The Human Transcription Factors

Site-saturation mutagenesis of 500 human protein domains

Missense variants that change the amino acid sequences of proteins cause one-third of human genetic diseases1. Tens of millions of missense variants exist in the current human population, and the vast majority of these have unknown functional consequences. Here we present a large-scale experimental analysis of human missense variants across many different proteins. Using DNA synthesis and cellular selection experiments we quantify the effect of more than 500,000 variants on the abundance of more than 500 human protein domains. This dataset reveals that 60% of pathogenic missense variants reduce protein stability. The contribution of stability to protein fitness varies across proteins and diseases and is particularly important in recessive disorders. We combine stability measurements with protein language models to annotate functional sites across proteins. Mutational effects on stability are largely conserved in homologous domains, enabling accurate stability prediction across entire protein families using energy models. Our data demonstrate the feasibility of assaying human protein variants at scale and provides a large consistent reference dataset for clinical variant interpretation and training and benchmarking of computational methods.

The role of transcription factors in the crosstalk between cancer-associated fibroblasts and tumor cells

BACKGROUND

Transcription factors (TFs) fulfill a critical role in the formation and maintenance of different cell types during the developmental process as well as disease. It is believed that cancer-associated fibroblasts (CAFs) are activation status of tissue-resident fibroblasts or derived from form other cell types via transdifferentiation or dedifferentiation. Despite a subgroup of CAFs exhibit anti-cancer effects, most of them are reported to exert effects on tumor progression, further indicating their heterogeneous origin.

AIM OF REVIEW

This review aimed to summarize and review the roles of TFs in the reciprocal crosstalk between CAFs and tumor cells, discuss the emerging mechanisms, and their roles in cell-fate decision, cellular reprogramming and advancing our understanding of the gene regulatory networks over the period of cancer initiation and progression.

KEY SCIENTIFIC CONCEPTS OF REVIEW

This manuscript delves into the key contributory factors of TFs that are involved in activating CAFs and maintaining their unique states. Additionally, it explores how TFs play a pivotal and multifaceted role in the reciprocal crosstalk between CAFs and tumor cells. This includes their involvement in processes such as epithelial-mesenchymal transition (EMT), proliferation, invasion, and metastasis, as well as metabolic reprogramming. TFs also have a role in constructing an immunosuppressive microenvironment, inducing resistance to radiation and chemotherapy, facilitating angiogenesis, and even 'educating' CAFs to support the malignancies of tumor cells. Furthermore, this manuscript delves into the current status of TF-targeted therapy and considers the future directions of TFs in conjunction with anti-CAFs therapies to address the challenges in clinical cancer treatment.

BACH1 as a key driver in rheumatoid arthritis fibroblast-like synoviocytes identified through gene network analysis

RNA-sequencing and differential gene expression studies have significantly advanced our understanding of pathogenic pathways underlying rheumatoid arthritis (RA). Yet, little is known about cell-specific regulatory networks and their contributions to disease. In this study, we focused on fibroblast-like synoviocytes (FLS), a cell type central to disease pathogenesis and joint damage in RA. We used a strategy that computed sample-specific gene regulatory networks to compare network properties between RA and osteoarthritis FLS. We identified 28 transcription factors (TFs) as key regulators central to the signatures of RA FLS. Six of these TFs are new and have not been previously implicated in RA through ex vivo or in vivo studies, and included BACH1, HLX, and TGIF1. Several of these TFs were found to be co-regulated, and BACH1 emerged as the most significant TF and regulator. The main BACH1 targets included those implicated in fatty acid metabolism and ferroptosis. The discovery of BACH1 was validated in experiments with RA FLS. Knockdown of BACH1 in RA FLS significantly affected the gene expression signatures, reduced cell adhesion and mobility, interfered with the formation of thick actin fibers, and prevented the polarized formation of lamellipodia, all required for the RA destructive behavior of FLS. This study establishes BACH1 as a central regulator of RA FLS phenotypes and suggests its potential as a therapeutic target to selectively modulate RA FLS.

Arrayed CRISPR libraries for the genome-wide activation, deletion and silencing of human protein-coding genes

Arrayed CRISPR libraries extend the scope of gene-perturbation screens to non-selectable cell phenotypes. However, library generation requires assembling thousands of vectors expressing single-guide RNAs (sgRNAs). Here, by leveraging massively parallel plasmid-cloning methodology, we show that arrayed libraries can be constructed for the genome-wide ablation (19,936 plasmids) of human protein-coding genes and for their activation and epigenetic silencing (22,442 plasmids), with each plasmid encoding an array of four non-overlapping sgRNAs designed to tolerate most human DNA polymorphisms. The quadruple-sgRNA libraries yielded high perturbation efficacies in deletion (75-99%) and silencing (76-92%) experiments and substantial fold changes in activation experiments. Moreover, an arrayed activation screen of 1,634 human transcription factors uncovered 11 novel regulators of the cellular prion protein PrPC, screening with a pooled version of the ablation library led to the identification of 5 novel modifiers of autophagy that otherwise went undetected, and 'post-pooling' individually produced lentiviruses eliminated template-switching artefacts and enhanced the performance of pooled screens for epigenetic silencing. Quadruple-sgRNA arrayed libraries are a powerful and versatile resource for targeted genome-wide perturbations.

A multiomic atlas identifies a treatment-resistant, bone marrow progenitor-like cell population in T cell acute lymphoblastic leukemia

Refractoriness to initial chemotherapy and relapse after remission are the main obstacles to curing T cell acute lymphoblastic leukemia (T-ALL). While tumor heterogeneity has been implicated in treatment failure, the cellular and genetic factors contributing to resistance and relapse remain unknown. Here we linked tumor subpopulations with clinical outcome, created an atlas of healthy pediatric hematopoiesis and applied single-cell multiomic analysis to a diverse cohort of 40 T-ALL cases. We identified a bone marrow progenitor (BMP)-like leukemia subpopulation associated with treatment failure and poor overall survival. The single-cell-derived molecular signature of BMP-like blasts predicted poor outcome across multiple subtypes of T-ALL and revealed that NOTCH1 mutations additively drive T-ALL blasts away from the BMP-like state. Through in silico and in vitro drug screenings, we identified a therapeutic vulnerability of BMP-like blasts to apoptosis-inducing agents including venetoclax. Collectively, our study establishes multiomic signatures for rapid risk stratification and targeted treatment of high-risk T-ALL.

Basic Epigenetic Mechanisms

The human genome consists of 23 chromosome pairs (22 autosomes and one pair of sex chromosomes), with 46 chromosomes in a normal cell. In the interphase nucleus, the 2 m long nuclear DNA is assembled with proteins forming chromatin. The typical mammalian cell nucleus has a diameter between 5 and 15 μm in which the DNA is packaged into an assortment of chromatin assemblies. The human brain has over 3000 cell types, including neurons, glial cells, oligodendrocytes, microglial, and many others. Epigenetic processes are involved in directing the organization and function of the genome of each one of the 3000 brain cell types. We refer to epigenetics as the study of changes in gene function that do not involve changes in DNA sequence. These epigenetic processes include histone modifications, DNA modifications, nuclear RNA, and transcription factors. In the interphase nucleus, the nuclear DNA is organized into different structures that are permissive or a hindrance to gene expression. In this chapter, we will review the epigenetic mechanisms that give rise to cell type-specific gene expression patterns.

Harnessing the ubiquitin proteasome system as a key player in stem cell biology

Intracellular proteins take part in almost every body function; thus, protein homeostasis is of utmost importance. The ubiquitin proteasome system (UPS) has a fundamental role in protein homeostasis. Its main role is to selectively eradicate impaired or misfolded proteins, thus halting any damage that could arise from the accumulation of these malfunctioning proteins. Proteasomes have a critical role in controlling protein homeostasis in all cell types, including stem cells. We will discuss the role of UPS enzymes as well as the 26S proteasome complex in stem cell biology from several angles. First, we shall overview common trends of proteasomal activity and gene expression of different proteasomal subunits and UPS enzymes upon passaging and differentiation of stem cells toward various cell lineages. Second, we shall explore the effect of modulating proteasomal activity in stem cells and navigate through the interrelation between proteasomes' activity and various proteasome-related transcription factors. Third, we will shed light on curated microRNAs and long non-coding RNAs using various bioinformatics tools that might have a possible role in regulating UPS in stem cells and possibly, upon manipulation, can enhance the differentiation process into different lineages and/or delay senescence upon cell passaging. This will help to decipher the role played by individual UPS enzymes and subunits as well as various interrelated molecular mediators in stem cells' maintenance and/or differentiation and open new avenues in stem cell research. This can ultimately provide a leap toward developing novel therapeutic interventions related to proteasome dysregulation.

Transcriptional regulators ensuring specific gene expression and decision-making at high TGFβ doses

TGFβ-signaling regulates cancer progression by controlling cell division, migration, and death. These outcomes are mediated by gene expression changes, but the mechanisms of decision-making toward specific fates remain unclear. Here, we combine SMAD transcription factor imaging, genome-wide RNA sequencing, and morphological assays to quantitatively link signaling, gene expression, and fate decisions in mammary epithelial cells. Fitting genome-wide kinetic models to our time-resolved data, we find that most of the TGFβ target genes can be explained as direct targets of SMAD transcription factors, whereas the remainder show signs of complex regulation, involving delayed regulation and strong amplification at high TGFβ doses. Knockdown experiments followed by global RNA sequencing revealed transcription factors interacting with SMADs in feedforward loops to control delayed and dose-discriminating target genes, thereby reinforcing the specific epithelial-to-mesenchymal transition at high TGFβ doses. We identified early repressors, preventing premature activation, and a late activator, boosting gene expression responses for a sufficiently strong TGFβ stimulus. Taken together, we present a global view of TGFβ-dependent gene regulation and describe specificity mechanisms reinforcing cellular decision-making.

Intermittent fasting shifts the diurnal transcriptome atlas of transcription factors

Intermittent fasting remains a safe and effective strategy to ameliorate various age-related diseases, but its specific mechanisms are not fully understood. Considering that transcription factors (TFs) determine the response to environmental signals, here, we profiled the diurnal expression of 600 samples across four metabolic tissues sampled every 4 over 24 h from mice placed on five different feeding regimens to provide an atlas of TFs in biological space, time, and feeding regimen. Results showed that 1218 TFs exhibited tissue-specific and temporal expression profiles in ad libitum mice, of which 974 displayed significant oscillations at least in one tissue. Intermittent fasting triggered more than 90% (1161 in 1234) of TFs to oscillate somewhere in the body and repartitioned their tissue-specific expression. A single round of fasting generally promoted TF expression, especially in skeletal muscle and adipose tissues, while intermittent fasting mainly suppressed TF expression. Intermittent fasting down-regulated aging pathway and upregulated the pathway responsible for the inhibition of mammalian target of rapamycin (mTOR). Intermittent fasting shifts the diurnal transcriptome atlas of TFs, and mTOR inhibition may orchestrate intermittent fasting-induced health improvements. This atlas offers a reference and resource to understand how TFs and intermittent fasting may contribute to diurnal rhythm oscillation and bring about specific health benefits.

Construction and validation of transcription‑factor‑based prognostic signature for TACE non‑response and characterization of tumor microenvironment infiltration in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors worldwide. Despite continuous development of treatment methods, overall survival rate of liver cancer is low. Transcatheter arterial chemoembolization (TACE) is a first-choice treatment for advanced liver cancer. Although it is generally effective, a number of patients do not benefit from it. Therefore, the present study was conducted to assess the response of patients following TACE. RNA-sequencing data and corresponding clinical information were extracted from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases. Models were constructed using weighted gene co-expression network analysis and least absolute shrinkage and selection operator-Cox regression analysis based on TCGA-LIHC and GSE104580 cohorts. The receiver operating characteristic curve was used for evaluation. Immunoassay, half-maximal inhibitory concentration analysis of risk groups, genomic enrichment analysis and nomogram construction were also performed. The predictive models were validated at the single-cell level using single-cell databases. Finally, the present study examined the expression of TACE refractoriness-related TFs (TRTs) in TACE-resistant and non-resistant cell lines in vitro. A risk categorization approach was created based on screening of four TRTs. The patients were split into high- and low-risk groups. There were significant variations in immune cell infiltration, medication sensitivity and overall survival (OS) between patients in the high-risk and low-risk groups. Multivariate Cox regression analysis showed that the risk score was an independent prognostic factor for OS. In the single-cell gene set, risk score was a good indicator of tumor microenvironment (TME). Reverse transcription-quantitative PCR revealed that three high-risk TRTs were upregulated in TACE-resistant cells. Prognosis and TME status of liver cancer patients following TACE could be assessed using a predictive model based on transcription factor correlation. This predictive model provided a reliable and simplified method to guide the clinical treatment of HCC.

Role and mechanism of COL3A1 in regulating the growth, metastasis, and drug sensitivity in cisplatin-resistant non-small cell lung cancer cells

Non-small cell lung cancer (NSCLC) is among the most difficult malignancies to treat. Type III collagen (COL3A1) can affect the progression and chemoresistance development of NSCLC. We herein explored the mechanism that drives COL3A1 dysregulation in NSCLC. Potential RNA-binding proteins (RBPs) and transcription factors (TFs) that could bind to COL3A1 were searched by bioinformatics. mRNA expression was detected by quantitative PCR. Protein expression was evaluated using immunoblotting and immunohistochemistry. The effects of the variables were assessed by gauging cell growth, invasiveness, migratory capacity, apoptosis, and cisplatin (DDP) sensitivity. The direct YY1/COL3A1 relationship was confirmed by ChIP and luciferase reporter experiments. Xenograft experiments were done to examine COL3A1's function in DDP efficacy. COL3A1 showed enhanced expression in DDP-resistant NSCLC. In H460/DDP and A549/DDP cells, downregulation of COL3A1 exerted inhibitory functions in cell growth, invasiveness, and migration, as well as promoting effects on cell DDP sensitivity and apoptosis. Mechanistically, ELAV-like RNA binding protein 1 (ELAVL1) enhanced the mRNA stability and expression of COL3A1, and Yin Yang 1 (YY1) promoted the transcription and expression of COL3A1. Furthermore, upregulation of COL3A1 reversed ELAVL1 inhibition- or YY1 deficiency-mediated functions in DDP-resistant NSCLC cells. Additionally, COL3A1 downregulation enhanced the anti-tumor efficacy of DDP in vivo. Our investigation demonstrates that COL3A1 upregulation, induced by both RBP ELAVL1 and TF YY1, exerts important functions in phenotypes of NSCLC cells with DDP resistance, offering an innovative opportunity in the treatment of drug-resistant NSCLC.

Footprint-C reveals transcription factor modes in local clusters and long-range chromatin interactions

The proximity ligation-based Hi-C and derivative methods are the mainstream tools to study genome-wide chromatin interactions. These methods often fragment the genome using enzymes functionally irrelevant to the interactions per se, restraining the efficiency in identifying structural features and the underlying regulatory elements. Here we present Footprint-C, which yields high-resolution chromatin contact maps built upon intact and genuine footprints protected by transcription factor (TF) binding. When analyzed at one-dimensional level, the billions of chromatin contacts from Footprint-C enable genome-wide analysis at single footprint resolution, and reveal preferential modes of local TF co-occupancy. At pairwise contact level, Footprint-C exhibits higher efficiency in identifying chromatin structural features when compared with other Hi-C methods, segregates chromatin interactions emanating from adjacent TF footprints, and uncovers multiway interactions involving different TFs. Altogether, Footprint-C results suggest that rich regulatory modes of TF may underlie both local residence and distal chromatin interactions, in terms of TF identity, valency, and conformational configuration.

Liver cancer multiomics reveals diverse protein kinase A disruptions convergently produce fibrolamellar hepatocellular carcinoma

Fibrolamellar Hepatocellular Carcinoma (FLC) is a rare liver cancer characterized by a fusion oncokinase of the genes DNAJB1 and PRKACA, the catalytic subunit of protein kinase A (PKA). A few FLC-like tumors have been reported showing other alterations involving PKA. To better understand FLC pathogenesis and the relationships among FLC, FLC-like, and other liver tumors, we performed a massive multi-omics analysis. RNA-seq data of 1412 liver tumors from FLC, hepatocellular carcinoma, hepatoblastoma and intrahepatic cholangiocarcinoma are analyzed, obtaining transcriptomic signatures unrestricted by experimental processing methods. These signatures reveal which dysregulations are unique to specific tumors and which are common to all liver cancers. Moreover, the transcriptomic FLC signature identifies a unifying phenotype for all FLC tumors regardless of how PKA was activated. We study this signature at multi-omics and single-cell levels in the first spatial transcriptomic characterization of FLC, identifying the contribution of tumor, normal, stromal, and infiltrating immune cells. Additionally, we study FLC metastases, finding small differences from the primary tumors.

Identification and Validation of Epithelial Cell Centre Regulatory Transcription Factors in the Gastric Cancer Microenvironment

Purpose

To identify the epithelial cell centre regulatory transcription factors in the gastric cancer (GC) microenvironment and provide a new strategy for the diagnosis and treatment of GC.

Methods

The GC single-cell dataset was downloaded from the Gene Expression Omnibus (GEO) database. The regulatory mechanisms of transcription factors in both pan-cancer and GC microenvironments were analysed using the Cancer Genome Atlas (TGCA) database. Real-time quantitative PCR (RT-qPCR) was used to determine the mRNA expression levels of Prospero homeobox gene 1 (PROX1) and Endothelial PAS domain-containing protein 1 (EPAS1) in the human gastric mucosal normal epithelial cell line (GES-1) and the GC cell line (AGS). Immunohistochemistry (IHC) was used to determine the amounts of PROX1 and EPAS1 protein expression in GC and adjacent tissues. GC patients' overall survival (OS) was tracked through outpatient, Inpatient case inquiry, or phone follow-up.

Results

The single-cell data from GSE184198 was re-annotated, resulting in nine cell subsets: T cells (13364), NK cells (606), B cells (2525), Epithelial cells (2497), DC cells (1167), Fibroblast cells (372), Endothelial cells (271), Neutrophils cells (246) and Macrophage cells (420). Analysis of cell subgroup signalling pathways revealed that communication intensity between epithelial cells and smooth muscle cells was highest. Transcription factors PROX1 and EPAS1 were notably active in epithelial cells. Cell communication analysis indicated that IFNG may interact with IFNGR1/2 and LIF with IL6ST and LIFR to regulate the downstream PROX1 and EPAS1. PROX1 and EPAS1 were upregulated and negatively correlated with tumour mutation burden (TMB). They also exhibited high positive correlations with immune checkpoints CTLA4 and PDCD1LG2, as well as with chemokines CCL24 and CXCL12 and their receptors CCR3 and CCR4. Additionally, PROX1 and EPAS1 were positively correlated with immunosuppressive factors ADORA2A, CD160, IL10, TGFBR1, KDR and CSF1R, as well as with immunostimulators CD276, PVR, TNFRSF25, ULBP1, CXCL12 and ENTPD1. In GC tissues and AGS, PROX1 and EPAS1 were both substantially expressed. In the meantime, they showed a positive correlation with clinicopathological features such TNM stage and degree of differentiation. In GC patients, the up-regulated group's PROX1 and EPAS1 prognosis was noticeably poorer than the down-regulated group's.

Conclusion

PROX1 and EPAS1 are likely central regulatory transcription factors in the epithelial cells of the GC environment, regulated by IFNG and LIF. They may contribute to GC progression by modulating the tumour's immune microenvironment.

Transcription factor prediction using protein 3D secondary structures

MOTIVATION

Transcription factors (TFs) are DNA-binding proteins that regulate gene expression. Traditional methods predict a protein as a TF if the protein contains any DNA-binding domains (DBDs) of known TFs. However, this approach fails to identify a novel TF that does not contain any known DBDs. Recently proposed TF prediction methods do not rely on DBDs. Such methods use features of protein sequences to train a machine learning model, and then use the trained model to predict whether a protein is a TF or not. Because the 3-dimensional (3D) structure of a protein captures more information than its sequence, using 3D protein structures will likely allow for more accurate prediction of novel TFs.

RESULTS

We propose a deep learning-based TF prediction method (StrucTFactor), which is the first method to utilize 3D secondary structural information of proteins. We compare StrucTFactor with recent state-of-the-art TF prediction methods based on ∼525 000 proteins across 12 datasets, capturing different aspects of data bias (including sequence redundancy) possibly influencing a method's performance. We find that StrucTFactor significantly (P-value < 0.001) outperforms the existing TF prediction methods, improving the performance over its closest competitor by up to 17% based on Matthews correlation coefficient.

AVAILABILITY AND IMPLEMENTATION

Data and source code are available at https://github.com/lieboldj/StrucTFactor and on our website at https://apps.cosy.bio/StrucTFactor.

Genome-wide single-cell and single-molecule footprinting of transcription factors with deaminase

Decades of research have established that mammalian transcription factors (TFs) bind to each gene's regulatory regions and cooperatively control tissue specificity, timing, and intensity of gene transcription. Mapping the combination of TF binding sites genome wide is critically important for understanding functional genomics. Here, we report a technique to measure TFs' binding sites on the human genome with a near single-base resolution by footprinting with deaminase (FOODIE) on a single-molecule and single-cell basis. Single-molecule sequencing reads after enzymatic deamination allow detection of the TF binding fraction on a particular footprint and the binding cooperativity of any two adjacent TFs, which can be either positive or negative. As a newcomer of single-cell genomics, single-cell FOODIE enables the detection of cell-type-specific TF footprints in a pure cell population in a heterogeneous tissue, such as the brain. We found that genes carrying out a certain biological function together in a housing-keeping correlated gene module (CGM) or a tissues-specific CGM are coordinated by shared TFs in the gene's promoters and enhancers, respectively. Scalable and cost-effective, FOODIE allows us to create an open FOODIE database for cell lines, with applicability to human tissues and clinical samples.

Identification of transcription factors associated with the disease-free survival of triple-negative breast cancer through weighted gene co-expression network analysis

Objective

Triple-negative breast cancer (TNBC) is a subtype of breast cancer that has a worse prognosis than the other subtypes of breast cancer because of its high recurrence and metastasis rates. The objective of this study is to identify the regulatory factors that are associated with the disease-free survival (DFS) of TNBC and potential biomarkers for TNBC treatment.

Material and Methods

We obtained the GSE97342 dataset from the Gene Expression Omnibus website and conducted weighted gene co-expression network analysis (WGCNA) to identify modules associated with the DFS of TNBC. Subsequently, biological functions of the modules were elucidated through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Cross-checking with the Human Transcription Factor Database facilitated the selection of hub transcription factors through univariate Cox regression analysis of overlapping transcription factors. Utilizing bioinformatics analysis, we assessed the prognostic significance of these hub transcription factors, investigated their target genes, and explored their associations with tumor immune cells in TNBC. Finally, the expression levels of the hub transcription factors were validated by immunohistochemical staining, quantitative reverse transcription polymerase chain reaction (qRT-PCR), and Western blotting.

Results

Through WGCNA analysis, we identified three modules correlated with DFS in TNBC. GO and KEGG analyses elucidated the biological functions of genes within these modules. Survival analysis pinpointed three hub transcription factors: Forkhead box D1 (FOXD1), aryl hydrocarbon receptor nuclear translocator 2 (ARNT2), and zinc finger protein 132 (ZNF132). The expression level of FOXD1 was negatively associated with the prognoses of patients with TNBC, whereas the other two genes were positively associated with the prognoses of patients with TNBC. Immunohistochemical staining, qRT-PCR, and Western blotting validated the expression levels of the hub transcription factors.

Conclusion

We discovered three hub transcription factors (FOXD1, ARNT2, and ZNF132) that were correlated with the DFS of TNBC. These correlations suggested their potential as prognostic predictors for patients with TNBC.

NetREm: Network Regression Embeddings reveal cell-type transcription factor coordination for gene regulation

Motivation

Transcription factor (TF) coordination plays a key role in gene regulation via direct and/or indirect protein-protein interactions (PPIs) and co-binding to regulatory elements on DNA. Single-cell technologies facilitate gene expression measurement for individual cells and cell-type identification, yet the connection between TF-TF coordination and target gene (TG) regulation of various cell types remains unclear.

Results

To address this, we introduce our innovative computational approach, Network Regression Embeddings (NetREm), to reveal cell-type TF-TF coordination activities for TG regulation. NetREm leverages network-constrained regularization, using prior knowledge of PPIs among TFs, to analyze single-cell gene expression data, uncovering cell-type coordinating TFs and identifying revolutionary TF-TG candidate regulatory network links. NetREm's performance is validated using simulation studies and benchmarked across several datasets in humans, mice, yeast. Further, we showcase NetREm's ability to prioritize valid novel human TF-TF coordination links in 9 peripheral blood mononuclear and 42 immune cell sub-types. We apply NetREm to examine cell-type networks in central and peripheral nerve systems (e.g. neuronal, glial, Schwann cells) and in Alzheimer's disease versus Controls. Top predictions are validated with experimental data from rat, mouse, and human models. Additional functional genomics data helps link genetic variants to our TF-TG regulatory and TF-TF coordination networks.

Availability and implementation

https://github.com/SaniyaKhullar/NetREm.

Integrated proteomic and transcriptomic landscape of human placenta in small for gestational age infants

Small for gestational age (SGA) infants affected by placental insufficiency are exposed to the risk of stillbirth and long-term complications. Based on RNA-seq and mass spectrometry, we identified dysregulated RNAs and proteins from the comparisons of SGA placental tissues and controls. We revealed two SGA-relevant co-expression modules (SRMs) that also significantly distinguished SGA from controls. Then we performed an integrated analysis of transcriptomic and proteomic profiles to trace their links to SGA as well as their significant correlations. For the core functional molecules we screened, we revealed their potential upstream regulators and validated them experimentally in an independent cohort. Overall, we pointed out insights into different molecular pathways for the pathological mechanisms of SGA and indicated potential target molecules that may be drivers of placental aberrations in the SGA infants.

Optimized reporters for multiplexed detection of transcription factor activity

In any given cell type, dozens of transcription factors (TFs) act in concert to control the activity of the genome by binding to specific DNA sequences in regulatory elements. Despite their considerable importance, we currently lack simple tools to directly measure the activity of many TFs in parallel. Massively parallel reporter assays (MPRAs) allow the detection of TF activities in a multiplexed fashion; however, we lack basic understanding to rationally design sensitive reporters for many TFs. Here, we use an MPRA to systematically optimize transcriptional reporters for 86 TFs and evaluate the specificity of all reporters across a wide array of TF perturbation conditions. We thus identified critical TF reporter design features and obtained highly sensitive and specific reporters for 62 TFs, many of which outperform available reporters. The resulting collection of "prime" TF reporters can be used to uncover TF regulatory networks and to illuminate signaling pathways. A record of this paper's transparent peer review process is included in the supplemental information.

Maize inbreds show allelic variation for diel transcription patterns

Circadian entrainment and external cues can cause gene transcript abundance to oscillate throughout the day, and these patterns of diel transcript oscillation vary across genes and plant species. Less is known about within-species allelic variation for diel patterns of transcript oscillation, or about how regulatory sequence variation influences diel transcription patterns. In this study, we evaluated diel transcript abundance for 24 diverse maize inbred lines. We observed extensive natural variation in diel transcription patterns, with two-fold variation in the number of genes that oscillate over the course of the day. A convolutional neural network trained to predict oscillation from promoter sequence identified sequences previously reported as binding motifs for known circadian clock genes in other plant systems. Genes showing diel transcription patterns that cosegregate with promoter sequence haplotypes are enriched for associations with photoperiod sensitivity and may have been indirect targets of selection as maize was adapted to longer day lengths at higher latitudes. These findings support the idea that cis-regulatory sequence variation influences patterns of gene expression, which in turn can have effects on phenotypic plasticity and local adaptation.

LM-Merger: A workflow for merging logical models with an application to gene regulation

Motivation

Gene regulatory network (GRN) models provide mechanistic understanding of genetic interactions that regulate gene expression and, consequently, influence cellular behavior. Dysregulated gene expression plays a critical role in disease progression and treatment response, making GRN models a promising tool for precision medicine. While researchers have built many models to describe specific subsets of gene interactions, more comprehensive models that cover a broader range of genes are challenging to build. This necessitates the development of automated approaches for merging existing models.

Results

We present LM-Merger, a workflow for semi-automatically merging logical GRN models. The workflow consists of five main steps: (a) model identification, (b) model standardization and annotation, (c) model verification, (d) model merging, and (d) model evaluation. We demonstrate the feasibility and benefit of this workflow with two pairs of published models pertaining to acute myeloid leukemia (AML). The integrated models were able to retain the predictive accuracy of the original models, while expanding coverage of the biological system. Notably, when applied to a new dataset, the integrated models outperformed the individual models in predicting patient response. This study highlights the potential of logical model merging to advance systems biology research and our understanding of complex diseases.

Availability and implementation

The workflow and accompanying tools, including modules for model standardization, automated logical model merging, and evaluation, are available at https://github.com/IlyaLab/LogicModelMerger/.

MatriCom: a scRNA-Seq data mining tool to infer ECM-ECM and cell-ECM communication systems

The ECM is a complex and dynamic meshwork of proteins that forms the framework of all multicellular organisms. Protein interactions within the ECM are critical to building and remodeling the ECM meshwork, while interactions between ECM proteins and cell surface receptors are essential for the initiation of signal transduction and the orchestration of cellular behaviors. Here, we report the development of MatriCom, a web application (https://matrinet.shinyapps.io/matricom) and a companion R package (https://github.com/Izzilab/MatriCom), devised to mine scRNA-Seq datasets and infer communications between ECM components and between different cell populations and the ECM. To impute interactions from expression data, MatriCom relies on a unique database, MatriComDB, that includes over 25,000 curated interactions involving matrisome components, with data on 80% of the ~1,000 genes that compose the mammalian matrisome. MatriCom offers the option to query open-access datasets sourced from large sequencing efforts (Tabula Sapiens, The Human Protein Atlas, HuBMAP) or to process user-generated datasets. MatriCom is also tailored to account for the specific rules governing ECM protein interactions and offers options to customize the output through stringency filters. We illustrate the usability of MatriCom with the example of the human kidney matrisome communication network. Last, we demonstrate how the integration of 46 scRNA-Seq datasets led to the identification of both ubiquitous and tissue-specific ECM communication patterns. We envision that MatriCom will become a powerful resource to elucidate the roles of different cell populations in ECM-ECM and cell-ECM interactions and their dysregulations in the context of diseases such as cancer or fibrosis.

Inferring gene regulatory networks of ALS from blood transcriptome profiles

One of the most robust approaches to the prediction of causal driver genes of complex diseases is to apply reverse engineering methods to infer a gene regulatory network (GRN) from gene expression profiles (GEPs). In this work, we analysed 794 GEPs of 1117 human whole-blood samples from Amyotrophic Lateral Sclerosis (ALS) patients and healthy subjects reported in the GSE112681 dataset. GRNs for ALS and healthy individuals were reconstructed by ARACNe-AP (Algorithm for the Reconstruction of Accurate Cellular Networks - Adaptive Partitioning). In order to examine phenotypic differences in the ALS population surveyed, several datasets were built by arranging GEPs according to sex, spinal or bulbar onset, and survival time. The designed reverse engineering methodology identified a significant number of potential ALS-promoting mechanisms and putative transcriptional biomarkers that were previously unknown. In particular, the characterization of ALS phenotypic networks by pathway enrichment analysis has identified a gender-specific disease signature, namely network activation related to the radiation damage response, reported in the networks of bulbar and female ALS patients. Also, focusing on a smaller interaction network, we selected some hub genes to investigate their inferred pathological and healthy subnetworks. The inferred GRNs revealed the interconnection of the four selected hub genes (TP53, SOD1, ALS2, VDAC3) with p53-mediated pathways, suggesting the potential neurovascular response to ALS neuroinflammation. In addition to being well consistent with literature data, our results provide a novel integrated view of ALS transcriptional regulators, expanding information on the possible mechanisms underlying ALS and also offering important insights for diagnostic purposes and for developing possible therapies for a disease yet incurable.

TExCNN: Leveraging Pre-Trained Models to Predict Gene Expression from Genomic Sequences

BACKGROUND/OBJECTIVES

Understanding the relationship between DNA sequences and gene expression levels is of significant biological importance. Recent advancements have demonstrated the ability of deep learning to predict gene expression levels directly from genomic data. However, traditional methods are limited by basic word encoding techniques, which fail to capture the inherent features and patterns of DNA sequences.

METHODS

We introduce TExCNN, a novel framework that integrates the pre-trained models DNABERT and DNABERT-2 to generate word embeddings for DNA sequences. We partitioned the DNA sequences into manageable segments and computed their respective embeddings using the pre-trained models. These embeddings were then utilized as inputs to our deep learning framework, which was based on convolutional neural network.

RESULTS

TExCNN outperformed current state-of-the-art models, achieving an average R2 score of 0.622, compared to the 0.596 score achieved by the DeepLncLoc model, which is based on the Word2Vec model and a text convolutional neural network. Furthermore, when the sequence length was extended from 10,500 bp to 50,000 bp, TExCNN achieved an even higher average R2 score of 0.639. The prediction accuracy improved further when additional biological features were incorporated.

CONCLUSIONS

Our experimental results demonstrate that the use of pre-trained models for word embedding generation significantly improves the accuracy of predicting gene expression. The proposed TExCNN pipeline performes optimally with longer DNA sequences and is adaptable for both cell-type-independent and cell-type-dependent predictions.

Deep learning insights into distinct patterns of polygenic adaptation across human populations

Response to spatiotemporal variation in selection gradients resulted in signatures of polygenic adaptation in human genomes. We introduce RAISING, a two-stage deep learning framework that optimizes neural network architecture through hyperparameter tuning before performing feature selection and prediction tasks. We tested RAISING on published and newly designed simulations that incorporate the complex interplay between demographic history and selection gradients. RAISING outperformed Phylogenetic Generalized Least Squares (PGLS), ridge regression and DeepGenomeScan, with significantly higher true positive rates (TPR) in detecting genetic adaptation. It reduced computational time by 60-fold and increased TPR by up to 28% compared to DeepGenomeScan on published data. In more complex demographic simulations, RAISING showed lower false discoveries and significantly higher TPR, up to 17-fold, compared to other methods. RAISING demonstrated robustness with least sensitivity to demographic history, selection gradient and their interactions. We developed a sliding window method for genome-wide implementation of RAISING to overcome the computational challenges of high-dimensional genomic data. Applied to African, European, South Asian and East Asian populations, we identified multiple genomic regions undergoing polygenic selection. Notably, ∼70% of the regions identified in Africans are unique, with broad patterns distinguishing them from non-Africans, corroborating the Out of Africa dispersal model.

Crosstalk between paralogs and isoforms influences p63-dependent regulatory element activity

The p53 family of transcription factors (p53, p63 and p73) regulate diverse organismal processes including tumor suppression, maintenance of genome integrity and the development of skin and limbs. Crosstalk between transcription factors with highly similar DNA binding profiles, like those in the p53 family, can dramatically alter gene regulation. While p53 is primarily associated with transcriptional activation, p63 mediates both activation and repression. The specific mechanisms controlling p63-dependent gene regulatory activity are not well understood. Here, we use massively parallel reporter assays (MPRA) to investigate how local DNA sequence context influences p63-dependent transcriptional activity. Most regulatory elements with a p63 response element motif (p63RE) activate transcription, although binding of the p63 paralog, p53, drives a substantial proportion of that activity. p63RE sequence content and co-enrichment with other known activating and repressing transcription factors, including lineage-specific factors, correlates with differential p63RE-mediated activities. p63 isoforms dramatically alter transcriptional behavior, primarily shifting inactive regulatory elements towards high p63-dependent activity. Our analysis provides novel insight into how local sequence and cellular context influences p63-dependent behaviors and highlights the key, yet still understudied, role of transcription factor paralogs and isoforms in controlling gene regulatory element activity.

Identification of two transcription factors that work coordinately to regulate early development in Entamoeba

The protozoan parasite Entamoeba has a life cycle that switches between infective cysts and invasive trophozoites. Encystation, a crucial process in parasite biology, is controlled by different mechanisms including transcriptional control. We identified two nuclear proteins in Entamoeba invadens, EIN_066100 and EIN_085620, that regulate parasite development by binding to a DNA motif (TCACTTTC) in the promoter regions of genes upregulated in the first 8 h of stage conversion. Overexpression of EIN_066100, a homolog of MAK16 protein, resulted in reduced amoebic proliferation without affecting encystation efficiency. Overexpression of EIN_085620, a protein with an RNA-recognition motif (RRM), led to increased encystation efficiency. Glutathione S-transferase (GST) pull down assays revealed that EIN_066100 interacts with EIN_085620 both in vivo and in vitro, and this interaction is mediated by the EIN_085620 RRM domain. By evaluating truncated proteins with deletions at either the N-terminal or C-terminal regions of EIN_066100, we elucidated the importance of its N-terminal region in proper protein localization, proliferation, encystation, and interaction with EIN_085620. Taken together, these results indicate a coordinated role of EIN_066100 and EIN_085620 in regulating Entamoeba development. This work sheds light on the molecular mechanisms in the earliest stages of Entamoeba encystation.IMPORTANCEAn important biological process in the biology of Entamoeba is stage conversion, which plays a crucial role in disease propagation, facilitating parasite survival outside the host and spreading to new hosts. Multiple mechanisms contribute to controlling the expression of amebic stage-specific genes such as epigenetic and transcriptional control. Identification of early transcriptional control regulators is crucial to understanding the initiation of the encystation cascade. We identified two nuclear proteins, EIN_066100 and EIN_085620, involved in the proliferation and developmental regulation of E. invadens. These proteins work by direct binding to each other and mediating encystation efficiency. Study of new regulators involved in Entamoeba development represents an important advance in a critical aspect of parasite biology.

Transcriptional regulation in the absence of inositol trisphosphate receptor calcium signaling

The activation of IP3 receptor (IP3R) Ca2+ channels generates agonist-mediated Ca2+ signals that are critical for the regulation of a wide range of biological processes. It is therefore surprising that CRISPR induced loss of all three IP3R isoforms (TKO) in HEK293 and HeLa cell lines yields cells that can survive, grow and divide, albeit more slowly than wild-type cells. In an effort to understand the adaptive mechanisms involved, we have examined the activity of key Ca2+ dependent transcription factors (NFAT, CREB and AP-1) and signaling pathways using luciferase-reporter assays, phosphoprotein immunoblots and whole genome transcriptomic studies. In addition, the diacylglycerol arm of the signaling pathway was investigated with protein kinase C (PKC) inhibitors and siRNA knockdown. The data showed that agonist-mediated NFAT activation was lost but CREB activation was maintained in IP3R TKO cells. Under base-line conditions transcriptome analysis indicated the differential expression of 828 and 311 genes in IP3R TKO HEK293 or HeLa cells, respectively, with only 18 genes being in common. Three main adaptations in TKO cells were identified in this study: 1) increased basal activity of NFAT, CREB and AP-1; 2) an increased reliance on Ca2+- insensitive PKC isoforms; and 3) increased production of reactive oxygen species and upregulation of antioxidant defense enzymes. We suggest that whereas wild-type cells rely on a Ca2+ and DAG signal to respond to stimuli, the TKO cells utilize the adaptations to allow key signaling pathways (e.g., PKC, Ras/MAPK, CREB) to transition to the activated state using a DAG signal alone.

Identifying the key regulators orchestrating Epstein-Barr virus reactivation

Epstein-Barr virus (EBV) infects more than 90% of the human population worldwide and establishes lifelong infection in hosts by switching between latent and lytic infection. EBV latency can be reactivated under appropriate conditions, leading to expression of the viral lytic genes and production of infectious progeny viruses. EBV reactivation involves crosstalk between various factors and signaling pathways, and the subsequent complicated virus-host interplays determine whether EBV continues to propagate. However, the detailed mechanisms underlying these processes remain unclear. In this review, we summarize the critical factors regulating EBV reactivation and the associated mechanisms. This encompasses the transcription and post-transcriptional regulation of immediate-early (IE) genes, the functions of viral factors on viral DNA replication and progeny virus production, the mechanisms through which viral proteins disrupt and inhibit the host's innate immune response, and the host factors that modulate EBV reactivation. Finally, we explore the potential applications of novel technologies in studying EBV reactivation, providing novel insights into the investigation of mechanisms governing EBV reactivation and the development of anti-EBV therapeutic strategies.

DysRegNet: Patient-specific and confounder-aware dysregulated network inference towards precision therapeutics

BACKGROUND AND PURPOSE

Gene regulation is frequently altered in diseases in unique and patient-specific ways. Hence, personalised strategies have been proposed to infer patient-specific gene-regulatory networks. However, existing methods do not scale well because they often require recomputing the entire network per sample. Moreover, they do not account for clinically important confounding factors such as age, sex or treatment history. Finally, a user-friendly implementation for the analysis and interpretation of such networks is missing.

EXPERIMENTAL APPROACH

We present DysRegNet, a method for inferring patient-specific regulatory alterations (dysregulations) from bulk gene expression profiles. We compared DysRegNet to the well-known SSN method, considering patient clustering, promoter methylation, mutations and cancer-stage data.

KEY RESULTS

We demonstrate that both SSN and DysRegNet produce interpretable and biologically meaningful networks across various cancer types. In contrast to SSN, DysRegNet can scale to arbitrary sample numbers and highlights the importance of confounders in network inference, revealing an age-specific bias in gene regulation in breast cancer. DysRegNet is available as a Python package (https://github.com/biomedbigdata/DysRegNet_package), and analysis results for 11 TCGA cancer types are available through an interactive web interface (https://exbio.wzw.tum.de/dysregnet).

CONCLUSION AND IMPLICATIONS

DysRegNet introduces a novel bioinformatics tool enabling confounder-aware and patient-specific network analysis to unravel regulatory alteration in complex diseases.

Development of artificial transcription factors and their applications in cell reprograming, genetic screen, and disease treatment

Gene dysregulations are associated with many human diseases, such as cancers and hereditary diseases. Artificial transcription factors (ATFs) are synthetic molecular tools to regulate the expression of disease-associated genes, which is of great significance in basic biological research and biomedical applications. Recent advances in the engineering of ATFs for regulating endogenous gene expression provide an expanded set of tools for understanding and treating diseases. However, the potential immunogenicity, large size, inefficient delivery, and off-target effects persist as obstacles for ATFs to be developed into therapeutics. Moreover, the activation of an endogenous gene following ATF activity lacks durability. In this review, we first describe the functional components of ATFs, including DNA-binding domains, transcriptional effector domains, and control switches. We then highlight examples of applications of ATFs, including cell reprogramming and differentiation, pathogenic gene screening, and disease treatment. Finally, we analyze and summarize major challenges for the clinical translation of ATFs and propose potential strategies to improve these useful molecular tools.

Identification of SPP1 + macrophages as an immune suppressor in hepatocellular carcinoma using single-cell and bulk transcriptomics

Introduction

Macrophages and T cells play crucial roles in liver physiology, but their functional diversity in hepatocellular carcinoma (HCC) remains largely unknown.

Methods

Two bulk RNA-sequencing (RNA-seq) cohorts for HCC were analyzed using gene co-expression network analysis. Key gene modules and networks were mapped to single-cell RNA-sequencing (scRNA-seq) data of HCC. Cell type fraction of bulk RNA-seq data was estimated by deconvolution approach using single-cell RNA-sequencing data as a reference. Survival analysis was carried out to estimate the prognosis of different immune cell types in bulk RNA-seq cohorts. Cell-cell interaction analysis was performed to identify potential links between immune cell types in HCC.

Results

In this study, we analyzed RNA-seq data from two large-scale HCC cohorts, revealing a major and consensus gene co-expression cluster with significant implications for immunosuppression. Notably, these genes exhibited higher enrichment in liver macrophages than T cells, as confirmed by scRNA-seq data from HCC patients. Integrative analysis of bulk and single-cell RNA-seq data pinpointed SPP1 + macrophages as an unfavorable cell type, while VCAN + macrophages, C1QA + macrophages, and CD8 + T cells were associated with a more favorable prognosis for HCC patients. Subsequent scRNA-seq investigations and in vitro experiments elucidated that SPP1, predominantly secreted by SPP1 + macrophages, inhibits CD8 + T cell proliferation. Finally, targeting SPP1 in tumor-associated macrophages through inhibition led to a shift towards a favorable phenotype.

Discussion

This study underpins the potential of SPP1 as a translational target in immunotherapy for HCC.

Absolute quantification of BACH1 and BACH2 transcription factors in B and plasma cells reveals their dynamic changes and unique roles

Changes in the absolute protein amounts of transcription factors are important for regulating gene expression during cell differentiation and in responses to changes in the cellular and extracellular environment. However, few studies have focused on the absolute quantification of mammalian transcription factors. In this study, we established an absolute quantification method for the transcription factors BACH1 and BACH2, which are expressed in B cells and regulated by direct heme binding. The method used purified recombinant proteins as controls in western blotting and was applied to mouse naïve B cells in the spleen, as well as activated B cells and plasma cells. BACH1 was present in naïve B cells at approximately half the levels of BACH2. In activated B cells, BACH1 decreased compared to naïve B cells, whilst BACH2 increased. In plasma cells, BACH1 increased back to the same extent as in naïve B cells, whilst BACH2 was not detected. Their target genes, Prdm1 and Hmox1, were highly induced in plasma cells. BACH1 was found to undergo degradation with lower concentrations of heme than BACH2. Therefore, BACH1 and BACH2 are similarly abundant in B cells but differ in heme sensitivity, potentially regulating gene expression differently depending on their heme responsiveness.

Short circuit: Transcription factor addiction as a growing vulnerability in cancer

Core regulatory circuitry refers to the network of lineage-specific transcription factors regulating expression of both their own coding genes, and that of other transcription factors. Such autoregulatory feedback loops coordinate the transcriptome and epigenome during development and cell fate decisions. This circuitry is hijacked during oncogenesis resulting in cancer cell fate being maintained by lineage-specific transcription factors. Major advances in functional genomics and chemical biology are paving the way for a new generation of cancer therapeutics aimed at disrupting this circuitry through both direct and indirect means. Here we review these critical advances in mechanistic understanding of transcription factor addiction in cancer and how the advent of proteolysis targeting chimeras and CRISPR screen assays are leading the way for a new paradigm in targeted cancer treatments.

Dynamic Evolution of Fibroblasts Revealed by Single-Cell RNA Sequencing of Human Pancreatic Cancer

SIGNIFICANCE

Pancreatic cancer remains a high unmet medical need. Understanding the interactions between stroma and cancer cells in this disease may unveil new opportunities for therapeutic intervention.

Genome-wide identification of alternative splicing related with transcription factors and splicing regulators in breast cancer stem cells responding to fasting-mimicking diet

Fasting-mimicking diet (FMD) can effectively inhibit the viability of breast cancer stem cells (CSCs). However, the molecular mechanisms underlying the inhibitory function of FMD on breast CSCs remain largely unknown. Elucidating the mechanisms by which FMD suppresses breast CSCs is beneficial to targeting breast CSCs. Herein, we systematically analyze alternative splicing and RNA binding protein (RBP) expression in breast CSCs during FMD. The analysis results show that a large number of regulated alternative splicing (RAS) and differentially expressed genes (DEGs) appear responding to FMD. Further studies show that there are potential regulatory relationships between transcription factors (TFs) with RAS (RAS-TFs) and their differentially expressed target genes (RAS-TF-DEGs). Moreover, differentially expressed RNA binding proteins (DERBPs) exhibit potential regulatory functions on RAS-TFs. In short, DERBPs potentially control the alternative splicing of TFs (RAS-TFs), regulating their target gene (RAS-TF-DEG) expression, which leads to the regulation of biological processes in breast CSCs during FMD. In addition, the alternative splicing and DEGs are compared between breast CSCs and differentiated cancer cells during FMD, providing new interpretations for the different responses of the two types of cells. Our studies will shed light on the understanding of the molecular mechanisms underlying breast CSC inhibition induced by FMD.

Cut&tag: a powerful epigenetic tool for chromatin profiling

Analysis of transcription factors and chromatin modifications at the genome-wide level provides insights into gene regulatory processes, such as transcription, cell differentiation and cellular response. Chromatin immunoprecipitation is the most popular and powerful approach for mapping chromatin, and other enzyme-tethering techniques have recently become available for living cells. Among these, Cleavage Under Targets and Tagmentation (CUT&Tag) is a relatively novel chromatin profiling method that has rapidly gained popularity in the field of epigenetics since 2019. It has also been widely adapted to map chromatin modifications and TFs in different species, illustrating the association of these chromatin epitopes with various physiological and pathological processes. Scalable single-cell CUT&Tag can be combined with distinct platforms to distinguish cellular identity, epigenetic features and even spatial chromatin profiling. In addition, CUT&Tag has been developed as a strategy for joint profiling of the epigenome, transcriptome or proteome on the same sample. In this review, we will mainly consolidate the applications of CUT&Tag and its derivatives on different platforms, give a detailed explanation of the pros and cons of this technique as well as the potential development trends and applications in the future.

TFTG: A comprehensive database for human transcription factors and their targets

Transcription factors (TFs) are major contributors to gene transcription, especially in controlling cell-specific gene expression and disease occurrence and development. Uncovering the relationship between TFs and their target genes is critical to understanding the mechanism of action of TFs. With the development of high-throughput sequencing techniques, a large amount of TF-related data has accumulated, which can be used to identify their target genes. In this study, we developed TFTG (Transcription Factor and Target Genes) database (http://tf.liclab.net/TFTG), which aimed to provide a large number of available human TF-target gene resources by multiple strategies, besides performing a comprehensive functional and epigenetic annotations and regulatory analyses of TFs. We identified extensive available TF-target genes by collecting and processing TF-associated ChIP-seq datasets, perturbation RNA-seq datasets and motifs. We also obtained experimentally confirmed relationships between TF and target genes from available resources. Overall, the target genes of TFs were obtained through integrating the relevant data of various TFs as well as fourteen identification strategies. Meanwhile, TFTG was embedded with user-friendly search, analysis, browsing, downloading and visualization functions. TFTG is designed to be a convenient resource for exploring human TF-target gene regulations, which will be useful for most users in the TF and gene expression regulation research.

SRSF3 suppresses RCC tumorigenesis and progression via regulating SP4 alternative splicing

Abnormal alternative splicing (AS) caused by dysregulated expression of splicing factors plays a crucial role in tumorigenesis and progression. The serine/arginine-rich (SR) RNA-binding protein family is a major class of splicing factors regulating AS. However, their roles and mechanisms in renal cell carcinoma (RCC) development and progression are not fully understood. Here, we found that SR splicing factor 3 (SRSF3) was an important splicing factor affecting RCC progression. SRSF3 was downregulated in RCC tissues and its low level was associated with decreased overall survival time of RCC patients. SRSF3 overexpression suppressed RCC cell malignancy. Mechanistically, the binding of SRSF3 to SP4 exon 3 led to the inclusion of SP4 exon 3 and the increase of long SP4 isoform (L-SP4) level in RCC cells. L-SP4, but not S-SP4 overexpression suppressed RCC cell malignancy. Meanwhile, L-SP4 participated in SRSF3-mediated anti-proliferation by transcriptionally promoting SMAD4 expression. Taken together, our findings provide new insights into the anticancer mechanism of SRSF3, suggesting that SRSF3 may serve as a novel potential therapeutic target for RCC.

Computational analysis and expression profiling of NAC transcription factor family involved in biotic stress response in Manihot esculenta

The Nascent polypeptide-Associated Complex (NAC) family is among the largest plant-specific TF families and plays an important role in plant growth, development, and stress responses. NAC TFs have been extensively studied in plants such as rice and Arabidopsis; however, their characterization, functions, evolution, and expression patterns in Manihot esculenta (cassava) under environmental stress remain largely unexplored. Here, we used bioinformatic analyses and biotic stress responses to investigate the physicochemical properties, chromosome location, phylogeny, gene structure, expression patterns, and cis-elements in promoter regions of the NAC TFs in cassava. We identified 119 M. esculenta NAC (MeNAC) gene families, unevenly distributed on 16 chromosomes. We investigated expression patterns of all identified MeNAC TFs under Xanthomonas axonopodis pv. manihotis (Xam) infection, strain CHN11, at different time points. Only 20 MeNAC TFs showed expression of significant bacterial resistance. Six MeNACs (MeNAC7, 26, 63, 65, 77, and 113) were selected for functional analysis. qRT-PCR assays revealed that MeNAC7, 26, 63, 65, 77, and 113 were induced in response to XamCHN11 infection and may participate in the molecular interaction of cassava and bacterial blight. Interestingly, MeNAC26, MeNAC63, MeNAC65, and MeNAC113 responded to XamCHN11 infection at 3 h post-inoculation. Furthermore, we identified 13 stress-related cis-elements in promoter regions of the MeNAC genes that are involved in diverse environmental stress responses. Phylogenetic analysis revealed that MeNAC genes with similar structures and motif distributions were grouped. This study provides valuable insights into the evolution, diversity, and characterization of MeNAC TFs. It lays the groundwork for a better understanding of their biological roles and molecular mechanisms in cassava.

Multi-layered knowledge graph neural network reveals pathway-level agreement of three breast cancer multi-gene assays

Multi-gene assays have been widely used to predict the recurrence risk for hormone receptor (HR)-positive breast cancer patients. However, these assays lack explanatory power regarding the underlying mechanisms of the recurrence risk. To address this limitation, we proposed a novel multi-layered knowledge graph neural network for the multi-gene assays. Our model elucidated the regulatory pathways of assay genes and utilized an attention-based graph neural network to predict recurrence risk while interpreting transcriptional subpathways relevant to risk prediction. Evaluation on three multi-gene assays-Oncotype DX, Prosigna, and EndoPredict-using SCAN-B dataset demonstrated the efficacy of our method. Through interpretation of attention weights, we found that all three assays are mainly regulated by signaling pathways driving cancer proliferation especially RTK-ERK-ETS-mediated cell proliferation for breast cancer recurrence. In addition, our analysis highlighted that the important regulatory subpathways remain consistent across different knowledgebases used for constructing the multi-level knowledge graph. Furthermore, through attention analysis, we demonstrated the biological significance and clinical relevance of these subpathways in predicting patient outcomes. The source code is available at http://biohealth.snu.ac.kr/software/ExplainableMLKGNN.

IGFBP7 promotes the proliferation and differentiation of primary myoblasts and intramuscular preadipocytes in chicken

Though it is well known that insulin-like growth factor (IGF) binding protein 7 (IGFBP7) plays an important role in myogenesis and adipogenesis in mammals, its impact on the proliferation, differentiation, and lipid deposition in chicken primary myoblasts (CPM) and intramuscular preadipocytes remains unexplored. In the present study, we firstly examined the correlation between SNPs within the genomic sequence of the IGFBP7 gene and carcass and blood chemical traits in a F2 resource population by genetic association analysis, and found that a significant correlation between the SNP (4_49499525) located in the intron region of IGFBP7 and serum high-density lipoproteins (HDL). We then examined the expression patterns of IGFBP7 across different stages of proliferation and differentiation in CPMs and intramuscular preadipocytes via qPCR, and explored the biological functions of IGFBP7 through gain- and loss-of-function experiments and a range of techniques including qPCR, CCK-8, EdU, flow cytometry, Western blot, immunofluorescence, and Oil Red O staining to detect the proliferation, differentiation, and lipid deposition in CPMs and intramuscular preadipocytes. We ascertained that the expression levels of the IGFBP7 gene increased as cell differentiation progresses in CPMs and intramuscular preadipocytes, and that IGFBP7 promotes the proliferation and differentiation of these cells, as well as facilitates intracellular lipid deposition. Furthermore, we investigated the regulatory mechanism of IGFBP7 expression by using co-transfection strategy and dual-luciferase reporter assay, and discovered that the myogenic transcription factors (MRF), myoblast determination factor (MyoD) and myogenin (MyoG), along with the adipocyte-specific transcription factor (TF) CCAAT/enhancer-binding protein α (C/EBPα), can bind to the core transcription activation region of the IGFBP7 promoter located 500 bp upstream from the transcription start site, thereby promoting IGFBP7 transcription and expression. Taken together, our study underscores the role of IGFBP7 as a positive regulator for myogenesis and adipogenesis, while also elucidating the functional and transcriptional regulatory mechanisms of IGFBP7 in chicken skeletal muscle development and intramuscular adipogenesis.

Identification of co-localised transcription factors based on paired motifs analysis

The interaction of transcription factors (TFs) with DNA precisely regulates gene transcription. In mammalian cells, thousands of TFs often interact with DNA cis-regulatory elements in a combinatorial manner rather than act alone. The identification of cooperativity between TFs can help to explore the mechanism of transcriptional regulation. However, little is known about the cooperative patterns of TFs in the genome. To identify which TFs prefer co-localisation, the authors conducted a paired motif analysis in the accessible regions of the human genome based on the Poisson background model. Especially, the authors distinguish the cooperative binding TFs and the competitive binding TFs according to the distance between TF motifs. In the K562 cell line, the authors find that TFs from a same family are always competing the same binding sites, such as FOS_JUN family, whereas KLF family TFs show significant cooperative binding in the adjacency region. Furthermore, the comparative analysis across 16 human cell lines indicates that most TF combination patterns are conserved, but there are still some cell-line-specific patterns. Finally, in human prostate cancer cells (PC-3) and human prostate normal cells (RWPE-2), the authors investigate the specific TF combination patterns in the disease cell and normal cell. The results show that the cooperative binding TF pairs shared by PC-3 and RWPE-2 account for over 90%. Simultaneously, the authors also identify 26 specific TF combination pairs in PC-3 cancer cells.

AEBP1 Silencing Protects Against Cerebral Ischemia/Reperfusion Injury by Regulating Neuron Ferroptosis and Microglia M2 Polarization Through PRKCA-PI3K-Akt Axis

Cerebral ischemia/reperfusion injury is one of the main causes of neuronal damage. Neuron ferroptosis and microglia polarization are considered as critical processes during cerebral ischemia/reperfusion. Adipocyte enhancer-binding protein 1 (AEBP1) usually acts as a transcriptional repressor which is involved in various diseases. However, it is still remains unknown whether AEBP1 could have important roles in regulating the neuron ferroptosis and microglia polarization in cerebral ischemia/reperfusion injury. The oxygen-glucose deprivation and reperfusion (OGD/R)-treated cells and middle cerebral artery occlusion (MCAO)-treated mice were used as in vitro and in vivo models. The differentially expressed factors were analyzed according to GEO datasets. Relative mRNA and protein expression levels were detected by qRT-PCR and western blot analysis. Cell viability was measured by CCK-8 assay. ROS, GSH and iron contents were detected using specifical assay kits. CD26 and CD206 levels were measured by immunofluorescence assay. Inflammatory cytokines were detected by ELISA. The association between AEBP1 and PRKCA was assessed by luciferase reporter and ChIP analyses. The neuron damage in mice was analyzed by TTC staining and neurological deficit score. Transcription factor AEBP1 was increased in OGD/R-treated HT22 and BV2 cells. AEBP1 silencing attenuated OGD/R-induced HT22 cell ferroptosis through increasing cell viability, GSH and GPX4 levels, and decreasing ROS, iron and ACSL4 levels. AEBP1 knockdown promoted microglia M2 polarization by increasing CD206-positive cells and Arg-1 level, and reducing iNOS, TNF-α, IL-1β and IL-6 levels in BV2 cells. AEBP1 transcriptionally repressed PRKCA expression, and further regulated PI3K/Akt signaling activation. Inhibition of PRKCA or PI3K/Akt reversed the effects of AEBP1 silencing on neuron ferroptosis and microglia M2 polarization. AEBP1 downregulation attenuated neuronal damage by decreasing infarct size and deficit scores in MCAO-treated mice. AEBP1 silencing mitigated neuron ferroptosis and promoted microglia M2 polarization through increasing PRKCA and activating PI3K/Akt signaling, indicating the potentially protective action of AEBP1 knockdown in cerebral ischemia/reperfusion injury.

Methylation and transcriptomic profiling reveals short term and long term regulatory responses in polarized macrophages

Macrophage plasticity allows the adoption of distinct functional states in response to environmental cues. While unique transcriptomic profiles define these states, focusing solely on transcription neglects potential long-term effects. The investigation of epigenetic changes can be used to understand how temporary stimuli can result in lasting effects. Epigenetic alterations play an important role in the pathophysiology of macrophages, including their trained innate immunity, enabling faster and more efficient inflammatory responses upon subsequent encounters to the same pathogen or insult. In this study, we used a multi-omics approach to elucidate the interplay between gene expression and DNA-methylation, to explore the potential long-term effects of diverse polarizing environments on macrophage activity. We identified a common core set of genes that are differentially methylated regardless of exposure type, indicating a potential common fundamental mechanism for adaptation to various stimuli. Functional analysis revealed that processes requiring rapid responses displayed transcriptomic regulation, whereas functions critical for long-term adaptations exhibited co-regulation at both transcriptomic and epigenetic levels. Our study uncovers a novel set of genes linked to the long-term effects of macrophage polarization. This discovery underscores the potential of epigenetics in elucidating how macrophages establish long-term memory and influence health outcomes.

Recruitment of homodimeric proneural factors by conserved CAT-CAT E-boxes drives major epigenetic reconfiguration in cortical neurogenesis

Proneural factors of the basic helix-loop-helix family coordinate neurogenesis and neurodifferentiation. Among them, NEUROG2 and NEUROD2 subsequently act to specify neurons of the glutamatergic lineage. Disruption of these factors, their target genes and binding DNA motifs has been linked to various neuropsychiatric disorders. Proneural factors bind to specific DNA motifs called E-boxes (hexanucleotides of the form CANNTG, composed of two CAN half sites on opposed strands). While corticogenesis heavily relies on E-box activity, the collaboration of proneural factors on different E-box types and their chromatin remodeling mechanisms remain largely unknown. Here, we conducted a comprehensive analysis using chromatin immunoprecipitation followed by sequencing (ChIP-seq) data for NEUROG2 and NEUROD2, along with time-matched single-cell RNA-seq, ATAC-seq and DNA methylation data from the developing mouse cortex. Our findings show that these factors are highly enriched in transiently active genomic regions during intermediate stages of neuronal differentiation. Although they primarily bind CAG-containing E-boxes, their binding in dynamic regions is notably enriched in CAT-CAT E-boxes (i.e. CATATG, denoted as 5'3' half sites for dimers), which undergo significant DNA demethylation and exhibit the highest levels of evolutionary constraint. Aided by HT-SELEX data reanalysis, structural modeling and DNA footprinting, we propose that these proneural factors exert maximal chromatin remodeling influence during intermediate stages of neurogenesis by binding as homodimers to CAT-CAT motifs. This study provides an in-depth integrative analysis of the dynamic regulation of E-boxes during neuronal development, enhancing our understanding of the mechanisms underlying the binding specificity of critical proneural factors.

The transcription factor FgSfp1 orchestrates mycotoxin deoxynivalenol biosynthesis in Fusarium graminearum

Fusarium graminearum (F. graminearum) and its derivative mycotoxin deoxynivalenol (DON) are highly concerned with food safety and sustainability worldwide. Although several transcription factors (TFs) had been elucidated, molecular mechanism participates in DON biosynthesis regulation remains largely unrevealed. Here, we first characterized a zinc finger-contained TF in F. graminearum, FgSfp1, which is indispensable for DON production since its depletion resulting in a 95.4% DON yielding reduction. Interestingly, contrast to previous knowledge, all TRI-cluster genes were abnormally upregulated in ΔFgSfp1 while Tri proteins abundance rationally decreased simultaneously. Further evidence show FgSfp1 could coordinate genetic translation pace by manipulating ribosomal biogenesis process. Specifically, FgSfp1-depletion leads to ribosome biogenesis assembly factor (RiBi) expression attenuation along with DON precursor acetyl-CoA synthase reduction since FgSfp1 actively interacts with RNA 2'-O-methylation enzyme FgNop1 revealed by Bi-FC. It subsequently influences mRNA translation pace. In conclusion, we elucidated that the FgSfp1 orchestrates DON biosynthesis via participating RNA posttranscriptional modification for ribosomal RNA maturation, offering insights into the DON biosynthesis regulation. Ultimately, this TF might be a key regulator for DON contamination control in the whole food chain.

Towards the genomic sequence code of DNA fragility for machine learning

Genomic DNA breakages and the subsequent insertion and deletion mutations are important contributors to genome instability and linked diseases. Unlike the research in point mutations, the relationship between DNA sequence context and the propensity for strand breaks remains elusive. Here, by analyzing the differences and commonalities across myriads of genomic breakage datasets, we extract the sequence-linked rules and patterns behind DNA fragility. We show the overall deconvolution of the sequence influence into short-, mid- and long-range effects, and the stressor-dependent differences in defining the range and compositional effects on DNA fragility. We summarize and release our feature compendium as a library that can be seamlessly incorporated into genomic machine learning procedures, where DNA fragility is of concern, and train a generalized DNA fragility model on cancer-associated breakages. Structural variants (SVs) tend to stabilize regions in which they emerge, with the effect most pronounced for pathogenic SVs. In contrast, the effects of chromothripsis are seen across regions less prone to breakages. We find that viral integration may bring genome fragility, particularly for cancer-associated viruses. Overall, this work offers novel insights into the genomic sequence basis of DNA fragility and presents a powerful machine learning resource to further enhance our understanding of genome (in)stability and evolution.

Paired CRISPR screens to map gene regulation in cis and trans

Recent massively-parallel approaches to decipher gene regulatory circuits have focused on the discovery of either cis -regulatory elements (CREs) or trans -acting factors. Here, we develop a scalable approach that pairs cis - and trans -regulatory CRISPR screens to systematically dissect how the key immune checkpoint PD-L1 is regulated. In human pancreatic ductal adenocarcinoma (PDAC) cells, we tile the PD-L1 locus using ∼25,000 CRISPR perturbations in constitutive and IFNγ-stimulated conditions. We discover 67 enhancer- or repressor-like CREs and show that distal CREs tend to contact the promoter of PD-L1 and related genes. Next, we measure how loss of all ∼2,000 transcription factors (TFs) in the human genome impacts PD-L1 expression and, using this, we link specific TFs to individual CREs and reveal novel PD-L1 regulatory circuits. For one of these regulatory circuits, we confirm the binding of predicted trans -factors (SRF and BPTF) using CUT&RUN and show that loss of either the CRE or TFs potentiates the anti-cancer activity of primary T cells engineered with a chimeric antigen receptor. Finally, we show that expression of these TFs correlates with PD-L1 expression in vivo in primary PDAC tumors and that somatic mutations in TFs can alter response and overall survival in immune checkpoint blockade-treated patients. Taken together, our approach establishes a generalizable toolkit for decoding the regulatory landscape of any gene or locus in the human genome, yielding insights into gene regulation and clinical impact.