Dexamethasone reshapes the transcriptome
of human airway smooth muscle cells
An independent reanalysis of paired untreated / dexamethasone-treated samples from four donors. Raw integer gene counts were modelled with donor explicitly blocked as a factor: ~ donor + treatment.
Agent process log — the sequence of steps the agent ran: verifying the GEO sample map, checking raw counts vs. FPKM, installing packages, running PyDESeq2, and querying g:Profiler.
Completion summary — the agent's own headline numbers at hand-off, matched against this report throughout.
This entire project — data retrieval, statistical modelling, enrichment queries, figures, and this HTML document — was produced end-to-end inside a single ChatGPT Work (agent mode) session, without hand-written code. Every figure, table, and number in this report was independently re-checked against the delivered analysis.py, run logs, and analysis_summary.json before publication; nothing here is taken on the agent's word alone.
Original prompt given to the agent (verbatim, Chinese) — click to expand
请独立完成一个公开 bulk RNA-seq 生物信息学分析项目，不需要我上传任何数据。
研究对象
使用 GEO 数据集 GSE52778，重点分析人类气道平滑肌细胞在 dexamethasone 处理后产生的转录组变化。
请优先使用其中经典的 8 个样本子集：
* 4 个不同供体来源的细胞
* 每个供体均包含 untreated 对照和 dexamethasone 处理
* 将 donor 视为配对或阻断因素
数据获取
1. 自行搜索并核实 GSE52778 的 GEO 页面、对应论文和样本信息。
2. 自行下载可用于分析的表达矩阵和样本 metadata。
3. 优先使用原始整数 count matrix。
4. 如果 GEO 页面没有直接提供适合的 count matrix，可以依次尝试：
* GEO supplementary files
* GEO Series Matrix
* Bioconductor airway 数据包
* 对应论文的补充数据
5. 不要下载大型 FASTQ 或 SRA 原始数据。
6. 记录所有下载链接、文件名、访问日期和数据来源。
7. 不要仅根据文件名猜测样本分组，必须根据 GEO metadata 或论文核实。
数据检查
完成以下检查：
1. 检查表达矩阵的行列含义。
2. 检查样本名称与 metadata 是否一一对应。
3. 判断数据是：
* 原始整数 counts
* 标准化 counts
* TPM、FPKM 或其他变换后的表达值
4. 检查缺失值、重复样本、低表达基因和异常样本。
5. 统计每个样本的 library size。
6. 检查供体效应和潜在批次效应。
7. 明确说明最终纳入和排除的样本。
统计设计
研究问题为：
dexamethasone 处理是否改变气道平滑肌细胞的基因表达？
由于同一个供体同时包含 untreated 和 dexamethasone 样本，请使用配对设计。
如果数据是原始整数 counts，优先使用类似以下设计：
~ donor + treatment
并使用适合 count 数据的差异表达方法，例如 DESeq2。
如果数据不是原始 counts，不要错误使用 DESeq2。请选择适合当前数据类型的方法，并解释理由。
分析内容
请完成：
1. 表达数据预处理和低表达基因过滤。
2. 样本 library size 可视化。
3. 样本相关性分析。
4. PCA 分析。
5. 样本聚类热图。
6. dexamethasone 与 untreated 的差异表达分析。
7. 多重检验校正。
8. 筛选显著上调和下调基因。
9. 将 Ensembl gene ID 转换为 gene symbol。
10. 无法可靠转换的基因保留原始 ID，不要伪造映射。
图表
至少生成：
* library size 图
* PCA 图
* 样本相关性热图
* 样本聚类图
* MA plot
* 火山图
* 前 30 个差异表达基因热图
* 前 10 个关键差异基因表达图
* 上调和下调基因数量统计图
* GO 富集结果图
所有图表必须包含清晰的标题、坐标轴、图例和样本分组信息。
功能富集
分别对显著上调基因和显著下调基因进行：
* GO Biological Process 富集
* KEGG pathway 富集
优先使用官方或可靠的注释数据库。
如果当前环境无法联网调用富集数据库：
1. 不要伪造富集结果。
2. 明确说明失败原因。
3. 输出可在本地继续运行的完整代码。
4. 可以基于已有可靠注释做有限解释，但必须标注证据来源。
生物学解释
重点讨论：
* 糖皮质激素反应
* 炎症相关通路
* 细胞外基质和气道重塑
* 气道平滑肌细胞功能
* 经典 dexamethasone 响应基因
请明确区分：
1. 数据直接支持的结果
2. 原论文已经报道的结论
3. 基于结果提出的合理推测
不要把相关性描述成因果关系。
输出文件
请最终生成并整理以下文件：
1. analysis.ipynb 或完整可运行分析脚本
2. sample_metadata_cleaned.csv
3. expression_matrix_cleaned.csv
4. differential_expression_full.xlsx
5. significant_upregulated_genes.csv
6. significant_downregulated_genes.csv
7. go_enrichment_results.xlsx
8. kegg_enrichment_results.xlsx
9. 所有 PNG 或 SVG 图表
10. 一个无需外部依赖即可打开的单页 HTML 报告
11. 一个 README.md
HTML 报告结构
单页 HTML 至少包括：
* 项目摘要
* 数据来源
* 实验设计
* 数据处理流程
* 质量控制
* PCA 和样本聚类
* 差异表达结果
* 功能富集
* 关键基因
* 生物学解释
* 局限性
* 可复现性说明
* 数据和代码来源
HTML 应简洁、专业、适合科研展示。
质量要求
* 所有结论必须来自真实计算结果。
* 不得伪造缺失数据、富集结果或统计值。
* 不得忽略 donor 配对设计。
* 不得仅复制原论文结论。
* 遇到下载失败时，请主动尝试替代数据源。
* 如果某一步无法完成，继续完成其余部分，并明确记录失败原因。
* 最后总结本次分析中最重要的 5 个发现和最主要的 5 个局限性。
Project summary
This analysis compares primary human airway smooth muscle cells treated with 1 µM dexamethasone for 18 hours against vehicle-treated controls. All eight samples were retained — no duplicates, no missing counts, and no outliers were flagged by distance on the first four principal components. Of the genes tested, 493 were significantly upregulated and 464 significantly downregulated (FDR<0.05 and |log2FC|≥1); 3,877 genes reached FDR<0.05 alone.
Data provenance & sample verification
The GEO record lists 16 samples spanning four experimental conditions; this project uses only the untreated and dexamethasone-treated sample from each of the four donors: GSM1275862/63, GSM1275866/67, GSM1275870/71, and GSM1275874/75, corresponding to SRR1039508/09, 12/13, 16/17, and 20/21. Treatment and donor fields were checked line-by-line against the official GEO metadata.
| source | file | accessed | url | role |
|---|---|---|---|---|
| NCBI GEO series record | GSE52778 web record | 2026-07-14 | link | Study design and sample-level metadata verification |
| NCBI GEO Series Matrix | GSE52778_series_matrix.txt.gz | 2026-07-14 | link | Official GEO metadata for all 16 samples; no expression table is present |
| NCBI GEO supplementary file | GSE52778_All_Sample_FPKM_Matrix.txt.gz | 2026-07-14 | link | FPKM matrix retained only for provenance/cross-check; not used for count modelling |
| NCBI GEO supplementary file | GSE52778_Dex_vs_Untreated_gene_exp.diff.gz | 2026-07-14 | link | Original Cuffdiff results retained for provenance; not substituted for this reanalysis |
| Bioconductor airway package 1.32.0 | airway_1.32.0.tar.gz (airway/data/airway.RData) | 2026-07-14 | link | Primary integer gene-count matrix and Ensembl release 75 annotation used in analysis |
| Himes et al., PLOS ONE (2014) | Article web page | 2026-07-14 | link | Original publication and biological context |
| g:Profiler g:GOSt API | go_enrichment_results.csv; kegg_enrichment_results.csv | 2026-07-14 | link | GO Biological Process and KEGG over-representation analysis |
Experimental design & processing pipeline
- Integer counts, Ensembl gene IDs, official gene symbols, and the sample map were loaded from
airway.RData. - Genes were retained if raw count ≥10 in at least four samples.
- Size factors were computed by the median-of-ratios method; exploratory plots use log2(normalized count + 1).
- PyDESeq2 0.5.4 fit a negative-binomial GLM:
~ donor + treatment, Wald test, BH-adjusted with independent filtering. - Direction-specific significant genes were defined as padj<0.05 and |log2FC|≥1.
- Up- and down-regulated gene lists were submitted separately to g:Profiler, restricted to GO:BP and KEGG, FDR-corrected.
Quality control
Library sizes range from 15.2–30.8 million reads. Of 63,677 genes, 30,208 had zero counts in every sample; 47,538 low-expression genes were filtered out. No explicit sequencing-batch field is available in the metadata; mean read length (87–126 bp) was checked only as a technical proxy correlated against principal components, and was not added as an extra term to the eight-sample primary model. Donor is modelled as an explicit blocking factor throughout.
PCA & sample clustering
Colour encodes treatment; point shape encodes donor. PC1 corresponds almost entirely to treatment (R² = 0.85), while PC2–PC4 mainly capture donor identity (donor R² on PC2 = 0.91). Clustering and correlation views are used to spot overall similarity and potential outliers; they do not replace the paired statistical design below.
Differential expression results
The MA plot and volcano plot both show a large, direction-consistent shift under dexamethasone: transcripts with higher baseline expression tend to move less in log-fold-change, and effect sizes are broadly symmetric between up- and down-regulated genes. The heatmap below shows the top 30 differentially expressed genes across all eight samples, and the paired trajectory plot tracks ten canonical glucocorticoid-response genes from untreated to dexamethasone within each donor.
Top differentially expressed genes
| display_gene | ensembl_gene_id | baseMean | log2FoldChange | padj | direction |
|---|---|---|---|---|---|
| SPARCL1 | ENSG00000152583 | 998 | 4.57 | 2.63e-162 | Up |
| SAMHD1 | ENSG00000101347 | 1.27e+04 | 3.76 | 8.76e-159 | Up |
| MAOA | ENSG00000189221 | 2.34e+03 | 3.35 | 3.39e-141 | Up |
| GPX3 | ENSG00000211445 | 1.23e+04 | 3.73 | 5.53e-140 | Up |
| DUSP1 | ENSG00000120129 | 3.41e+03 | 2.94 | 1.04e-137 | Up |
| CACNB2 | ENSG00000165995 | 496 | 3.29 | 9.81e-121 | Up |
| KLF15 | ENSG00000163884 | 561 | 4.46 | 4.12e-100 | Up |
| PER1 | ENSG00000179094 | 777 | 3.19 | 5.25e-96 | Up |
| VCAM1 | ENSG00000162692 | 510 | -3.69 | 1.25e-95 | Down |
| ADAMTS1 | ENSG00000154734 | 3.03e+04 | 2.34 | 5.32e-95 | Up |
| MT2A | ENSG00000125148 | 3.66e+03 | 2.21 | 1.44e-92 | Up |
| NEXN | ENSG00000162614 | 5.4e+03 | 2.03 | 1.34e-90 | Up |
| STEAP2 | ENSG00000157214 | 3.01e+03 | 1.97 | 9.03e-88 | Up |
| CCDC69 | ENSG00000198624 | 2.06e+03 | 2.91 | 2.12e-87 | Up |
| KCTD12 | ENSG00000178695 | 2.66e+03 | -2.53 | 4.03e-87 | Down |
Functional enrichment
Upregulated and downregulated gene sets were enriched separately against GO Biological Process and KEGG pathways via the g:Profiler API (accessed 2026-07-14; 493 upregulated genes returned 515 terms, 464 downregulated genes returned 1,066 terms). Upregulated genes are enriched for glucocorticoid response, extracellular matrix organisation, and muscle-related processes; downregulated genes are enriched for inflammatory response, cytokine-mediated signalling, and the KEGG cytokine–cytokine receptor interaction and TRP-channel inflammatory mediator pathways. These are gene-set overlaps, not evidence of pathway activity or causal mechanism.
GO Biological Process — top terms
| direction | term_id | term_name | p_value_adjusted | intersection_size |
|---|---|---|---|---|
| Down | GO:0007154 | cell communication | 1.32e-19 | 206 |
| Down | GO:0023052 | signaling | 2.07e-19 | 204 |
| Down | GO:0051239 | regulation of multicellular organismal process | 2.07e-19 | 124 |
| Down | GO:0048731 | system development | 2.11e-18 | 148 |
| Down | GO:0032501 | multicellular organismal process | 1.41e-17 | 214 |
| Down | GO:0007275 | multicellular organism development | 7.45e-17 | 159 |
| Down | GO:0032502 | developmental process | 1.21e-16 | 196 |
| Down | GO:0051716 | cellular response to stimulus | 1.21e-16 | 213 |
| Down | GO:0007165 | signal transduction | 2.11e-16 | 186 |
| Down | GO:0048856 | anatomical structure development | 3.72e-16 | 183 |
| Down | GO:0050896 | response to stimulus | 7.04e-16 | 240 |
| Down | GO:0051240 | positive regulation of multicellular organismal process | 1.86e-15 | 81 |
KEGG pathways — top terms
| direction | term_id | term_name | p_value_adjusted | intersection_size |
|---|---|---|---|---|
| Down | KEGG:04750 | Inflammatory mediator regulation of TRP channels | 1.65e-05 | 14 |
| Down | KEGG:04060 | Cytokine-cytokine receptor interaction | 0.000537 | 21 |
| Down | KEGG:04713 | Circadian entrainment | 0.00135 | 11 |
| Up | KEGG:04820 | Cytoskeleton in muscle cells | 0.00153 | 19 |
| Down | KEGG:04360 | Axon guidance | 0.00158 | 15 |
| Down | KEGG:05200 | Pathways in cancer | 0.00205 | 28 |
| Up | KEGG:00350 | Tyrosine metabolism | 0.00423 | 7 |
| Down | KEGG:04611 | Platelet activation | 0.00712 | 11 |
| Down | KEGG:04540 | Gap junction | 0.00859 | 9 |
| Down | KEGG:04020 | Calcium signaling pathway | 0.00859 | 16 |
| Down | KEGG:04923 | Regulation of lipolysis in adipocytes | 0.0115 | 7 |
| Down | KEGG:04925 | Aldosterone synthesis and secretion | 0.0115 | 9 |
Key genes & biological interpretation
Dexamethasone is associated with a large, direction-consistent shift in expression; the paired design controls for baseline donor differences. The canonical glucocorticoid-response gene panel — CRISPLD2, FKBP5, DUSP1, KLF15, PER1, TSC22D3, CEBPD, C7, CCDC69, and ZBTB16 — is significantly upregulated here, and each shows a consistent within-donor trajectory across all four donors. GO enrichment for glucocorticoid response, ECM organisation, and muscle processes in the upregulated set, and inflammatory/cytokine signalling in the downregulated set, both come directly from this computation; enrichment reflects gene-set overlap, not confirmed pathway activity or mechanism.
Himes et al. used a contemporary Cuffdiff/FPKM workflow and reported 316 BH-corrected significant genes, validated by qRT-PCR for several classic response genes, and additionally showed dexamethasone induces CRISPLD2 and studied its relationship to IL-1β/IL-6/IL-8 responses using independent functional experiments. This reanalysis, using a count-based paired model with different filtering and annotation, finds 3,877 FDR-significant genes; the numeric discrepancy reflects differing methods rather than a failure to reproduce the original study. The functional-perturbation evidence belongs to the original paper — it was not regenerated by the eight RNA-seq samples analysed here.
Given enrichment for inflammatory regulation, extracellular-matrix organisation, and smooth-muscle-related processes, it is plausible that dexamethasone simultaneously influences anti-inflammatory transcriptional programmes and cell states relevant to airway remodelling. Bulk RNA-seq alone can only establish expression associations; it cannot demonstrate specific pathway activity, protein function, or a causal change in airway phenotype.
Five key findings
- All four complete donor pairs were verified against GEO metadata and included; pairing is written explicitly into the statistical model.
- Input is raw integer counts, satisfying the assumptions of a negative-binomial model; the GEO-provided FPKM file was never mistakenly substituted in.
- 3,877 genes reach FDR<0.05; of these, 957 also meet |log2FC|≥1 (493 up, 464 down).
- Multiple canonical glucocorticoid-response genes are significant in this independent reanalysis and show consistent paired trajectories across donors.
- Direction-specific GO/KEGG enrichment links the results to inflammation, ECM/remodelling, and airway-smooth-muscle function — interpreted as associational, not causal.
Five main limitations
- Only four biological donors are available, leaving limited statistical power and limited ability to generalise to the wider population.
- GEO metadata describes all donors as white males, limiting demographic generalisability of the findings.
- The design covers a single dexamethasone dose and a single 18-hour time point, so dose- or time-dependence cannot be inferred.
- The count matrix uses hg19-era Ensembl release 75 annotation; gene mappings may differ from current reference annotations.
- No explicit sequencing-batch variable exists; read length was assessed only as a limited technical proxy, and bulk RNA-seq cannot resolve within-sample cellular heterogeneity or establish causal mechanism.
Reproducibility
The complete entry point is analysis.py. Fixed data URLs, file SHA-256 checksums, filtering rules, the statistical model, significance thresholds, and enrichment API logs are all shipped with the project. PyDESeq2 is a Python reimplementation of the DESeq2 method; results are expected to closely track R/DESeq2 but exact numerical parity is not guaranteed. Access date: 2026-07-14.