Human transcription factor protein interaction networks

A comprehensive Schizosaccharomyces pombe atlas of physical transcription factor interactions with proteins and chromatin

Transcription factors (TFs) are key regulators of gene expression, yet many of their targets and modes of action remain unknown. In Schizosaccharomyces pombe, one-third of TFs are solely homology predicted, with few experimentally validated. We created a comprehensive library of 89 endogenously tagged S. pombe TFs, mapping their protein and chromatin interactions using immunoprecipitation-mass spectrometry and chromatin immunoprecipitation sequencing. Our study identified protein interactors for half the TFs, with over a quarter potentially forming stable complexes. We discovered DNA-binding sites for most TFs across 2,027 unique genomic regions, revealing motifs for 38 TFs and uncovering a complex network of extensive TF cross- and autoregulation. Characterization of the largest TF family revealed conserved DNA sequence preferences but diverse binding patterns and identified a repressive heterodimer, Ntu1/Ntu2, linked to perinuclear gene localization. Our TFexplorer webtool makes all data interactively accessible, offering insights into TF interactions and regulatory mechanisms with broad biological relevance.

Widespread variation in molecular interactions and regulatory properties among transcription factor isoforms

Most human transcription factor (TF) genes encode multiple protein isoforms differing in DNA-binding domains, effector domains, or other protein regions. The global extent to which this results in functional differences between isoforms remains unknown. Here, we systematically compared 693 isoforms of 246 TF genes, assessing DNA binding, protein binding, transcriptional activation, subcellular localization, and condensate formation. Relative to reference isoforms, two-thirds of alternative TF isoforms exhibit differences in one or more molecular activities, which often could not be predicted from sequence. We observed two primary categories of alternative TF isoforms: "rewirers" and "negative regulators," both of which were associated with differentiation and cancer. Our results support a model wherein the relative expression levels of, and interactions involving, TF isoforms add an understudied layer of complexity to gene regulatory networks, demonstrating the importance of isoform-aware characterization of TF functions and providing a rich resource for further studies.

Protein-interaction network analysis reveals a role of Prp19 splicing factor in transcription of both intron-containing and intron-lacking genes

We have previously demonstrated that the transcription-dependent interaction of the promoter and terminator ends of a gene, which results in the formation of a gene loop, is facilitated by the interaction of the general transcription factor TFIIB with the CF1, CPF and Rat1 termination complexes. To further elucidate the protein-protein interactions that stabilize gene loop, we performed mass spectrometry of affinity purified termination complexes from chromatin fraction. Quantitative proteomic analysis revealed additional interactions of termination factors with TFIID and SAGA complex. Since gene looping of intron-containing genes involves additional contacts of the promoter and terminator with the intron, we examined if termination factors interact with the splicing factors as well. All three termination complexes displayed statistically significant interactions with Prp19, Prp43, Sub2, Snu114, Brr2 and Smb1 splicing factors. Since Prp43 and Prp19 consistently emerged as the interactor of both initiation and termination factors, we affinity-purified both and performed mass spectrometry. Prp19 exhibited interactions with subunits of TFIID, CPF complex, and the RSC chromatin remodeling complex. These interactions were observed exclusively in the chromatin context, thereby implicating the factor in transcription of protein coding genes. Since fewer than 4% of yeast genes contain introns, we hypothesized that Prp19 might have a broader role in RNAPII transcription cycle. Auxin-mediated depletion of Prp19 resulted in about two-fold decrease in transcription of a subset of both intron-containing and intron-lacking genes. Specifically, the promoter recruitment of TBP registered a significant decline in the absence of Prp19. Chromatin immunoprecipitation (ChIP) analysis revealed crosslinking of Prp19 to the promoter proximal as well as downstream regions of both intronic and non-intronic genes. These findings demonstrate that Prp19 has a novel role in the initiation step of transcription in yeast.

TLE4 is a repressor of the oncogenic activity of TLX3 in T-cell acute lymphoblastic leukemia

T-cell acute lymphoblastic leukemia (T-ALL) is an aggressive hematological disease originating from the malignant transformation of T-cell progenitors, caused by the accumulation of genetic aberrations. One-fifth of T-ALL patients are characterized by ectopic expression of the homeobox transcription factor TLX3. However, the role of TLX3 in T-ALL remains elusive, partly due to the lack of suitable study models. Strikingly, this TLX3-positive subgroup has a high frequency of FLT3 mutations, predominantly FLT3-ITD, in pediatric cases. To investigate this, we generated ex vivo cultured pro-T cells driven by the co-expression of TLX3 and FLT3-ITD, which conferred IL7 independent growth. This model allowed us to confirm that TLX3 expression and FLT3 signaling cooperate to transform T-cells and induce an oncogenic context. Data from this cell model, combined with gene expression data from TLX3 positive T-ALL cases, revealed a strong downregulation of the transcriptional repressor TLE4. Furthermore, TLE4 showed to have a repressive effect on ex vivo TLX3 T-ALL cell growth, likely caused by a partial reversal of the TLX3 transcriptional profile. In conclusion, we developed a TLX3+FLT3-ITD pro-T cell model and used it to illustrate that TLX3 directly represses TLE4 expression, which is beneficial for the oncogenic function of TLX3.

WDR26 depletion alters chromatin accessibility and gene expression profiles in mammalian cells

WD-repeat containing protein 26 (WDR26) is an essential component of the CTLH E3 ligase complex. Mutations in WDR26 lead to Skraban-Deardorff, an intellectual disability syndrome with clinical features resembling other disorders arising from defects in transcriptional regulation and chromatin structure. However, the role of WDR26 and its associated CTLH complex in regulating chromatin or transcription has not been elucidated. Here, we assessed how loss of WDR26 affects chromatin accessibility and gene expression. Transcriptome analysis of WDR26 knockout HeLa cells revealed over 2000 differentially expressed genes, while ATAC-Seq analysis showed over 32,000 differentially accessible chromatin regions, the majority mapping to intergenic and intronic regions and 13 % mapping to promoters. Above all, we found that WDR26 loss affected expression of genes regulated by AP-1 and NF-1 transcription factors and resulted in dramatic changes in their chromatin accessibility. Overall, our analyses implicate WDR26 and the CTLH complex in chromatin regulation.

Lysine-specific demethylase 1a is obligatory for gene regulation during kidney development

Histone methyltransferases and demethylases play crucial roles in gene regulation and are vital for proper functioning of multiple tissues. Lysine-specific histone demethylase 1A (Kdm1a), is responsible for the demethylation of specific lysines, namely K4 and K9, on histone H3. In this study, we investigated the functions of Kdm1a during mouse kidney development upon targeted deletion in renal progenitor cells. Loss of Kdm1a in Six2-positive nephron progenitors resulted in significant reduction in renal mass, tissue structural changes and impaired function. To further understand the molecular function of Kdm1a during kidney development, we conducted multi-omics analyses that included transcriptome profiling, Chromatin immunoprecipitation (ChIP) sequencing, and methylome assessments. These omic analyses identified Kdm1a as a critical gene regulator required for sustained expression of several nephron segment marker genes, as well as vast number of solute carrier (Slc) genes and a few imprinted genes. Absence of Kdm1a in kidneys led to an increase in global H3K9 methylation peaks, which correlated with the transcriptional downregulation of numerous genes. Among these were markers of nephron progenitors and presumptive tubular precursors. We also observed that specific gene bodies exhibited altered DNA methylation patterns at intragenic differentially methylated regions (DMRs) upon Kdm1a deletion, while the overall global levels of DNA methylation remained unchanged. Our data point to a key regulatory role for Kdm1a in the renal progenitor epigenome, influencing kidney specific gene expression in the developing nephrons. Together the study highlights an indispensable role for Kdm1a for proper development of mouse kidneys, and its absence leading to significant developmental and functional impairment.

Direct ionic stress sensing and mitigation by the transcription factor NFAT5

Rising temperatures and water scarcity caused by climate change are increasingly exposing our cells and tissues to ionic stress, a consequence of elevated cytoplasmic ionic strength that can disrupt protein, organelle, and genome function. Here, we unveil a single-protein mechanism for ionic strength sensing and mitigation in animal cells, one that is notably different from the analogous high osmolarity glycerol kinase cascade in yeast. The Rel family transcription factor NFAT5 directly senses intracellular ionic strength using a C-terminal prion-like domain (PLD). In response to elevated intracellular ionic strength, this PLD is necessary and sufficient to coordinate an adaptive gene expression program by recruiting the transcriptional coactivator BRD4. The purified NFAT5 PLD forms condensates in response to elevated solution ionic strength in vitro, and human NFAT5 alone is sufficient to reconstitute a mammalian transcriptional response to ionic stress in yeast. We propose that ion-sensitive conformational changes in a PLD directly regulate transcription to maintain ionic strength homeostasis in animal cells.

Transcription factors form a ternary complex with NIPBL/MAU2 to localize cohesin at enhancers

While the cohesin complex is a key player in genome architecture, how it localizes to specific chromatin sites is not understood. Recently, we and others have proposed that direct interactions with transcription factors lead to the localization of the cohesin-loader complex (NIPBL/MAU2) within enhancers. Here, we identify two clusters of LxxLL motifs within the NIPBL sequence that regulate NIPBL dynamics, interactome, and NIPBL-dependent transcriptional programs. One of these clusters interacts with MAU2 and is necessary for the maintenance of the NIPBL-MAU2 heterodimer. The second cluster binds specifically to the ligand-binding domains of steroid receptors. For the glucocorticoid receptor (GR), we examine in detail its interaction surfaces with NIPBL and MAU2. Using AlphaFold2 and molecular docking algorithms, we uncover a GR-NIPBL-MAU2 ternary complex and describe its importance in GR-dependent gene regulation. Finally, we show that multiple transcription factors interact with NIPBL-MAU2, likely using interfaces other than those characterized for GR.

Master transcription factor binding sites constitute the core of early replication control elements

Eukaryotic genomes replicate in a defined temporal order called the replication timing (RT) program. RT is developmentally regulated with potential to drive cell fate transitions, but mechanisms controlling RT remain elusive. We previously identified "Early Replication Control Elements" (ERCEs) necessary for early RT, domain-wide transcription, 3D chromatin architecture and compartmentalization in mouse embryonic stem cells (mESCs) but, deletions identifying ERCEs were large and encompassed many putative regulatory elements. Here, we show that ERCEs are compound elements whose RT activity can largely be accounted for by multiple sites of diverse master transcription factor binding (subERCEs), distinguished from other such sites by their long-range interactions. While deletion of subERCEs had large effects on both transcription and RT, deleting transcription start sites eliminated nearly all transcription with moderate effects on RT. Our results suggest a model in which subERCEs respond to diverse master transcription factors by functioning both as transcription enhancers and as elements that organize chromatin domains structurally and support early RT, potentially providing a feed-forward loop to drive robust epigenomic change during cell fate transitions.

Emerging Roles for Transcription Factors During Mitosis

The genome is dynamically reorganized, partitioned, and divided during mitosis. Despite their role in organizing interphase chromatin, transcription factors were largely believed to be mitotic spectators evicted from chromatin during mitosis, only able to reestablish their position on DNA upon entry into G1. However, a panoply of evidence now contradicts this early belief. Numerous transcription factors are now known to remain active during mitosis to achieve diverse purposes, including chromosome condensation, regulation of the centromere/kinetochore function, and control of centrosome homeostasis. Inactivation of transcription factors during mitosis results in chromosome segregation errors, key features of cancer. Moreover, active transcription and the production of centromere-derived transcripts during mitosis are also known to play key roles in maintaining chromosomal stability. Finally, many transcription factors are associated with chromosomal instability through poorly defined mechanisms. Herein, we will review the emerging roles of transcription factors and transcription during mitosis with a focus on their role in promoting the faithful segregation of sister chromatids.

Modeling and designing enhancers by introducing and harnessing transcription factor binding units

Enhancers serve as pivotal regulators of gene expression throughout various biological processes by interacting with transcription factors (TFs). While transcription factor binding sites (TFBSs) are widely acknowledged as key determinants of TF binding and enhancer activity, the significant role of their surrounding context sequences remains to be quantitatively characterized. Here we propose the concept of transcription factor binding unit (TFBU) to modularly model enhancers by quantifying the impact of context sequences surrounding TFBSs using deep learning models. Based on this concept, we develop DeepTFBU, a comprehensive toolkit for enhancer design. We demonstrate that designing TFBS context sequences can significantly modulate enhancer activities and produce cell type-specific responses. DeepTFBU is also highly efficient in the de novo design of enhancers containing multiple TFBSs. Furthermore, DeepTFBU enables flexible decoupling and optimization of generalized enhancers. We prove that TFBU is a crucial concept, and DeepTFBU is highly effective for rational enhancer design.

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.

Roles of Post-Translational Modifications of Transcription Factors Involved in Breast Cancer Hypoxia

BC is the most commonly diagnosed cancer and the second leading cause of cancer death among women worldwide. Cellular stress is a condition that leads to disrupted homeostasis by extrinsic and intrinsic factors. Among other stressors, hypoxia is a driving force for breast cancer (BC) progression and a general hallmark of solid tumors. Thus, intratumoral hypoxia is an important determinant of invasion, metastasis, treatment failure, prognosis, and patient mortality. Acquisition of the epithelial-mesenchymal transition (EMT) phenotype is also a consequence of tumor hypoxia. The cellular response to hypoxia is mainly regulated by the hypoxia signaling pathway, governed by hypoxia-inducible factors (HIFs), mainly HIF1α. HIFs are a family of transcription factors (TFs), which induce the expression of target genes involved in cell survival and proliferation, metabolic reprogramming, angiogenesis, resisting apoptosis, invasion, and metastasis. HIF1α cooperates with a large number of other TFs. In this review, we focused on the crosstalk and cooperation between HIF1α and other TFs involved in the cellular response to hypoxia in BC. We identified a cluster of TFs, proposed as the HIF1α-TF interactome, that orchestrates the transcription of target genes involved in hypoxia, due to their post-translational modifications (PTMs), including phosphorylation/dephosphorylation, ubiquitination/deubiquitination, SUMOylation, hydroxylation, acetylation, S-nitrosylation, and palmitoylation. PTMs of these HIF1α-related TFs drive their stability and activity, degradation and turnover, and the bidirectional translocation between the cytoplasm or plasma membrane and nucleus of BC cells, as well as the transcription/activation of proteins encoded by oncogenes or inactivation of tumor suppressor target genes. Consequently, PTMs of TFs in the HIF1α interactome are crucial regulatory mechanisms that drive the cellular response to oxygen deprivation in BC cells.

Transcription factor specificity protein (SP) family in renal physiology and diseases

Dysregulated specificity proteins (SPs), members of the C2H2 zinc-finger family, are crucial transcription factors (TFs) with implications for renal physiology and diseases. This comprehensive review focuses on the role of SP family members, particularly SP1 and SP3, in renal physiology and pathology. A detailed analysis of their expression and cellular localization in the healthy human kidney is presented, highlighting their involvement in fatty acid metabolism, electrolyte regulation, and the synthesis of important molecules. The review also delves into the diverse roles of SPs in various renal diseases, including renal ischemia/reperfusion injury, diabetic nephropathy, renal interstitial fibrosis, and lupus nephritis, elucidating their molecular mechanisms and potential as therapeutic targets. The review further discusses pharmacological modulation of SPs and its implications for treatment. Our findings provide a comprehensive understanding of SPs in renal health and disease, offering new avenues for targeted therapeutic interventions and precision medicine in nephrology.

Further Development of SAMPDI-3D: A Machine Learning Method for Predicting Binding Free Energy Changes Caused by Mutations in Either Protein or DNA

BACKGROUND/OBJECTIVES

Predicting the effects of protein and DNA mutations on the binding free energy of protein-DNA complexes is crucial for understanding how DNA variants impact wild-type cellular function. As many cellular interactions involve protein-DNA binding, accurately predicting changes in binding free energy (ΔΔG) is valuable for distinguishing pathogenic mutations from benign ones.

METHODS

This study describes the development and optimization of the SAMPDI-3Dv2 machine learning method, which is trained on an expanded database of experimentally measured ΔΔGs. This enhanced model incorporates new features, including the 3D structure of the mutant protein, features of the mutant structure, and a position-specific scoring matrix (PSSM). Benchmarking was conducted using 5-fold cross-validation.

RESULTS

The updated SAMPDI-3D model (SAMPDI-3Dv2) achieved Pearson correlation coefficients (PCCs) of 0.68 for protein and 0.80 for DNA mutations. These results represent significant improvements over existing tools. Additionally, the method's rapid execution time enables genome-scale predictions.

CONCLUSIONS

The improved SAMPDI-3Dv2 shows enhanced predictive performance for analyzing mutations in protein-DNA complexes. By leveraging structural information and an expanded training dataset, SAMPDI-3Dv2 provides researchers with a more accurate and efficient tool for mutation analysis, contributing to identifying pathogenic variants and improving our understanding of cellular function.

Proteomics: An In-Depth Review on Recent Technical Advances and Their Applications in Biomedicine

Proteins hold pivotal importance since many diseases manifest changes in protein activity. Proteomics techniques provide a comprehensive exploration of protein structure, abundance, and function in biological samples, enabling the holistic characterization of overall changes in organisms. Nowadays, the breadth of emerging methodologies in proteomics is unprecedentedly vast, with constant optimization of technologies in sample processing, data collection, data analysis, and its scope of application is steadily transitioning from the bench to the clinic. Here, we offer an insightful review of the technical developments in proteomics and its applications in biomedicine over the past 5 years. We focus on its profound contributions in profiling disease spectra, discovering new biomarkers, identifying promising drug targets, deciphering alterations in protein conformation, and unearthing protein-protein interactions. Moreover, we summarize the cutting-edge technologies and potential breakthroughs in the proteomics pipeline and provide the principal challenges in proteomics. Based on these, we aspire to broaden the applicability of proteomics and inspire researchers to enhance our understanding of complex biological systems by utilizing such techniques.

Transcriptome profiling of maize transcription factor mutants to probe gene regulatory network predictions

Transcription factors play important roles in regulation of gene expression and phenotype. A variety of approaches have been utilized to develop gene regulatory networks to predict the regulatory targets for each transcription factor, such as yeast-1-hybrid screens and gene co-expression network analysis. Here we identified potential transcription factor targets and used a reverse genetics approach to test the predictions of several gene regulatory networks in maize. Loss-of-function mutant alleles were isolated for 22 maize transcription factors. These mutants did not exhibit obvious morphological phenotypes. However, transcriptomic profiling identified differentially expressed genes in each of the mutant genotypes, and targeted metabolic profiling indicated variable phenolic accumulation in some mutants. An analysis of expression levels for predicted target genes based on yeast-1-hybrid screens identified a small subset of predicted targets that exhibit altered expression levels. The analysis of predicted targets from gene co-expression network-based methods found significant enrichments for prediction sets of some transcription factors, but most predicted targets did not exhibit altered expression. This could result from false-positive gene co-expression network predictions, a transcription factor with a secondary regulatory role resulting in minor effects on gene regulation, or redundant gene regulation by other transcription factors. Collectively, these findings suggest that loss-of-function for single uncharacterized transcription factors might have limited phenotypic impacts but can reveal subsets of gene regulatory network predicted targets with altered expression.

ATAC and SAGA histone acetyltransferase modules facilitate transcription factor binding to nucleosomes independent of their acetylation activity

Transcription initiation involves the coordination of multiple events, starting with activators binding specific DNA target sequences, which recruit transcription coactivators to open chromatin and enable binding of general transcription factors and RNA polymerase II to promoters. Two key human transcriptional coactivator complexes, ATAC (ADA-two-A-containing) and SAGA (Spt-Ada-Gcn5 acetyltransferase), containing histone acetyltransferase (HAT) activity, target genomic loci to increase promoter accessibility. To better understand the function of ATAC and SAGA HAT complexes, we used in vitro biochemical and biophysical assays to characterize human ATAC and SAGA HAT module interactions with nucleosomes and how a transcription factor (TF) coordinates these interactions. We found that ATAC and SAGA HAT modules bind nucleosomes with high affinity, independent of their HAT activity and the tested TF. ATAC and SAGA HAT modules directly interact with the VP16 activator domain and this domain enhances acetylation activity of both HAT modules. Surprisingly, ATAC and SAGA HAT modules increase TF binding to its DNA target site within the nucleosome by an order of magnitude independent of histone acetylation. Altogether, our results reveal synergistic coordination between HAT modules and a TF, where ATAC and SAGA HAT modules (i) acetylate histones to open chromatin and (ii) facilitate TF targeting within nucleosomes independently of their acetylation activity.

Recent Advances in Mass Spectrometry-Based Protein Interactome Studies

The foundation of all biological processes is the network of diverse and dynamic protein interactions with other molecules in cells known as the interactome. Understanding the interactome is crucial for elucidating molecular mechanisms but has been a longstanding challenge. Recent developments in mass spectrometry (MS)-based techniques, including affinity purification, proximity labeling, cross-linking, and co-fractionation mass spectrometry (MS), have significantly enhanced our abilities to study the interactome. They do so by identifying and quantifying protein interactions yielding profound insights into protein organizations and functions. This review summarizes recent advances in MS-based interactomics, focusing on the development of techniques that capture protein-protein, protein-metabolite, and protein-nucleic acid interactions. Additionally, we discuss how integrated MS-based approaches have been applied to diverse biological samples, focusing on significant discoveries that have leveraged our understanding of cellular functions. Finally, we highlight state-of-the-art bioinformatic approaches for predictions of interactome and complex modeling, as well as strategies for combining experimental interactome data with computation methods, thereby enhancing the ability of MS-based techniques to identify protein interactomes. Indeed, advances in MS technologies and their integrations with computational biology provide new directions and avenues for interactome research, leveraging new insights into mechanisms that govern the molecular architecture of living cells and, thereby, our comprehension of biological processes.

A foundation model of transcription across human cell types

Transcriptional regulation, which involves a complex interplay between regulatory sequences and proteins, directs all biological processes. Computational models of transcription lack generalizability to accurately extrapolate to unseen cell types and conditions. Here we introduce GET (general expression transformer), an interpretable foundation model designed to uncover regulatory grammars across 213 human fetal and adult cell types1,2. Relying exclusively on chromatin accessibility data and sequence information, GET achieves experimental-level accuracy in predicting gene expression even in previously unseen cell types3. GET also shows remarkable adaptability across new sequencing platforms and assays, enabling regulatory inference across a broad range of cell types and conditions, and uncovers universal and cell-type-specific transcription factor interaction networks. We evaluated its performance in prediction of regulatory activity, inference of regulatory elements and regulators, and identification of physical interactions between transcription factors and found that it outperforms current models4 in predicting lentivirus-based massively parallel reporter assay readout5,6. In fetal erythroblasts7, we identified distal (greater than 1 Mbp) regulatory regions that were missed by previous models, and, in B cells, we identified a lymphocyte-specific transcription factor-transcription factor interaction that explains the functional significance of a leukaemia risk predisposing germline mutation8-10. In sum, we provide a generalizable and accurate model for transcription together with catalogues of gene regulation and transcription factor interactions, all with cell type specificity.

Nuclear Factor I Family Members are Key Transcription Factors Regulating Gene Expression

The Nuclear Factor I (NFI) family of transcription factors (TFs) plays key roles in cellular differentiation, proliferation, and homeostasis. As such, NFI family members engage in a large number of interactions with other proteins and chromatin. However, despite their well-established significance, the NFIs' interactomes, their dynamics, and their functions have not been comprehensively examined. Here, we employed complementary omics-level techniques, i.e. interactomics (affinity purification mass spectrometry (AP-MS) and proximity-dependent biotinylation (BioID)), and chromatin immunoprecipitation sequencing (ChIP-Seq), to obtain a comprehensive view of the NFI proteins and their interactions in different cell lines. Our analyses included all four NFI family members, and a less-studied short isoform of NFIB (NFIB4), which lacks the DNA binding domain. We observed that, despite exhibiting redundancy, each family member had unique high-confidence interactors and target genes, suggesting distinct roles within the transcriptional regulatory networks. The study revealed that NFIs interact with other TFs to co-regulate a broad range of regulatory networks and cellular processes. Notably, time-dependent proximity-labeling unveiled a highly dynamic nature of NFI protein-protein interaction networks and hinted at the temporal modulation of NFI interactions. Furthermore, gene ontology (GO) enrichment analysis of NFI interactome and targetome revealed the involvement of NFIs in transcriptional regulation, chromatin organization, cellular signaling pathways, and pathways related to cancer. Additionally, we observed that NFIB4 engages with proteins associated with mRNA regulation, which suggests that NFIs have roles beyond traditional DNA binding and transcriptional modulation. We propose that NFIs may function as potential pioneering TFs, given their role in regulating the DNA binding ability of other TFs and their interactions with key chromatin remodeling complexes, thereby influencing a wide range of cellular processes. These insights into NFI protein-protein interactions and their dynamic, context-dependent nature provide a deeper understanding of gene regulation mechanisms and hint at the role of NFIs as master regulators.

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.

PIN1 is a novel interaction partner and a negative upstream regulator of the transcription factor NFIB

NFIB is a transcription factor of the Nuclear Factor One (NFI) family that is essential for embryonic development. Post-translational control of NFIB or its upstream regulators have not been well characterized. Here, we show that PIN1 binds NFIB in a phosphorylation-dependent manner, via its WW domain. PIN1 interacts with the well-conserved N-terminal domains of all NFIs. Moreover, PIN1 attenuates the transcriptional activity of NFIB; this attenuation requires substrate binding by PIN1 but not its isomerase activity. Paradoxically, we found stabilization of NFIB by PIN1. We propose that PIN1 represses NFIB function not by regulating its abundance but by inducing a conformational change. These results identify NFIB as a novel PIN1 target and posit a role for PIN1 in post-translational regulation of NFIB and other NFIs.

diaPASEF-Powered Chemoproteomics Enables Deep Kinome Interaction Profiling

Protein-protein interactions (PPIs) underlie most biological functions. Devastating human conditions like cancers, neurological disorders, and infections, hijack PPI networks to initiate disease, and to drive disease progression. Understanding precisely how diseases remodel PPI networks can, therefore, help clarify disease mechanisms and identify therapeutic targets. Protein kinases control most cellular processes through protein phosphorylation. The 518 human kinases, known as the kinome, are frequently dysregulated in disease and highly druggable with ATP-competitive inhibitors. Kinase activity, localization, and substrate recognition are regulated by dynamic PPI networks composed of scaffolding and adapter proteins, other signaling enzymes like small GTPases and E3 ligases, and phospho-substrates. Accordingly, mapping kinase PPI networks can help determine kinome activation states, and, in turn, cellular activation states; this information can be used for studying kinase-mediated cell signaling, and for prioritizing kinases for drug discovery. Previously, we have developed a high-throughput method for kinome PPI mapping based on mass spectrometry (MS)-based chemoproteomics that we named kinobead competition and correlation analysis (kiCCA). Here, we introduce 2 nd generation (gen) kiCCA which utilizes data-independent acquisition (dia) with parallel accumulation serial fragmentation (PASEF) MS and a re-designed CCA algorithm with improved selection criteria and the ability to predict multiple kinase interaction partners of the same proteins. Using neuroblastoma cell line models of the noradrenergic-mesenchymal transition (NMT), we demonstrate that 2 nd gen kiCCA (1) identified 6.1-times more kinase PPIs in native cell extracts compared to our 1 st gen approach, (2) determined kinase-mediated signaling pathways that underly the neuroblastoma NMT, and (3) accurately predicted pharmacological targets for manipulating NMT states. Our 2 nd gen kiCCA method is broadly useful for cell signaling research and kinase drug discovery.

The molecular consequences of FOXF1 missense mutations associated with alveolar capillary dysplasia with misalignment of pulmonary veins

BACKGROUND

Alveolar capillary dysplasia with misalignment of pulmonary veins (ACD/MPV) is a fatal congenital lung disorder strongly associated with genomic alterations in the Forkhead box F1 (FOXF1) gene and its regulatory region. However, little is known about how FOXF1 genomic alterations cause ACD/MPV and what molecular mechanisms are affected by these mutations. Therefore, the effect of ACD/MPV patient-specific mutations in the FOXF1 gene on the molecular function of FOXF1 was studied.

METHODS

Epitope-tagged FOXF1 constructs containing one of the ACD/MPV-associated mutations were expressed in mammalian cell lines to study the effect of FOXF1 mutations on protein function. EMSA binding assays and luciferase assays were performed to study the effect on target gene binding and activation. Immunoprecipitation followed by SDS‒PAGE and western blotting were used to study protein‒protein interactions. Protein phosphorylation was studied using phos-tag western blotting.

RESULTS

An overview of the localization of ACD/MPV-associated FOXF1 mutations revealed that the G91-S101 region was frequently mutated. A three-dimensional model of the forkhead DNA-binding domain of FOXF1 showed that the G91-S101 region consists of an α-helix and is predicted to be important for DNA binding. We showed that FOXF1 missense mutations in this region differentially affect the DNA binding of the FOXF1 protein and influence the transcriptional regulation of target genes depending on the location of the mutation. Furthermore, we showed that some of these mutations can affect the FOXF1 protein at the posttranscriptional level, as shown by altered phosphorylation by MST1 and MST2 kinases.

CONCLUSION

Missense mutations in the coding region of the FOXF1 gene alter the molecular function of the FOXF1 protein at multiple levels, such as phosphorylation, DNA binding and target gene activation. These results indicate that FOXF1 molecular pathways may be differentially affected in ACD/MPV patients carrying missense mutations in the DNA-binding domain and may explain the phenotypic heterogeneity of ACD/MPV.

Development of compact transcriptional effectors using high-throughput measurements in diverse contexts

Transcriptional effectors are protein domains known to activate or repress gene expression; however, a systematic understanding of which effector domains regulate transcription across genomic, cell type and DNA-binding domain (DBD) contexts is lacking. Here we develop dCas9-mediated high-throughput recruitment (HT-recruit), a pooled screening method for quantifying effector function at endogenous target genes and test effector function for a library containing 5,092 nuclear protein Pfam domains across varied contexts. We also map context dependencies of effectors drawn from unannotated protein regions using a larger library tiling chromatin regulators and transcription factors. We find that many effectors depend on target and DBD contexts, such as HLH domains that can act as either activators or repressors. To enable efficient perturbations, we select context-robust domains, including ZNF705 KRAB, that improve CRISPRi tools to silence promoters and enhancers. We engineer a compact human activator called NFZ, by combining NCOA3, FOXO3 and ZNF473 domains, which enables efficient CRISPRa with better viral delivery and inducible control of chimeric antigen receptor T cells.

Rapid profiling of transcription factor-cofactor interaction networks reveals principles of epigenetic regulation

Transcription factor (TF)-cofactor (COF) interactions define dynamic, cell-specific networks that govern gene expression; however, these networks are understudied due to a lack of methods for high-throughput profiling of DNA-bound TF-COF complexes. Here, we describe the Cofactor Recruitment (CoRec) method for rapid profiling of cell-specific TF-COF complexes. We define a lysine acetyltransferase (KAT)-TF network in resting and stimulated T cells. We find promiscuous recruitment of KATs for many TFs and that 35% of KAT-TF interactions are condition specific. KAT-TF interactions identify NF-κB as a primary regulator of acutely induced histone 3 lysine 27 acetylation (H3K27ac). Finally, we find that heterotypic clustering of CBP/P300-recruiting TFs is a strong predictor of total promoter H3K27ac. Our data support clustering of TF sites that broadly recruit KATs as a mechanism for widespread co-occurring histone acetylation marks. CoRec can be readily applied to different cell systems and provides a powerful approach to define TF-COF networks impacting chromatin state and gene regulation.

Application of omics technologies in studies on antitumor effects of Traditional Chinese Medicine

Traditional Chinese medicine (TCM) is considered to be one of the most comprehensive and influential form of traditional medicine. It plays an important role in clinical treatment and adjuvant therapy for cancer. However, the complex composition of TCM presents challenges to the comprehensive and systematic understanding of its antitumor mechanisms, which hinders further development of TCM with antitumor effects. Omics technologies can immensely help in elucidating the mechanism of action of drugs. They utilize high-throughput sequencing and detection techniques to provide deeper insights into biological systems, revealing the intricate mechanisms through which TCM combats tumors. Multi-omics approaches can be used to elucidate the interrelationships among different omics layers by integrating data from various omics disciplines. By analyzing a large amount of data, these approaches further unravel the complex network of mechanisms underlying the antitumor effects of TCM and explain the mutual regulations across different molecular levels. In this study, we presented a comprehensive overview of the recent progress in single-omics and multi-omics research focused on elucidating the mechanisms underlying the antitumor effects of TCM. We discussed the significance of omics technologies in advancing research on the antitumor properties of TCM and also provided novel research perspectives and methodologies for further advancing this research field.

Blocker-SELEX: a structure-guided strategy for developing inhibitory aptamers disrupting undruggable transcription factor interactions

Despite the well-established significance of transcription factors (TFs) in pathogenesis, their utilization as pharmacological targets has been limited by the inherent challenges in modulating their protein interactions. The lack of defined small-molecule binding pockets and the nuclear localization of TFs do not favor the use of traditional tools. Aptamers possess large molecular weights, expansive blocking surfaces and efficient cellular internalization, making them compelling tools for modulating TF interactions. Here, we report a structure-guided design strategy called Blocker-SELEX to develop inhibitory aptamers (iAptamers) that selectively block TF interactions. Our approach leads to the discovery of iAptamers that cooperatively disrupt SCAF4/SCAF8-RNAP2 interactions, dysregulating RNAP2-dependent gene expression, which impairs cell proliferation. This approach is further applied to develop iAptamers blocking WDR5-MYC interactions. Overall, our study highlights the potential of iAptamers in disrupting pathogenic TF interactions, implicating their potential utility in studying the biological functions of TF interactions and in nucleic acids drug discovery.

The activity of early-life gene regulatory elements is hijacked in aging through pervasive AP-1-linked chromatin opening

A mechanistic connection between aging and development is largely unexplored. Through profiling age-related chromatin and transcriptional changes across 22 murine cell types, analyzed alongside previous mouse and human organismal maturation datasets, we uncovered a transcription factor binding site (TFBS) signature common to both processes. Early-life candidate cis-regulatory elements (cCREs), progressively losing accessibility during maturation and aging, are enriched for cell-type identity TFBSs. Conversely, cCREs gaining accessibility throughout life have a lower abundance of cell identity TFBSs but elevated activator protein 1 (AP-1) levels. We implicate TF redistribution toward these AP-1 TFBS-rich cCREs, in synergy with mild downregulation of cell identity TFs, as driving early-life cCRE accessibility loss and altering developmental and metabolic gene expression. Such remodeling can be triggered by elevating AP-1 or depleting repressive H3K27me3. We propose that AP-1-linked chromatin opening drives organismal maturation by disrupting cell identity TFBS-rich cCREs, thereby reprogramming transcriptome and cell function, a mechanism hijacked in aging through ongoing chromatin opening.

A proximity proteomics pipeline with improved reproducibility and throughput

Proximity labeling (PL) via biotinylation coupled with mass spectrometry (MS) captures spatial proteomes in cells. Large-scale processing requires a workflow minimizing hands-on time and enhancing quantitative reproducibility. We introduced a scalable PL pipeline integrating automated enrichment of biotinylated proteins in a 96-well plate format. Combining this with optimized quantitative MS based on data-independent acquisition (DIA), we increased sample throughput and improved protein identification and quantification reproducibility. We applied this pipeline to delineate subcellular proteomes across various compartments. Using the 5HT2A serotonin receptor as a model, we studied temporal changes of proximal interaction networks induced by receptor activation. In addition, we modified the pipeline for reduced sample input to accommodate CRISPR-based gene knockout, assessing dynamics of the 5HT2A network in response to perturbation of selected interactors. This PL approach is universally applicable to PL proteomics using biotinylation-based PL enzymes, enhancing throughput and reproducibility of standard protocols.

Real-Time Measurement of a Weak Interaction of a Transcription Factor Motif with a Protein Hub at Single-Molecule Precision

Transcription factors often interact with other protein cofactors, regulating gene expression. Direct detection of these brief events using existing technologies remains challenging due to their transient nature. In addition, intrinsically disordered domains, intranuclear location, and lack of cofactor-dependent active sites of transcription factors further complicate the quantitative analysis of these critical processes. Here, we create a genetically encoded label-free sensor to identify the interaction between a motif of the MYC transcription factor, a primary cancer driver, and WDR5, a chromatin-associated protein hub. Using an engineered nanopore equipped with this motif, WDR5 is probed through reversible captures and releases in a one-by-one and time-resolved fashion. Our single-molecule kinetic measurements indicate a weak-affinity interaction arising from a relatively slow complex association and a fast dissociation of WDR5 from the tethered motif. Further, we validate this subtle interaction by determinations in an ensemble using single nanodisc-wrapped nanopores immobilized on a biolayer interferometry sensor. This study also provides the proof-of-concept for a sensor that reveals unique recognition signatures of different protein binding sites. Our foundational work may be further developed to produce sensing elements for analytical proteomics and cancer nanomedicine.

IRF2BP2 counteracts the ATF7/JDP2 AP-1 heterodimer to prevent inflammatory overactivation in acute myeloid leukemia (AML) cells

Acute myeloid leukemia (AML) is a hematological malignancy characterized by abnormal proliferation and accumulation of immature myeloid cells in the bone marrow. Inflammation plays a crucial role in AML progression, but excessive activation of cell-intrinsic inflammatory pathways can also trigger cell death. IRF2BP2 is a chromatin regulator implicated in AML pathogenesis, although its precise role in this disease is not fully understood. In this study, we demonstrate that IRF2BP2 interacts with the AP-1 heterodimer ATF7/JDP2, which is involved in activating inflammatory pathways in AML cells. We show that IRF2BP2 is recruited by the ATF7/JDP2 dimer to chromatin and counteracts its gene-activating function. Loss of IRF2BP2 leads to overactivation of inflammatory pathways, resulting in strongly reduced proliferation. Our research indicates that a precise equilibrium between activating and repressive transcriptional mechanisms creates a pro-oncogenic inflammatory environment in AML cells. The ATF7/JDP2-IRF2BP2 regulatory axis is likely a key regulator of this process and may, therefore, represent a promising therapeutic vulnerability for AML. Thus, our study provides new insights into the molecular mechanisms underlying AML pathogenesis and identifies a potential therapeutic target for AML treatment.

GET: a foundation model of transcription across human cell types

Transcriptional regulation, involving the complex interplay between regulatory sequences and proteins, directs all biological processes. Computational models of transcription lack generalizability to accurately extrapolate in unseen cell types and conditions. Here, we introduce GET, an interpretable foundation model designed to uncover regulatory grammars across 213 human fetal and adult cell types. Relying exclusively on chromatin accessibility data and sequence information, GET achieves experimental-level accuracy in predicting gene expression even in previously unseen cell types. GET showcases remarkable adaptability across new sequencing platforms and assays, enabling regulatory inference across a broad range of cell types and conditions, and uncovering universal and cell type specific transcription factor interaction networks. We evaluated its performance on prediction of regulatory activity, inference of regulatory elements and regulators, and identification of physical interactions between transcription factors. Specifically, we show GET outperforms current models in predicting lentivirus-based massive parallel reporter assay readout with reduced input data. In fetal erythroblasts, we identify distal (>1Mbp) regulatory regions that were missed by previous models. In B cells, we identified a lymphocyte-specific transcription factor-transcription factor interaction that explains the functional significance of a leukemia-risk predisposing germline mutation. In sum, we provide a generalizable and accurate model for transcription together with catalogs of gene regulation and transcription factor interactions, all with cell type specificity.

Exploring affinity chromatography in proteomics: A comprehensive review

Over the past decades, the proteomics field has undergone rapid growth. Progress in mass spectrometry and bioinformatics, together with separation methods, has brought many innovative approaches to the study of the molecular biology of the cell. The potential of affinity chromatography was recognized immediately after its first application in proteomics, and since that time, it has become one of the cornerstones of many proteomic protocols. Indeed, this chromatographic technique exploiting the specific binding between two molecules has been employed for numerous purposes, from selective removal of interfering (over)abundant proteins or enrichment of scarce biomarkers in complex biological samples to mapping the post-translational modifications and protein interactions with other proteins, nucleic acids or biologically active small molecules. This review presents a comprehensive survey of this versatile analytical tool in current proteomics. To navigate the reader, the haphazard space of affinity separations is classified according to the experiment's aims and the separated molecule's nature. Different types of available ligands and experimental strategies are discussed in further detail for each of the mentioned procedures.

Mapping protein-protein interactions by mass spectrometry

Protein-protein interactions (PPIs) are essential for numerous biological activities, including signal transduction, transcription control, and metabolism. They play a pivotal role in the organization and function of the proteome, and their perturbation is associated with various diseases, such as cancer, neurodegeneration, and infectious diseases. Recent advances in mass spectrometry (MS)-based protein interactomics have significantly expanded our understanding of the PPIs in cells, with techniques that continue to improve in terms of sensitivity, and specificity providing new opportunities for the study of PPIs in diverse biological systems. These techniques differ depending on the type of interaction being studied, with each approach having its set of advantages, disadvantages, and applicability. This review highlights recent advances in enrichment methodologies for interactomes before MS analysis and compares their unique features and specifications. It emphasizes prospects for further improvement and their potential applications in advancing our knowledge of PPIs in various biological contexts.

A proximity proteomics pipeline with improved reproducibility and throughput

Proximity labeling (PL) through biotinylation coupled with mass spectrometry (MS) has emerged as a powerful technique for capturing spatial proteomes within living cells. Large-scale sample processing for proximity proteomics requires a workflow that minimizes hands-on time while enhancing quantitative reproducibility. Here, we present a scalable PL pipeline integrating automated enrichment of biotinylated proteins in a 96-well plate format. By combining this pipeline with an optimized quantitative MS acquisition method based on data-independent acquisition (DIA), we not only significantly increased sample throughput but also improved the reproducibility of protein identification and quantification. We applied this pipeline to delineate subcellular proteomes across various cellular compartments, including endosomes, late endosomes/lysosomes, the Golgi apparatus, and the plasma membrane. Moreover, employing 5HT2A serotonin receptor as a model, we investigated temporal changes of proximal interaction networks induced by the receptor's activation with serotonin. Finally, to demonstrate the applicability of our PL pipeline across multiple experimental conditions, we further modified the PL pipeline for reduced sample input amounts to accommodate CRISPR-based gene knockout, and assessed the dynamics of the 5HT2A network in response to the perturbation of selected proximal interactors. Importantly, the presented PL approach is universally applicable to PL proteomics using biotinylation-based PL enzymes, increasing both throughput and reproducibility of standard protocols.

Promoter Methylation Leads to Hepatocyte Nuclear Factor 4A Loss and Pancreatic Cancer Aggressiveness

Background and Aims

Decoding pancreatic ductal adenocarcinoma heterogeneity and the consequent therapeutic selection remains a challenge. We aimed to characterize epigenetically regulated pathways involved in pancreatic ductal adenocarcinoma progression.

Methods

Global DNA methylation analysis in pancreatic cancer patient tissues and cell lines was performed to identify differentially methylated genes. Targeted bisulfite sequencing and in vitro methylation reporter assays were employed to investigate the direct link between site-specific methylation and transcriptional regulation. A series of in vitro loss-of-function and gain-of function studies and in vivo xenograft and the KPC (LSL-Kras G12D/+ ; LSL-Trp53 R172H/+ ; Pdx1-Cre) mouse models were used to assess pancreatic cancer cell properties. Gene and protein expression analyses were performed in 3 different cohorts of pancreatic cancer patients and correlated to clinicopathological parameters.

Results

We identify Hepatocyte Nuclear Factor 4A (HNF4A) as a novel target of hypermethylation in pancreatic cancer and demonstrate that site-specific proximal promoter methylation drives HNF4A transcriptional repression. Expression analyses in patients indicate the methylation-associated suppression of HNF4A expression in pancreatic cancer tissues. In vitro and in vivo studies reveal that HNF4A is a novel tumor suppressor in pancreatic cancer, regulating cancer growth and aggressiveness. As evidenced in both the KPC mouse model and human pancreatic cancer tissues, HNF4A expression declines significantly in the early stages of the disease. Most importantly, HNF4 loss correlates with poor overall patient survival.

Conclusion

HNF4A silencing, mediated by promoter DNA methylation, drives pancreatic cancer development and aggressiveness leading to poor patient survival.

Widespread variation in molecular interactions and regulatory properties among transcription factor isoforms

Most human Transcription factors (TFs) genes encode multiple protein isoforms differing in DNA binding domains, effector domains, or other protein regions. The global extent to which this results in functional differences between isoforms remains unknown. Here, we systematically compared 693 isoforms of 246 TF genes, assessing DNA binding, protein binding, transcriptional activation, subcellular localization, and condensate formation. Relative to reference isoforms, two-thirds of alternative TF isoforms exhibit differences in one or more molecular activities, which often could not be predicted from sequence. We observed two primary categories of alternative TF isoforms: "rewirers" and "negative regulators", both of which were associated with differentiation and cancer. Our results support a model wherein the relative expression levels of, and interactions involving, TF isoforms add an understudied layer of complexity to gene regulatory networks, demonstrating the importance of isoform-aware characterization of TF functions and providing a rich resource for further studies.

Rapid profiling of transcription factor-cofactor interaction networks reveals principles of epigenetic regulation

Transcription factor (TF)-cofactor (COF) interactions define dynamic, cell-specific networks that govern gene expression; however, these networks are understudied due to a lack of methods for high-throughput profiling of DNA-bound TF-COF complexes. Here we describe the Cofactor Recruitment (CoRec) method for rapid profiling of cell-specific TF-COF complexes. We define a lysine acetyltransferase (KAT)-TF network in resting and stimulated T cells. We find promiscuous recruitment of KATs for many TFs and that 35% of KAT-TF interactions are condition specific. KAT-TF interactions identify NF-κB as a primary regulator of acutely induced H3K27ac. Finally, we find that heterotypic clustering of CBP/P300-recruiting TFs is a strong predictor of total promoter H3K27ac. Our data supports clustering of TF sites that broadly recruit KATs as a mechanism for widespread co-occurring histone acetylation marks. CoRec can be readily applied to different cell systems and provides a powerful approach to define TF-COF networks impacting chromatin state and gene regulation.

Exploring Options for Proximity-Dependent Biotinylation Experiments: Comparative Analysis of Labeling Enzymes and Affinity Purification Resins

Proximity-dependent biotinylation (PDB) techniques provide information about the molecular neighborhood of a protein of interest, yielding insights into its function and localization. Here, we assessed how different labeling enzymes and streptavidin resins influence PDB results. We compared the high-confidence interactors of the DNA/RNA-binding protein transactive response DNA-binding protein 43 kDa (TDP-43) identified using either miniTurbo (biotin ligase) or APEX2 (peroxidase) enzymes. We also evaluated two commercial affinity resins for purification of biotinylated proteins: conventional streptavidin sepharose versus a new trypsin-resistant streptavidin conjugated to magnetic resin, which significantly reduces the level of contamination by streptavidin peptides following on-bead trypsin digestion. Downstream analyses involved liquid chromatography coupled to mass spectrometry in data-dependent acquisition mode, database searching, and statistical analysis of high-confidence interactors using SAINTexpress. The APEX2-TDP-43 experiment identified more interactors than miniTurbo-TDP-43, although miniTurbo provided greater overlap with previously documented TDP-43 interactors. Purifications on sepharose resin yielded more interactors than magnetic resin in small-scale experiments using a range of magnetic resin volumes. We suggest that resin-specific background protein binding profiles and different lysate-to-resin ratios cumulatively affect the distributions of prey protein abundance in experimental and control samples, which impact statistical confidence scores. Overall, we highlight key experimental variables to consider for the empirical optimization of PDB experiments.

Dynamic YAP expression in the non-parenchymal liver cell compartment controls heterologous cell communication

INTRODUCTION

The Hippo pathway and its transcriptional effectors yes-associated protein (YAP) and transcriptional coactivator with PDZ-binding motif (TAZ) are targets for cancer therapy. It is important to determine if the activation of one factor compensates for the inhibition of the other. Moreover, it is unknown if YAP/TAZ-directed perturbation affects cell-cell communication of non-malignant liver cells.

MATERIALS AND METHODS

To investigate liver-specific phenotypes caused by YAP and TAZ inactivation, we generated mice with hepatocyte (HC) and biliary epithelial cell (BEC)-specific deletions for both factors (YAPKO, TAZKO and double knock-out (DKO)). Immunohistochemistry, single-cell sequencing, and proteomics were used to analyze liver tissues and serum.

RESULTS

The loss of BECs, liver fibrosis, and necrosis characterized livers from YAPKO and DKO mice. This phenotype was weakened in DKO tissues compared to specimens from YAPKO animals. After depletion of YAP in HCs and BECs, YAP expression was induced in non-parenchymal cells (NPCs) in a cholestasis-independent manner. YAP positivity was detected in subgroups of Kupffer cells (KCs) and endothelial cells (ECs). The secretion of pro-inflammatory chemokines and cytokines such as C-X-C motif chemokine ligand 11 (CXCL11), fms-related receptor tyrosine kinase 3 ligand (FLT3L), and soluble intercellular adhesion molecule-1 (ICAM1) was increased in the serum of YAPKO animals. YAP activation in NPCs could contribute to inflammation via TEA domain transcription factor (TEAD)-dependent transcriptional regulation of secreted factors.

CONCLUSION

YAP inactivation in HCs and BECs causes liver damage, and concomitant TAZ deletion does not enhance but reduces this phenotype. Additionally, we present a new mechanism by which YAP contributes to cell-cell communication originating from NPCs.

Interaction network of human early embryonic transcription factors

Embryonic genome activation (EGA) occurs during preimplantation development and is characterized by the initiation of de novo transcription from the embryonic genome. Despite its importance, the regulation of EGA and the transcription factors involved in this process are poorly understood. Paired-like homeobox (PRDL) family proteins are implicated as potential transcriptional regulators of EGA, yet the PRDL-mediated gene regulatory networks remain uncharacterized. To investigate the function of PRDL proteins, we are identifying the molecular interactions and the functions of a subset family of the Eutherian Totipotent Cell Homeobox (ETCHbox) proteins, seven PRDL family proteins and six other transcription factors (TFs), all suggested to participate in transcriptional regulation during preimplantation. Using mass spectrometry-based interactomics methods, AP-MS and proximity-dependent biotin labeling, and chromatin immunoprecipitation sequencing we derive the comprehensive regulatory networks of these preimplantation TFs. By these interactomics tools we identify more than a thousand high-confidence interactions for the 21 studied bait proteins with more than 300 interacting proteins. We also establish that TPRX2, currently assigned as pseudogene, is a transcriptional activator.

Unveiling the structure and interactions of SOG1, a NAC domain transcription factor: An in-silico perspective

SOG1 is a crucial plant-specific NAC domain family transcription factor and functions as the central regulator of DNA damage response, acting downstream of ATM and ATR kinases. In this study, various in-silico approaches have been employed for the characterization of SOG1 transcription factor in a comparative manner with its orthologues from various plant species. Amino acid sequences of more than a hundred SOG1 or SOG1-like proteins were retrieved and their relationship was determined through phylogenetic and motif analyses. Various physiochemical properties and secondary structural components of SOG1 orthologues were determined in selective plant species including Arabidopsis thaliana, Oryza sativa, Amborella trichopoda, and Physcomitrella patens. Furthermore, fold recognition or threading and homology-based three-dimensional models of SOG1 were constructed followed by subsequent evaluation of quality and accuracy of the generated protein models. Finally, extensive DNA-Protein and Protein-Protein interaction studies were performed using the HADDOCK server to give an insight into the mechanism of how SOG1 binds with the promoter region of its target genes or interacts with other proteins to regulate the DNA damage responses in plants. Our docking analysis data have shown the molecular mechanism of SOG1's binding with 5'-CTT(N)7AAG-3' and 5'-(N)4GTCAA(N)4-3' consensus sequences present in the promoter region of its target genes. Moreover, SOG1 physically interacts and forms a thermodynamically stable complex with NAC103 and BRCA1 proteins, which possibly serve as coactivators or mediators in the transcription regulatory network of SOG1. Overall, our in-silico study will provide meaningful information regarding the structural and functional characterization of the SOG1 transcription factor.

Novel Nuclear Roles for Testis-Specific ACTL7A and ACTL7B Supported by In Vivo Characterizations and AI Facilitated In Silico Mechanistic Modeling with Implications for Epigenetic Regulation in Spermiogenesis

A mechanistic role for nuclear function of testis-specific actin related proteins (ARPs) is proposed here through contributions of ARP subunit swapping in canonical chromatin regulatory complexes. This is significant to our understanding of both mechanisms controlling regulation of spermiogenesis, and the expanding functional roles of the ARPs in cell biology. Among these roles, actins and ARPs are pivotal not only in cytoskeletal regulation, but also in intranuclear chromatin organization, influencing gene regulation and nucleosome remodeling. This study focuses on two testis-specific ARPs, ACTL7A and ACTL7B, exploring their intranuclear activities and broader implications utilizing combined in vivo, in vitro, and in silico approaches. ACTL7A and ACTL7B, previously associated with structural roles, are hypothesized here to serve in chromatin regulation during germline development. This study confirms the intranuclear presence of ACTL7B in spermatocytes and round spermatids, revealing a potential role in intranuclear processes, and identifies a putative nuclear localization sequence conserved across mammalian ACTL7B, indicating a potentially unique mode of nuclear transport which differs from conventional actin. Ablation of ACTL7B leads to varied transcriptional changes reported here. Additionally, in the absence of ACTL7A or ACTL7B there is a loss of intranuclear localization of HDAC1 and HDAC3, which are known regulators of epigenetic associated acetylation changes that in turn regulate gene expression. Thus, these HDACs are implicated as contributors to the aberrant gene expression observed in the KO mouse testis transcriptomic analysis. Furthermore, this study employed and confirmed the accuracy of in silico models to predict ARP interactions with Helicase-SANT-associated (HSA) domains, uncovering putative roles for testis-specific ARPs in nucleosome remodeling complexes. In these models, ACTL7A and ACTL7B were found capable of binding to INO80 and SWI/SNF nucleosome remodeler family members in a manner akin to nuclear actin and ACTL6A. These models thus implicate germline-specific ARP subunit swapping within chromatin regulatory complexes as a potential regulatory mechanism for chromatin and associated molecular machinery adaptations in nuclear reorganizations required during spermiogenesis. These results hold implications for male fertility and epigenetic programing in the male-germline that warrant significant future investigation. In summary, this study reveals that ACTL7A and ACTL7B play intranuclear gene regulation roles in male gametogenesis, adding to the multifaceted roles identified also spanning structural, acrosomal, and flagellar stability. ACTL7A and ACTL7B unique nuclear transport, impact on HDAC nuclear associations, impact on transcriptional processes, and proposed mechanism for involvement in nucleosome remodeling complexes supported by AI facilitated in silico modeling contribute to a more comprehensive understanding of the indispensable functions of ARPs broadly in cell biology, and specifically in male fertility.

Identification of immune-related regulatory networks and diagnostic biomarkers in thyroid eye disease

BACKGROUND

Thyroid eye disease (TED) is an orbit-associated autoimmune inflammatory disorder intricately linked to immune dysregulation. Complete pathogenesis of TED remains elusive. This work aimed to mine pathogenesis of TED from immunological perspective and identify diagnostic genes.

METHODS

Gene expression microarray data for TED patients were downloaded from Gene Expression Omnibus, immune-related genes (IRGs) were from ImmPort database, and TED-related transcription factors (TFs) were from Cirtrome Cancer database. Differential analysis, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed. Regulatory networks of TFs and IRGs were constructed with Cytoscape. Diagnostic biomarkers in TED were identified through LASSO. Immune cell infiltration analysis was performed using CIBERSORT.

RESULTS

Twenty-three immune-related DEmRNAs were revealed and were primarily enriched in humoral immune response, positive regulation of inflammatory response, IL-17, and TNF pathways. Co-expression regulatory network included four TFs and 16 immune-related DEmRNAs. Seven diagnostic genes were identified, with Area Under the Curve (AUC) of 0.993 for training set and AUC value of 0.836 for validation set. TED patients exhibited elevated infiltration levels by macrophages M2, mast cells, and CD8 T cells among 22 immune cell types, whereas macrophages M2 and mast cells resting were significantly lower than normal group.

CONCLUSIONS

The seven feature genes had high diagnostic value for TED patients. Our work explored regulatory network and diagnostic biomarkers, laying theoretical basis for TED diagnosis and treatment.

Revealing protein trafficking by proximity labeling-based proteomics

Protein trafficking is a fundamental process with profound implications for both intracellular and intercellular functions. Proximity labeling (PL) technology has emerged as a powerful tool for capturing precise snapshots of subcellular proteomes by directing promiscuous enzymes to specific cellular locations. These enzymes generate reactive species that tag endogenous proteins, enabling their identification through mass spectrometry-based proteomics. In this comprehensive review, we delve into recent advancements in PL-based methodologies, placing particular emphasis on the label-and-fractionation approach and TransitID, for mapping proteome trafficking. These methodologies not only facilitate the exploration of dynamic intracellular protein trafficking between organelles but also illuminate the intricate web of intercellular and inter-organ protein communications.

Molecular Atlas of HER2+ Breast Cancer Cells Treated with Endogenous Ligands: Temporal Insights into Mechanisms of Trastuzumab Resistance

Trastuzumab therapy in HER2+ breast cancer patients has mixed success owing to acquired resistance to therapy. A detailed understanding of downstream molecular cascades resulting from trastuzumab resistance is yet to emerge. In this study, we investigate the cellular mechanisms underlying acquired resistance using trastuzumab-sensitive and -resistant cancer cells (BT474 and BT474R) treated with endogenous ligands EGF and HRG across time. We probe early receptor organization through microscopy and signaling events through multiomics measurements and assess the bioenergetic state through mitochondrial measurements. Integrative analyses of our measurements reveal significant alterations in EGF-treated BT474 HER2 membrane dynamics and robust downstream activation of PI3K/AKT/mTORC1 signaling. EGF-treated BT474R shows a sustained interferon-independent activation of the IRF1/STAT1 cascade, potentially contributing to trastuzumab resistance. Both cell lines exhibit temporally divergent metabolic demands and HIF1A-mediated stress responses. BT474R demonstrates inherently increased mitochondrial activity. HRG treatment in BT474R leads to a pronounced reduction in AR expression, affecting downstream lipid metabolism with implications for treatment response. Our results provide novel insights into mechanistic changes underlying ligand treatment in BT474 and BT474R and emphasize the pivotal role of endogenous ligands. These results can serve as a framework for furthering the understanding of trastuzumab resistance, with therapeutic implications for women with acquired resistance.

Comparative analysis of models in predicting the effects of SNPs on TF-DNA binding using large-scale in vitro and in vivo data

Non-coding variants associated with complex traits can alter the motifs of transcription factor (TF)-deoxyribonucleic acid binding. Although many computational models have been developed to predict the effects of non-coding variants on TF binding, their predictive power lacks systematic evaluation. Here we have evaluated 14 different models built on position weight matrices (PWMs), support vector machines, ordinary least squares and deep neural networks (DNNs), using large-scale in vitro (i.e. SNP-SELEX) and in vivo (i.e. allele-specific binding, ASB) TF binding data. Our results show that the accuracy of each model in predicting SNP effects in vitro significantly exceeds that achieved in vivo. For in vitro variant impact prediction, kmer/gkm-based machine learning methods (deltaSVM_HT-SELEX, QBiC-Pred) trained on in vitro datasets exhibit the best performance. For in vivo ASB variant prediction, DNN-based multitask models (DeepSEA, Sei, Enformer) trained on the ChIP-seq dataset exhibit relatively superior performance. Among the PWM-based methods, tRap demonstrates better performance in both in vitro and in vivo evaluations. In addition, we find that TF classes such as basic leucine zipper factors could be predicted more accurately, whereas those such as C2H2 zinc finger factors are predicted less accurately, aligning with the evolutionary conservation of these TF classes. We also underscore the significance of non-sequence factors such as cis-regulatory element type, TF expression, interactions and post-translational modifications in influencing the in vivo predictive performance of TFs. Our research provides valuable insights into selecting prioritization methods for non-coding variants and further optimizing such models.

Dual DNA/RNA-binding factor regulates dynamics of hnRNP splicing condensates

Despite decades of research, mechanisms by which co-transcriptional alternative splicing events are targeted to the correct genomic locations to drive cell fate decisions remain unknown. By combining structural and molecular approaches, we define a new mechanism by which an essential transcription factor (TF) targets co-transcriptional splicing through physical and functional interaction with RNA and RNA binding proteins (RBPs). We show that an essential TF co-transcriptionally regulates sex-specific alternative splicing by directly interacting with a subset of target RNAs on chromatin and modulating the dynamics of hnRNPA2 homolog nuclear splicing condensates.