Multiome Perturb-seq unlocks scalable discovery of integrated perturbation effects on the transcriptome and epigenome.

Single-cell CRISPR screens link genetic perturbations to transcriptional states, but high-throughput methods connecting these induced changes to their regulatory foundations are limited. Here we introduce Multiome Perturb-seq, extending single-cell CRISPR screens to simultaneously measure perturbation-induced changes in gene expression and chromatin accessibility. We apply Multiome Perturb-seq in a CRISPRi screen of 13 chromatin remodelers in human RPE-1 cells, achieving efficient assignment of sgRNA identities to single nuclei via an improved method for capturing barcode transcripts from nuclear RNA. We organize expression and accessibility measurements into coherent programs describing the integrated effects of perturbations on cell state, finding that ARID1A and SUZ12 knockdowns induce programs enriched for developmental features. Pseudotime analysis of perturbations connects accessibility changes to changes in gene expression, highlighting the value of multimodal profiling. Overall, our method provides a scalable and simply implemented system to dissect the regulatory logic underpinning cell state.

Sex-biased Regulation of Extracellular Matrix Genes in COPD.

Compared to men, women often develop COPD at an earlier age with worse respiratory symptoms despite lower smoking exposure. However, most preventive, and therapeutic strategies ignore biological sex differences in COPD. Our goal was to better understand sex-specific gene regulatory processes in lung tissue and the molecular basis for sex differences in COPD onset and severity. We analyzed lung tissue gene expression and DNA methylation data from 747 individuals in the Lung Tissue Research Consortium (LTRC), and 85 individuals in an independent dataset. We identified sex differences in COPD-associated gene regulation using gene regulatory networks. We used linear regression to test for sex-biased associations of methylation with lung function, emphysema, smoking, and age. Analyzing gene regulatory networks in the control group, we identified that genes involved in the extracellular matrix (ECM) have higher transcriptional factor targeting in females than in males. However, this pattern is reversed in COPD, with males showing stronger regulatory targeting of ECM-related genes than females. Smoking exposure, age, lung function, and emphysema were all associated with sex-specific differential methylation of ECM-related genes. We identified sex-based gene regulatory patterns of ECM-related genes associated with lung function and emphysema. Multiple factors including epigenetics, smoking, aging, and cell heterogeneity influence sex-specific gene regulation in COPD. Our findings underscore the importance of considering sex as a key factor in disease susceptibility and severity.

Gene regulatory networks reveal sex difference in lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma (LUAD) has been observed to have significant sex differences in incidence, prognosis, and response to therapy. However, the molecular mechanisms responsible for these disparities have not been investigated extensively. METHODS: Sample-specific gene regulatory network methods were used to analyze RNA sequencing data from non-cancerous human lung samples from The Genotype Tissue Expression Project (GTEx) and lung adenocarcinoma primary tumor samples from The Cancer Genome Atlas (TCGA); results were validated on independent data. RESULTS: We found that genes associated with key biological pathways including cell proliferation, immune response and drug metabolism are differentially regulated between males and females in both healthy lung tissue and tumor, and that these regulatory differences are further perturbed by tobacco smoking. We also discovered significant sex bias in transcription factor targeting patterns of clinically actionable oncogenes and tumor suppressor genes, including AKT2 and KRAS. Using differentially regulated genes between healthy and tumor samples in conjunction with a drug repurposing tool, we identified several small-molecule drugs that might have sex-biased efficacy as cancer therapeutics and further validated this observation using an independent cell line database. CONCLUSIONS: These findings underscore the importance of including sex as a biological variable and considering gene regulatory processes in developing strategies for disease prevention and management.

Lung adenocarcinoma (LUAD) is a disease that affects males and females differently. Biological sex not only influences chances of developing the disease, but also how the disease progresses and how effective various therapies may be. We analyzed sex-specific gene regulatory networks consisting of transcription factors and the genes they regulate in both healthy lung tissue and in LUAD and identified sex-biased differences. We found that genes associated with cell proliferation, immune response, and drug metabolism are differentially targeted by transcription factors between males and females. We also found that several genes that are drug targets in LUAD, are also regulated differently between males and females. Importantly, these differences are also influenced by an individual's smoking history. Extending our analysis using a drug repurposing tool, we found candidate drugs with evidence that they might work better for one sex or the other. These results demonstrate that considering the differences in gene regulation between males and females will be essential if we are to develop precision medicine strategies for preventing and treating LUAD.

Optimal Inference of Hidden Markov Models Through Expert-Acquired Data.

This paper focuses on inferring a general class of hidden Markov models (HMMs) using data acquired from experts. Expert-acquired data contain decisions/actions made by humans/users for various objectives, such as navigation data reflecting drivers' behavior, cybersecurity data carrying defender decisions, and biological data containing the biologist's actions (e.g., interventions and experiments). Conventional inference methods rely on temporal changes in data without accounting for expert knowledge. This paper incorporates expert knowledge into the inference of HMMs by modeling expert behavior as an imperfect reinforcement learning agent. The proposed method optimally quantifies experts' perceptions about the system model, which, alongside the temporal changes in data, contributes to the inference process. The proposed inference method is derived through a combination of dynamic programming and optimal recursive Bayesian estimation. The applicability of this method is demonstrated to a wide range of inference criteria, such as maximum likelihood and maximum a posteriori. The performance of the proposed method is investigated through a comprehensive numerical experiment using a benchmark problem and biological networks.

A Low Expression of NRF2 Enhances Oxidative Stress and Autophagy in Myofibroblasts, Promoting Progression of Chronic Obstructive Pulmonary Disease.

BACKGROUND: The inflammation phenotypes are often closely related to oxidative stress and autophagy pathway activation, which could be developed as a treatment target. AIMS: The aim of this study was to explore the underlying mechanism of inflammation in chronic obstructive pulmonary disease (COPD). METHODS: The lung tissue single-cell RNA-seq (scRNA-seq) dataset of GSE171541 was downloaded from the Gene Expression Omnibus (GEO) database. The marker genes were obtained from the CellMarker database. "Seurat" and "harmony" R packages were used for single-cell profiling analysis. Then, the "AUCell" R package was employed to calculate the reactive oxygen species (ROS) and autophagy pathway scores. Gene regulation network analysis was performed by applying the "SCENIC" package, followed by conducting correlation analysis with Spearman's rank correlation method. The cigarettes were used to develop a traumatic model in mice, and the expression of relevant genes was measured by qRT-PCR. RESULTS: The scRNA-seq analysis classified 12 cell subgroups in which the contractility of myofibroblasts was closely associated with the progression of COPD. Further analysis showed that ROS and autophagy pathways were significantly activated in myofibroblasts and that the nuclear factor erythroid 2-related factor 2 (NRF2) and its mediated oxidative stress pathway were inhibited in myofibroblasts. In addition, the downregulated NRF2 gene was negatively correlated with the expression of autophagy and ROS activation. In the traumatic mice model, NRF2 was downregulated in COPD mice but further elevated in the COPD+NRF2 mice group. Interestingly, the mRNA levels of Kelchlike ECH-associated protein 1 (Keap1), NADPH oxidase (NOX), and Cathepsin B (CTSB) were upregulated in COPD group in comparison to the control group but they were downregulated by NRF2. These results suggested that low-expressed NFR2 promoted autophagy and ROS pathway activation in myofibroblasts for COPD progression. CONCLUSION: We identified a cell myofibroblast cluster closely associated with COPD progression using the scRNA-seq analysis. The downregulated NFR2, as a key risk factor, mediated myofibroblast death by activating the oxidative stress and autophagy pathway for COPD progression.

Dynamics and bifurcations in genetic circuits with fibration symmetries.

Circuit building blocks of gene regulatory networks (GRN) have been identified through the fibration symmetries of the underlying biological graph. Here, we analyse analytically six of these circuits that occur as functional and synchronous building blocks in these networks. Of these, the lock-on, toggle switch, Smolen oscillator, feed-forward fibre and Fibonacci fibre circuits occur in living organisms, notably Escherichia coli; the sixth, the repressilator, is a synthetic GRN. We consider synchronous steady states determined by a fibration symmetry (or balanced colouring) and determine analytic conditions for local bifurcation from such states, which can in principle be either steady-state or Hopf bifurcations. We identify conditions that characterize the first bifurcation, the only one that can be stable near the bifurcation point. We model the state of each gene in terms of two variables: mRNA and protein concentration. We consider all possible 'admissible' models-those compatible with the network structure-and then specialize these general results to simple models based on Hill functions and linear degradation. The results systematically classify using graph symmetries the complexity and dynamics of these circuits, which are relevant to understand the functionality of natural and synthetic cells.

Nonlinear causal discovery with confounders.

This article introduces a causal discovery method to learn nonlinear relationships in a directed acyclic graph with correlated Gaussian errors due to confounding. First, we derive model identifiability under the sublinear growth assumption. Then, we propose a novel method, named the Deconfounded Functional Structure Estimation (DeFuSE), consisting of a deconfounding adjustment to remove the confounding effects and a sequential procedure to estimate the causal order of variables. We implement DeFuSE via feedforward neural networks for scalable computation. Moreover, we establish the consistency of DeFuSE under an assumption called the strong causal minimality. In simulations, DeFuSE compares favorably against state-of-the-art competitors that ignore confounding or nonlinearity. Finally, we demonstrate the utility and effectiveness of the proposed approach with an application to gene regulatory network analysis. The Python implementation is available at https://github.com/chunlinli/defuse.

Integrated transcriptomic and proteomic analysis of exogenous abscisic acid regulation on tuberous root development in Pseudostellaria heterophylla.

Abscisic acid (ABA) significantly regulates plant growth and development, promoting tuberous root formation in various plants. However, the molecular mechanisms of ABA in the tuberous root development of Pseudostellaria heterophylla are not yet fully understood. This study utilized Illumina sequencing and de novo assembly strategies to obtain a reference transcriptome associated with ABA treatment. Subsequently, integrated transcriptomic and proteomic analyses were used to determine gene expression profiles in P. heterophylla tuberous roots. ABA treatment significantly increases the diameter and shortens the length of tuberous roots. Clustering analysis identified 2,256 differentially expressed genes and 679 differentially abundant proteins regulated by ABA. Gene co-expression and protein interaction networks revealed ABA positively induced 30 vital regulators. Furthermore, we identified and assigned putative functions to transcription factors (PhMYB10, PhbZIP2, PhbZIP, PhSBP) that mediate ABA signaling involved in the regulation of tuberous root development, including those related to cell wall metabolism, cell division, starch synthesis, hormone metabolism. Our findings provide valuable insights into the complex signaling networks of tuberous root development modulated by ABA. It provided potential targets for genetic manipulation to improve the yield and quality of P. heterophylla, which could significantly impact its cultivation and medicinal value.

Peripheral whole blood microRNA expression in relation to vascular function: a population-based study.

BACKGROUND: As key regulators of gene expression, microRNAs affect many cardiovascular mechanisms and have been associated with several cardiovascular diseases. In this study, we aimed to investigate the relation of whole blood microRNAs with several quantitative measurements of vascular function, and explore their biological role through an integrative microRNA-gene expression analysis. METHODS: Peripheral whole blood microRNA expression was assessed through RNA-Seq in 2606 participants (45.8% men, mean age: 53.93, age range: 30 to 95 years) from the Rhineland Study, an ongoing population-based cohort study in Bonn, Germany. Weighted gene co-expression network analysis was used to cluster microRNAs with highly correlated expression levels into 14 modules. Through linear regression models, we investigated the association between each module's expression and quantitative markers of vascular health, including pulse wave velocity, total arterial compliance index, cardiac index, stroke index, systemic vascular resistance index, reactive skin hyperemia and white matter hyperintensity burden. For each module associated with at least one trait, one or more hub-microRNAs driving the association were defined. Hub-microRNAs were further characterized through mapping to putative target genes followed by gene ontology pathway analysis. RESULTS: Four modules, represented by hub-microRNAs miR-320 family, miR-378 family, miR-3605-3p, miR-6747-3p, miR-6786-3p, and miR-330-5p, were associated with total arterial compliance index. Importantly, the miR-320 family module was also associated with white matter hyperintensity burden, an effect partially mediated through arterial compliance. Furthermore, hub-microRNA miR-192-5p was related to cardiac index. Functional analysis corroborated the relevance of the identified microRNAs for vascular function by revealing, among others, enrichment for pathways involved in blood vessel morphogenesis and development, angiogenesis, telomere organization and maintenance, and insulin secretion. CONCLUSIONS: We identified several microRNAs robustly associated with cardiovascular function, especially arterial compliance and cardiac output. Moreover, our results highlight miR-320 as a regulator of cerebrovascular damage, partly through modulation of vascular function. As many of these microRNAs were involved in biological processes related to vasculature development and aging, our results contribute to the understanding of vascular physiology and provide putative targets for cardiovascular disease prevention.

Mining key genes associated with phosphorus deficiency through genome-wide identification and characterization of cucumber SPX family genes.

BACKGROUND: Proteins harboring the SPX domain are crucial for the regulation of phosphate (Pi) homeostasis in plants. This study aimed to identify and analyze the entire SPX gene family within the cucumber genome. RESULTS: The cucumber genome encompassed 16 SPX domain-containing genes, which were distributed across six chromosomes and categorized into four distinct subfamilies: SPX, SPX-MFS, SPX-EXS and SPX-RING, based on their structure characteristics. Additionally, gene duplications and synteny analysis were conducted for CsSPXs, revealing that their promoter regions were enriched with a variety of hormone-responsive, biotic/abiotic stress and typical P1BS-related elements. Tissue expression profiling of CsSPX genes revealed that certain members were specifically expressed in particular organs, suggesting essential roles in cucumber growth and development. Under low Pi stress, CsSPX1 and CsSPX2 exhibited a particularly strong response to Pi starvation. It was observed that the cucumber cultivar Xintaimici displayed greater tolerance to low Pi compared to black-spined cucumber under low Pi stress conditions. Protein interaction networks for the 16 CsSPX proteins were predicted, and yeast two-hybrid assay revealed that CsPHR1 interacted with CsSPX2, CsSPX3, CsSPX4 and CsSPX5, implying their involvement in the Pi signaling pathway in conjunction with CsPHR1. CONCLUSION: This research lays the foundation for further exploration of the function of the CsSPX genes in response to low Pi stress and for elucidating the underlying mechanism.

Integrative multi-omics increase resolution of the sea urchin posterior gut gene regulatory network at single cell level.

Drafting gene regulatory networks (GRNs) requires embryological knowledge pertaining to the cell type families, information on the regulatory genes, causal data from gene knockdown experiments and validations of the identified interactions by cis-regulatory analysis. We use multi-omics involving next-generation sequencing (-seq) to obtain the necessary information for drafting Strongylocentrotus purpuratus posterior gut GRN. Here we present an update to the GRN using i) a single cell RNA-seq derived cell atlas highlighting the 2 day post fertilization (dpf) sea urchin gastrula cell type families, as well as the genes expressed at single cell level, ii) a set of putative cis-regulatory modules and transcription factor (TF) binding sites obtained from chromatin accessibility ATAC-seq data, and iii) interactions directionality obtained from differential bulk RNA-seq following knockdown of the TF Sp-Pdx1, a key regulator of gut patterning in sea urchins. Combining these datasets, we draft the GRN for the hindgut Sp-Pdx1 positive cells in the 2 dpf gastrula embryo. Overall, our data suggests the complex connectivity of the posterior gut GRN and increases the resolution of gene regulatory cascades operating within it.

TFTF: An R-Based Integrative Tool for Decoding Human Transcription Factor-Target Interactions.

Transcription factors (TFs) are crucial in modulating gene expression and sculpting cellular and organismal phenotypes. The identification of TF-target gene interactions is pivotal for comprehending molecular pathways and disease etiologies but has been hindered by the demanding nature of traditional experimental approaches. This paper introduces a novel web application and package utilizing the R program, which predicts TF-target gene relationships and vice versa. Our application integrates the predictive power of various bioinformatic tools, leveraging their combined strengths to provide robust predictions. It merges databases for enhanced precision, incorporates gene expression correlation for accuracy, and employs pan-tissue correlation analysis for context-specific insights. The application also enables the integration of user data with established resources to analyze TF-target gene networks. Despite its current limitation to human data, it provides a platform to explore gene regulatory mechanisms comprehensively. This integrated, systematic approach offers researchers an invaluable tool for dissecting the complexities of gene regulation, with the potential for future expansions to include a broader range of species.

DeepIMAGER: Deeply Analyzing Gene Regulatory Networks from scRNA-seq Data.

Understanding the dynamics of gene regulatory networks (GRNs) across diverse cell types poses a challenge yet holds immense value in unraveling the molecular mechanisms governing cellular processes. Current computational methods, which rely solely on expression changes from bulk RNA-seq and/or scRNA-seq data, often result in high rates of false positives and low precision. Here, we introduce an advanced computational tool, DeepIMAGER, for inferring cell-specific GRNs through deep learning and data integration. DeepIMAGER employs a supervised approach that transforms the co-expression patterns of gene pairs into image-like representations and leverages transcription factor (TF) binding information for model training. It is trained using comprehensive datasets that encompass scRNA-seq profiles and ChIP-seq data, capturing TF-gene pair information across various cell types. Comprehensive validations on six cell lines show DeepIMAGER exhibits superior performance in ten popular GRN inference tools and has remarkable robustness against dropout-zero events. DeepIMAGER was applied to scRNA-seq datasets of multiple myeloma (MM) and detected potential GRNs for TFs of RORC, MITF, and FOXD2 in MM dendritic cells. This technical innovation, combined with its capability to accurately decode GRNs from scRNA-seq, establishes DeepIMAGER as a valuable tool for unraveling complex regulatory networks in various cell types.

DeepGRNCS: deep learning-based framework for jointly inferring gene regulatory networks across cell subpopulations.

Inferring gene regulatory networks (GRNs) allows us to obtain a deeper understanding of cellular function and disease pathogenesis. Recent advances in single-cell RNA sequencing (scRNA-seq) technology have improved the accuracy of GRN inference. However, many methods for inferring individual GRNs from scRNA-seq data are limited because they overlook intercellular heterogeneity and similarities between different cell subpopulations, which are often present in the data. Here, we propose a deep learning-based framework, DeepGRNCS, for jointly inferring GRNs across cell subpopulations. We follow the commonly accepted hypothesis that the expression of a target gene can be predicted based on the expression of transcription factors (TFs) due to underlying regulatory relationships. We initially processed scRNA-seq data by discretizing data scattering using the equal-width method. Then, we trained deep learning models to predict target gene expression from TFs. By individually removing each TF from the expression matrix, we used pre-trained deep model predictions to infer regulatory relationships between TFs and genes, thereby constructing the GRN. Our method outperforms existing GRN inference methods for various simulated and real scRNA-seq datasets. Finally, we applied DeepGRNCS to non-small cell lung cancer scRNA-seq data to identify key genes in each cell subpopulation and analyzed their biological relevance. In conclusion, DeepGRNCS effectively predicts cell subpopulation-specific GRNs. The source code is available at https://github.com/Nastume777/DeepGRNCS.

Single-nucleus multi-omics of Parkinson's disease reveals a glutamatergic neuronal subtype susceptible to gene dysregulation via alteration of transcriptional networks.

The genetic architecture of Parkinson's disease (PD) is complex and multiple brain cell subtypes are involved in the neuropathological progression of the disease. Here we aimed to advance our understanding of PD genetic complexity at a cell subtype precision level. Using parallel single-nucleus (sn)RNA-seq and snATAC-seq analyses we simultaneously profiled the transcriptomic and chromatin accessibility landscapes in temporal cortex tissues from 12 PD compared to 12 control subjects at a granular single cell resolution. An integrative bioinformatic pipeline was developed and applied for the analyses of these snMulti-omics datasets. The results identified a subpopulation of cortical glutamatergic excitatory neurons with remarkably altered gene expression in PD, including differentially-expressed genes within PD risk loci identified in genome-wide association studies (GWAS). This was the only neuronal subtype showing significant and robust overexpression of SNCA. Further characterization of this neuronal-subpopulation showed upregulation of specific pathways related to axon guidance, neurite outgrowth and post-synaptic structure, and downregulated pathways involved in presynaptic organization and calcium response. Additionally, we characterized the roles of three molecular mechanisms in governing PD-associated cell subtype-specific dysregulation of gene expression: (1) changes in cis-regulatory element accessibility to transcriptional machinery; (2) changes in the abundance of master transcriptional regulators, including YY1, SP3, and KLF16; (3) candidate regulatory variants in high linkage disequilibrium with PD-GWAS genomic variants impacting transcription factor binding affinities. To our knowledge, this study is the first and the most comprehensive interrogation of the multi-omics landscape of PD at a cell-subtype resolution. Our findings provide new insights into a precise glutamatergic neuronal cell subtype, causal genes, and non-coding regulatory variants underlying the neuropathological progression of PD, paving the way for the development of cell- and gene-targeted therapeutics to halt disease progression as well as genetic biomarkers for early preclinical diagnosis.

Developing the 'omic toolkit of comparative physiologists.

Typical 'omic analyses reduce complex biological systems to simple lists of supposedly independent variables, failing to account for changes in the wider transcriptional landscape. In this commentary, we discuss the utility of network approaches for incorporating this wider context into the study of physiological phenomena. We highlight opportunities to build on traditional network tools by utilising cutting-edge techniques to account for higher order interactions (i.e. beyond pairwise associations) within datasets, allowing for more accurate models of complex 'omic systems. Finally, we show examples of previous works utilising network approaches to gain additional insight into their organisms of interest. As 'omics grow in both their popularity and breadth of application, so does the requirement for flexible analytical tools capable of interpreting and synthesising complex datasets.

Inferring Interaction Networks from Transcriptomic Data: Methods and Applications.

Transcriptomic data is a treasure trove in modern molecular biology, as it offers a comprehensive viewpoint into the intricate nuances of gene expression dynamics underlying biological systems. This genetic information must be utilized to infer biomolecular interaction networks that can provide insights into the complex regulatory mechanisms underpinning the dynamic cellular processes. Gene regulatory networks and protein-protein interaction networks are two major classes of such networks. This chapter thoroughly investigates the wide range of methodologies used for distilling insightful revelations from transcriptomic data that include association-based methods (based on correlation among expression vectors), probabilistic models (using Bayesian and Gaussian models), and interologous methods. We reviewed different approaches for evaluating the significance of interactions based on the network topology and biological functions of the interacting molecules and discuss various strategies for the identification of functional modules. The chapter concludes with highlighting network-based techniques of prioritizing key genes, outlining the centrality-based, diffusion- based, and subgraph-based methods. The chapter provides a meticulous framework for investigating transcriptomic data to uncover assembly of complex molecular networks for their adaptable analyses across a broad spectrum of biological domains.

Physiological Roles of an Acinetobacter-specific σ Factor.

The Gram-negative pathogen Acinetobacter baumannii is considered an "urgent threat" to human health due to its propensity to become antibiotic resistant. Understanding the distinct regulatory paradigms used by A. baumannii to mitigate cellular stresses may uncover new therapeutic targets. Many γ-proteobacteria use the extracytoplasmic function (ECF) σ factor, RpoE, to invoke envelope homeostasis networks in response to stress. Acinetobacter species contain the poorly characterized ECF "SigAb;" however, it is unclear if SigAb has the same physiological role as RpoE. Here, we show that SigAb is a metal stress-responsive ECF that appears unique to Acinetobacter species and distinct from RpoE. We combine promoter mutagenesis, motif scanning, and ChIP-seq to define the direct SigAb regulon, which consists of sigAb itself, the stringent response mediator, relA, and the uncharacterized small RNA, "sabS." However, RNA-seq of strains overexpressing SigAb revealed a large, indirect regulon containing hundreds of genes. Metal resistance genes are key elements of the indirect regulon, as CRISPRi knockdown of sigAb or sabS resulted in increased copper sensitivity and excess copper induced SigAb-dependent transcription. Further, we found that two uncharacterized genes in the sigAb operon, "aabA" and "aabB", have anti-SigAb activity. Finally, employing a targeted Tn-seq approach that uses CRISPR-associated transposons, we show that sigAb, aabA, and aabB are important for fitness even during optimal growth conditions. Our work reveals new physiological roles for SigAb and SabS, provides a novel approach for assessing gene fitness, and highlights the distinct regulatory architecture of A. baumannii.

Early B-Cell Factor 1: An Archetype for a Lineage-Restricted Transcription Factor Linking Development to Disease.

The development of highly specialized blood cells from hematopoietic stem cells (HSCs) in the bone marrow (BM) is dependent upon a stringently orchestrated network of stage- and lineage-restricted transcription factors (TFs). Thus, the same stem cell can give rise to various types of differentiated blood cells. One of the key regulators of B-lymphocyte development is early B-cell factor 1 (EBF1). This TF belongs to a small, but evolutionary conserved, family of proteins that harbor a Zn-coordinating motif and an IPT/TIG (immunoglobulin-like, plexins, transcription factors/transcription factor immunoglobulin) domain, creating a unique DNA-binding domain (DBD). EBF proteins play critical roles in diverse developmental processes, including body segmentation in the Drosophila melanogaster embryo, and retina formation in mice. While several EBF family members are expressed in neuronal cells, adipocytes, and BM stroma cells, only B-lymphoid cells express EBF1. In the absence of EBF1, hematopoietic progenitor cells (HPCs) fail to activate the B-lineage program. This has been attributed to the ability of EBF1 to act as a pioneering factor with the ability to remodel chromatin, thereby creating a B-lymphoid-specific epigenetic landscape. Conditional inactivation of the Ebf1 gene in B-lineage cells has revealed additional functions of this protein in relation to the control of proliferation and apoptosis. This may explain why EBF1 is frequently targeted by mutations in human leukemia cases. This chapter provides an overview of the biochemical and functional properties of the EBF family proteins, with a focus on the roles of EBF1 in normal and malignant B-lymphocyte development.

The prognostic impact of pathogenic stromal cell-associated genes in lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma (LUAD) stands as the most prevalent subtype among lung cancers. Interactions between stromal and cancer cells influence tumor growth, invasion, and metastasis. However, the regulatory mechanisms of stromal cells in the lung adenocarcinoma tumor microenvironment remain unclear. This study seeks to elucidate the regulatory connections among critical pathogenic genes and their associated expression variations within distinct stromal cell subtypes. METHOD: Analysis and investigation were conducted on a total of 114,019 single-cell RNA data and 346 The Cancer Genome Atlas (TCGA) LUAD-related samples using bioinformatics and statistical algorithms. Differential gene expression analysis was performed for tumor samples and controls, followed by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Differential genes between stromal cells and other cell clusters were identified and intersected with the differential genes from TCGA. We employed a combination of LASSO regression and multivariable Cox regression to identify the ultimate set of pathogenic gene. Survival models were trained to predict the relationship between patient survival and these pathogenic genes. Analysis of transcription factor (TF) cell specificity and pseudotime trajectories within stromal cell subpopulations revealed that vascular endothelial cells (ECs) and matrix cancer-associated fibroblasts (CAFs) are key in regulation of the prognosis-associated genes CAV2, COL1A1, TIMP1, ETS2, AKAP12, ID1 and COL1A2. RESULTS: Seven pathogenic genes associated with LUAD in stromal cells were identified and used to develop a survival model. High expression of these genes is linked to a greater risk of poor survival. Stromal cells were categorized into eight subtypes and one unannotated cluster. Mesothelial cells, vascular endothelial cells (ECs), and matrix cancer-associated fibroblasts (CAFs) showed cell-specific regulation of the pathogenic genes. CONCLUSIONS: The seven disease-causing genes in vascular ECs and matrix CAFs can be used to detect the survival status of LUAD patients, providing new directions for future targeted drug design.