Gene Expression Omnibus: NCBI gene expression and hybridization array data repository

Rapid formation of stable autotetraploid rice from genome-doubled F1 hybrids of japonica-indica subspecies

Theory predicts that in the absence of selection, a newly formed segmental allopolyploid will become 'autopolyploidized' if homoeologous exchanges (HEs) occur freely. Moreover, because selection against meiotic abnormalities is expected to be strong in the initial generations, we anticipate HEs to be uncommon in evolved segmental allopolyploids. Here we analysed the whole-genome composition of 202 phenotypically homogeneous and stable rice tetraploid recombinant inbred lines (TRILs) derived from Oryza sativa subsp. japonica subsp. indica hybridization/whole-genome doubling. We measured functional traits related to growth, development and reproductive fitness, and analysed meiotic chromosomal behaviour of the TRILs. We uncover factors that constrain the genomic composition of the TRILs, including asymmetric parental contribution and exclusive uniparental segment retention. Intriguingly, some TRILs that have high fertility and abiotic stress resilience co-occur with largely stabilized meiosis. Our findings comprise evidence supporting the evolutionary possibility of HE-catalysed 'allo-to-auto' polyploidy transitions in nature, with implications for creating new polyploid crops.

Genome-Scale Metabolic Modeling of Human Pancreas with Focus on Type 2 Diabetes

Type 2 diabetes (T2D) is characterized by relative insulin deficiency due to pancreatic beta cell dysfunction and insulin resistance in different tissues. Not only beta cells but also other islet cells (alpha, delta, and pancreatic polypeptide [PP]) are critical for maintaining glucose homeostasis in the body. In this overarching context and given that a deeper understanding of T2D pathophysiology and novel molecular targets is much needed, studies that integrate experimental and computational biology approaches offer veritable prospects for innovation. In this study, we report on single-cell RNA sequencing data integration with a generic Human1 model to generate context-specific genome-scale metabolic models for alpha, beta, delta, and PP cells for nondiabetic and T2D states and, importantly, at single-cell resolution. Moreover, flux balance analysis was performed for the investigation of metabolic activities in nondiabetic and T2D pancreatic cells. By altering glucose and oxygen uptakes to the metabolic networks, we documented the ways in which hypoglycemia, hyperglycemia, and hypoxia led to changes in metabolic activities in various cellular subsystems. Reporter metabolite analysis revealed significant transcriptional changes around several metabolites involved in sphingolipid and keratan sulfate metabolism in alpha cells, fatty acid metabolism in beta cells, and myoinositol phosphate metabolism in delta cells. Taken together, by leveraging genome-scale metabolic modeling, this research bridges the gap between metabolic theory and clinical practice, offering a comprehensive framework to advance our understanding of pancreatic metabolism in T2D, and contributes new knowledge toward the development of targeted precision medicine interventions.

Roles of sensory receptors in non-sensory organs: the kidney and beyond

Olfactory receptors (ORs), taste receptors and opsins are well-known for their pivotal roles in mediating the senses of smell, taste and sight, respectively. However, in the past two decades, research has shown that these sensory receptors also regulate physiological processes in a variety of non-sensory tissues. Although ORs, taste receptors and opsins have all been shown to have physiological roles beyond their traditional locations, most work in the kidney has focused on ORs. To date, renal ORs have been shown to have roles in blood pressure regulation (OLFR78 and OLFR558) and glucose homeostasis (OLFR1393). However, sensory receptors remain drastically understudied outside of traditional sensory systems, in part because of inherent challenges in studying these receptors. Increased knowledge of the physiological and pathophysiological roles of sensory receptors has the potential to substantially improve understanding of the function of numerous organs and systems, including the kidney. In addition, most sensory receptors are G protein-coupled receptors, which are considered to be the most druggable class of proteins, and thus could potentially be exploited as future therapeutic targets.

Host centric drug repurposing for viral diseases

Computational approaches for drug repurposing for viral diseases have mainly focused on a small number of antivirals that directly target pathogens (virus centric therapies). In this work, we combine ideas from collaborative filtering and network medicine for making predictions on a much larger set of drugs that could be repurposed for host centric therapies, that are aimed at interfering with host cell factors required by a pathogen. Our idea is to create matrices quantifying the perturbation that drugs and viruses induce on human protein interaction networks. Then, we decompose these matrices to learn embeddings of drugs, viruses, and proteins in a low dimensional space. Predictions of host-centric antivirals are obtained by taking the dot product between the corresponding drug and virus representations. Our approach is general and can be applied systematically to any compound with known targets and any virus whose host proteins are known. We show that our predictions have high accuracy and that the embeddings contain meaningful biological information that may provide insights into the underlying biology of viral infections. Our approach can integrate different types of information, does not rely on known drug-virus associations and can be applied to new viral diseases and drugs.

Identification of dilated cardiomyopathy-linked key genes by bioinformatics methods and evaluating the impact of tannic acid and monosodium glutamate in rats

Dilated cardiomyopathy (DCM) is the most common type of myocardial dysfunction, affecting mostly young adults, but its therapeutic diagnosis and biomarkers for prognosis are lacking. This study aimed to investigate the possible effect of the common food additive monosodium glutamate (MSG) and tannic acid (TA), a phenolic compound, on the key molecular actors responsible for DCM. DCM-related publicly available microarray datasets (GSE120895, GSE17800, and GSE19303) were downloaded from the comprehensive Gene Expression Omnibus (GEO) database, and analyzed to identify differentially expressed genes (DEGs). By integrating DEGs and gene-disease validity curation results, overlapping genes were screened and identified as hub genes. Protein-protein interaction (PPI) network and ontology analysis were performed to make sense of the identified biological data. Finally, mRNA expression changes of identified hub genes in the heart tissues of rats treated with MSG and TA were measured by the qPCR method. Six upregulated (IGF1, TTN, ACTB, LMNA, EDN1, and NPPB) DEGs were identified between the DCM and healthy control samples as the hub genes. qPCR results revealed that the mRNA levels of these genes involved in DCM development increased significantly in rat heart tissues exposed to MSG. In contrast, this increase was remarkably alleviated by TA treatment. Our results provide new insights into critical molecular mechanisms that should be focused on in future DCM studies. Moreover, MSG may play a critical role in DCM formation, and TA may be used as a promising therapeutic agent in DCM.

Tet Methylcytosine Dioxygenase 2 (TET2) Mutation Drives a Global Hypermethylation Signature in Patients With Pulmonary Arterial Hypertension (PAH): Correlation With Altered Gene Expression Relevant to a Common T Cell Phenotype

Epigenetic changes in gene expression due to DNA methylation regulate pulmonary vascular structure and function. Genetic or acquired alterations in DNA methylation/demethylation can promote the development of pulmonary arterial hypertension (PAH). Here, we performed epigenome-wide mapping of DNA methylation in whole blood from 10 healthy people and 19 age/sex-matched PAH patients from the PAH Biobank. Exome sequencing confirmed the absence of known mutations in PAH-associated gene variants identifying subjects with or without mutations of TET2, a putative PAH gene encoding the demethylating enzyme, TET2. DNA of patients with PAH and no TET2 mutation was hypermethylated compared to healthy controls. Patients with PAH and a TET2 mutation had greater DNA CpG methylation than mutation-free PAH patients. Unique Differentially Methylated Regions (DMR) were more common in patients with PAH with TET2 mutations (1164) than in PAH without mutations (262). We correlated methylome findings with a public PAH transcriptomic RNA dataset, prioritizing targets that are both hypermethylated in our cohort and downregulated at the RNA level. Relative to controls, functional analysis reveals enriched functions related to T cell differentiation in PAH patients with a TET2 mutation. We identified genes with downregulated expression that were hypermethylated in PAH patients (with or without a TET2 mutation). In both cases, a conserved T cell phenotype emerged. Pan-chromosomal hypermethylation in PAH is greatest in patients with TET2 mutations. Observed hypermethylation of genes involved in the pathogenesis of PAH, such as EIF2AK4, and transcription factors that regulate T cell development, such as TCF7, merit further study and may contribute to the inflammation in PAH.

Patterns and variations of copy number alterations in acute myeloid leukemia: insights from the LeukAtlas database

Recent pan-cancer analysis revealed the global pattern and potential aetiologies of copy number variation signatures in human cancers, particularly those derived from non-hematopoietic tissues. In sharp contrast, the generally low CNV burden in leukemia leaves the CNV landscape and variations largely unexplored, impeding understanding of CNV in leukemia development. Through a comprehensive compilation of public datasets, we constructed LeukAtlas ( https://ngdc.cncb.ac.cn/leukemia ), a user-friendly database encompassing 12,597 CNVs from 1446 AML samples across diverse subtypes and age groups, providing tools for multidimensional CNV analysis. Our analyses suggested the CNV levels significantly varied among AML patients. We discovered two previously unknown CNV patterns in adult AML patients, dominated by segmental LOH and/or minor gain, which have been shown to be associated with chromosomal instability in solid tumors. Additionally, we defined two potential new AML subgroups based on CNVs status, providing new stratification markers within the existing karyotype framework. Representing the most extensive CNV collection in AML, LeukAtlas is a valuable resource for exploring the role of CNVs in the pathogenesis and prognosis stratification of leukemia. Interrogation of this database uncovers novel subclasses with unique CNV profiles and reveals heterogeneous CNV patterns in AML, demonstrating the potential role of chromosomal instability in AML progression.

Foundation models in bioinformatics

With the adoption of foundation models (FMs), artificial intelligence (AI) has become increasingly significant in bioinformatics and has successfully addressed many historical challenges, such as pre-training frameworks, model evaluation and interpretability. FMs demonstrate notable proficiency in managing large-scale, unlabeled datasets, because experimental procedures are costly and labor intensive. In various downstream tasks, FMs have consistently achieved noteworthy results, demonstrating high levels of accuracy in representing biological entities. A new era in computational biology has been ushered in by the application of FMs, focusing on both general and specific biological issues. In this review, we introduce recent advancements in bioinformatics FMs employed in a variety of downstream tasks, including genomics, transcriptomics, proteomics, drug discovery and single-cell analysis. Our aim is to assist scientists in selecting appropriate FMs in bioinformatics, according to four model types: language FMs, vision FMs, graph FMs and multimodal FMs. In addition to understanding molecular landscapes, AI technology can establish the theoretical and practical foundation for continued innovation in molecular biology.

A comprehensive bioinformatics analysis identifies mitophagy biomarkers and potential Molecular mechanisms in hypertensive nephropathy

Mitophagy, the selective removal of damaged mitochondria, plays a critical role in kidney diseases, but its involvement in hypertensive nephropathy (HTN) is not well understood. To address this gap, we investigated mitophagy-related genes in HTN, identifying potential biomarkers for diagnosis and treatment. Transcriptome datasets from the Gene Expression Omnibus database were analyzed, resulting in the identification of seven mitophagy related differentially expressed genes (MR-DEGs), namely PINK1, ULK1, SQSTM1, ATG5, ATG12, MFN2, and UBA52. Further, we explored the correlation between MR-DEGs, immune cells, and inflammatory factors. The identified genes demonstrated a strong correlation with Mast cells, T-cells, TGFβ3, IL13, and CSF3. Machine learning techniques were employed to screen important genes, construct diagnostic models, and evaluate their accuracy. Consensus clustering divided the HTN patients into two mitophagy subgroups, with Subgroup 2 showing higher levels of immune cell infiltration and inflammatory factors. The functions of their proteins primarily involve complement, coagulation, lipids, and vascular smooth muscle contraction. Single-cell RNA sequencing revealed that mitophagy was most significant in proximal tubule cells (PTC) in HTN patients. Pseudotime analysis of PTC confirmed the expression changes observed in the transcriptome. Intercellular communication analysis suggested that mitophagy might regulate PTC's participation in intercellular crosstalk. Notably, specific transcription factors such as HNF4A, PPARA, and STAT3 showed strong correlations with mitophagy-related genes in PTC, indicating their potential role in modulating PTC function and influencing the onset and progression of HTN. This study offers a comprehensive analysis of mitophagy in HTN, enhancing our understanding of the pathogenesis, diagnosis, and treatment of HTN.

Pattern Recognition-Driven Detection of Circadian-Disruptive Compounds from Gene Expressions: High-Throughput Screening and Experimental Verification

Circadian rhythms regulate the timing of numerous biological functions in organisms. Besides well-known external stimuli like the light-dark cycle and temperature, circadian rhythms can also be modulated by environmental substances. However, this area remains largely underexplored. Here, we developed a robust Pattern Recognition-Driven Prediction Approach (PRD-PA) that enables the identification of circadian-disruptive compounds from large-scale zebrafish transcriptomic profiling. The approach utilizes a circadian gene panel consisting of over 270 Circadian-Indicating Genes (CIGs) with stable and robust periodicity and combines it with a predictive model, known as the Differential Gene Expression Values of an Individual Comparison Model (DGVICM), that can effectively predict internal circadian phases from transcriptomic samples. By analyzing 692 aggregated gene expression profiles across 40 environmental substances, several were identified as having significant circadian-disruptive potential. These include glucocorticoids (e.g., prednisone (PRE) and triamcinolone (TRI)), the antithyroid agent propylthiouracil (PTU), and the widely used UV filter benzophenone-3 (BP-3). Both glucocorticoids and PTU are well-documented disruptors of circadian rhythms, and BP-3's circadian-disrupting properties were validated through experimental exposures. Moreover, BP-3 analogs, including 2,4-dihydroxybenzophenone (BP-1) and 2,2'-dihydroxy-4-methoxybenzophenone (BP-8), were also found to exhibit similar circadian-disruptive effects. Overall, the present findings demonstrated the reliability of the PRD-PA approach for circadian disruption screening and highlighted the presence of diverse circadian-disruptive substances in our environment.

Identification of lens-regulated genes driving anterior eye development

Signals from the lens regulate multiple aspects of eye development, including establishment of eye size, patterning of the presumptive iris and ciliary body in the anterior optic cup and migration and differentiation of neural crest cells. To advance understanding of the molecular mechanism by which the lens regulates eye development, we performed transcriptome profiling of embryonic chicken retinas after lens removal. Genes associated with nervous system development were upregulated in lens-removed eyes, but the presumptive ciliary body and iris region did not adopt a neural retina identity following lens removal. Lens-regulated genes implicated in periocular mesenchyme, cornea and anterior optic cup development were identified, including factors not previously implicated in eye development. Unexpectedly, transcriptomic differences were identified in retinas from male versus female chicken embryos, suggesting sexual dimorphism from early stages. In situ hybridisation of embryonic chicken eyes and analyses of datasets from embryonic mouse and adult human eyes confirmed expression of candidate genes, including multiple WNT genes, in tissues important for anterior eye development and function. Remarkably, pharmacological activation of canonical WNT signalling restored eye development and size in the absence of the lens. These analyses have identified candidate genes and biological pathways involved in eye development, providing avenues for new research in this area.

Targeting capture and eradicate circulating tumor cells by activated platelet derived vehicle for inhibiting triple-negative breast cancer metastasis

Circulating tumor cells (CTCs) are cardinal intermediaries in the metastatic cascade, particularly in triple-negative breast cancer (TNBC), owing to their high-affinity interactions that bolster survival and dissemination. Addressing this pivotal mechanism, we have developed APEVs@DOX, a pioneering biomimetic delivery system. Utilizing activated platelet membranes as a scaffold, APEVs@DOX recapitulates the natural affinity between platelets and CTCs, enabling targeted delivery of doxorubicin. Our results, substantiated by meticulous in vitro and in vivo experimentation, revealed 78 % reduction in lung metastasis nodules in murine models relative to controls, affirming APEVs@DOX's proficiency in CTCs capture and eradication. This study not only illuminates the potential of CTCs-targeted therapies in the precision medicine armamentarium for TNBC but also contributes empirical data to guide the strategic design of anti-metastatic interventions. The therapeutic impact of APEVs@DOX in curtailing metastatic spread offers a beacon of hope for advancing TNBC treatment paradigms.

A novel framework to build saliva-based DNA methylation biomarkers: Quantifying systemic chronic inflammation as a case study

Accessible and non-invasive biomarkers that measure human ageing processes and the risk of developing age-related disease are paramount in preventative healthcare. Here, we describe a novel framework to train saliva-based DNA methylation (DNAm) biomarkers that are reproducible and biologically interpretable. By leveraging a reliability dataset with replicates across tissues, we demonstrate that it is possible to transfer knowledge from blood DNAm to saliva DNAm data using DNAm proxies of blood proteins (EpiScores). We apply these methods to create a new saliva-based epigenetic clock (InflammAge) that quantifies systemic chronic inflammation (SCI) in humans. Using a large blood DNAm human cohort with linked electronic health records and over 18,000 individuals (Generation Scotland), we demonstrate that InflammAge significantly associates with all-cause mortality, disease outcomes, lifestyle factors, and immunosenescence; in many cases outperforming the widely used SCI biomarker C-reactive protein (CRP). We propose that our biomarker discovery framework and InflammAge will be useful to improve understanding of the molecular mechanisms underpinning human ageing and to assess the impact of gero-protective interventions.

Activation of the Drosophila innate immune system accelerates growth in cooperation with oncogenic Ras

Innate immunity in Drosophila acts as an organismal surveillance system for external stimuli or cellular fitness and triggers context-specific responses to fight infections and maintain tissue homeostasis. However, uncontrolled activation of innate immune pathways can be detrimental. In mammals, innate immune signaling is often overactivated in malignant cells and contributes to tumor progression. Drosophila tumor models have been instrumental in the discovery of interactions between pathways that promote tumorigenesis, but little is known about whether and how the Toll innate immune pathway interacts with oncogenes. Here we use a Drosophila epithelial in vivo model to investigate the interplay between Toll signaling and oncogenic Ras. In the absence of oncogenic Ras (RasV12), Toll signaling suppresses differentiation and induces apoptosis. In contrast, in the context of RasV12, cells are protected from cell death and Dorsal promotes cell survival and proliferation to drive hyperplasia. Taken together, we show that the tissue-protective functions of innate immune activity can be hijacked by pre-malignant cells to induce tumorous overgrowth.

Diversity, Complexity, and Challenges of Viral Infectious Disease Data in the Big Data Era: A Comprehensive Review

Viral infectious diseases, characterized by their intricate nature and wide-ranging diversity, pose substantial challenges in the domain of data management. The vast volume of data generated by these diseases, spanning from the molecular mechanisms within cells to large-scale epidemiological patterns, has surpassed the capabilities of traditional analytical methods. In the era of artificial intelligence (AI) and big data, there is an urgent necessity for the optimization of these analytical methods to more effectively handle and utilize the information. Despite the rapid accumulation of data associated with viral infections, the lack of a comprehensive framework for integrating, selecting, and analyzing these datasets has left numerous researchers uncertain about which data to select, how to access it, and how to utilize it most effectively in their research.This review endeavors to fill these gaps by exploring the multifaceted nature of viral infectious diseases and summarizing relevant data across multiple levels, from the molecular details of pathogens to broad epidemiological trends. The scope extends from the micro-scale to the macro-scale, encompassing pathogens, hosts, and vectors. In addition to data summarization, this review thoroughly investigates various dataset sources. It also traces the historical evolution of data collection in the field of viral infectious diseases, highlighting the progress achieved over time. Simultaneously, it evaluates the current limitations that impede data utilization.Furthermore, we propose strategies to surmount these challenges, focusing on the development and application of advanced computational techniques, AI-driven models, and enhanced data integration practices. By providing a comprehensive synthesis of existing knowledge, this review is designed to guide future research and contribute to more informed approaches in the surveillance, prevention, and control of viral infectious diseases, particularly within the context of the expanding big-data landscape.

GeneCOCOA: Detecting context-specific functions of individual genes using co-expression data

Extraction of meaningful biological insight from gene expression profiling often focuses on the identification of statistically enriched terms or pathways. These methods typically use gene sets as input data, and subsequently return overrepresented terms along with associated statistics describing their enrichment. This approach does not cater to analyses focused on a single gene-of-interest, particularly when the gene lacks prior functional characterization. To address this, we formulated GeneCOCOA, a method which utilizes context-specific gene co-expression and curated functional gene sets, but focuses on a user-supplied gene-of-interest (GOI). The co-expression between the GOI and subsets of genes from functional groups (e.g. pathways, GO terms) is derived using linear regression, and resulting root-mean-square error values are compared against background values obtained from randomly selected genes. The resulting p values provide a statistical ranking of functional gene sets from any collection, along with their associated terms, based on their co-expression with the gene of interest in a manner specific to the context and experiment. GeneCOCOA thereby provides biological insight into both gene function, and putative regulatory mechanisms by which the expression of the GOI is controlled. Despite its relative simplicity, GeneCOCOA outperforms similar methods in the accurate recall of known gene-disease associations. We furthermore include a differential GeneCOCOA mode, thus presenting the first implementation of a gene-focused approach to experiment-specific gene set enrichment analysis. GeneCOCOA is formulated as an R package for ease-of-use, available at https://github.com/si-ze/geneCOCOA.

HISSTA: a human in situ single-cell transcriptome atlas

MOTIVATION

Spatial transcriptomics holds great promise for revolutionizing biology and medicine by providing gene expression profiles with spatial information. Until recently, spatial resolution has been limited, but advances in high-throughput in situ imaging technologies now offer new opportunities by covering thousands of genes at a single-cell or even subcellular resolution, necessitating databases dedicated to comprehensive coverage and analysis with user-friendly intefaces.

RESULTS

We introduce the HISSTA database, which facilitates the archival and analysis of in situ transcriptome data at single-cell resolution from various human tissues. We have collected and annotated spatial transcriptome data generated by MERFISH, CosMx SMI, and Xenium techniques, encompassing 112 samples and 28 million cells across 16 tissue types from 63 studies. To decipher spatial contexts, we have implemented advanced tools for cell type annotation, spatial colocalization, spatial cellular communication, and niche analyses. Notably, all datasets and annotations are interactively accessible through Vitessce, allowing users to focus on regions of interest and examine gene expression in detail. HISSTA is a unique database designed to manage the rapidly growing dataset of in situ transcriptomes at single-cell resolution. Given its comprehensive data content and advanced analysis tools with interactive visualizations, HISSTA is poised to significantly impact cancer diagnosis, precision medicine, and digital pathology.

AVAILABILITY AND IMPLEMENTATION

HISSTA is freely accessible at https://kbds.re.kr/hissta/. The source code is available at https://doi.org/10.5281/zenodo.14904523.

Single-cell 3D genome reconstruction in the haploid setting using rigidity theory

This article considers the problem of 3-dimensional genome reconstruction for single-cell data, and the uniqueness of such reconstructions in the setting of haploid organisms. We consider multiple graph models as representations of this problem, and use techniques from graph rigidity theory to determine identifiability. Biologically, our models come from Hi-C data, microscopy data, and combinations thereof. Mathematically, we use unit ball and sphere packing models, as well as models consisting of distance and inequality constraints. In each setting, we describe and/or derive new results on realisability and uniqueness. We then propose a 3D reconstruction method based on semidefinite programming and apply it to synthetic and real data sets using our models.

Human giant GTPase GVIN1 forms an antimicrobial coatomer around the intracellular bacterial pathogen Burkholderia thailandensis

Several human pathogens exploit the kinetic forces generated by polymerizing actin to power their intracellular motility. Human cell-autonomous immune responses activated by the cytokine interferon-gamma (IFNγ) interfere with such microbial actin-based motility, yet the underlying molecular mechanisms are poorly defined. Here, we identify the IFNγ-inducible human giant GTPases GVIN1 as a novel host defense protein that blocks the bacterial pathogen Burkholderia thailandensis from high-jacking the host's actin polymerization machinery. We found that GVIN1 proteins form a coatomer around cytosolic bacteria and prevent Burkholderia from establishing force-generating actin comet tails. Coatomers formed by a second IFNγ-inducible GTPase, human guanylate binding protein 1 (GBP1), constitute a GVIN1-independent but mechanistically related anti-motility pathway. We show that coating with either GVIN1 or GBP1 displaces the Burkholderia outer membrane protein BimA, an actin nucleator that is essential for actin tail formation. Both GVIN1 and GBP1 coatomers require additional IFNγ-inducible co-factors to disrupt the membrane localization of BimA, demonstrating the existence of two parallel-acting IFNγ-inducible defense modules that evolved to target a virulence trait critical for the pathogenesis of numerous bacterial infectious agents.

Amogel: a multi-omics classification framework using associative graph neural networks with prior knowledge for biomarker identification

The advent of high-throughput sequencing technologies, such as DNA microarray and DNA sequencing, has enabled effective analysis of cancer subtypes and targeted treatment. Furthermore, numerous studies have highlighted the capability of graph neural networks (GNN) to model complex biological systems and capture non-linear interactions in high-throughput data. GNN has proven to be useful in leveraging multiple types of omics data, including prior biological knowledge from various sources, such as transcriptomics, genomics, proteomics, and metabolomics, to improve cancer classification. However, current works do not fully utilize the non-linear learning potential of GNN and lack of the integration ability to analyse high-throughput multi-omics data simultaneously with prior biological knowledge. Nevertheless, relying on limited prior knowledge in generating gene graphs might lead to less accurate classification due to undiscovered significant gene-gene interactions, which may require expert intervention and can be time-consuming. Hence, this study proposes a graph classification model called associative multi-omics graph embedding learning (AMOGEL) to effectively integrate multi-omics datasets and prior knowledge through GNN coupled with association rule mining (ARM). AMOGEL employs an early fusion technique using ARM to mine intra-omics and inter-omics relationships, forming a multi-omics synthetic information graph before the model training. Moreover, AMOGEL introduces multi-dimensional edges, with multi-omics gene associations or edges as the main contributors and prior knowledge edges as auxiliary contributors. Additionally, it uses a gene ranking technique based on attention scores, considering the relationships between neighbouring genes. Several experiments were performed on BRCA and KIPAN cancer subtypes to demonstrate the integration of multi-omics datasets (miRNA, mRNA, and DNA methylation) with prior biological knowledge of protein-protein interactions, KEGG pathways and Gene Ontology. The experimental results showed that the AMOGEL outperformed the current state-of-the-art models in terms of classification accuracy, F1 score and AUC score. The findings of this study represent a crucial step forward in advancing the effective integration of multi-omics data and prior knowledge to improve cancer subtype classification.

Using weighted gene co-expression network analysis to identify key genes related to preeclampsia

Introduction

The pathogenesis of preeclampsia remains unclear, highlighting the need for the creation of dependable biomarkers. This study aimed to pinpoint genetic risk factors linked to preeclampsia through the utilization of weighted gene co-expression network analysis (WGCNA).

Methods

A gene expression profile dataset from the placentas of patients with preeclampsia was acquired from the Gene Expression Omnibus (GEO) database and employed as a discovery cohort to construct a WGCNA network. Functional enrichment analysis, pathway analysis, and the construction of protein-protein interaction (PPI) networks were performed on core genes within these modules to pinpoint hub genes. The GSE25906 dataset was utilized as a validation cohort to evaluate the diagnostic significance of the hub genes. Immunohistochemistry assays were employed to validate the protein expression levels of these genes in placental tissues from both preeclampsia and control groups.

Results

Through WGCNA, 33 co-expression modules were identified, with 4 modules significantly associated with multiple clinical traits (≥3). Among these, 75 core genes were highlighted, predominantly enriched in pathways related to the adaptive immune response and platelet activation. Notably, TYROBP, PLEK, LCP2, HCK, and ITGAM emerged as hub genes with high PPI network scores and strong diagnostic potential, all prominently associated with immunity-related pathways. Protein expression analysis revealed that these genes were downregulated in placental tissues from preeclampsia patients compared to healthy controls.

Discussions

TYROBP, PLEK, LCP2, HCK, and ITGAM are closely linked to preeclampsia and hold promise as potential biomarkers for its diagnosis and for advancing the understanding of its pathogenesis.

Microevolution of Cryptococcus neoformans in high CO2 converges on mutations isolated from patients with relapsed cryptococcosis

Cryptococcus neoformans is an environmental fungus that causes an estimated 180,000 deaths annually and transitions from the external environment to the host environment to cause disease. CO2 concentrations in the atmosphere (0.04%) are dramatically lower than in mammalian tissues (5%). Environmental C. neoformans strains that cannot tolerate 5% CO2 are less virulent than CO2-tolerant strains. Microevolution at elevated CO2 generates loss-of-function mutations in the nucleotide binding protein Avc1 that confer CO2 tolerance to CO2-intolerant strains. Mechanistically, Avc1 positively regulates the expression of plasma membrane transporters, including PDR9, a phospholipid floppase that negatively modulates CO2 fitness. Deletion of AVC1 in five CO2-intolerant environmental strains increases competitive fitness in host CO2 and in a mouse infection model. Importantly, strains with similar AVC1 mutations emerge in patients with relapsed cryptococcosis. Therefore, this microevolutionary convergence strongly suggests that adaptation to host CO2 is a significant driver of C. neoformans fitness during infection.

Lignature: A Comprehensive Database of Ligand Signatures to Predict Cell-Cell Communication

Ligand-receptor interactions mediate intercellular communication, inducing transcriptional changes that regulate physiological and pathological processes. Ligand-induced transcriptomic signatures can be used to predict active ligands; however, the absence of a comprehensive set of ligand-response signatures has limited their practical application in predicting ligand-receptor interactions. To bridge this gap, we developed Lignature, a curated database encompassing intracellular transcriptomic signatures for 362 human ligands, significantly expanding the repertoire of ligands with available intracellular response signatures. Lignature compiles signatures from published transcriptomic datasets and established resources such as CytoSig and ImmuneDictionary, generating both gene- and pathway-based signatures for each ligand. We applied Lignature to predict active ligands driving transcriptomic changes in controlled in vitro experiments and real-world single-cell sequencing datasets. Lignature outperformed existing methods such as NicheNet, achieving higher accuracy in identifying active ligands at both the gene and pathway levels. These results establish Lignature as a robust platform for ligand signaling inference, providing a powerful tool to explore ligand-receptor interactions across diverse experimental and physiological contexts.

TENT5C extends Odf1 poly(A) tail to sustain sperm morphogenesis and fertility

Changes in the poly(A) tail length of Odf1 and other transcripts critical for male fertility have been linked to translational activation during sperm formation 1-3. The mRNA poly(A) polymerase TENT5C is required for fastening the flagellum to the sperm head, but its role in shaping the poly(A) tail profile of the spermatid transcriptome remains limited 4,5. Here, we comprehensively document how changes in mRNA poly(A) tail length across the transcriptome reflect transcript metabolism in spermatids. In the absence of TENT5C polymerase activity, the poly(A) tail length of Odf1 transcripts is reduced, and the local distribution of ODF1 proteins in spermatids is disrupted. We show that mice expressing a catalytically inactive TENT5C produce headless spermatozoa with outer dense fibers detached from the axoneme, and other flagellar abnormalities associated with ODF1 deficiency 6. We propose that TENT5C poly(A) polymerase activity regulates the spatial translation of Odf1 mRNAs during spermiogenesis, a process critical for sperm morphogenesis and fertility. These findings highlight the power of poly(A) tail profiling to identify abnormal mRNA processing causative of infertility.

Deletion of an sRNA primes development in a multicellular bacterium

Small non-coding RNAs (sRNAs) regulate gene expression of many biological processes. During growth, some myxobacteria produce an sRNA-Pxr-that blocks fruiting-body development, an aggregative multicellular process typically triggered by starvation. Deleting the pxr gene allows Myxococcus xanthus to develop despite nutrient availability, but Pxr binding targets and the genes regulated by Pxr remain unknown. Here, after showing that Pxr controls the temporal dynamics of development, we compare the transcriptomes of vegetative M. xanthus cells possessing vs. lacking pxr. Over half of the genes impacted by pxr deletion are linked to development, including known and previously undiscovered critical regulators. Pxr also positively regulates genes associated with general metabolic processes. Our study discovers phenotypic effects of Pxr regulation with ecological importance, identifies the suite of genes this sRNA controls during vegetative growth and reveals a previously unknown developmental regulator. These findings provide insights into the molecular mechanism controlling myxobacterial development.

Beckwith-Wiedemann syndrome and large offspring syndrome involve alterations in methylome, transcriptome, and chromatin configuration

Beckwith-Wiedemann Syndrome (BWS) is the most common epigenetic overgrowth syndrome, caused by epigenetic alterations on chromosome 11p15. In ∼50% of patients with BWS, the imprinted region KvDMR1 (IC2) is hypomethylated. Nearly all children with BWS develop organ overgrowth and up to 28% develop cancer during childhood. The global epigenetic alterations beyond the 11p15 region in BWS are not currently known. Uncovering these alterations at the methylome, transcriptome, and chromatin architecture levels are necessary steps to improve the diagnosis and understanding of patients with BWS. Here we characterized the complete epigenetic profiles of BWS IC2 individuals together with the animal model of BWS, bovine large offspring syndrome (LOS). A novel finding of this research is the identification of two molecular subgroups of BWS IC2 individuals. Genome-wide alternations were detected for DNA methylation, transcript abundance, alternative splicing events of RNA, chromosome compartments, and topologically associating domains (TADs) in BWS and LOS, with shared alterations identified between species. Altered chromosome compartments and TADs were correlated with differentially expressed genes in BWS and LOS. Together, we highlight genes and genomic regions that have the potential to serve as targets for biomarker development to improve current molecular diagnostic methodologies for BWS.

An Arrayed CRISPR Screen Identifies Knockout Combinations Improving Antibody Productivity in HEK293 Cells

Mammalian cells are used to express complex biologics, such as multispecific antibodies. While multispecifics enable promising new strategies for treating human disease, their production at high expression titer and purity can be challenging. To understand how cells respond to antibody and multispecific expression, five molecules were selected for bulk RNA sequencing (RNA-seq) early after the transfection of a human embryonic kidney 293 (HEK293) host. All five molecules shared a differential expression signature of secretory and protein folding stresses, but this signature was stronger for molecules with low titer. We then designed an arrayed CRISPR knockout screen of 206 differentially expressed target genes and 223 literature-motivated targets to identify knockouts that affect antibody productivity. Eight novel knockout targets were identified that increased expression titers by 20-80%. Notably, seven of these top eight hits were from the differentially expressed set of candidate-gene knockouts. The top knockout target, HIST2H3C, showed evidence for additivity with five other hits, including a knockout combination that increased the titer of a difficult-to-express antibody by up to 100%. Findings for both HIST2H3C and INHBE knockout targets generalized to an alternate HEK293 host expressing an additional antibody and a multispecific host with no meaningful impact on product purity. Thus, we propose HIST2H3C and INHBE disruption as a promising and novel strategy for host-cell engineering to improve antibody and multispecific productivity.

Regulatory T cells in the mouse hypothalamus control immune activation and ameliorate metabolic impairments in high-calorie environments

The hypothalamus in the central nervous system (CNS) has important functions in controlling systemic metabolism. A calorie-rich diet triggers CNS immune activation, impairing metabolic control and promoting obesity and Type 2 Diabetes (T2D), but the mechanisms driving hypothalamic immune activation remain unclear. Here we identify regulatory T cells (Tregs) as key modulators of hypothalamic immune responses. In mice, calorie-rich environments activate hypothalamic CD4+ T cells, infiltrating macrophages and microglia while reducing hypothalamic Tregs. mRNA profiling of hypothalamic CD4+ T cells reveals a Th1-like activation state, with increased Tbx21, Cxcr3 and Cd226 but decreased Ccr7 and S1pr1. Importantly, results from Treg loss-of function and gain-of-function experiments show that Tregs limit hypothalamic immune activation and reverse metabolic impairments induced by hyper-caloric feeding. Our findings thus help refine the current model of Treg-centered immune-metabolic crosstalk in the brain and may contribute to the development of precision immune modulation for obesity and diabetes.

In Silico Identification of ANKRD22 as a Theragnostic Target for Pancreatic Cancer and Fostamatinib's Therapeutic Potential

Pancreatic cancer (PC) is one of the most tremendously malignant cancers with a poor prognosis, especially when it advances to metastasis. Besides, PC patients have encountered resistance to recent therapeutic approaches. In recent work, we effectively determined ANKRD22 by re-analyzing RNA-seq datasets from cell lines and human tissues deriving from PC. We demonstrated that ANKRD22 expression was remarkably high in the PC group compared to the normal group at both gene expression and protein levels. ANKRD22 resulted in a worse overall survival (OS) rate of PC patients (HR = 1.7, p = 0.0082). Intriguingly, ANKRD22 was statistically highly expressed in the mutated KRAS group relative to the wildtype group (p < 0.05). Similarly, compared to the wildtype TP53, in the mutated TP53, ANKRD22 also significantly expressed (p < 0.05); their concurrent expression, ANKRD22 and KRAS; ANKRD22 and TP53 exacerbated the survival outcome relative to the co-expression of low ANKRD22 and unaltered genes (p < 0.001; HR > 2.6). We explored the potential pathways and biological processes ANKRD22 might not only contribute to promoting PC, including cell-cycle regulation, E2F1 targets, and apoptosis but also foster the dissemination of PC by involve in invasion and migration processes. In the investigation of drugs that might target ANKRD22, we figured out fostamatinib. Molecular docking and molecular dynamic simulation (MDs) techniques provided extensive insights into the binding mode of ANKRD22 and fostamatinib. ANKRD22 exhibited strong binding affinity (ΔG = -7.0 kcal/mol in molecular docking and ∆Gbind = -38.66 ± 6.09 kcal/mol in MDs). Taken together, ANKRD22 could be a promising theragnostic target that might be inhibited by fostamatinib, thereby suppressing PC growth.

Pervasive formation of double-stranded RNAs by overlapping sense/antisense transcripts in budding yeast mitosis and meiosis

Previous RNA profiling studies revealed coexpression of overlapping sense/antisense (s/a) transcripts in pro- and eukaryotic organisms. Functional analyses in yeast have shown that certain s/a mRNA/mRNA and mRNA/lncRNA pairs form stable double-stranded RNAs (dsRNAs) that affect transcript stability. Little is known, however, about the genome-wide prevalence of dsRNA formation and its potential functional implications during growth and development in diploid budding yeast. To address this question, we monitored dsRNAs in a Saccharomyces cerevisiae strain expressing the ribonuclease DCR1 and the RNA-binding protein AGO1 from Naumovozyma castellii We identify dsRNAs at 347 s/a loci that express partially or completely overlapping transcripts during mitosis, meiosis, or both stages of the diploid life cycle. We associate dsRNAs with s/a loci previously thought to be exclusively regulated by antisense interference, and others that encode antisense RNAs, which down-regulate sense mRNA-encoded protein levels. To facilitate hypothesis building, we developed the sense/antisense double-stranded RNA (SensR) expression viewer. Users are able to retrieve different graphical displays of dsRNA and RNA expression data using genome coordinates and systematic or standard names for mRNAs and different types of stable or cryptic long noncoding RNAs (lncRNAs). Our data are a useful resource for improving yeast genome annotation and for work on RNA-based regulatory mechanisms controlling transcript and protein levels. The data are also interesting from an evolutionary perspective, since natural antisense transcripts that form stable dsRNAs have been detected in many species from bacteria to humans. The SensR viewer is freely accessible at https://sensr.genouest.org.

A semidominant point mutation of Mediator tail subunit MED5b in Arabidopsis leads to altered enrichment of H3K27me3 and reduced expression of targets of MYC2

The Mediator complex coordinates regulatory input for transcription driven by RNA polymerase II in eukaryotes. reduced epidermal fluorescence4-3 (ref4-3) is a semidominant mutation that results in a single amino acid substitution in the Mediator tail subunit Med5b. Previous characterization of ref4-3 revealed altered expression of a variety of loci in Arabidopsis, including those contributing to phenylpropanoid biosynthesis. Examination of existing RNA-seq data indicated that loci enriched for the transcriptionally repressive chromatin modification H3K27me3 are overrepresented among genes that are misregulated in ref4-3. We used ChIP-seq and RNA-seq to examine the possibility that perturbation of H3K27me3 homeostasis in ref4-3 plants contributed to altered transcript levels. We observed that ref4-3 results in a modest global reduction of H3K27me3 at enriched loci and that this reduction is not dependent on gene expression; however, altered H3K27me3 was not strongly predictive of altered expression in ref4-3 plants. Instead, our analyses revealed a substantial enrichment of targets of the MYC2 transcriptional regulator among genes that exhibit decreased expression in ref4-3. Consistent with previous characterization of ref4-3, we observed that ref4-3-dependent decreased expression of MYC2 targets can be suppressed by loss of another Mediator tail subunit, MED25. This observation is consistent with previous biochemical characterization of MYC2. Our data highlight the diverse and distinct impacts that a single amino acid change in the tail subunit of Mediator can have on transcriptional circuits and raise the prospect that Mediator directly contributes to H3K27me3 homeostasis in plants.

Identification of candidate biomarkers correlated with the pathogenesis of breast cancer patients

Breast cancer (BC) is the second leading cause of cancer-related death in females, followed by lung cancer. Disadvantages exist in conventional diagnostic techniques of BC, such as radiation risk. The present study integrated bioinformatics analysis with machine learning to elucidate potential key candidate genes associated with the tumorigenesis of BC. Eleven datasets were downloaded from the Gene Expression Omnibus (GEO) database and were consolidated into two independent cohorts (training cohort and validation cohort) after batch-effect removal. We employed "limma" package to screen differentially expressed genes (DEGs) between BC and adjacent normal breast samples. Subsequently, the most reliable diagnostic indicators were identified utilizing LASSO-Logistic regression, SVM-RFE and multivariate stepwise Logistic regression analysis. Logistic model and nomogram were created based on these hub genes and applied in external validation cohort to verify the robustness of the model. As a result, a total of six hub genes connected with BC pathogenesis were identified, including CD300LG, IGSF10, FAM83D, MAMDC2, COMP and SEMA3G. Then, a diagnostic model of BC on the basis of these genes was established. ROC analysis of the diagnostic model illustrated that AUC of the training cohort was 0.978 (0.962, 0.995). In the validation cohort, AUC of training set and validation set were 0.936 (0.910, 0.961) and 0.921 (0.870, 0.972), respectively. This indicated that the model was reliable in separating BC patients from healthy individuals. The model may assist in early diagnosis of BC with implications for improving the prognosis of BC patients.

Transcriptomic Analysis of CAD Cell Differentiation

CAD cells were derived from Cath.a cells, a mouse central nervous system catecholaminergic cell line. Serum-starved CAD cells undergo morphological changes and resemble isolated neurons when observed by microscopy. We carried out an RNAseq transcriptomic analysis to examine differentiated CAD cells for expression signatures related to neuronal functions, identifying ~1900 transcripts whose expression changed with differentiation. Pathview analysis identified ~80 KEGG pathway gene sets that were differentially expressed, including upregulation of at least 13 neuron-related pathways. This dataset can be explored more deeply, allowing further investigation into expression changes relevant to studying neuronal functions in this easy-to-culture model system.

Zinc-limited Mycobacterium tuberculosis stimulate distinct responses in macrophages compared with standard zinc-replete bacteria

Tuberculosis (TB) is notoriously difficult to treat, likely due to the complex host-pathogen interactions driven by pathogen heterogeneity. An understudied area of TB pathogenesis is host responses to Mycobacterium tuberculosis bacteria (Mtb) that are limited in zinc ions. This distinct population resides in necrotic granulomas and sputum and could be the key player in tuberculosis pathogenicity. In this study, we tested the hypothesis that macrophages differentiate between Mtb grown under zinc limitation or in the standard zinc-replete medium. Using several macrophage infection models, such as murine RAW 264.7 and murine bone marrow-derived macrophages (BMDMs), as well as human THP-1-derived macrophages, we show that macrophages infected with zinc-limited Mtb have increased bacterial burden compared with macrophages infected with zinc-replete Mtb. We further demonstrate that macrophage infection with zinc-limited Mtb trigger higher production of reactive oxygen species (ROS) and cause more macrophage death. Furthermore, the increased ROS production is linked to the increased phagocytosis of zinc-limited Mtb, whereas cell death is not. Finally, transcriptional analysis of RAW 264.7 macrophages demonstrates that macrophages have more robust pro-inflammatory responses when infected with zinc-limited Mtb than zinc-replete Mtb. Together, our findings suggest that Mtb's access to zinc affects their interaction with macrophages and that zinc-limited Mtb may be influencing TB progression. Therefore, zinc availability in bacterial growth medium should be considered in TB drug and vaccine developments.

NAD+ prevents chronic kidney disease by activating renal tubular metabolism

Chronic kidney disease (CKD) is associated with renal metabolic disturbances, including impaired fatty acid oxidation (FAO). Nicotinamide adenine dinucleotide (NAD+) is a small molecule that participates in hundreds of metabolism-related reactions. NAD+ levels are decreased in CKD, and NAD+ supplementation is protective. However, both the mechanism of how NAD+ supplementation protects from CKD, as well as the cell types involved, are poorly understood. Using a mouse model of Alport syndrome, we show that nicotinamide riboside (NR), an NAD+ precursor, stimulated renal PPARα signaling and restored FAO in the proximal tubules, thereby protecting from CKD in both sexes. Bulk RNA-sequencing showed that renal metabolic pathways were impaired in Alport mice and activated by NR in both sexes. These transcriptional changes were confirmed by orthogonal imaging techniques and biochemical assays. Single-nuclei RNA sequencing and spatial transcriptomics, both the first of their kind to our knowledge from Alport mice, showed that NAD+ supplementation restored FAO in proximal tubule cells. Finally, we also report, for the first time to our knowledge, sex differences at the transcriptional level in this Alport model. In summary, the data herein identify a nephroprotective mechanism of NAD+ supplementation in CKD, and they demonstrate that this benefit localizes to the proximal tubule cells.

Integrated Analysis of Ferroptosis and Immune Infiltration in Ulcerative Colitis Based on Bioinformatics

Introduction

Ulcerative colitis (UC) is an inflammatory bowel disease influenced by genetic, immune, and environmental factors. This study investigates the link between ferroptosis, a cell death process related to oxidative stress and iron metabolism, and immune infiltration in UC.

Materials and Methods

We analyzed UC patient transcription data from the Gene Expression Omnibus (GEO) and identified ferroptosis-related genes using FerrDB. Using STRING and Cytoscape, we analyzed protein-protein interactions to identify hub UC Differentially Expressed Genes (UCDEGs) and performed functional enrichment with GO and KEGG pathways. Machine learning helped further identify key UC Differentially Expressed Ferroptosis-related genes (UCDE-FRGs), which were validated using additional GEO datasets and immunohistochemical staining.

Results

A total of 11 hub UCDEGs (CCL2, ICAM1, TLR2, CXCL9, MMP9, CXCL10, IL1B, CXCL8, PTPRC, FCGR3A, and IL1A) and 3 key UCDE-FRGs (DUOX2, LCN2 and IDO1) were identified. GO and KEGG functional enrichment indicates that these genes play a role in immunity and ferroptosis. Analysis of immune cell infiltration showed that there were a large number of Plasma cells, Monocytes, M0/M1 Macrophages and Neutrophils in the UC. Correlation analysis revealed 3 key UCDE-FRGs associated with immune-infiltrated cells in UC. IHC results showed that the expression levels of 3 key UCDE-FRGs in UC were all higher than that in the healthy controls.

Conclusion

In summary, this study identified three key genes related to UC ferroptosis and immunity, namely DUOX2, IDO1 and LCN2. These findings suggest that immune infiltration plays an important role in UC caused by ferroptosis, and that there is mutual regulation between UC and immune-infiltrated cells. Our research revealed the potential application of immune and ferroptosis in the diagnosis, treatment and prognosis of UC, providing new strategies for clinical management.

Redox-inactive CC-type glutaredoxins interfere with TGA transcription factor-dependent repression of target promoters in roots

Changes in nitrogen (N) availability in the soil trigger transcriptional responses in plants to optimize N acquisition, allocation, and remobilization. In roots of N-starved Arabidopsis (Arabidopsis thaliana) plants, transcriptional activation of genes encoding, for example, low-affinity nitrate transporters, depends on 4 related C-TERMINALLY ENCODED PEPTIDE DOWNSTREAM (CEPD) proteins, also known as ROXY6, ROXY7, ROXY8, and ROXY9. All 21 ROXYs found in A. thaliana interact with members of the TGACG-binding (TGA) family of transcription factors. Here, we demonstrate that 2 Clade I TGAs (TGA1, TGA4) serve as molecular links between CEPDs and their target promoters in roots. In the roxy6 roxy7 roxy8 roxy9 quadruple mutant (named cepd in this manuscript), transcriptional activation of N-starvation-inducible genes is impaired, most likely due to the association of Clade I TGAs with a repressive complex at their target promoters. In wild-type plants, this repressive complex is nonfunctional, and gene expression may be regulated by the N supply-regulated ratio of CEPDs over opposing ROXYs containing the TOPLESS-interacting ALWL motif. Although CEPDs resemble glutaredoxins with glutathione-dependent oxidoreductase activity, a ROXY9 variant with a mutation in the catalytic cysteine in its putative active site can confer wild-type-like regulation of target genes. This finding demonstrates that ROXY9 does not function through redox-dependent mechanisms.

Understanding dysbiosis and resilience in the human gut microbiome: biomarkers, interventions, and challenges

The healthy gut microbiome is important in maintaining health and preventing various chronic and metabolic diseases through interactions with the host via different gut-organ axes, such as the gut-brain, gut-liver, gut-immune, and gut-lung axes. The human gut microbiome is relatively stable, yet can be influenced by numerous factors, such as diet, infections, chronic diseases, and medications which may disrupt its composition and function. Therefore, microbial resilience is suggested as one of the key characteristics of a healthy gut microbiome in humans. However, our understanding of its definition and indicators remains unclear due to insufficient experimental data. Here, we review the impact of key drivers including intrinsic and extrinsic factors such as diet and antibiotics on the human gut microbiome. Additionally, we discuss the concept of a resilient gut microbiome and highlight potential biomarkers including diversity indices and some bacterial taxa as recovery-associated bacteria, resistance genes, antimicrobial peptides, and functional flexibility. These biomarkers can facilitate the identification and prediction of healthy and resilient microbiomes, particularly in precision medicine, through diagnostic tools or machine learning approaches especially after antimicrobial medications that may cause stable dysbiosis. Furthermore, we review current nutrition intervention strategies to maximize microbial resilience, the challenges in investigating microbiome resilience, and future directions in this field of research.

Hierarchical Lineage Tracing Reveals Diverse Pathways of AML Treatment Resistance

Cancer cells adapt to treatment, leading to the emergence of clones that are more aggressive and resistant to anti-cancer therapies. We have a limited understanding of the development of treatment resistance as we lack technologies to map the evolution of cancer under the selective pressure of treatment. To address this, we developed a hierarchical, dynamic lineage tracing method called FLARE (Following Lineage Adaptation and Resistance Evolution). We use this technique to track the progression of acute myeloid leukemia (AML) cell lines through exposure to Cytarabine (AraC), a front-line treatment in AML, in vitro and in vivo. We map distinct cellular lineages in murine and human AML cell lines predisposed to AraC persistence and/or resistance via the upregulation of cell adhesion and motility pathways. Additionally, we highlight the heritable expression of immunoproteasome 11S regulatory cap subunits as a potential mechanism aiding AML cell survival, proliferation, and immune escape in vivo. Finally, we validate the clinical relevance of these signatures in the TARGET-AML cohort, with a bisected response in blood and bone marrow. Our findings reveal a broad spectrum of resistance signatures attributed to significant cell transcriptional changes. To our knowledge, this is the first application of dynamic lineage tracing to unravel treatment response and resistance in cancer, and we expect FLARE to be a valuable tool in dissecting the evolution of resistance in a wide range of tumor types.

LncRNA ENST00000581911 Regulates Extraocular Muscle Remodeling by Interacting With KHSRP in Thyroid Eye Disease

Purpose

Thyroid eye disease (TED) is a visually debilitating and cosmetically disfiguring orbital disorder, characterized by the remodeling of extraocular muscles (EOMs). This study aimed to investigate the role of long non-coding RNA (lncRNA) ENST00000581911 in the EOMs of TED.

Methods

LncRNA microarray analysis was performed on EOM tissues sampled from patients with TED and patients with concomitant esotropia. LncRNA ENST00000581911 was identified and subjected to bioinformatics analysis. High-throughput RNA sequencing, CCK-8 assay, CFSE staining, and ELISA were used to investigate the regulatory function of ENST00000581911 in vitro. Furthermore, RNA pull-down, liquid chromatography-tandem mass spectrometry (LC-MS/MS), and western blot (WB) analyses were applied to identify the RNA-binding protein (RBP) interacting with ENST00000581911.

Results

A total of 1261 lncRNAs were found to be differentially expressed in the EOMs of TED, with 648 upregulated and 613 downregulated lncRNAs. Among these, the upregulated lncRNA ENST00000581911 exhibited the highest expression level, as validated by quantitative real-time PCR (qRT-PCR). Functional analysis demonstrated that ENST00000581911 might be involved in inflammatory response, regulation of muscle contraction, and amino sugar and nucleotide sugar metabolism. RNA sequencing of ENST00000581911-overexpressing and control orbital fibroblasts (OFs) showed that ENST00000581911 might play a regulatory role in DNA replication, extracellular matrix, and cell cycle. Furthermore, KHSRP was identified as the RBP of ENST00000581911. Overexpression of ENST00000581911 promoted cell proliferation and hyaluronic acid secretion in OFs, whereas silencing KHSRP attenuated these effects.

Conclusions

This study provides novel insights into the role of lncRNA ENST00000581911 in the pathogenesis of EOM remodeling in TED. ENST00000581911 may serve as a potential therapeutic target of TED.

Establishment of a prediction model and immune infiltration characteristics of atherosclerosis progression based on neutrophil extracellular traps-related genes

Neutrophil extracellular traps (NETs) are a novel regulatory mechanism of neutrophils, which can promote endothelial cell inflammation through direct or indirect pathways and play a crucial role in the occurrence and development of atherosclerosis (AS). This study aimed to explore the mechanism of NETs in AS progression using bioinformatics methods. We acquired datasets from Gene Expression Omnibus (GEO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) and used Weighted Gene Co-expression Network Analysis (WGCNA) to identify communal genes shared by NET-related genes. Gene Ontology (GO) and KEGG enrichment analyses were conducted. Machine learning algorithms were used to identify hub genes, then protein-protein interaction (PPI), CO-expression network construction, nomogram model building and validation, and immune infiltration analysis were performed. Data were verified by qPCR. Four datasets related to AS progression were included. Module genes shared 27 genes with NRGs. Pathways related to immune regulation, leukocyte migration, and others were identified. Machine learning revealed SLC25A4 and C5AR1 as hub genes. SLC25A4 and C5AR1 were confirmed to have predictive value for intraplaque hemorrhage (IPH), advanced AS plaques, ruptured plaques, and unstable plaques. These pathologic changes are closely related to AS progression and are the main contents of AS progression. Immune infiltration analysis revealed 4 immune cells associated with IPH, among them resting dendritic cells, which were closely related to SLC25A4. In qPCR validation, SLC25A4 and C5AR1 were shown to be consistent with the bioinformatic analysis results. These findings provided novel insights into the molecular characteristics of NRGs and potential therapies for AS progression.

EpiMapper: A new tool for analyzing high-throughput sequencing from CUT&Tag

Since the invention of next-generation sequencing, new methods have been developed to understand the regulation of gene expression through epigenetic markers. Among these, CUT&Tag (Cleavage Under Targets and Tagmentation) analysis has emerged as an efficient epigenomic profiling technique with low input requirements, high sensitivity, and low background signals. Although wet-lab techniques are available, data analysis remains challenging for scientists without expert-level computational skills. Therefore, we developed EpiMapper, a new Python package that simplifies the data analysis of CUT&Tag sequencing and similar techniques, such as ATAC-seq (Assay for Transposase-Accessible Chromatin using sequencing) or ChIP-seq (chromatin immunoprecipitation [ChIP] assays with sequencing), and allows biomedical scientists to easily interpret the results. This new package includes every necessary step, from quality control to annotation and differential peak analysis. In particular, EpiMapper has improved functionality (e.g., reproducibility assessment) compared to previous analysis protocols and visualization plots and provides new features, such as genome annotation and differential peak analysis. Using three case studies, two on CUT&Tag and one on ATAC-seq data, the EpiMapper Python package successfully reproduced previous results.

Pathways underlying selective neuronal vulnerability in Alzheimer's disease: Contrasting the vulnerable locus coeruleus to the resilient substantia nigra

INTRODUCTION

Alzheimer's disease (AD) selectively affects certain brain regions, yet the mechanisms of selective vulnerability remain poorly understood. The neuromodulatory subcortical system, which includes nuclei exhibiting a range of vulnerability and resilience to AD-type degeneration, presents a framework for uncovering these mechanisms.

METHODS

We leveraged transcriptomics and immunohistochemistry in paired samples from human post mortem tissue representing a vulnerable and resilient region-the locus coeruleus (LC) and substantia nigra (SN). These regions have comparable anatomical features but distinct vulnerability to AD.

RESULTS

We identified significant differences in cholesterol homeostasis, antioxidant pathways, KRAS signaling, and estrogen signaling at a bulk transcriptomic level. Notably, evidence of sigma-2 receptor upregulation was detected in the LC.

DISCUSSION

Our findings highlight pathways differentiating the LC and SN, potentially explaining the LC's selective vulnerability in AD. Such pathways offer potential targets of disease-modifying therapies for AD.

HIGHLIGHTS

Intraindividual comparative RNAseq was used to study selective vulnerability. Metallothionein genes are significantly enriched in the substantia nigra. Cholesterol homeostatic genes are significantly enriched in the locus coeruleus. The locus coeruleus is likely more susceptible to toxic amyloid beta oligomers.

Long Non-Coding RNAs in Non-Alcoholic Fatty Liver Disease; Friends or Foes?

Metabolic dysfunction-associated fatty liver disease (MAFLD) is a range of conditions that start with the accumulation of fat in the liver (hepatic steatosis) and can progress to more severe stages like steatohepatitis (NASH) and fibrosis without drinking alcohol. Environmental and genetic variables both contribute to MAFLD's development, with various biological processes and mediators involved at every phase. Long non-coding RNAs (lncRNAs) are a class of RNA molecules that are not translated into protein and are over 200 nucleotides long. They can impact genes that encode protein by controlling transcriptional and post-transcriptional procedures. Dysregulation of lncRNA has been connected to several liver diseases, including MAFLD. Recent research has linked lncRNAs to MAFLD pathology in both patients and animal models. However, the roles of most lncRNAs in MAFLD pathology are still not well recognized. This review provides a comprehensive catalog of recently reported lncRNAs in the pathogenesis of MAFLD and summarizes the current knowledge of lncRNAs usage as therapeutic strategies in MAFLD, the most common liver disease. Collectively, lncRNA's targeting could potentially offer a therapeutic approach by modulating MAFLD.

Deep profiling of gene expression across 18 human cancers

Clinical and biological information in large datasets of gene expression across cancers could be tapped with unsupervised deep learning. However, difficulties associated with biological interpretability and methodological robustness have made this impractical. Here we describe an unsupervised deep-learning framework for the generation of low-dimensional latent spaces for gene-expression data from 50,211 transcriptomes across 18 human cancers. The framework, which we named DeepProfile, outperformed dimensionality-reduction methods with respect to biological interpretability and allowed us to unveil that genes that are universally important in defining latent spaces across cancer types control immune cell activation, whereas cancer-type-specific genes and pathways define molecular disease subtypes. By linking latent variables in DeepProfile to secondary characteristics of tumours, we discovered that mutation burden is closely associated with the expression of cell-cycle-related genes, and that the activity of biological pathways for DNA-mismatch repair and MHC class II antigen presentation are consistently associated with patient survival. We also found that tumour-associated macrophages are a source of survival-correlated MHC class II transcripts. Unsupervised learning can facilitate the discovery of biological insight from gene-expression data.

Shared pyroptosis pathways and crosstalk genes underpin inflammatory links between periodontitis and atherosclerosis

OBJECTIVE

This study aimed to identify crosstalk genes shared between periodontitis (PD) and atherosclerosis (AS) and to investigate their potential connections with pyroptosis-related genes. The goal was to uncover common regulatory mechanisms underlying these two inflammatory conditions.

METHODS

Gene expression datasets for PD (GSE10334) and AS (GSE43292) were retrieved from public databases. Following batch effect correction and normalization, differential expression analysis was conducted using the limma package in R. Functional enrichment analysis was performed with the clusterProfiler package to identify key pathways, while heatmaps and pathway networks were constructed to visualize the relationships among pyroptosis genes and crosstalk genes. Weighted gene co-expression network analysis (WGCNA) was applied to identify critical modules, and the diagnostic potential of core genes was evaluated via receiver operating characteristic (ROC) analysis. Protein-protein interaction (PPI) networks were also constructed to explore molecular interactions.

RESULTS

A total of 28 downregulated and 105 upregulated genes were identified in the PD dataset, while the AS dataset revealed 55 downregulated and 56 upregulated genes. Thirteen crosstalk genes were identified between the two datasets. Enrichment analyses of these crosstalk genes highlighted their involvement in inflammation- and immune-related pathways. The observed association of pyrototic phenotypes with PD and AS indicated significant overexpression of pyroptosis-related genes such as CASP1, NLRP3, and GSDMD, suggesting the participation of pyroptosis in the progression of disease. The WGCNA suggested that pyroptosis genes are closely relevant to immune responses and cell death processes. Data up to October 2023 were used to perform receiver operating characteristics (ROC) curves to confirm the diagnostic value of the enriched core genes, and all of them presented AUC values >0.8, which meant that they were key genes with effective diagnostic power.

CONCLUSION

We report a novel study that identifies differentially expressed genes and pyroptosis-related pathways in PD and AS with shared inflammatory mechanisms. These results underscore the crucial role of pyroptosis in disease progression, suggesting its potential as a focus of diagnostic and therapeutic strategies. These findings provide insights for dissecting the molecular basis of inflammatory diseases.

Toll-Like Receptor 4 and 8 are Overexpressed in Lung Biopsies of Human Non-small Cell Lung Carcinoma

PURPOSE

Lung cancer is the leading cause of cancer death worldwide which includes two main types of carcinoma distinguished in non-small cell lung cancer (NSCLC) involving epithelial cells, and small cell lung cancer (SCLC) affecting neuronal cells and hormone secreting cells. Studies have shown a causal link between inflammation/innate immunity and onset of NSCLC. The present study aimed to evaluate the expression of Toll-like receptors (TLRs) 4 and TLR8 in peripheral blood mononuclear cells (PBMC) and in lung tissues of patients with NSCLC, useful for future prognostic tools for NSCLC.

METHODS

Patients surgically treated for NSCLC with anatomical resections and patients with benign disease were enrolled. The expression levels of TLR4 and TLR8 were determined by real time PCR and by immunohistochemical analysis in PBMC and in lung tissues, respectively. A preliminary in silico analysis including 1194 arrays from healthy and cancer tissues were extracted by Genevestigator database. The association between TLRs gene expression and survival outcome was also investigated.

RESULTS

Bioinformatics analysis revealed that downregulation of TLR4 and TLR8 positively impacts the survival in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). However, no significant differences in TLR4 and TLR8 gene expression between case and control groups were observed in PBMC. A positive correlation was found in their expression levels. Interestingly, immunohistochemical analysis showed that the levels of TLR4 and TLR8 were higher in the lung tissues of patients with NSCLC than in the control group in terms of staining intensity and positive cells.

CONCLUSION

Albeit the precise role of TLRs is not fully defined, this study identified the potential involvement of TLR4 and TLR8 in the pathogenesis of NSCLC. Our data led us to hypothesize their potential role in overall survival which deserves to be explored further to establish whether TLR4 and TLR8 can represent positive prognostic indicators of disease in NSCLC.

Actuating Extracellular Matrices Decouple the Mechanical and Biochemical Effects of Muscle Contraction on Motor Neurons

Emerging in vivo evidence suggests that repeated muscle contraction, or exercise, impacts peripheral nerves. However, the difficulty of isolating the muscle-specific impact on motor neurons in vivo, as well as the inability to decouple the biochemical and mechanical impacts of muscle contraction in this setting, motivates investigating this phenomenon in vitro. This study demonstrates that tuning the mechanical properties of fibrin enables longitudinal culture of highly contractile skeletal muscle monolayers, enabling functional characterization of and long-term secretome harvesting from exercised tissues. Motor neurons stimulated with exercised muscle-secreted factors significantly upregulate neurite outgrowth and migration, with an effect size dependent on muscle contraction intensity. Actuating magnetic microparticles embedded within fibrin hydrogels enable dynamically stretching motor neurons and non-invasively mimicking the mechanical effects of muscle contraction. Interestingly, axonogenesis is similarly upregulated in both mechanically and biochemically stimulated motor neurons, but RNA sequencing reveals different transcriptomic signatures between groups, with biochemical stimulation having a greater impact on cell signaling related to axonogenesis and synapse maturation. This study leverages actuating extracellular matrices to robustly validate a previously hypothesized role for muscle contraction in regulating motor neuron growth and maturation from the bottom-up through both mechanical and biochemical signaling.

DUSP1 Promotes Osimertinib Drug-Tolerant Persistence by Inhibiting MAPK/ERK Signaling in Non-small Cell Lung Cancer

EGFR tyrosine kinase inhibitors (EGFR-TKIs) are the first-line treatment for EGFR-mutant non-small cell lung cancer (NSCLC) patients, which remarkably improve the clinical outcomes. However, drug resistance has greatly impaired the efficacy of EGFR-TKIs and contributes to cancer treatment failure. DUSP1, a negative regulator of MAPK signaling pathway, was discovered to mediate drug resistance in multiple types of cancers. Our study aimed to explore the role of DUSP1 in NSCLC cell resistance to osimertinib, a third-generation EGFR-TKI. Human NSCLC cell lines PC-9 and HCC827 were exposed to increasing concentrations of osimertinib for over 6 months to generate osimertinib resistant cells (PC-9-OR and HCC827-OR). The viabilities of osimertinib-resistant and parental sensitive NSCLC cells in response to osimertinib stimulation were detected by MTS assay and the IC50 values for osimertinib were obtained. The differentially expressed genes in osimertinib-resistant and sensitive NSCLC cells were identified by analyzing the GEO dataset GSE106765 using bioinformatic tools. DUSP1 expression was knocked down by using the short hairpin RNAs (shRNAs). Then, the effects of DUSP1 silencing on osimertinib-resistant and sensitive NSCLC cell resistance to osimertinib, viability, proliferation and apoptosis were assessed through loss-of-function experiments. The expression of key molecules (JNK, ERK, and p38 MAPK) in the MAPK signaling pathway was detected through western blotting analysis. DUSP1 was overexpressed in osimertinib-resistant NSCLC cells versus parental sensitive cells. DUSP1 silencing attenuated the resistance of NSCLC cells to osimertinib. DUSP1 silencing markedly inhibited osimertinib-resistant and sensitive NSCLC cell proliferation but enhanced cell apoptosis. Mechanically, DUSP1 knockdown increased phosphorylated-JNK, ERK, and p38 MAPK levels in NSCLC cells. Treatment with SB203580, the p38 MAPK inhibitor, reversed the effects of DUSP1 silencing on osimertinib-resistant NSCLC cell resistance to osimertinib, cell proliferation and apoptosis. DUSP1 downregulation restores the sensitivity of NSCLC cells to osimertinib via activating the MAPK signaling pathway.

Monitoring cystic fibrosis airway infections with Pseudomonas aeruginosa with anti-OprF serum antibodies

BACKGROUND

The management of cystic fibrosis (CF) requires knowledge of the patient's microbiological status. The serology of anti-Pseudomonas aeruginosa antibodies against exoenzymes or water-soluble antigens has gained diagnostic value, particularly to detect the onset of colonization with P. aeruginosa. However, the diversity and variable expression of these antigens, which was unknown when the ELISAs became common diagnostic procedures at CF clinics, prohibits the quantitative evaluation of bacterial antigen load during intermittent and chronic infection.

METHODS

An ELISA was developed to measure the serum IgG antibody levels against P. aeruginosa porin OprF, a species-specific, conserved, immunogenic and constitutively expressed protein present in the outer membrane and extracellular vesicles.

RESULTS

Serial serum samples were collected from 310 people with CF (pwCF) over a period of up to 15 years. Compared to a reference of P. aeruginosa - negative CF sera set to 1, OprF antibody titers ranged from 0.3 to 13.2 (median: 1.7) in 56 intermittently colonized patients and from 0.5 to 51.2 (median: 11.8) in 176 chronically colonized pwCF showing higher anti-OprF antibody levels during chronic than during intermittent colonization with P. aeruginosa (P = 0, Z = - 21.7, effect size 0.62). Inhalation with twice daily 80 mg tobramycin decreased OprF antibody titers (P = 5 × 10-5), particularly during the third and fourth year of chronic colonization.

CONCLUSION

The OprF ELISA should be an appropriate tool to monitor Pseudomonas serology at all stages of infection and disease severity and to study the impact of short- and long-term therapeutic interventions.