Integrative omics analysis identifies biomarkers of idiopathic pulmonary fibrosis.

Idiopathic pulmonary fibrosis (IPF) is an interstitial lung disease characterized by chronic progressive pulmonary fibrosis and a poor prognosis. Genetic studies, including transcriptomic and proteomics, have provided new insight into revealing mechanisms of IPF. Herein we provided a novel strategy to identify biomarkers by integrative analysis of transcriptomic and proteomic profiles of IPF patients. We examined the landscape of IPF patients' gene expression in the transcription and translation phases and investigated the expression and functions of two new potential biomarkers. Differentially expressed (DE) mRNAs were mainly enriched in pathways associated with immune system activities and inflammatory responses, while DE proteins are related to extracellular matrix production and wound repair. The upregulated genes in both phases are associated with wound repair and cell differentiation, while the downregulated genes in both phases are associated with reduced immune activities and the damage of the alveolar tissues. On this basis, we identified thirteen potential marker genes. Among them, we validated the expression changes of butyrophilin-like 9 (BTNL9) and plasmolipin (PLLP) and investigated their functional pathways in the IPF mechanism. Both genes are downregulated in the tissues of IPF patients and Bleomycin-induced mice, and co-expression analysis indicates that they have a protective effect by inhibiting extracellular matrix production and promoting wound repair in alveolar epithelial cells.

Issues of Z-factor and an approach to avoid them for quality control in high-throughput screening studies.

MOTIVATION: High-throughput screening (HTS) is a vital automation technology in biomedical research in both industry and academia. The well-known Z-factor has been widely used as a gatekeeper to assure assay quality in an HTS study. However, many researchers and users may not have realized that Z-factor has major issues. RESULTS: In this article, the following four major issues are explored and demonstrated so that researchers may use the Z-factor appropriately. First, the Z-factor violates the Pythagorean theorem of statistics. Second, there is no adjustment of sampling error in the application of the Z-factor for quality control (QC) in HTS studies. Third, the expectation of the sample-based Z-factor does not exist. Fourth, the thresholds in the Z-factor-based criterion lack a theoretical basis. Here, an approach to avoid these issues was proposed and new QC criteria under homoscedasticity were constructed so that researchers can choose a statistically grounded criterion for QC in the HTS studies. We implemented this approach in an R package and demonstrated its utility in multiple CRISPR/CAS9 or siRNA HTS studies. AVAILABILITY AND IMPLEMENTATION: The R package qcSSMDhomo is freely available from GitHub: https://github.com/Karena6688/qcSSMDhomo. The file qcSSMDhomo_1.0.0.tar.gz (for Windows) containing qcSSMDhomo is also available at Bioinformatics online. qcSSMDhomo is distributed under the GNU General Public License. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Profiles of sensitization and comorbidity in asthma patients with markedly increased serum total IgE (>1000kU/L).

Background: Immunoglobulin E (IgE) plays an important role in asthma, but a few patients exhibit extremely high levels of serum total IgE. Objective: This study aimed to investigate the profiles of comorbidity and/or complications, severity, and sensitizations in patients with asthma and with a total IgE level > 1000 kU/L. Methods: We retrospectively analyzed 170 patients with asthma and with total IgE levels of >1000 kU/L from the inpatient data base. Available information, including age, gender, body mass index, diagnosis, results of routine blood tests, pulmonary function, fractional exhaled nitric oxide, induced sputum (if any), IgE (both total and specific) tests and medication records were analyzed. Results: In the study subjects, >80% were adults, and the average total IgE level was median (interquartile range) 1438 kU/L (1181-2255 kU/L). Approximately 15% of the subjects had at least one comorbidity and/or complication, and 78.82% of the subjects were positive for at least one allergen. Airway infections (44.71%) and rhinosinusitis (41.18%) accounted for the two most common conditions despite age groups. Total IgE levels did not differ among the subjects with different conditions. Overall, mites had the highest positive rate (59.4%). Serum total IgE levels were positively correlated with house-dust mite specific IgE (sIgE) levels (r = 0.23; p < 0.05), peripheral blood eosinophil counts (r = 0.21; p < 0.01), and the number of confirmed sIgE positivity (r = 0.19; p < 0.01), and optimal scaling analysis showed that asthma severity was associated with Aspergillus fumigatus sIgE levels. Conclusion: In the subjects with asthma and with a total IgE level of >1000 kU/L, the two most common conditions were airway infections and rhinosinusitis, despite sensitization. A. fumigatus sIgE levels were closely associated with total IgE levels and asthma severity.

Organism dual RNA-seq reveals the importance of BarA/UvrY in Vibrio parahaemolyticus virulence.

Elucidation of host-pathogen interaction is essential for developing effective strategies to combat bacterial infection. Dual RNA-Seq using cultured cells or tissues/organs as the host of pathogen has emerged as a novel strategy to understand the responses concurrently from both pathogen and host at cellular level. However, bacterial infection mostly causes systematic responses from the host at organism level where the interplay is urgently to be understood but inevitably being neglected by the current practice. Here, we developed an approach that simultaneously monitor the genome-wide infection-linked transcriptional alterations in both pathogenic Vibrio parahaemolyticus and the infection host nematode Caenorhabditis elegans. Besides the dynamic alterations in transcriptomes of both C. elegans and V. parahaemolyticus during infection, we identify a two-component system, BarA/UvrY, that is important for virulence in host. BarA/UvrY not only controls the virulence factors in V. parahaemolyticus including Type III and Type VI secretion systems, but also attenuates innate immune responses in C. elegans, including repression on the MAP kinase-mediated cascades. Thus, our study exemplifies the use of dual RNA-Seq at organism level to uncover previously unrecognized interplay between host and pathogen.

Transcriptome analysis reveals lncRNA-mediated complex regulatory network response to DNA damage in the liver tissue of Rattus norvegicus.

DNA is prone to damages, which would result in genetic disorders and enhance risk of tumorigenesis. Hence, understanding the molecular mechanisms of DNA damage and repair will provide deep insights into tumorigenesis, carcinogenesis as well as the corresponding treatments. Aiming at investigating potential long noncoding RNAs (lncRNAs) response against DNA damage, we performed a comprehensive transcriptomic analysis based on RNA sequencing data of the liver tissue from Rattus norvegicus, in which DNA damage was induced using aflatoxin B1, ifosfamide and N-nitrosodimethylamine. Through our analyses, numerous novel lncRNAs are identified for the first time, and differential network analysis discloses lncRNA-mediated regulatory networks related to DNA-damage response. The result shows that these DNA-damage-inducing chemicals might disrupt many lncRNA-mediated interactions involved in diverse biological processes and pathways, for example, immune function and cell adhesion. In contrast, the host might also activate a few RNA interactions in response to DNA damage, involving response to drug and regulation of cell cycle.

Re-analysis of the coral Acropora digitifera transcriptome reveals a complex lncRNAs-mRNAs interaction network implicated in Symbiodinium infection.

BACKGROUND: Being critically important to the ecosystem, the stability of coral reefs is directly related to the marine and surrounding terrestrial environments. However, coral reefs are now undergoing massive and accelerating devastation due to bleaching. The fact that the breakdown of symbiosis between coral and the dinoflagellate, zooxanthellae, has been well elucidated as the main cause of bleaching, implying the establishment of symbiosis with zooxanthellae plays a crucial role in maintaining coral survival. However, the relevant molecular and cellular mechanisms have not been well studied yet. In this study, based on the deep RNA-sequencing data derived from Mohamed, A. R. et al., an integrated transcriptome analysis was performed to deeply investigate global transcriptome changes of the coral Acropora digitifera in response to infection by dinoflagellate of the genus Symbiodinium. RESULTS: The results revealed that compared to RefTranscriptome_v1.0 (A. digitifera transcriptome assembly v1.0), numerous novel transcripts and isoforms were identified, the Symbiodinium-infected coral larvae at 4 h generated the highest proportion of specific isoforms. Alternative splicing analysis showed that intron retention predominated in all alternative transcripts among six statuses. Additionally, 8117 lncRNAs were predicted via a stringent stepwise filtering pipeline. A complex lncRNAs-mRNAs network including 815 lncRNAs and 6395 mRNAs were established, in which 21 lncRNAs were differentially expressed at 4 h post infection. Functional clustering analysis for those differentially lncRNAs-coexpressed mRNAs demonstrated that several biological processes and pathways related to protein kinase activity, metabolic pathways, mitochondrion, ribosome, etc. were enriched. CONCLUSIONS: Our study not only refined A. digitifera transcriptome via identification of novel transcripts and isoforms, but also predicted a high-confidence dataset of lncRNAs. Functional study based on the construction of lncRNAs-mRNAs co-expression network has disclosed a complex lncRNA-mediated regulation in response to Symbiodinium infection exhibited in A. digitifera. Once validated, these lncRNAs could be good potential targets for treatment and prevention of bleaching in coral.

CGManalyzer: an R package for analyzing continuous glucose monitoring studies.

Summary: The R package CGManalyzer contains functions for analyzing data from a continuous glucose monitoring (CGM) study. It covers a wide and comprehensive range of data analysis methods including reading a series of datasets, obtaining summary statistics of glucose levels, plotting data, transforming the time stamp format, fixing missing values, evaluating the mean of daily difference and continuous overlapping net glycemic action, calculating multiscale sample entropy, conducting pairwise comparison, displaying results using various plots including a new type of plot called an antenna plot, etc. This package has been developed from our work in directly analyzing data from various CGM devices such as the FreeStyle Libre, Glutalor, Dexcom and Medtronic CGM. Thus, this package should greatly facilitate the analysis of various CGM studies. Availability and implementation: The package for Windows is available from CRAN: http://cran.r-project.org/mirrors.html. The source file CGManalyzer_1.0.tar.gz is available in the Supplementary Material and at the website of Zhang's lab https://quantitativelab.fhs.umac.mo/analytic-tool/. Contact: douglaszhang@umac.mo. Supplementary information: Supplementary data are available at Bioinformatics online.

Detection of sputum by interpreting the time-frequency distribution of respiratory sound signal using image processing techniques.

Motivation: Sputum in the trachea is hard to expectorate and detect directly for the patients who are unconscious, especially those in Intensive Care Unit. Medical staff should always check the condition of sputum in the trachea. This is time-consuming and the necessary skills are difficult to acquire. Currently, there are few automatic approaches to serve as alternatives to this manual approach. Results: We develop an automatic approach to diagnose the condition of the sputum. Our approach utilizes a system involving a medical device and quantitative analytic methods. In this approach, the time-frequency distribution of respiratory sound signals, determined from the spectrum, is treated as an image. The sputum detection is performed by interpreting the patterns in the image through the procedure of preprocessing and feature extraction. In this study, 272 respiratory sound samples (145 sputum sound and 127 non-sputum sound samples) are collected from 12 patients. We apply the method of leave-one out cross-validation to the 12 patients to assess the performance of our approach. That is, out of the 12 patients, 11 are randomly selected and their sound samples are used to predict the sound samples in the remaining one patient. The results show that our automatic approach can classify the sputum condition at an accuracy rate of 83.5%. Availability and implementation: The matlab codes and examples of datasets explored in this work are available at Bioinformatics online. Contact: yesoyou@gmail.com or douglaszhang@umac.mo. Supplementary information: Supplementary data are available at Bioinformatics online.

Multicellular gene network analysis identifies a macrophage-related gene signature predictive of therapeutic response and prognosis of gliomas.

BACKGROUND: The tumor-associated microenvironment plays important roles in tumor progression and drug resistance. However, systematic investigations of macrophage-tumor cell interactions to identify novel macrophage-related gene signatures in gliomas for predicting patient prognoses and responses to targeted therapies are lacking. METHODS: We developed a multicellular gene network approach to investigating the prognostic role of macrophage-tumor cell interactions in tumor progression and drug resistance in gliomas. Multicellular gene networks connecting macrophages and tumor cells were constructed from re-grouped drug-sensitive and drug-resistant samples of RNA-seq data in mice gliomas treated with BLZ945 (a CSF1R inhibitor). Subsequently, a differential network-based COX regression model was built to identify the risk signature using a cohort of 310 glioma samples from the Chinese Glioma Genome Atlas database. A large independent validation set of 690 glioma samples from The Cancer Genome Atlas database was used to test the prognostic significance and accuracy of the gene signature in predicting prognosis and targeted therapeutic response of glioma patients. RESULTS: A macrophage-related gene signature was developed consisting of twelve genes (ANPEP, DPP4, PRRG1, GPNMB, TMEM26, PXDN, CDH6, SCN3A, SEMA6B, CCDC37, FANCA, NETO2), which was tested in the independent validation set to examine its prognostic significance and accuracy. The generation of 1000 random gene signatures by a bootstrapping scheme justified the non-random nature of the macrophage-related gene signature. Moreover, the discovered gene signature was verified to be predictive of the sensitivity or resistance of glioma patients to molecularly targeted therapeutics and outperformed other existing gene signatures. Additionally, the macrophage-related gene signature was an independent and the strongest prognostic factor when adjusted for clinicopathologic risk factors and other existing gene signatures. CONCLUSION: The multicellular gene network approach developed herein indicates profound roles of the macrophage-mediated tumor microenvironment in the progression and drug resistance of gliomas. The identified macrophage-related gene signature has good prognostic value for predicting resistance to targeted therapeutics and survival of glioma patients, implying that combining current targeted therapies with new macrophage-targeted therapy may be beneficial for the long-term treatment outcomes of glioma patients.

Meta-analysis of adherence to highly active antiretroviral therapy in patients with HIV infection in China.

With the widespread implementation of antiretroviral therapy in many countries, the HIV/AIDS epidemic has declined. However, little is known about the prevalence of adherence rate to Highly Active Antiretroviral Therapy (HAART) in patients with HIV infection in China. This is the first meta-analysis of cross-sectional studies of treatment adherence (≥ 95%) to HAART in Chinese patients. Both English (PubMed, PsycINFO, EMBASE, and Web of Science) and Chinese (WanFang, CNKI, and SinoMed) databases were systematically and independently searched by three investigators. Studies with adherence rate estimates of HAART were included. Adherence rate estimates of each eligible study were extracted and pooled using the random-effects model. A total of 40 studies conducted in China were eligible and analyzed. The mean rate of ≥ 95% adherence to HAART was 81.1% (95%CI: 75.1%-88.0%, I2 = 97.3%) at one week, 80.9% (95%CI: 74.7%-85.9%, I2 = 96.6%) at one month, and 68.3% (95%CI: 46.1%-84.4%, I2 = 97.1%) at 3 months or longer. Subgroup analyses revealed that samples with no gender predominance, low education level, middle economic region, rural area, older age (42.3 years), and recent publication (2013 or later) were correlated to higher HAART adherence. The average rate of HAART adherence was relatively high in China, which indicates effective HIV/AIDS policy, prevention and control measures. However, the HAART adherence rate decreased over the study time period.

Elevated serum levels of periostin in patients with allergic bronchopulmonary aspergillosis.

BACKGROUND: Serum periostin levels have been reported to be an indicator of Th2 inflammation in asthmatic patients. OBJECTIVE: This study aimed to investigate serum levels of periostin in patients with allergic bronchopulmonary aspergillosis (ABPA) and to evaluate its diagnostic and monitoring value in the disease. METHODS: Patients with ABPA (n = 19) and asthma (n = 24), including severe asthma with fungal sensitisation (SAFS, n = 11) and severe asthma without fungal sensitization (SAwFS, n = 13), were enrolled. Serum levels of periostin were analysed by enzyme-linked immunosorbent assay. Serum total IgE and Aspergillus fumigatus specific IgE, IgG were measured by ImmunoCAP. Levels of cytokines (IFN-γ, IL-4, IL-5, IL-8, IL-10, IL-13 and IL-17A) were measured by Meso Scale Discovery (MSD). RESULTS: Serum levels of periostin in ABPA patients (85.55 ng/mL, [68.28-166] ng/mL) were higher than those in SAFS (50.99 ng/mL, [32.02-71.80] ng/mL; P < 0.01). Among the analysed cytokines, IL-5 levels in ABPA (1.55 pg/mL, [0.96-3.33] pg/mL) were higher than those in SAFS (0.31 pg/mL, [0.26-0.56] pg/mL; P < 0.05) or SAwFS (0.34 pg/mL, [0.21-0.56] pg/mL; P < 0.01). Serum periostin levels was positively associated with total IgE levels (r = 0.319, P < 0.05), serum IL-5 levels (r = 0.484, P < 0.01) and blood eosinophil counts (r = 0.428, P < 0.05). In ROC analysis, the clinical reference value of periostin was 68.8 ng/mL for differential diagnosis of ABPA and SAFS, with the area under the curve (AUC) of 0.81. Longitudinally, serum periostin levels did not change significantly after treatment in ABPA. CONCLUSIONS: These findings suggested that serum levels of periostin were up-regulated in ABPA patients, which may be associated with eosinophilic inflammation.

An Improved Method of Handling Missing Values in the Analysis of Sample Entropy for Continuous Monitoring of Physiological Signals.

Medical devices generate huge amounts of continuous time series data. However, missing values commonly found in these data can prevent us from directly using analytic methods such as sample entropy to reveal the information contained in these data. To minimize the influence of missing points on the calculation of sample entropy, we propose a new method to handle missing values in continuous time series data. We use both experimental and simulated datasets to compare the performance (in percentage error) of our proposed method with three currently used methods: skipping the missing values, linear interpolation, and bootstrapping. Unlike the methods that involve modifying the input data, our method modifies the calculation process. This keeps the data unchanged which is less intrusive to the structure of the data. The results demonstrate that our method has a consistent lower average percentage error than other three commonly used methods in multiple common physiological signals. For missing values in common physiological signal type, different data size and generating mechanism, our method can more accurately extract the information contained in continuously monitored data than traditional methods. So it may serve as an effective tool for handling missing values and may have broad utility in analyzing sample entropy for common physiological signals. This could help develop new tools for disease diagnosis and evaluation of treatment effects.

Studies on Aminoglycoside Susceptibility Identify a Novel Function of KsgA To Secure Translational Fidelity during Antibiotic Stress.

Antibiotic resistance has become a global crisis. Studies on the mechanism of bacterial tolerance to antibiotics will not only increase our conceptual understanding of bacterial death but also provide potential targets for novel inhibitors. We screened a mutant library containing a full set of in-frame deletion mutants of Escherichia coli K-12 and identified 140 genes that possibly contribute to gentamicin tolerance. The deletion of ksgA increased the inhibition and killing potency against mid-log-phase bacteria by aminoglycosides. Initially identified as a 16S rRNA methyltransferase, KsgA also has additional functions as a ribosomal biogenesis factor and a DNA glycosylase. We found that the methyltransferase activity of KsgA is responsible for the tolerance, as demonstrated by a site-directed mutagenesis analysis. In contrast to the mechanism for cold sensitivity, the decreased tolerance to aminoglycoside is not related to the failure of ribosomal biogenesis. Furthermore, the DNA glycosylase activity of KsgA contributes minimally to kanamycin tolerance. Importantly, we discovered that KsgA secures protein translational fidelity upon kanamycin killing, in contrast to its role during cold stress and kasugamycin treatment. The results suggest that the compromise in protein translational fidelity in the absence of KsgA is the root cause of an increased sensitivity to a bactericidal aminoglycoside. In addition, KsgA in the pathogenic Acinetobacter baumannii contributes not only to the tolerance against aminoglycoside killing but also to virulence in the host, warranting its potential application as a target for inhibitors that potentiate aminoglycoside therapeutic killing as well as disarm bacterial virulence simultaneously.

Entropy Change of Biological Dynamics in Asthmatic Patients and Its Diagnostic Value in Individualized Treatment: A Systematic Review.

Asthma is a chronic respiratory disease featured with unpredictable flare-ups, for which continuous lung function monitoring is the key for symptoms control. To find new indices to individually classify severity and predict disease prognosis, continuous physiological data collected from monitoring devices is being studied from different perspectives. Entropy, as an analysis method for quantifying the inner irregularity of data, has been widely applied in physiological signals. However, based on our knowledge, there is no such study to summarize the complexity differences of various physiological signals in asthmatic patients. Therefore, we organized a systematic review to summarize the complexity differences of important signals in patients with asthma. We searched several medical databases and systematically reviewed existing asthma clinical trials in which entropy changes in physiological signals were studied. As a conclusion, we find that, for airflow, heart rate variability, center of pressure and respiratory impedance, their entropy values decrease significantly in asthma patients compared to those of healthy people, while, for respiratory sound and airway resistance, their entropy values increase along with the progression of asthma. Entropy of some signals, such as respiratory inter-breath interval, shows strong potential as novel indices of asthma severity. These results will give valuable guidance for the utilization of entropy in physiological signals. Furthermore, these results should promote the development of management and diagnosis of asthma using continuous monitoring data in the future.

Entropy for the Complexity of Physiological Signal Dynamics.

Recently, the rapid development of large data storage technologies, mobile network technology, and portable medical devices makes it possible to measure, record, store, and track analysis of biological dynamics. Portable noninvasive medical devices are crucial to capture individual characteristics of biological dynamics. The wearable noninvasive medical devices and the analysis/management of related digital medical data will revolutionize the management and treatment of diseases, subsequently resulting in the establishment of a new healthcare system. One of the key features that can be extracted from the data obtained by wearable noninvasive medical device is the complexity of physiological signals, which can be represented by entropy of biological dynamics contained in the physiological signals measured by these continuous monitoring medical devices. Thus, in this chapter I present the major concepts of entropy that are commonly used to measure the complexity of biological dynamics. The concepts include Shannon entropy, Kolmogorov entropy, Renyi entropy, approximate entropy, sample entropy, and multiscale entropy. I also demonstrate an example of using entropy for the complexity of glucose dynamics.

displayHTS: a R package for displaying data and results from high-throughput screening experiments.

The R package displayHTS implements recently developed methods and figures for displaying data and hit selection results in high-throughput screening (HTS) experiments. It generates not only certain useful distinctive graphics such as the plate-well series plot, plate image and dual-flashlight plot but also other commonly used figures such as volcano plot and plate correlation plot. These figures are critical for visualizing the data and displaying important features of HTS data and hit selection results.

Bayesian adaptive determination of the sample size required to assure acceptably low adverse event risk.

An emerging concern with new therapeutic agents, especially treatments for type 2 diabetes, a prevalent condition that increases an individual's risk of heart attack or stroke, is the likelihood of adverse events, especially cardiovascular events, that the new agents may cause. These concerns have led to regulatory requirements for demonstrating that a new agent increases the risk of an adverse event relative to a control by no more than, say, 30% or 80% with high (e.g., 97.5%) confidence. We describe a Bayesian adaptive procedure for determining if the sample size for a development program needs to be increased and, if necessary, by how much, to provide the required assurance of limited risk. The decision is based on the predictive likelihood of a sufficiently high posterior probability that the relative risk is no more than a specified bound. Allowance can be made for between-center as well as within-center variability to accommodate large-scale developmental programs, and design alternatives (e.g., many small centers, few large centers) for obtaining additional data if needed can be explored. Binomial or Poisson likelihoods can be used, and center-level covariates can be accommodated. The predictive likelihoods are explored under various conditions to assess the statistical properties of the method.

RespirAnalyzer: an R package for analyzing data from continuous monitoring of respiratory signals.

Motivation: The analysis of data obtained from continuous monitoring of respiratory signals (CMRS) holds significant importance in improving patient care, optimizing sports performance, and advancing scientific understanding in the field of respiratory health. Results: The R package RespirAnalyzer provides an analytic tool specifically for feature extraction, fractal and complexity analysis for CMRS data. The package covers a wide and comprehensive range of data analysis methods including obtaining inter-breath intervals (IBI) series, plotting time series, obtaining summary statistics of IBI series, conducting power spectral density, multifractal detrended fluctuation analysis (MFDFA) and multiscale sample entropy analysis, fitting the MFDFA results with the extended binomial multifractal model, displaying results using various plots, etc. This package has been developed from our work in directly analyzing CMRS data and is anticipated to assist fellow researchers in computing the related features of their CMRS data, enabling them to delve into the clinical significance inherent in these features. Availability and implementation: The package for Windows is available from both Comprehensive R Archive Network (CRAN): https://cran.r-project.org/web/packages/RespirAnalyzer/index.html and GitHub: https://github.com/dongxinzheng/RespirAnalyzer.