Cut&tag: a powerful epigenetic tool for chromatin profiling

Analysis of transcription factors and chromatin modifications at the genome-wide level provides insights into gene regulatory processes, such as transcription, cell differentiation and cellular response. Chromatin immunoprecipitation is the most popular and powerful approach for mapping chromatin, and other enzyme-tethering techniques have recently become available for living cells. Among these, Cleavage Under Targets and Tagmentation (CUT&Tag) is a relatively novel chromatin profiling method that has rapidly gained popularity in the field of epigenetics since 2019. It has also been widely adapted to map chromatin modifications and TFs in different species, illustrating the association of these chromatin epitopes with various physiological and pathological processes. Scalable single-cell CUT&Tag can be combined with distinct platforms to distinguish cellular identity, epigenetic features and even spatial chromatin profiling. In addition, CUT&Tag has been developed as a strategy for joint profiling of the epigenome, transcriptome or proteome on the same sample. In this review, we will mainly consolidate the applications of CUT&Tag and its derivatives on different platforms, give a detailed explanation of the pros and cons of this technique as well as the potential development trends and applications in the future.

TFTG: A comprehensive database for human transcription factors and their targets

Transcription factors (TFs) are major contributors to gene transcription, especially in controlling cell-specific gene expression and disease occurrence and development. Uncovering the relationship between TFs and their target genes is critical to understanding the mechanism of action of TFs. With the development of high-throughput sequencing techniques, a large amount of TF-related data has accumulated, which can be used to identify their target genes. In this study, we developed TFTG (Transcription Factor and Target Genes) database (http://tf.liclab.net/TFTG), which aimed to provide a large number of available human TF-target gene resources by multiple strategies, besides performing a comprehensive functional and epigenetic annotations and regulatory analyses of TFs. We identified extensive available TF-target genes by collecting and processing TF-associated ChIP-seq datasets, perturbation RNA-seq datasets and motifs. We also obtained experimentally confirmed relationships between TF and target genes from available resources. Overall, the target genes of TFs were obtained through integrating the relevant data of various TFs as well as fourteen identification strategies. Meanwhile, TFTG was embedded with user-friendly search, analysis, browsing, downloading and visualization functions. TFTG is designed to be a convenient resource for exploring human TF-target gene regulations, which will be useful for most users in the TF and gene expression regulation research.

A contrastive learning approach to integrate spatial transcriptomics and histological images

The rapid growth of spatially resolved transcriptomics technology provides new perspectives on spatial tissue architecture. Deep learning has been widely applied to derive useful representations for spatial transcriptome analysis. However, effectively integrating spatial multi-modal data remains challenging. Here, we present ConGcR, a contrastive learning-based model for integrating gene expression, spatial location, and tissue morphology for data representation and spatial tissue architecture identification. Graph convolution and ResNet were used as encoders for gene expression with spatial location and histological image inputs, respectively. We further enhanced ConGcR with a graph auto-encoder as ConGaR to better model spatially embedded representations. We validated our models using 16 human brains, four chicken hearts, eight breast tumors, and 30 human lung spatial transcriptomics samples. The results showed that our models generated more effective embeddings for obtaining tissue architectures closer to the ground truth than other methods. Overall, our models not only can improve tissue architecture identification's accuracy but also may provide valuable insights and effective data representation for other tasks in spatial transcriptome analyses.

Chimeric antigens displaying GPR65 extracellular loops on a soluble scaffold enabled the discovery of antibodies, which recognized native receptor

GPR65 is a proton-sensing G-protein coupled receptor associated with multiple immune-mediated inflammatory diseases, whose function is relatively poorly understood. With few reagents commercially available to probe the biology of receptor, generation of an anti-GPR65 monoclonal antibody was desired. Using soluble chimeric scaffolds, such as ApoE3, displaying the extracellular loops of GPR65, together with established phage display technology, native GPR65 loop-specific antibodies were identified. Phage-derived loop-binding antibodies recognized the wild-type native receptor to which they had not previously been exposed, generating confidence in the use of chimeric soluble proteins to act as efficient surrogates for membrane protein extracellular loop antigens. This technique provides promise for the rational design of chimeric antigens in facilitating the discovery of specific antibodies to GPCRs.

eIF4E orchestrates mRNA processing, RNA export and translation to modify specific protein production

The eukaryotic translation initiation factor eIF4E acts as a multifunctional factor that simultaneously influences mRNA processing, export, and translation in many organisms. Its multifactorial effects are derived from its capacity to bind to the methyl-7-guanosine cap on the 5'end of mRNAs and thus can act as a cap chaperone for transcripts in the nucleus and cytoplasm. In this review, we describe the multifactorial roles of eIF4E in major mRNA-processing events including capping, splicing, cleavage and polyadenylation, nuclear export and translation. We discuss the evidence that eIF4E acts at two levels to generate widescale changes to processing, export and ultimately the protein produced. First, eIF4E alters the production of components of the mRNA processing machinery, supporting a widescale reprogramming of multiple mRNA processing events. In this way, eIF4E can modulate mRNA processing without physically interacting with target transcripts. Second, eIF4E also physically interacts with both capped mRNAs and components of the RNA processing or translation machineries. Further, specific mRNAs are sensitive to eIF4E only in particular mRNA processing events. This selectivity is governed by the presence of cis-acting elements within mRNAs known as USER codes that recruit relevant co-factors engaging the appropriate machinery. In all, we describe the molecular bases for eIF4E's multifactorial function and relevant regulatory pathways, discuss the basis for selectivity, present a compendium of ~80 eIF4E-interacting factors which play roles in these activities and provide an overview of the relevance of its functions to its oncogenic potential. Finally, we summarize early-stage clinical studies targeting eIF4E in cancer.

Gene choice in cancer cells is exclusive in ion transport but concurrent in DNA replication

Cancers share common cellular and physiological features. Little is known about whether distinctive gene expression patterns can be displayed at the single-cell level by gene families in cancer cells. The expression of gene homologs within a family can exhibit concurrence and exclusivity. Concurrence can promote all-or-none expression patterns of related genes and underlie alternative physiological states. Conversely, exclusive gene families express the same or similar number of homologs in each cell, allowing a broad repertoire of cell identities to be generated. We show that gene families involved in the cell-cycle and antigen presentation are expressed concurrently. Concurrence in the DNA replication complex MCM reflects the replicative status of cells, including cell lines and cancer-derived organoids. Exclusive expression requires precise regulatory mechanism, but cancer cells retain this form of control for ion homeostasis and extend it to gene families involved in cell migration. Thus, the cell adhesion-based identity of healthy cells is transformed to an identity based on migration in the population of cancer cells, reminiscent of epithelial-mesenchymal transition.

Direction-aware functional class scoring enrichment analysis of infinium DNA methylation data

Infinium Methylation BeadChip arrays remain one of the most popular platforms for epigenome-wide association studies, but tools for downstream pathway analysis have their limitations. Functional class scoring (FCS) is a group of pathway enrichment techniques that involve the ranking of genes and evaluation of their collective regulation in biological systems, but the implementations described for Infinium methylation array data do not retain direction information, which is important for mechanistic understanding of genomic regulation. Here, we evaluate several candidate FCS methods that retain directional information. According to simulation results, the best-performing method involves the mean aggregation of probe limma t-statistics by gene followed by a rank-ANOVA enrichment test using the mitch package. This method, which we call 'LAM,' outperformed an existing over-representation analysis method in simulations, and showed higher sensitivity and robustness in an analysis of real lung tumour-normal paired datasets. Using matched RNA-seq data, we examine the relationship of methylation differences at promoters and gene bodies with RNA expression at the level of pathways in lung cancer. To demonstrate the utility of our approach, we apply it to three other contexts where public data were available. First, we examine the differential pathway methylation associated with chronological age. Second, we investigate pathway methylation differences in infants conceived with in vitro fertilization. Lastly, we analyse differential pathway methylation in 19 disease states, identifying hundreds of novel associations. These results show LAM is a powerful method for the detection of differential pathway methylation complementing existing methods. A reproducible vignette is provided to illustrate how to implement this method.

Transgenerational epigenetic self-memory of Dio3 dosage is associated with Meg3 methylation and altered growth trajectories and neonatal hormones

Intergenerational and transgenerational epigenetic effects resulting from conditions in previous generations can contribute to environmental adaptation as well as disease susceptibility. Previous studies in rodent and human models have shown that abnormal developmental exposure to thyroid hormone affects endocrine function and thyroid hormone sensitivity in later generations. Since the imprinted type 3 deiodinase gene (Dio3) regulates sensitivity to thyroid hormones, we hypothesize its epigenetic regulation is altered in descendants of thyroid hormone overexposed individuals. Using DIO3-deficient mice as a model of developmental thyrotoxicosis, we investigated Dio3 total and allelic expression and growth and endocrine phenotypes in descendants. We observed that male and female developmental overexposure to thyroid hormone altered total and allelic Dio3 expression in genetically intact descendants in a tissue-specific manner. This was associated with abnormal growth and neonatal levels of thyroid hormone and leptin. Descendant mice also exhibited molecular abnormalities in the Dlk1-Dio3 imprinted domain, including increased methylation in Meg3 and altered foetal brain expression of other genes of the Dlk1-Dio3 imprinted domain. These molecular abnormalities were also observed in the tissues and germ line of DIO3-deficient ancestors originally overexposed to thyroid hormone in utero. Our results provide a novel paradigm of epigenetic self-memory by which Dio3 gene dosage in a given individual, and its dependent developmental exposure to thyroid hormone, influences its own expression in future generations. This mechanism of epigenetic self-correction of Dio3 expression in each generation may be instrumental in descendants for their adaptive programming of developmental growth and adult endocrine function.

Interaction of noncoding RNAs with hippo signaling pathway in cancer cells and cancer stem cells

The Hippo signaling pathway has a regulatory function in the organogenesis process and cellular homeostasis, switching the cascade reactions of crucial kinases acts to turn off/on the Hippo pathway, altering the downstream gene expression and thereby regulating proliferation, apoptosis, or stemness. Disruption of this pathway can lead to the occurrence of various disorders and different types of cancer. Recent findings highlight the importance of ncRNAs, such as microRNA, circular RNA, and lncRNAs, in modulating the Hippo pathway. Defects in ncRNAs can disrupt Hippo pathway balance, increasing tumor cells, tumorigenesis, and chemotherapeutic resistance. This review summarizes ncRNAs' inhibitory or stimulatory role in - Hippo pathway regulation in cancer and stem cells. Identifying the relation between ncRNAs and the components of this pathway could pave the way for developing new biomarkers in the treatment and diagnosis of cancers.

Functional roles of conserved lncRNAs and circRNAs in eukaryotes

Long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) have emerged as critical regulators in essentially all biological processes across eukaryotes. They exert their functions through chromatin remodeling, transcriptional regulation, interacting with RNA-binding proteins (RBPs), serving as microRNA sponges, etc. Although non-coding RNAs are typically more species-specific than coding RNAs, a number of well-characterized lncRNA (such as XIST and NEAT1) and circRNA (such as CDR1as and ciRS-7) are evolutionarily conserved. The studies on conserved lncRNA and circRNAs across multiple species could facilitate a comprehensive understanding of their roles and mechanisms, thereby overcoming the limitations of single-species studies. In this review, we provide an overview of conserved lncRNAs and circRNAs, and summarize their conserved roles and mechanisms.

TET1 displays catalytic and non-catalytic functions in the adult mouse cortex

TET1/2/3 dioxygenases iteratively demethylate 5-methylcytosine, beginning with the formation of 5-hydroxymethylcytosine (5hmC). The post-mitotic brain maintains higher levels of 5hmC than most peripheral tissues, and TET1 ablation studies have underscored the critical role of TET1 in brain physiology. However, deletion of Tet1 precludes the disentangling of the catalytic and non-catalytic functions of TET1. Here, we dissect these functions of TET1 by comparing adult cortex of Tet1 wildtype (Tet1 WT), a novel Tet1 catalytically dead mutant (Tet1 HxD), and Tet1 knockout (Tet1 KO) mice. Using DNA methylation array, we uncover that Tet1 HxD and KO mutations perturb the methylation status of distinct subsets of CpG sites. Gene ontology (GO) analysis on specific differential 5hmC regions indicates that TET1's catalytic activity is linked to neuronal-specific functions. RNA-Seq further shows that Tet1 mutations predominantly impact the genes that are associated with alternative splicing. Lastly, we performed High-performance Liquid Chromatography Mass-Spectrometry lipidomics on WT and mutant cortices and uncover accumulation of lysophospholipids lysophosphatidylethanolamine and lysophosphatidylcholine in Tet1 HxD cortex. In summary, we show that Tet1 HxD does not completely phenocopy Tet1 KO, providing evidence that TET1 modulates distinct cortical functions through its catalytic and non-catalytic roles.

Node features of chromosome structure networks and their connections to genome annotation

The 3D conformations of chromosomes can encode biological significance, and the implications of such structures have been increasingly appreciated recently. Certain chromosome structural features, such as A/B compartmentalization, are frequently extracted from Hi-C pairwise genome contact information (physical association between different regions of the genome) and compared with linear annotations of the genome, such as histone modifications and lamina association. We investigate how additional properties of chromosome structure can be deduced using an abstract graph representation of the contact heatmap, and describe specific network properties that can have a strong connection with some of these biological annotations. We constructed chromosome structure networks (CSNs) from bulk Hi-C data and calculated a set of site-resolved (node-based) network properties. These properties are useful for characterizing certain aspects of chromosomal structure. We examined the ability of network properties to differentiate several scenarios, such as haploid vs diploid cells, partially inverted nuclei vs conventional architecture, depletion of chromosome architectural proteins, and structural changes during cell development. We also examined the connection between network properties and a series of other linear annotations, such as histone modifications and chromatin states including poised promoter and enhancer labels. We found that semi-local network properties exhibit greater capability in characterizing genome annotations compared to diffusive or ultra-local node features. For example, the local square clustering coefficient can be a strong classifier of lamina-associated domains. We demonstrated that network properties can be useful for highlighting large-scale chromosome structure differences that emerge in different biological situations.

A novel adipose loss-of-function mutant in Drosophila

To identify genes required for brain growth, we took an RNAi knockdown reverse genetic approach in Drosophila. One potential candidate isolated from this effort is the anti-lipogenic gene adipose (adp). Adp has an established role in the negative regulation of lipogenesis in the fat body of the fly and adipose tissue in mammals. While fat is key to proper development in general, adp has not been investigated during brain development. Here, we found that RNAi knockdown of adp in neuronal stem cells and neurons results in reduced brain lobe volume and sought to replicate this with a mutant fly. We generated a novel adp mutant that acts as a loss-of-function mutant based on buoyancy assay results. We found that despite a change in fat content in the body overall and a decrease in the number of larger (>5 µm) brain lipid droplets, there was no change in the brain lobe volume of mutant larvae. Overall, our work describes a novel adp mutant that can functionally replace the long-standing adp60 mutant and shows that the adp gene has no obvious involvement in brain growth.

One-carbon metabolism nutrients impact the interplay between DNA methylation and gene expression in liver, enhancing protein synthesis in Atlantic salmon

Supplementation of one-carbon (1C) metabolism micronutrients, which include B-vitamins and methionine, is essential for the healthy growth and development of Atlantic salmon (Salmo salar). However, the recent shift towards non-fish meal diets in salmon aquaculture has led to the need for reassessments of recommended micronutrient levels. Despite the importance of 1C metabolism in growth performance and various cellular regulations, the molecular mechanisms affected by these dietary alterations are less understood. To investigate the molecular effect of 1C nutrients, we analysed gene expression and DNA methylation using two types of omics data: RNA sequencing (RNA-seq) and reduced-representation bisulphite sequencing (RRBS). We collected liver samples at the end of a feeding trial that lasted 220 days through the smoltification stage, where fish were fed three different levels of four key 1C nutrients: methionine, vitamin B6, B9, and B12. Our results indicate that the dosage of 1C nutrients significantly impacts genetic and epigenetic regulations in the liver of Atlantic salmon, particularly in biological pathways related to protein synthesis. The interplay between DNA methylation and gene expression in these pathways may play an important role in the mechanisms underlying growth performance affected by 1C metabolism.

Alterations of DNA methylation profile in peripheral blood of children with simple obesity

Purpose

To investigate the association between DNA methylation and childhood simple obesity.

Methods

Genome-wide analysis of DNA methylation was conducted on peripheral blood samples from 41 children with simple obesity and 31 normal controls to identify differentially methylated sites (DMS). Subsequently, gene functional analysis of differentially methylated genes (DMGs) was carried out. After screening the characteristic DMGs based on specific conditions, the methylated levels of these DMS were evaluated and verified by pyrosequencing. Receiver operating characteristic (ROC) curve analysis assessed the predictive efficacy of corresponding DMGs. Finally, Pearson correlation analysis revealed the correlation between specific DMS and clinical data.

Results

The overall DNA methylation level in the obesity group was significantly lower than in normal. A total of 241 DMS were identified. Functional pathway analysis revealed that DMGs were primarily involved in lipid metabolism, carbohydrate metabolism, amino acid metabolism, human diseases, among other pathways. The characteristic DMS within the genes Transcription factor A mitochondrial (TFAM) and Piezo type mechanosensitive ion channel component 1(PIEZO1) were recognized as CpG-cg05831083 and CpG-cg14926485, respectively. Furthermore, the methylation level of CpG-cg05831083 significantly correlated with body mass index (BMI) and vitamin D.

Conclusions

Abnormal DNA methylation is closely related to childhood simple obesity. The altered methylation of CpG-cg05831083 and CpG-cg14926485 could potentially serve as biomarkers for childhood simple obesity.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13755-024-00275-w.

Preimplantation genetic testing as a preventive strategy for the transmission of mitochondrial DNA disorders

Mitochondrial diseases are distinct types of metabolic and/or neurologic abnormalities that occur as a consequence of dysfunction in oxidative phosphorylation, affecting several systems in the body. There is no effective treatment modality for mitochondrial disorders so far, emphasizing the clinical significance of preventing the inheritance of these disorders. Various reproductive options are available to reduce the probability of inheriting mitochondrial disorders, including in vitro fertilization (IVF) using donated oocytes, preimplantation genetic testing (PGT), and prenatal diagnosis (PND), among which PGT not only makes it possible for families to have genetically-owned children but also PGT has the advantage that couples do not have to decide to terminate the pregnancy if a mutation is detected in the fetus. PGT for mitochondrial diseases originating from nuclear DNA includes analyzing the nuclear genome for the presence or absence of corresponding mutations. However, PGT for mitochondrial disorders arising from mutations in mitochondrial DNA (mtDNA) is more intricate, due to the specific characteristics of mtDNA such as multicopy nature, heteroplasmy phenomenon, and exclusive maternal inheritance. Therefore, the present review aims to discuss the utility and challenges of PGT as a preventive approach to inherited mitochondrial diseases caused by mtDNA mutations.

Unitig-centered pan-genome machine learning approach for predicting antibiotic resistance and discovering novel resistance genes in bacterial strains

In current genomic research, the widely used methods for predicting antimicrobial resistance (AMR) often rely on prior knowledge of known AMR genes or reference genomes. However, these methods have limitations, potentially resulting in imprecise predictions owing to incomplete coverage of AMR mechanisms and genetic variations. To overcome these limitations, we propose a pan-genome-based machine learning approach to advance our understanding of AMR gene repertoires and uncover possible feature sets for precise AMR classification. By building compacted de Brujin graphs (cDBGs) from thousands of genomes and collecting the presence/absence patterns of unique sequences (unitigs) for Pseudomonas aeruginosa, we determined that using machine learning models on unitig-centered pan-genomes showed significant promise for accurately predicting the antibiotic resistance or susceptibility of microbial strains. Applying a feature-selection-based machine learning algorithm led to satisfactory predictive performance for the training dataset (with an area under the receiver operating characteristic curve (AUC) of > 0.929) and an independent validation dataset (AUC, approximately 0.77). Furthermore, the selected unitigs revealed previously unidentified resistance genes, allowing for the expansion of the resistance gene repertoire to those that have not previously been described in the literature on antibiotic resistance. These results demonstrate that our proposed unitig-based pan-genome feature set was effective in constructing machine learning predictors that could accurately identify AMR pathogens. Gene sets extracted using this approach may offer valuable insights into expanding known AMR genes and forming new hypotheses to uncover the underlying mechanisms of bacterial AMR.

kmerDB: A database encompassing the set of genomic and proteomic sequence information for each species

The decrease in sequencing expenses has facilitated the creation of reference genomes and proteomes for an expanding array of organisms. Nevertheless, no established repository that details organism-specific genomic and proteomic sequences of specific lengths, referred to as kmers, exists to our knowledge. In this article, we present kmerDB, a database accessible through an interactive web interface that provides kmer-based information from genomic and proteomic sequences in a systematic way. kmerDB currently contains 202,340,859,107 base pairs and 19,304,903,356 amino acids, spanning 54,039 and 21,865 reference genomes and proteomes, respectively, as well as 6,905,362 and 149,305,183 genomic and proteomic species-specific sequences, termed quasi-primes. Additionally, we provide access to 5,186,757 nucleic and 214,904,089 peptide sequences absent from every genome and proteome, termed primes. kmerDB features a user-friendly interface offering various search options and filters for easy parsing and searching. The service is available at: www.kmerdb.com.

The crucial roles and research advances of cGAS‑STING pathway in liver diseases

Inflammation responses have identified as a key mediator of in various liver diseases with high morbidity and mortality. cGAS-STING signalling is essential in innate immunity since it triggers release of type I interferons and various of proinflammatory cytokines. The potential connection between cGAS-STING pathway and liver inflammatory diseases has recently been reported widely. In our review, the impact of cGAS-STING on liver inflammation and regulatory mechanism are summarized. Furthermore, many inhibitors of cGAS-STING signalling as promising agents to cure liver inflammation are also explored in detail. A comprehensive knowledge of molecular mechanisms of cGAS-STING signalling in liver inflammation is vital for exploring novel treatments and providing recommendations and perspectives for future utilization.

Chromatin phase separation and nuclear shape fluctuations are correlated in a polymer model of the nucleus

Abnormal cell nuclear shapes are hallmarks of diseases, including progeria, muscular dystrophy, and many cancers. Experiments have shown that disruption of heterochromatin and increases in euchromatin lead to nuclear deformations, such as blebs and ruptures. However, the physical mechanisms through which chromatin governs nuclear shape are poorly understood. To investigate how heterochromatin and euchromatin might govern nuclear morphology, we studied chromatin microphase separation in a composite coarse-grained polymer and elastic shell simulation model. By varying chromatin density, heterochromatin composition, and heterochromatin-lamina interactions, we show how the chromatin phase organization may perturb nuclear shape. Increasing chromatin density stabilizes the lamina against large fluctuations. However, increasing heterochromatin levels or heterochromatin-lamina interactions enhances nuclear shape fluctuations by a "wetting"-like interaction. In contrast, fluctuations are insensitive to heterochromatin's internal structure. Our simulations suggest that peripheral heterochromatin accumulation could perturb nuclear morphology, while nuclear shape stabilization likely occurs through mechanisms other than chromatin microphase organization.

Genome-wide analysis for root and leaf architecture traits associated with drought tolerance at the seedling stage in a highly ecologically diverse wheat population

Drought stress occurred at early growth stages in wheat affecting the following growth stages. Therefore, selecting promising drought-tolerant genotypes with highly adapted traits at the seedling stage is an important task for wheat breeders and geneticists. Few research efforts were conducted on the genetic control for drought-adaptive traits at the seedling stage in wheat. In this study, a set of 146 highly diverse spring wheat core collections representing 28 different countries was evaluated under drought stress at the seedling stage. All genotypes were exposed to drought stress for 13 days by water withholding. Leaf traits including seedling length, leaf wilting, days to wilting, leaf area, and leaf rolling were scored. Moreover, root traits such as root length, maximum width, emergence angle, tip angle, and number of roots were scored. Considerable significant genetic variation was found among all genotypes tested in these experiments. The heritability estimates ranged from 0.74 (leaf witling) to 0.99 (root tip angle). A set of nine genotypes were selected and considered drought-tolerant genotypes. Among all leaf traits, shoot length had significant correlations with all root traits under drought stress. The 146 genotypes were genotyped using the Infinium Wheat 15 K single nucleotide polymorphism (SNP) array and diversity arrays technology (DArT) marker platform. The result of genotyping revealed 12,999 SNPs and 2150 DArT markers which were used to run a genome-wide association study (GWAS). The results of GWAS revealed 169 markers associated with leaf and root traits under drought stress. Out of the 169 markers, 82 were considered major quantitative trait loci (QTL). The GWAS revealed 95 candidate genes were identified with 53 genes showing evidence for drought tolerance in wheat, while the remaining candidate genes were considered novel. No shared markers were found between leaf and root traits. The results of the study provided mapping novel markers associated with new root traits at the seedling stage. Also, the selected genotypes from different countries could be employed in future wheat breeding programs not only for improving adaptive drought-tolerant traits but also for expanding genetic diversity.

HiOmics: A cloud-based one-stop platform for the comprehensive analysis of large-scale omics data

Analyzing the vast amount of omics data generated comprehensively by high-throughput sequencing technology is of utmost importance for scientists. In this context, we propose HiOmics, a cloud-based platform equipped with nearly 300 plugins designed for the comprehensive analysis and visualization of omics data. HiOmics utilizes the Element Plus framework to craft a user-friendly interface and harnesses Docker container technology to ensure the reliability and reproducibility of data analysis results. Furthermore, HiOmics employs the Workflow Description Language and Cromwell engine to construct workflows, ensuring the portability of data analysis and simplifying the examination of intricate data. Additionally, HiOmics has developed DataCheck, a tool based on Golang, which verifies and converts data formats. Finally, by leveraging the object storage technology and batch computing capabilities of public cloud platforms, HiOmics enables the storage and processing of large-scale data while maintaining resource independence among users.

Resistance and aerobic training increases genome-wide DNA methylation in women with polycystic ovary syndrome

Physical activity is a first-line treatment for polycystic ovary syndrome (PCOS). Resistance or aerobic exercise improves metabolic complications, reproductive outcomes, and quality of life in PCOS. DNA methylation reprogramming during exercise may be the major modifier behind these changes. We sought to evaluate genome-wide DNA methylation changes after supervised resistance and aerobic exercise in women with PCOS. Exercises were performed in 56 women with PCOS (resistance, n = 30; aerobic, n = 26), for 16 weeks (wks), three times per week, in 50-minute to one-hour sessions. Anthropometric indices and hormonal and metabolic parameters were measured before and after training. Genome-wide leukocyte DNA methylation was analysed by Infinium Human MethylationEPIC 850K BeadChip microarrays (Illumina). Both resistance and aerobic exercise improved anthropometric indices, metabolic dysfunction, and hyperandrogenism in PCOS after the training programme, but no differences were observed between the two exercises. Resistance and aerobic exercise increased genome-wide DNA methylation, although resistance changed every category in the CpG island context (islands, shores, shelve, and open sea), whereas aerobic exercise altered CpG shores and the open sea. Using a stringent FDR (>40), 6 significantly differentially methylated regions (DMRs) were observed in the resistance exercise cohort and 14 DRMs in the aerobic cohort, all of which were hypermethylated. The increase in genome-wide DNA methylation may be related to the metabolic and hormonal changes observed in PCOS after resistance and aerobic exercise. Since the mammalian genome is hypermethylated globally to prevent genomic instability and ageing, resistance and aerobic exercise may promote health and longevity through environmentally induced epigenetic changes.

Bifunctional black phosphorus quantum dots platform: Delivery and remarkable immunotherapy enhancement of STING agonist

Cancer immunotherapy has been developed to improve therapeutic effects for patients by activating the innate immune stimulator of interferon gene (STING) pathway. However, most patients cannot benefit from this therapy, mainly due to the problems of excessively low immune responses and lack of tumor specificity. Herein, we report a solution to these two problems by developing a bifunctional platform of black phosphorus quantum dots (BPQDs) for STING agonists. Specifically, BPQDs could connect targeted functional groups and regulate surface zeta potential by coordinating metal ions to increase loading (over 5 times) while maintaining high universality (7 STING agonists). The controlled release of STING agonists enabled specific interactions with their proteins, activating the STING pathway and stimulating the secretion release of immunosuppressive factors by phosphorylating TBK1 and IFN-IRF3 and secreting high levels of immunostimulatory cytokines, including IL-6, IFN-α, and IFN-β. Moreover, the immunotherapy was enhanced was enhanced mild photothermal therapy (PTT) of BPQDs platform, producing enough T cells to eliminate tumors and prevent tumor recurrence. This work facilitates further research on targeted delivery of small-molecule immune drugs to enhance the development of clinical immunotherapy.

Causal association and shared genetics between telomere length and COVID-19 outcomes: New evidence from the latest large-scale summary statistics

Background

Observational studies suggested that leukocyte telomere length (LTL) is shortened in COVID-19 patients. However, the genetic association and causality remained unknown.

Methods

Based on the genome-wide association of LTL (N = 472,174) and COVID-19 phenotypes (N = 1086,211-2597,856), LDSC and SUPERGNOVA were used to estimate the genetic correlation. Cross-trait GWAS meta-analysis, colocalization, fine-mapping analysis, and transcriptome-wide association study were conducted to explore the shared genetic etiology. Mendelian randomization (MR) was utilized to infer the causality. Upstream and downstream two-step MR was performed to investigate the potential mediating effects.

Results

LDSC identified a significant genetic association between LTL and all COVID-19 phenotypes (rG < 0, p < 0.05). Six significant regions were observed for LTL and COVID-19 susceptibility and hospitalization, respectively. Colocalization analysis found rs144204502, rs34517439, and rs56255908 were shared causal variants between LTL and COVID-19 phenotypes. Numerous biological pathways associated with LTL and COVID-19 outcomes were identified, mainly involved in -immune-related pathways. MR showed that longer LTL was significantly associated with a lower risk of COVID-19 severity (OR [95% CI] = 0.81 [0.71-0.92], p = 1.24 ×10-3) and suggestively associated with lower risks of COVID-19 susceptibility (OR [95% CI] = 0.96 [0.92-1.00], p = 3.44 ×10-2) and COVID-19 hospitalization (OR [95% CI] = 0.89 [0.80-0.98], p = 1.89 ×10-2). LTL partially mediated the effects of BMI, smoking, and education on COVID-19 outcomes. Furthermore, six proteins partially mediated the causality of LTL on COVID-19 outcomes, including BNDF, QPCT, FAS, MPO, SFTPB, and APOF.

Conclusions

Our findings suggested that shorter LTL was genetically associated with a higher risk of COVID-19 phenotypes, with shared genetic etiology and potential causality.

Genome and transcriptomic analysis of the adaptation of Escherichia coli to environmental stresses

In natural niches, bacteria are forced to spend most of their lives under various environmental stresses, such as nutrient limitation, heavy metal pollution, heat and antibiotic stress. To cope with adverse environments, bacterial genome can during the life cycle, produce potential adaptive mutants. The genomic changes, especially mutations, in the genes that encode RNA polymerase and transcription factors, might lead to variations in the transcriptome. These variations enable bacteria to cope with environmental stresses through physiological adaptation in response to stress. This paper reviews the recent contributions of genomic and transcriptomic analyses in understanding the adaption mechanism of Escherichia coli to environmental stresses. Various genomic changes have been observed in E. coli strains in laboratory or under natural stresses, including starvation, heavy metals, acidic conditions, heat shock and antibiotics. The mutations include slight changes (one to several nucleotides), deletions, insertions, chromosomal rearrangements and variations in copy numbers. The transcriptome of E. coli largely changes due to genomic mutations. However, the transcriptional profiles vary due to variations in stress selections. Cellular adaptation to the selections is associated with transcriptional changes resulting from genomic mutations. Changes in genome and transcriptome are cooperative and jointly affect the adaptation of E. coli to different environments. This comprehensive review reveals that coordination of genome mutations and transcriptional variations needs to be explored further to provide a better understanding of the mechanisms of bacterial adaptation to stresses.

The implications for urological malignancies of non-coding RNAs in the the tumor microenvironment

Urological malignancies are a major global health issue because of their complexity and the wide range of ways they affect patients. There's a growing need for in-depth research into these cancers, especially at the molecular level. Recent studies have highlighted the importance of non-coding RNAs (ncRNAs) – these don't code for proteins but are crucial in controlling genes – and the tumor microenvironment (TME), which is no longer seen as just a background factor but as an active player in cancer progression. Understanding how ncRNAs and the TME interact is key for finding new ways to diagnose and predict outcomes in urological cancers, and for developing new treatments. This article reviews the basic features of ncRNAs and goes into detail about their various roles in the TME, focusing specifically on how different ncRNAs function and act in urological malignancies.

Beyond mutations: Accounting for quantitative changes in the analysis of protein evolution

Molecular phylogenetic research has relied on the analysis of the coding sequences by genes or of the amino acid sequences by the encoded proteins. Enumerating the numbers of mismatches, being indicators of mutation, has been central to pertinent algorithms. Specific amino acids possess quantifiable characteristics that enable the conversion from "words" (strings of letters denoting amino acids or bases) to "waves" (strings of quantitative values representing the physico-chemical properties) or to matrices (coordinates representing the positions in a comprehensive property space). The application of such numerical representations to evolutionary analysis takes into account not only the occurrence of mutations but also their properties as influences that drive speciation, because selective pressures favor certain mutations over others, and this predilection is represented in the characteristics of the incorporated amino acids (it is not born out solely by the mismatches). Besides being more discriminating sources for tree-generating algorithms than match/mismatch, the number strings can be examined for overall similarity with average mutual information, autocorrelation, and fractal dimension. Bivariate wavelet analysis aids in distinguishing hypermutable versus conserved domains of the protein. The matrix depiction is readily subjected to comparisons of distances, and it allows the generation of heat maps or graphs. This analysis preserves the accepted taxa order where tree construction with standard approaches yields conflicting results (for the protein S100A6). It also aids hypothesis generation about the origin of mitochondrial proteins. These analytical algorithms have been automated in R and are applicable to various processes that are describable in matrix format.

Circular RNA LMBR1 inhibits bladder cancer progression by enhancing expression of the protein ALDH1A3

Background

Circular RNAs (circRNAs) have been identified as playing an integral role in the development of bladder cancer (BC). However, the mechanism by which circRNAs operate in the chemical carcinogenesis of BC remains unclear.

Methods

To explore this mechanism, we used RNA high-throughput sequencing to identify differentially expressed circRNA in bladder epithelial cells and chemically induced malignant transformed BC cells. Subsequently, in vitro experiments were conducted to investigate the biological function and molecular mechanism of circLMBR1 in BC. Finally, animal experiments were conducted to examine the clinical relevance of circLMBR1 in vivo.

Results

Our profiling of circular RNA expression during cellular malignant transformation induced by chemical carcinogens identified a subset of circRNAs associated with cell transformation. We verified that the expression of circLMBR1 in bladder epithelial malignant transformed cells was decreased compared with control cells, as well as in BC tissues and bladder cell lines. Furthermore, circLMBR1 was seen to inhibit the proliferation, invasion, and migration of BC cells both in vitro and in vivo. Mechanistically, circLMBR1 was found to exert its antitumor effect by binding to the protein ALDH1A3.

Conclusions

Our findings have revealed that circLMBR1 inhibits the progression of BC cells by binding to ALDH1A3 and upregulating its expression. As such, circLMBR1 serves as a promising predictor of BC and may provide a novel therapeutic target for the treatment of BC.

Dynamic expedition of leading mutations in SARS-CoV-2 spike glycoproteins

The continuous evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which caused the recent pandemic, has generated countless new variants with varying fitness. Mutations of the spike glycoprotein play a particularly vital role in shaping its evolutionary trajectory, as they have the capability to alter its infectivity and antigenicity. We present a time-resolved statistical method, Dynamic Expedition of Leading Mutations (deLemus), to analyze the evolutionary dynamics of the SARS-CoV-2 spike glycoprotein. The proposed L -index of the deLemus method is effective in quantifying the mutation strength of each amino acid site and outlining evolutionarily significant sites, allowing the comprehensive characterization of the evolutionary mutation pattern of the spike glycoprotein.

Automated characterization and analysis of expression compatibility between regulatory sequences and metabolic genes in Escherichia coli

Utilizing standardized artificial regulatory sequences to fine-tuning the expression of multiple metabolic pathways/genes is a key strategy in the creation of efficient microbial cell factories. However, when regulatory sequence expression strengths are characterized using only a few reporter genes, they may not be applicable across diverse genes. This introduces great uncertainty into the precise regulation of multiple genes at multiple expression levels. To address this, our study adopted a fluorescent protein fusion strategy for a more accurate assessment of target protein expression levels. We combined 41 commonly-used metabolic genes with 15 regulatory sequences, yielding an expression dataset encompassing 520 unique combinations. This dataset highlighted substantial variation in protein expression level under identical regulatory sequences, with relative expression levels ranging from 2.8 to 176-fold. It also demonstrated that improving the strength of regulatory sequences does not necessarily lead to significant improvements in the expression levels of target proteins. Utilizing this dataset, we have developed various machine learning models and discovered that the integration of promoter regions, ribosome binding sites, and coding sequences significantly improves the accuracy of predicting protein expression levels, with a Spearman correlation coefficient of 0.72, where the promoter sequence exerts a predominant influence. Our study aims not only to provide a detailed guide for fine-tuning gene expression in the metabolic engineering of Escherichia coli but also to deepen our understanding of the compatibility issues between regulatory sequences and target genes.

Circle-seq based method for eccDNA synthesis and its application as a canonical promoter independent vector for robust microRNA overexpression

Extrachromosomal circular DNA (eccDNA) has recently gained increasing attention due to its significant role in cancer and other pathophysiologic states. The majority of circular DNAs detected by Circle-seq are small-size eccDNAs with enigmatic functions. One major technical hurdle is to synthesize eccDNA for functional identification. Here, we describe CAES (Circle-seq based Artificial EccDNA Synthesis), a promising and reliable method for artificial eccDNA synthesis. Eight eccDNAs carrying different microRNA genes (eccMIR) found in gastric cancer tissues, ranging from 329 bp to 2189 bp in size, were created utilizing the CAES method. Exonuclease V and single restriction-endonuclease digestion identified the circular structure of synthetic eccDNAs. The DNA circularization efficiency afforded by CAES ranged from 15.6% to 31.1%, which was negatively correlated with the eccDNA length. In addition, we demonstrated that CAES-synthesized eccMIRs can express both miRNA-3p and - 5p molecules efficiently independent of a canonical promoter in human cell lines. Further assays proved that these eccMIRs were functional as they were able to repress the luciferase gene containing a miRNA-target sequence in the 3'UTR as well as the endogenous mRNA targets. Finally, kinetics study revealed that eccDNA exhibited a decay rate similar to the standard plasmids and linear DNA in cultured cells. Together, this study offers a rapid and convenient method for Circle-seq users to synthesize artificial eccDNAs. It also demonstrates the promising potential of eccMIR as a bacterial DNA-free vector for safe and robust miRNA overexpression in both basic research and therapeutic applications.

Cysteine hyperoxidation rewires communication pathways in the nucleosome and destabilizes the dyad

Gene activity is tightly controlled by reversible chemical modifications called epigenetic marks, which are of various types and modulate gene accessibility without affecting the DNA sequence. Despite an increasing body of evidence demonstrating the role of oxidative-type modifications of histones in gene expression regulation, there remains a complete absence of structural data at the atomistic level to understand the molecular mechanisms behind their regulatory action. Owing to μs time-scale MD simulations and protein communication networks analysis, we describe the impact of histone H3 hyperoxidation (i.e., S-sulfonylation) on the nucleosome core particle dynamics. Our results reveal the atomic-scale details of the intrinsic structural networks within the canonical histone core and their perturbation by hyperoxidation of the histone H3 C110. We show that this modification involves local rearrangements of the communication networks and destabilizes the dyad, and that one modification is enough to induce a maximal structural signature. Our results suggest that cysteine hyperoxidation in the nucleosome core particle might favor its disassembly.

The African swine fever virus MGF300-4L protein is associated with viral pathogenicity by promoting the autophagic degradation of IKKβ and increasing the stability of IκBα

African swine fever (ASF) is a highly contagious, often fatal viral disease caused by African swine fever virus (ASFV), which imposes a substantial economic burden on the global pig industry. When screening for the virus replication-regulating genes in the left variable region of the ASFV genome, we observed a notable reduction in ASFV replication following the deletion of the MGF300-4L gene. However, the role of MGF300-4L in ASFV infection remains unexplored. In this study, we found that MGF300-4L could effectively inhibit the production of proinflammatory cytokines IL-1β and TNF-α, which are regulated by the NF-κB signaling pathway. Mechanistically, we demonstrated that MGF300-4L interacts with IKKβ and promotes its lysosomal degradation via the chaperone-mediated autophagy. Meanwhile, the interaction between MGF300-4L and IκBα competitively inhibits the binding of the E3 ligase β-TrCP to IκBα, thereby inhibiting the ubiquitination-dependent degradation of IκBα. Remarkably, although ASFV encodes other inhibitors of NF-κB, the MGF300-4L gene-deleted ASFV (Del4L) showed reduced virulence in pigs, indicating that MGF300-4L plays a critical role in ASFV pathogenicity. Importantly, the attenuation of Del4L was associated with a significant increase in the production of IL-1β and TNF-α early in the infection of pigs. Our findings provide insights into the functions of MGF300-4L in ASFV pathogenicity, suggesting that MGF300-4L could be a promising target for developing novel strategies and live attenuated vaccines against ASF.

AlloViz: A tool for the calculation and visualisation of protein allosteric communication networks

Allostery, the presence of functional interactions between distant parts of proteins, is a critical concept in the field of biochemistry and molecular biology, particularly in the context of protein function and regulation. Understanding the principles of allosteric regulation is essential for advancing our knowledge of biology and developing new therapeutic strategies. This paper presents AlloViz, an open-source Python package designed to quantitatively determine, analyse, and visually represent allosteric communication networks on the basis of molecular dynamics (MD) simulation data. The software integrates well-known techniques for understanding allosteric properties simplifying the process of accessing, rationalising, and representing protein allostery and communication routes. It overcomes the inefficiency of having multiple methods with heterogeneous implementations and showcases the advantages of using MD simulations and multiple replicas to obtain statistically sound information on protein dynamics; it also enables the calculation of "consensus-like" scores aggregating methods that consider multiple structural aspects of allosteric networks. We demonstrate the features of AlloViz on two proteins: β-arrestin 1, a key player for regulating G protein-coupled receptor (GPCR) signalling, and the protein tyrosine phosphatase 1B, an important pharmaceutical target for allosteric inhibitors. The software includes comprehensive documentation and examples, tutorials, and a user-friendly graphical interface.

Adipose tissue-selective ablation of ADAM10 results in divergent metabolic phenotypes following long-term dietary manipulation

A Disintegrin And Metalloproteinase domain-containing protein 10 (ADAM10), is involved in several metabolic and inflammatory pathways. We speculated that ADAM10 plays a modulatory role in adipose tissue inflammation and metabolism. To this end, we studied adipose tissue-specific ADAM10 knock-out mice (aKO). While young, regular chow diet-fed aKO mice showed increased insulin sensitivity, following prolonged (33 weeks) high-fat diet (HFD) exposure, aKO mice developed obesity and insulin resistance. Compared to controls, aKO mice showed less inflammatory adipokine profile despite the significant increase in adiposity. In brown adipose tissue, aKO mice on HFD had changes in CD8+ T cell populations indicating a lesser inflammatory pattern. Following HFD, both aKO and control littermates demonstrated decreased adipose tissue pro-inflammatory macrophages, and increased anti-inflammatory accumulation, without differences between the genotypes. Collectively, our observations indicate that selective deletion of ADAM10 in adipocytes results in a mitigated inflammatory response, leading to increased insulin sensitivity in young mice fed with regular diet. This state of insulin sensitivity, following prolonged HFD, facilitates energy storage resulting in increased fat accumulation which ultimately leads to the development of a phenotype of obesity and insulin resistance. In conclusion, the data indicate that ADAM10 has a modulatory effect of inflammation and whole-body energy metabolism.

Porin deficiency or plasmid copy number increase mediated carbapenem-resistant Escherichia coli resistance evolution

This study investigated resistance evolution mechanisms of conjugated plasmids and bacterial hosts under different concentrations of antibiotic pressure. Ancestral strain ECNX52 was constructed by introducing the blaNDM-5-carrying IncX3 plasmid into E. coli C600, and was subjected to laboratory evolution under different concentrations of meropenem pressure. Minimal inhibitory concentrations and conjugation frequency were determined. Fitness of these strains was assessed. Whole genome sequencing and transcriptional changes were performed. Ancestral host or plasmids were recombined with evolved hosts or plasmids to verify plasmid or host factors in resistance evolution. Role of the repA mutation on plasmid copy number was determined. Two out of the four clones (EM2N1 and EM2N3) exhibited four-fold increase in MIC when exposed to a continuous pressure of 2 μg/mL MEM (1/32 MIC), by down regulating expression of outer membrane protein ompF. Besides, all four clones displayed four-fold increase in MIC and higher conjugation frequency when subjected to a continuous pressure of 4 μg/mL MEM (1/16 MIC), attributing to increasing plasmid copy number generated by repA D140Y (GAT→TAT) mutation. Bacterial hosts and conjugative plasmids can undergo resistance evolution under certain concentrations of antimicrobial pressure by reducing the expression of outer membrane proteins or increasing plasmid copy numbers.

A novel framework for human leukocyte antigen (HLA) genotyping using probe capture-based targeted next-generation sequencing and computational analysis

Human leukocyte antigen (HLA) genes play pivotal roles in numerous immunological applications. Given the immense number of polymorphisms, achieving accurate high-throughput HLA typing remains challenging. This study aimed to harness the human pan-genome reference consortium (HPRC) resources as a potential benchmark for HLA reference materials. We meticulously annotated specific four field-resolution alleles for 11 HLA genes (HLA-A, -B, -C, -DPA1, -DPB1, -DQA1, -DQB1, -DRB1, -DRB3, -DRB4 and -DRB5) from 44 high-quality HPRC personal genome assemblies. For sequencing, we crafted HLA-specific probes and conducted capture-based targeted sequencing of the genomic DNA of the HPRC cohort, ensuring focused and comprehensive coverage of the HLA region of interest. We used publicly available short-read whole-genome sequencing (WGS) data from identical samples to offer a comparative perspective. To decipher the vast amount of sequencing data, we employed seven distinct software tools: OptiType, HLA-VBseq, HISAT genotype, SpecHLA, T1K, QzType, and DRAGEN. Each tool offers unique capabilities and algorithms for HLA genotyping, allowing comprehensive analysis and validation of the results. We then compared these results with benchmarks derived from personal genome assemblies. Our findings present a comprehensive four-field-resolution HLA allele annotation for 44 HPRC samples. Significantly, our innovative targeted next-generation sequencing (NGS) approach for HLA genes showed superior accuracy compared with conventional short-read WGS. An integrated analysis involving QzType, T1K, and DRAGEN was developed, achieving 100% accuracy for all 11 HLA genes. In conclusion, our study highlighted the combination of targeted short-read sequencing and astute computational analysis as a robust approach for HLA genotyping. Furthermore, the HPRC cohort has emerged as a valuable assembly-based reference in this realm.

Non-coding 886 (nc886/vtRNA2-1), the epigenetic odd duck - implications for future studies

Non-coding 886 (nc886, vtRNA2-1) is the only human polymorphically imprinted gene, in which the methylation status is not determined by genetics. Existing literature regarding the establishment, stability and consequences of the methylation pattern, as well as the nature and function of the nc886 RNAs transcribed from the locus, are contradictory. For example, the methylation status of the locus has been reported to be stable through life and across somatic tissues, but also susceptible to environmental effects. The nature of the produced nc886 RNA(s) has been redefined multiple times, and in carcinogenesis, these RNAs have been reported to have conflicting roles. In addition, due to the bimodal methylation pattern of the nc886 locus, traditional genome-wide methylation analyses can lead to false-positive results, especially in smaller datasets. Herein, we aim to summarize the existing literature regarding nc886, discuss how the characteristics of nc886 give rise to contradictory results, as well as to reinterpret, reanalyse and, where possible, replicate the results presented in the current literature. We also introduce novel findings on how the distribution of the nc886 methylation pattern is associated with the geographical origins of the population and describe the methylation changes in a large variety of human tumours. Through the example of this one peculiar genetic locus and RNA, we aim to highlight issues in the analysis of DNA methylation and non-coding RNAs in general and offer our suggestions for what should be taken into consideration in future analyses.

Ionotropic purinergic receptor 7 (P2X7) channel structure and pharmacology provides insight regarding non-nucleotide agonism

P2X7 is a member of the Ionotropic Purinergic Receptor (P2X) family. The P2X family of receptors is composed of seven (P2X1-7), ligand-gated, nonselective cation channels. Changes in P2X expression have been reported in multiple disease models. P2Xs have large complex extracellular domains that function as receptors for a variety of ligands, including endogenous and synthetic agonists and antagonists. ATP is the canonical agonist. ATP affinity ranges from nanomolar to micromolar for most P2XRs, but P2X7 has uniquely poor ATP affinity. In many physiological settings, it may be difficult to achieve the millimolar extracellular ATP concentrations needed for P2X7 channel activation; however, channel function is implicated in pain sensation, immune cell function, cardiovascular disease, cancer, and osteoporosis. Multiple high-resolution P2X7 structures have been solved in apo-, ATP-, and antagonist-bound states. P2X7 structural data reveal distinct allosteric and orthosteric antagonist-binding sites. Both allosteric and orthosteric P2X7 antagonists are well documented to inhibit ATP-evoked channel current. However, a growing body of evidence supports P2X7 activation by non-nucleotide agonists, including extracellular histone proteins and human cathelicidin-derived peptides (LL-37). Interestingly, P2X7 non-nucleotide agonism is not inhibited by allosteric antagonists, but is inhibited by orthosteric antagonists. Herein, we review P2X7 function with a focus on the efficacy of available pharmacology on P2X7 channel current activation by non-nucleotide agonists in effort to understand agonist/antagonist efficacy, and consider the impact of these data on the current understanding of P2X7 in physiology and disease given these limitations of P2X7-selective antagonists and incomplete knockout mouse models.

NeuralBeds: Neural embeddings for efficient DNA data compression and optimized similarity search

The availability of high throughput sequencing tools coupled with the declining costs in the production of DNA sequences has led to the generation of enormous amounts of omics data curated in several databases such as NCBI and EMBL. Identification of similar DNA sequences from these databases is one of the fundamental tasks in bioinformatics. It is essential for discovering homologous sequences in organisms, phylogenetic studies of evolutionary relationships among several biological entities, or detection of pathogens. Improving DNA similarity search is of outmost importance because of the increased complexity of the evergrowing repositories of sequences. Therefore, instead of using the conventional approach of comparing raw sequences, e.g., in fasta format, a numerical representation of the sequences can be used to calculate their similarities and optimize the search process. In this study, we analyzed different approaches for numerical embeddings, including Chaos Game Representation, hashing, and neural networks, and compared them with classical approaches such as principal component analysis. It turned out that neural networks generate embeddings that are able to capture the similarity between DNA sequences as a distance measure and outperform the other approaches on DNA similarity search, significantly.

Genetic confounds of transgenerational epigenetic inheritance in mice

Transgenerational epigenetic inheritance in mammals remains a controversial phenomenon. A recent study by Takahashi et al. provides evidence for this mode of inheritance in mice by using a CRISPR/Cas9-based epigenetic editing technique to modify DNA methylation levels at specific promoters and then demonstrating the inheritance of the gain in methylation in offspring. In this technical commentary, we argue that the method used in the original study inherently amplifies the likelihood of genetic changes that thereafter lead to the heritability of epigenetic changes. We provide evidence that genetic changes from multiple sources do indeed occur in these experiments and explore several avenues by which these changes could be causal to the apparent inheritance of epigenetic changes. We conclude a genetic basis of inheritance cannot be ruled out and thus transgenerational epigenetic inheritance has not been adequately established by the original study.

Are weight loss and metabolic outcomes of bariatric surgery influenced by candidate glucocorticoid receptor gene polymorphisms? A prospective study

BACKGROUND

Bariatric surgery is the most effective treatment for severe obesity. There can be variation in the degree of weight reduction following bariatric surgery. It is unknown whether single nucleotide polymorphisms (SNPs) in the glucocorticoid receptor locus (GRL) affect postoperative weight loss and metabolic outcomes.

MATERIALS/METHODS

We studied the association between selected candidate SNPs and postoperative weight loss and metabolic outcomes in patients with severe obesity undergoing bariatric surgery. The polymorphisms rs41423247 (Bcl1), rs56149945 (N363S) and rs6189/rs6190 (ER22/23EK) were analysed.

RESULTS

The 139 participants included 95 women (68.3%) and had a median (interquartile range) age of 53.0 (46.0-60.0) years and mean (SD) weight of 140.8 (28.8) kg and body mass index of 50.3 (8.6) kg/m2. At baseline, 59 patients had type 2 diabetes (T2D), 60 had hypertension and 35 had obstructive sleep apnoea syndrome treated with continuous positive airway pressure (CPAP). 84 patients (60.4%) underwent gastric bypass and 55 (39.6%) underwent sleeve gastrectomy. There were no significant differences in weight loss, glycated haemoglobin (HbA1c) or lipid profile categorized by genotype status, sex or median age. There was significant weight reduction after bariatric surgery with a postoperative BMI of 34.1 (6.8) kg/m2 at 24 months (p < 0.001).

CONCLUSION

While GRL polymorphisms with a known deleterious effect on adipose tissue mass and function may have a small, additive effect on the prevalence of obesity and related metabolic disorders in the population, we suggest that the relatively weak biological influence of these SNPs is readily overcome by bariatric surgery.

Branching in poly(amine-co-ester) polyplexes impacts mRNA transfection

Branching is a key structural parameter of polymers, which can have profound impacts on physicochemical properties. It has been demonstrated that branching is a modulating factor for mRNA delivery and transfection using delivery vehicles built from cationic polymers, but the influence of polymer branching on mRNA delivery remains relatively underexplored compared to other polymer features such as monomer composition, hydrophobicity, pKa, or the type of terminal group. In this study, we examined the impact of branching on the physicochemical properties of poly(amine-co-esters) (PACE) and their efficiency in mRNA transfection in vivo and in vitro under various conditions. PACE polymers were synthesized with various degrees of branching ranging from 0 to 0.66, and their transfection efficiency was systemically evaluated. We observed that branching improves the stability of polyplexes but reduces the pH buffering capacity. Therefore, the degree of branching (DB) must be optimized in a delivery route specific manner due to differences in challenges faced by polyplexes in different physiological compartments. Through a systematic analysis of physicochemical properties and mRNA transfection in vivo and in vitro, this study highlights the influence of polymer branching on nucleic acid delivery.

Development of a multi-targeted chemotherapeutic approach based on G-quadruplex stabilisation and carbonic anhydrase inhibition

A novel class of compounds designed to hit two anti-tumour targets, G-quadruplex structures and human carbonic anhydrases (hCAs) IX and XII is proposed. The induction/stabilisation of G-quadruplex structures by small molecules has emerged as an anticancer strategy, disrupting telomere maintenance and reducing oncogene expression. hCAs IX and XII are well-established anti-tumour targets, upregulated in many hypoxic tumours and contributing to metastasis. The ligands reported feature a berberine G-quadruplex stabiliser scaffold connected to a moiety inhibiting hCAs IX and XII. In vitro experiments showed that our compounds selectively stabilise G-quadruplex structures and inhibit hCAs IX and XII. The crystal structure of a telomeric G-quadruplex in complex with one of these ligands was obtained, shedding light on the ligand/target interaction mode. The most promising ligands showed significant cytotoxicity against CA IX-positive HeLa cancer cells in hypoxia, and the ability to stabilise G-quadruplexes within tumour cells.

Optimal variable identification for accurate detection of causal expression Quantitative Trait Loci with applications in heart-related diseases

Gene expression plays a pivotal role in various diseases, contributing significantly to their mechanisms. Most GWAS risk loci are in non-coding regions, potentially affecting disease risk by altering gene expression in specific tissues. This expression is notably tissue-specific, with genetic variants substantially influencing it. However, accurately detecting the expression Quantitative Trait Loci (eQTL) is challenging due to limited heritability in gene expression, extensive linkage disequilibrium (LD), and multiple causal variants. The single variant association approach in eQTL analysis is limited by its susceptibility to capture the combined effects of multiple variants, and a bias towards common variants, underscoring the need for a more robust method to accurately identify causal eQTL variants. To address this, we developed an algorithm, CausalEQTL, which integrates L 0 +L 1 penalized regression with an ensemble approach to localize eQTL, thereby enhancing prediction performance precisely. Our results demonstrate that CausalEQTL outperforms traditional models, including LASSO, Elastic Net, Ridge, in terms of power and overall performance. Furthermore, analysis of heart tissue data from the GTEx project revealed that eQTL sites identified by our algorithm provide deeper insights into heart-related tissue eQTL detection. This advancement in eQTL mapping promises to improve our understanding of the genetic basis of tissue-specific gene expression and its implications in disease. The source code and identified causal eQTLs for CausalEQTL are available on GitHub: https://github.com/zhc-moushang/CausalEQTL.

PRIMITI: A computational approach for accurate prediction of miRNA-target mRNA interaction

Current medical research has been demonstrating the roles of miRNAs in a variety of cellular mechanisms, lending credence to the association between miRNA dysregulation and multiple diseases. Understanding the mechanisms of miRNA is critical for developing effective diagnostic and therapeutic strategies. miRNA-mRNA interactions emerge as the most important mechanism to be understood despite their experimental validation constraints. Accordingly, several computational models have been developed to predict miRNA-mRNA interactions, albeit presenting limited predictive capabilities, poor characterisation of miRNA-mRNA interactions, and low usability. To address these drawbacks, we developed PRIMITI, a PRedictive model for the Identification of novel miRNA-Target mRNA Interactions. PRIMITI is a novel machine learning model that utilises CLIP-seq and expression data to characterise functional target sites in 3'-untranslated regions (3'-UTRs) and predict miRNA-target mRNA repression activity. The model was trained using a reliable negative sample selection approach and the robust extreme gradient boosting (XGBoost) model, which was coupled with newly introduced features, including sequence and genetic variation information. PRIMITI achieved an area under the receiver operating characteristic (ROC) curve (AUC) up to 0.96 for a prediction of functional miRNA-target site binding and 0.96 for a prediction of miRNA-target mRNA repression activity on cross-validation and an independent blind test. Additionally, the model outperformed state-of-the-art methods in recovering miRNA-target repressions in an unseen microarray dataset and in a collection of validated miRNA-mRNA interactions, highlighting its utility for preliminary screening. PRIMITI is available on a reliable, scalable, and user-friendly web server at https://biosig.lab.uq.edu.au/primiti.

Circ_0011058 alleviates RA pathology through the circ_0011058/miR-335-5p/CUL4B signal axis

Our previous study found that Cullin 4B (CUL4B) inhibited rheumatoid arthritis (RA) pathology through glycogen synthase kinase-3beta (GSK3β)/canonical Wnt signalling pathway. In this work, pre-experiment and bioinformatics analysis suggested that circ_0011058 may lead to the up-regulation of CUL4B expression by inhibiting miR-335-5p. Therefore, we studied whether circ_0011058 can promote the expression of CUL4B through sponging the miR-335-5p and further promote the pathological development of RA. Bioinformatics prediction, real-time quantitative PCR (RT-qPCR), western blot (WB), double luciferase reporter gene and other relevant methods were used to study the inhibition of circ_0011058 on RA pathology and its molecular mechanism. Results showed that the expression of circ_0011058 was significantly increased in adjuvant arthritis (AA) rats and RA fibroblast-like synoviocytes (FLS). The knockout of circ_0011058 inhibited the proliferation of AA FLS and RA FLS, decreased the levels of interleukin-1 beta (IL-1β), interleukin 6 (IL-6), interleukin 8 (IL-8), and inhibited the expression of matrix metalloproteinase 3 (MMP3), fibronectin, which showed that circ_0011058 had a strong role in promoting RA pathology. Furthermore, miR-335-5p expression was reduced in AA rats and RA FLS. The highly expressed circ_0011058 directly sponged the miR-335-5p, which led to the increase of CUL4B expression and promoted the activation of the GSK3β/canonical signalling pathway. Finally, we confirmed that miR-335-5p mediated the roles of circ_0011058 in promoting RA pathological development, which showed that the circ_0011058/miR-335-5p/CUL4B signal axis was involved in RA pathology. This work was of great significance for clarifying the roles of circ_0011058 in RA pathology, and further work was needed to establish whether circ_0011058 was a potential therapeutic target or diagnostic marker for RA.

scSID: A lightweight algorithm for identifying rare cell types by capturing differential expression from single-cell sequencing data

Single-cell RNA sequencing (scRNA-seq) is currently an important technology for identifying cell types and studying diseases at the genetic level. Identifying rare cell types is biologically important as one of the downstream data analyses of single-cell RNA sequencing. Although rare cell identification methods have been developed, most of these suffer from insufficient mining of intercellular similarities, low scalability, and being time-consuming. In this paper, we propose a single-cell similarity division algorithm (scSID) for identifying rare cells. It takes cell-to-cell similarity into consideration by analyzing both inter-cluster and intra-cluster similarities, and discovers rare cell types based on the similarity differences. We show that scSID outperforms other existing methods by benchmarking it on different experimental datasets. Application of scSID to multiple datasets, including 68K PBMC and intestine, highlights its exceptional scalability and remarkable ability to identify rare cell populations.

Deep-HPI-pred: An R-Shiny applet for network-based classification and prediction of Host-Pathogen protein-protein interactions

Host-pathogen interactions (HPIs) are vital in numerous biological activities and are intrinsically linked to the onset and progression of infectious diseases. HPIs are pivotal in the entire lifecycle of diseases: from the onset of pathogen introduction, navigating through the mechanisms that bypass host cellular defenses, to its subsequent proliferation inside the host. At the heart of these stages lies the synergy of proteins from both the host and the pathogen. By understanding these interlinking protein dynamics, we can gain crucial insights into how diseases progress and pave the way for stronger plant defenses and the swift formulation of countermeasures. In the framework of current study, we developed a web-based R/Shiny app, Deep-HPI-pred, that uses network-driven feature learning method to predict the yet unmapped interactions between pathogen and host proteins. Leveraging citrus and CLas bacteria training datasets as case study, we spotlight the effectiveness of Deep-HPI-pred in discerning Protein-protein interaction (PPIs) between them. Deep-HPI-pred use Multilayer Perceptron (MLP) models for HPI prediction, which is based on a comprehensive evaluation of topological features and neural network architectures. When subjected to independent validation datasets, the predicted models consistently surpassed a Matthews correlation coefficient (MCC) of 0.80 in host-pathogen interactions. Remarkably, the use of Eigenvector Centrality as the leading topological feature further enhanced this performance. Further, Deep-HPI-pred also offers relevant gene ontology (GO) term information for each pathogen and host protein within the system. This protein annotation data contributes an additional layer to our understanding of the intricate dynamics within host-pathogen interactions. In the additional benchmarking studies, the Deep-HPI-pred model has proven its robustness by consistently delivering reliable results across different host-pathogen systems, including plant-pathogens (accuracy of 98.4% and 97.9%), human-virus (accuracy of 94.3%), and animal-bacteria (accuracy of 96.6%) interactomes. These results not only demonstrate the model's versatility but also pave the way for gaining comprehensive insights into the molecular underpinnings of complex host-pathogen interactions. Taken together, the Deep-HPI-pred applet offers a unified web service for both identifying and illustrating interaction networks. Deep-HPI-pred applet is freely accessible at its homepage: https://cbi.gxu.edu.cn/shiny-apps/Deep-HPI-pred/ and at github: https://github.com/tahirulqamar/Deep-HPI-pred.