Diagnosis and treatment efficacy
Both of the patients exhibited menstrual cycle anomalies, amenorrhoea, oligomenorrhoea or long cycles, clinical and/or biochemical hyperandrogenism and ultrasound appearance of polycystic ovaries. Based on Rotterdam criteria [3], PCOS was diagnosed. After treatment with CDD, we observed an obvious decease of BMI, TG(Triglyceride), TC(total cholesterol) and HOMA-IR in both patients (Table1).
Transcriptome sequencing
We processed the raw data generated by transcriptome sequencing. Clean data was obtained after filtering the unqualified reads by cutadapt. Raw reads filtering as follows: (1) Remove reads containing adaptors; (2) Remove reads containing N > 10% (N represents base that could not be determined); (3) The Q-score (Quality value) of over 50% bases of the read is <= 5. Raw sequencing reads, valid reads, Q20%, Q30% and GC content was also evaluated (Supplementary Table 1). Annotation information was analyzed including chromosomes, genes, transcripts and GO annotation. Hisat was used to align valid reads to reference genome and reads matched to the genome were calculated based on the gene location, including (1) alignment between sequencing data and reference genome; (2) distribution of mapped reads on chromosome (Supplementary Table 2). FPKM (fragments per kilobase of exon model per million mapped reads) was used to estimate the gene expression level. Distribution of gene expression value in each sample was summarized in Supplementary Table 3. As there is difference in the distribution of expressed gene number and gene expression level, FPKM can be divided into different intervals. Gene numbers in different intervals were calculated (Supplementary Table 4).
Identification of differential expressed genes associated with CDD treatment
To determine differential expressed genes (DEGs), RNA-Seq was performed on two PCOS patients with pre- and post-treatment of CDD. According to the inspected RNA-seq data, all the quality control parameters were within the acceptable ranges. For patient I, a total of 29,024 annotated Ensembl genes were detected and included in subsequent analysis, of which 662 genes were upregulated and 429 genes were downregulated in post-treatment of CDD (Extended Table 1). After applying statistical analysis (P < 0.05), a FPKM criterion (either one FPKM of pre- (T1) or post-treatment (T2) of CDD >= 1.0), and q-value < 0.05, 130 genes were significantly differentially expressed in patient I with post-treatment of CDD (T2), including 62 upregulated genes and 68 downregulated genes (Extended Table 2). For patient II, a total of 29,034 annotated Ensembl genes were detected and included in subsequent analysis, of which 897 genes were upregulated and 900 genes were downregulated in post-treatment of CDD (Extended Table 3). After applying same statistical analysis and filtering criterion as patient I, 128 genes were significantly differentially expressed in patient I with post-treatment of CDD (T4), including 49 upregulated genes and 79 downregulated genes (Extended Table 4). By comparing the genes identified in both patients, we found 4 differential expressed genes shared by both patients with post-treatment of CDD (Table 2; Extended Table 5). Of them, S1PR1 is upregulated, and AL034397.3, GNA12 and MAP1LC3B are downregulated.
Identification of differential expressed transcripts associated with CDD treatment
For patient I, a total of 114,144 annotated Ensembl genes were detected and included in subsequent analysis, of which 3,623 genes were upregulated and 3,621 genes were downregulated in post-treatment (T2) of CDD compared with pre-treatment (T1) (Extended Table 6). After applying statistical analysis (P < 0.05), a FPKM criterion (either one FPKM of T1 or T2 >= 1.0), and q-value < 0.05, 1,362 transcripts were significantly differentially expressed in patient I with post-treatment of CDD (T2) compared with pre-treatment (T1), including 746 upregulated genes and 616 downregulated genes (Extended Table 7). For patient II, a total of 114,143 annotated Ensembl transcripts were detected and included in subsequent analysis, of which 4,150 transcripts were upregulated and 4,154 transcripts were downregulated in post-treatment (T4) of CDD compared with pre-treatment (T3) (Extended Table 8). After applying identical statistical analysis (P < 0.05) with patient I, we found that 2,022 transcripts were significantly differentially expressed in patient II with post-treatment (T4) of CDD compared with pre-treatment (T3), including 931 upregulated transcripts and 1091 downregulated transcripts (Extended Table 9). To find out the recurrent differential transcripts, we selected those transcripts with identical “start” and “end” in both patients and fold change > 2.5. We obtained totally 27 differential expressed transcripts shared by two patients with CDD treatment (Table 3; Extended Table 10).
GO enrichment
To further analyze differential gene expression associated with CDD, GO enrichment analysis was performed using the rWikiPathways R package. For patient I, 1,091 differential expressed genes (662 genes were upregulated and 429 genes were downregulated) were fed into pathway analysis with 29,024 annotated Ensembl genes as background genes (Extended Table 1). Top 15 pathways were presented including viral gene expression and protein synthesis (Figure 1A; Extended Table 11). For patient II, 1,797 differential expressed genes (897 genes were upregulated and 900 genes were downregulated) were put into pathway analysis with 29,024 annotated Ensembl genes as background genes (Extended Table 3). Top 15 pathways were presented including immune response (Figure 1B; Extended Table 12).