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A transcriptional regulatory network (TRN) is a collection of transcription regulators with their associated downstream genes, which is highly condition-specific. Understanding how cell states can be programmed through small molecules/drugs or conditions by modulating the whole gene expression system granted us the potential to amend abnormal cells and cure diseases. Condition Orientated Regulatory Networks (CORN, https://qinlab.sysu.edu.cn/home) is a library of condition (small molecule/drug treatments and gene knockdowns)-based transcriptional regulatory sub-networks (TRSNs) that come with an online TRSN matching tool. It allows users to browse condition-associated TRSNs or match those TRSNs by inputting transcriptomic changes of interest. CORN utilizes transcriptomic changes data after specific conditional treatment in cells, and in vivo transcription factor (TF) binding data in cells, by combining TF binding information and calculations of significant expression alterations of TFs and genes after the conditional treatments, TRNs under the effect of different conditions were constructed. In short, CORN associated 1805 different types of specific conditions (small molecule/drug treatments and gene knockdowns) to 9553 TRSNs in 25 human cell lines, involving 204TFs. By linking and curating specific conditions to responsive TRNs, the scientific community can now perceive how TRNs are altered and controlled by conditions alone in an organized manner for the first time. This study demonstrated with examples that CORN can aid the understanding of molecular pathology, pharmacology and drug repositioning, and screened drugs with high potential for cancer and coronavirus disease 2019 (COVID-19) treatments. [ABSTRACT FROM AUTHOR]

Cell fate conversion by overexpressing defined factors is a powerful tool in regenerative medicine. However, identifying key factors for cell fate conversion requires laborious experimental efforts; thus, many of such conversions have not been achieved yet. Nevertheless, cell fate conversions found in many published studies were incomplete as the expression of important gene sets could not be manipulated thoroughly. Therefore, the identification of master transcription factors for complete and efficient conversion is crucial to render this technology more applicable clinically. In the past decade, systematic analyses on various single-cell and bulk OMICs data have uncovered numerous gene regulatory mechanisms, and made it possible to predict master gene regulators during cell fate conversion. By virtue of the sparse structure of master transcription factors and the group structure of their simultaneous regulatory effects on the cell fate conversion process, this study introduces a novel computational method predicting master transcription factors based on group sparse optimization technique integrating data from multi-OMICs levels, which can be applicable to both single-cell and bulk OMICs data with a high tolerance of data sparsity. When it is compared with current prediction methods by cross-referencing published and validated master transcription factors, it possesses superior performance. In short, this method facilitates fast identification of key regulators, give raise to the possibility of higher successful conversion rate and in the hope of reducing experimental cost. [ABSTRACT FROM AUTHOR]

Simple Summary: Cytokines are a group of small proteins in the body that play an important part in boosting the immune system. Thymic stromal lymphopoietin (TSLP) is a cytokine that plays an important role in the maturation of T cells. Two variants of TSLP, long-form (lfTSLP) and short-form (sfTSLP), have been found, however their roles in cancers are not known. In this study, we discovered that sfTSLP, but not lfTSLP, is predominantly expressed in ovarian and endometrial cancers. The switch that turns the sfTSLP gene on or off is controlled by external modifications of DNA. Our results also found that sfTSLP promotes tumour growth through activating several signal pathways in cancer cells. Thymic stromal lymphopoietin (TSLP) is an epithelial cell derived cytokine belonging to the IL-7 family and a key initiator of allergic inflammation. Two main isoforms of TSLP, classified as long- (lfTSLP) and short-form (sfTSLP), have been reported in human, but their expression patterns and role(s) in cancers are not yet clear. mRNA expression was examined by isoform-specific RT-PCR and RNA in situ hybridisation. Epigenetic regulation was investigated by chromatin immunoprecipitation-PCR and bisulfite sequencing. Tumour progression was investigated by gene overexpression, cell viability assay, cancer organoid culture and transwell invasion. Signals were investigated by proteome profiler protein array and RNA-sequencing. With the use of isoform-specific primers and probes, we uncovered that only sfTSLP was expressed in the cell lines and tumour tissues of human ovarian and endometrial cancers. We also showed the epigenetic regulation of sfTSLP: sfTSLP transcription was regulated by histone acetylation at promoters in ovarian cancer cells, whereas silencing of the sfTSLP transcripts was regulated by promoter DNA methylation in endometrial cancer cells. In vitro study showed that ectopically overexpressing sfTSLP promoted tumour growth but not invasion. Human phosphokinase array application demonstrated that the sfTSLP overexpression activated phosphorylation of multiple intracellular kinases (including GSK3α/β, AMPKα1, p53, AKT1/2, ERK1/2 and Src) in ovarian cancer cells in a context-dependent manner. We further investigated the impact of sfTSLP overexpression on transcriptome by RNA-sequencing and found that EFNB2 and PBX1 were downregulated in ovarian and endometrial cancer cells, suggesting their role in sfTSLP-mediated tumour growth. In conclusion, sfTSLP is predominantly expressed in ovarian and endometrial cancers and promotes tumour growth. [ABSTRACT FROM AUTHOR]

DNA- and RNA-binding proteins (DRBPs) typically possess multiple functions to bind both DNA and RNA and regulate gene expression from more than one level. They are controllers for post-transcriptional processes, such as splicing, polyadenylation, transportation, translation, and degradation of RNA transcripts in eukaryotic organisms, as well as regulators on the transcriptional level. Although DRBPs are reported to play critical roles in various developmental processes and diseases, it is still unclear how they work with DNAs and RNAs simultaneously and regulate genes at the transcriptional and post-transcriptional levels. To investigate the functional mechanism of DRBPs, we collected data from a variety of databases and literature and identified 118 DRBPs, which function as both transcription factors (TFs) and splicing factors (SFs), thus called DRBP-SF. Extensive investigations were conducted on four DRBP-SFs that were highly expressed in chronic myeloid leukemia (CML), heterogeneous nuclear ribonucleoprotein K (HNRNPK), heterogeneous nuclear ribonucleoprotein L (HNRNPL), non-POU domain-containing octamer-binding protein (NONO), and TAR DNA-binding protein 43 (TARDBP). By integrating and analyzing ChIP-seq, CLIP-seq, RNA-seq, and shRNA-seq data in K562 using binding and expression target analysis and Statistical Utility for RBP Functions, we discovered a two-layer regulatory network system centered on these four DRBP-SFs and proposed three possible regulatory models where DRBP-SFs can connect transcriptional and alternative splicing regulatory networks cooperatively in CML. The exploration of the identified DRBP-SFs provides new ideas for studying DRBP and regulatory networks, holding promise for further mechanistic discoveries of the two-layer gene regulatory system that may play critical roles in the occurrence and development of CML., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Wang, Zong, Wu, Leung, Hu and Qin.)

Background: Crustacea, the second largest subphylum of Arthropoda, includes species of major ecological and economic importance, such as crabs, lobsters, crayfishes, shrimps, and barnacles. With the rapid development of crustacean aquaculture and biodiversity loss, understanding the gene regulatory mechanisms of growth, reproduction, and development of crustaceans is crucial to both aquaculture development and biodiversity conservation of this group of organisms. In these biological processes, transcription factors (TFs) play a vital role in regulating gene expression. However, crustacean transcription factors are still largely unknown, because the lack of complete genome sequences of most crustacean species hampers the studies on their transcriptional regulation on a system-wide scale. Thus, the current TF databases derived from genome sequences contain TF information for only a few crustacean species and are insufficient to elucidate the transcriptional diversity of such a large animal group., Results: Our database CrusTF ( http://qinlab.sls.cuhk.edu.hk/CrusTF ) provides comprehensive information for evolutionary and functional studies on the crustacean transcriptional regulatory system. CrusTF fills the knowledge gap of transcriptional regulation in crustaceans by exploring publicly available and newly sequenced transcriptomes of 170 crustacean species and identifying 131,941 TFs within 63 TF families. CrusTF features three categories of information: sequence, function, and evolution of crustacean TFs. The database enables searching, browsing and downloading of crustacean TF sequences. CrusTF infers DNA binding motifs of crustacean TFs, thus facilitating the users to predict potential downstream TF targets. The database also presents evolutionary analyses of crustacean TFs, which improve our understanding of the evolution of transcriptional regulatory systems in crustaceans., Conclusions: Given the importance of TF information in evolutionary and functional studies on transcriptional regulatory systems of crustaceans, this database will constitute a key resource for the research community of crustacean biology and evolutionary biology. Moreover, CrusTF serves as a model for the construction of TF database derived from transcriptome data. A similar approach could be applied to other groups of organisms, for which transcriptomes are more readily available than genomes.