TFCat: the curated catalog of mouse and human transcription factors

Transcriptome Complexity Disentangled: A Regulatory Molecules Approach

Transcription factors (TFs) and microRNAs (miRNAs) are fundamental regulators of gene expression, cell state, and biological processes. This study investigated whether a small subset of TFs and miRNAs could accurately predict genome-wide gene expression. We analyzed 8895 samples across 31 cancer types from The Cancer Genome Atlas and identified 28 miRNA and 28 TF clusters using unsupervised learning. Medoids of these clusters could differentiate tissues of origin with 92.8% accuracy, demonstrating their biological relevance. We developed Tissue-Agnostic and Tissue-Aware models to predict 20,000 gene expressions using the 56 selected medoid miRNAs and TFs. The Tissue-Aware model attained an R2 of 0.70 by incorporating tissue-specific information. Despite measuring only 1/400th of the transcriptome, the prediction accuracy was comparable to that achieved by the 1000 landmark genes. This suggests the transcriptome has an intrinsically low-dimensional structure that can be captured by a few regulatory molecules. Our approach could enable cheaper transcriptome assays and analysis of low-quality samples. It also provides insights into genes that are heavily regulated by miRNAs/TFs versus alternative mechanisms. However, model transportability was impacted by dataset discrepancies, especially in miRNA distribution. Overall, this study demonstrates the potential of a biology-guided approach for robust transcriptome representation.

Boosting transcriptional activities by employing repeated activation domains in transcription factors

Enhancing the transcriptional activation activity of transcription factors (TFs) has multiple applications in organism improvement, metabolic engineering, and other aspects of plant science, but the approaches remain unclear. Here, we used gene activation assays and genetic transformation to investigate the transcriptional activities of two MYB TFs, PRODUCTION OF ANTHOCYANIN PIGMENT 1 (AtPAP1) from Arabidopsis (Arabidopsis thaliana) and EsMYBA1 from Epimedium (Epimedium sagittatum), and their synthetic variants in a range of plant species from several families. Using anthocyanin biosynthesis as a convenient readout, we discovered that homologous naturally occurring TFs showed differences in the transcriptional activation ability and that similar TFs induced large changes in the genetic program when heterologously expressed in different species. In some cases, shuffling the DNA-binding domains and transcriptional activation domains (ADs) between homologous TFs led to synthetic TFs that had stronger activation potency than the original TFs. More importantly, synthetic TFs derived from MYB, NAC, bHLH, and ethylene-insensitive3-like (EIL) family members containing tandemly repeated ADs had greatly enhanced activity compared to their natural counterparts. These findings enhance our understanding of TF activity and demonstrate that employing tandemly repeated ADs from natural TFs is a simple and widely applicable strategy to enhance the activation potency of synthetic TFs.

The proximity-based protein interactome and regulatory logics of the transcription factor p65 NF-κB/RELA

The protein interactome of p65/RELA, the most active subunit of the transcription factor (TF) NF-κB, has not been previously determined in living cells. Using p65-miniTurbo fusion proteins and biotin tagging, we identify >350 RELA interactors from untreated and IL-1α-stimulated cells, including many TFs (47% of all interactors) and >50 epigenetic regulators belonging to different classes of chromatin remodeling complexes. A comparison with the interactomes of two point mutants of p65 reveals that the interactions primarily require intact dimerization rather than DNA-binding properties. A targeted RNAi screen for 38 interactors and subsequent functional transcriptome and bioinformatics studies identify gene regulatory (sub)networks, each controlled by RELA in combination with one of the TFs ZBTB5, GLIS2, TFE3/TFEB, or S100A8/A9. The large, dynamic and versatile high-resolution interactome of RELA and its gene regulatory logics provides a rich resource and a new framework for explaining how RELA cooperativity determines gene expression patterns.

GraphPro: An interpretable graph neural network-based model for identifying promoters in multiple species

Promoters are DNA sequences that bind with RNA polymerase to initiate transcription, regulating this process through interactions with transcription factors. Accurate identification of promoters is crucial for understanding gene expression regulation mechanisms and developing therapeutic approaches for various diseases. However, experimental techniques for promoter identification are often expensive, time-consuming, and inefficient, necessitating the development of accurate and efficient computational models for this task. Enhancing the model's ability to recognize promoters across multiple species and improving its interpretability pose significant challenges. In this study, we introduce a novel interpretable model based on graph neural networks, named GraphPro, for multi-species promoter identification. Initially, we encode the sequences using k-tuple nucleotide frequency pattern, dinucleotide physicochemical properties, and dna2vec. Subsequently, we construct two feature extraction modules based on convolutional neural networks and graph neural networks. These modules aim to extract specific motifs from the promoters, learn their dependencies, and capture the underlying structural features of the promoters, providing a more comprehensive representation. Finally, a fully connected neural network predicts whether the input sequence is a promoter. We conducted extensive experiments on promoter datasets from eight species, including Human, Mouse, and Escherichia coli. The experimental results show that the average Sn, Sp, Acc and MCC values of GraphPro are 0.9123, 0.9482, 0.8840 and 0.7984, respectively. Compared with previous promoter identification methods, GraphPro not only achieves better recognition accuracy on multiple species, but also outperforms all previous methods in cross-species prediction ability. Furthermore, by visualizing GraphPro's decision process and analyzing the sequences matching the transcription factor binding motifs captured by the model, we validate its significant advantages in biological interpretability. The source code for GraphPro is available at https://github.com/liuliwei1980/GraphPro.

Differential cell type-specific function of the aryl hydrocarbon receptor and its repressor in diet-induced obesity and fibrosis

OBJECTIVE

The aryl hydrocarbon receptor (AhR) is a ligand-activated transcription factor regulating xenobiotic responses as well as physiological metabolism. Dietary AhR ligands activate the AhR signaling axis, whereas AhR activation is negatively regulated by the AhR repressor (AhRR). While AhR-deficient mice are known to be resistant to diet-induced obesity (DIO), the influence of the AhRR on DIO has not been assessed so far.

METHODS

In this study, we analyzed AhRR-/- mice and mice with a conditional deletion of either AhRR or AhR in myeloid cells under conditions of DIO and after supplementation of dietary AhR ligands. Moreover, macrophage metabolism was assessed using Seahorse Mito Stress Test and ROS assays as well as transcriptomic analysis.

RESULTS

We demonstrate that global AhRR deficiency leads to a robust, but not as profound protection from DIO and hepatosteatosis as AhR deficiency. Under conditions of DIO, AhRR-/- mice did not accumulate TCA cycle intermediates in the circulation in contrast to wild-type (WT) mice, indicating protection from metabolic dysfunction. This effect could be mimicked by dietary supplementation of AhR ligands in WT mice. Because of the predominant expression of the AhRR in myeloid cells, AhRR-deficient macrophages were analyzed for changes in metabolism and showed major metabolic alterations regarding oxidative phosphorylation and mitochondrial activity. Unbiased transcriptomic analysis revealed increased expression of genes involved in de novo lipogenesis and mitochondrial biogenesis. Mice with a genetic deficiency of the AhRR in myeloid cells did not show alterations in weight gain after high fat diet (HFD) but demonstrated ameliorated liver damage compared to control mice. Further, deficiency of the AhR in myeloid cells also did not affect weight gain but led to enhanced liver damage and adipose tissue fibrosis compared to controls.

CONCLUSIONS

AhRR-deficient mice are resistant to diet-induced metabolic syndrome. Although conditional ablation of either the AhR or AhRR in myeloid cells did not recapitulate the phenotype of the global knockout, our findings suggest that enhanced AhR signaling in myeloid cells deficient for AhRR protects from diet-induced liver damage and fibrosis, whereas myeloid cell-specific AhR deficiency is detrimental.

Identification of DNA motif pairs on paired sequences based on composite heterogeneous graph

Motivation

The interaction between DNA motifs (DNA motif pairs) influences gene expression through partnership or competition in the process of gene regulation. Potential chromatin interactions between different DNA motifs have been implicated in various diseases. However, current methods for identifying DNA motif pairs rely on the recognition of single DNA motifs or probabilities, which may result in local optimal solutions and can be sensitive to the choice of initial values. A method for precisely identifying DNA motif pairs is still lacking.

Results

Here, we propose a novel computational method for predicting DNA Motif Pairs based on Composite Heterogeneous Graph (MPCHG). This approach leverages a composite heterogeneous graph model to identify DNA motif pairs on paired sequences. Compared with the existing methods, MPCHG has greatly improved the accuracy of motifs prediction. Furthermore, the predicted DNA motifs demonstrate heightened DNase accessibility than the background sequences. Notably, the two DNA motifs forming a pair exhibit functional consistency. Importantly, the interacting TF pairs obtained by predicted DNA motif pairs were significantly enriched with known interacting TF pairs, suggesting their potential contribution to chromatin interactions. Collectively, we believe that these identified DNA motif pairs held substantial implications for revealing gene transcriptional regulation under long-range chromatin interactions.

DeepReg: a deep learning hybrid model for predicting transcription factors in eukaryotic and prokaryotic genomes

Deep learning models (DLMs) have gained importance in predicting, detecting, translating, and classifying a diversity of inputs. In bioinformatics, DLMs have been used to predict protein structures, transcription factor-binding sites, and promoters. In this work, we propose a hybrid model to identify transcription factors (TFs) among prokaryotic and eukaryotic protein sequences, named Deep Regulation (DeepReg) model. Two architectures were used in the DL model: a convolutional neural network (CNN), and a bidirectional long-short-term memory (BiLSTM). DeepReg reached a precision of 0.99, a recall of 0.97, and an F1-score of 0.98. The quality of our predictions, the bias-variance trade-off approach, and the characterization of new TF predictions were evaluated and compared against those produced by DeepTFactor, as well as against experimental data from three model organisms. Predictions based on our DLM tended to exhibit less variance and bias than those from DeepTFactor, thus increasing reliability and decreasing overfitting.

Factors determining the sensitivity to proteasome inhibitors of multiple myeloma cells

Multiple myeloma is an incurable cancer that originates from antibody-producing plasma cells. It is characterized by an intrinsic ability to produce large amounts of immunoglobulin-like proteins. The high rate of synthesis makes myeloma cells dependent on protein processing mechanisms related to the proteasome. This dependence made proteasome inhibitors such as bortezomib and carfilzomib one of the most important classes of drugs used in multiple myeloma treatment. Inhibition of the proteasome is associated with alteration of a number of important biological processes leading, in consequence, to inhibition of angiogenesis. The effect of drugs in this group and the degree of patient response to the treatment used is itself an extremely complex process that depends on many factors. At cellular level the change in sensitivity to proteasome inhibitors may be related to differences in the expression level of proteasome subunits, the degree of proteasome loading, metabolic adaptation, transcriptional or epigenetic factors. These are just some of the possibilities that may influence differences in response to proteasome inhibitors. This review describes the main cellular factors that determine the degree of response to proteasome inhibitor drugs, as well as information on the key role of the proteasome and the performance characteristics of the inhibitors that are the mainstay of multiple myeloma treatment.

PMAIP1, a novel diagnostic and potential therapeutic biomarker in osteoporosis

BACKGROUND

Osteoporosis is a common endocrine metabolic bone disease, which may lead to severe consequences. However, the unknown molecular mechanism of osteoporosis, the observable side effects of present treatments and the inability to fundamentally improve bone metabolism seriously restrict the impact of prevention and treatment. The study aims to identify potential biomarkers from osteoclast progenitors, specifically peripheral blood monocytes on predicting the osteoporotic phenotype.

METHODS

Datasets were obtained from Gene Expression Omnibus (GEO). Based on the differentially expressed genes (DEGs) and GSEA results, GO and KEGG analyses were performed using the DAVID database and Metascape database. PPI network, TF network, drug-gene interaction network, and ceRNA network were established to determine the hub genes. Its osteogenesis, migration, and proliferation abilities in bone marrow mesenchymal stem cells (BMSCs) were validated through RT-qPCR, WB, ALP staining, VK staining, wound healing assay, transwell assay, and CCK-8 assay.

RESULTS

A total of 63 significant DEGs were screened. Functional and pathway enrichment analysis discovered that the functions of the significant DEGs (SDEGs) are mainly related to immunity and metal ions. A comprehensive evaluation of all the network analyses, PMAIP1 was defined as osteoporosis's core gene. This conclusion was further confirmed in clinical cohort data. A series of experiments demonstrated that the PMAIP1 gene can promote the osteogenesis, migration and proliferation of BMSC cells.

CONCLUSIONS

All of these outcomes showed a new theoretical basis for further research in the treatment of osteoporosis, and PMAIP1 was identified as a potential biomarker for osteoporosis diagnosis and treatment.

Differential Gene Expression following DHX36/G4R1 Knockout Is Associated with G-Quadruplex Content and Cancer

G-quadruplexes (G4s) are secondary DNA and RNA structures stabilized by positive cations in a central channel formed by stacked tetrads of Hoogsteen base-paired guanines. G4s form from G-rich sequences across the genome, whose biased distribution in regulatory regions points towards a gene-regulatory role. G4s can themselves be regulated by helicases, such as DHX36 (aliases: G4R1 and RHAU), which possess the necessary activity to resolve these stable structures. G4s have been shown to both positively and negatively regulate gene expression when stabilized by ligands, or through the loss of helicase activity. Using DHX36 knockout Jurkat cell lines, we identified widespread, although often subtle, effects on gene expression that are associated with the presence or number of observed G-quadruplexes in promoters or gene regions. Genes that significantly change their expression, particularly those that show a significant increase in RNA abundance under DHX36 knockout, are associated with a range of cellular functions and processes, including numerous transcription factors and oncogenes, and are linked to several cancers. Our work highlights the direct and indirect role of DHX36 in the transcriptome of T-lymphocyte leukemia cells and the potential for DHX36 dysregulation in cancer.

TFCheckpoint database update, a cross-referencing system for transcription factors from human, mouse and rat

Prior knowledge about DNA-binding transcription factors (dbTFs), transcription co-regulators (coTFs) and general transcriptional factors (GTFs) is crucial for the study and understanding of the regulation of transcription. This is reflected by the many publications and database resources describing knowledge about TFs. We previously launched the TFCheckpoint database, an integrated resource focused on human, mouse and rat dbTFs, providing users access to a comprehensive overview of these proteins. Here, we describe TFCheckpoint 2.0 (https://www.tfcheckpoint.org/index.php), comprising 13 collections of dbTFs, coTFs and GTFs. TFCheckpoint 2.0 provides an easy and versatile cross-referencing system for users to view and download collections that may otherwise be cumbersome to find, compare and retrieve.

PacBio Full-Length Transcriptome of a Tetraploid Sinocyclocheilus multipunctatus Provides Insights into the Evolution of Cavefish

Sinocyclocheilus multipunctatus is a second-class nationally protected wild animal in China. As one of the cavefish, S. multipunctatus has strong adaptability to harsh subterranean environments. In this study, we used PacBio SMRT sequencing technology to generate a first representative full-length transcriptome for S. multipunctatus. Sequence clustering analysis obtained 232,126 full-length transcripts. Among all transcripts, 40,487 were annotated in public databases, while 70,300 microsatellites, 2384 transcription factors, and 16,321 long non-coding RNAs were identified. The phylogenetic tree showed that S. multipunctatus shows a closer relationship to Carassius auratus and Cyprinus carpio, phylogenetically diverging from the common ancestor ~14.74 million years ago (Mya). We also found that between 15.6 and 17.5 Mya, S. multipunctatus also experienced an additional whole-genome duplication (WGD) event, which may have promoted the species evolution of S. multipunctatus. Meanwhile, the overall rates of evolutionary of polyploid S. multipunctatus were significantly higher than those of the other cyprinids, and 220 positively selected genes (PSGs) were identified in two sub-genomes of S. multipunctatus. These PSGs are likely to fulfill critical roles in the process of adapting to diverse cave environments. This study has the potential to facilitate future investigations into the genomic characteristics of S. multipunctatus and provide valuable insights into revealing the evolutionary history of polyploid S. multipunctatus.

Molecular mechanisms of multi-omic regulation in breast cancer

Breast cancer is a complex disease that is influenced by the concurrent influence of multiple genetic and environmental factors. Recent advances in genomics and other high throughput biomolecular techniques (-omics) have provided numerous insights into the molecular mechanisms underlying breast cancer development and progression. A number of these mechanisms involve multiple layers of regulation. In this review, we summarize the current knowledge on the role of multiple omics in the regulation of breast cancer, including the effects of DNA methylation, non-coding RNA, and other epigenomic changes. We comment on how integrating such diverse mechanisms is envisioned as key to a more comprehensive understanding of breast carcinogenesis and cancer biology with relevance to prognostics, diagnostics and therapeutics. We also discuss the potential clinical implications of these findings and highlight areas for future research. Overall, our understanding of the molecular mechanisms of multi-omic regulation in breast cancer is rapidly increasing and has the potential to inform the development of novel therapeutic approaches for this disease.

Identification of the novel FOXP3-dependent Treg cell transcription factor MEOX1 by high-dimensional analysis of human CD4+ T cells

CD4+ T cells play a central role in the adaptive immune response through their capacity to activate, support and control other immune cells. Although these cells have become the focus of intense research, a comprehensive understanding of the underlying regulatory networks that orchestrate CD4+ T cell function and activation is still incomplete. Here, we analyzed a large transcriptomic dataset consisting of 48 different human CD4+ T cell conditions. By performing reverse network engineering, we identified six common denominators of CD4+ T cell functionality (CREB1, E2F3, AHR, STAT1, NFAT5 and NFATC3). Moreover, we also analyzed condition-specific genes which led us to the identification of the transcription factor MEOX1 in Treg cells. Expression of MEOX1 was comparable to FOXP3 in Treg cells and can be upregulated by IL-2. Epigenetic analyses revealed a permissive epigenetic landscape for MEOX1 solely in Treg cells. Knockdown of MEOX1 in Treg cells revealed a profound impact on downstream gene expression programs and Treg cell suppressive capacity. These findings in the context of CD4+ T cells contribute to a better understanding of the transcriptional networks and biological mechanisms controlling CD4+ T cell functionality, which opens new avenues for future therapeutic strategies.

Transcription factors in the pathogenesis of pulmonary arterial hypertension-Current knowledge and therapeutic potential

Pulmonary arterial hypertension (PAH) is a disease characterized by elevated pulmonary vascular resistance and pulmonary artery pressure. Mortality remains high in severe cases despite significant advances in management and pharmacotherapy. Since currently approved PAH therapies are unable to significantly reverse pathological vessel remodeling, novel disease-modifying, targeted therapeutics are needed. Pathogenetically, PAH is characterized by vessel wall cell dysfunction with consecutive remodeling of the pulmonary vasculature and the right heart. Transcription factors (TFs) regulate the process of transcribing DNA into RNA and, in the pulmonary circulation, control the response of pulmonary vascular cells to macro- and microenvironmental stimuli. Often, TFs form complex protein interaction networks with other TFs or co-factors to allow for fine-tuning of gene expression. Therefore, identification of the underlying molecular mechanisms of TF (dys-)function is essential to develop tailored modulation strategies in PAH. This current review provides a compendium-style overview of TFs and TF complexes associated with PAH pathogenesis and highlights their potential as targets for vasculoregenerative or reverse remodeling therapies.

Identifying transcript-level differential expression in primary human immune cells

BACKGROUND

Multipotential hematopoietic stem cells differentiate into a wide variety of immune cells with a diversity of functions, including the ability to respond to a variety of stimuli. Importantly, numerous studies have demonstrated the importance of gene transcription in defining cell identity and functions. While these studies have primarily been performed at the level of the gene, it is known that key immune genes such as CD44 and CD45 generate multiple different transcripts that are differentially expressed across different immune cells, and that encode proteins with different sequences and functions. Prior genomic surveys have shown that the mechanisms for generating diversity in expressed transcripts (alternate splicing, alternate transcription start sites, etc.) are very active in immune cells, but have been lacking in terms of identifying genes with multiple transcripts, that are differentially expressed, and likely to affect cell functions.

METHODS

We first identified the set of genes that had at least two transcripts expressed in our RNA sequencing dataset generated from purified populations of neutrophils, monocytes and five lymphocyte populations (B, NK, γδ T, CD4 + T and CD8 + T) from twelve healthy donors. Next, we developed a heuristic approach to identify genes where two or more transcripts have distinct expression patterns across lymphoid and/or myeloid populations. We then focused our annotation and interpretation on differentially expressed transcripts that affect the coding sequence. This process was repeated to identify transcripts that were differentially expressed between monocytes and populations of macrophages and LPS-stimulated macrophages derived from these monocytes in vitro.

RESULTS

We found that over 55 % of genes had two or more expressed transcripts, with an average ∼3 transcripts per gene, and that 70 % of these had at least two of the transcripts that encoded proteins with different sequences. As expected, we identified a complex pattern of differential expression for multiple transcripts encoding the CD45 transmembrane protein, but we also found similar evidence for ten other genes (CD300A, FYB1, GPI, LITAF, PSMA1, PTMA, RPL32, SEPTIN9, SH3BP2, SH3KBP1) when comparing the expression patterns of transcripts within myeloid and lymphoid cells. We also identified five genes with differentially expressed transcripts associated with the transition from monocytes to macrophages (FNBP1, KLF6, and SEPTIN9) or between macrophages and LPS-stimulated macrophages (CD44, OAZ2, and SEPTIN9). For the most part, we found that the different transcripts of these genes are expected to impact specific biological functions, for example the different transcripts of SEPTIN9 likely regulate the cytoskeleton in immune cells via their interactions with actins filaments and microtubules.

CONCLUSIONS

This analytic approach successfully identified multi-transcript genes that are differentially expressed across immune cells and could be applied to other transcriptomic data.

DATA AVAILABILITY STATEMENT

Researchers can request access to the individual-level data from the current study by contacting the Montreal Heart Institute ethics committee at the following institutional email address: cer.icm@icm-mhi.org.

DeepLncPro: an interpretable convolutional neural network model for identifying long non-coding RNA promoters

Long non-coding RNA (lncRNA) plays important roles in a series of biological processes. The transcription of lncRNA is regulated by its promoter. Hence, accurate identification of lncRNA promoter will be helpful to understand its regulatory mechanisms. Since experimental techniques remain time consuming for gnome-wide promoter identification, developing computational tools to identify promoters are necessary. However, only few computational methods have been proposed for lncRNA promoter prediction and their performances still have room to be improved. In the present work, a convolutional neural network based model, called DeepLncPro, was proposed to identify lncRNA promoters in human and mouse. Comparative results demonstrated that DeepLncPro was superior to both state-of-the-art machine learning methods and existing models for identifying lncRNA promoters. Furthermore, DeepLncPro has the ability to extract and analyze transcription factor binding motifs from lncRNAs, which made it become an interpretable model. These results indicate that the DeepLncPro can server as a powerful tool for identifying lncRNA promoters. An open-source tool for DeepLncPro was provided at https://github.com/zhangtian-yang/DeepLncPro.

GCN4 Enhances the Transcriptional Regulation of AreA by Interacting with SKO1 To Mediate Nitrogen Utilization in Ganoderma lucidum

Nitrogen is an essential nutrient for cell growth and proliferation. Limitations of nitrogen availability in organisms elicit a series of rapid transcriptional reprogramming mechanisms, which involve the participation of many transcription factors.

Deregulated transcription factors and poor clinical outcomes in cancer patients

Transcription factors are a group of proteins, which possess DNA-binding domains, bind to DNA strands of promoters or enhancers, and initiate transcription of genes with cooperation of RNA polymerase and other co-factors. They play crucial roles in regulating transcription during embryogenesis and development. Their physiological status in different cell types is also important to maintain cellular homeostasis. Therefore, any deregulation of transcription factors will lead to the development of cancer cells and tumor progression. Based on their functions in cancer cells, transcription factors could be either oncogenic or tumor suppressive. Furthermore, transcription factors have been shown to modulate cancer stem cells, epithelial-mesenchymal transition (EMT) and drug response; therefore, measuring deregulated transcription factors is hypothesized to predict treatment outcomes of patients with cancers and targeting deregulated transcription factors could be an encouraging strategy for cancer therapy. Here, we summarize the current knowledge of major deregulated transcription factors and their effects on causing poor clinical outcome of patients with cancer. The information presented here will help to predict the prognosis and drug response and to design novel drugs and therapeutic strategies for the treatment of cancers by targeting deregulated transcription factors.

A map of cis-regulatory modules and constituent transcription factor binding sites in 80% of the mouse genome

BACKGROUND

Mouse is probably the most important model organism to study mammal biology and human diseases. A better understanding of the mouse genome will help understand the human genome, biology and diseases. However, despite the recent progress, the characterization of the regulatory sequences in the mouse genome is still far from complete, limiting its use to understand the regulatory sequences in the human genome.

RESULTS

Here, by integrating binding peaks in ~ 9,000 transcription factor (TF) ChIP-seq datasets that cover 79.9% of the mouse mappable genome using an efficient pipeline, we were able to partition these binding peak-covered genome regions into a cis-regulatory module (CRM) candidate (CRMC) set and a non-CRMC set. The CRMCs contain 912,197 putative CRMs and 38,554,729 TF binding sites (TFBSs) islands, covering 55.5% and 24.4% of the mappable genome, respectively. The CRMCs tend to be under strong evolutionary constraints, indicating that they are likely cis-regulatory; while the non-CRMCs are largely selectively neutral, indicating that they are unlikely cis-regulatory. Based on evolutionary profiles of the genome positions, we further estimated that 63.8% and 27.4% of the mouse genome might code for CRMs and TFBSs, respectively.

CONCLUSIONS

Validation using experimental data suggests that at least most of the CRMCs are authentic. Thus, this unprecedentedly comprehensive map of CRMs and TFBSs can be a good resource to guide experimental studies of regulatory genomes in mice and humans.

Full-length transcriptome analysis of the bloom-forming dinoflagellate Akashiwo sanguinea by single-molecule real-time sequencing

The dinoflagellate Akashiwo sanguinea is a harmful algal species and commonly observed in estuarine and coastal waters around the world. Harmful algal blooms (HABs) caused by this species lead to serious environmental impacts in the coastal waters of China since 1998 followed by huge economic losses. However, the full-length transcriptome information of A. sanguinea is still not fully explored, which hampers basic genetic and functional studies. Herein, single-molecule real-time (SMRT) sequencing technology was performed to characterize the full-length transcript in A. sanguinea. Totally, 83.03 Gb SMRT sequencing clean reads were generated, 983,960 circular consensus sequences (CCS) with average lengths of 3,061 bp were obtained, and 81.71% (804,016) of CCS were full-length non-chimeric reads (FLNC). Furthermore, 26,461 contigs were obtained after being corrected with Illumina library sequencing, with 20,037 (75.72%) successfully annotated in the five public databases. A total of 13,441 long non-coding RNA (lncRNA) transcripts, 3,137 alternative splicing (AS) events, 514 putative transcription factors (TFs) members from 23 TF families, and 4,397 simple sequence repeats (SSRs) were predicted, respectively. Our findings provided a sizable insights into gene sequence characteristics of A. sanguinea, which can be used as a reference sequence resource for A. sanguinea draft genome annotation, and will contribute to further molecular biology research on this harmful bloom algae.

Atlas of regulated target genes of transcription factors (ART-TF) in human ES cells

Background

Transcription factors (TFs) play central roles in maintaining “stemness” of embryonic stem (ES) cells and their differentiation into several hundreds of adult cell types. The regulatory competence of TFs is routinely assessed by detecting target genes to which they bind. However, these data do not indicate which target genes are activated, repressed, or not affected by the change of TF abundance. There is a lack of large-scale studies that compare the genome binding of TFs with the expression change of target genes after manipulation of each TF.

Results

In this paper we associated human TFs with their target genes by two criteria: binding to genes, evaluated from published ChIP-seq data (n = 1868); and change of target gene expression shortly after induction of each TF in human ES cells. Lists of direction- and strength-specific regulated target genes are generated for 311 TFs (out of 351 TFs tested) with expected proportion of false positives less than or equal to 0.30, including 63 new TFs not present in four existing databases of target genes. Our lists of direction-specific targets for 152 TFs (80.0%) are larger that in the TRRUST database. In average, 30.9% of genes that respond greater than or equal to twofold to the induction of TFs are regulated targets. Regulated target genes indicate that the majority of TFs are either strong activators or strong repressors, whereas sets of genes that responded greater than or equal to twofold to the induction of TFs did not show strong asymmetry in the direction of expression change. The majority of human TFs (82.1%) regulated their target genes primarily via binding to enhancers. Repression of target genes is more often mediated by promoter-binding than activation of target genes. Enhancer-promoter loops are more abundant among strong activator and repressor TFs.

Conclusions

We developed an atlas of regulated targets of TFs (ART-TF) in human ES cells by combining data on TF binding with data on gene expression change after manipulation of individual TFs. Sets of regulated gene targets were identified with a controlled rate of false positives. This approach contributes to the understanding of biological functions of TFs and organization of gene regulatory networks. This atlas should be a valuable resource for ES cell-based regenerative medicine studies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04924-3.

Core transcription programs controlling injury-induced neurodegeneration of retinal ganglion cells

Regulatory programs governing neuronal death and axon regeneration in neurodegenerative diseases remain poorly understood. In adult mice, optic nerve crush (ONC) injury by severing retinal ganglion cell (RGC) axons results in massive RGC death and regenerative failure. We performed an in vivo CRISPR-Cas9-based genome-wide screen of 1,893 transcription factors (TFs) to seek repressors of RGC survival and axon regeneration following ONC. In parallel, we profiled the epigenetic and transcriptional landscapes of injured RGCs by ATAC-seq and RNA-seq to identify injury-responsive TFs and their targets. These analyses converged on four TFs as critical survival regulators, of which ATF3/CHOP preferentially regulate pathways activated by cytokines and innate immunity and ATF4/C/EBPγ regulate pathways engaged by intrinsic neuronal stressors. Manipulation of these TFs protects RGCs in a glaucoma model. Our results reveal core transcription programs that transform an initial axonal insult into a degenerative process and suggest novel strategies for treating neurodegenerative diseases.

Doxorubicin impacts chromatin binding of HMGB1, Histone H1 and retinoic acid receptor

Doxorubicin (Dox), a widely used anticancer DNA-binding drug, affects chromatin in multiple ways, and these effects contribute to both its efficacy and its dose-limiting side effects, especially cardiotoxicity. Here, we studied the effects of Dox on the chromatin binding of the architectural proteins high mobility group B1 (HMGB1) and the linker histone H1, and the transcription factor retinoic acid receptor (RARα) by fluorescence recovery after photobleaching (FRAP) and fluorescence correlation spectroscopy (FCS) in live cells. At lower doses, Dox increased the binding of HMGB1 to DNA while decreasing the binding of the linker histone H1. At higher doses that correspond to the peak plasma concentrations achieved during chemotherapy, Dox reduced the binding of HMGB1 as well. This biphasic effect is interpreted in terms of a hierarchy of competition between the ligands involved and Dox-induced local conformational changes of nucleosome-free DNA. Combined, FRAP and FCS mobility data suggest that Dox decreases the overall binding of RARα to DNA, an effect that was only partially overcome by agonist binding. The intertwined interactions described are likely to contribute to both the effects and side effects of Dox.

m6A-mediated modulation coupled with transcriptional regulation shapes long noncoding RNA repertoire of the cGAS-STING signaling

The cGAS-STING signaling plays pivotal roles not only in host antiviral defense but also in various noninfectious contexts. Compared with protein-coding genes, much less was known about long noncoding RNAs involved in this pathway. Here, we performed an integrative study to elucidate the lncRNA repertoire and the mechanisms modulating lncRNA’s expression following cGAS-STING signaling activation. We uncovered a reliable set of 672 lncRNAs closely linked to cGAS-STING signaling activation (cs-lncRNA), which might be associated with type-I interferon response and infection-related phenotypes. The ChIP-seq analysis demonstrated that cs-lncRNA was strongly regulated at the transcriptional level. We further found N6-methyladenosine (m⁶A) regulatory machinery was indispensable for establishing cs-lncRNA repertoire via modulating m⁶A modification on cs-lncRNA transcripts and promoting the expression of signaling transduction key components, including IFNAR1. Loss of IFNAR1 led to the dysregulation of cs-lncRNAs resembled that of loss of an essential subunit of m⁶A writer METTL14. We also found m⁶A system affected transcriptional machinery to modulate cs-lncRNAs by targeting multiple crucial transcription factors. Inhibiting an m⁶A modification regulated transcription factor, EZH2, markedly enhanced the expression pattern of cs-lncRNAs. Taken together, our results uncovered the composition of the cs-lncRNAs and revealed m⁶A-mediated modulation coupled with transcriptional regulation significantly shaped cs-lncRNA repertoire.

Human transcription factor protein interaction networks

Transcription factors (TFs) interact with several other proteins in the process of transcriptional regulation. Here, we identify 6703 and 1536 protein–protein interactions for 109 different human TFs through proximity-dependent biotinylation (BioID) and affinity purification mass spectrometry (AP-MS), respectively. The BioID analysis identifies more high-confidence interactions, highlighting the transient and dynamic nature of many of the TF interactions. By performing clustering and correlation analyses, we identify subgroups of TFs associated with specific biological functions, such as RNA splicing or chromatin remodeling. We also observe 202 TF-TF interactions, of which 118 are interactions with nuclear factor 1 (NFI) family members, indicating uncharacterized cross-talk between NFI signaling and other TF signaling pathways. Moreover, TF interactions with basal transcription machinery are mainly observed through TFIID and SAGA complexes. This study provides a rich resource of human TF interactions and also act as a starting point for future studies aimed at understanding TF-mediated transcription.

Transcription factors (TFs) interact with several other proteins in the process of transcriptional regulation. Here the authors identify 6703 and 1536 protein–protein interactions for 109 different human TFs through BioID and AP-MS analyses, respectively.

Identification and External Validation of a Transcription Factor-Related Prognostic Signature in Pediatric Neuroblastoma

Background

Neuroblastoma is a common solid tumor originating from the sympathetic nervous system, commonly found in children, and it is one of the leading causes of tumor-related deaths in children. In addition to pathological features, molecular-level features, such as how much gene expression is present and the mutational profile, may provide useful information for the precise treatment of neuroblastoma. Transcription factors (TFs) play an important regulatory role in all aspects of cellular life activities. But there are currently no studies on transcription factor-based biomarkers of neuroblastoma prognosis, and this study is much needed.

Methods

We downloaded RNA transcriptome data and clinical data from the TARGET database to construct a prognostic model. The prognostic model was constructed by using univariate Cox analysis, LASSO, and multivariate Cox regression. We divided the patients into low-risk and high-risk groups using the median value of the risk score as the cut-off. Then, we validated the prognostic model with the dataset GSE49710.

Results

We constructed a prognostic model consisting of eight genes (SATB1, ZNF564, SOX14, EN1, IKZF2, SLC2A4RG, FOXJ2, and ZNF521). Patients in the high-risk group had a lower survival rate than those in the low-risk group. The area under the 3-year ROC curve of the model reached 0.825, suggesting a good predictive efficacy. We performed target gene prediction for the eight transcription factors in the model using six online databases and found that TUT1 may be a target gene for transcription factor EN1 and is associated with immune infiltration.

Conclusion

This prognostic model consisting of eight transcription factor-associated genes demonstrated reliable predictive efficacy. This prediction model may provide new potential targets for the treatment of neuroblastoma and personalized monitoring of neuroblastoma patients with high and low risk.

TF-Marker: a comprehensive manually curated database for transcription factors and related markers in specific cell and tissue types in human

Transcription factors (TFs) play key roles in biological processes and are usually used as cell markers. The emerging importance of TFs and related markers in identifying specific cell types in human diseases increases the need for a comprehensive collection of human TFs and related markers sets. Here, we developed the TF-Marker database (TF-Marker, http://bio.liclab.net/TF-Marker/), aiming to provide cell/tissue-specific TFs and related markers for human. By manually curating thousands of published literature, 5905 entries including information about TFs and related markers were classified into five types according to their functions: (i) TF: TFs which regulate expression of the markers; (ii) T Marker: markers which are regulated by the TF; (iii) I Marker: markers which influence the activity of TFs; (iv) TFMarker: TFs which play roles as markers and (v) TF Pmarker: TFs which play roles as potential markers. The 5905 entries of TF-Marker include 1316 TFs, 1092 T Markers, 473 I Markers, 1600 TFMarkers and 1424 TF Pmarkers, involving 383 cell types and 95 tissue types in human. TF-Marker further provides a user-friendly interface to browse, query and visualize the detailed information about TFs and related markers. We believe TF-Marker will become a valuable resource to understand the regulation patterns of different tissues and cells.

Homodimeric and Heterodimeric Interactions among Vertebrate Basic Helix-Loop-Helix Transcription Factors

The basic helix–loop–helix transcription factor (bHLH TF) family is involved in tissue development, cell differentiation, and disease. These factors have transcriptionally positive, negative, and inactive functions by combining dimeric interactions among family members. The best known bHLH TFs are the E-protein homodimers and heterodimers with the tissue-specific TFs or ID proteins. These cooperative and dynamic interactions result in a complex transcriptional network that helps define the cell’s fate. Here, the reported dimeric interactions of 67 vertebrate bHLH TFs with other family members are summarized in tables, including specifications of the experimental techniques that defined the dimers. The compilation of these extensive data underscores homodimers of tissue-specific bHLH TFs as a central part of the bHLH regulatory network, with relevant positive and negative transcriptional regulatory roles. Furthermore, some sequence-specific TFs can also form transcriptionally inactive heterodimers with each other. The function, classification, and developmental role for all vertebrate bHLH TFs in four major classes are detailed.

A GO catalogue of human DNA-binding transcription factors

To control gene transcription, DNA-binding transcription factors recognise specific sequence motifs in gene regulatory regions. A complete and reliable GO annotation of all DNA-binding transcription factors is key to investigating the delicate balance of gene regulation in response to environmental and developmental stimuli. The need for such information is demonstrated by the many lists of transcription factors that have been produced over the past decade. The COST Action Gene Regulation Ensemble Effort for the Knowledge Commons (GREEKC) Consortium brought together experts in the field of transcription with the aim of providing high quality and interoperable gene regulatory data. The Gene Ontology (GO) Consortium provides strict definitions for gene product function, including factors that regulate transcription. The collaboration between the GREEKC and GO Consortia has enabled the application of those definitions to produce a new curated catalogue of over 1400 human DNA-binding transcription factors, that can be accessed at https://www.ebi.ac.uk/QuickGO/targetset/dbTF. This catalogue has facilitated an improvement in the GO annotation of human DNA-binding transcription factors and led to the GO annotation of almost sixty thousand DNA-binding transcription factors in over a hundred species. Thus, this work will aid researchers investigating the regulation of transcription in both biomedical and basic science.

Integrated transcriptome profiling in THP-1 macrophages infected with bunyavirus SFTSV

Severe fever with thrombocytopenia syndrome virus (SFTSV) is a tick-borne bunyavirus that causes an emerging hemorrhagic fever termed SFTS with high mortality. However, knowledge of SFTSV-host interactions is largely limited. Here, we performed a global transcriptome analysis of mRNAs and lncRNAs in THP-1 macrophages infected with SFTSV for 24 and 48 h. A total of 2,334 differentially expressed mRNAs and 154 differentially expressed lncRNAs were identified with 577 mRNAs and 31 lncRNAs commonly changed at both time points. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that differentially expressed mRNAs were mainly associated with innate immune, cytokine signaling, systemic lupus erythematosus, and alcoholism. Differentially expressed lncRNAs were enriched in systemic lupus erythematosus, alcoholism, and ribosome. Bioinformatic analysis also revealed hub regulatory mRNAs including IL6, TNF, UBA52, SRC, IL10, CXCL10, and CDK1 and core regulatory lncRNAs including XLOC_083027 and XLOC_113317. Transcription factor analysis of the differentially expressed mRNAs revealed that IRF1, SPI1, SPIB, ELF5, and FEV were enriched during SFTSV infection. Taken together, our studies illustrate the complex interaction between THP-1 macrophages and SFTSV.

The transcription factor reservoir and chromatin landscape in activated plasmacytoid dendritic cells

Background

Transcription factors (TFs) control gene expression by direct binding to regulatory regions of target genes but also by impacting chromatin landscapes and modulating DNA accessibility for other TFs. In recent years several TFs have been defined that control cell fate decisions and effector functions in the immune system. Plasmacytoid dendritic cells (pDCs) are an immune cell type with the unique capacity to produce high amounts of type I interferons quickly in response to contact with viral components. Hereby, this cell type is involved in anti-infectious immune responses but also in the development of inflammatory and autoimmune diseases. To date, the global TF reservoir in pDCs early after activation remains to be fully characterized.

Results

To fill this gap, we have performed a comprehensive analysis in naïve versus TLR9-activated murine pDCs in a time course study covering early timepoints after stimulation (2 h, 6 h, 12 h) integrating gene expression (RNA-Seq) and chromatin landscape (ATAC-Seq) studies. To unravel the biological processes underlying the changes in TF expression on a global scale gene ontology (GO) analyses were performed. We found that 70% of all genes annotated as TFs in the mouse genome (1014 out of 1636) are expressed in pDCs for at least one stimulation time point and are covering a wide range of TF classes defined by their specific DNA binding mechanisms. GO analysis revealed involvement of TLR9-induced TFs in epigenetic modulation, NFκB and JAK-STAT signaling, and protein production in the endoplasmic reticulum. pDC activation predominantly “turned on” the chromatin regions associated with TF genes. Our in silico analyses pointed at the AP-1 family of TFs as less noticed but possibly important players in these cells after activation. AP-1 family members exhibit (1) increased gene expression, (2) enhanced chromatin accessibility in their promoter region, and (3) a TF DNA binding motif that is globally enriched in genomic regions that were found more accessible in pDCs after TLR9 activation.

Conclusions

In this study we define the complete set of TLR9-regulated TFs in pDCs. Further, this study identifies the AP-1 family of TFs as potentially important but so far less well characterized regulators of pDC function.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12863-021-00991-2.

Alveolar macrophage transcriptomic profiling in COPD shows major lipid metabolism changes

Background

Immune cells play a major role in the pathogenesis of COPD. Changes in the distribution and cellular functions of major immune cells, such as alveolar macrophages (AMs) and neutrophils are well known; however, their transcriptional reprogramming and contribution to the pathophysiology of COPD are still not fully understood.

Method

To determine changes in transcriptional reprogramming and lipid metabolism in the major immune cell type within bronchoalveolar lavage fluid, we analysed whole transcriptomes and lipidomes of sorted CD45⁺Lin⁻HLA-DR⁺CD66b⁻Autofluorescence^hi AMs from controls and COPD patients.

Results

We observed global transcriptional reprogramming featuring a spectrum of activation states, including pro- and anti-inflammatory signatures. We further detected significant changes between COPD patients and controls in genes involved in lipid metabolism, such as fatty acid biosynthesis in GOLD2 patients. Based on these findings, assessment of a total of 202 lipid species in sorted AMs revealed changes of cholesteryl esters, monoacylglycerols and phospholipids in a disease grade-dependent manner.

Conclusions

Transcriptome and lipidome profiling of COPD AMs revealed GOLD grade-dependent changes, such as in cholesterol metabolism and interferon-α and γ responses.

AMs from COPD patients undergo GOLD grade-specific transcriptional reprogramming and acquire a complex activation profile. Among the observed changes are gene programmes involved in lipid metabolism that translate into alterations in the AM lipidome.https://bit.ly/3sYAqgd

Cannabinoid receptor 2 is necessary to induce toll-like receptor-mediated microglial activation

The tight regulation of microglia activity is key for precise responses to potential threats, while uncontrolled and exacerbated microglial activity is neurotoxic. Microglial toll-like receptors (TLRs) are indispensable for sensing different types of assaults and triggering an innate immune response. Cannabinoid receptor 2 (CB2) signaling is a key pathway to control microglial homeostasis and activation, and its activation is connected to changes in microglial activity. We aimed to investigate how CB2 signaling impacts TLR-mediated microglial activation. Here, we demonstrate that deletion of CB2 causes a dampened transcriptional response to prototypic TLR ligands in microglia. Loss of CB2 results in distinct microglial gene expression profiles, morphology, and activation. We show that the CB2-mediated attenuation of TLR-induced microglial activation is mainly p38 MAPK-dependent. Taken together, we demonstrate that CB2 expression and signaling are necessary to fine-tune TLR-induced activation programs in microglia.

Identification of a Novel Transcription Factor Prognostic Index for Breast Cancer

Transcription factors (TFs) are the mainstay of cancer and have a widely reported influence on the initiation, progression, invasion, metastasis, and therapy resistance of cancer. However, the prognostic values of TFs in breast cancer (BC) remained unknown. In this study, comprehensive bioinformatics analysis was conducted with data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. We constructed the co-expression network of all TFs and linked it to clinicopathological data. Differentially expressed TFs were obtained from BC RNA-seq data in TCGA database. The prognostic TFs used to construct the risk model for progression free interval (PFI) were identified by Cox regression analyses, and the PFI was analyzed by the Kaplan-Meier method. A receiver operating characteristic (ROC) curve and clinical variables stratification analysis were used to detect the accuracy of the prognostic model. Additionally, we performed functional enrichment analysis by analyzing the differential expressed gene between high-risk and low-risk group. A total of nine co-expression modules were identified. The prognostic index based on 4 TFs (NR3C2, ZNF652, EGR3, and ARNT2) indicated that the PFI was significantly shorter in the high-risk group than their low-risk counterpart (p < 0.001). The ROC curve for PFI exhibited acceptable predictive accuracy, with an area under the curve value of 0.705 and 0.730. In the stratification analyses, the risk score index is an independent prognostic variable for PFI. Functional enrichment analyses showed that high-risk group was positively correlated with mTORC1 signaling pathway. In conclusion, the TF-related signature for PFI constructed in this study can independently predict the prognosis of BC patients and provide a deeper understanding of the potential biological mechanism of TFs in BC.

Accurate prediction of cis-regulatory modules reveals a prevalent regulatory genome of humans

cis-regulatory modules(CRMs) formed by clusters of transcription factor (TF) binding sites (TFBSs) are as important as coding sequences in specifying phenotypes of humans. It is essential to categorize all CRMs and constituent TFBSs in the genome. In contrast to most existing methods that predict CRMs in specific cell types using epigenetic marks, we predict a largely cell type agonistic but more comprehensive map of CRMs and constituent TFBSs in the gnome by integrating all available TF ChIP-seq datasets. Our method is able to partition 77.47% of genome regions covered by available 6092 datasets into a CRM candidate (CRMC) set (56.84%) and a non-CRMC set (43.16%). Intriguingly, the predicted CRMCs are under strong evolutionary constraints, while the non-CRMCs are largely selectively neutral, strongly suggesting that the CRMCs are likely cis-regulatory, while the non-CRMCs are not. Our predicted CRMs are under stronger evolutionary constraints than three state-of-the-art predictions (GeneHancer, EnhancerAtlas and ENCODE phase 3) and substantially outperform them for recalling VISTA enhancers and non-coding ClinVar variants. We estimated that the human genome might encode about 1.47M CRMs and 68M TFBSs, comprising about 55% and 22% of the genome, respectively; for both of which, we predicted 80%. Therefore, the cis-regulatory genome appears to be more prevalent than originally thought.

Uncovering the RNA-binding protein landscape in the pluripotency network of human embryonic stem cells

Embryonic stem cell (ESC) self-renewal and cell fate decisions are driven by a broad array of molecular signals. While transcriptional regulators have been extensively studied in human ESCs (hESCs), the extent to which RNA-binding proteins (RBPs) contribute to human pluripotency remains unclear. Here, we carry out a proteome-wide screen and identify 810 proteins that bind RNA in hESCs. We reveal that RBPs are preferentially expressed in hESCs and dynamically regulated during early stem cell differentiation. Notably, many RBPs are affected by knockdown of OCT4, a master regulator of pluripotency, several dozen of which are directly targeted by this factor. Using cross-linking and immunoprecipitation (CLIP-seq), we find that the pluripotency-associated STAT3 and OCT4 transcription factors interact with RNA in hESCs and confirm the binding of STAT3 to the conserved NORAD long-noncoding RNA. Our findings indicate that RBPs have a more widespread role in human pluripotency than previously appreciated.

VB10, a new blood biomarker for differential diagnosis and recovery monitoring of acute viral and bacterial infections

BACKGROUND

Precise differential diagnosis between acute viral and bacterial infections is important to enable appropriate therapy, avoid unnecessary antibiotic prescriptions and optimize the use of hospital resources. A systems view of host response to infections provides opportunities for discovering sensitive and robust molecular diagnostics.

METHODS

We combine blood transcriptomes from six independent datasets (n = 756) with a knowledge-based human protein-protein interaction network, identifies subnetworks capturing host response to each infection class, and derives common response cores separately for viral and bacterial infections. We subject the subnetworks to a series of computational filters to identify a parsimonious gene panel and a standalone diagnostic score that can be applied to individual samples. We rigorously validate the panel and the diagnostic score in a wide range of publicly available datasets and in a newly developed Bangalore-Viral Bacterial (BL-VB) cohort.

FINDING

We discover a 10-gene blood-based biomarker panel (Panel-VB) that demonstrates high predictive performance to distinguish viral from bacterial infections, with a weighted mean AUROC of 0.97 (95% CI: 0.96-0.99) in eleven independent datasets (n = 898). We devise a new stand-alone patient-wise score (VB10) based on the panel, which shows high diagnostic accuracy with a weighted mean AUROC of 0.94 (95% CI 0.91-0.98) in 2996 patient samples from 56 public datasets from 19 different countries. Further, we evaluate VB10 in a newly generated South Indian (BL-VB, n = 56) cohort and find 97% accuracy in the confirmed cases of viral and bacterial infections. We find that VB10 is (a) capable of accurately identifying the infection class in culture-negative indeterminate cases, (b) reflects recovery status, and (c) is applicable across different age groups, covering a wide spectrum of acute bacterial and viral infections, including uncharacterized pathogens. We tested our VB10 score on publicly available COVID-19 data and find that our score detected viral infection in patient samples.

INTERPRETATION

Our results point to the promise of VB10 as a diagnostic test for precise diagnosis of acute infections and monitoring recovery status. We expect that it will provide clinical decision support for antibiotic prescriptions and thereby aid in antibiotic stewardship efforts.

FUNDING

Grand Challenges India, Biotechnology Industry Research Assistance Council (BIRAC), Department of Biotechnology, Govt. of India.

Direct cell reprogramming: approaches, mechanisms and progress

The reprogramming of somatic cells with defined factors, which converts cells from one lineage into cells of another, has greatly reshaped our traditional views on cell identity and cell fate determination. Direct reprogramming (also known as transdifferentiation) refers to cell fate conversion without transitioning through an intermediary pluripotent state. Given that the number of cell types that can be generated by direct reprogramming is rapidly increasing, it has become a promising strategy to produce functional cells for therapeutic purposes. This Review discusses the evolution of direct reprogramming from a transcription factor-based method to a small-molecule-driven approach, the recent progress in enhancing reprogrammed cell maturation, and the challenges associated with in vivo direct reprogramming for translational applications. It also describes our current understanding of the molecular mechanisms underlying direct reprogramming, including the role of transcription factors, epigenetic modifications, non-coding RNAs, and the function of metabolic reprogramming, and highlights novel insights gained from single-cell omics studies.

Comprehensive analysis of full-length transcriptomes of Schizothorax prenanti by single-molecule long-read sequencing

Schizothorax prenanti (S. prenanti) is one of the most important aquaculture species in the southwest of China. However, information of the full-length transcripts in S. prenanti remains unknown. In this study, single-molecule real-time (SMRT) sequencing was performed to generate full-length transcriptomes of S.prenanti. In total, 23.26 Gb of clean reads were generated. A total of 312,587 circular consensus sequences (CCS) were obtained with average lengths of 2634 bp and 84.16% (270,662) of CCS were full-length non-chimeric reads. After being corrected with Illumina library sequencing, 18,005 contigs were obtained, with 17,797 (98.81%) successfully annotated in eight public databases, including 15,839 complete open reading frames (ORFs) with an average length of 1330 bp. Furthermore, a total of 4152 alternative splicing (AS) events and 250 long non-coding RNA (lncRNA) transcripts were detected. Additionally, a total of 1129 putative transcription factors (TFs) members from 56 TF families and 11,660 simple sequence repeats (SSRs) were identified. This study provided a valuable resource of full-length transcripts for further research on S. prenanti.

Potential interactions between the TBX4-FGF10 and SHH-FOXF1 signaling during human lung development revealed using ChIP-seq

Background

The epithelial-mesenchymal signaling involving SHH-FOXF1, TBX4-FGF10, and TBX2 pathways is an essential transcriptional network operating during early lung organogenesis. However, precise regulatory interactions between different genes and proteins in this pathway are incompletely understood.

Methods

To identify TBX2 and TBX4 genome-wide binding sites, we performed chromatin immunoprecipitation followed by next-generation sequencing (ChIP-seq) in human fetal lung fibroblasts IMR-90.

Results

We identified 14,322 and 1,862 sites strongly-enriched for binding of TBX2 and TBX4, respectively, 43.95% and 18.79% of which are located in the gene promoter regions. Gene Ontology, pathway enrichment, and DNA binding motif analyses revealed a number of overrepresented cues and transcription factor binding motifs relevant for lung branching that can be transcriptionally regulated by TBX2 and/or TBX4. In addition, TBX2 and TBX4 binding sites were found enriched around and within FOXF1 and its antisense long noncoding RNA FENDRR, indicating that the TBX4-FGF10 cascade may directly interact with the SHH-FOXF1 signaling.

Conclusions

We highlight the complexity of transcriptional network driven by TBX2 and TBX4 and show that disruption of this crosstalk during morphogenesis can play a substantial role in etiology of lung developmental disorders.

Disease severity-specific neutrophil signatures in blood transcriptomes stratify COVID-19 patients

BACKGROUND

The SARS-CoV-2 pandemic is currently leading to increasing numbers of COVID-19 patients all over the world. Clinical presentations range from asymptomatic, mild respiratory tract infection, to severe cases with acute respiratory distress syndrome, respiratory failure, and death. Reports on a dysregulated immune system in the severe cases call for a better characterization and understanding of the changes in the immune system.

METHODS

In order to dissect COVID-19-driven immune host responses, we performed RNA-seq of whole blood cell transcriptomes and granulocyte preparations from mild and severe COVID-19 patients and analyzed the data using a combination of conventional and data-driven co-expression analysis. Additionally, publicly available data was used to show the distinction from COVID-19 to other diseases. Reverse drug target prediction was used to identify known or novel drug candidates based on finding from data-driven findings.

RESULTS

Here, we profiled whole blood transcriptomes of 39 COVID-19 patients and 10 control donors enabling a data-driven stratification based on molecular phenotype. Neutrophil activation-associated signatures were prominently enriched in severe patient groups, which was corroborated in whole blood transcriptomes from an independent second cohort of 30 as well as in granulocyte samples from a third cohort of 16 COVID-19 patients (44 samples). Comparison of COVID-19 blood transcriptomes with those of a collection of over 3100 samples derived from 12 different viral infections, inflammatory diseases, and independent control samples revealed highly specific transcriptome signatures for COVID-19. Further, stratified transcriptomes predicted patient subgroup-specific drug candidates targeting the dysregulated systemic immune response of the host.

CONCLUSIONS

Our study provides novel insights in the distinct molecular subgroups or phenotypes that are not simply explained by clinical parameters. We show that whole blood transcriptomes are extremely informative for COVID-19 since they capture granulocytes which are major drivers of disease severity.

Disease severity-specific neutrophil signatures in blood transcriptomes stratify COVID-19 patients

BACKGROUND

The SARS-CoV-2 pandemic is currently leading to increasing numbers of COVID-19 patients all over the world. Clinical presentations range from asymptomatic, mild respiratory tract infection, to severe cases with acute respiratory distress syndrome, respiratory failure, and death. Reports on a dysregulated immune system in the severe cases call for a better characterization and understanding of the changes in the immune system.

METHODS

In order to dissect COVID-19-driven immune host responses, we performed RNA-seq of whole blood cell transcriptomes and granulocyte preparations from mild and severe COVID-19 patients and analyzed the data using a combination of conventional and data-driven co-expression analysis. Additionally, publicly available data was used to show the distinction from COVID-19 to other diseases. Reverse drug target prediction was used to identify known or novel drug candidates based on finding from data-driven findings.

RESULTS

Here, we profiled whole blood transcriptomes of 39 COVID-19 patients and 10 control donors enabling a data-driven stratification based on molecular phenotype. Neutrophil activation-associated signatures were prominently enriched in severe patient groups, which was corroborated in whole blood transcriptomes from an independent second cohort of 30 as well as in granulocyte samples from a third cohort of 16 COVID-19 patients (44 samples). Comparison of COVID-19 blood transcriptomes with those of a collection of over 3100 samples derived from 12 different viral infections, inflammatory diseases, and independent control samples revealed highly specific transcriptome signatures for COVID-19. Further, stratified transcriptomes predicted patient subgroup-specific drug candidates targeting the dysregulated systemic immune response of the host.

CONCLUSIONS

Our study provides novel insights in the distinct molecular subgroups or phenotypes that are not simply explained by clinical parameters. We show that whole blood transcriptomes are extremely informative for COVID-19 since they capture granulocytes which are major drivers of disease severity.

From start to end: Phase separation and transcriptional regulation

Phase separation is the basis for the formation of membrane-less organelles in cells and is involved in many biological processes. Many biological macromolecules, such as proteins and nucleic acids, exert their biological functions by forming phase-separated condensates, and phase separation is closely related to various human diseases. Gene transcriptional regulation is an indispensable part of gene expression and normal function in cells. Its abnormal regulation often causes the occurrence of different diseases. In recent years, the occurrence of phase separation during transcriptional regulation has become an area of intense research. This review summarizes the process of phase separation involved in heterochromatin formation and chromatin remodeling, transcriptional regulation and post-transcriptional regulation. It provides a reference for understanding gene regulation during cell identity and disease development.

nSARS-Cov-2, pulmonary edema and thrombosis: possible molecular insights using miRNA-gene circuits in regulatory networks

Background

Given the worldwide spread of the novel Severe Acute Respiratory Syndrome Coronavirus 2 (nSARS-CoV-2) infection pandemic situation, research to repurpose drugs, identify novel drug targets, vaccine candidates have created a new race to curb the disease. While the molecular signature of nSARS-CoV-2 is still under investigation, growing literature shows similarity among nSARS-CoV-2, pulmonary edema, and thromboembolic disorders due to common symptomatic features. A network medicine approach is used to to explore the molecular complexity of the disease and to uncover common molecular trajectories of edema and thrombosis with nSARS-CoV-2.

Results and conclusion

A comprehensive nSARS-CoV-2 responsive miRNA: Transcription Factor (TF): gene co-regulatory network was built using host-responsive miRNAs and it’s associated tripartite, Feed-Forward Loops (FFLs) regulatory circuits were identified. These regulatory circuits regulate signaling pathways like virus endocytosis, viral replication, inflammatory response, pulmonary vascularization, cell cycle control, virus spike protein stabilization, antigen presentation, etc. A unique miRNA-gene regulatory circuit containing a consortium of four hub FFL motifs is proposed to regulate the virus-endocytosis and antigen-presentation signaling pathways. These regulatory circuits also suggest potential correlations/similarity in the molecular mechanisms during nSARS-CoV-2 infection, pulmonary diseases and thromboembolic disorders and thus could pave way for repurposing of drugs. Some important miRNAs and genes have also been proposed as potential candidate markers. A detailed molecular snapshot of TGF signaling as the common pathway, that could play an important role in controlling common pathophysiologies among diseases, is also put forth.

Supplementary information

Supplementary information accompanies this paper at 10.1186/s41544-020-00057-y.

Human transcription factors responsive to initial reprogramming predominantly undergo legitimate reprogramming during fibroblast conversion to iPSCs

The four transcription factors OCT4, SOX2, KLF4, and MYC (OSKM) together can convert human fibroblasts to induced pluripotent stem cells (iPSCs). It is, however, perplexing that they can do so only for a rare population of the starting cells with a long latency. Transcription factors (TFs) define identities of both the starting fibroblasts and the end product, iPSCs, and are also of paramount importance for the reprogramming process. It is critical to upregulate or activate the iPSC-enriched TFs while downregulate or silence the fibroblast-enriched TFs. This report explores the initial TF responses to OSKM as the molecular underpinnings for both the potency aspects and the limitation sides of the OSKM reprogramming. The authors first defined the TF reprogramome, i.e., the full complement of TFs to be reprogrammed. Most TFs were resistant to OSKM reprogramming at the initial stages, an observation consistent with the inefficiency and long latency of iPSC reprogramming. Surprisingly, the current analyses also revealed that most of the TFs (at least 83 genes) that did respond to OSKM induction underwent legitimate reprogramming. The initial legitimate transcriptional responses of TFs to OSKM reprogramming were also observed in the reprogramming fibroblasts from a different individual. Such early biased legitimate reprogramming of the responsive TFs aligns well with the robustness aspect of the otherwise inefficient and stochastic OSKM reprogramming.

Multiple Isolated Transcription Factors Act as Switches and Contribute to Species Uniqueness

Mammals have variable numbers (1300-2000) of transcription factors (TFs), but the reasons for this large variation are unclear. To investigate general TF patterns, we de novo identified 156,906 TFs from 96 mammalian species. We identified more than 500 human isolated TFs that are rarely reported in human TF-to-TF networks. Mutations in the genes of these TFs were less lethal than those of connected TFs. Consequently, these isolated TFs are more tolerant of changes and have become unique during speciation. They may also serve as a source of variation for TF evolution. Reconciliation of TF-family phylogenetic trees with a mammalian species tree revealed an average of 37.8% TF gains and 15.0% TF losses over 177 million years, which implies that isolated TFs are pervasive in mammals. Compared with non-TF interacting genes, TF-interacting genes have unique TF profiles and have higher expression levels in mice than in humans. Different expression levels of the same TF-interacting gene contribute to species-specific phenotypes. Formation and loss of isolated TFs enabling unique TF profiles may provide variable switches that adjust divergent expression profiles of target genes to generate species-specific phenotypes, thereby making species unique.

Transcription Factors in Cartilage Homeostasis and Osteoarthritis

Osteoarthritis (OA) is the most common degenerative joint disease, and it is characterized by articular cartilage loss. In part, OA is caused by aberrant anabolic and catabolic activities of the chondrocyte, the only cell type present in cartilage. These chondrocyte activities depend on the intra- and extracellular signals that the cell receives and integrates into gene expression. The key proteins for this integration are transcription factors. A large number of transcription factors exist, and a better understanding of the transcription factors activated by the various signaling pathways active during OA can help us to better understand the complex etiology of OA. In addition, establishing such a profile can help to stratify patients in different subtypes, which can be a very useful approach towards personalized therapy. In this review, we discuss crucial transcription factors for extracellular matrix metabolism, chondrocyte hypertrophy, chondrocyte senescence, and autophagy in chondrocytes. In addition, we discuss how insight into these factors can be used for treatment purposes.

Single-Cell RNA-Seq Mapping of Human Thymopoiesis Reveals Lineage Specification Trajectories and a Commitment Spectrum in T Cell Development

The challenges in recapitulating in vivo human T cell development in laboratory models have posed a barrier to understanding human thymopoiesis. Here, we used single-cell RNA sequencing (sRNA-seq) to interrogate the rare CD34+ progenitor and the more differentiated CD34- fractions in the human postnatal thymus. CD34+ thymic progenitors were comprised of a spectrum of specification and commitment states characterized by multilineage priming followed by gradual T cell commitment. The earliest progenitors in the differentiation trajectory were CD7- and expressed a stem-cell-like transcriptional profile, but had also initiated T cell priming. Clustering analysis identified a CD34+ subpopulation primed for the plasmacytoid dendritic lineage, suggesting an intrathymic dendritic specification pathway. CD2 expression defined T cell commitment stages where loss of B cell potential preceded that of myeloid potential. These datasets delineate gene expression profiles spanning key differentiation events in human thymopoiesis and provide a resource for the further study of human T cell development.

A transcriptome-based approach to identify functional modules within and across primary human immune cells

Genome-wide transcriptomic analyses have provided valuable insight into fundamental biology and disease pathophysiology. Many studies have taken advantage of the correlation in the expression patterns of the transcriptome to infer a potential biologic function of uncharacterized genes, and multiple groups have examined the relationship between co-expression, co-regulation, and gene function on a broader scale. Given the unique characteristics of immune cells circulating in the blood, we were interested in determining whether it was possible to identify functional co-expression modules in human immune cells. Specifically, we sequenced the transcriptome of nine immune cell types from peripheral blood cells of healthy donors and, using a combination of global and targeted analyses of genes within co-expression modules, we were able to determine functions for these modules that were cell lineage-specific or shared among multiple cell lineages. In addition, our analyses identified transcription factors likely important for immune cell lineage commitment and/or maintenance.