Genomic Interactions Research Articles

Genome-wide analysis of Smad and Schnurri transcription factors in C. elegans demonstrates widespread interaction and a function in collagen secretion.

Smads and their transcription factor partners mediate the transcriptional responses of target cells to secreted ligands of the transforming growth factor-β (TGF-β) family, including those of the conserved bone morphogenetic protein (BMP) family, yet only a small number of direct target genes have been well characterized. In C. elegans, the BMP2/4 ortholog DBL-1 regulates multiple biological functions, including body size, via a canonical receptor-Smad signaling cascade. Here, we identify functional binding sites for SMA-3/Smad and its transcriptional partner SMA-9/Schnurri based on ChIP-seq peaks (identified by modEncode) and expression differences of nearby genes identified from RNA-seq analysis of corresponding mutants. We found that SMA-3 and SMA-9 have both overlapping and unique target genes. At a genome-wide scale, SMA-3/Smad acts as a transcriptional activator, whereas SMA-9/Schnurri direct targets include both activated and repressed genes. Mutations in sma-9 partially suppress the small body size phenotype of sma-3, suggesting some level of antagonism between these factors and challenging the prevailing model for Schnurri function. Functional analysis of target genes revealed a novel role in body size for genes involved in one-carbon metabolism and in the endoplasmic reticulum (ER) secretory pathway, including the disulfide reductase dpy-11. Our findings indicate that Smads and SMA-9/Schnurri have previously unappreciated complex genetic and genomic regulatory interactions that in turn regulate the secretion of extracellular components like collagen into the cuticle to mediate body size regulation.

ParSite is a multicolor DNA labeling system that allows for simultaneous imaging of triple genomic loci in living cells.

The organization of the human genome in space and time is critical for transcriptional regulation and cell fate determination. However, robust methods for tracking genome organization or genomic interactions over time in living cells are lacking. Here, we developed a multicolor DNA labeling system, ParSite, to simultaneously track triple genomic loci in the U2OS cells. The tricolor ParSite system is derived from the T. thermophilus ParB/ParSc (TtParB/ParSc) system by rational design. We mutated the interface between TtParB and ParSc and generated a new pair of TtParBm and ParSm for genomic DNA labeling. The insertions of 16 base-pair palindromic ParSc and ParSm into genomic loci allow dual-color DNA imaging in living cells. A pair of genomic loci labeled by ParSite could be colocalized with p53-binding protein 1 (53BP1) in response to CRISPR/Cas9-mediated double-strand breaks (DSBs). The ParSite permits tracking promoter and terminator dynamics of the APP gene, which spans 290 kilobases in length. Intriguingly, the hybrid ParS (ParSh) of half-ParSc and half-ParSm enables for the visualization of a third locus independent of ParSc or ParSm. We simultaneously labeled 3 loci with a genomic distance of 36, 89, and 352 kilobases downstream the C3 repeat locus, respectively. In sum, the ParSite is a robust DNA labeling system for tracking multiple genomic loci in space and time in living cells.

Chromosomal rDNA Distribution Patterns in Clonal Cobitis Triploid Hybrids (Teleostei, Cobitidae): Insights into Parental Genomic Contributions.

Background: Interspecific hybridization between relative species Cobitis taenia (with a diploid genome designated as TT), Cobitis elongatoides (EE) and Cobitis tanaitica (NN) and the successive polyploidization with transitions from sexuality to asexuality experienced by triploid Cobitis hybrids likely influence their chromosomal rearrangements, including rearrangements of ribosomal DNA (rDNA) distribution patterns. Previously, we documented distinct karyotypic differences: C. elongatoides exhibited bi-armed chromosomes while C. taenia showed uni-armed chromosomes with rDNA-positive hybridization signals, respectively. Methods: In this study, fluorescence in situ hybridization (FISH) with 5S rDNA and 28S rDNA probes was used to analyze and compare chromosomal distribution patterns of rDNAs in clonally reproduced triploid Cobitis hybrids of different genomic constitutions ETT, ETN, EEN and EET (referred to using acronyms denoting the haploid genomes of their parent species), and their parental species. Results:Cobitis triploid hybrids exhibited intermediate karyotypes with ribosome synthesis sites on chromosomes inherited from both parents, showing no evidence of nucleolar dominance. The rDNA pattern derived from the C. elongatoides genome was more stable in the hybrids' karyotypes. Two and one submetacentric chromosomes with co-localized rDNAs were effective markers to ascertain C. elongatoides diploid (EE) and haploid (E) genomes within the genome of triploid hybrids, respectively. Fewer 5S rDNA loci were observed in diploid (TT) and haploid (T) chromosome sets from C. taenia in ETT and EET females. C. taenia and C. tanaitica exhibited similar rDNA distribution patterns. Conclusions: The karyotypes of triploid Cobitis hybrids reflect the genomic contributions of their parental species. Variability in rDNA distribution patterns suggests complex genomic interactions in Cobitis hybrids resulting from polyploidization and hybridization, potentially influencing their reproductive potential.

Machine and Deep Learning Methods for Predicting 3D Genome Organization.

Three-dimensional (3D) chromatin interactions, such as enhancer-promoter interactions (EPIs), loops, topologically associating domains (TADs), and A/B compartments, play critical roles in a wide range of cellular processes by regulating gene expression. Recent development of chromatin conformation capture technologies has enabled genome-wide profiling of various 3D structures, even with single cells. However, current catalogs of 3D structures remain incomplete and unreliable due to differences in technology, tools, and low data resolution. Machine learning methods have emerged as an alternative to obtain missing 3D interactions and/or improve resolution. Such methods frequently use genome annotation data (ChIP-seq, DNAse-seq, etc.), DNA sequencing information (k-mers and transcription factor binding site (TFBS) motifs), and other genomic properties to learn the associations between genomic features and chromatin interactions. In this review, we discuss computational tools for predicting three types of 3D interactions (EPIs, chromatin interactions, and TAD boundaries) and analyze their pros and cons. We also point out obstacles to the computational prediction of 3D interactions and suggest future research directions.

Chromatin Immunoprecipitation for Standard, Rare, or Weakly Binding Proteins.

Various proteins interact with specific genome regions, playing crucial roles in gene regulation. Chromatin Immunoprecipitation (ChIP) is the most commonly used method to study protein-DNA interactions in vivo. By combining ChIP with high-throughput sequencing, ChIP-seq allows for studying the genome-wide localization of proteins. Although several ChIP protocols are available for plant tissues, they are primarily designed for histone modifications and abundant proteins with high DNA-binding affinity, which are considered as the "standard targets." Here we describe a ChIP protocol for plant tissues not only optimized for the standard targets but also adapted for proteins with low abundances or weak DNA-binding ability. Successful execution of the protocol enables reliable generation of DNA templates for quantitative PCR or libraries for next-generation sequencing, which makes it an effective tool for analyzing genomic interactions of a wide range of proteins.

Single-Cell Hi-C Analysis Workflow with Pairtools.

Single-cell Hi-C (scHi-C) is a collection of protocols for studying genomic interactions within individual cells. Although data analysis for scHi-C resembles data analysis for bulk Hi-C, the unique challenges of scHi-C, such as high noise and protocol-specific biases, require specialized data processing strategies. In this tutorial chapter, we focus on using pairtools, a suite of tools optimized for scHi-C data, demonstrating its application on a Drosophila snHi-C dataset. While centered on pairtools for snHi-C data, the principles outlined are applicable across scHi-C variants with minor adjustments. This educational chapter aims to guide researchers in using open-source tools for scHi-C analysis, emphasizing critical steps of contact pair extraction, detection of ligation junctions, filtration, and deduplication.

The three-dimensional genome drives the evolution of asymmetric gene duplicates via enhancer capture-divergence.

Previous evolutionary models of duplicate gene evolution have overlooked the pivotal role of genome architecture. Here, we show that proximity-based regulatory recruitment by distally duplicated genes is an efficient mechanism for modulating tissue-specific production of preexisting proteins. By leveraging genomic asymmetries, we performed a coexpression analysis on Drosophila melanogaster tissue data to show the generality of enhancer capture-divergence (ECD) as a significant evolutionary driver of asymmetric, distally duplicated genes. We use the recently evolved gene HP6/Umbrea as an example of the ECD process. By assaying genome-wide chromosomal conformations in multiple Drosophila species, we show that HP6/Umbrea was inserted near a preexisting, long-distance three-dimensional genomic interaction. We then use this data to identify a newly found enhancer (FLEE1), buried within the coding region of the highly conserved, essential gene MFS18, that likely neofunctionalized HP6/Umbrea. Last, we demonstrate ancestral transcriptional coregulation of HP6/Umbrea's future insertion site, illustrating how enhancer capture provides a highly evolvable, one-step solution to Ohno's dilemma.

EpiGePT: a pretrained transformer-based language model for context-specific human epigenomics

The inherent similarities between natural language and biological sequences have inspired the use of large language models in genomics, but current models struggle to incorporate chromatin interactions or predict in unseen cellular contexts. To address this, we propose EpiGePT, a transformer-based model designed for predicting context-specific human epigenomic signals. By incorporating transcription factor activities and 3D genome interactions, EpiGePT outperforms existing methods in epigenomic signal prediction tasks, especially in cell-type-specific long-range interaction predictions and genetic variant impacts, advancing our understanding of gene regulation. A free online prediction service is available at http://health.tsinghua.edu.cn/epigept.

Current transcriptome database and biomarker discovery for immunotherapy by immune checkpoint blockade.

Immune checkpoint blockade (ICB) has revolutionized the current immuno-oncology and significantly improved clinical outcome for cancer treatment. Despite the advancement in clinics, only a small subset of patients derives immune response to the ICB therapy. Therefore, a robust predictive biomarker that identifies potential candidate becomes increasingly crucial in delivering this technology to the public. In this review, we first discuss the biomarkers that focus on tumor genome, tumor microenvironment and tumor-host interaction. Then, we compare existing databases for biomarker discovery for ICB response. We also present IOhub - an interactive web portal that incorporates 36 bulk and 10 single-cell transcriptome datasets for benchmark analysis of the current biomarkers. Finally, we highlight the trending interest in antibody drug conjugate and combination treatment and their use in precision immuno-oncology.

Multiomics and Molecular Docking Approaches to Elucidate Desmostachya Bipinnata-Derived Multitargeting Agents for Oral Squamous Cell Carcinoma

Background Oral squamous cell carcinoma (OSCC) is a prevalent form of head and neck cancer characterized by aggressive behavior and a poor prognosis. Conventional therapies have demonstrated limited effectiveness, underscoring the need for innovative strategies that target the molecular mechanisms involved in OSCC progression. Multitargeting agents present a promising approach by simultaneously addressing several key pathways, potentially addressing issues of treatment resistance. Desmostachya bipinnata, a medicinal plant renowned for its anticancer properties, contains bioactive compounds that may serve as effective treatments for OSCC. Objectives This study aims to investigate the therapeutic potential of bioactive compounds from Desmostachya bipinnata in treating OSCC. It uses bioinformatics and molecular docking techniques to identify key molecular targets and pathways, evaluate compound binding affinities, and propose novel multitargeting agents for OSCC therapy. Materials and Methods This study aimed to explore the therapeutic potential of Desmostachya bipinnata compounds for OSCC using bioinformatics and molecular docking. Six of the 19 compounds screened were excluded due to toxicity, leaving 14 for further analysis. GeneCards, DisGeNet, and Gene Expression Omnibus (GEO) databases identified 3,278 OSCC-related genes, and SwissTargetPrediction predicted 221 targets. Protein–protein interaction and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis pinpointed significant hub genes. Molecular docking of four selected compounds (linoleic acid, kaempferol, daucosterol, stigmasterol-glucoside) with six key targets (MMP2, PTGS2, STAT3, MAPK1, MMP9, AKT1) revealed strong binding affinities, suggesting potential therapeutic efficacy. Results This study evaluated potential therapeutic compounds from Desmostachya bipinnata for OSCC through a comprehensive approach. After assessing the toxicity of 19 compounds, six were excluded due to predicted adverse effects, leaving 14 for further analysis. We identified 3,278 OSCC-related genes by integrating data from GeneCards, DisGeNet, and GEO databases. Using SwissTargetPrediction, we narrowed down 221 unique targets for these compounds and identified 95 common targets with OSCC genes. Protein–protein interaction analysis via STRING and Cytoscape, along with Molecular Complex Detection (MCODE), highlighted a significant gene cluster. Expression analysis with Gene Expression Profiling Interactive Analysis (GEPIA) led to the exclusion of low-expressing genes (IL6, MAPK3, ESR1, BCL2), focusing on MMP2, PTGS2, STAT3, MAPK1, MMP9, and AKT1, which are involved in cancer-related pathways. Molecular docking studies showed that linoleic acid, kaempferol, daucosterol, and stigmasterol-glucoside exhibit strong binding affinities to these targets, suggesting their potential as effective therapeutic agents. Activity predictions confirmed their antineoplastic properties, underscoring their potential utility in OSCC treatment. Conclusion The findings indicate that Desmostachya bipinnata compounds exhibit promising multitargeting activity against OSCC. The strong binding affinities and interaction profiles of these compounds with key OSCC-related targets support their potential as effective therapeutic agents. Further experimental validation is needed to confirm these results and explore the clinical applicability of these compounds in OSCC treatment.

Analysis of gene expression level dominance and homoeologous expression bias in different tissues of a new synthetic amphidiploid, Raphanobrassica (Brassica rapa × Raphanus sativus)

Polyploidization is an evolutionarily and mechanistically intriguing process, that requires harmonization of genomic and regulatory interactions among divergent genome groups. Here, we examined gene expression pattern changes in the leaves and petals of Raphanobrassica, a new synthetic amphidiploid generated by a distant cross between radish (RR, 2n = 18) and purple cabbage (AA, 2n = 20). The degree and direction of expression level dominance (ELD), defined as the total expression level of two homoeologous genes, was assessed, and homoeologous expression bias (HEB) analyzed, to assess the relative contributions of homoeologous genes to the transcriptome. Comparative analysis between Raphanobrassica and its parental species revealed that most differentially expressed genes were additive (leaves, 81.63 %; flowers, 82.71 %). ELD favors the R genome in both Raphanobrassica tissues, primarily driven by up- or down-regulation of non-dominant parent genes, with radish (RR) as the dominant parent in ELD-R and cabbage (AA) in ELD-A HEB indicated a preference for the A genome in both tissues (leaves, 26.03 %; flowers, 32.74 %). To further elucidate the ELD phenomenon, we compared expression levels of individual homoeologous genes with those in the diploid parents. ELD toward genes expressed at higher levels in parents was mediated either by down-regulation of genes from the dominant parent or up-regulation of those from the non-dominant parent, whereas ELD toward genes expressed at lower levels in parents was attributable solely to down-regulation of non-dominant parent homoeologous genes. Our findings provide novel insights into gene expression changes in heterodiploids generated by hybridization prior to polyploidization, laying a foundation for understanding genome-wide expression pattern alterations during polyploid formation.

Enhancing immuno-oncology investigations through multidimensional decoding of tumor microenvironment with IOBR 2.0.

The use of large transcriptome datasets has greatly improved our understanding of the tumor microenvironment (TME) and helped develop precise immunotherapies. The growing application of multi-omics, single-cell RNA sequencing (scRNA-seq), and spatial transcriptome sequencing has led to many new insights, yet these findings still require clinical validation in large cohorts. To advance multi-omics integration in TME research, we have upgraded the Immuno-Oncology Biological Research (IOBR) package to IOBR 2.0, restructuring and standardizing its analytical workflow. IOBR 2.0 offers six modules for TME analysis based on multi-omics data, including data preprocessing, TME estimation, TME infiltration pattern identification, cellular interaction analysis, genome and TME interaction, and feature visualization, as well as modeling. Additionally, IOBR 2.0 enables constructing gene signatures and reference matrices from scRNA-seq data for TME deconvolution. The user-friendly pipeline provides comprehensive insights into tumor-immune interactions, and a detailed GitBook(https://iobr.github.io/book/) offers a complete manual and analysis guide for each module.

ChromEMT: visualizing and reconstructing chromatin ultrastructure and 3D organization in situ.

Structure determines function. The discovery of the DNA double-helix structure revealed how genetic information is stored and copied. In the mammalian cell nucleus, up to two meters of DNA is compacted by histones to form nucleosome/DNA particle chains that form euchromatin and heterochromatin domains, chromosome territories and mitotic chromosomes upon cell division. A critical question is what are the structures, interactions and 3D organization of DNA as chromatin in the nucleus and how do they determine DNA replication timing, gene expression and ultimately cell fate. To visualize genomic DNA across these different length scales in the nucleus, we developed ChromEMT, a method that selectively enhances the electron density and contrast of DNA and interacting nucleosome particles, which enables nucleosome chains, chromatin domains, chromatin ultrastructure and 3D organization to be imaged and reconstructed by using multi-tilt electron microscopy tomography (EMT). ChromEMT exploits a membrane-permeable, fluorescent DNA-binding dye, DRAQ5, which upon excitation drives the photo-oxidation and precipitation of diaminobenzidine polymers on the surface of DNA/nucleosome particles that are visible in the electron microscope when stained with osmium. Here, we describe a detailed protocol for ChromEMT, including DRAQ5 staining, photo-oxidation, sample preparation and multi-tilt EMT that can be applied broadly to reconstruct genomic DNA structure and 3D interactions in cells and tissues and different kingdoms of life. The entire procedure takes ~9 days and requires expertise in electron microscopy sample sectioning and acquisition of multi-tilt EMT data sets.

A review of deep learning models for the prediction of chromatin interactions with DNA and epigenomic profiles.

Advances in three-dimensional (3D) genomics have revealed the spatial characteristics of chromatin interactions in gene expression regulation, which is crucial for understanding molecular mechanisms in biological processes. High-throughput technologies like ChIA-PET, Hi-C, and their derivatives methods have greatly enhanced our knowledge of 3D chromatin architecture. However, the chromatin interaction mechanisms remain largely unexplored. Deep learning, with its powerful feature extraction and pattern recognition capabilities, offers a promising approach for integrating multi-omics data, to build accurate predictive models of chromatin interaction matrices. This review systematically summarizes recent advances in chromatin interaction matrix prediction models. By integrating DNA sequences and epigenetic signals, we investigate the latest developments in these methods. This article details various models, focusing on how one-dimensional (1D) information transforms into the 3D structure chromatin interactions, and how the integration of different deep learning modules specifically affects model accuracy. Additionally, we discuss the critical role of DNA sequence information and epigenetic markers in shaping 3D genome interaction patterns. Finally, this review addresses the challenges in predicting chromatin interaction matrices, in order to improve the precise mapping of chromatin interaction matrices and DNA sequence, and supporting the transformation and theoretical development of 3D genomics across biological systems.

Genome-wide analysis of Smad and Schnurri transcription factors in C. elegans demonstrates widespread interaction and a function in collagen secretion.

Smads and their transcription factor partners mediate the transcriptional responses of target cells to secreted ligands of the Transforming Growth Factor-β (TGF-β) family, including those of the conserved bone morphogenetic protein (BMP) family, yet only a small number of direct target genes have been well characterized. In C. elegans, the BMP2/4 ortholog DBL-1 regulates multiple biological functions, including body size, via a canonical receptor-Smad signaling cascade. Here, we identify functional binding sites for SMA-3/Smad and its transcriptional partner SMA-9/Schnurri based on ChIP-seq peaks (identified by modEncode) and expression differences of nearby genes identified from RNA-seq analysis of corresponding mutants. We found that SMA-3 and SMA-9 have both overlapping and unique target genes. At a genome-wide scale, SMA-3/Smad acts as a transcriptional activator, whereas SMA-9/Schnurri direct targets include both activated and repressed genes. Mutations in sma-9 partially suppress the small body size phenotype of sma-3, suggesting some level of antagonism between these factors and challenging the prevailing model for Schnurri function. Functional analysis of target genes revealed a novel role in body size for genes involved in one-carbon metabolism and in the endoplasmic reticulum (ER) secretory pathway, including the disulfide reductase dpy-11. Our findings indicate that Smads and SMA-9/Schnurri have previously unappreciated complex genetic and genomic regulatory interactions that in turn regulate the secretion of extracellular components like collagen into the cuticle to mediate body size regulation.

Single-Sample Networks Reveal Intra-Cytoband Co-Expression Hotspots in Breast Cancer Subtypes.

Breast cancer is a heterogeneous disease comprising various subtypes with distinct molecular characteristics, clinical outcomes, and therapeutic responses. This heterogeneity evidences significant challenges for diagnosis, prognosis, and treatment. Traditional genomic co-expression network analyses often overlook individual-specific interactions critical for personalized medicine. In this study, we employed single-sample gene co-expression network analysis to investigate the structural and functional genomic alterations across breast cancer subtypes (Luminal A, Luminal B, Her2-enriched, and Basal-like) and compared them with normal breast tissue. We utilized RNA-Seq gene expression data to infer gene co-expression networks. The LIONESS algorithm allowed us to construct individual networks for each patient, capturing unique co-expression patterns. We focused on the top 10,000 gene interactions to ensure consistency and robustness in our analysis. Network metrics were calculated to characterize the topological properties of both aggregated and single-sample networks. Our findings reveal significant fragmentation in the co-expression networks of breast cancer subtypes, marked by a change from interchromosomal (TRANS) to intrachromosomal (CIS) interactions. This transition indicates disrupted long-range genomic communication, leading to localized genomic regulation and increased genomic instability. Single-sample analyses confirmed that these patterns are consistent at the individual level, highlighting the molecular heterogeneity of breast cancer. Despite these pronounced alterations, the proportion of CIS interactions did not significantly correlate with patient survival outcomes across subtypes, suggesting limited prognostic value. Furthermore, we identified high-degree genes and critical cytobands specific to each subtype, providing insights into subtype-specific regulatory networks and potential therapeutic targets. These genes play pivotal roles in oncogenic processes and may represent important keys for targeted interventions. The application of single-sample co-expression network analysis proves to be a powerful tool for uncovering individual-specific genomic interactions.

Phenotypic homogenization and potential fitness constraints following non-native introgression in an endemic sportfish.

Introgressive hybridization may lead to contrasting evolutionary outcomes that are difficult to predict, since they depend on the fitness effects of endogenous genomic interactions and environmental factors. Conservation of endemic biodiversity may be more effective with require direct measurement of introgressed ancestry and fitness in wild populations, especially for keystone taxa at risk of hybridization following species introductions. We assessed the relationship of non-native ancestry with growth and body condition in the basin-restricted Neosho Bass (Micropterus velox; NB), focusing on two streams in the NB native range that are admixed extensively with non-native Smallmouth Bass (M. dolomieu; SMB). We quantified genetic composition of 116 fish from Big Sugar Creek (N=46) and Elk River (N=70) at 14 microsatellite loci. Using back-calculated total length-at-age estimated from sagittal otoliths, we assessed whether genetic ancestry explained variation in von Bertalanffy growth model parameters, accounting for sex and stream effects. We then assessed the relationship of ancestry and body condition. We found no differences in growth parameters by sex, stream, or ancestry, suggesting phenotypic homogenization which could be mediated by selection on body size. We found a negative correlation between SMB ancestry and condition, including lower condition in Big Sugar Creek, possibly reflecting a trade-off between maximum length and condition with respect to overall fitness. We show that ongoing non-native introgression, which may be augmented by anthropogenic SMB introductions, may attenuate evolutionary differentiation between species and directly influence fitness, possibly having critical implications for long-term persistence and management of adaptive potential in a popular and ecologically important endemic sportfish.

Concomitant gut dysbiosis and defective gut barrier serve as the bridges between hypercortisolism and chronic systemic inflammation in Cushing's disease.

The aim of this study was to investigate the gut microbial signatures and related pathophysiological implications in patients with Cushing's disease (CD). Twenty-seven patients with CD and 45 healthy controls were enrolled. Based on obtained metagenomics data, we performed correlation, network study, and genome interaction group (GIG) analysis. Fecal metabolomics and serum enzyme linked immunosorbent assay (ELISA) analysis were conducted in dichotomized CD patients. Caco-2 cells were incubated with gradient concentrations of cortisol for subsequent transepithelial electrical resistance (TEER) measurement, FITC-dextran transwell permeability assay, qPCR, and western blot analysis. Gut microbial composition in patients with CD was notably different from that in healthy controls. Network analysis revealed that Eubacterium siraeum might serve as the core specie in the gut microbial system of CD patients. Subsequent GIG analysis identified the positive correlations between GIG9 and UFC. Further serum ELISA and fecal metabolomics uncovered that CD patients with elevated UFC levels were characterized with increased lipopolysaccharide binding protein (LBP). Moreover, remarkable positive association was found between LBP level and relative abundance of E. siraeum. TEER and FITC-dextran transwell assays demonstrated that hypercortisolism induced increased gut permeability. Further qPCR and western blot analysis suggested that dysregulated AhR/Claudin 2 axis might be involved in the development of hypercortisolism-induced defective gut barrier function. Disease activity associated dysbiosis and defective gut barrier might jointly facilitate the development of systemic inflammation in patients with CD.

De novo assembly and characterization of the complete mitochondrial genome of Phellodendron amurense reveals three repeat‐mediated recombination

Phellodendron amurense Rupr., a rare herb renowned for its medicinal and ecological significance, has remained genetically unexplored at the mitochondrial level until now. This study presents the first-ever systematic assembly and annotation of the complete mitochondrial genome of P. amurense, achieved through a hybrid strategy combining Illumina and Nanopore sequencing data. The mitochondrial genome spans 566,285 bp with a GC content of 45.51 %, structured into two circular molecules. Our comprehensive analysis identified 32 protein-coding genes (PCGs), 33 tRNA genes, and 3 rRNA genes, alongside 181 simple sequence repeats, 19 tandem repeats, and 310 dispersed repeats. Notably, multiple genome conformations were predicted due to repeat-mediated homologous recombination. Additionally, we assembled the chloroplast genome, identifying 21 mitochondrial plastid sequences that provide insights into organelle genome interactions. A total of 380 RNA-editing sites within the mitochondrial PCGs were predicted, enhancing our understanding of gene regulation and function. Phylogenetic analysis using mitochondrial PCGs from 30 species revealed evolutionary relationships, confirming the homology between P. amurense and Citrus species. This foundational study offers a valuable genetic resource for the Rutaceae family, facilitating further research into genetic evolution and molecular diversity in plant mitochondrial genomes.

Research Progress on Genomic Regions and Candidate Genes Related to Milk Composition Traits of Dairy Goats Based on Functional Genomics: A Narrative Review.

Goat milk has gained global attention for its unique nutritional properties and potential health benefits. Advancements in functional genomic technologies have significantly progressed genetic research on milk composition traits in dairy goats. This review summarizes various research methodologies applied in this field. Genome-wide association studies (GWAS) have identified genomic regions associated with major milk components, with the diacylglycerol acyltransferase 1 (DGAT1) gene and casein gene cluster consistently linked to milk composition traits. Transcriptomics has revealed gene expression patterns in mammary tissue across lactation stages, while the role of non-coding RNAs (such as miRNAs and circRNAs) in regulating milk composition has been confirmed. Proteomic and metabolomic studies have not only helped us gain a more comprehensive understanding of goat milk composition characteristics but have also provided crucial support for the functional validation of genes related to milk components. The integration of multi-omics data has emerged as an effective strategy for elucidating complex regulatory networks from a systems biology perspective. Despite progress, challenges remain, including refining reference genomes, collecting large-scale phenotypic data, and conducting functional validations. Future research should focus on improving reference genomes, expanding study populations, investigating functional milk components, exploring epigenetic regulation and non-coding RNAs, and studying microbiome-host genome interactions. These efforts will inform more precise genomic and marker-assisted selection strategies, advancing genetic improvements in milk composition traits in dairy goats.