Non-invasive Analysis of the Sputum Transcriptome Discriminates Clinical Phenotypes of Asthma

Whole transcriptome wide gene expression profiles in the sputum and circulation from 100 asthma patients were measured using the Affymetrix HuGene 1.0ST arrays. Unsupervised clustering analysis based on pathways from KEGG were used to identify TEA clusters of patients from the sputum gene expression profiles. The identified TEA clusters have significantly different pre-bronchodilator FEV1, bronchodilator responsiveness, exhaled nitric oxide levels, history of hospitalization for asthma and history of intubation. Evaluation of TEA clusters in children from Asthma BRIDGE cohort confirmed the identified differences in intubation and hospitalization. Furthermore, evaluation of the TH2 gene signatures suggested a much lower prevalence of TH2 high disease than previously reported

A novel pathway-based distance score enhances assessment of disease heterogeneity in gene expression

Distance-based unsupervised clustering of gene expression data is commonly used to identify heterogeneity in biologic samples. However, high noise levels in gene expression data and the relatively high correlation between genes are often encountered, so traditional distances such as Euclidean distance may not be effective at discriminating the biological differences between samples. In this study, we developed a novel computational method to assess the biological differences based on pathways by assuming that ontologically defined biological pathways in biologically similar samples have similar behavior. Application of this distance score results in more accurate, robust, and biologically meaningful clustering results in both simulated data and real data when compared to traditional methods. It also has comparable or better performance compared to Pathifier

Testing gene set enrichment for subset of genes: Sub-GSE

<p>Abstract</p> <p>Background</p> <p>Many methods have been developed to test the enrichment of genes related to certain phenotypes or cell states in gene sets. These approaches usually combine gene expression data with functionally related gene sets as defined in databases such as GeneOntology (GO), KEGG, or BioCarta. The results based on gene set analysis are generally more biologically interpretable, accurate and robust than the results based on individual gene analysis. However, while most available methods for gene set enrichment analysis test the enrichment of the entire gene set, it is more likely that only a subset of the genes in the gene set may be related to the phenotypes of interest.</p> <p>Results</p> <p>In this paper, we develop a novel method, termed Sub-GSE, which measures the enrichment of a predefined gene set, or pathway, by testing its subsets. The application of Sub-GSE to two simulated and two real datasets shows Sub-GSE to be more sensitive than previous methods, such as GSEA, GSA, and SigPath, in detecting gene sets assiated with a phenotype of interest. This is particularly true for cases in which only a fraction of the genes in the gene set are associated with the phenotypes. Furthermore, the application of Sub-GSE to two real data sets demonstrates that it can detect more biologically meaningful gene sets than GSEA.</p> <p>Conclusion</p> <p>We developed a new method to measure the gene set enrichment. Applications to two simulated datasets and two real datasets show that this method is sensitive to the associations between gene sets and phenotype. The program Sub-GSE can be downloaded from <url>http://www-rcf.usc.edu/~fsun</url>.</p

Inferring activity changes of transcription factors by binding association with sorted expression profiles

<p>Abstract</p> <p>Background</p> <p>The identification of transcription factors (TFs) associated with a biological process is fundamental to understanding its regulatory mechanisms. From microarray data, however, the activity changes of TFs often cannot be directly observed due to their relatively low expression levels, post-transcriptional modifications, and other complications. Several approaches have been proposed to infer TF activity changes from microarray data. In some models, a linear relationship between gene expression and TF-gene binding strength is assumed. In some other models, the target genes of a TF are first determined by a significance cutoff to binding affinity scores, and then expression differentiation is checked between the target and other genes.</p> <p>Results</p> <p>We propose a novel method, referred to as BASE (binding association with sorted expression), to infer TF activity changes from microarray expression profiles with the help of binding affinity data. It searches the maximum association between bind affinity profile of a TF and expression change profile along the direction of sorted differentiation. The method does not make hard target gene selection, rather, the significances of TF activity changes are evaluated by permutation tests of binding association at the end. To show the effectiveness of this method, we apply it to three typical examples using different kinds of binding affinity data, namely, ChIP-chip data, motif discovery data, and positional weighted matrix scanning data, respectively. The implications obtained from all three examples are consistent with established biological results. Moreover, the inferences suggest new and biological meaningful hypotheses for further investigation.</p> <p>Conclusion</p> <p>The proposed method makes transcription inference from profiles of expression and binding affinity. The same machinery can be used to deal with various kinds of binding affinity data. The method does not require a linear assumption, and has the desirable property of scale-invariance with respect to TF-specific binding affinity. This method is easy to implement and can be routinely applied for transcriptional inferences in microarray studies.</p

Genomic Androgen Receptor-Occupied Regions with Different Functions, Defined by Histone Acetylation, Coregulators and Transcriptional Capacity

Background: The androgen receptor (AR) is a steroid-activated transcription factor that binds at specific DNA locations and plays a key role in the etiology of prostate cancer. While numerous studies have identified a clear connection between AR binding and expression of target genes for a limited number of loci, high-throughput elucidation of these sites allows for a deeper understanding of the complexities of this process. Methodology/Principal Findings: We have mapped 189 AR occupied regions (ARORs) and 1,388 histone H3 acetylation (AcH3) loci to a 3 % continuous stretch of human genomic DNA using chromatin immunoprecipitation (ChIP) microarray analysis. Of 62 highly reproducible ARORs, 32 (52%) were also marked by AcH3. While the number of ARORs detected in prostate cancer cells exceeded the number of nearby DHT-responsive genes, the AcH3 mark defined a subclass of ARORs much more highly associated with such genes – 12 % of the genes flanking AcH3+ARORs were DHT-responsive, compared to only 1 % of genes flanking AcH32ARORs. Most ARORs contained enhancer activities as detected in luciferase reporte

Identification and validation of differentially expressed transcripts by RNA-sequencing of formalin-fixed, paraffin-embedded (FFPE) lung tissue from patients with Idiopathic Pulmonary Fibrosis

Background: Idiopathic Pulmonary Fibrosis (IPF) is a lethal lung disease of unknown etiology. A major limitation in transcriptomic profiling of lung tissue in IPF has been a dependence on snap-frozen fresh tissues (FF). In this project we sought to determine whether genome scale transcript profiling using RNA Sequencing (RNA-Seq) could be applied to archived Formalin-Fixed Paraffin-Embedded (FFPE) IPF tissues. Results: We isolated total RNA from 7 IPF and 5 control FFPE lung tissues and performed 50 base pair paired-end sequencing on Illumina 2000 HiSeq. TopHat2 was used to map sequencing reads to the human genome. On average similar to 62 million reads (53.4% of similar to 116 million reads) were mapped per sample. 4,131 genes were differentially expressed between IPF and controls (1,920 increased and 2,211 decreased (FDR lt 0.05). We compared our results to differentially expressed genes calculated from a previously published dataset generated from FF tissues analyzed on Agilent microarrays (GSE47460). The overlap of differentially expressed genes was very high (760 increased and 1,413 decreased, FDR lt 0.05). Only 92 differentially expressed genes changed in opposite directions. Pathway enrichment analysis performed using MetaCore confirmed numerous IPF relevant genes and pathways including extracellular remodeling, TGF-beta, and WNT. Gene network analysis of MMP7, a highly differentially expressed gene in both datasets, revealed the same canonical pathways and gene network candidates in RNA-Seq and microarray data. For validation by NanoString nCounter (R) we selected 35 genes that had a fold change of 2 in at least one dataset (10 discordant, 10 significantly differentially expressed in one dataset only and 15 concordant genes). High concordance of fold change and FDR was observed for each type of the samples (FF vs FFPE) with both microarrays (r = 0.92) and RNA-Seq (r = 0.90) and the number of discordant genes was reduced to four. Conclusions: Our results demonstrate that RNA sequencing of RNA obtained from archived FFPE lung tissues is feasible. The results obtained from FFPE tissue are highly comparable to FF tissues. The ability to perform RNA-Seq on archived FFPE IPF tissues should greatly enhance the availability of tissue biopsies for research in IPF

Approaches for integrating heterogeneous RNA-seq data reveal cross-talk between microbes and genes in asthmatic patients.

Sputum induction is a non-invasive method to evaluate the airway environment, particularly for asthma. RNA sequencing (RNA-seq) of sputum samples can be challenging to interpret due to the complex and heterogeneous mixtures of human cells and exogenous (microbial) material. In this study, we develop a pipeline that integrates dimensionality reduction and statistical modeling to grapple with the heterogeneity. LDA(Latent Dirichlet allocation)-link connects microbes to genes using reduced-dimensionality LDA topics. We validate our method with single-cell RNA-seq and microscopy and then apply it to the sputum of asthmatic patients to find known and novel relationships between microbes and genes

Identification and validation of differentially expressed transcripts by RNA-sequencing of formalin-fixed, paraffin-embedded (FFPE) lung tissue from patients with Idiopathic Pulmonary Fibrosis

Background: Idiopathic Pulmonary Fibrosis (IPF) is a lethal lung disease of unknown etiology. A major limitation in transcriptomic profiling of lung tissue in IPF has been a dependence on snap-frozen fresh tissues (FF). In this project we sought to determine whether genome scale transcript profiling using RNA Sequencing (RNA-Seq) could be applied to archived Formalin-Fixed Paraffin-Embedded (FFPE) IPF tissues. Results: We isolated total RNA from 7 IPF and 5 control FFPE lung tissues and performed 50 base pair paired-end sequencing on Illumina 2000 HiSeq. TopHat2 was used to map sequencing reads to the human genome. On average similar to 62 million reads (53.4% of similar to 116 million reads) were mapped per sample. 4,131 genes were differentially expressed between IPF and controls (1,920 increased and 2,211 decreased (FDR lt 0.05). We compared our results to differentially expressed genes calculated from a previously published dataset generated from FF tissues analyzed on Agilent microarrays (GSE47460). The overlap of differentially expressed genes was very high (760 increased and 1,413 decreased, FDR lt 0.05). Only 92 differentially expressed genes changed in opposite directions. Pathway enrichment analysis performed using MetaCore confirmed numerous IPF relevant genes and pathways including extracellular remodeling, TGF-beta, and WNT. Gene network analysis of MMP7, a highly differentially expressed gene in both datasets, revealed the same canonical pathways and gene network candidates in RNA-Seq and microarray data. For validation by NanoString nCounter (R) we selected 35 genes that had a fold change of 2 in at least one dataset (10 discordant, 10 significantly differentially expressed in one dataset only and 15 concordant genes). High concordance of fold change and FDR was observed for each type of the samples (FF vs FFPE) with both microarrays (r = 0.92) and RNA-Seq (r = 0.90) and the number of discordant genes was reduced to four. Conclusions: Our results demonstrate that RNA sequencing of RNA obtained from archived FFPE lung tissues is feasible. The results obtained from FFPE tissue are highly comparable to FF tissues. The ability to perform RNA-Seq on archived FFPE IPF tissues should greatly enhance the availability of tissue biopsies for research in IPF

Interplay of tRNA-Derived Fragments and T Cell Activation in Breast Cancer Patient Survival

Effector CD8+ T cell activation and its cytotoxic function are positively correlated with improved survival in breast cancer. tRNA-derived fragments (tRFs) have recently been found to be involved in gene regulation in cancer progression. However, it is unclear how interactions between expression of tRFs and T cell activation affect breast cancer patient survival. We used Kaplan–Meier survival and multivariate Cox regression models to evaluate the effect of interactions between expression of tRFs and T cell activation on survival in 1081 breast cancer patients. Spearman correlation analysis and weighted gene co-expression network analysis were conducted to identify genes and pathways that were associated with tRFs. tRFdb-5024a, 5P_tRNA-Leu-CAA-4-1, and ts-49 were positively associated with overall survival, while ts-34 and ts-58 were negatively associated with overall survival. Significant interactions were detected between T cell activation and ts-34 and ts-49. In the T cell exhaustion group, patients with a low level of ts-34 or a high level of ts-49 showed improved survival. In contrast, there was no significant difference in the activation group. Breast cancer related pathways were identified for the five tRFs. In conclusion, the identified five tRFs associated with overall survival may serve as therapeutic targets and improve immunotherapy in breast cancer

The association of periodontal diseases and Sjogren’s syndrome: A systematic review and meta-analysis

BackgroundThe relationship between periodontal diseases and Sjogren’s syndrome were found inconsistent in current studies. Our objective is to clarify the relationship between periodontal diseases and Sjogren’s syndrome.MethodsA systematic review was performed and reported according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Electronic databases (EMBASE, PubMed, Web of Science, and Cochrane Library, from inceptions until 24 November 2021) were searched. The Newcastle-Ottawa Scale (NOS) and Agency for Healthcare Research and Quality (AHRQ) were applied to evaluate the quality of studies. Quality assessment of the certainty of evidence was performed based on the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) guidelines. When the output is the ratio, Odds ratio (OR) of periodontal diseases with Sjogren’s syndrome were calculated. When the output is the mean, weighted mean difference (WMD) of periodontal diseases with Sjogren’s syndrome was calculated. We conducted meta-analysis and estimated the pool sensitivity. Begg’s test was used to test the possibility of publication bias. We also carried out meta-regression to clarify the source of heterogeneity (I2 &gt; 50%). Finally, we performed a trial sequential analysis (TSA) to identify the false positive or false negative outcomes that might occur during repeated updates.Results21 studies were included in this systematic review, with a total of 11435 subjects. Meta-analysis of 5 studies showed that there is a positive correlation between periodontitis and Sjogren’s syndrome (OR = 2.12, 95% CI = 1.43–3.17; 5 studies, 6927 participants; low certainty of evidence). Meta-analysis of 16 studies showed that the periodontal condition of patients with Sjogren’s syndrome was worse compared with the control group, and the scores of clinical periodontal parameters were relatively high.ConclusionSjogren’s syndrome patients seem to be more likely to be diagnosed with periodontal diseases. However, our results should be interpreted with caution considering the high heterogeneity.Systematic review registration[https://www.crd.york.ac.uk/prospero/], identifier [CRD42021261322]