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Background Seed dormancy and germination are key components of plant regeneration strategies. Aconitum barbatum is a plant commonly found in northeast China. Although it has potential for use in gardening and landscaping, its seed dormancy and regeneration strategy, which adapt to its natural habitat, are not well understood. Our aim was to identify conditions for breaking A. barbatum seed dormancy and determine its dormancy type. Embryo growth and germination were determined by collecting seeds over time in the field. Laboratory experiments that control light, temperature, and stratification period were conducted to assess dormancy breaking and germination, and GA 3 was used to identify dormancy type. Results Seeds of A. barbatum have undeveloped embryos with physiological dormancy at maturity in autumn. The embryo-to-seed length ratio increases from 0.33 to 0.78 before the emergence of the radical. Under natural environmental conditions, embryo development begins in early winter. Laboratory experiments have shown that long-term incubation under 4 °C (cold stratification) promotes embryo development and seed dormancy break. With an extension of cold stratification, an increase in germination percentages was observed when seeds were transferred from 4 °C to warmer temperatures. Seeds exposed to light during incubation show a higher germination percentage than those kept in the dark. Seed germination can also be enhanced by a 100 mg/L GA 3 concentration. Conclusions Seeds of A. barbatum display intermediate complex morphophysiological dormancy at maturity. In addition to the underdeveloped embryo, there are also physiological barriers that prevent the embryo from germinating. Dormancy breaking of A. barbatum seeds can be achieved by natural winter cold stratification, allowing seeds to germinate and sprout seedlings at the beginning of the following growing season. Our findings provide valuable insights into the seed dormancy and regeneration strategy of A. barbatum , which could facilitate its effective utilization in gardening and landscaping.

Non-small cell lung cancer (NSCLC) is one of the leading causes of cancer deaths. Afatinib is the first-line anti-cancer agent for treatment of NSCLC. However, unexpected resistance has been a major obstacle for its clinical efficacy. In this study, we dissected afatinib resistance from the perspective of N6-Methyladenosine (m6A) modification. First, we depicted the m6A modification profiles for the afatinib resistant and sensitive NSCLC cell lines (H1299 and A549). We found that the sum enrichment scores of the resistant cell line (H1299) was much higher than that of the sensitive cell line (A549). Next, we identified the functionally m6A-modified genes, which were the intersection of the differentially m6A methylated genes and the differentially expressed genes between H1299 and A549, as well as negative correlation between m6A modification levels and gene expression levels. In addition, functional enrichment analysis of the functionally m6A-modified genes indicated that m6A methylation might modify cell cycle to affect afatinib response. Furthermore, the functionally m6A-modified genes were over-represented in the putative drug resistance-associated genes and the FDA-approved drug targets, and had significantly higher average degree and clustering coefficient than other genes in protein–protein interaction (PPI) network. We also identified five network modules, which were all related to drug resistance functions. Finally, survival analysis demonstrated that m6A modification could affect prognosis of NSCLC patients. In conclusion, we conducted a first attempt to dissect m6A methylation affection on afatinib resistance in NSCLC, and brought inspiration for the study of epigenetic roles in drug resistance.

Finding target genes and target pathways of existing drugs for drug repositioning in multiple sclerosis (MS) based on transcriptomic changes in MS immune cells. Based on transcriptome data from Gene Expression Omnibus (GEO) database, differentially expressed genes (DEGs) in MS patients without treatment were identified by bioinformatics analysis according to the type of immune cells, as well as DEGs in MS patients before and after drug administration. Hub target genes of the drug for MS were analyzed by constructing the protein-protein interaction network, and candidate drugs targeting 2 or more hub target genes were obtained through the connectivity map (CMap) database and Drugbank database. Then, the enriched pathways of MS patients without treatment and the enriched pathways of MS patients before and after drug administration were intersected to obtain the target pathways of the drug for MS, and the candidate drugs targeting 2 or more target pathways were obtained through Kyoto Encyclopedia of Genes and Genomes (KEGG) database. We obtained 50 hub target genes for CD4 T cells in Fingolimod for MS, 15 hub target genes for Plasmacytoid dendritic cells (pDCs) and 7 hub target genes for Peripheral blood mononuclear cells (PBMC) in interferon-β (IFN-β) for MS. 6 candidate drugs targeting two or more hub targets (Fostamatinib, Copper, Artenimol, Phenethyl isothiocyanate, Aspirin and Zinc) were obtained. In addition, we obtained 4 target pathways for CD19 B cells and 15 target pathways for CD4 T cells in Fingolimod for MS, 7 target pathways for pDCs and 6 target pathways for PBMC in IFN-β for MS, most of which belong to the immune system and viral infectious disease pathways. We obtained 69 candidate drugs targeting two target pathways. We found that applying candidate drugs that target both the \"PI3K-Akt signaling pathway\" and \"Chemokine signaling pathway\" (e.g., Nemiralisib and Umbralisib) or applying tyrosine kinase inhibitors (e.g., Fostamatinib) may be potential therapies for the treatment of MS.

The timing of seed germination significantly affects the fitness and life cycle of plants. Torilis scabra is a perennial medicinal herb occurring in mixed forests but the increasing use and modification of forestlands in recent decades has led to the degeneration of its natural habitat. Nonetheless, the requirements for germination in T. scabra remain unclear. The present study focused on identifying conditions necessary to break T. scabra seed dormancy and describing its seed dormancy type. By periodically collecting seeds that were sown in the field, germination phenology was studied. The impact of light, temperature, and warm/cold stratification on breaking seed dormancy and promoting germination was also determined through incubating seeds in laboratory conditions. Additionally, the effect of GA3 was explored to more accurately identify the type of dormancy present. The results demonstrated that the seeds of T. scabra possessed small, undeveloped embryos with physiological dormancy at the time of maturity. In the field, embryo growth initiated in early spring and the embryo–seed length ratio increased by ~300% before the radical emerged. In the laboratory, the embryo–seed length ratio increased from 0.24 to 0.82 when seeds were subjected to cold stratification at 4 °C and then transferred to 15/25 °C. Germination was observed across a broad temperature range after cold stratification. GA3 also helped to break dormancy but after-ripening did not. Taken together, the results suggest that seeds of T. scabra have non-deep simple morphophysiological dormancy.

Extensive changes in DNA methylation have been observed in schizophrenia (SC) and bipolar disorder (BP), and may contribute to the pathogenesis of these disorders. Here, we performed genome-scale DNA methylation profiling using methylated DNA immunoprecipitation followed by sequencing (MeDIP-seq) on two brain regions (including frontal cortex and anterior cingulate) in 5 SC, 7 BP and 6 normal subjects. Comparing with normal controls, we identified substantial differentially methylated regions (DMRs) in these two brain regions of SC and BP. To our surprise, different brain regions show completely distinct distributions of DMRs across the genomes. In frontal cortex of both SC and BP subjects, we observed widespread hypomethylation as compared to normal controls, preferentially targeting the terminal ends of the chromosomes. In contrast, in anterior cingulate, both SC and BP subjects displayed extensive gain of methylation. Notably, in these two brain regions of SC and BP, only a few DMRs overlapped with promoters, whereas a greater proportion occurs in introns and intergenic regions. Functional enrichment analysis indicated that important psychiatric disorder-related biological processes such as neuron development, differentiation and projection may be altered by epigenetic changes located in the intronic regions. Transcriptome analysis revealed consistent dysfunctional processes with those determined by DMRs. Furthermore, DMRs in the same brain regions from SC and BP could successfully distinguish BP and/or SC from normal controls while differentially expressed genes could not. Overall, our results support a major role for brain-region-dependent aberrant DNA methylation in the pathogenesis of these two disorders.

Triple-negative breast cancer (TNBC) is a highly heterogeneous disease with different molecular subtypes. Although progress has been made, the identification of TNBC subtype-associated biomarkers is still hindered by traditional RNA-seq or array technologies, since bulk data detected by them usually have some non-disease tissue samples, or they are confined to measure the averaged properties of whole tissues. To overcome these constraints and discover TNBC subtype-specific prognosis signatures (TSPSigs), we proposed a single-cell RNA-seq-based bioinformatics approach for identifying TSPSigs. Notably, the TSPSigs we developed mostly were found to be disease-related and involved in cancer development through investigating their enrichment analysis results. In addition, the prognostic power of TSPSigs was successfully confirmed in four independent validation datasets. The multivariate analysis results showed that TSPSigs in two TNBC subtypes-BL1 and LAR, were two independent prognostic factors. Further, analysis results of the TNBC cell lines revealed that the TSPSigs expressions and drug sensitivities had significant associations. Based on the preceding data, we concluded that TSPSigs could be exploited as novel candidate prognostic markers for TNBC patients and applied to individualized treatment in the future.

MicroRNAs, a new class of key regulators of gene expression, have been shown to be involved in diverse biological processes and linked to many human diseases. To elucidate miRNA function from a global perspective, we constructed a conserved miRNA co-expression network by integrating multiple human and mouse miRNA expression data. We found that these conserved co-expressed miRNA pairs tend to reside in close genomic proximity, belong to common families, share common transcription factors, and regulate common biological processes by targeting common components of those processes based on miRNA targets and miRNA knockout/transfection expression data, suggesting their strong functional associations. We also identified several co-expressed miRNA sub-networks. Our analysis reveals that many miRNAs in the same sub-network are associated with the same diseases. By mapping known disease miRNAs to the network, we identified three cancer-related miRNA sub-networks. Functional analyses based on targets and miRNA knockout/transfection data consistently show that these sub-networks are significantly involved in cancer-related biological processes, such as apoptosis and cell cycle. Our results imply that multiple co-expressed miRNAs can cooperatively regulate a given biological process by targeting common components of that process, and the pathogenesis of disease may be associated with the abnormality of multiple functionally cooperative miRNAs rather than individual miRNAs. In addition, many of these co-expression relationships provide strong evidence for the involvement of new miRNAs in important biological processes, such as apoptosis, differentiation and cell cycle, indicating their potential disease links.

Engineered organoids by sequential introduction of key mutations could help modeling the dynamic cancer progression. However, it remains difficult to determine gene paths which were sufficient to capture cancer behaviors and to broadly explain cancer mechanisms. Here, as a case study of colorectal cancer (CRC), functional and dynamic characterizations of five types of engineered organoids with different mutation combinations of five driver genes ( , and ) showed that sequential introductions of all five driver mutations could induce enhanced activation of more hallmark signatures, tending to cancer. Comparative analysis of engineered organoids and corresponding CRC tissues revealed sequential introduction of key mutations could continually shorten the biological distance from engineered organoids to CRC tissues. Nevertheless, there still existed substantial biological gaps between the engineered organoid even with five key mutations and CRC samples. Thus, we proposed an integrative strategy to prioritize gene cascading paths for shrinking biological gaps between engineered organoids and CRC tissues. Our results not only recapitulated the well-known adenoma-carcinoma sequence model (e.g., AKST-organoid with driver mutations in , and ), but also provided potential paths for delineating alternative pathogenesis underlying CRC populations (e.g., A-organoid with mutation). Our strategy also can be applied to both organoids with more mutations and other cancers, which can improve and innovate mechanism across cancer patients for drug design and cancer therapy.

Accumulating evidence shows a tight link between inflammation and cancer. However, comprehensive identification of pivotal transcription factors (i.e., core TFs) mediating the dysregulated links remains challenging, mainly due to a lack of samples that can effectively reflect the connections between inflammation and tumorigenesis. Here, we constructed a series of TF-mediated regulatory networks from a large compendium of expression profiling of normal colonic tissues, inflammatory bowel diseases (IBDs) and colorectal cancer (CRC), which contains 1201 samples in total, and then proposed a network-based approach to characterize potential links bridging inflammation and cancer. For this purpose, we computed significantly dysregulated relationships between inflammation and their linked cancer networks, and then 24 core TFs with their dysregulated genes were identified. Collectively, our approach provides us with quite important insight into inflammation-associated tumorigenesis in colorectal cancer, which could also be applied to identify functionally dysregulated relationships mediating the links between other different disease phenotypes.

Analysis of the biological pathways involved in complex human diseases is an important step in elucidating the pathogenesis and mechanism of diseases. Most pathway analysis approaches identify disease-related biological pathways using overlapping genes between pathways and diseases. However, these approaches ignore the functional biological association between pathways and diseases. In this paper, we designed a novel computational framework for prioritising disease-risk pathways based on functional profiling. The disease gene set and biological pathways were translated into functional profiles in the context of GO annotations. We then implemented a semantic similarity measurement for calculating the concordance score between a functional profile of disease genes and a functional profile of pathways (FPP); the concordance score was then used to prioritise and infer disease-risk pathways. A freely accessible web toolkit, 'Functional Profiling-based Pathway Prioritisation' (FPPP), was developed (http://bioinfo.hrbmu.edu.cn/FPPP). During validation, our method successfully identified known disease-pathway pairs with area under the ROC curve (AUC) values of 96.73 and 95.02% in tests using both pathway randomisation and disease randomisation. A robustness analysis showed that FPPP is reliable even when using data containing noise. A case study based on a dilated cardiomyopathy data set indicated that the high-ranking pathways from FPPP are well known to be linked with this disease. Furthermore, we predicted the risk pathways of 413 diseases by using FPPP to build a disease similarity landscape that systematically reveals the global modular organisation of disease associations.