Project description:In this data set, we reported for the first time that huanglongbing disease (HLB) induces major changes in the expression of global genes in flavedo, vascular and juice vesicle tissues of citrus fruit. 68 Total samples were analyzed. cDNA generation, array analysis, and statistical tests were performed as a service at the Interdisciplinary Center for Biotechnology Research (ICBR) Microarray Core facility at the University of Florida (Gainesville, FL). The linear models were used for array analysis (Smyth GK et al. Bioinformatics, 2005, 2067-2075). The linear models were firstly used to assess differential expression, and then an empirical Bayes method was used to moderate the standard errors. 13 comparisons were performed for the study. The comparisons in Citrus sinensis cv. Hamlin included: SC vs. CC (genes that respond to infection in symptomatic vascular core); SJV vs. CJV (genes that respond to infection in symptomatic juice vesicle); SS vs. CS (genes that respond to infection in symptomatic seed); SP vs. CP (genes that respond to infection in symptomatic peel). The comparisons in Citrus sinensis cv. Valencia included: SP vs. HP (genes that respond to infection in symptomatic peel); ASP vs. HP (genes that respond to infection in asymptomatic peel); SP vs. ASP (genes that respond to infection in symptomatic peel compared to asymptomatic peel); SC vs. HC (genes that respond to infection in symptomatic vascular core); ASC vs. HC (genes that respond to infection in asymptomatic vascular core); SC vs. ASC (genes that respond to infection in symptomatic vascular core compared to asymptomatic vascular core); SJV vs. HJV (genes that respond to infection in symptomatic juice vesicle); ASJV vs. HJV (genes that respond to infection in asymptomatic juice vesicle); SJV vs. ASJV (genes that respond to infection in symptomatic juice vesicle compared to asymptomatic juice vesicle). ESTs with significant expression changes (P value <0.001; false discovery rate <0.01 with equal or higher than 2-fold changes in expression) were selected for further analysis.

Project description:The human nasopharynx is colonized by a diverse community of commensal microbiota linked to many respiratory diseases, yet their interactions with the host remain unclear. In this study, we introduced a dual-transcriptomics analysis strategy, which can characterize the host transcriptome and microbiome from nasal samples simultaneously. RNA sequencing reads from human nasal swab samples were pre-processed and aligned to the human genome for host gene expression counting, while unmapped reads were further aligned to microbiota genome. After taxonomic classification, microbial abundance matrix was derived at each taxonomic level for differential and host-microbiota interaction analysis. We applied this workflow to a local SARS-CoV-2 cohort with 76 infected patients, among whom 55 (72.37%) were symptomatic at enrollment. Nasal swabs were collected from all 76 patients at enrollment and from 73 patients at one-week later follow-up. We detected a median of 4.81% reads unmapped from the human genome across all 149 samples, among which around half (median 48.63%) were successfully mapped to microbiome genome. Meta-transcriptomic analysis detected significantly higher SARS-related coronavirus loads in samples from the symptomatic group at enrollment, and both groups showed decreased loads one week later. Compared with benchmarking 16S rRNA sequencing on 53 samples, our computational strategy showed high correlation of relative abundance in all top 20 genus. A total of 685 bacteria species were identified to show a relative abundance >= 0.01% in at least 10% samples. Differential abundance analysis identified 66 species (DASs) from 6 phyla with significantly decreased abundance in samples from the symptomatic group compared to the asymptomatic group at enrollment. Integrating these symptom-associated DASs with host’s gene expression using an expression quantitative trait bacteria (eQTB) model, we found 58 symptom-associated DASs identified at enrollment were significantly associated with one to 16 genes. GSEA showed a series of symptom-associated DASs were significantly correlated with pathways related to the activation of olfactory, keratinocyte differentiation, and DNA methylation. In summary, our dual-transcriptomic analysis strategy effectively characterized host-microbiome interactions, offering insights into microbial contributions to respiratory diseases.

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Endometriosis Pain: Genes linked to Inflammation Distinguish Symptomatic from Asymptomatic Disease

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