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.

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.

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.

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.

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.

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.

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.

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.

Utilizing Machine Learning Techniques to Predict the Efficacy of Aerobic Exercise Intervention on Young Hypertensive Patients Based on Cardiopulmonary Exercise Testing.

Recently, the incidence of hypertension has significantly increased among young adults. While aerobic exercise intervention (AEI) has long been recognized as an effective treatment, individual differences in response to AEI can seriously influence clinicians' decisions. In particular, only a few studies have been conducted to predict the efficacy of AEI on lowering blood pressure (BP) in young hypertensive patients. As such, this paper aims to explore the implications of various cardiopulmonary metabolic indicators in the field by mining patients' cardiopulmonary exercise testing (CPET) data before making treatment plans. CPET data are collected "breath by breath" by using an oxygenation analyzer attached to a mask and then divided into four phases: resting, warm-up, exercise, and recovery. To mitigate the effects of redundant information and noise in the CPET data, a sparse representation classifier based on analytic dictionary learning was designed to accurately predict the individual responsiveness to AEI. Importantly, the experimental results showed that the model presented herein performed better than the baseline method based on BP change and traditional machine learning models. Furthermore, the data from the exercise phase were found to produce the best predictions compared with the data from other phases. This study paves the way towards the customization of personalized aerobic exercise programs for young hypertensive patients.

Distinct Mucoinflammatory Phenotype and the Immunomodulatory Long Noncoding Transcripts Associated with SARS-CoV-2 Airway Infection.

Respiratory epithelial cells are the primary target for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We investigated the 3D human airway tissue model to evaluate innate epithelial cell responses to SARS-CoV-2 infection. A SARS-CoV-2 clinical isolate productively infected the 3D-airway model with a time-dependent increase in viral load (VL) and concurrent upregulation of airway immunomodulatory factors ( IL-6, ICAM-1 , and SCGB1A1 ) and respiratory mucins ( MUC5AC, MUC5B, MUC2 , and MUC4) , and differential modulation of select long noncoding RNAs (lncRNAs i.e., LASI, TOSL, NEAT1 , and MALAT1 ). Next, we examined these immunomodulators in the COVID-19 patient nasopharyngeal swab samples collected from subjects with high- or low-VLs (∻100-fold difference). As compared to low-VL, high-VL patients had prominent mucoinflammatory signature with elevated expression of IL-6, ICAM-1, SCGB1A1, SPDEF, MUC5AC, MUC5B , and MUC4 . Interestingly, LASI, TOSL , and NEAT1 lncRNA expressions were also markedly elevated in high-VL patients with no change in MALAT1 expression. In addition, dual-staining of LASI and SARS-CoV-2 nucleocapsid N1 RNA showed predominantly nuclear/perinuclear localization at 24 hpi in 3D-airway model as well as in high-VL COVID-19 patient nasopharyngeal cells, which exhibited high MUC5AC immunopositivity. Collectively, these findings suggest SARS-CoV-2 induced lncRNAs may play a role in acute mucoinflammatory response observed in symptomatic COVID-19 patients.

Characterization of 475 Novel, Putative Small RNAs (sRNAs) in Carbon-Starved Salmonella enterica Serovar Typhimurium.

An increasingly apparent role of noncoding RNA (ncRNAs) is to coordinate gene expression during environmental stress. A mounting body of evidence implicates small RNAs (sRNAs) as key drivers of Salmonella stress survival. Generally thought to be 50-500 nucleotides in length and to occur in intergenic regions, sRNAs typically regulate protein expression through base pairing with mRNA targets. In this work, through employing a refined definition of sRNAs allowing for shorter sequences and sRNA loci to overlap with annotated protein-coding gene loci, we have identified 475 previously unannotated sRNAs that are significantly differentially expressed during carbon starvation (C-starvation). Northern blotting and quantitative RT-PCRs confirm the expressions and identities of several of these novel sRNAs, and our computational analyses find the majority to be highly conserved and structurally related to known sRNAs. Importantly, we show that deletion of one of the sRNAs dynamically expressed during C-starvation, sRNA4130247, significantly impairs the Salmonella C-starvation response (CSR), confirming its involvement in the Salmonella CSR. In conclusion, the work presented here provides the first-ever characterization of intragenic sRNAs in Salmonella, experimentally confirms that sRNAs dynamically expressed during the CSR are directly involved in stress survival, and more than doubles the Salmonella enterica sRNAs described to date.

A long noncoding RNA antisense to ICAM-1 is involved in allergic asthma associated hyperreactive response of airway epithelial cells.

Epithelial cells of the conducting airways are a pivotal first line of defense against airborne pathogens and allergens that orchestrate inflammatory responses and mucociliary clearance. Nonetheless, the molecular mechanisms responsible for epithelial hyperreactivity associated with allergic asthma are not completely understood. Transcriptomic analysis of human airway epithelial cells (HAECs), differentiated in-vitro at air-liquid interface (ALI), showed 725 differentially expressed immediate-early transcripts, including putative long noncoding RNAs (lncRNAs). A novel lncRNA on the antisense strand of ICAM-1 or LASI was identified, which was induced in LPS-primed HAECs along with mucin MUC5AC and its transcriptional regulator SPDEF. LPS-primed expression of LASI, MUC5AC, and SPDEF transcripts were higher in ex-vivo cultured asthmatic HAECs that were further augmented by LPS treatment. Airway sections from asthmatics with increased mucus load showed higher LASI expression in MUC5AC+ goblet cells following multi-fluorescent in-situ hybridization and immunostaining. LPS- or IL-13-induced LASI transcripts were mostly enriched in the nuclear/perinuclear region and were associated with increased ICAM-1, IL-6, and CXCL-8 expression. Blocking LASI expression reduced the LPS or IL-13-induced epithelial inflammatory factors and MUC5AC expression, suggesting that the novel lncRNA LASI could play a key role in LPS-primed trained airway epithelial responses that are dysregulated in allergic asthma.

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 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.

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.

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.

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.

Exploring Observability of Attractor Cycles in Boolean Networks for Biomarker Detection.

Boolean Network (BN) is a simple and popular mathematical model that has attracted significant attention from systems biology due to its capacity to reveal genetic regulatory network behavior. In addition, observability, as an important network feature, plays a vital role in deciphering the underlying mechanisms driving a genetic regulatory network and has been widely investigated. Prior studies examined observability of BNs and other complex networks. That said, observability of attractor, which can serve as a biomarker for disease, has not been fully examined in the literature. In this study, we formulated a new definition for singleton or cyclic attractor observability in BNs and developed an effective methodology to resolve the captured problem. We also showed complexity is of O(Pmn), when the maximal period of cyclic attractor is P, the number of attractor is m and the number of genes is n. Importantly, we have confirmed our method can faithfully predict the expression pattern of segment polarity genes in Drosophila melanogaster and showed it can effectively and efficiently deal with the captured observability problem.