG classes, both those CDEs seen before as well as new, unseen CDEs. In addition, it reduces the mapping burden and improves the mapping quality.

Muscle fatigue detection and treatment system driven by internet of things.

BACKGROUND: Internet of things is fast becoming the norm in everyday life, and integrating the Internet into medical treatment, which is increasing day by day, is of high utility to both clinical doctors and patients. While there are a number of different health-related problems encountered in daily life, muscle fatigue is a common problem encountered by many. METHODS: To facilitate muscle fatigue detection, a pulse width modulation (PWM) and ESP8266-based fatigue detection and recovery system is introduced in this paper to help alleviate muscle fatigue. The ESP8266 is employed as the main controller and communicator, and PWM technology is employed to achieve adaptive muscle recovery. Muscle fatigue can be detected by surface electromyography signals and monitored in real-time via a wireless network. RESULTS: With the help of the proposed system, human muscle fatigue status can be monitored in real-time, and the recovery vibration motor status can be optimized according to muscle activity state. DISCUSSION: Environmental factors had little effect on the response time and accuracy of the system, and the response time was stable between 1 and 2 s. As indicated by the consistent change of digital value, muscle fatigue was clearly diminished using this system. CONCLUSIONS: Experiments show that environmental factors have little effect on the response time and accuracy of the system. The response time is stably between 1 and 2 s, and, as indicated by the consistent change of digital value, our systems clearly diminishes muscle fatigue. Additionally, the experimental results show that the proposed system requires minimal power and is both sensitive and stable.

A semantics-oriented computational approach to investigate microRNA regulation on glucocorticoid resistance in pediatric acute lymphoblastic leukemia.

BACKGROUND: Acute lymphoblastic leukemia is the most prevalent neoplasia among children. Despite the tremendous achievements of state-of-the-art treatment strategies, drug resistance is still a major cause of chemotherapy failure leading to relapse in pediatric acute lymphoblastic leukemia. The underlying mechanisms of such phenomenon are not yet clear and subject to further exploration. Prior research has shown that microRNAs can act as post-transcriptional regulators of many genes related to drug resistance. However, details of microRNA regulation mechanisms in pediatric acute lymphoblastic leukemia are far from completely understood. METHODS: We utilized a computational approach based upon emerging biomedical and biological ontologies and semantic technologies to investigate the important roles of microRNA: mRNA regulation on glucocorticoid resistance in pediatric acute lymphoblastic leukemia. In particular, various filtering mechanisms were designed based on the user-provided MeSH term to narrow down the most promising microRNAs in an effective manner. RESULTS: During our manual search on background literature, we found a total of 18 candidate microRNAs that possibly regulate glucocorticoid resistance in pediatric acute lymphoblastic leukemia. After the first-round filtering using the Broader-Match option where both the user-provided MeSH term and its direct parent term were utilized, the number of targets for 18 microRNAs was reduced from 232 to 74. During the second-round filtering with the Exact-Match option where only the MeSH term itself was utilized, the number of targets was further reduced to 19. Finally, we conducted semantic searches in the OmniSearch software tool on the five likely regulating microRNAs and identified two most likely microRNAs. CONCLUSIONS: We successfully identified two microRNAs, hsa-miR-142-3p and hsa-miR-17-5p, which are computationally predicted to closely relate to glucocorticoid resistance, thus potentially serving as novel biomarkers and therapeutic targets in pediatric acute lymphoblastic leukemia.

Novel small RNA (sRNA) landscape of the starvation-stress response transcriptome of Salmonella enterica serovar typhimurium.

Small RNAs (sRNAs) are short (∼50-200 nucleotides) noncoding RNAs that regulate cellular activities across bacteria. Salmonella enterica starved of a carbon-energy (C) source experience a host of genetic and physiological changes broadly referred to as the starvation-stress response (SSR). In an attempt to identify novel sRNAs contributing to SSR control, we grew log-phase, 5-h C-starved and 24-h C-starved cultures of the virulent Salmonella enterica subspecies enterica serovar Typhimurium strain SL1344 and comprehensively sequenced their small RNA transcriptomes. Strikingly, after employing a novel strategy for sRNA discovery based on identifying dynamic transcripts arising from "gene-empty" regions, we identify 58 wholly undescribed Salmonella sRNA genes potentially regulating SSR averaging an ∼1,000-fold change in expression between log-phase and C-starved cells. Importantly, the expressions of individual sRNA loci were confirmed by both comprehensive transcriptome analyses and northern blotting of select candidates. Of note, we find 43 candidate sRNAs share significant sequence identity to characterized sRNAs in other bacteria, and ∼70% of our sRNAs likely assume characteristic sRNA structural conformations. In addition, we find 53 of our 58 candidate sRNAs either overlap neighboring mRNA loci or share significant sequence complementarity to mRNAs transcribed elsewhere in the SL1344 genome strongly suggesting they regulate the expression of transcripts via antisense base-pairing. Finally, in addition to this work resulting in the identification of 58 entirely novel Salmonella enterica genes likely participating in the SSR, we also find evidence suggesting that sRNAs are significantly more prevalent than currently appreciated and that Salmonella sRNAs may actually number in the thousands.

An oxidative DNA "damage" and repair mechanism localized in the VEGF promoter is important for hypoxia-induced VEGF mRNA expression.

In hypoxia, mitochondria-generated reactive oxygen species not only stimulate accumulation of the transcriptional regulator of hypoxic gene expression, hypoxia inducible factor-1 (Hif-1), but also cause oxidative base modifications in hypoxic response elements (HREs) of hypoxia-inducible genes. When the hypoxia-induced base modifications are suppressed, Hif-1 fails to associate with the HRE of the VEGF promoter, and VEGF mRNA accumulation is blunted. The mechanism linking base modifications to transcription is unknown. Here we determined whether recruitment of base excision DNA repair (BER) enzymes in response to hypoxia-induced promoter modifications was required for transcription complex assembly and VEGF mRNA expression. Using chromatin immunoprecipitation analyses in pulmonary artery endothelial cells, we found that hypoxia-mediated formation of the base oxidation product 8-oxoguanine (8-oxoG) in VEGF HREs was temporally associated with binding of Hif-1α and the BER enzymes 8-oxoguanine glycosylase 1 (Ogg1) and redox effector factor-1 (Ref-1)/apurinic/apyrimidinic endonuclease 1 (Ape1) and introduction of DNA strand breaks. Hif-1α colocalized with HRE sequences harboring Ref-1/Ape1, but not Ogg1. Inhibition of BER by small interfering RNA-mediated reduction in Ogg1 augmented hypoxia-induced 8-oxoG accumulation and attenuated Hif-1α and Ref-1/Ape1 binding to VEGF HRE sequences and blunted VEGF mRNA expression. Chromatin immunoprecipitation-sequence analysis of 8-oxoG distribution in hypoxic pulmonary artery endothelial cells showed that most of the oxidized base was localized to promoters with virtually no overlap between normoxic and hypoxic data sets. Transcription of genes whose promoters lost 8-oxoG during hypoxia was reduced, while those gaining 8-oxoG was elevated. Collectively, these findings suggest that the BER pathway links hypoxia-induced introduction of oxidative DNA modifications in promoters of hypoxia-inducible genes to transcriptional activation.

SARS-Cov-2 small viral RNA suppresses gene expression via complementary binding to mRNA 3' UTR.

SARS-CoV-2 (SC2) has been intensely studied since its emergence. However, the mechanisms of host immune dysregulation triggered by SC2 remain poorly understood. That said, it is well established that many prominent viral families encode microRNAs (miRNAs) or related small viral RNAs (svRNAs) capable of regulating human genes involved in immune function. Importantly, recent reports have shown that SC2 encodes its own svRNAs. In this study, we have identified 12 svRNAs expressed during SC2 infection and show that one of these svRNAs can regulate target gene expression via complementary binding to mRNA 3' untranslated regions (3'UTRs) much like human microRNAs.

Characterization of a novel sRNA contributing to biofilm formation in Salmonella enterica serovar Typhimurium.

Small RNAs (sRNAs) are short noncoding RNAs of ~50-200 nucleotides believed to primarily function in regulating crucial activities in bacteria during periods of cellular stress. This study examined the relevance of specific sRNAs on biofilm formation in nutrient starved Salmonella enterica serovar Typhimurium. Eight unique sRNAs were selected for deletion primarily based on their genomic location and/or putative targets. Quantitative and qualitative analyses confirm one of these, sRNA1186573, is required for efficient biofilm formation in S. enterica further highlighting the significance of sRNAs during Salmonella stress response.

Global Switch from DICER-dependent MicroRNA to DICER-independent SnoRNA-derived RNA Biogenesis in Malignancy.

SnoRNAs are frequently processed into snoRNA-derived RNAs (sdRNAs) that function much like traditional microRNAs (miRNAs). That said, our analyses suggest a global switch from DICER-dependent (predominately miRNA) to DICER-independent (predominately sdRNA) biogenesis/gene regulation in colon cancer. Whereas the expressions of 259 of 288 appreciably expressed miRNAs are significantly decreased (avg. 6.4% of WT) in human colon cancer DICER-KOs, 95 of 103 sdRNAs are conversely, significantly increased (avg. 679.3%) in DICER-KOs as compared to WT. As many diseases are characterized by DICER deficiency, this putative global switch to DICER-independent sdRNA regulations may contribute to an array of human diseases.

Human YRNA 4 (HY4) plasma levels are a prognostic indicator of SARS-CoV-2 infection clinical severity.

SARS-CoV-2 infection can result in a range of outcomes from asymptomatic/mild disease to severe COVID-19/fatality. In this study, we investigated the differential expression of small noncoding RNAs (sncRNAs) between patient cohorts defined by disease severity. We collected plasma samples, stratified these based on clinical outcomes, and sequenced their circulating sncRNAs. Excitingly, we found YRNA HY4 displays significant differential expression (p=0.025) between patients experiencing mild and severe disease. In agreement with recent reports identifying plasma YRNAs as indicators of influenza infection severity, our results strongly suggest that circulating HY4 levels represent a powerful prognostic indicator of likely SARS-CoV-2 patient infection outcome.

G-quadruplex recognition activities of E. Coli MutS.

BACKGROUND: Guanine quadruplex (G4 DNA) is a four-stranded structure that contributes to genome instability and site-specific recombination. G4 DNA folds from sequences containing tandemly repetitive guanines, sequence motifs that are found throughout prokaryote and eukaryote genomes. While some cellular activities have been identified with binding or processing G4 DNA, the factors and pathways governing G4 DNA metabolism are largely undefined. Highly conserved mismatch repair factors have emerged as potential G4-responding complexes because, in addition to initiating heteroduplex correction, the human homologs bind non-B form DNA with high affinity. Moreover, the MutS homologs across species have the capacity to recognize a diverse range of DNA pairing variations and damage, suggesting a conserved ability to bind non-B form DNA. RESULTS: Here, we asked if E. coli MutS and a heteroduplex recognition mutant, MutS F36A, were capable of recognizing and responding to G4 DNA structures. We find by mobility shift assay that E. coli MutS binds to G4 DNA with high affinity better than binding to G-T heteroduplexes. In the same assay, MutS F36A failed to recognize G-T mismatched oligonucleotides, as expected, but retained an ability to bind to G4 DNA. Association with G4 DNA by MutS is not likely to activate the mismatch repair pathway because nucleotide binding did not promote release of MutS or MutS F36A from G4 DNA as it does for heteroduplexes. G4 recognition activities occur under physiological conditions, and we find that M13 phage harboring G4-capable DNA poorly infected a MutS deficient strain of E. coli compared to M13mp18, suggesting functional roles for mismatch repair factors in the cellular response to unstable genomic elements. CONCLUSIONS: Taken together, our findings demonstrate that E. coli MutS has a binding activity specific for non-B form G4 DNA, but such binding appears independent of canonical heteroduplex repair activation.

Immunomodulatory LncRNA on antisense strand of ICAM-1 augments SARS-CoV-2 infection-associated airway mucoinflammatory phenotype.

Noncoding RNAs are important regulators of mucoinflammatory response, but little is known about the contribution of airway long noncoding RNAs (lncRNAs) in COVID-19. RNA-seq analysis showed a more than 4-fold increased expression of IL-6, ICAM-1, CXCL-8, and SCGB1A1 inflammatory factors; MUC5AC and MUC5B mucins; and SPDEF, FOXA3, and FOXJ1 transcription factors in COVID-19 patient nasal samples compared with uninfected controls. A lncRNA on antisense strand to ICAM-1 or LASI was induced 2-fold in COVID-19 patients, and its expression was directly correlated with viral loads. A SARS-CoV-2-infected 3D-airway model largely recapitulated these clinical findings. RNA microscopy and molecular modeling indicated a possible interaction between viral RNA and LASI lncRNA. Notably, blocking LASI lncRNA reduced the SARS-CoV-2 replication and suppressed MUC5AC mucin levels and associated inflammation, and select LASI-dependent miRNAs (e.g., let-7b-5p and miR-200a-5p) were implicated. Thus, LASI lncRNA represents an essential facilitator of SARS-CoV-2 infection and associated airway mucoinflammatory response.

Corrigendum: Increased expression of LASI LncRNA regulates the cigarette smoke and COPD associated airway inflammation and mucous cell hyperplasia.

[This corrects the article DOI: 10.3389/fimmu.2022.803362.].

Increased Expression of LASI lncRNA Regulates the Cigarette Smoke and COPD Associated Airway Inflammation and Mucous Cell Hyperplasia.

Research Impact: Cigarette smoke (CS) exposure is strongly associated with chronic obstructive pulmonary disease (COPD). In respiratory airways, CS exposure disrupts airway barrier functions, mucous/phlegm production, and basic immune responses of airway epithelial cells. Based on our recent identification of a specific immunomodulatory long noncoding RNA (lncRNA), we investigated its role in CS-induced responses in bronchial airways of cynomolgus macaque model of CS-induced COPD and in former smokers with and without COPD. The lncRNA was significantly upregulated in CS-induced macaque airways and in COPD airways that exhibited higher mucus expression and goblet cell hyperplasia. Experimental models of cells derived from COPD subjects recapitulated the augmented inflammation and mucus expression following the smoke challenge. Blocking of lncRNA expression in cell culture setting suppressed the smoke-induced and COPD-associated dysregulated mucoinflammatory response suggesting that this airway specific immunomodulatory lncRNA may represent a novel target to mitigate the smoke-mediated inflammation and mucus hyperexpression. Rationale: In conducting airways, CS disrupts airway epithelial functions, mucociliary clearances, and innate immune responses that are primarily orchestrated by human bronchial epithelial cells (HBECs). Mucus hypersecretion and dysregulated immune response are the hallmarks of chronic bronchitis (CB) that is often exacerbated by CS. Notably, we recently identified a long noncoding RNA (lncRNA) antisense to ICAM-1 (LASI) that mediates airway epithelial responses. Objective: To investigate the role of LASI lncRNA in CS-induced airway inflammation and mucin hyperexpression in an animal model of COPD, and in HBECs and lung tissues from former smokers with and without COPD. To interrogate LASI lncRNA role in CS-mediated airway mucoinflammatory responses by targeted gene editing. Methods: Small airway tissue sections from cynomolgus macaques exposed to long-term mainstream CS, and those from former smokers with and without COPD were analyzed. The structured-illumination imaging, RNA fluorescence in-situ hybridization (FISH), and qRT-PCR were used to characterize lncRNA expression and the expression of inflammatory factors and airway mucins in a cell culture model of CS extract (CSE) exposure using HBECs from COPD (CHBEs) in comparison with cells from normal control (NHBEs) subjects. The protein levels of mucin MUC5AC, and inflammatory factors ICAM-1, and IL-6 were determined using specific ELISAs. RNA silencing was used to block LASI lncRNA expression and lentivirus encoding LASI lncRNA was used to achieve LASI overexpression (LASI-OE). Results: Compared to controls, LASI lncRNA was upregulated in CS-exposed macaques and in COPD smoker airways, correlating with mucus hyperexpression and mucus cell hyperplasia in severe COPD airways. At baseline, the unstimulated CHBEs showed increased LASI lncRNA expression with higher expression of secretory mucin MUC5AC, and inflammatory factors, ICAM-1, and IL-6 compared to NHBEs. CSE exposure of CHBEs resulted in augmented inflammation and mucus expression compared to controls. While RNA silencing-mediated LASI knockdown suppressed the mucoinflammatory response, cells overexpressing LASI lncRNA showed elevated mRNA levels of inflammatory factors. Conclusions: Altogether, LASI lncRNA may represent a novel target to control the smoke-mediated dysregulation in airway responses and COPD exacerbations.

MicroRNA-like snoRNA-Derived RNAs (sdRNAs) Promote Castration-Resistant Prostate Cancer.

We have identified 38 specifically excised, differentially expressed snoRNA fragments (sdRNAs) in TCGA prostate cancer (PCa) patient samples as compared to normal prostate controls. SnoRNA-derived fragments sdRNA-D19b and -A24 emerged among the most differentially expressed and were selected for further experimentation. We found that the overexpression of either sdRNA significantly increased PC3 (a well-established model of castration-resistant prostate cancer (CRPC)) cell proliferation, and that sdRNA-D19b overexpression also markedly increased the rate of PC3 cell migration. In addition, both sdRNAs provided drug-specific resistances with sdRNA-D19b levels correlating with paclitaxel resistance and sdRNA-24A conferring dasatinib resistance. In silico and in vitro analyses revealed that two established PCa tumor suppressor genes, CD44 and CDK12, represent targets for sdRNA-D19b and sdRNA-A24, respectively. This outlines a biologically coherent mechanism by which sdRNAs downregulate tumor suppressors in AR-PCa to enhance proliferative and metastatic capabilities and to encourage chemotherapeutic resistance. Aggressive proliferation, rampant metastasis, and recalcitrance to chemotherapy are core characteristics of CRPC that synergize to produce a pathology that ranks second in cancer-related deaths for men. This study defines sdRNA-D19b and -A24 as contributors to AR-PCa, potentially providing novel biomarkers and therapeutic targets of use in PCa clinical intervention.

Adenosine deamination in human transcripts generates novel microRNA binding sites.

Animals regulate gene expression at multiple levels, contributing to the complexity of the proteome. Among these regulatory events are post-transcriptional gene silencing, mediated by small non-coding RNAs (e.g. microRNAs), and adenosine-to-inosine (A-to-I) editing, generated by adenosine deaminases that act on double-stranded RNA (ADAR). Recent data suggest that these regulatory processes are connected at a fundamental level. A-to-I editing can affect Drosha processing or directly alter the microRNA (miRNA) sequences responsible for mRNA targeting. Here, we analyzed the previously reported adenosine deaminations occurring in human cDNAs, and asked if there was a relationship between A-to-I editing events in the mRNA 3' untranslated regions (UTRs) and mRNA:miRNA binding. We find significant correlations between A-to-I editing and changes in miRNA complementarities. In all, over 3000 of the 12 723 distinct adenosine deaminations assessed were found to form 7-mer complementarities (known as seed matches) to a subset of human miRNAs. In 200 of the ESTs, we also noted editing within a specific 13 nucleotide motif. Strikingly, deamination of this motif simultaneously creates seed matches to three (otherwise unrelated) miRNAs. Our results suggest the creation of miRNA regulatory sites as a novel function for ADAR activity. Consequently, many miRNA target sites may only be identifiable through examining expressed sequences.