Additional file 2: Figure S1. of CRISPR/Cas9-mediated viral interference in plants

Dot blot analysis of the TYLCV genome accumulation in NBCas9OE. Figure S2 CRISPR/Cas9-mediated virus interference in TYLCV sap inoculated plants. Figure S3. Targeting of CP region of TYLCV by CRISPR/Cas9. Figure S4. RCA analysis of the TYLCV genome accumulation. Figure S5. DNA blot analysis of the TYLCV genome accumulation. Figure S6. Reduction of TYLCV symptoms on NB-Cas9OE plants expressing IR-sgRNA. Figure S7. Reduction of TYLCV symptoms in NB-Cas9OE plants expressing CP-gRNA or RCRII-gRNA. Figure S8. Reduction of TYLCV symptoms in NB-Cas9OE plants coexpressing IR-sgRNA and CP-sgRNA. Figure S9. Restriction enzyme recognition site loss analysis from multiplexed targeting of IR and CP sequences. Figure S10. Alignment of the Sanger sequence reads of IR and CP regions of TYLCV from multiplexed targeting of IR and CP sequences. Figure S11. Recovery of TYLCV symptoms in NB-Cas9OE plants expressing IR-CP-gRNA. Figure S12. Southern blot analysis for the TYLCV genome accumulation. (PDF 3075 kb

Additional file 3: of CRISPR/Cas9-mediated viral interference in plants

Supplementary methods. (PDF 71 kb

Additional file 1: Table S1. of CRISPR/Cas9-mediated viral interference in plants

Primers used in this study. Table S2 Summary of different sgRNA used for targeting of TYLCV genome. (PDF 3026 kb

Additional file 4: of CRISPR/Cas9-mediated viral interference in plants

Supplementary sequences and maps. Supplemental sequence 1. TYLCV 2.3 genome sequence and map. Supplemental sequence 2. IR-gRNA (TYLCV) sequence and map. Supplemental sequence 3. CP-gRNA (TYLCV) sequence and map. Supplemental sequence 4. RCRII-gRNA (TYLCV) sequence and map. Supplementary sequence 5. IR-gRNA (BCTV Worland) sequence and map. Supplementary sequence 6. RCRII-gRNA (BCTV Worland) sequence and map. Supplementary sequence 7. BCTV (Worland) sequence and map. Supplementary sequence 8. MeMV sequence and map. Supplementary sequence 9. Non-specific-sgRNA sequence and map. (PDF 3031 kb

DataSheet1_Genomic profiling and network-level understanding uncover the potential genes and the pathways in hepatocellular carcinoma.xlsx

Data integration with phenotypes such as gene expression, pathways or function, and protein-protein interactions data has proven to be a highly promising technique for improving human complex diseases, particularly cancer patient outcome prediction. Hepatocellular carcinoma is one of the most prevalent cancers, and the most common cause is chronic HBV and HCV infection, which is linked to the majority of cases, and HBV and HCV play a role in multistep carcinogenesis progression. We examined the list of known hepatocellular carcinoma biomarkers with the publicly available expression profile dataset of hepatocellular carcinoma infected with HCV from day 1 to day 10 in this study. The study covers an overexpression pattern for the selected biomarkers in clinical hepatocellular carcinoma patients, a combined investigation of these biomarkers with the gathered temporal dataset, temporal expression profiling changes, and temporal pathway enrichment following HCV infection. Following a temporal analysis, it was discovered that the early stages of HCV infection tend to be more harmful in terms of expression shifting patterns, and that there is no significant change after that, followed by a set of genes that are consistently altered. PI3K, cAMP, TGF, TNF, Rap1, NF-kB, Apoptosis, Longevity regulating pathway, signaling pathways regulating pluripotency of stem cells, Cytokine-cytokine receptor interaction, p53 signaling, Wnt signaling, Toll-like receptor signaling, and Hippo signaling pathways are just a few of the most commonly enriched pathways. The majority of these pathways are well-known for their roles in the immune system, infection and inflammation, and human illnesses like cancer. We also find that ADCY8, MYC, PTK2, CTNNB1, TP53, RB1, PRKCA, TCF7L2, PAK1, ITPR2, CYP3A4, UGT1A6, GCK, and FGFR2/3 appear to be among the prominent genes based on the networks of genes and pathways based on the copy number alterations, mutations, and structural variants study.</p