GTRD: an integrated view of transcription regulation

Machine learning identification of a novel vasculogenic mimicry-related signature and FOXM1's role in promoting vasculogenic mimicry in clear cell renal cell carcinoma

BACKGROUND

Clear Cell Renal Cell Carcinoma (ccRCC), the predominant subtype of renal cell carcinoma (RCC), ranks among the most common malignancies worldwide. Vasculogenic mimicry (VM) plays a pivotal role in tumor progression, being closely linked with heightened chemoresistance and adverse prognosis in cancer patients. Nonetheless, the broader impact of vasculogenic mimicry-related genes (VRGs) on ccRCC patient prognosis, tumor microenvironment characteristics, and treatment response remains incompletely understood.

METHODS

Consensus clustering identified VRG-associated subtypes. We developed a machine learning framework integrating 12 algorithms to establish a consistent VM-related signature (VRG_score). The predictive value of VRG_score for ccRCC prognosis and treatment response was assessed. FOXM1's clinical relevance was explored using the UCLCAN database. FOXM1 expression in tumor and adjacent tissues was assessed using Western Blotting, IHC, RNA-seq, and Chip-qPCR methods, and its regulatory mechanism was confirmed.

RESULTS

We examined VRG mutation and expression patterns in ccRCC at the gene level, identifying two distinct molecular clusters. A consensus VRG_score was formulated using a machine learning computational framework and Cox regression, displaying strong predictive power for prognosis and clinical translation. Additionally, FOXM1 was found to be upregulated in ccRCC, correlating with clinical pathological features and positively regulating PYCR1, thereby activating the PI3K/AKT/mTOR signaling pathway and promoting VM formation.

CONCLUSION

This study constructed a VM-related signature and revealed that FOXM1 promotes VM formation in renal cell carcinoma through the PYCR1-PI3K/AKT/mTOR signaling axis, serving as a prognostic indicator and potential therapeutic target.

Dysregulation of REST and its target genes impacts the fate of neural progenitor cells in down syndrome

Increasing shreds of evidence suggest that neurogenic-to-gliogenic shift may be critical to the abnormal neurodevelopment observed in individuals with Down syndrome (DS). REST, the Repressor Element-1 Silencing Transcription factor, regulates the differentiation and development of neural cells. Downregulation of REST may lead to defects in post-differentiation neuronal morphology in the brain of the DS fetal. This study aims to elucidate the role of REST in DS-derived NPCs using bioinformatics analyses and laboratory validations. We identified and validated vital REST-targeted DEGs: CD44, TGFB1, FN1, ITGB1, and COL1A1. Interestingly, these genes are involved in neurogenesis and gliogenesis in DS-derived NPCs. Furthermore, we identified nuclear REST loss and the neuroblast marker, DCX, was downregulated in DS human trisomic induced pluripotent stem cells (hiPSCs)-derived NPCs, whereas the glioblast marker, NFIA, was upregulated. Our findings indicate that the loss of REST is critical in the neurogenic-to-gliogenic shift observed in DS-derived NPCs. REST and its target genes may collectively regulate the NPC phenotype.

Clonally expanded, targetable, natural killer-like NKG7 T cells seed the aged spinal cord to disrupt myeloid-dependent wound healing

Spinal cord injury (SCI) increasingly affects aged individuals, where functional impairment and mortality are highest. However, the aging-dependent mechanisms underpinning tissue damage remain elusive. Here, we find that natural killer-like T (NKLT) cells seed the intact aged human and murine spinal cord and multiply further after injury. NKLT cells accumulate in the spinal cord via C-X-C motif chemokine receptor 6 and ligand 16 signaling to clonally expand by engaging with major histocompatibility complex (MHC)-I-expressing myeloid cells. NKLT cells expressing natural killer cell granule protein 7 (Nkg7) disrupt myeloid-cell-dependent wound healing in the aged injured cord. Nkg7 deletion in mice curbs NKLT cell degranulation to normalize the myeloid cell phenotype, thus promoting tissue repair and axonal integrity. Monoclonal antibodies neutralizing CD8+ T cells after SCI enhance neurological recovery by promoting wound healing. Our results unveil a reversible role for NKG7+CD8+ NKLT cells in exacerbating tissue damage, suggesting a clinically relevant treatment for SCI.

Asymmetry of Motif Conservation Within Their Homotypic Pairs Distinguishes DNA-Binding Domains of Target Transcription Factors in ChIP-Seq Data

Transcription factors (TFs) are the main regulators of eukaryotic gene expression. The cooperative binding of at least two TFs to genomic DNA is a major mechanism of transcription regulation. Massive analysis of the co-occurrence of overrepresented pairs of motifs for different target TFs studied in ChIP-seq experiments can clarify the mechanisms of TF cooperation. We categorized the target TFs from M. musculus ChIP-seq and A. thaliana ChIP-seq/DAP-seq experiments according to the structure of their DNA-binding domains (DBDs) into classes. We studied homotypic pairs of motifs, using the same recognition model for each motif. Asymmetric and symmetric pairs consist of motifs of remote and close recognition scores. We found that asymmetric pairs of motifs predominate for all TF classes. TFs from the murine/plant 'Basic helix-loop-helix (bHLH)', 'Basic leucine zipper (bZIP)', and 'Tryptophan cluster' classes and murine 'p53 domain' and 'Rel homology region' classes showed the highest enrichment of asymmetric homotypic pairs of motifs. Pioneer TFs, despite their DBD types, have a higher significance of asymmetry within homotypic pairs of motifs compared to other TFs. Asymmetry within homotypic CEs is a promising new feature decrypting the mechanisms of gene transcription regulation.

Dissecting the functions and regulatory mechanisms of disulfidoptosis-related RPN1 in pan-cancer: modulation of immune microenvironment and cellular senescence

Introduction

Cancer's inherent heterogeneity, marked by diverse genetic and molecular alterations, presents significant challenges for developing effective treatments. One such alteration is the regulation of disulfidoptosis, a recently discovered programmed cell death pathway. RPN1, a key regulator associated with disulfidoptosis, may influence various aspects of tumor biology, including immune evasion and cellular senescence. This study aims to dissect the role of RPN1 in pan-cancer and its potential as a therapeutic target.

Methods

We employed a pan-cancer analysis to explore RPN1 expression and its association with clinical outcomes across multiple tumor types. Immune cell infiltration and expression of immune checkpoint genes were analyzed in relation to RPN1. Additionally, cellular senescence markers were assessed in RPN1 knockdown tumor cells. Gene regulatory mechanisms were studied through gene copy number variations, DNA methylation analysis, and transcriptional regulation by SP1.

Results

RPN1 is overexpressed in a wide range of tumor types and correlates with poor clinical outcomes, including overall survival, disease-specific survival, and progression-free intervals. Our analysis shows that RPN1 is involved in immune evasion, correlating with the presence of myeloid dendritic cells, macrophages, and tumor-associated fibroblasts, and influencing T-cell activity. RPN1 knockdown led to reduced tumor cell proliferation and induced cellular senescence, marked by increased senescence-associated biomarkers and β-galactosidase activity. RPN1 expression was found to be regulated by gene copy number variations, reduced DNA methylation, and transcriptional control via SP1.

Discussion

These findings highlight RPN1 as a key pan-cancer regulator, influencing immune microenvironment interactions and cellular senescence. The regulation of disulfidoptosis by RPN1 presents a promising avenue for therapeutic intervention. Targeting RPN1 could enhance immunotherapy efficacy and help mitigate tumor progression, offering a potential strategy for cancer treatment.

CircRREB1 Mediates Metabolic Reprogramming and Stemness Maintenance to Facilitate Pancreatic Ductal Adenocarcinoma Progression

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal tumor with limited treatment options and poor patient survival. Circular RNAs (circRNA) play crucial regulatory roles in the occurrence and development of various cancers, including PDAC. In this study, using circRNA sequencing of diverse PDAC samples, we identified circRREB1 as an oncogenic circRNA that is significantly upregulated in PDAC and is correlated with an unfavorable patient prognosis. Functionally, loss of circRREB1 markedly inhibited glycolysis and stemness, whereas elevated circRREB1 elicited the opposite effects. Mechanistically, circRREB1 interacted with PGK1, disrupting the association between PTEN and PGK1 and increasing PGK1 phosphorylation to activate glycolytic flux. Moreover, circRREB1 promoted WNT7B transcription by directly interacting with YBX1 and facilitating its nuclear translocation, consequently activating the Wnt/β-catenin signaling pathway to maintain PDAC stemness. Overall, these results highlight circRREB1 as a key regulator of metabolic and stemness properties of PDAC. Significance: CircRREB1 stimulates PGK1 to induce glycolysis and activates the Wnt/β-catenin signaling pathway to maintain stemness in pancreatic cancer, indicating the potential of circRREB1 as a biomarker and therapeutic target.

Genome-wide epigenetic profiling and transcriptome analysis in pediatric Obstructive Sleep Apnea: A focus on Black female children

Obstructive Sleep Apnea (OSA) is a common sleep-related breathing disorder characterized by airway obstruction during sleep. Diagnosing pediatric OSA is challenging, particularly in underrepresented populations, leading to disparities in treatment and long-term negative health outcomes. Our study aimed to identify alternative diagnostic tools by investigating genome-wide epigenetic changes and associated transcriptomic alterations in Black female, pediatric patients with OSA. Whole-genome bisulfite sequencing and RNA sequencing were performed on saliva samples from healthy controls and children with OSA. Analysis of differential methylation and gene expression patterns revealed dysregulated inflammation and metabolism pathways in children with OSA. Chromosomes 19 and 22 exhibited elevated methylation signatures in this patient population. Integration of methylation and gene expression data identified specific molecular markers, including NAP1L4, CCR1, and LIF. The study emphasizes the need to consider both genetic and environmental factors in pediatric OSA, and the identified markers may offer avenues for further research.

RNA-seq analysis and in vivo experiments identified the protective effect of kaempferol on idiopathic pulmonary fibrosis by regulating the PPARG/TNC signaling pathway to reduce ECM deposition

Idiopathic pulmonary fibrosis (IPF) is a chronic age-related lung disease with a high mortality rate. Kaempferol (KMP), an active ingredient in common plants and foods with anti-inflammatory, antioxidant and immunomodulatory properties, has been shown to be effective against fibrotic diseases. However, the molecular mechanisms underlying the treatment of IPF with KMP remain unclear. Therefore, IPF mice were established by intratracheal instillation of bleomycin (BLM) to explore the efficacy and underlying mechanism of KMP in the treatment of IPF. We found that KMP improved the body weight changes of BLM-induced IPF mice, alleviated inflammatory infiltration and collagen deposition, and decreased the expression levels of hydroxyproline, α-SMA, Col3a1, Mmp2, Timp1, Vim, Fn, TNF-α, TGF-β1, IL-6 and IL-8, while up-regulating the expression E-cadherin in lung tissues. The transcriptomic results showed that KMP may exert therapeutic effects against IPF by regulating the PPARG/TNC signaling pathway to reduce extracellular matrix (ECM) deposition. Interestingly, ROC curve analysis suggested that TNC and PPARG had good diagnostic performance for IPF, and TF prediction revealed that PPARG is an important upstream gene regulating TNC, and the IF experiment confirmed the co-localization of TNC and PPARG. Molecular docking showed that KMP bound well to PPARG and TNC, and IF results revealed that KMP significantly reduced the interaction between PPARG and TNC. Furthermore, RT-PCR, WB, IHC and IF experiments confirmed that KMP elevated the expression of PPARG and inhibited the expression of TNC, thus inhibiting the ECM-receptor interaction pathway and ultimately serving as a therapeutic treatment for IPF mice. These findings revealed that KMP reduced inflammatory infiltration and collagen deposition in the lungs of IPF mice and that the PPARG/TNC signaling pathway may be an important mechanism for the treatment of IPF with KMP, which provides a new perspective for the development of therapeutic approaches for IPF.

Gene module reconstruction identifies cellular differentiation processes and the regulatory logic of specialized secretion in zebrafish

During differentiation, cells become structurally and functionally specialized, but comprehensive views of the underlying remodeling processes are elusive. Here, we leverage single-cell RNA sequencing (scRNA-seq) developmental trajectories to reconstruct differentiation using two secretory tissues as models-the zebrafish notochord and hatching gland. First, we integrated expression and functional similarities to identify gene modules, revealing dozens of modules representing known and newly associated differentiation processes and their dynamics. Second, we focused on the unfolded protein response (UPR) transducer module to study how general versus cell-type-specific secretory functions are regulated. Profiling loss- and gain-of-function embryos identified that the UPR transcription factors creb3l1, creb3l2, and xbp1 are master regulators of a general secretion program. creb3l1/creb3l2 additionally activate an extracellular matrix secretion program, while xbp1 partners with bhlha15 to activate a gland-like secretion program. Our study presents module identification via multi-source integration for reconstructing differentiation (MIMIR) and illustrates how transcription factors confer general and specialized cellular functions.

Aberrant expression of collagen type X in solid tumor stroma is associated with EMT, immunosuppressive and pro-metastatic pathways, bone marrow stromal cell signatures, and poor survival prognosis

Background

Collagen type X (ColXα1, encoded by COL10A1) is expressed specifically in the cartilage-to-bone transition, in bone marrow cells, and in osteoarthritic (OA) cartilage. We have previously shown that ColXα1 is expressed in breast tumor stroma, correlates with tumor-infiltrating lymphocytes, and predicts poor adjuvant therapy outcomes in ER+/HER2+ breast cancer. However, the underlying molecular mechanisms for these effects are unknown. In this study, we performed bioinformatic analysis of COL10A1-associated gene modules in breast and pancreatic cancer as well as in cells from bone marrow and OA cartilage. These findings provide important insights into the mechanisms of transcriptional and extracellular matrix changes which impact the local stromal microenvironment and tumor progression.

Methods

Immunohistochemistry was performed to examine collagen type X expression in solid tumors. WGCNA was used to generate COL10A1-associated gene networks in breast and pancreatic tumor cohorts using RNA-Seq data from The Cancer Genome Atlas. Computational analysis was employed to assess the impact of these gene networks on development and progression of cancer and OA. Data processing and statistical analysis was performed using R and various publicly-available computational tools.

Results

Expression of COL10A1 and its associated gene networks highlights inflammatory and immunosuppressive microenvironments, which identify aggressive breast and pancreatic tumors and contribute to metastatic potential in a sex-dependent manner. Both cancer types are enriched in stroma, and COL10A1 implicates bone marrow-derived fibroblasts as drivers of the epithelial-to-mesenchymal transition (EMT) in these tumors. Heightened expression of COL10A1 and its associated gene networks is correlated with poorer patient outcomes in both breast and pancreatic cancer. Common transcriptional changes and chondrogenic activity are shared between cancer and OA cartilage, suggesting that similar microenvironmental alterations may underlie both diseases.

Conclusions

COL10A1-associated gene networks may hold substantial value as regulators and biomarkers of aggressive tumor phenotypes with implications for therapy development and clinical outcomes. Identification of tumors which exhibit high expression of COL10A1 and its associated genes may reveal the presence of bone marrow-derived stromal microenvironments with heightened EMT capacity and metastatic potential. Our analysis may enable more effective risk assessment and more precise treatment of patients with breast and pancreatic cancer.

Perspectives on Codebook: sequence specificity of uncharacterized human transcription factors

We describe an effort ("Codebook") to determine the sequence specificity of 332 putative and largely uncharacterized human transcription factors (TFs), as well as 61 control TFs. Nearly 5,000 independent experiments across multiple in vitro and in vivo assays produced motifs for just over half of the putative TFs analyzed (177, or 53%), of which most are unique to a single TF. The data highlight the extensive contribution of transposable elements to TF evolution, both in cis and trans, and identify tens of thousands of conserved, base-level binding sites in the human genome. The use of multiple assays provides an unprecedented opportunity to benchmark and analyze TF sequence specificity, function, and evolution, as further explored in accompanying manuscripts. 1,421 human TFs are now associated with a DNA binding motif. Extrapolation from the Codebook benchmarking, however, suggests that many of the currently known binding motifs for well-studied TFs may inaccurately describe the TF's true sequence preferences.

Automatic construction of Petri net models for computational simulations of molecular interaction network

Petri nets are commonly applied in modeling biological systems. However, construction of a Petri net model for complex biological systems is often time consuming, and requires expertise in the research area, limiting their application. To address this challenge, we developed GINtoSPN, an R package that automates the conversion of multi-omics molecular interaction network extracted from the Global Integrative Network (GIN) into Petri nets in GraphML format. These GraphML files can be directly used for Signaling Petri Net (SPN) simulation. To demonstrate the utility of this tool, we built a Petri net model for neurofibromatosis type I. Simulation of NF1 gene knockout, compared to normal skin fibroblast cells, revealed persistent accumulation of Ras-GTPs as expected. Additionally, we identified several other genes substantially affected by the loss of NF1's function, exhibiting individual-specific variability. These results highlight the effectiveness of GINtoSPN in streamlining the modeling and simulation of complex biological systems.

Stem cell transcriptional profiles from mouse subspecies reveal cis-regulatory evolution at translation genes

A key goal of evolutionary genomics is to harness molecular data to draw inferences about selective forces that have acted on genomes. The field progresses in large part through the development of advanced molecular-evolution analysis methods. Here we explored the intersection between classical sequence-based tests for selection and an empirical expression-based approach, using stem cells from Mus musculus subspecies as a model. Using a test of directional, cis-regulatory evolution across genes in pathways, we discovered a unique program of induction of translation genes in stem cells of the Southeast Asian mouse M. m. castaneus relative to its sister taxa. We then mined population-genomic sequences to pursue underlying regulatory mechanisms for this expression divergence, finding robust evidence for alleles unique to M. m. castaneus at the upstream regions of the translation genes. We interpret our data under a model of changes in lineage-specific pressures across Mus musculus in stem cells with high translational capacity. Our findings underscore the rigor of integrating expression and sequence-based methods to generate hypotheses about evolutionary events from long ago.

Cell-free DNA assay for malignancy classification of high-risk lung nodules

OBJECTIVE

Although low-dose computed tomography has been proven effective to reduce lung cancer-specific mortality, a considerable proportion of surgically resected high-risk lung nodules were still confirmed pathologically benign. There is an unmet need of a novel method for malignancy classification in lung nodules.

METHODS

We recruited 307 patients with high-risk lung nodules who underwent curative surgery, and 247 and 60 cases were pathologically confirmed malignant and benign lung lesions, respectively. Plasma samples from each patient were collected before surgery and performed low-depth (5×) whole-genome sequencing. We extracted cell-free DNA characteristics and determined radiomic features. We built models to classify the malignancy using our data and further validated models with 2 independent lung nodule cohorts.

RESULTS

Our models using one type of profile were able to distinguish lung cancer and benign lung nodules at an area under the curve metrics of 0.69 to 0.91 in the study cohort. Integrating all the 5 base models using cell-free DNA profiles, the cell-free DNA-based ensemble model achieved an area under the curve of 0.95 (95% CI, 0.92-0.97) in the study cohort and 0.98 (95% CI, 0.96-1.00) in the validation cohort. At a specificity of 95.0%, the sensitivity reached 80.0% in the study cohort. With the same threshold, the specificity and sensitivity had similar performances in both validation cohorts. Furthermore, the performance of area under the curve reached 0.97 in both the study and validation cohorts when considering the radiomic profile.

CONCLUSIONS

The cell-free DNA profiles-based method is an efficient noninvasive tool to distinguish malignancies and high-risk but pathologically benign lung nodules.

Distinct negative-sense RNA viruses induce a common set of transcripts encoding proteins forming an extensive network

The large group of negative-strand RNA viruses (NSVs) comprises many important pathogens. To identify conserved patterns in host responses, we systematically compared changes in the cellular RNA levels after infection of human hepatoma cells with nine different NSVs of different virulence degrees. RNA sequencing experiments indicated that the amount of viral RNA in host cells correlates with the number of differentially expressed host cell transcripts. Time-resolved differential gene expression analysis revealed a common set of 178 RNAs that are regulated by all NSVs analyzed. A newly developed open access web application allows downloads and visualizations of all gene expression comparisons for individual viruses over time or between several viruses. Most of the genes included in the core set of commonly differentially expressed genes (DEGs) encode proteins that serve as membrane receptors, signaling proteins and regulators of transcription. They mainly function in signal transduction and control immunity, metabolism, and cell survival. One hundred sixty-five of the DEGs encode host proteins from which 47 have already been linked to the regulation of viral infections in previous studies and 89 proteins form a complex interaction network that may function as a core hub to control NSV infections.IMPORTANCEThe infection of cells with negative-strand RNA viruses leads to the differential expression of many host cell RNAs. The differential spectrum of virus-regulated RNAs reflects a large variety of events including anti-viral responses, cell remodeling, and cell damage. Here, these virus-specific differences and similarities in the regulated RNAs were measured in a highly standardized model. A newly developed app allows interested scientists a wide range of comparisons and visualizations.

Two subtle problems with overrepresentation analysis

Motivation

Overrepresentation analysis (ORA) is used widely to assess the enrichment of functional categories in a gene list compared to a background list. ORA is therefore a critical method in the interpretation of 'omics data, relating gene lists to biological functions and themes. Although ORA is hugely popular, we and others have noticed two potentially undesired behaviours of some ORA tools. The first one we call the 'background problem', because it involves the software eliminating large numbers of genes from the background list if they are not annotated as belonging to any category. The second one we call the 'false discovery rate problem', because some tools underestimate the true number of parallel tests conducted.

Results

Here, we demonstrate the impact of these issues on several real RNA-seq datasets and use simulated RNA-seq data to quantify the impact of these problems. We show that the severity of these problems depends on the gene set library, the number of genes in the list, and the degree of noise in the dataset. These problems can be mitigated by changing packages/websites for ORA or by changing to another approach such as functional class scoring.

Availability and implementation

An R/Shiny tool has been provided at https://oratool.ziemann-lab.net/ and the supporting materials are available from Zenodo (https://zenodo.org/records/13823301).

The molecular anatomy of cashmere goat hair follicle during cytodifferentiation stage

BACKGROUND

Cashmere, named as "soft gold", derives from the secondary hair follicles (SHFs) of cashmere goat which is vital to Northwest China's economy. The cytodifferentiation stage (E120), mirroring the complete hair follicle (HF) structure of adult goats and marking a critical phase in SHF development. Therefore, this study aims to enhance the understanding of SHF development and its impact on fiber quality, informing breeding strategies.

RESULTS

From the scRNA-seq data analysis, the intricate processes and transcriptional dynamics of inner layer cell differentiation of HFs were unveiled in this study. we identified nine cell populations during cytodifferentiation and key structures such as the hair shaft and inner root sheath. And we discovered three main inner layer lineages and seven subpopulations, clarifying their roles in specialization and signaling. Pseudotime mapping analysis showed cell evolution from early stage to mature stages marked by unique gene expressions, and the intermediate stage on the differentiation of each lineage was revealed. The identification and spatial localization of specific transcription factors, such as GATA3, LEF1 and PRDM1, as well as keratin genes highlight regulatory pathways involved in HF development, which was further validated by immunofluorescence. These findings suggested the potential strategies to improve fiber quality, and the discovery of diverse cell types and their developmental molecular mechanisms, particularly in this species-specific context, offered a nuanced view of the regulatory mechanisms driving HF development in cashmere goats.

CONCLUSION

Overall, these findings provide a systematic molecular atlas of skin, defining three major branches and cell states of inner layer cells of HF, and determining how the branch-specific transcription factors, keratins, and signals coordinate HF morphogenesis during cytodifferentiation stage. This research not only advances skin tissue research in goats but also holds broader implications for the understanding of HF regeneration and development across various species.

Bioinformatics in Russia: history and present-day landscape

Bioinformatics has become an interdisciplinary subject due to its universal role in molecular biology research. The current status of Russia's bioinformatics research in Russia is not known. Here, we review the history of bioinformatics in Russia, present the current landscape, and highlight future directions and challenges. Bioinformatics research in Russia is driven by four major industries: information technology, pharmaceuticals, biotechnology, and agriculture. Over the past three decades, despite a delayed start, the field has gained momentum, especially in protein and nucleic acid research. Dedicated and shared centers for genomics, proteomics, and bioinformatics are active in different regions of Russia. Present-day bioinformatics in Russia is characterized by research issues related to genetics, metagenomics, OMICs, medical informatics, computational biology, environmental informatics, and structural bioinformatics. Notable developments are in the fields of software (tools, algorithms, and pipelines), use of high computation power (e.g. by the Siberian Supercomputer Center), and large-scale sequencing projects (the sequencing of 100 000 human genomes). Government funding is increasing, policies are being changed, and a National Genomic Information Database is being established. An increased focus on eukaryotic genome sequencing, the development of a common place for developers and researchers to share tools and data, and the use of biological modeling, machine learning, and biostatistics are key areas for future focus. Universities and research institutes have started to implement bioinformatics modules. A critical mass of bioinformaticians is essential to catch up with the global pace in the discipline.

A novel approach for in vivo DNA footprinting using short double-stranded cell-free DNA from plasma

Here, we present a method for enrichment of double-stranded cfDNA with an average length of ∼40 bp from cfDNA for high-throughput DNA sequencing. This class of cfDNA is enriched at gene promoters and binding sites of transcription factors or structural DNA-binding proteins, so that a genome-wide DNA footprint is directly captured from liquid biopsies. In short double-stranded cfDNA from healthy individuals, we find significant enrichment of 203 transcription factor motifs. Additionally, short double-stranded cfDNA signals at specific genomic regions correlate negatively with DNA methylation, positively with H3K4me3 histone modifications and gene transcription. The diagnostic potential of short double-stranded cell-free DNA (cfDNA) in blood plasma has not yet been recognized. When comparing short double-stranded cfDNA from patient samples of pancreatic ductal adenocarcinoma with colorectal carcinoma or septic with postoperative controls, we identify 136 and 241 differentially enriched loci, respectively. Using these differentially enriched loci, the disease types can be clearly distinguished by principal component analysis, demonstrating the diagnostic potential of short double-stranded cfDNA signals as a new class of biomarkers for liquid biopsies.

Targeting m6A mRNA demethylase FTO alleviates manganese-induced cognitive memory deficits in mice

Manganese (Mn) induced learning and memory deficits through mechanisms that are not fully understood. In this study, we discovered that the demethylase FTO was significantly downregulated in hippocampal neurons in an experimental a mouse model of Mn exposure. This decreased expression of FTO was associated with Mn-induced learning and memory impairments, as well as the dysfunction in synaptic plasticity and damage to regional neurons. The overexpression of FTO, or its positive modulation with agonists, provides protection against neurological damage and cognitive impairments. Mechanistically, FTO interacts synergistically with the reader YTHDF3 to facilitate the degradation of GRIN1 and GRIN3B through the m6A modification pathway. Additionally, Mn decreases the phosphorylation of SOX2, which specifically impairs the transcriptional regulation of FTO activity. Additionally, we found that the natural compounds artemisinin and apigenin that can bind molecularly with SOX2 and reduce Mn-induced cognitive dysfunction in mice. Our findings suggest that the SOX2-FTO-Grins axis represents a viable target for addressing Mn-induced neurotoxicity and cognitive impairments.

scDAPP: a comprehensive single-cell transcriptomics analysis pipeline optimized for cross-group comparison

Single-cell transcriptomics profiling has increasingly been used to evaluate cross-group (or condition) differences in cell population and cell-type gene expression. This often leads to large datasets with complex experimental designs that need advanced comparative analysis. Concurrently, bioinformatics software and analytic approaches also become more diverse and constantly undergo improvement. Thus, there is an increased need for automated and standardized data processing and analysis pipelines, which should be efficient and flexible too. To address these, we develop the single-cell Differential Analysis and Processing Pipeline (scDAPP), a R-based workflow for comparative analysis of single cell (or nucleus) transcriptomic data between two or more groups and at the levels of single cells or 'pseudobulking' samples. The pipeline automates many steps of pre-processing using data-learnt parameters, uses previously benchmarked software, and generates comprehensive intermediate data and final results that are valuable for both beginners and experts of scRNA-seq analysis. Moreover, the analytic reports, augmented by extensive data visualization, increase the transparency of computational analysis and parameter choices, while facilitate users to go seamlessly from raw data to biological interpretation. scDAPP is freely available under the MIT license, with source code, documentation and sample data at the GitHub (https://github.com/bioinfoDZ/scDAPP).

Multi-objective context-guided consensus of a massive array of techniques for the inference of Gene Regulatory Networks

BACKGROUND AND OBJECTIVE

Gene Regulatory Network (GRN) inference is a fundamental task in biology and medicine, as it enables a deeper understanding of the intricate mechanisms of gene expression present in organisms. This bioinformatics problem has been addressed in the literature through multiple computational approaches. Techniques developed for inferring from expression data have employed Bayesian networks, ordinary differential equations (ODEs), machine learning, information theory measures and neural networks, among others. The diversity of implementations and their respective customization have led to the emergence of many tools and multiple specialized domains derived from them, understood as subsets of networks with specific characteristics that are challenging to detect a priori. This specialization has introduced significant uncertainty when choosing the most appropriate technique for a particular dataset. This proposal, named MO-GENECI, builds upon the basic idea of the previous proposal GENECI and optimizes consensus among different inference techniques, through a carefully refined multi-objective evolutionary algorithm guided by various objective functions, linked to the biological context at hand.

METHODS

MO-GENECI has been tested on an extensive and diverse academic benchmark of 106 gene regulatory networks from multiple sources and sizes. The evaluation of MO-GENECI compared its performance to individual techniques using key metrics (AUROC and AUPR) for gene regulatory network inference. Friedman's statistical ranking provided an ordered classification, followed by non-parametric Holm tests to determine statistical significance.

RESULTS

MO-GENECI's Pareto front approximation facilitates easy selection of an appropriate solution based on generic input data characteristics. The best solution consistently emerged as the winner in all statistical tests, and in many cases, the median precision solution showed no statistically significant difference compared to the winner.

CONCLUSIONS

MO-GENECI has not only demonstrated achieving more accurate results than individual techniques, but has also overcome the uncertainty associated with the initial choice due to its flexibility and adaptability. It is shown intelligently to select the most suitable techniques for each case. The source code is hosted in a public repository at GitHub under MIT license: https://github.com/AdrianSeguraOrtiz/MO-GENECI. Moreover, to facilitate its installation and use, the software associated with this implementation has been encapsulated in a Python package available at PyPI: https://pypi.org/project/geneci/.

Genomic background sequences systematically outperform synthetic ones in de novo motif discovery for ChIP-seq data

Efficient de novo motif discovery from the results of wide-genome mapping of transcription factor binding sites (ChIP-seq) is dependent on the choice of background nucleotide sequences. The foreground sequences (ChIP-seq peaks) represent not only specific motifs of target transcription factors, but also the motifs overrepresented throughout the genome, such as simple sequence repeats. We performed a massive comparison of the 'synthetic' and 'genomic' approaches to generate background sequences for de novo motif discovery. The 'synthetic' approach shuffled nucleotides in peaks, while in the 'genomic' approach selected sequences from the reference genome randomly or only from gene promoters according to the fraction of A/T nucleotides in each sequence. We compiled the benchmark collections of ChIP-seq datasets for mouse, human and Arabidopsis, and performed de novo motif discovery. We showed that the genomic approach has both more robust detection of the known motifs of target transcription factors and more stringent exclusion of the simple sequence repeats as possible non-specific motifs. The advantage of the genomic approach over the synthetic approach was greater in plants compared to mammals. We developed the AntiNoise web service (https://denovosea.icgbio.ru/antinoise/) that implements a genomic approach to extract genomic background sequences for twelve eukaryotic genomes.

Identification of a circadian-based prognostic signature predicting cancer-associated fibroblasts infiltration and immunotherapy response in bladder cancer

Circadian rhythm disruption impacts the efficiency of both chemotherapy and immunotherapy, yet identifying the key factors involved remains challenging. Circadian rhythm disruption can trigger aberrant fibroblasts activation, suggesting potential roles of cancer-associated fibroblasts (CAFs) in addressing this issue. In this paper, TCGA-BLCA patients were classified into two subgroups based on the expression of core circadian rhythm genes (CCRGs). The CCRG-based subgroups showed distinct fibroblast-related signals, from which a risk model composed of five fibroblast-related genes was finally established with excellent survival prognostic value in both TCGA and GEO datasets. The risk model was positively associated with the infiltration of CAFs and can efficiently predict the immunotherapy response in BLCA. Besides, high-risk score was associated with reduced sensitivity to a majority of traditional chemotherapeutic drugs such as oxaliplatin and gemcitabine. Further, the correlation between CCRGs and the risk genes was analyzed. Among the five risk genes, FAM20C displayed the most extensive correlation with the CCRGs and exhibited the strongest connection with CAFs infiltration. Moreover, FAM20C independently served as a predictor for the response to immunotherapy in BLCA. In conclusion, this study has identified a circadian-based signature for evaluating CAFs infiltration and predicting the efficacy of chemotherapy and immunotherapy. The central gene FAM20C has emerged as a promising candidate which merits further investigations.

Cell-free DNA from germline TP53 mutation carriers reflect cancer-like fragmentation patterns

Germline pathogenic TP53 variants predispose individuals to a high lifetime risk of developing multiple cancers and are the hallmark feature of Li-Fraumeni syndrome (LFS). Our group has previously shown that LFS patients harbor shorter plasma cell-free DNA fragmentation; independent of cancer status. To understand the functional underpinning of cfDNA fragmentation in LFS, we conducted a fragmentomic analysis of 199 cfDNA samples from 82 TP53 mutation carriers and 30 healthy TP53-wildtype controls. We find that LFS individuals exhibit an increased prevalence of A/T nucleotides at fragment ends, dysregulated nucleosome positioning at p53 binding sites, and loci-specific changes in chromatin accessibility at development-associated transcription factor binding sites and at cancer-associated open chromatin regions. Machine learning classification resulted in robust differentiation between TP53 mutant versus wildtype cfDNA samples (AUC-ROC = 0.710-1.000) and intra-patient longitudinal analysis of ctDNA fragmentation signal enabled early cancer detection. These results suggest that cfDNA fragmentation may be a useful diagnostic tool in LFS patients and provides an important baseline for cancer early detection.

DNA-binding factor footprints and enhancer RNAs identify functional non-coding genetic variants

BACKGROUND

Genome-wide association studies (GWAS) have revealed a multitude of candidate genetic variants affecting the risk of developing complex traits and diseases. However, the highlighted regions are typically in the non-coding genome, and uncovering the functional causative single nucleotide variants (SNVs) is challenging. Prioritization of variants is commonly based on genomic annotation with markers of active regulatory elements, but current approaches still poorly predict functional variants. To address this, we systematically analyze six markers of active regulatory elements for their ability to identify functional variants.

RESULTS

We benchmark against molecular quantitative trait loci (molQTL) from assays of regulatory element activity that identify allelic effects on DNA-binding factor occupancy, reporter assay expression, and chromatin accessibility. We identify the combination of DNase footprints and divergent enhancer RNA (eRNA) as markers for functional variants. This signature provides high precision, but with a trade-off of low recall, thus substantially reducing candidate variant sets to prioritize variants for functional validation. We present this as a framework called FINDER-Functional SNV IdeNtification using DNase footprints and eRNA.

CONCLUSIONS

We demonstrate the utility to prioritize variants using leukocyte count trait and analyze variants in linkage disequilibrium with a lead variant to predict a functional variant in asthma. Our findings have implications for prioritizing variants from GWAS, in development of predictive scoring algorithms, and for functionally informed fine mapping approaches.

HAS2 facilitates glioma cell malignancy and suppresses ferroptosis in an FZD7-dependent manner

Glioma is the most common malignant tumor in the central nervous system, and it is crucial to uncover the factors that influence prognosis. In this study, we utilized Mfuzz to identify a gene set that showed a negative correlation with overall survival in patients with glioma. Gene Ontology (GO) enrichment analyses were then undertaken to gain insights into the functional characteristics and pathways associated with these genes. The expression distribution of Hyaluronan Synthase 2 (HAS2) was explored across multiple datasets, revealing its expression patterns. In vitro and in vivo experiments were carried out through gene knockdown and overexpression to validate the functionality of HAS2. Potential upstream transcription factors of HAS2 were predicted using transcriptional regulatory databases, and these predictions were experimentally validated using ChIP-PCR and dual-luciferase reporter gene assays. The results showed that elevated expression of HAS2 in glioma indicates poor prognosis. HAS2 was found to play a role in activating an antiferroptosis pathway in glioma cells. Inhibiting HAS2 significantly increased cellular sensitivity to ferroptosis-inducing agents. Finally, we determined that the oncogenic effect of HAS2 is mediated by the key receptor of the WNT pathway, FZD7.

DiPRO1 distinctly reprograms muscle and mesenchymal cancer cells

We have recently identified the uncharacterized ZNF555 protein as a component of a productive complex involved in the morbid function of the 4qA locus in facioscapulohumeral dystrophy. Subsequently named DiPRO1 (Death, Differentiation, and PROliferation related PROtein 1), our study provides substantial evidence of its role in the differentiation and proliferation of human myoblasts. DiPRO1 operates through the regulatory binding regions of SIX1, a master regulator of myogenesis. Its relevance extends to mesenchymal tumors, such as rhabdomyosarcoma (RMS) and Ewing sarcoma, where DiPRO1 acts as a repressor via the epigenetic regulators TIF1B and UHRF1, maintaining methylation of cis-regulatory elements and gene promoters. Loss of DiPRO1 mimics the host defense response to virus, awakening retrotransposable repeats and the ZNF/KZFP gene family. This enables the eradication of cancer cells, reprogramming the cellular decision balance towards inflammation and/or apoptosis by controlling TNF-α via NF-kappaB signaling. Finally, our results highlight the vulnerability of mesenchymal cancer tumors to si/shDiPRO1-based nanomedicines, positioning DiPRO1 as a potential therapeutic target.

Tonic type 2 immunity is a critical tissue checkpoint controlling autoimmunity in the skin

Immunoregulatory mechanisms established in the lymphoid organs are vital for preventing autoimmunity. However, the presence of similar mechanisms in non-lymphoid tissues remains unclear. Through transcriptomic and lipidomic analyses, we find a negative association between psoriasis and fatty acid metabolism, as well as Th2 signature. Homeostatic expression of liver X receptor (LXR) and peroxisome proliferator-activated receptor gamma (PPARγ) is essential for maintaining fatty acid metabolism and for conferring resistance to psoriasis in mice. Perturbation of signal transducer and activator of transcription 6 (STAT6) diminishes the homeostatic levels of LXR and PPARγ. Furthermore, mice lacking STAT6, interleukin 4 receptor alpha (IL-4Rα), or IL-13, but not IL-4, exhibit increased susceptibility to psoriasis. Under steady state, innate lymphoid cells (ILCs) are the primary producers of IL-13. In human skin, inhibiting tonic type 2 immunity exacerbates psoriasis-like inflammation and IL-17A, while activating LXR or PPARγ inhibits them. Hence, we propose that tonic type 2 immunity, driven by IL-13-producing ILCs, represents a crucial tissue checkpoint that represses autoimmunity and maintains lipid homeostasis in the skin.

Single-cell analysis reveals transcriptional dynamics in healthy primary parathyroid tissue

Studies on human parathyroids are generally limited to hyperfunctioning glands owing to the difficulty in obtaining normal human tissue. We therefore obtained non-human primate (NHP) parathyroids to provide a suitable alternative for sequencing that would bear a close semblance to human organs. Single-cell RNA expression analysis of parathyroids from four healthy adult M. mulatta reveals a continuous trajectory of epithelial cell states. Pseudotime analysis based on transcriptomic signatures suggests a progression from GCM2 hi progenitors to mature parathyroid hormone (PTH)-expressing epithelial cells with increasing core mitochondrial transcript abundance along pseudotime. We sequenced, as a comparator, four histologically characterized hyperfunctioning human parathyroids with varying oxyphil and chief cell abundance and leveraged advanced computational techniques to highlight similarities and differences from non-human primate parathyroid expression dynamics. Predicted cell-cell communication analysis reveals abundant endothelial cell interactions in the parathyroid cell microenvironment in both human and NHP parathyroid glands. We show abundant RARRES2 transcripts in both human adenoma and normal primate parathyroid cells and use coimmunostaining to reveal high levels of RARRES2 protein (also known as chemerin) in PTH-expressing cells, which could indicate that RARRES2 plays an unrecognized role in parathyroid endocrine function. The data obtained are the first single-cell RNA transcriptome to characterize nondiseased parathyroid cell signatures and to show a transcriptomic progression of cell states within normal parathyroid glands, which can be used to better understand parathyroid cell biology.

Vitamin D Receptor Regulates the Expression of the Grainyhead-Like 1 Gene

Vitamin D plays an important pleiotropic role in maintaining global homeostasis of the human body. Its functions go far beyond skeletal health, playing a crucial role in a plethora of cellular functions, as well as in extraskeletal health, ensuring the proper functioning of multiple human organs, including the skin. Genes from the Grainyhead-like (GRHL) family code for transcription factors necessary for the development and maintenance of various epithelia. Even though they are involved in many processes regulated by vitamin D, a direct link between vitamin D-mediated cellular pathways and GRHL genes has never been described. We employed various bioinformatic methods, quantitative real-time PCR, chromatin immunoprecipitation, reporter gene assays, and calcitriol treatments to investigate this issue. We report that the vitamin D receptor (VDR) binds to a regulatory region of the Grainyhead-like 1 (GRHL1) gene and regulates its expression. Ectopic expression of VDR and treatment with calcitriol alters the expression of the GRHL1 gene. The evidence presented here indicates a role of VDR in the regulation of expression of GRHL1 and correspondingly a role of GRHL1 in mediating the actions of vitamin D.

Computational Reconstruction of the Transcription Factor Regulatory Network Induced by Auxin in Arabidopsis thaliana L

In plant hormone signaling, transcription factor regulatory networks (TFRNs), which link the master transcription factors to the biological processes under their control, remain insufficiently characterized despite their crucial function. Here, we identify a TFRN involved in the response to the key plant hormone auxin and define its impact on auxin-driven biological processes. To reconstruct the TFRN, we developed a three-step procedure, which is based on the integrated analysis of differentially expressed gene lists and a representative collection of transcription factor binding profiles. Its implementation is available as a part of the CisCross web server. With the new method, we distinguished two transcription factor subnetworks. The first operates before auxin treatment and is switched off upon hormone application, the second is switched on by the hormone. Moreover, we characterized the functioning of the auxin-regulated TFRN in control of chlorophyll and lignin biosynthesis, abscisic acid signaling, and ribosome biogenesis.

TFscope: systematic analysis of the sequence features involved in the binding preferences of transcription factors

Characterizing the binding preferences of transcription factors (TFs) in different cell types and conditions is key to understand how they orchestrate gene expression. Here, we develop TFscope, a machine learning approach that identifies sequence features explaining the binding differences observed between two ChIP-seq experiments targeting either the same TF in two conditions or two TFs with similar motifs (paralogous TFs). TFscope systematically investigates differences in the core motif, nucleotide environment and co-factor motifs, and provides the contribution of each key feature in the two experiments. TFscope was applied to > 305 ChIP-seq pairs, and several examples are discussed.

ChIP-Atlas 3.0: a data-mining suite to explore chromosome architecture together with large-scale regulome data

ChIP-Atlas (https://chip-atlas.org/) presents a suite of data-mining tools for analyzing epigenomic landscapes, powered by the comprehensive integration of over 376 000 public ChIP-seq, ATAC-seq, DNase-seq and Bisulfite-seq experiments from six representative model organisms. To unravel the intricacies of chromatin architecture that mediates the regulome-initiated generation of transcriptional and phenotypic diversity within cells, we report ChIP-Atlas 3.0 that enhances clarity by incorporating additional tracks for genomic and epigenomic features within a newly consolidated 'annotation track' section. The tracks include chromosomal conformation (Hi-C and eQTL datasets), transcriptional regulatory elements (ChromHMM and FANTOM5 enhancers), and genomic variants associated with diseases and phenotypes (GWAS SNPs and ClinVar variants). These annotation tracks are easily accessible alongside other experimental tracks, facilitating better elucidation of chromatin architecture underlying the diversification of transcriptional and phenotypic traits. Furthermore, 'Diff Analysis,' a new online tool, compares the query epigenome data to identify differentially bound, accessible, and methylated regions using ChIP-seq, ATAC-seq and DNase-seq, and Bisulfite-seq datasets, respectively. The integration of annotation tracks and the Diff Analysis tool, coupled with continuous data expansion, renders ChIP-Atlas 3.0 a robust resource for mining the landscape of transcriptional regulatory mechanisms, thereby offering valuable perspectives, particularly for genetic disease research and drug discovery.

CREB3 suppresses hepatocellular carcinoma progression by depressing AKT signaling through competitively binding with insulin receptor and transcriptionally activating RNA-binding motif protein 38

cAMP responsive element binding protein 3 (CREB3), belonging to bZIP family, was reported to play multiple roles in various cancers, but its role in hepatocellular carcinoma (HCC) is still unclear. cAMP responsive element binding protein 3 like 3 (CREB3L3), another member of bZIP family, was thought to be transcription factor (TF) to regulate hepatic metabolism. Nevertheless, except for being TFs, other function of bZIP family were poorly understood. In this study, we found CREB3 inhibited growth and metastasis of HCC in vitro and in vivo. RNA sequencing indicated CREB3 regulated AKT signaling to influence HCC progression. Mass spectrometry analysis revealed CREB3 interacted with insulin receptor (INSR). Mechanistically, CREB3 suppressed AKT phosphorylation by inhibiting the interaction of INSR with insulin receptor substrate 1 (IRS1). In our study, CREB3 was firstly proved to affect activation of substrates by interacting with tyrosine kinase receptor. Besides, CREB3 could act as a TF to transactivate RNA-binding motif protein 38 (RBM38) expression, leading to suppressed AKT phosphorylation. Rescue experiments further confirmed the independence between the two functional manners. In conclusion, CREB3 acted as a tumor suppressor in HCC, which inhibited AKT phosphorylation through independently interfering interaction of INSR with IRS1, and transcriptionally activating RBM38.

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.

Multi-dimensional cell-free DNA-based liquid biopsy for sensitive early detection of gastric cancer

BACKGROUND

Gastric cancer is the fifth most common cancer type. Most patients are diagnosed at advanced stages with poor prognosis. A non-invasive assay for the detection of early-stage gastric cancer is highly desirable for reducing associated mortality.

METHODS

We collected a prospective study cohort of 110 stage I-II gastric cancer patients and 139 non-cancer individuals. We performed whole-genome sequencing with plasma samples and profiled four types of cell-free DNA (cfDNA) characteristics, fragment size pattern, copy number variation, nucleosome coverage pattern, and single nucleotide substitution. With these differential profiles, we developed an ensemble model to detect gastric cancer signals. Further, we validated the assay in an in-house first validation cohort of 73 gastric cancer patients and 94 non-cancer individuals and an independent second validation cohort of 47 gastric cancer patients and 49 non-cancer individuals. Additionally, we evaluated the assay in a hypothetical 100,000 screening population by Monte Carlo simulation.

RESULTS

Our cfDNA-based assay could distinguish early-stage gastric cancer from non-cancer at an AUROC of 0.962 (95% CI: 0.942-0.982) in the study cohort, 0.972 (95% CI: 0.953-0.992) in the first validation cohort and 0.937 (95% CI: 0.890-0.983) in the second validation cohort. The model reached a specificity of 92.1% (128/139) and a sensitivity of 88.2% (97/110) in the study cohort. In the first validation cohort, 91.5% (86/94) of non-cancer individuals and 91.8% (67/73) of gastric cancer patients were correctly identified. In the second validation cohort, 89.8% (44/49) of non-cancer individuals and 87.2% (41/47) of gastric cancer patients were accurately classified.

CONCLUSIONS

We introduced a liquid biopsy assay using multiple dimensions of cfDNA characteristics that could accurately identify early-stage gastric cancer from non-cancerous conditions. As a cost-effective non-invasive approach, it may provide population-wide benefits for the early detection of gastric cancer.

TRIAL REGISTRATION

This study was registered on ClinicalTrials.gov under the identifier NCT05269056 on March 7, 2022.

Genomic alterations and transcriptional phenotypes in circulating tumor DNA and matched metastatic tumor

Background

Profiling circulating cell-free DNA (cfDNA) has become a fundamental practice in cancer medicine, but the effectiveness of cfDNA at elucidating tumor-derived molecular features has not been systematically compared to standard single-lesion tumor biopsies in prospective cohorts of patients. The use of plasma instead of tissue to guide therapy is particularly attractive for patients with small cell lung cancer (SCLC), a cancer whose aggressive clinical course making it exceedingly challenging to obtain tumor biopsies.

Methods

Here, a prospective cohort of 49 plasma samples obtained before, during, and after treatment from 20 patients with recurrent SCLC, we study cfDNA low pass whole genome (0.1X coverage) and exome (130X) sequencing in comparison with time-point matched tumor, characterized using exome and transcriptome sequencing.

Results

Direct comparison of cfDNA versus tumor biopsy reveals that cfDNA not only mirrors the mutation and copy number landscape of the corresponding tumor but also identifies clinically relevant resistance mechanisms and cancer driver alterations not found in matched tumor biopsies. Longitudinal cfDNA analysis reliably tracks tumor response, progression, and clonal evolution. Genomic sequencing coverage of plasma DNA fragments around transcription start sites shows distinct treatment-related changes and captures the expression of key transcription factors such as NEUROD1 and REST in the corresponding SCLC tumors, allowing prediction of SCLC neuroendocrine phenotypes and treatment responses.

Conclusions

These findings have important implications for non-invasive stratification and subtype-specific therapies for patients with SCLC, now treated as a single disease.

Structure-based learning to predict and model protein-DNA interactions and transcription-factor co-operativity in cis-regulatory elements

Transcription factor (TF) binding is a key component of genomic regulation. There are numerous high-throughput experimental methods to characterize TF-DNA binding specificities. Their application, however, is both laborious and expensive, which makes profiling all TFs challenging. For instance, the binding preferences of ∼25% human TFs remain unknown; they neither have been determined experimentally nor inferred computationally. We introduce a structure-based learning approach to predict the binding preferences of TFs and the automated modelling of TF regulatory complexes. We show the advantage of using our approach over the classical nearest-neighbor prediction in the limits of remote homology. Starting from a TF sequence or structure, we predict binding preferences in the form of motifs that are then used to scan a DNA sequence for occurrences. The best matches are either profiled with a binding score or collected for their subsequent modeling into a higher-order regulatory complex with DNA. Co-operativity is modelled by: (i) the co-localization of TFs and (ii) the structural modeling of protein-protein interactions between TFs and with co-factors. We have applied our approach to automatically model the interferon-β enhanceosome and the pioneering complexes of OCT4, SOX2 (or SOX11) and KLF4 with a nucleosome, which are compared with the experimentally known structures.

Peripheral Blood Mononuclear Cell Gene Expression Associated with Pulmonary Microvascular Perfusion: The Multi-Ethnic Study of Atherosclerosis Chronic Obstructive Pulmonary Disease

Rationale: Chronic obstructive pulmonary disease (COPD) and emphysema are associated with endothelial damage and altered pulmonary microvascular perfusion. The molecular mechanisms underlying these changes are poorly understood in patients, in part because of the inaccessibility of the pulmonary vasculature. Peripheral blood mononuclear cells (PBMCs) interact with the pulmonary endothelium. Objectives: To test the association between gene expression in PBMCs and pulmonary microvascular perfusion in COPD. Methods: The Multi-Ethnic Study of Atherosclerosis (MESA) COPD Study recruited two independent samples of COPD cases and controls with ⩾10 pack-years of smoking history. In both samples, pulmonary microvascular blood flow, pulmonary microvascular blood volume, and mean transit time were assessed on contrast-enhanced magnetic resonance imaging, and PBMC gene expression was assessed by microarray. Additional replication was performed in a third sample with pulmonary microvascular blood volume measures on contrast-enhanced dual-energy computed tomography. Differential expression analyses were adjusted for age, gender, race/ethnicity, educational attainment, height, weight, smoking status, and pack-years of smoking. Results: The 79 participants in the discovery sample had a mean age of 69 ± 6 years, 44% were female, 25% were non-White, 34% were current smokers, and 66% had COPD. There were large PBMC gene expression signatures associated with pulmonary microvascular perfusion traits, with several replicated in the replication sets with magnetic resonance imaging (n = 47) or dual-energy contrast-enhanced computed tomography (n = 157) measures. Many of the identified genes are involved in inflammatory processes, including nuclear factor-κB and chemokine signaling pathways. Conclusions: PBMC gene expression in nuclear factor-κB, inflammatory, and chemokine signaling pathways was associated with pulmonary microvascular perfusion in COPD, potentially offering new targetable candidates for novel therapies.

Prediction of Transcription Factor Binding Sites on Cell-Free DNA Based on Deep Learning

Transcription factors (TFs) are important regulatory elements for vital cellular activities, and the identification of transcription factor binding sites (TFBS) can help to explore gene regulatory mechanisms. Research studies have proved that cfDNA (cell-free DNA) shows relatively higher coverage at TFBS due to the protection by TF from degradation by nucleases and short fragments of cfDNA are enriched in TFBS. However, there are still great difficulties in the noninvasive identification of TFBSs from experimental techniques. In this study, we propose a deep learning-based approach that can noninvasively predict TFBSs of cfDNA by learning sequence information from known TFBSs through convolutional neural networks. Under the addition of long short-term memory, our model achieved an area under the curve of 84%. Based on this model to predict cfDNA, we found consistent motifs in cfDNA fragments and lower coverage occurred upstream and downstream of these cfDNA fragments, which is consistent with a previous study. We also found that the binding sites of the same TF differ in different cell lines. TF-specific target genes were detected from cfDNA and were enriched in cancer-related pathways. In summary, our method of locating TFBSs from plasma has the potential to reflect the intrinsic regulatory mechanism from a noninvasive perspective and provide technical guidance for dynamic monitoring of disease in clinical practice.

Gene expression supports a single origin of horns and antlers in hoofed mammals

Horns, antlers, and other bony cranial appendages of even-toed hoofed mammals (ruminant artiodactyls) challenge traditional morphological homology assessments. Cranial appendages all share a permanent bone portion with family-specific integument coverings, but homology determination depends on whether the integument covering is an essential component or a secondary elaboration of each structure. To enhance morphological homology assessments, we tested whether juvenile cattle horn bud transcriptomes share homologous gene expression patterns with deer antlers relative to pig outgroup tissues, treating the integument covering as a secondary elaboration. We uncovered differentially expressed genes that support horn and antler homology, potentially distinguish them from non-cranial-appendage bone and other tissues, and highlight the importance of phylogenetic outgroups in homology assessments. Furthermore, we found differentially expressed genes that could support a shared cranial neural crest origin for horns and antlers and expression patterns that refine our understanding of the timing of horn and antler differentiation.

GRouNdGAN: GRN-guided simulation of single-cell RNA-seq data using causal generative adversarial networks

We introduce GRouNdGAN, a gene regulatory network (GRN)-guided reference-based causal implicit generative model for simulating single-cell RNA-seq data, in silico perturbation experiments, and benchmarking GRN inference methods. Through the imposition of a user-defined GRN in its architecture, GRouNdGAN simulates steady-state and transient-state single-cell datasets where genes are causally expressed under the control of their regulating transcription factors (TFs). Training on six experimental reference datasets, we show that our model captures non-linear TF-gene dependencies and preserves gene identities, cell trajectories, pseudo-time ordering, and technical and biological noise, with no user manipulation and only implicit parameterization. GRouNdGAN can synthesize cells under new conditions to perform in silico TF knockout experiments. Benchmarking various GRN inference algorithms reveals that GRouNdGAN effectively bridges the existing gap between simulated and biological data benchmarks of GRN inference algorithms, providing gold standard ground truth GRNs and realistic cells corresponding to the biological system of interest.

scDAPP: a comprehensive single-cell transcriptomics analysis pipeline optimized for cross-group comparison

Single-cell transcriptomics profiling has increasingly been used to evaluate cross-group differences in cell population and cell-type gene expression. This often leads to large datasets with complex experimental designs that need advanced comparative analysis. Concurrently, bioinformatics software and analytic approaches also become more diverse and constantly undergo improvement. Thus, there is an increased need for automated and standardized data processing and analysis pipelines, which should be efficient and flexible too. To address these, we develop the single-cell Differential Analysis and Processing Pipeline (scDAPP), a R-based workflow for comparative analysis of single cell (or nucleus) transcriptomic data between two or more groups and at the levels of single cells or "pseudobulking" samples. The pipeline automates many steps of pre-processing using data-learnt parameters, uses previously benchmarked software, and generates comprehensive intermediate data and final results that are valuable for both beginners and experts of scRNA-seq analysis. Moreover, the analytic reports, augmented by extensive data visualization, increase the transparency of computational analysis and parameter choices, while facilitate users to go seamlessly from raw data to biological interpretation. Availability and Implementation: scDAPP is freely available for non-commercial usage as an R package under the MIT license. Source code, documentation and sample data are available at the GitHub (https://github.com/bioinfoDZ/scDAPP).

Glioblastoma stem cells deliver ABCB4 transcribed by ATF3 via exosomes conferring glioblastoma resistance to temozolomide

Glioblastoma stem cells (GSCs) play a key role in glioblastoma (GBM) resistance to temozolomide (TMZ) chemotherapy. With the increase in research on the tumour microenvironment, exosomes secreted by GSCs have become a new focus in GBM research. However, the molecular mechanism by which GSCs affect drug resistance in GBM cells via exosomes remains unclear. Using bioinformatics analysis, we identified the specific expression of ABCB4 in GSCs. Subsequently, we established GSC cell lines and used ultracentrifugation to extract secreted exosomes. We conducted in vitro and in vivo investigations to validate the promoting effect of ABCB4 and ABCB4-containing exosomes on TMZ resistance. Finally, to identify the transcription factors regulating the transcription of ABCB4, we performed luciferase assays and chromatin immunoprecipitation-quantitative PCR. Our results indicated that ABCB4 is highly expressed in GSCs. Moreover, high expression of ABCB4 promoted the resistance of GSCs to TMZ. Our study found that GSCs can also transmit their highly expressed ABCB4 to differentiated glioma cells (DGCs) through exosomes, leading to high expression of ABCB4 in these cells and promoting their resistance to TMZ. Mechanistic studies have shown that the overexpression of ABCB4 in GSCs is mediated by the transcription factor ATF3. In conclusion, our results indicate that GSCs can confer resistance to TMZ in GBM by transmitting ABCB4, which is transcribed by ATF3, through exosomes. This mechanism may lead to drug resistance and recurrence of GBM. These findings contribute to a deeper understanding of the mechanisms underlying drug resistance in GBM and provide novel insights into its treatment.

Her2 amplification, Rel-A, and Bach1 can influence APOBEC3A expression in breast cancer cells

APOBEC-induced mutations occur in 50% of sequenced human tumors, with APOBEC3A (A3A) being a major contributor to mutagenesis in breast cancer cells. The mechanisms that cause A3A activation and mutagenesis in breast cancers are still unknown. Here, we describe factors that influence basal A3A mRNA transcript levels in breast cancer cells. We found that basal A3A mRNA correlates with A3A protein levels and predicts the amount of APOBEC signature mutations in a panel of breast cancer cell lines, indicating that increased basal transcription may be one mechanism leading to breast cancer mutagenesis. We also show that alteration of ERBB2 expression can drive A3A mRNA levels, suggesting the enrichment of the APOBEC mutation signature in Her2-enriched breast cancer could in part result from elevated A3A transcription. Hierarchical clustering of transcripts in primary breast cancers determined that A3A mRNA was co-expressed with other genes functioning in viral restriction and interferon responses. However, reduction of STAT signaling via inhibitors or shRNA in breast cancer cell lines had only minor impact on A3A abundance. Analysis of single cell RNA-seq from primary tumors indicated that A3A mRNA was highest in infiltrating immune cells within the tumor, indicating that correlations of A3A with STAT signaling in primary tumors may be result from higher immune infiltrates and are not reflective of STAT signaling controlling A3A expression in breast cancer cells. Analysis of ATAC-seq data in multiple breast cancer cell lines identified two transcription factor sites in the APOBEC3A promoter region that could promote A3A transcription. We determined that Rel-A, and Bach1, which have binding sites in these peaks, elevated basal A3A expression. Our findings highlight a complex and variable set of transcriptional activators for A3A in breast cancer cells.

Sexually dimorphic control of affective state processing and empathic behaviors

Recognizing the affective states of social counterparts and responding appropriately fosters successful social interactions. However, little is known about how the affective states are expressed and perceived and how they influence social decisions. Here, we show that male and female mice emit distinct olfactory cues after experiencing distress. These cues activate distinct neural circuits in the piriform cortex (PiC) and evoke sexually dimorphic empathic behaviors in observers. Specifically, the PiC → PrL pathway is activated in female observers, inducing a social preference for the distressed counterpart. Conversely, the PiC → MeA pathway is activated in male observers, evoking excessive self-grooming behaviors. These pathways originate from non-overlapping PiC neuron populations with distinct gene expression signatures regulated by transcription factors and sex hormones. Our study unveils how internal states of social counterparts are processed through sexually dimorphic mechanisms at the molecular, cellular, and circuit levels and offers insights into the neural mechanisms underpinning sex differences in higher brain functions.

Proteasome Inhibition Reprograms Chromatin Landscape in Breast Cancer

The 26S proteasome is the major protein degradation machinery in cells. Cancer cells use the proteasome to modulate gene expression networks that promote tumor growth. Proteasome inhibitors have emerged as effective cancer therapeutics, but how they work mechanistically remains unclear. Here, using integrative genomic analysis, we discovered unexpected reprogramming of the chromatin landscape and RNA polymerase II (RNAPII) transcription initiation in breast cancer cells treated with the proteasome inhibitor MG132. The cells acquired dynamic changes in chromatin accessibility at specific genomic loci termed differentially open chromatin regions (DOCR). DOCRs with decreased accessibility were promoter proximal and exhibited unique chromatin architecture associated with divergent RNAPII transcription. Conversely, DOCRs with increased accessibility were primarily distal to transcription start sites and enriched in oncogenic superenhancers predominantly accessible in non-basal breast tumor subtypes. These findings describe the mechanisms by which the proteasome modulates the expression of gene networks intrinsic to breast cancer biology.

SIGNIFICANCE

Our study provides a strong basis for understanding the mechanisms by which proteasome inhibitors exert anticancer effects. We find open chromatin regions that change during proteasome inhibition, are typically accessible in non-basal breast cancers.

SRRM2 splicing factor modulates cell fate in early development

Embryo development is an orchestrated process that relies on tight regulation of gene expression to guide cell differentiation and fate decisions. The Srrm2 splicing factor has recently been implicated in developmental disorders and diseases, but its role in early mammalian development remains unexplored. Here, we show that Srrm2 dosage is critical for maintaining embryonic stem cell pluripotency and cell identity. Srrm2 heterozygosity promotes loss of stemness, characterised by the coexistence of cells expressing naive and formative pluripotency markers, together with extensive changes in gene expression, including genes regulated by serum-response transcription factor (SRF) and differentiation-related genes. Depletion of Srrm2 by RNA interference in embryonic stem cells shows that the earliest effects of Srrm2 heterozygosity are specific alternative splicing events on a small number of genes, followed by expression changes in metabolism and differentiation-related genes. Our findings unveil molecular and cellular roles of Srrm2 in stemness and lineage commitment, shedding light on the roles of splicing regulators in early embryogenesis, developmental diseases and tumorigenesis.