RESULT: SPME GC-MS analysis showed the highest terpenoid accumulation on the 6th day post-inoculation (dpi) compared to the other treatment time points (0 dpi, 3 dpi, and 9 dpi). Among the increased terpenoid compounds, α-cedrene, valencene and β-bisabolene were prominent. P. minor inoculated for 6 days was selected for miRNA library construction using next generation sequencing. Differential gene expression analysis showed that 58 miRNAs belonging to 30 families had significantly altered regulation.
Among these 58 differentially expressed genes (DEGs), 27 [corrected] miRNAs were upregulated, whereas 31 [corrected] miRNAs were downregulated. Two putative novel pre-miRNAs were identified and validated through reverse transcriptase PCR. Prediction of target transcripts potentially involved in the mevalonate pathway (MVA) was carried out by psRobot software, resulting in four miRNAs: pmi-miR530, pmi-miR6173, pmi-miR6300 and a novel miRNA, pmi-Nov_13. In addition, two miRNAs, miR396a and miR398f/g, were predicted to have their target transcripts in the non-mevalonate pathway (MEP). In addition, a novel miRNA, pmi-Nov_12, was identified to have a target gene involved in green leaf volatile (GLV) biosynthesis. RT-qPCR analysis showed that pmi-miR6173, pmi-miR6300 and pmi-nov_13 were downregulated, while miR396a and miR398f/g were upregulated. Pmi-miR530 showed upregulation at 9 dpi, and dynamic expression was observed for pmi-nov_12. Pmi-6300 and pmi-miR396a cleavage sites were detected through degradome sequence analysis. Furthermore, the relationship between miRNA metabolites and mRNA metabolites was validated using correlation analysis.
CONCLUSION: Our findings suggest that six studied miRNAs post-transcriptionally regulate terpenoid biosynthesis in P. minor. This regulatory behaviour of miRNAs has potential as a genetic tool to regulate terpenoid biosynthesis in P. minor.
RESULTS: In this study, we propose the Context Based Dependency Network (CBDN), a method that is able to infer gene regulatory networks with the regulatory directions from gene expression data only. To determine the regulatory direction, CBDN computes the influence of source to target by evaluating the magnitude changes of expression dependencies between the target gene and the others with conditioning on the source gene. CBDN extends the data processing inequality by involving the dependency direction to distinguish between direct and transitive relationship between genes. We also define two types of important regulators which can influence a majority of the genes in the network directly or indirectly. CBDN can detect both of these two types of important regulators by averaging the influence functions of candidate regulator to the other genes. In our experiments with simulated and real data, even with the regulatory direction taken into account, CBDN outperforms the state-of-the-art approaches for inferring gene regulatory network. CBDN identifies the important regulators in the predicted network: 1. TYROBP influences a batch of genes that are related to Alzheimer's disease; 2. ZNF329 and RB1 significantly regulate those 'mesenchymal' gene expression signature genes for brain tumors.
CONCLUSION: By merely leveraging gene expression data, CBDN can efficiently infer the existence of gene-gene interactions as well as their regulatory directions. The constructed networks are helpful in the identification of important regulators for complex diseases.
AIM OF STUDY: The overall aim of this study was to investigate the gene expression profile of Ligno TG-K via de novo RNA-seq and pathway analysis. We also aimed to identify highly expressed genes encoding compounds that contribute to its cytotoxic and antioxidant properties, as well as perform a comparative transcriptomics analysis between Ligno TG-K and its sister species, L. rhinocerus TM02®.
MATERIALS AND METHODS: Total RNA from fresh 3-month-old cultivated L. tigris sclerotia (Ligno TG-K) was extracted and analyzed via de novo RNA sequencing. Expressed genes were analyzed using InterPro and NCBI-Nr databases for domain identification and homology search. Functional categorization based on gene functions and pathways was performed using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Clusters of Orthologous Genes (COG) databases. Selected genes were subsequently subjected to phylogenetic analysis.
RESULTS: Our transcriptomics analysis of Ligno TG-K revealed that 68.06% of its genes are expressed in the sclerotium; 80.38% of these were coding transcripts. Our analysis identified highly expressed transcripts encoding proteins with prospective medicinal properties. These included serine proteases (FPKM = 7356.68), deoxyribonucleases (FPKM = 3777.98), lectins (FPKM = 3690.87), and fungal immunomodulatory proteins (FPKM = 2337.84), all of which have known associations with anticancer activities. Transcripts linked to proteins with antioxidant activities, such as superoxide dismutase (FPKM = 1161.69) and catalase (FPKM = 1905.83), were also highly expressed. Results of our sequence alignments revealed that these genes and their orthologs can be found in other mushrooms. They exhibit significant sequence similarities, suggesting possible parallels in their anticancer and antioxidant bioactivities.
CONCLUSION: This study is the first to provide a reference transcriptome profile of genes expressed in the sclerotia of L. tigris. The current study also presents distinct COG profiles of highly expressed genes in Ligno TG-K and L. rhinocerus TM02®, highlighting that any distinctions uncovered may be attributed to their interspecies variations and inherent characteristics that are unique to each species. Our findings suggest that Ligno TG-K contains bioactive compounds with prospective medicinal properties that warrant further investigations.
CLASSIFICATION: Systems biology and omics.
FINDINGS: We optimized the assembly of a Hevea bark transcriptome based on 16 Gb Illumina PE RNA-Seq reads using the Oases assembler across a range of k-mer sizes. We then assessed assembly quality based on transcript N50 length and transcript mapping statistics in relation to (a) known Hevea cDNAs with complete open reading frames, (b) a set of core eukaryotic genes and (c) Hevea genome scaffolds. This was followed by a systematic transcript mapping process where sub-assemblies from a series of incremental amounts of bark transcripts were aligned to transcripts from the entire bark transcriptome assembly. The exercise served to relate read amounts to the degree of transcript mapping level, the latter being an indicator of the coverage of gene transcripts expressed in the sample. As read amounts or datasize increased toward 16 Gb, the number of transcripts mapped to the entire bark assembly approached saturation. A colour matrix was subsequently generated to illustrate sequencing depth requirement in relation to the degree of coverage of total sample transcripts.
CONCLUSIONS: We devised a procedure, the "transcript mapping saturation test", to estimate the amount of RNA-Seq reads needed for deep coverage of transcriptomes. For Hevea de novo assembly, we propose generating between 5-8 Gb reads, whereby around 90% transcript coverage could be achieved with optimized k-mers and transcript N50 length. The principle behind this methodology may also be applied to other non-model plants, or with reads from other second generation sequencing platforms.
RESULTS: A total of 75,350,240 sequence reads were obtained via Hi-seq 2500 sequencing technology. A total of 5473 significant differentially expressed genes were called. Gene ontology functional categorisation showed that cellular process, catalytic activity, and cell part categories had the highest number of expressed genes, while the metabolic pathways category possessed the highest number of expressed genes in the KEGG pathway analysis. The additional sequence dataset will further enrich existing M. fascicularis transcriptome assemblies, and provide a dataset for further downstream studies.
Methods: Human ALDH1+ BCSCs were grown in serum-free Dulbecco's Modified Eagle Medium (DMEM)/F12, while MCF-7 and MDA-MB-231 were cultured in DMEM supplemented with 10% foetal bovine serum under standard conditions. Total RNA was extracted using the Tripure Isolation Reagent. The relative mRNA expressions of OCT4, ALDH1A1 and CD44 associated with stemness as well as TGF-β1, TβR1, ERα1 and MnSOD associated with aggressiveness in BCSCs and MCF-7 cells were determined using the quantitative real-time PCR (qRT-PCR).
Results: The mRNA expressions of OCT4 (5.19-fold ± 0.338; P = 0.001), ALDH1A1 (3.67-fold ± 0.523; P = 0.006), CD44 (2.65-fold ± 0.307; P = 0.006), TGF-β1 (22.89-fold ± 6.840; P = 0.015), TβR1 (3.74-fold ± 1.446; P = 0.045) and MnSOD (4.6-fold ± 1.096; P = 0.014) were higher in BCSCs than in MCF-7 but were almost similar to MDA-MB-231 cells. In contrast, the ERα1 expression of BCSCs (0.97-fold ± 0.080; P = 0.392) was similar to MCF-7 cells, indicating that BSCSs are oestrogen-dependent breast cancer cells.
Conclusion: The oestrogen-dependent BCSCs express stemness and aggressiveness genes at a higher level compared to oestrogen-dependent MCF-7 but are almost similar to oestrogen-independent MDA-MB-231 cells.