Toward understanding the role of genomic repeat elements in neurodegenerative diseases

Neurodegenerative diseases cause great medical and economic burdens for both patients and society; however, the complex molecular mechanisms thereof are not yet well understood. With the development of high-coverage sequencing technology, researchers have started to notice that genomic repeat regions, previously neglected in search of disease culprits, are active contributors to multiple neurodegenerative diseases. In this review, we describe the association between repeat element variants and multiple degenerative diseases through genome-wide association studies and targeted sequencing. We discuss the identification of disease-relevant repeat element variants, further powered by the advancement of long-read sequencing technologies and their related tools, and summarize recent findings in the molecular mechanisms of repeat element variants in brain degeneration, such as those causing transcriptional silencing or RNA-mediated gain of toxic function. Furthermore, we describe how in silico predictions using innovative computational models, such as deep learning language models, could enhance and accelerate our understanding of the functional impact of repeat element variants. Finally, we discuss future directions to advance current findings for a better understanding of neurodegenerative diseases and the clinical applications of genomic repeat elements.

Potential role and therapeutic implications of glutathione peroxidase 4 in the treatment of Alzheimer's disease

Alzheimer's disease is an age-related neurodegenerative disorder with a complex and incompletely understood pathogenesis. Despite extensive research, a cure for Alzheimer's disease has not yet been found. Oxidative stress mediates excessive oxidative responses, and its involvement in Alzheimer's disease pathogenesis as a primary or secondary pathological event is widely accepted. As a member of the selenium-containing antioxidant enzyme family, glutathione peroxidase 4 reduces esterified phospholipid hydroperoxides to maintain cellular redox homeostasis. With the discovery of ferroptosis, the central role of glutathione peroxidase 4 in anti-lipid peroxidation in several diseases, including Alzheimer's disease, has received widespread attention. Increasing evidence suggests that glutathione peroxidase 4 expression is inhibited in the Alzheimer's disease brain, resulting in oxidative stress, inflammation, ferroptosis, and apoptosis, which are closely associated with pathological damage in Alzheimer's disease. Several therapeutic approaches, such as small molecule drugs, natural plant products, and non-pharmacological treatments, ameliorate pathological damage and cognitive function in Alzheimer's disease by promoting glutathione peroxidase 4 expression and enhancing glutathione peroxidase 4 activity. Therefore, glutathione peroxidase 4 upregulation may be a promising strategy for the treatment of Alzheimer's disease. This review provides an overview of the gene structure, biological functions, and regulatory mechanisms of glutathione peroxidase 4, a discussion on the important role of glutathione peroxidase 4 in pathological events closely related to Alzheimer's disease, and a summary of the advances in small-molecule drugs, natural plant products, and non-pharmacological therapies targeting glutathione peroxidase 4 for the treatment of Alzheimer's disease. Most prior studies on this subject used animal models, and relevant clinical studies are lacking. Future clinical trials are required to validate the therapeutic effects of strategies targeting glutathione peroxidase 4 in the treatment of Alzheimer's disease.

The impact of endocrine-disrupting chemicals on stem cells: Mechanisms and implications for human health

Endocrine-disrupting chemicals (EDCs) are compounds, either natural or man-made, that interfere with the normal functioning of the endocrine system. There is increasing evidence that exposure to EDCs can have profound adverse effects on reproduction, metabolic disorders, neurological alterations, and increased risk of hormone-dependent cancer. Stem cells (SCs) are integral to these pathological processes, and it is therefore crucial to understand how EDCs may influence SC functionality. This review examines the literature on different types of EDCs and their effects on various types of SCs, including embryonic, adult, and cancer SCs. Possible molecular mechanisms through which EDCs may influence the phenotype of SCs are also evaluated. Finally, the possible implications of these effects on human health are discussed. The available literature demonstrates that EDCs can influence the biology of SCs in a variety of ways, including by altering hormonal pathways, DNA damage, epigenetic changes, reactive oxygen species production and alterations in the gene expression patterns. These disruptions may lead to a variety of cell fates and diseases later in adulthood including increased risk of endocrine disorders, obesity, infertility, reproductive abnormalities, and cancer. Therefore, the review emphasizes the importance of raising broader awareness regarding the intricate impact of EDCs on human health.

Non-coding RNAs in acute ischemic stroke: from brain to periphery

Acute ischemic stroke is a clinical emergency and a condition with high morbidity, mortality, and disability. Accurate predictive, diagnostic, and prognostic biomarkers and effective therapeutic targets for acute ischemic stroke remain undetermined. With innovations in high-throughput gene sequencing analysis, many aberrantly expressed non-coding RNAs (ncRNAs) in the brain and peripheral blood after acute ischemic stroke have been found in clinical samples and experimental models. Differentially expressed ncRNAs in the post-stroke brain were demonstrated to play vital roles in pathological processes, leading to neuroprotection or deterioration, thus ncRNAs can serve as therapeutic targets in acute ischemic stroke. Moreover, distinctly expressed ncRNAs in the peripheral blood can be used as biomarkers for acute ischemic stroke prediction, diagnosis, and prognosis. In particular, ncRNAs in peripheral immune cells were recently shown to be involved in the peripheral and brain immune response after acute ischemic stroke. In this review, we consolidate the latest progress of research into the roles of ncRNAs (microRNAs, long ncRNAs, and circular RNAs) in the pathological processes of acute ischemic stroke-induced brain damage, as well as the potential of these ncRNAs to act as biomarkers for acute ischemic stroke prediction, diagnosis, and prognosis. Findings from this review will provide novel ideas for the clinical application of ncRNAs in acute ischemic stroke.

Exploiting fly models to investigate rare human neurological disorders

Rare neurological diseases, while individually are rare, collectively impact millions globally, leading to diverse and often severe neurological symptoms. Often attributed to genetic mutations that disrupt protein function or structure, understanding their genetic basis is crucial for accurate diagnosis and targeted therapies. To investigate the underlying pathogenesis of these conditions, researchers often use non-mammalian model organisms, such as Drosophila (fruit flies), which is valued for their genetic manipulability, cost-efficiency, and preservation of genes and biological functions across evolutionary time. Genetic tools available in Drosophila, including CRISPR-Cas9, offer a means to manipulate gene expression, allowing for a deep exploration of the genetic underpinnings of rare neurological diseases. Drosophila boasts a versatile genetic toolkit, rapid generation turnover, and ease of large-scale experimentation, making it an invaluable resource for identifying potential drug candidates. Researchers can expose flies carrying disease-associated mutations to various compounds, rapidly pinpointing promising therapeutic agents for further investigation in mammalian models and, ultimately, clinical trials. In this comprehensive review, we explore rare neurological diseases where fly research has significantly contributed to our understanding of their genetic basis, pathophysiology, and potential therapeutic implications. We discuss rare diseases associated with both neuron-expressed and glial-expressed genes. Specific cases include mutations in CDK19 resulting in epilepsy and developmental delay, mutations in TIAM1 leading to a neurodevelopmental disorder with seizures and language delay, and mutations in IRF2BPL causing seizures, a neurodevelopmental disorder with regression, loss of speech, and abnormal movements. And we explore mutations in EMC1 related to cerebellar atrophy, visual impairment, psychomotor retardation, and gain-of-function mutations in ACOX1 causing Mitchell syndrome. Loss-of-function mutations in ACOX1 result in ACOX1 deficiency, characterized by very-long-chain fatty acid accumulation and glial degeneration. Notably, this review highlights how modeling these diseases in Drosophila has provided valuable insights into their pathophysiology, offering a platform for the rapid identification of potential therapeutic interventions. Rare neurological diseases involve a wide range of expression systems, and sometimes common phenotypes can be found among different genes that cause abnormalities in neurons or glia. Furthermore, mutations within the same gene may result in varying functional outcomes, such as complete loss of function, partial loss of function, or gain-of-function mutations. The phenotypes observed in patients can differ significantly, underscoring the complexity of these conditions. In conclusion, Drosophila represents an indispensable and cost-effective tool for investigating rare neurological diseases. By facilitating the modeling of these conditions, Drosophila contributes to a deeper understanding of their genetic basis, pathophysiology, and potential therapies. This approach accelerates the discovery of promising drug candidates, ultimately benefiting patients affected by these complex and understudied diseases.

Physiology and transcriptome of Eucommia ulmoides seeds at different germination stages

E. ulmoides (Eucommia ulmoides) has significant industrial and medicinal value and high market demand. E. ulmoides grows seedlings through sowing. According to previous studies, plant hormones have been shown to regulate seed germination. To understand the relationship between hormones and E. ulmoides seed germination, we focused on examining the changes in various indicators during the germination stage of E. ulmoides seeds. We measured the levels of physiological and hormone indicators in E. ulmoides seeds at different germination stages and found that the levels of abscisic acid (ABA), gibberellin (GA), and indole acetic acid (IAA) significantly varied as the seeds germinated. Furthermore, we confirmed that ABA, GA, and IAA are essential hormones in the germination of E. ulmoides seeds using Gene Ontology and Kyoto Encyclopedia of Genes and Genomics enrichment analyses of the transcriptome. The discovery of hormone-related synthesis pathways in the control group of Eucommia seeds at different germination stages further confirmed this conclusion. This study provides a basis for further research into the regulatory mechanisms of E. ulmoides seeds at different germination stages and the relationship between other seed germination and plant hormones.

Epigenetic silencing ZSCAN23 promotes pancreatic cancer growth by activating Wnt signaling

Pancreatic ductal adenocarcinoma (PDAC) is the most malignant tumor. Zinc finger and SCAN domain-containing protein 23 (ZSCAN23) is a new member of the SCAN domain family. The expression regulation and biological function remain to be elucidated. In this study, we explored the epigenetic regulation and the function of ZSCAN23 in PDAC. ZSCAN23 was methylated in 60.21% (171/284) of PDAC and its expression was regulated by promoter region methylation. The expression of ZSCAN23 inhibited cell proliferation, colony formation, migration, invasion, and induced apoptosis and G1/S phase arrest. ZSCAN23 suppressed Panc10.05 cell xenograft growth in mice. Mechanistically, ZSCAN23 inhibited Wnt signaling by interacting with myosin heavy chain 9 (MYH9) in pancreatic cancer cells. ZSCAN23 is frequently methylated in PDAC and may serve as a detective marker. ZSCAN23 suppresses PDAC cell growth both in vitro and in vivo.

M6A modification regulates tumor suppressor DIRAS1 expression in cervical cancer cells

DIRAS family GTPase 1 (DIRAS1) has been reported as a potential tumor suppressor in other human cancer. However, its expression pattern and role in cervical cancer remain unknown. Knockdown of DIRAS1 significantly promoted the proliferation, growth, migration, and invasion of C33A and SiHa cells cultured in vitro. Overexpression of DIRAS1 significantly inhibited the viability and motility of C33A and SiHa cells. Compared with normal cervical tissues, DIRAS1 mRNA levels were significantly lower in cervical cancer tissues. DIRAS1 protein expression was also significantly reduced in cervical cancer tissues compared with para-cancerous tissues. In addition, DIRAS1 expression level in tumor tissues was significantly negatively correlated with the pathological grades of cervical cancer patients. DNA methylation inhibitor (5-Azacytidine) and histone deacetylation inhibitor (SAHA) resulted in a significant increase in DIRAS1 mRNA levels in C33A and SiHa cells, but did not affect DIRAS1 protein levels. FTO inhibitor (FB23-2) significantly down-regulated intracellular DIRAS1 mRNA levels, but significantly up-regulated DIRAS1 protein levels. Moreover, the down-regulation of METTL3 and METTL14 expression significantly inhibited DIRAS1 protein expression, whereas the down-regulation of FTO and ALKBH5 expression significantly increased DIRAS1 protein expression. In conclusion, DIRAS1 exerts a significant anti-oncogenic function and its expression is significantly downregulated in cervical cancer cells. The m6A modification may be a key mechanism to regulate DIRAS1 mRNA stability and protein translation efficiency in cervical cancer.

Risk-appropriate regulations for gene-editing technologies

This paper explores the scope for the newly emerging technologies, based on gene editing (GE) contributing to addressing the global challenges that we face. These challenges relate to food security, climate change and biodiversity depletion. In particular, it examines the science and evidence behind the most appropriate forms of agricultural production to meet these challenges, the targets set in the Global Biodiversity Framework (GBF) agreed to at the end of 2022 and the possible role of GE technologies in contributing to meeting these targets. It then examines the most risk-appropriate regulatory environment required to best facilitate the adoption of GE technology, drawing on the experiences of the impact of regulatory systems for other innovations used in agricultural and food production systems such as genetically modified organisms (GMOs).

The prognostic marker KRT81 is involved in suppressing CD8 + T cells and predicts immunotherapy response for triple-negative breast cancer

Triple-negative breast Cancer (TNBC) is an aggressive subtype lacking estrogen, progesterone, and HER2 receptors. Known for limited targeted therapies, it poses challenges and requires personalized treatment strategies. Differential analysis revealed a significant decrease in keratin 81 (KRT81) expression in non-TNBC samples and an increase in TNBC samples, lower KRT81 expression correlated with better TNBC patient outcomes. It emerged as an independent predictive factor for TNBC, with associations found between its expression and clinically relevant features. We further developed a nomogram for survival probability assessment based on Cox regression results, demonstrating its accuracy through calibration curves. Gene annotation analysis indicated that KRT81 is involved in immune-related pathways and tumor cell adhesion. KRT81 is associated with immune cell infiltration of Follicular helper T cells (Tfh) and CD8 + T cells, suggesting its potential impact on the immunological microenvironment. The study delved into KRT81's predictive value for immunotherapy responses, high expression of KRT81 was associated with greater potential for immune evasion. Single-cell RNA sequencing analysis pinpointed KRT81 expression within a specific malignant subtype which was a risk factor for TNBC. Furthermore, KRT81 promoted TNBC cell proliferation, migration, invasion, and adhesion was confirmed by gene knockout or overexpression assay. Co-culture experiments further indicated KRT81's potential role in inhibiting CD8 + T cells, and correlation analysis implied KRT81 was highly correlated with immune checkpoint CD276, providing insights into its involvement in the immune microenvironment via CD276. In conclusion, this comprehensive study positions KRT81 as a promising prognostic marker for predicting tumor progression and immunotherapy responses in TNBC.

The recent genetic modification techniques for improve soil conservation, nutrient uptake and utilization

Advances in genetic modification (GM) techniques have generated huge interest in improving nutrient utilization, maximizing nutrient uptake, and conserving soil in the pursuit of sustainable agriculture. Unfortunately, little is still known about the recent advancements in the application of GM tactics to enhance each of these areas. This review explores the latest GM strategies intended to support soil conservation, maximize nutrient uptake, and improve nutrient utilization in farming, highlighting the critical roles that soil health and nutrient management play in sustainable farming. GM strategies such as improving the efficiency of nutrient uptake through enhanced root systems and increased nutrient transport mechanisms are well discussed. This study suggests that addressing potential obstacles, such as ethical and regulatory concerns, is a necessity for long-term sustainability applications of GM technologies to raise agricultural yields.

Design and evaluation of mRNA encoding recombinant neutralizing antibodies for botulinum neurotoxin type B intoxication prophylaxis

Among all natural and synthetic toxins, botulinum neurotoxins (BoNTs), produced by Clostridium botulinum in an anaerobic environment, are the most toxic polymer proteins. Currently, the most effective modalities for botulism prevention and treatment are vaccination and antitoxin use, respectively. However, these modalities are associated with long response time for active immunization, side effects, and donor limitations. As such, the development of more promising botulism prevention and treatment modalities is warranted. Here, we designed an mRNA encoding B9-hFc - a heavy-chain antibody fused to VHH and human Fc that can neutralize BoNT serotype B (BoNT/B) effectively - and assessed its expression in vitro and in vivo. The results confirmed that our mRNA demonstrates good expression in vitro and in vivo. Moreover, a single mRNA lipid nanoparticle injection effectively prevents BoNT/B intoxication in vivo, with effects comparable to those of protein antibodies. In conclusion, we explored and clarified whether mRNA drugs encoding neutralizing antibodies prevent BoNT/B intoxication. Our results provide an efficient strategy for further research on the prevention and treatment of intoxication by botulinum toxin.

Biobanks and biomarkers: Their current and future role in biomedical research

The importance and utility of biobanks has increased exponentially since their inception and creation. Initially used as part of translational research, they now contribute over 40% of data for all cancer research papers in the United States of America and play a crucial role in all aspects of healthcare. Multiple classification systems exist but a simplified approach is to either classify as population-based or disease-oriented entities. Whilst historically publicly funded institutions, there has been a significant increase in industry funded entities across the world which has changed the dynamic of biobanks offering new possibilities but also new challenges. Biobanks face legal questions over data sharing and intellectual property as well as ethical and sustainability questions particularly as the world attempts to move to a low-carbon economy. International collaboration is required to address some of these challenges but this in itself is fraught with complexity and difficulty. This review will examine the current utility of biobanks in the modern healthcare setting as well as the current and future challenges these vital institutions face.

Discovery and biochemical characterization of thermostable glycerol oxidases

Alditol oxidases are promising tools for the biocatalytic oxidation of glycerol to more valuable chemicals. By integrating in silico bioprospecting with cell-free protein synthesis and activity screening, an effective pipeline was developed to rapidly identify enzymes that are active on glycerol. Three thermostable alditol oxidases from Actinobacteria Bacterium, Streptomyces thermoviolaceus, and Thermostaphylospora chromogena active on glycerol were discovered. The characterization of these three flavoenzymes demonstrated their glycerol oxidation activities, preference for alkaline conditions, and excellent thermostabilities with melting temperatures higher than 75 °C. Structural elucidation of the alditol oxidase from Actinobacteria Bacterium highlighted a constellation of side chains that engage the substrate through several hydrogen bonds, a histidine residue covalently bound to the FAD prosthetic group, and a tunnel leading to the active site. Upon computational simulations of substrate binding, a double mutant targeting a residue pair at the tunnel entrance was created and found to display an improved thermal stability and catalytic efficiency for glycerol oxidation. The hereby described alditol oxidases form a valuable panel of oxidative biocatalysts that can perform regioselective oxidation of glycerol and other polyols. KEY POINTS: • Rapid pipeline designed to identify putative oxidases • Biochemical and structural characterization of alditol oxidases • Glycerol oxidation to more valuable derivatives.

Heterochromatin in plant meiosis

Heterochromatin is an organizational property of eukaryotic chromosomes, characterized by extensive DNA and histone modifications, that is associated with the silencing of transposable elements and repetitive sequences. Maintaining heterochromatin is crucial for ensuring genomic integrity and stability during the cell cycle. During meiosis, heterochromatin is important for homologous chromosome synapsis, recombination, and segregation, but our understanding of meiotic heterochromatin formation and condensation is limited. In this review, we focus on the dynamics and features of heterochromatin and how it condenses during meiosis in plants. We also discuss how meiotic heterochromatin influences the interaction and recombination of homologous chromosomes during prophase I.

Differential gene expression analysis pipelines and bioinformatic tools for the identification of specific biomarkers: A review

In recent years, the role of bioinformatics and computational biology together with omics techniques and transcriptomics has gained tremendous importance in biomedicine and healthcare, particularly for the identification of biomarkers for precision medicine and drug discovery. Differential gene expression (DGE) analysis is one of the most used techniques for RNA-sequencing (RNA-seq) data analysis. This tool, which is typically used in various RNA-seq data processing applications, allows the identification of differentially expressed genes across two or more sample sets. Functional enrichment analyses can then be performed to annotate and contextualize the resulting gene lists. These studies provide valuable information about disease-causing biological processes and can help in identifying molecular targets for novel therapies. This review focuses on differential gene expression (DGE) analysis pipelines and bioinformatic techniques commonly used to identify specific biomarkers and discuss the advantages and disadvantages of these techniques.

Posttranscriptional Regulation of Intestinal Mucosal Growth and Adaptation by Noncoding RNAs in Critical Surgical Disorders

The human intestinal epithelium has an impressive ability to respond to insults and its homeostasis is maintained by well-regulated mechanisms under various pathophysiological conditions. Nonetheless, acute injury and inhibited regeneration of the intestinal epithelium occur commonly in critically ill surgical patients, leading to the translocation of luminal toxic substances and bacteria to the bloodstream. Effective therapies for the preservation of intestinal epithelial integrity and for the prevention of mucosal hemorrhage and gut barrier dysfunction are limited, primarily because of a poor understanding of the mechanisms underlying mucosal disruption. Noncoding RNAs (ncRNAs), which include microRNAs (miRNAs), long ncRNAs (lncRNAs), circular RNAs (circRNAs), and small vault RNAs (vtRNAs), modulate a wide array of biological functions and have been identified as orchestrators of intestinal epithelial homeostasis. Here, we feature the roles of many important ncRNAs in controlling intestinal mucosal growth, barrier function, and repair after injury-particularly in the context of postoperative recovery from bowel surgery. We review recent literature surrounding the relationships between lncRNAs, microRNAs, and RNA-binding proteins and how their interactions impact cell survival, proliferation, migration, and cell-to-cell interactions in the intestinal epithelium. With advancing knowledge of ncRNA biology and growing recognition of the importance of ncRNAs in maintaining the intestinal epithelial integrity, ncRNAs provide novel therapeutic targets for treatments to preserve the gut epithelium in individuals suffering from critical surgical disorders.

A survey of k-mer methods and applications in bioinformatics

The rapid progression of genomics and proteomics has been driven by the advent of advanced sequencing technologies, large, diverse, and readily available omics datasets, and the evolution of computational data processing capabilities. The vast amount of data generated by these advancements necessitates efficient algorithms to extract meaningful information. K-mers serve as a valuable tool when working with large sequencing datasets, offering several advantages in computational speed and memory efficiency and carrying the potential for intrinsic biological functionality. This review provides an overview of the methods, applications, and significance of k-mers in genomic and proteomic data analyses, as well as the utility of absent sequences, including nullomers and nullpeptides, in disease detection, vaccine development, therapeutics, and forensic science. Therefore, the review highlights the pivotal role of k-mers in addressing current genomic and proteomic problems and underscores their potential for future breakthroughs in research.

Observational methods for human studies of transgenerational effects

There are substantial challenges in studying human transgenerational epigenetic outcomes resulting from environmental conditions. The task requires specialized methods and tools that incorporate specific knowledge of multigenerational relationship combinations of probands and their ancestors, phenotype data for individuals, environmental information of ancestors and their descendants, which can span historical to present datasets, and informative environmental data that chronologically aligns with ancestors and descendants over space and time. As a result, there are few epidemiologic studies of potential transgenerational effects in human populations, thus limiting the knowledge of ancestral environmental conditions and the potential impacts we face with modern human health outcomes. In an effort to overcome some of the challenges in studying human transgenerational effects, we present two transgenerational study designs: transgenerational space-time cluster detection and transgenerational case-control study design. Like other epidemiological methods, these methods determine whether there are statistical associations between phenotypic outcomes (e.g., adverse health outcomes) among probands and the shared environments and environmental factors facing their ancestors. When the ancestor is a paternal grandparent, a statistically significant association provides some evidence that a transgenerational inheritable factor may be involved. Such results may generate useful hypotheses that can be explored using epigenomic data to establish conclusive evidence of transgenerational heritable effects. Both methods are proband-centric: They are designed around the phenotype of interest in the proband generation for case selection and family pedigree creation. In the examples provided, we incorporate at least three generations of paternal lineage in both methods to observe a potential transgenerational effect.

Regulation of STAT5 phosphorylation and interaction with SHP1 by lnc-AC004893, a long non-coding RNA overexpressed in myeloproliferative neoplasms

OBJECTIVES

Constitutive activation of Janus kinase 2 (JAK2)/signal transducer and activator of transcription (STAT) signaling pathway is central to the pathogenesis of myeloproliferative neoplasms (MPNs). Long noncoding RNAs (lncRNAs) regulate diverse biological processes. However, the role of lncRNAs in MPN pathogenesis is not well studied.

METHODS

The expression of lnc-AC004893 in MPN patients was measured by quantitative real-time PCR (qRT-PCR). Gene-specific short hairpin RNAs (shRNAs) were designed to inhibit the expression of lnc-AC004893, and western blot was performed to explore the role of lnc-AC004893 via regulating the JAK2/STAT5 signaling pathway. Furthermore, co-IP was performed to determine the binding ability of lnc-AC004893 and STAT5 protein. Finally, the BaF3-JAK2V617F-transplanted mouse model was used to assess the biological role of lnc-ac004893 in vivo.

RESULTS

We report that lnc-AC004893, a poorly conserved pseudogene-209, is substantially upregulated in MPN cells compared with normal controls (NCs). Knockdown of lnc-AC004893 by specific shRNAs suppressed cell proliferation and decreased colony formation. Furthermore, the knockdown of lnc-AC004893 reduced the expression of p-STAT5 but not total STAT5 in HEL and murine IL-3-dependent Ba/F3 cells, which present constitutive and inducible activation of JAK2/STAT5 signaling. In addition, inhibition of murine lnc-ac004893 attenuated BaF3-JAK2V617F-transplanted phenotypes and extended the overall survival. Mechanistically, knockdown of lnc-AC004893 enhanced the binding ability of STAT5 and protein tyrosine phosphatase SHP1. Furthermore, knockdown of lnc-AC004893 decreased STAT5-lnc-AC004893 interaction but not SHP1-lnc-AC004893 interaction.

CONCLUSION

Lnc-AC004893 regulates STAT5 phosphorylation by affecting the interaction of STAT5 and SHP1. Lnc-AC004893 might be a potential therapeutic target for MPN patients.

Whole slide images as non-fungible tokens: A decentralized approach to secure, scalable data storage and access

Background

Distributed ledger technology (DLT) enables the creation of tamper-resistant, decentralized, and secure digital ledgers. A non-fungible token (NFT) represents a record on-chain associated with a digital or physical asset, such as a whole-slide image (WSI). The InterPlanetary File System (IPFS) represents an off-chain network, hypermedia, and file sharing peer-to-peer protocol for storing and sharing data in a distributed file system. Today, we need cheaper, more efficient, highly scalable, and transparent solutions for WSI data storage and access of medical records and medical imaging data.

Methods

WSIs were created from non-human tissues and H&E-stained sections were scanned on a Philips Ultrafast WSI scanner at 40× magnification objective lens (1 μm/pixel). TIFF images were stored on IPFS, while NFTs were minted on the Ethereum blockchain network in ERC-1155 standard. WSI-NFTs were stored on MetaMask and OpenSea was used to display the WSI-NFT collection. Filebase storage application programing interface (API) were used to create dedicated gateways and content delivery networks (CDN).

Results

A total of 10 WSI-NFTs were minted on the Ethereum blockchain network, found on our collection "Whole Slide Images as Non-fungible Tokens Project" on Open Sea: https://opensea.io/collection/untitled-collection-126765644. WSI TIFF files ranged in size from 1.6 to 2.2 GB and were stored on IPFS and pinned on 3 separate nodes. Under optimal conditions, and using a dedicated CDN, WSI reached retrieved at speeds of over 10 mb/s, however, download speeds and WSI retrieval times varied significantly depending on the file and gateway used. Overall, the public IPFS gateway resulted in variably poorer WSI download retrieval performance compared to gateways provided by Filebase storage API.

Conclusion

Whole-slide images, as the most complex and substantial data files in healthcare, demand innovative solutions. In this technical report, we identify pitfalls in IPFS, and demonstrate proof-of-concept using a 3-layer architecture for scalable, decentralized storage, and access. Optimized through dedicated gateways and CDNs, which can be effectively applied to all medical data and imaging modalities across the healthcare sector. DLT and off-chain network solutions present numerous opportunities for advancements in clinical care, education, and research. Such approaches uphold the principles of equitable healthcare data ownership, security, and democratization, and are poised to drive significant innovation.

Upregulation of GPR133 expression impaired the phagocytosis of macrophages in recurrent spontaneous miscarriage

Decidual macrophages are the second-largest immune cell group at the maternal-foetal interface. They participate in apoptotic cell removal, and protect the foetus from microorganisms or pathogens. Dysfunction of decidual macrophages gives rise to pregnancy complications such as preeclampsia and recurrent spontaneous miscarriage (RSM). However, the mechanisms by which decidual macrophages are involved in the occurrence of adverse pregnancy outcomes have not been elucidated. Here we integrated DNA methylation and gene expression data from decidua macrophages to identify potential risk factors related to RSM. GPR133 was significantly hypomethylated and upregulated in decidual macrophages from RSM patients. Further demethylation analysis demonstrated that GPR133 expression in decidual macrophages was significantly increased by 5-Aza-dC treatment. In addition, the influence of GPR133 on the phagocytic ability of macrophages was explored. Phagocytosis was impaired in the decidual macrophages of RSM patients with increased GPR133 expression. Increased GPR133 expression induced by demethylation treatment in the decidual macrophages of healthy control patients led to a significant decrease in phagocytic function. Importantly, knockdown of GPR133 resulted in a significant improvement in the phagocytic function of THP-1 macrophages. In conclusion, the existing studies have shown the influence of GPR133 on the phagocytic function of decidual macrophages and pregnancy outcomes, providing new data and ideas for future research on the role of decidual macrophages in RSM.

RBProkCNN: Deep learning on appropriate contextual evolutionary information for RNA binding protein discovery in prokaryotes

RNA-binding proteins (RBPs) are central to key functions such as post-transcriptional regulation, mRNA stability, and adaptation to varied environmental conditions in prokaryotes. While the majority of research has concentrated on eukaryotic RBPs, recent developments underscore the crucial involvement of prokaryotic RBPs. Although computational methods have emerged in recent years to identify RBPs, they have fallen short in accurately identifying prokaryotic RBPs due to their generic nature. To bridge this gap, we introduce RBProkCNN, a novel machine learning-driven computational model meticulously designed for the accurate prediction of prokaryotic RBPs. The prediction process involves the utilization of eight shallow learning algorithms and four deep learning models, incorporating PSSM-based evolutionary features. By leveraging a convolutional neural network (CNN) and evolutionarily significant features selected through extreme gradient boosting variable importance measure, RBProkCNN achieved the highest accuracy in five-fold cross-validation, yielding 98.04% auROC and 98.19% auPRC. Furthermore, RBProkCNN demonstrated robust performance with an independent dataset, showcasing a commendable 95.77% auROC and 95.78% auPRC. Noteworthy is its superior predictive accuracy when compared to several state-of-the-art existing models. RBProkCNN is available as an online prediction tool (https://iasri-sg.icar.gov.in/rbprokcnn/), offering free access to interested users. This tool represents a substantial contribution, enriching the array of resources available for the accurate and efficient prediction of prokaryotic RBPs.

Single-cell multi-omics in the study of digestive system cancers

Digestive system cancers are prevalent diseases with a high mortality rate, posing a significant threat to public health and economic burden. The diagnosis and treatment of digestive system cancer confront conventional cancer problems, such as tumor heterogeneity and drug resistance. Single-cell sequencing (SCS) emerged at times required and has developed from single-cell RNA-seq (scRNA-seq) to the single-cell multi-omics era represented by single-cell spatial transcriptomics (ST). This article comprehensively reviews the advances of single-cell omics technology in the study of digestive system tumors. While analyzing and summarizing the research cases, vital details on the sequencing platform, sample information, sampling method, and key findings are provided. Meanwhile, we summarize the commonly used SCS platforms and their features, as well as the advantages of multi-omics technologies in combination. Finally, the development trends and prospects of the application of single-cell multi-omics technology in digestive system cancer research are prospected.

Methods in DNA methylation array dataset analysis: A review

Understanding the intricate relationships between gene expression levels and epigenetic modifications in a genome is crucial to comprehending the pathogenic mechanisms of many diseases. With the advancement of DNA Methylome Profiling techniques, the emphasis on identifying Differentially Methylated Regions (DMRs/DMGs) has become crucial for biomarker discovery, offering new insights into the etiology of illnesses. This review surveys the current state of computational tools/algorithms for the analysis of microarray-based DNA methylation profiling datasets, focusing on key concepts underlying the diagnostic/prognostic CpG site extraction. It addresses methodological frameworks, algorithms, and pipelines employed by various authors, serving as a roadmap to address challenges and understand changing trends in the methodologies for analyzing array-based DNA methylation profiling datasets derived from diseased genomes. Additionally, it highlights the importance of integrating gene expression and methylation datasets for accurate biomarker identification, explores prognostic prediction models, and discusses molecular subtyping for disease classification. The review also emphasizes the contributions of machine learning, neural networks, and data mining to enhance diagnostic workflow development, thereby improving accuracy, precision, and robustness.

Evaluation of miRNA-146a, miRNA-34a, and pro-inflammatory cytokines as a potential early indicators for type 1 diabetes mellitus

Background

Type I diabetes mellitus (T1DM) is one of the most common chronic autoimmune diseases worldwide. miRNAs are a class of small non-coding RNA molecules that have been linked to immune system functions, β-cell metabolism, proliferation, and death, all of which contribute to pathogenesis of TIDM. Dysregulated miRNAs have been identified in Egyptian TIDM patients.

Aim

Several miRNAs were profiled in Egyptian TIDM patients to determine whether they can be used as molecular biomarkers for T1DM. The relationship between the investigated miRNAs and pro-inflammatory cytokines (TNF-α and IL-6) has also been evaluated in the development of TIDM, in addition to the creation of a proposed model for TIDM prediction.

Patients & methods

Case-control study included 177 Egyptian patients with confirmed type I diabetes mellitus and 177 healthy individuals. MiRNA-34 and miRNA-146 were detected in serum samples using real-time PCR, whereas TNF-α and IL-6 levels were assessed using ELIZA.

Results

Patients with TIDM showed a significant decrease in the expression of miRNA-146, with a cut-off value ≤ 3.3, 48 % specificity, and 92.1 % sensitivity, whereas miRNA-34 had the highest sensitivity (95.5 %) and specificity (97.2 %) for differentiating diabetic patients from controls. Furthermore, other diagnostic proinflammatory markers showed lower sensitivity and specificity.

Conclusion

Serum levels of miRNA-34a, miRNA-146, IL-6, and TNF-α provide new insights into T1DM pathogenesis and could be used for screening and diagnosis purposes. They can be also a potential therapeutic target, as well as allowing for more strategies to improve T1DM disease outcomes.

The perinucleolar compartment: structure, function, and utility in anti-cancer drug development

The perinucleolar compartment (PNC) was initially identified as a nuclear structure enriched for the polypyrimidine tract-binding protein. Since then, the PNC has been implicated in carcinogenesis. The prevalence of this compartment is positively correlated with disease progression in various types of cancer, and its expression in primary tumors is linked to worse patient outcomes. Using the PNC as a surrogate marker for anti-cancer drug efficacy has led to the development of a clinical candidate for anti-metastasis therapies. The PNC is a multicomponent nuclear body situated at the periphery of the nucleolus. Thus far, several non-coding RNAs and RNA-binding proteins have been identified as the PNC components. Here, we summarize the current understanding of the structure and function of the PNC, as well as its recurrent links to cancer progression and metastasis.

Moura A, Filho ODMM, Pessoa C, Miranda Furtado CL. Hypomethylation at H19DMR in penile squamous cell carcinoma is not related to HPV infection

Penile squamous cell carcinoma (SCC) is a rare and aggressive tumour mainly related to lifestyle behaviour and human papillomavirus (HPV) infection. Environmentally induced loss of imprinting (LOI) at the H19 differentially methylated region (H19DMR) is associated with many cancers in the early events of tumorigenesis and may be involved in the pathogenesis of penile SCC. We sought to evaluate the DNA methylation pattern at H19DMR and its association with HPV infection in men with penile SCC by bisulfite sequencing (bis-seq). We observed an average methylation of 32.2% ± 11.6% at the H19DMR of penile SCC and did not observe an association between the p16INK4a+ (p = 0.59) and high-risk HPV+ (p = 0.338) markers with methylation level. The average methylation did not change according to HPV positive for p16INK4a+ or hrHPV+ (35.4% ± 10%) and negative for both markers (32.4% ± 10.1%) groups. As the region analysed has a binding site for the CTCF protein, the hypomethylation at the surrounding CpG sites might alter its insulator function. In addition, there was a positive correlation between intense polymorphonuclear cell infiltration and hypomethylation at H19DMR (p = 0.035). Here, we report that hypomethylation at H19DMR in penile SCC might contribute to tumour progression and aggressiveness regardless of HPV infection.

Epigenetic changes in pyloric caeca of Atlantic salmon fed diets containing increasing levels of lipids and choline

An earlier study of ours investigating the effect of dietary lipid levels on the choline requirement of Atlantic salmon showed increasing severity of intestinal steatosis with increasing lipid levels. As choline is involved in epigenetic regulation by being the key methyl donor, pyloric caeca samples from the study were analysed for epigenetic effects of dietary lipid and choline levels. The diets varied in lipid levels between 16% and 28%, and choline levels between 1.9 and 2.3 g/kg. The diets were fed for 8 weeks to Atlantic salmon of 25 g of initial weight. Using reduced representation bisulfite sequencing (RRBS), this study revealed that increasing dietary lipid levels induced methylation differences in genes involved in membrane transport and signalling pathways, and in microRNAs important for the regulation of lipid homoeostasis. Increasing choline levels also affected genes involved in fatty acid biosynthesis and transport, lipolysis, and lipogenesis, as well as important immune genes. Our observations confirmed that choline is involved in epigenetic regulation in Atlantic salmon, as has been reported for higher vertebrates. This study showed the need for the inclusion of biomarkers of epigenetic processes in studies that must be conducted to define optimal choline levels in diets for Atlantic salmon.

Mdpg: a novel multi-disease diagnosis prediction method based on patient knowledge graphs

Diagnosis prediction, a key factor in enhancing healthcare efficiency, remains a focal point in clinical decision support research. However, the time-series, sparse and multi-noise characteristics of electronic health record (EHR) data make it a great challenge. Existing methods commonly address these issues using RNNs and incorporating medical prior knowledge from medical knowledge bases, but they neglect the local spatial characteristics and spatial-temporal correlation of the data. Consequently, we propose MDPG, a diagnosis prediction model based on patient knowledge graphs. Initially, we represent the electronic visit records of patients as a patient-centered temporal knowledge graph, capturing the local spatial structure and temporal characteristics of the visit information. Subsequently, we design the spatial graph convolution block, temporal self-attention block, and spatial-temporal synchronous graph convolution block to capture the spatial, temporal, and spatial-temporal correlations embedded in them, respectively. Ultimately, we accomplish the prediction of patients' future states through multi-label classification. We conduct comprehensive experiments on two real-world datasets independently and evaluate the results using visit-level precision@k and code-level accuracy@k metrics. The experimental results demonstrate that MDPG outperforms all baseline models, yielding the best performance.

Genetic variant classification by predicted protein structure: A case study on IRF6

Next-generation genome sequencing has revolutionized genetic testing, identifying numerous rare disease-associated gene variants. However, to impute pathogenicity, computational approaches remain inadequate and functional testing of gene variant is required to provide the highest level of evidence. The emergence of AlphaFold2 has transformed the field of protein structure determination, and here we outline a strategy that leverages predicted protein structure to enhance genetic variant classification. We used the gene IRF6 as a case study due to its clinical relevance, its critical role in cleft lip/palate malformation, and the availability of experimental data on the pathogenicity of IRF6 gene variants through phenotype rescue experiments in irf6-/- zebrafish. We compared results from over 30 pathogenicity prediction tools on 37 IRF6 missense variants. IRF6 lacks an experimentally derived structure, so we used predicted structures to explore associations between mutational clustering and pathogenicity. We found that among these variants, 19 of 37 were unanimously predicted as deleterious by computational tools. Comparing in silico predictions with experimental findings, 12 variants predicted as pathogenic were experimentally determined as benign. Even with the recently published AlphaMissense model, 15/18 (83%) of the predicted pathogenic variants were experimentally determined as benign. In comparison, mapping variants to the protein revealed deleterious mutation clusters around the protein binding domain, whereas N-terminal variants tend to be benign, suggesting the importance of structural information in determining pathogenicity of mutations in this gene. In conclusion, incorporating gene-specific structural features of known pathogenic/benign mutations may provide meaningful insights into pathogenicity predictions in a gene-specific manner and facilitate the interpretation of variant pathogenicity.

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.

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.

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.

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.

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.

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.