This study aimed to explore the common differentially expressed genes (DEGs) between atherosclerosis (AS) and nonalcoholic fatty liver disease (NAFLD) through bioinformatics. The GSE89632 and GSE100927 datasets from the open-source GEO database were selected for analysis in this study. DEGs between the control and disease groups were identified from the datasets of NAFLD and AS, leading to the identification of genes that are commonly dysregulated in both conditions. Gene set enrichment analysis (GSEA) was performed on 2 datasets, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses to investigate the potential biological functions and signaling pathways associated with the common DEGs. A protein-protein interaction network was constructed using the STRING database to identify hub genes. The diagnostic efficacy of these hub genes was evaluated through receiver operating characteristic curve analysis of the GSE63067 and GSE57691 datasets. A total of 45 common DEGs were identified. GSEA indicated that there were common pathways in the GSE89632 and GSE100927 datasets. The GO and KEGG pathway enrichment analyses revealed that the co-expressed genes were mainly enriched in regulating cytokine production, increasing responsiveness to external stimuli, and macrophage activation. The identified signaling pathways primarily included cytokine-cytokine receptor interaction, the toll-like receptor signaling pathway, and the interleukin-17 signaling pathway. Ten hub genes were OSM, IL1B, CCL3, CSF3, IL1RN, TAGLN, CNN1, RGCC, MYH11, and ACTG2. Common DEGs were identified between AS and NAFLD, indicating that these diseases may mutually influence and exacerbate each other during their progression. Exploring their shared pathogenic mechanisms may provide insights into potential therapeutic targets and contribute to the prevention of AS development in patients with NAFLD.

A growing number of micro-peptides (miPs) have been identified in recent years, but their biological roles remain largely unexplored. We identified a conserved 6 kDa miP, named SMIM30, as a potential metabolic regulator. To study the physiological function of Smim30, we generated a loss-of-function mouse strain using the CRISPR/Cas9-mediated knock-in strategy. When fed both normal chow and high-fat diets, these mice exhibited elevated blood glucose and insulin levels, with reduced insulin sensitivity. We further showed that Smim30 loss in adipose tissue drives systemic insulin resistance, though intriguingly, adipocyte-expressed Smim30 is dispensable to this effect. Instead, Smim30 is mainly expressed in adipose tissue-residential macrophages, and loss of Smim30 led to increased macrophage infiltration and production of pro-inflammatory cytokines and chemokines. Smim30 also modulated inflammatory responses in ex vivo/in vitro macrophage systems, which is conserved in both human and mouse. Collectively, Smim30 plays a key role in maintaining adipose tissue insulin sensitivity and safeguarding systemic metabolic homeostasis, offering potential as both a diagnostic biomarker and therapeutic target for metabolic disorders.

Growing evidence links human long noncoding RNAs (lncRNAs) to metabolic disease pathogenesis, yet no FDA-approved drugs target human lncRNAs. Most human lncRNAs lack conservation in other mammals, complicating efforts to define their roles and identify therapeutic targets. Here, we leveraged the concept of functionally conserved lncRNAs (FCLs) - lncRNAs that share function despite no sequence similarity - to develop a framework for identifying human lncRNAs as therapeutic targets for metabolic disorders. We used expression quantitative trait loci mapping and functional conservation analyses to pinpoint human lncRNAs influenced by disease-associated SNPs and with potential functionally conserved mouse equivalents. We identified human and mouse GULLs (glucose and lipid lowering), which regulate glucose and lipid metabolism by binding CRTC2, thereby modulating gluconeogenic genes via CREB and lipogenic genes via SREBP1. Despite their lack of sequence similarity, both lncRNAs demonstrated similar metabolic effects in obese mice, with more pronounced benefits from long-term activation. To identify druggable sites, we mapped GULLs' binding motifs to CRTC2 (termed GULFs). Standalone human GULF, an RNA oligomer resembling FDA-approved siRNAs, significantly improved glucose and lipid levels in obese mice. This framework highlights functionally conserved human lncRNAs as promising therapeutic targets, exemplified by GULLs' potential as a glucose- and lipid-lowering therapeutic.

Chemotherapy-induced neuropathic pain (CINP) is a serious adverse effect of commonly used chemotherapeutics. Neurosteroid allopregnanolone is suggested to modulate the expression of various receptors or enzymes that involved in pain perception, presenting an analgesic potential. Here, we investigated if allopregnanolone attenuates extracellular signal-regulated kinase (ERK) and its downstream prostaglandin E2 (PGE2) expression in the dorsal spinal cord concomitant with neuropathic pain relief in paclitaxel (PTX)-induced neuropathic pain model rats. The results showed PTX upregulated phosphorylated ERK (p-ERK), PGE2 level, and PGE2 receptor E-prostanoid 2 (EP2) expression in the spinal dorsal horn. Besides, p-ERK inhibitor PD98059 or microglia inhibitor minocycline reduced microglial activation, p-ERK expression, PGE2 release, EP2 expression, and partially alleviated PTX-induced mechanical hypersensitivity. Further, allopregnanolone level in the dorsal spinal cord was observed to decrease in CINP rats, and intragastric administration of exogenous allopregnanolone dose-dependently alleviated PTX-induced mechanical hypersensitivity. Mechanistically, allopregnanolone dose-dependently alleviated PTX-induced microglial activation, p-ERK, PGE2, and EP2 upregulation, as well as cytokines expression in the dorsal spinal cord in CINP rats. Furthermore, subcutaneous injection of allopregnanolone synthesis inhibitor medroxyprogesterone could reduce endogenous allopregnanolone and block all effects of exogenous allopregnanolone in CINP rats. Taken together, these results suggest allopregnanolone presents an analgesic effect for PTX-induced mechanical hypersensitivity, partially via inhibiting the dorsal spinal cord PGE2-EP2 mediated microglia-neuron signaling.

Background & Aims The human liver transcriptome is complex and highly dynamic, e.g. one gene may produce multiple distinct transcripts, each with distinct posttranscriptional modifications. Direct knowledge of transcriptome dynamics, however, is largely obscured by the inaccessibility of the human liver to treatments and the insufficient annotation of the human liver transcriptome at transcript and RNA modification levels. Methods We generated mice that carry humanized livers of identical genetic background and subjected them to representative metabolic treatments. We then analyzed the humanized livers with nanopore single-molecule direct RNA sequencing to determine the expression level, m6A modification and poly(A) tail length of all RNA transcript isoforms. Our system allows for the de novo annotation of human liver transcriptomes to reflect metabolic responses and for the study of transcriptome dynamics in parallel. Results Our analysis uncovered a vast number of novel genes and transcripts. Our transcript-level analysis of human liver transcriptomes also identified a multitude of regulated metabolic pathways that were otherwise invisible using conventional short-read RNA sequencing. We revealed for the first time the dynamic changes in m6A and poly(A) tail length of human liver transcripts, many of which are transcribed from key metabolic genes. Furthermore, we performed comparative analyses of gene regulation between humans and mice, and between two individuals using the liver-specific humanized mice, revealing that transcriptome dynamics are highly species- and genetic background-dependent. Conclusion Our work revealed a complex metabolic response landscape of the human liver transcriptome and provides a novel resource to understand transcriptome dynamics of the human liver in response to physiologically relevant metabolic stimuli (https://caolab.shinyapps.io/human_hepatocyte_landscape/). Impact and implications Direct knowledge of the human liver transcriptome is currently very limited, hindering the overall understanding of human liver pathophysiology. We combined a liver-specific humanized mouse model and long-read direct RNA sequencing technology to establish a de novo annotation of the human liver transcriptome and identified a multitude of regulated metabolic pathways that were otherwise invisible using conventional technologies. The extensive regulatory information on human genes we provided could enable basic scientists to infer the pathological relevance of their genes of interest and physician scientists to better pinpoint the changes in metabolic networks underlying a specific pathophysiology.

Understanding tissue-specific RNA landscapes is essential for uncovering the functional mechanisms of key organs in mammals. However, current knowledge remains limited, as short-read RNA sequencing-the predominant method for assessing gene expression-depends on incomplete gene annotations and struggles to resolve the diverse transcripts produced by genes. To address these limitations, an integrative approach combining nanopore direct RNA sequencing (DRS), ATAC-Seq, and short-read RNA-seq is used. This method enabled the analysis of RNA landscapes across major mouse organs under fasting and fed conditions, representing two extremes of the caloric cycle. This study uncovered tens of thousands of novel transcripts and identified hundreds of genes with tissue-specific expression, revealing additional layers of regulated pathways within each organ that conventional short-read RNA-seq cannot resolve. By profiling transcript expression across multiple organs under identical conditions, it is conducted comparative analyses exposing significant differences in transcript isoforms and regulations. Moreover, nanopore DRS revealed dynamic changes in poly(A) tail length and m6A modifications of transcripts, many regulated in a tissue-specific manner. These changes likely contribute to functional differentiation and metabolic specialization of various organs. Collectively, this findings reveal previously unrecognized layers of gene regulation, offering new insights into the metabolic basis of organ function.

Direct knowledge of gene regulation in human liver by metabolic stimuli could fundamentally advance our understanding of metabolic physiology. This information, however, is largely unknown, and this void is deeply rooted in a paradox that is caused by the inaccessibility of human liver to treatments and the insufficient annotation of liver transcriptome. Recent advances have uncovered immense complexity of transcriptome, i.e., multiple transcripts produced from one gene and extensive RNA modifications, which are all highly regulated and often condition-dependent. Establishing an inclusive annotation to study liver transcriptome dynamics thus requires human liver samples of diverse conditions, which are paradoxically unavailable due to the inaccessibility of human liver to treatments. In this work, we addressed these challenges by coupling an isogenic humanized mouse model with Nanopore single-molecule direct RNA sequencing (DRS). We first generated mice that carried humanized livers of identical genetic background, which were equivalent to clones of a single human liver, and then subjected the mice to representative metabolic treatments. We then analyzed the humanized livers with Nanopore DRS, which directly reads full-length native RNAs to determine the expression level, m6A modification and poly(A) tail length of all RNA transcript isoforms. Thus, our system allows for constructing a de novo annotation of human liver transcriptomes reflecting metabolic responses and studying transcriptome dynamics in conjunction. Our analysis uncovered a vast number of novel genes and transcripts that have not been previously reported. Our transcript-level analysis of human liver transcriptomes also identified a multitude of regulated metabolic pathways that were otherwise invisible using conventional short read RNA-seq. We also revealed for the first time the dynamic changes in m6A and poly(A) tail length of human liver transcripts many of which are transcribed from key metabolic genes. Furthermore, we performed comparative analyses of gene regulation between human and mouse and between two individuals using the liver-specific humanized mice. This revealed that transcriptome dynamics are highly species- and genetic background-dependent, which may only be faithfully studied in a humanized system that entails clones of the same human liver. Hence our work revealed a complex metabolic responsive landscape of human liver transcriptome and also provided a framework to understand transcriptome dynamics of human liver in response to physiologically relevant metabolic stimuli.

Background: It remains unclear whether transfer RNA-derived small RNAs (tsRNAs) play a role in pathological cardiac hypertrophy (PCH). We aimed to clarify the expression profile of tsRNAs and disclose their relationship with the clinical phenotype of PCH and the putative role. Methods: Small RNA sequencing was performed on the plasma of PCH patients and healthy volunteers. In the larger sample size and angiotensin II (Ang II)-stimulated H9c2 cells, the data were validated by real-time qPCR. Atrial natriuretic peptide (ANP) and brain natriuretic peptide (BNP) were examined in Ang II-stimulated H9c2 cells. The potential role of tsRNAs in the pathogenesis of PCH was explored by bioinformatics analysis. Results: A total of 4185 differentially expressed tsRNAs were identified, of which four and five tsRNAs were observed to be significantly upregulated and downregulated, respectively. Of the five downregulated tsRNAs, four were verified to be significantly downregulated in the larger sample group, including tRF-30-3JVIJMRPFQ5D, tRF-16-R29P4PE, tRF-21-NB8PLML3E, and tRF-21-SWRYVMMV0, and the AUC values for diagnosis of concentric hypertrophy were 0.7893, 0.7825, 0.8475, and 0.8825, respectively. The four downregulated tsRNAs were negatively correlated with the left ventricular posterior wall dimensions in PCH patients (r = −0.4227; r = −0.4517; r = −0.5567; r = −0.4223). The levels of ANP and BNP, as well as cell size, were decreased in Ang II–stimulated H9c2 cells with 21-NB8PLML3E mimic transfection. Bioinformatics analysis revealed that the target genes of tRF-21-NB8PLML3E were mainly enriched in the metabolic pathway and involved in the regulation of ribosomes. Conclusions: The plasma tRF-21-NB8PLML3E might be considered as a biomarker and offers early screening potential in patients with PCH.

Chemical modifications of RNAs, known as the epitranscriptome, are emerging as widespread regulatory mechanisms underlying gene regulation. The field of epitranscriptomics advances recently due to improved transcriptome-wide sequencing strategies for mapping RNA modifications and intensive characterization of writers, erasers, and readers that deposit, remove, and recognize RNA modifications, respectively. Herein, we review recent advances in characterizing plant epitranscriptome and its regulatory mechanisms in post-transcriptional gene regulation and diverse physiological processes, with main emphasis on N-6-methyladenosine (m(6)A) and 5-methylcytosine (m(5)C). We also discuss the potential and challenges for utilization of epitranscriptome editing in crop improvement.

A growing number of long noncoding RNAs (lncRNAs) have emerged as vital metabolic regulators. However, most human lncRNAs are nonconserved and highly tissue specific, vastly limiting our ability to identify human lncRNA metabolic regulators (hLMRs). In this study, we established a pipeline to identify putative hLMRs that are metabolically sensitive, disease relevant, and population applicable. We first progressively processed multilevel human transcriptome data to select liver lncRNAs that exhibit highly dynamic expression in the general population, show differential expression in a nonalcoholic fatty liver disease (NAFLD) population, and respond to dietary intervention in a small NAFLD cohort. We then experimentally demonstrated the responsiveness of selected hepatic lncRNAs to defined metabolic milieus in a liver-specific humanized mouse model. Furthermore, by extracting a concise list of protein-coding genes that are persistently correlated with lncRNAs in general and NAFLD populations, we predicted the specific function for each hLMR. Using gain- and loss-of-function approaches in humanized mice as well as ectopic expression in conventional mice, we validated the regulatory role of one nonconserved hLMR in cholesterol metabolism by coordinating with an RNA-binding protein, PTBP1, to modulate the transcription of cholesterol synthesis genes. Our work overcame the heterogeneity intrinsic to human data to enable the efficient identification and functional definition of disease-relevant human lncRNAs in metabolic homeostasis.

A number of difficulties exist when studying long noncoding RNAs (lncRNAs) from a biological standpoint. As it is uncertain what percentage of human lncRNAs play meaningful roles in biology or consists of transcriptional artifacts, one prominent challenge is to decide which lncRNAs to study out of a potential 70,000 putative lncRNA genes. Integration of GWAS and eQTL signals has led to the identification of functional genes for disease susceptibility (Barbeira et al., Nat Commun 9(1):1825, 2018). In this chapter we describe a protocol for building bioinformatic evidence for lncRNA and trait/disease association.

Dietary supplementation is a widely adapted strategy to maintain nutritional balance for improving health and preventing chronic diseases. Conflicting results in studies of similar design, however, suggest that there is substantial heterogenicity in individuals' responses to nutrients, and personalized nutrition is required to achieve the maximum benefit of dietary supplementation. In recent years, nutrigenomics studies have been increasingly utilized to characterize the detailed genomic response to a specific nutrient, but it remains a daunting task to define the signatures responsible for interindividual variations to dietary supplements for tissues with limited accessibility. In this work, we used the hepatic response to omega-3 fatty acids as an example to probe such signatures. Through comprehensive analysis of nutrigenomic response to eicosapentaneoid acid (EPA) and/or docosahexaenoic acid (DHA) including both protein coding and long noncoding RNA (lncRNA) genes in human hepatocytes, we defined the EPA- and/or DHA-specific signature genes in hepatocytes. By analyzing gene expression variations in livers of healthy and relevant disease populations, we identified a set of protein coding and lncRNA signature genes whose responses to omega-3 fatty acid exhibit very high interindividual variabilities. The large variabilities of individual responses to omega-3 fatty acids were further validated in human hepatocytes from ten different donors. Finally, we profiled RNAs in exosomes isolated from the circulation of a liver-specific humanized mouse model, in which the humanized liver is the sole source of human RNAs, and confirmed the in vivo detectability of some signature genes, supporting their potential as biomarkers for nutrient response. Taken together, we have developed an efficient and practical procedure to identify nutrient-responsive gene signatures as well as accessible biomarkers for interindividual variations.

Bile acids, regarded as the body's detergent for digesting lipids, also function as critical signaling molecules that regulate cholesterol and triglyceride levels in the body. Bile acids are the natural ligands of the nuclear receptor, FXR, which controls an intricate network of cellular pathways to maintain metabolic homeostasis. In recent years, growing evidence supports that many cellular actions of the bile acid/FXR pathway are mediated by long non-coding RNAs (lncRNAs), and lncRNAs are in turn powerful regulators of bile acid levels and FXR activities. In this review, we highlight the substantial progress made in the understanding of the functional and mechanistic role of lncRNAs in bile acid metabolism and how lncRNAs connect bile acid activity to additional metabolic processes. We also discuss the potential of lncRNA studies in elucidating novel molecular mechanisms of the bile acid/FXR pathway and the promise of lncRNAs as potential diagnostic markers and therapeutic targets for diseases associated with altered bile acid metabolism.

The invention discloses application of long-chain non-coding RNA GAS5 in preparation of a drug for promoting nerve regeneration and repairing nerve injury. Research results show that by down-regulating or inhibiting expression of body GAS5, regeneration of dorsal root node DRG neuron axons can be promoted, and then peripheral nerve injury repair is facilitated.

Mouse is the most widely used animal model in biomedical research, but it remains unknown what causes the large number of differentially regulated genes between human and mouse livers identified in recent years. In this report, we aim to determine whether these divergent gene regulations are primarily caused by environmental factors or some of them are the result of cell-autonomous differences in gene regulation in human and mouse liver cells. The latter scenario would suggest that many human genes are subject to human-specific regulation and can only be adequately studied in a human or humanized system. To understand the similarity and divergence of gene regulation between human and mouse livers, we performed stepwise comparative analyses in human, mouse, and humanized livers with increased stringency to gradually remove the impact of factors external to liver cells, and used bioinformatics approaches to retrieve gene networks to ascertain the regulated biological processes. We first compared liver gene regulation by fatty liver disease in human and mouse under the condition where the impact of genetic and gender biases was minimized, and identified over 50% of all commonly regulated genes, that exhibit opposite regulation by fatty liver disease in human and mouse. We subsequently performed more stringent comparisons when a single specific transcriptional or post-transcriptional event was modulated in vitro or vivo or in liver-specific humanized mice in which human and mouse hepatocytes colocalize and share a common circulation. Intriguingly and strikingly, the pattern of a high percentage of oppositely regulated genes persists under well-matched conditions, even in the liver of the humanized mouse model, which represents the most closely matched in vivo condition for human and mouse liver cells that is experimentally achievable. Gene network analyses further corroborated the results of oppositely regulated genes and revealed substantial differences in regulated biological processes in human and mouse cells. We also identified a list of regulated lncRNAs that exhibit very limited conservation and could contribute to these differential gene regulations. Our data support that cell-autonomous differences in gene regulation might contribute substantially to the divergent gene regulation between human and mouse livers and there are a significant number of biological processes that are subject to human-specific regulation and need to be carefully considered in the process of mouse to human translation.

A growing number of long non-coding RNAs (lncRNAs) have emerged as vital metabolic regulators in research animals suggesting that lncRNAs could also play an important role in human metabolism. However, most human lncRNAs are non-conserved, vastly limiting our ability to identify human lncRNA metabolic regulators (hLMRs). As the sequence-function relation of lncRNAs has yet to be established, the identification of lncRNA metabolic regulators in animals often relies on their regulations by experimental metabolic conditions. But it is very challenging to apply this strategy to human lncRNAs because well-controlled human data are much limited in scope and often confounded by genetic heterogeneity. In this study, we establish an efficient pipeline to identify putative hLMRs that are metabolically sensitive, disease-relevant, and population applicable. We first progressively processed human transcriptome data to select human liver lncRNAs that exhibit highly dynamic expression in the general population, show differential expression in a metabolic disease population, and response to dietary intervention in a small disease cohort. We then experimentally demonstrated the responsiveness of selected hepatic lncRNAs to defined metabolic milieus in a liver-specific humanized mouse model. Furthermore, by extracting a concise list of protein-coding genes that are persistently correlated with lncRNAs in general and metabolic disease populations, we predicted the specific function for each hLMR. Using gain- and loss-of-function approaches in humanized mice as well as ectopic expression in conventional mice, we were able to validate the regulatory role of one non-conserved hLMR in cholesterol metabolism. Mechanistically, this hLMR binds to an RNA-binding protein, PTBP1, to modulate the transcription of cholesterol synthesis genes. In summary, our study provides a pipeline to overcome the variabilities intrinsic to human data to enable the efficient identification and functional definition of hLMRs. The combination of this bioinformatic framework and humanized murine model will enable broader systematic investigation of the physiological role of disease-relevant human lncRNAs in metabolic homeostasis.

Long non-coding RNA Knowledgebase (lncRNAKB) is an integrated resource for exploring lncRNA biology in the context of tissue-specificity and disease association. A systematic integration of annotations from six independent databases resulted in 77,199 human lncRNA (224,286 transcripts). The user-friendly knowledgebase covers a comprehensive breadth and depth of lncRNA annotation. lncRNAKB is a compendium of expression patterns, derived from analysis of RNA-seq data in thousands of samples across 31 solid human normal tissues (GTEx). Thousands of co-expression modules identified via network analysis and pathway enrichment to delineate lncRNA function are also accessible. Millions of expression quantitative trait loci ( cis -eQTL) computed using whole genome sequence genotype data (GTEx) can be downloaded at lncRNAKB that also includes tissue-specificity, phylogenetic conservation and coding potential scores. Tissue-specific lncRNA-trait associations encompassing 323 GWAS (UK Biobank) are also provided. LncRNAKB is accessible at http://www.lncrnakb.org/ , and the data are freely available through Open Science Framework ( https://doi.org/10.17605/OSF.IO/RU4D2 ).

Unlike protein-coding genes, the majority of human long non-coding RNAs (lncRNAs) are considered non-conserved. Although lncRNAs have been shown to function in diverse pathophysiological processes in mice, it remains largely unknown whether human lncRNAs have such in vivo functions. Here, we describe an integrated pipeline to define the in vivo function of non-conserved human lncRNAs. We first identify lncRNAs with high function potential using multiple indicators derived from human genetic data related to cardiometabolic traits, then define lncRNA’s function and specific target genes by integrating its correlated biological pathways in humans and co-regulated genes in a humanized mouse model. Finally, we demonstrate that the in vivo function of human-specific lncRNAs can be successfully examined in the humanized mouse model, and experimentally validate the predicted function of an obesity-associated lncRNA, LINC01018, in regulating the expression of genes in fatty acid oxidation in humanized livers through its interaction with RNA-binding protein HuR.

LncRNAs (long noncoding RNAs) are transcripts that are at least 200 nucleotides long and lack any predicted coding potential. Whereas significant progress has been made in deciphering the function of mouse lncRNAs, critical gaps remain in understanding how human lncRNAs exercise their function in a physiological context. As most human lncRNAs are currently considered nonconserved and often do not have homologs in mouse, the technical bottleneck is the lack of a suitable model to study the physiological function. Chimeric mice with repopulated human hepatocytes have emerged as promising tools to study human-specific, liver enriched lncRNAs. Among all liver-specific humanized mouse models, TK-NOG is relatively easy to prepare and holds a higher repopulation rate for a prolonged period of time. In this chapter, we will illustrate how to establish humanized TK-NOG mice for in vivo analysis of human lncRNAs in detail.