Nothing Special   »   [go: up one dir, main page]

You seem to have javascript disabled. Please note that many of the page functionalities won't work as expected without javascript enabled.
 
 
Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Article Types

Countries / Regions

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Search Results (2,493)

Search Parameters:
Keywords = lncRNAs

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
19 pages, 11582 KiB  
Article
Small Molecule Inhibitor of Protein Kinase C DeltaI (PKCδI) Decreases Inflammatory Pathways and Gene Expression and Improves Metabolic Function in Diet-Induced Obese Mouse Model
by Brenna Osborne, Rekha S. Patel, Meredith Krause-Hauch, Ashley Lui, Gitanjali Vidyarthi and Niketa A. Patel
Biology 2024, 13(11), 943; https://doi.org/10.3390/biology13110943 (registering DOI) - 18 Nov 2024
Abstract
Obesity promotes metabolic diseases such as type 2 diabetes and cardiovascular disease. PKCδI is a serine/threonine kinase which regulates cell growth, differentiation, and survival. Caspase-3 cleavage of PKCδI releases the C-terminal catalytic fragment (PKCδI_C), which promotes inflammation and apoptosis. We previously demonstrated an [...] Read more.
Obesity promotes metabolic diseases such as type 2 diabetes and cardiovascular disease. PKCδI is a serine/threonine kinase which regulates cell growth, differentiation, and survival. Caspase-3 cleavage of PKCδI releases the C-terminal catalytic fragment (PKCδI_C), which promotes inflammation and apoptosis. We previously demonstrated an increase in PKCδI_C in human obese adipose tissue (AT) and adipocytes. Subsequently, we designed a small molecule drug called NP627 and demonstrated that NP627 specifically inhibited the release of PKCδI_C in vitro. Here, we evaluate the in vivo safety and efficacy of NP627 in a diet-induced obese (DIO) mouse model. The results demonstrate that NP627 treatment in DIO mice increased glucose uptake and inhibited the cleavage of PKCδI_C in the AT as well as in the kidney, spleen, and liver. Next, RNAseq analysis was performed on the AT from the NP627-treated DIO mice. The results show increases in ADIPOQ and CIDEC, upregulation of AMPK, PI3K-AKT, and insulin signaling pathways, while inflammatory pathways were decreased post-NP627 administration. Further, levels of lncRNAs associated with metabolic pathways were affected by NP627 treatment. In conclusion, the study demonstrates that NP627, a small-molecule inhibitor of PKCδI activity, is not toxic and that it improves the metabolic function of DIO mice in vivo. Full article
Show Figures

Figure 1

Figure 1
<p>Schematic of study design, created in BioRender. Patel, N (2024). Reprinted with permission from Patel, N (2024). Copyright 2024 Patel, N.</p>
Full article ">Figure 2
<p>Mice were weighed before and after NP627 administration, and their respective weights were graphed. Statistical analysis using Student’s <span class="html-italic">t</span>-test was performed; ns = not significant.</p>
Full article ">Figure 3
<p>(<b>a</b>) An IPGTT was performed. Glucose readings were taken every 30 min for 2 h. Statistical analysis was performed using Welch’s <span class="html-italic">t</span>-test, * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> ≤ 0.001; ns = not significant. (<b>b</b>) The area under the curve (AUC) was calculated as AUC (mmol/L·min) = 1/2 × (BG 0 min + BG 30 min) × 30 min + 1/2 × (BG 30 min + BG 60 min) × 30 min + 1/2 × (BG 60 min + BG 90 min) × 30 min + 1/2 × (BG 90 min + BG 120 min) × 30 min. BG = blood glucose. Data are expressed as mean ± SEM, n = 5. Statistical analysis was performed using a paired parametric <span class="html-italic">t</span>-test, ** <span class="html-italic">p</span> &lt; 0.01.</p>
Full article ">Figure 4
<p>(<b>a</b>) Total protein was extracted from adipose tissue of lean, DIO, and DIO mice treated with NP627 (200 ng/kg b.w., 500 ng/kg b.w., or 1 μg/kg b.w.), and a Western blot was performed using antibodies against PKCδI and β-actin. Samples were run in triplicate. Densitometric units obtained by Amersham IQTL analysis software were normalized to β-actin. Welch’s <span class="html-italic">t</span>-test was used to determine statistical significance: ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> ≤ 0.001. (<b>b</b>) Total protein levels from the adipose tissue, liver, kidney, and spleen from lean, DIO, and DIO + 200 ng/kg b.w. NP627 mice were analyzed by Western blot, and a representative blot is shown. (<b>c</b>) Graph of densitometric units of 3 lean mice were calculated by Amersham IQTL software and normalized to β-actin. (<b>d</b>) Graph of densitometric units of 3 DIO mice and 3 DIO + 1 μg/kg b.w. NP627 mice were calculated by Amersham IQTL software and normalized to β-actin. Statistical analysis was performed in GraphPad using Welch’s <span class="html-italic">t</span>-test, **** <span class="html-italic">p</span> ≤ 0.0001, comparing DIO versus DIO + 1 μg/kg b.w. NP627.</p>
Full article ">Figure 5
<p>Hematoxylin and eosin staining was performed on the adipose tissue, kidney, spleen, and liver on DIO (PBS) and DIO mice treated with 1 μg/kg b.w. NP627. Slides were imaged using Keyence BZ-X810.</p>
Full article ">Figure 6
<p>(<b>a</b>) Clustered heatmap of differentially expressed genes (DEGs) between lean, DIO, and DIO + NP627. (<b>b</b>) Distance heatmap showing how closely related the samples are within their respective groups. (<b>c</b>) Venn diagram demonstrating upregulated and downregulated genes that were significantly reversed due to NP627 treatment. (<b>d</b>) Volcano plot of genes that differ significantly between lean, DIO, and DIO + NP627 groups.</p>
Full article ">Figure 7
<p>(<b>a</b>) A pathway enrichment table was created showing pathways that were upregulated/downregulated between their respective groups. Blue indicates significantly downregulated pathways, and red indicates significantly upregulated pathways. Blue represents a smaller log10 false discovery rate (FDR), representing a more significant correlation. Red represents a larger log10 FDR. (<b>b</b>) KEGG pathway mapping of the insulin-signaling pathway. (<b>c</b>) KEGG pathway map of the AMPK-signaling pathway. (<b>d</b>) KEGG pathway map outlining genes of the PI3K-AKT-signaling pathway. (<b>e</b>) Western blotting using p-AKT, AKT, and β-actin antibodies was performed on adipose tissue from DIO and DIO + NP627 mice. β-actin was used for normalization purposes. Densitometric units from the immunoblot were calculated using Amersham IQTL analysis software. Statistical significance was determined in GraphPad using a parametric paired <span class="html-italic">t</span>-test, ** <span class="html-italic">p</span> &lt; 0.01.</p>
Full article ">Figure 7 Cont.
<p>(<b>a</b>) A pathway enrichment table was created showing pathways that were upregulated/downregulated between their respective groups. Blue indicates significantly downregulated pathways, and red indicates significantly upregulated pathways. Blue represents a smaller log10 false discovery rate (FDR), representing a more significant correlation. Red represents a larger log10 FDR. (<b>b</b>) KEGG pathway mapping of the insulin-signaling pathway. (<b>c</b>) KEGG pathway map of the AMPK-signaling pathway. (<b>d</b>) KEGG pathway map outlining genes of the PI3K-AKT-signaling pathway. (<b>e</b>) Western blotting using p-AKT, AKT, and β-actin antibodies was performed on adipose tissue from DIO and DIO + NP627 mice. β-actin was used for normalization purposes. Densitometric units from the immunoblot were calculated using Amersham IQTL analysis software. Statistical significance was determined in GraphPad using a parametric paired <span class="html-italic">t</span>-test, ** <span class="html-italic">p</span> &lt; 0.01.</p>
Full article ">Figure 7 Cont.
<p>(<b>a</b>) A pathway enrichment table was created showing pathways that were upregulated/downregulated between their respective groups. Blue indicates significantly downregulated pathways, and red indicates significantly upregulated pathways. Blue represents a smaller log10 false discovery rate (FDR), representing a more significant correlation. Red represents a larger log10 FDR. (<b>b</b>) KEGG pathway mapping of the insulin-signaling pathway. (<b>c</b>) KEGG pathway map of the AMPK-signaling pathway. (<b>d</b>) KEGG pathway map outlining genes of the PI3K-AKT-signaling pathway. (<b>e</b>) Western blotting using p-AKT, AKT, and β-actin antibodies was performed on adipose tissue from DIO and DIO + NP627 mice. β-actin was used for normalization purposes. Densitometric units from the immunoblot were calculated using Amersham IQTL analysis software. Statistical significance was determined in GraphPad using a parametric paired <span class="html-italic">t</span>-test, ** <span class="html-italic">p</span> &lt; 0.01.</p>
Full article ">Figure 7 Cont.
<p>(<b>a</b>) A pathway enrichment table was created showing pathways that were upregulated/downregulated between their respective groups. Blue indicates significantly downregulated pathways, and red indicates significantly upregulated pathways. Blue represents a smaller log10 false discovery rate (FDR), representing a more significant correlation. Red represents a larger log10 FDR. (<b>b</b>) KEGG pathway mapping of the insulin-signaling pathway. (<b>c</b>) KEGG pathway map of the AMPK-signaling pathway. (<b>d</b>) KEGG pathway map outlining genes of the PI3K-AKT-signaling pathway. (<b>e</b>) Western blotting using p-AKT, AKT, and β-actin antibodies was performed on adipose tissue from DIO and DIO + NP627 mice. β-actin was used for normalization purposes. Densitometric units from the immunoblot were calculated using Amersham IQTL analysis software. Statistical significance was determined in GraphPad using a parametric paired <span class="html-italic">t</span>-test, ** <span class="html-italic">p</span> &lt; 0.01.</p>
Full article ">Figure 8
<p>(<b>a</b>) Chord diagram showing relationship between genes and inflammatory pathway nodes. (<b>b</b>) Total RNA was extracted from adipose tissue of DIO and DIO + NP627 mice. Real-time qPCR was performed in triplicate using SYBR Green to measure the absolute quantification (AQ) of inflammatory genes IL-1β, IL-6, TNFα, and MCP-1, and normalized with β-actin. Statistical analysis was performed in GraphPad using an unpaired <span class="html-italic">t</span>-test: * <span class="html-italic">p</span> &lt; 0.05, *** <span class="html-italic">p</span> &lt; 0.001, and ns = not significant. (<b>c</b>) Western blot analysis was performed on adipose tissue from DIO, and DIO + NP627 mice using JESS (automated Western blotting), using antibodies against TNFα and IL-1β. The graph represents ± SEM chemiluminescence units calculated by JESS software Compass. Statistical analysis was performed in GraphPad using Welch’s <span class="html-italic">t</span>-test: * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01.</p>
Full article ">Figure 8 Cont.
<p>(<b>a</b>) Chord diagram showing relationship between genes and inflammatory pathway nodes. (<b>b</b>) Total RNA was extracted from adipose tissue of DIO and DIO + NP627 mice. Real-time qPCR was performed in triplicate using SYBR Green to measure the absolute quantification (AQ) of inflammatory genes IL-1β, IL-6, TNFα, and MCP-1, and normalized with β-actin. Statistical analysis was performed in GraphPad using an unpaired <span class="html-italic">t</span>-test: * <span class="html-italic">p</span> &lt; 0.05, *** <span class="html-italic">p</span> &lt; 0.001, and ns = not significant. (<b>c</b>) Western blot analysis was performed on adipose tissue from DIO, and DIO + NP627 mice using JESS (automated Western blotting), using antibodies against TNFα and IL-1β. The graph represents ± SEM chemiluminescence units calculated by JESS software Compass. Statistical analysis was performed in GraphPad using Welch’s <span class="html-italic">t</span>-test: * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01.</p>
Full article ">Figure 9
<p>RNAseq data was analyzed for lncRNA expression in lean vs DIO vs DIO + NP627. Graph represents FKPM (fragments per kilobase of exon per million fragments mapped) gene counts (n = 4). Statistical significance was determined in GraphPad using Welch’s <span class="html-italic">t</span>-test; * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> ≤ 0.0001, ns = not significant.</p>
Full article ">Figure 10
<p>Schematic of action of NP627, created in BioRender. Patel, N (2024). Reprinted with permission from Patel, N (2024). Copyright 2024 Patel, N.</p>
Full article ">
30 pages, 652 KiB  
Review
Psychiatric Symptoms in Wilson’s Disease—Consequence of ATP7B Gene Mutations or Just Coincidence?—Possible Causal Cascades and Molecular Pathways
by Grażyna Gromadzka, Agnieszka Antos, Zofia Sorysz and Tomasz Litwin
Int. J. Mol. Sci. 2024, 25(22), 12354; https://doi.org/10.3390/ijms252212354 - 18 Nov 2024
Viewed by 156
Abstract
Wilson’s disease (WD) is an autosomal recessive disorder of copper metabolism. The genetic defect in WD affects the ATP7B gene, which encodes the ATP7B transmembrane protein, which is essential for maintaining normal copper homeostasis in the body. It is primarily expressed in the [...] Read more.
Wilson’s disease (WD) is an autosomal recessive disorder of copper metabolism. The genetic defect in WD affects the ATP7B gene, which encodes the ATP7B transmembrane protein, which is essential for maintaining normal copper homeostasis in the body. It is primarily expressed in the liver and acts by incorporating copper into ceruloplasmin (Cp), the major copper transport protein in the blood. In conditions of excess copper, ATP7B transports it to bile for excretion. Mutations in ATP7B lead to impaired ATP7B function, resulting in copper accumulation in hepatocytes leading to their damage. The toxic “free”—unbound to Cp—copper released from hepatocytes then accumulates in various organs, contributing to their damage and clinical manifestations of WD, including hepatic, neurological, hematological, renal, musculoskeletal, ophthalmological, psychiatric, and other effects. While most clinical manifestations of WD correspond to identifiable organic or cellular damage, the pathophysiology underlying its psychiatric manifestations remains less clearly understood. A search for relevant articles was conducted in PubMed/Medline, Science Direct, Scopus, Willy Online Library, and Google Scholar, combining free text and MeSH terms using a wide range of synonyms and related terms, including “Wilson’s disease”, “hepatolenticular degeneration”, “psychiatric manifestations”, “molecular mechanisms”, “pathomechanism”, and others, as well as their combinations. Psychiatric symptoms of WD include cognitive disorders, personality and behavioral disorders, mood disorders, psychosis, and other mental disorders. They are not strictly related to the location of brain damage, therefore, the question arises whether these symptoms are caused by WD or are simply a coincidence or a reaction to the diagnosis of a genetic disease. Hypotheses regarding the etiology of psychiatric symptoms of WD suggest a variety of molecular mechanisms, including copper-induced CNS toxicity, oxidative stress, mitochondrial dysfunction, mitophagy, cuproptosis, ferroptosis, dysregulation of neurotransmission, deficiencies of neurotrophic factors, or immune dysregulation. New studies on the expression of noncoding RNA in WD are beginning to shed light on potential molecular pathways involved in psychiatric symptomatology. However, current evidence is still insufficient to definitively establish the cause of psychiatric symptoms in WD. It is possible that the etiology of psychiatric symptoms varies among individuals, with multiple biological and psychological mechanisms contributing to them simultaneously. Future studies with larger samples and comprehensive analyses are necessary to elucidate the mechanisms underlying the psychiatric manifestations of WD and to optimize diagnostics and therapeutic approaches. Full article
(This article belongs to the Section Molecular Pathology, Diagnostics, and Therapeutics)
Show Figures

Figure 1

Figure 1
<p>The main mechanisms that may be important in the pathogenesis of psychiatric symptoms of WD.</p>
Full article ">
10 pages, 454 KiB  
Commentary
LNC-ing Genetics in Mitochondrial Disease
by Rick Kamps and Emma Louise Robinson
Non-Coding RNA 2024, 10(6), 57; https://doi.org/10.3390/ncrna10060057 (registering DOI) - 15 Nov 2024
Viewed by 209
Abstract
Primary mitochondrial disease (MD) is a group of rare genetic diseases reported to have a prevalence of 1:5000 and is currently without a cure. This group of diseases includes mitochondrial encephalopathy, lactic acidosis, and stroke-like episodes (MELAS), maternally inherited diabetes and deafness (MIDD), [...] Read more.
Primary mitochondrial disease (MD) is a group of rare genetic diseases reported to have a prevalence of 1:5000 and is currently without a cure. This group of diseases includes mitochondrial encephalopathy, lactic acidosis, and stroke-like episodes (MELAS), maternally inherited diabetes and deafness (MIDD), Leber’s hereditary optic neuropathy (LHON), Leigh syndrome (LS), Kearns–Sayre syndrome (KSS), and myoclonic epilepsy and ragged-red fiber disease (MERRF). Additionally, secondary mitochondrial dysfunction has been implicated in the most common current causes of mortality and morbidity, including cardiovascular disease (CVD) and cancer. Identifying key genetic contributors to both MD and secondary mitochondrial dysfunction may guide clinicians to assess the most effective treatment course and prognosis, as well as informing family members of any hereditary risk of disease transmission. Identifying underlying genetic causes of primary and secondary MD involves either genome sequencing (GS) or small targeted panel analysis of known disease-causing nuclear- or mitochondrial genes coding for mitochondria-related proteins. Due to advances in GS, the importance of long non-coding RNA (lncRNA) as functional contributors to the pathophysiology of MD is being unveiled. A limited number of studies have thus far reported the importance of lncRNAs in relation to MD causation and progression, and we are entering a new area of attention for clinical geneticists in specific rare malignancies. This commentary provides an overview of what is known about the role of lncRNAs as genetic and molecular contributors to disease pathophysiology and highlights an unmet need for a deeper understanding of mitochondrial dysfunction in serious human disease burdens. Full article
29 pages, 2028 KiB  
Review
Non-Coding RNAs as Potential Diagnostic/Prognostic Markers for Hepatocellular Carcinoma
by Federica Tonon, Chiara Grassi, Domenico Tierno, Alice Biasin, Mario Grassi, Gabriele Grassi and Barbara Dapas
Int. J. Mol. Sci. 2024, 25(22), 12235; https://doi.org/10.3390/ijms252212235 - 14 Nov 2024
Viewed by 354
Abstract
The increasing incidence of hepatocellular carcinoma (HCC), together with the poor effectiveness of the available treatments, make early diagnosis and effective screening of utmost relevance. Liquid biopsy represents a potential novel approach to early HCC detection and monitoring. The identification of blood markers [...] Read more.
The increasing incidence of hepatocellular carcinoma (HCC), together with the poor effectiveness of the available treatments, make early diagnosis and effective screening of utmost relevance. Liquid biopsy represents a potential novel approach to early HCC detection and monitoring. The identification of blood markers has many desirable features, including the absence of any significant risk for the patients, the possibility of being used as a screening tool, and the ability to perform multiple tests, thus allowing for the real-time monitoring of HCC evolution. Unfortunately, the available blood markers for HCC have several limitations, mostly related to specificity and sensitivity. In this context, employing non-coding RNAs (ncRNAs) may represent an interesting and novel diagnostic approach. ncRNAs, which include, among others, micro interfering RNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs), regulate human gene expression via interactions with their target mRNA. Notably, their expression can be altered in HCC, thus reflecting disease status. In this review, we discuss some notable works that describe the use of miRNAs, lncRNAs, and circRNAs as HCC biomarkers. Despite some open aspects related to ncRNA use, the presented works strongly support the potential effectiveness of these molecules as diagnostic/prognostic markers for HCC. Full article
Show Figures

Figure 1

Figure 1
<p>miRNA biogenesis and functions. In the cell nucleus, a long precursor named pri-miRNA is processed by the enzyme Drosha to pre-miRNA; this, in turn, is exported to the cytoplasm by the Exportin 5 enzyme. Here, the DICER enzyme produces double-stranded mature RNA (miRNA). The miRNA antisense strand is then loaded onto RISC, allowing for the recognition of the target mRNA, resulting in translation inhibition (via an imperfect base pairing) or mRNA degradation (via perfect base pairing). Recent findings indicate that via direct/indirect mechanisms, miRNA can also promote gene expression. This figure was created with BioRender.com (accessed on 23 September 2024).</p>
Full article ">Figure 2
<p>LncRNA biogenesis and functions. LncRNAs can be spliced, capped, and poly-adenylated. Following transcription, they assume a three-dimensional (3D) structure responsible for the biological effects, such as the recruitment of transcription activators/repressors to the promoters of their target genes, thus regulating gene expression; the inhibition of miRNA activity via the sponging effect; and the ability to act as scaffolds for protein to support the formation of protein complexes. This figure was created with BioRender.com (accessed on 23 September 2024).</p>
Full article ">Figure 3
<p>CircRNA biogenesis and functions. Although circRNAs have multiple biogenesis mechanisms, a common one is represented by back-splicing. Back-splicing can be induced by protein-dimerization, sequence complementarity of flanking introns, and exon-skipping mechanisms. Following the formation of a circular RNA, circRNA is exported into the cytoplasm, where it can bind miRNAs via complementary regions; undergo translation to generate small peptides; and interact with proteins, gene promoters, and specific mRNAs. This figure was created with BioRender.com (accessed on 23 September 2024).</p>
Full article ">Figure 4
<p>Extracellular vesicles. Extracellular vesicles (EVs) are a heterogeneous group of lipid bilayer particles synthesized and secreted by different cell types into the extracellular environment. EVs encapsulate various bioactive molecules, such as proteins, lipids, and nucleic acids. Nowadays, it is known that EVs can contain ncRNAs and can deliver these molecules to distant cells both under physiological and pathological conditions. This figure was created with BioRender.com (accessed on 23 September 2024).</p>
Full article ">
28 pages, 2627 KiB  
Review
Targeting PDGF/PDGFR Signaling Pathway by microRNA, lncRNA, and circRNA for Therapy of Vascular Diseases: A Narrow Review
by Chao-Nan Ma, Shan-Rui Shi, Xue-Ying Zhang, Guo-Song Xin, Xiang Zou, Wen-Lan Li and Shou-Dong Guo
Biomolecules 2024, 14(11), 1446; https://doi.org/10.3390/biom14111446 - 14 Nov 2024
Viewed by 417
Abstract
Despite the significant progress in diagnostic and therapeutic strategies, vascular diseases, such as cardiovascular diseases (CVDs) and respiratory diseases, still cannot be successfully eliminated. Vascular cells play a key role in maintaining vascular homeostasis. Notably, a variety of cells produce and secrete platelet-derived [...] Read more.
Despite the significant progress in diagnostic and therapeutic strategies, vascular diseases, such as cardiovascular diseases (CVDs) and respiratory diseases, still cannot be successfully eliminated. Vascular cells play a key role in maintaining vascular homeostasis. Notably, a variety of cells produce and secrete platelet-derived growth factors (PDGFs), which promote mitosis and induce the division, proliferation, and migration of vascular cells including vascular smooth muscle cells (SMCs), aortic SMCs, endothelial cells, and airway SMCs. Therefore, PDGF/PDGR receptor signaling pathways play vital roles in regulating the homeostasis of blood vessels and the onset and development of CVDs, such as atherosclerosis, and respiratory diseases including asthma and pulmonary arterial hypertension. Recently, accumulating evidence has demonstrated that microRNA, long-chain non-coding RNA, and circular RNA are involved in the regulation of PDGF/PDGFR signaling pathways through competitive interactions with target mRNAs, contributing to the occurrence and development of the above-mentioned diseases. These novel findings are useful for laboratory research and clinical studies. The aim of this article is to conclude the recent progresses in this field, particular the mechanisms of action of these non-coding RNAs in regulating vascular remodeling, providing potential strategies for the diagnosis, prevention, and treatment of vascular-dysfunction-related diseases, particularly CVDs and respiratory diseases. Full article
Show Figures

Figure 1

Figure 1
<p>PDGF dimers bind PDGFRα and PDGFRβ to activate distinct signaling pathways that are involved in the regulation of vascular cell proliferation, migration, and invasion. According to the available references, four different disulphide-linked dimers including PDGF-AA, PDGF-AB, PDGF-BB, and PDGF-CC are involved in regulating the phenotype switch of vascular cells, thereby regulating vessel homeostasis in different organs. ERK: extracellular signal-regulated kinase; JAK: Janus kinase; MMP: matrix metalloproteinase; Myc: myelocytomatosis oncogene gene; NF-κB: nuclear factor kappa-B; PDGF: platelet-derived growth factors; PDGFR: PDGF receptor; STAT: signal transducer and activator of transcription; VEGF: vascular endothelial growth factor; VSMC: vascular smooth muscle cells.</p>
Full article ">Figure 2
<p>miRNAs target genes that are involved in the PDGF/PDGFR signaling pathways, modulating vascular cell phenotype switch and vascular diseases, such as atherosclerosis and Kawasaki disease. miRNAs are found to regulate various signaling pathways including CDK1/P21, IGF-1, KLF4, CyclinD1, PCNA, NF-κB p65, and MEKK1/ERK/KLF4 in different vascular cells. CDK1: cyclin-dependent kinase 1; ICAM-1: intercellular cell adhesion molecule-1; IGF: insulin-like growth factor; IGF-1R: IGR-1 receptor; KLF4: Krüppel-like factor 4; LDL: low-density lipoprotein; MEKK1: mitogen-activated protein kinase kinase 1; LOX-1: lectin-like oxidized LDL receptor-1; OX-LDL: oxidized low-density lipoprotein; PCNA: proliferating cell nuclear antigen; P21: kinase inhibitor cdkn1a; RelA: reticuloendotheliosis viral oncogene homolog A; SMCs: smooth muscle cells.</p>
Full article ">Figure 3
<p>miRNAs target genes that are involved in the PDGF/PDGFR signaling pathways, modulating phenotype switch of PASMCs and airway SMCs, affecting the onset and development of pulmonary arterial hypertension and asthma. miRNAs regulate pulmonary hypertension via activating various molecules including C-Kit, P27Kip1, STAT3, TGF-β1/Smad2/3, PI3K/HDAC4, and NOR-1. miRNAs modulate asthma via targeting the signaling pathways, such as JAK2/STAT3, CyclinD1, NOR-1, and FGF1/MAPK/STAT1. C-Kit: mast/stem cell growth factor receptor kit; FGF1: fibroblast growth factor 1; JAK2: Janus kinase 2; Kip: kinase inhibition protein; MAPK: mitogen-activated protein kinase; NOR-1: neuron-derived orphan receptor 1; PASMC: primary pulmonary artery smooth muscle cell; Smad: drosophila mothers against decapentaplegic protein; STAT: signal transducer and activator of transcription; TGF-β1: transforming growth factor-β1.</p>
Full article ">Figure 4
<p>lncRNAs regulate different pathways via sponging miRNAs to modulate cell proliferation and migration as well as inflammation, thereby affecting the occurrence and development of atherosclerosis, asthma, and liver fibrosis. AKT: protein kinase B; AMPK: AMP-activated protein kinase; ATG7: autophagy-related 7; PI3K: phosphatidylinositol 3-kinase; PTEN: phosphatase and tensin homolog deleted on chromosome ten.</p>
Full article ">Figure 5
<p>Some circRNAs are found to sponge miRNAs to regulate molecules that are involved in the PDGF/PDGFR signaling pathways, controlling cell proliferation and migration, modulating the onset and development of atherosclerosis and asthma. FGF1: fibroblast growth factor 1; FRS2: fibroblast growth factor receptor substrate 2; HMGB1: high-mobility group box 1; IGF1: insulin-like growth factor 1; KCNA1: voltage-gated potassium channel subfamily A member 1; mTOR: mammalian target of rapamycin; TRAF6: tumor-necrosis-factor-receptor-associated factor 6; VAMP2: vesicle-associated membrane protein 2; VEGFA: vascular endothelial growth factor A.</p>
Full article ">
13 pages, 5037 KiB  
Article
LINC01614 Promotes Oral Squamous Cell Carcinoma by Regulating FOXC1
by Hongze Che, Xun Zhang, Luo Cao, Wenjun Huang and Qing Lu
Genes 2024, 15(11), 1461; https://doi.org/10.3390/genes15111461 - 13 Nov 2024
Viewed by 294
Abstract
Background: Long non-coding RNAs (lncRNAs) are pivotal mediators during the development of carcinomas; however, it remains to be investigated whether lncRNAs are implicated in oral squamous cell carcinoma (OSCC). Methods: In this study, quantitative real-time PCR was conducted for detecting the expression of [...] Read more.
Background: Long non-coding RNAs (lncRNAs) are pivotal mediators during the development of carcinomas; however, it remains to be investigated whether lncRNAs are implicated in oral squamous cell carcinoma (OSCC). Methods: In this study, quantitative real-time PCR was conducted for detecting the expression of LINC01614 in OSCC cell lines. The biological functions of LINC01614 were assessed by loss- and gain-of-function experiments conducted both in vivo and in vitro. Cellular proliferation, migration, and invasion were investigated herein, and dual luciferase reporter assays were additionally performed to explore the relationships among LINC01614, miR-138-5p, and Forkhead box C1 (FOXC1). Results: The research presented herein revealed that OSCC cells express high levels of LINC01614. Functional experiments employing cellular and animal models demonstrated that LINC01614 knockdown repressed the malignant phenotypes of OSCC cells, including their growth, invasiveness, and migration. Further investigation revealed that LINC01614 absorbs miR-138-5p miRNA by functioning as a competing endogenous RNA to downregulate the abundance of FOXC1. Conclusions: The findings revealed that LINC01614 contributes to the progression of OSCC by targeting the FOXC1 signaling pathway. The study provides insights into a novel mechanistic process to regulate the development of OSCC, and established a possible target for the therapeutic management of OSCC. Full article
(This article belongs to the Section Human Genomics and Genetic Diseases)
Show Figures

Figure 1

Figure 1
<p>LINC01614 is upregulated in OSCC. (<b>A</b>,<b>B</b>) Expression patterns of LINC01614 in normal and cancer tissues, as reported in the GEPIA database. (<b>C</b>) Expression patterns of LINC01614 in HNSC based on data retrieved from the GEPIA database. (<b>D</b>) Expression levels of LINC01614 in CAL-27 and SCC-9 OSCC cell lines and control HOK cells. (<b>E</b>) The protein-coding potential of LINC01614 was assessed using the CPC tool. (<b>F</b>) Subcellular localization of LINC01614, as determined using the lncLocator. (<b>G</b>) Determination of the cytoplasmic and nuclear expression of LINC01614 in CAL-27 cells by qRT-PCR. (<b>H</b>) Relationship between the expression of LINC01614 and overall survival, as determined by Kaplan–Meier analysis. (<b>I</b>) Relationship between the stage of OSCC tumors and LINC01614 expression. *** <span class="html-italic">p</span> &lt; 0.001.</p>
Full article ">Figure 2
<p>LINC01614 promoted the proliferation, migration, and invasion of OSCC cells in vitro. (<b>A</b>) Detection of the transfection efficacy of si-LINC01614 in OSCC cells. (<b>B</b>–<b>D</b>) Growth of OSCC cells with LINC01614 knockdown as determined by CCK-8 and colony formation assays. (<b>E</b>) Effects of si-LINC01614 on the migration of OSCC cells post-transfection, as determined by wound healing assays. (<b>F</b>) Alterations in the invasive potential of OSCC cells following the downregulation of LINC01614 expression, as determined by Transwell assays. (<b>G</b>) Effects of LINC01614 downregulation on the regulation of apoptosis in OSCC cells. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001.</p>
Full article ">Figure 3
<p>Si-LINC01614 suppressed the progression of OSCC by targeting miR-138-5p (<b>A</b>) The consensus results obtained from the databases, miRCode and miRDB, are depicted in the intersection area of the Venn diagram. (<b>B</b>) Prediction of the miR-138-5p binding site of LINC01614 using miRDB. (<b>C</b>) Alterations in the expression of miR-138-5p following transfection with miR-138-5p mimics or inhibitors, or LINC01614. (<b>D</b>) Effects of the miR-138-5p and NC mimics on the luciferase activities of the wild-type and mutant LINC01614 as determined by dual luciferase reporter gene analysis. (<b>E</b>,<b>F</b>) Results of CCK-8 and colony formation assays of OSCC cells following transfection with the miR-138-5p inhibitor, miR-138-5p inhibitor+si-LINC01614, or control. (<b>G</b>) Determination of the migration of OSCC cells transfected with the miR-138-5p inhibitor or miR-138-5p inhibitor+si-LINC01614 and control cells by wound healing assays. (<b>H</b>) Effects of the miR-138-5p inhibitor on the invasion of CAL-27 and SCC-9 cells with LINC01614 knockdown as determined by Transwell assays. (<b>I</b>) Apoptotic potential of OSCC cells following co-transfection with the miR-138-5p inhibitor and si-LINC01614 or NC. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001.</p>
Full article ">Figure 4
<p>LINC01614 expression counteracted the effects of miR-138-5p upregulation on OSCC cells. (<b>A</b>,<b>B</b>) Alterations in the proliferative potential of OSCC cells following transfection with miR-138-5p mimics or co-transfection with miR-138-5p mimics and LINC01614, as determined by colony formation and CCK-8 assays. (<b>C</b>,<b>D</b>) Effects of LINC01614 on the migratory and invasive potential of OSCC cells following transfection with miR-138-5p mimics, as determined by wound healing and Transwell assays. (<b>E</b>) Detection of apoptosis in OSCC cells overexpressing LINC01614 or NC following transfection with miR-138-5p mimics or NC by flow cytometry. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001.</p>
Full article ">Figure 5
<p>MiR-138-5p targets FOXC1 to negatively regulate the expression of FOXC1. (<b>A</b>) Prediction of the potential target genes of miR-138-5p using the ENCORI, TargetScan, miRDB, and miRTarBase databases. (<b>B</b>) The effects of co-transfection with miR-138-5p mimics/NC and FOXC1-WT or FOXC1-MUT were examined using dual-luciferase reporter gene assays. (<b>C</b>) Effects of FOXC1 expression in OSCC cells transfected with miR-138-5p mimics or inhibitors. (<b>D</b>–<b>G</b>) Effects of co-transfection with the miR-138-5p inhibitor and control or si-FOXC1, on the growth, migration, invasion, and apoptosis of OSCC cells, as determined by colony formation, wound healing, and Transwell assays, and flow cytometric analyses. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001.</p>
Full article ">Figure 6
<p>FOXC1 silencing counteracted the promoting effects of LINC01614 overexpression in OSCC cells. (<b>A</b>,<b>B</b>) Proliferative ability of OSCC cells (LINC01614, LINC01614+si-FOXC1, or NC) as determined by CCK-8 and colony formation assays. (<b>C</b>) Effects of co-transfection with LINC01614 and control or si-FOXC1 on the migratory potential of CAL-27 cells by wound healing assays. (<b>D</b>) Determination of the invasive potential of CAL-27 or SCC-9 cells transfected with LINC01614 or NC post-transfection with si-FOXC1 by Transwell assays. (<b>E</b>) Detection of the apoptosis of OSCC cells transfected with LINC01614 or LINC01614+si-FOXC1 and control OSCC cells by flow cytometry. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001.</p>
Full article ">Figure 7
<p>LINC01614 promoted the progression of OSCC in vivo. (<b>A</b>) Representative images of the subcutaneous OSCC tumors of nude mice. (<b>B</b>,<b>C</b>) Volumes and weights of the OSCC tumors. (<b>D</b>) Schematic depicting the role of the LINC01614/miR-138-5p/FOXC1 axis in the progression of OSCC. * <span class="html-italic">p</span> &lt; 0.05.</p>
Full article ">
14 pages, 6802 KiB  
Article
Novel Differentially Expressed LncRNAs Regulate Artemisinin Biosynthesis in Artemisia annua
by Tingyu Ma, Tianyuan Zhang, Jingyuan Song, Xiaofeng Shen, Li Xiang and Yuhua Shi
Life 2024, 14(11), 1462; https://doi.org/10.3390/life14111462 - 12 Nov 2024
Viewed by 415
Abstract
Long non-coding RNAs (lncRNAs) are crucial in regulating secondary metabolite production in plants, but their role in artemisinin (ART) biosynthesis, a key anti-malarial compound from Artemisia annua, remains unclear. Here, by investigating high-artemisinin-producing (HAP) and lowartemisinin-producing (LAP) genotypes, we found that the final [...] Read more.
Long non-coding RNAs (lncRNAs) are crucial in regulating secondary metabolite production in plants, but their role in artemisinin (ART) biosynthesis, a key anti-malarial compound from Artemisia annua, remains unclear. Here, by investigating high-artemisinin-producing (HAP) and lowartemisinin-producing (LAP) genotypes, we found that the final artemisinin content in A. annua is influenced by the quantity of the precursor compounds. We report on RNA deep sequencing in HAP and LAP genotypes. Based on the application of a stringent pipeline, 1419 novel lncRNAs were identified. Moreover, we identified 256 differentially expressed lncRNAs between HAP and LAP. We then established correlations between lncRNAs and artemisinin biosynthesis genes in order to identify a molecular framework for the differential expression of the pathway between the two genotypes. Three potential lncRNAs (MSTRG.33718.2, MSTRG.30396.1 and MSTRG.2697.4) linked to the key artemisinin biosynthetic genes (ADS: Amorpha-4,11-diene synthase, DXS: 1-deoxy-D-xylulose-5-phosphate synthase, and HMGS: 3-hydroxyl-3-methyglutaryl CoA synthase) were detected. Importantly, we observed that up-regulation of these lncRNAs positively modulates the target artemisinin biosynthetic genes, potentially leading to high artemisinin biosynthesis in HAP. In contrast, BAS (beta-amyrin synthase), which is involved in the artemisinin competing pathway, was strongly down-regulated in HAP compared to LAP, in line with the expression pattern of the linked lncRNA MSTRG.30396.1. By identifying and characterizing lncRNAs that are potentially linked to the regulation of key biosynthetic genes, this work provides new insights into the complex regulatory networks governing artemisinin production in A. annua. Such findings could pave the way for innovative approaches in metabolic engineering, potentially enhancing artemisinin yields and addressing challenges in sustainable production. Full article
(This article belongs to the Section Genetics and Genomics)
Show Figures

Figure 1

Figure 1
<p>A simplified biosynthesis pathway in <span class="html-italic">A. annua</span> showing the route of synthesis of artemisinin and artemisinin B biosynthesis (<b>A</b>) Metabolite content in the two <span class="html-italic">A. annua</span> genotypes: HAP (high-artemisinin-producing type) in green, and LAP (low-artemisinin-producing type) in gray (<b>B</b>). A chromatogram for the seven studied metabolites including artemisinin, artemisitene, arteannuin B, dihydroartemisinic acid, artemisinic acid, artemisinic aldehyde and amorpha-4,11-diene (<b>C</b>).</p>
Full article ">Figure 2
<p>Genome-wide identification of lncRNAs in <span class="html-italic">Artemisia annua</span>. Flowchart of prediction of novel lncRNAs in <span class="html-italic">A. annua</span> (<b>A</b>). Venn diagram showing shared lncRNAs from CPC and CNCI prediction tools (<b>B</b>). Distribution of lncRNAs within <span class="html-italic">A. annua</span> genome (<b>C</b>).</p>
Full article ">Figure 3
<p>Expression overview of lncRNAs from <span class="html-italic">Artemisia annua</span> genotypes HAP (high-artemisinin-producing type) and LAP (low-artemisinin-producing type). Heatmap of expressed lncRNAs (<b>A</b>). Boxplot showing log10(FPKM) value of lncRNAs (<b>B</b>). Principal component analysis depicting LAP and HAP projection on two first principal component axes (<b>C</b>). Volcano plot of exhibiting differentially expressed, up-regulated and down-regulated lncRNAs from HAP vs. LAP analysis. Red dots: lncRNAs with a q-value &lt; 0.05 and a significant differential expression. Black dots: lncRNAs without a significant difference expression (<b>D</b>).</p>
Full article ">Figure 4
<p>Common and unique enriched KEGG and GO enrichment of target genes of cis- and trans-lncRNAs in HAP (high-artemisinin-producing type) and LAP (low-artemisinin-producing type) genotypes. (<b>A</b>) Common enriched KEGG pathways of lncRNAs cis-acting target genes. (<b>B</b>) Common enriched KEGG pathways of two genotypes of lncRNA trans-acting target genes. (<b>C</b>) Unique enriched GO terms of cis-acting target genes. (<b>D</b>) Unique enriched GO terms of lncRNA trans-acting target genes.</p>
Full article ">Figure 5
<p>Analysis of cis-acting lncRNAs and their cis-targeted genes. Amorpha-4-1-diene synthase (<span class="html-italic">ADS</span>), beta-amyrin synthase (<span class="html-italic">BAS</span>), 1-deoxy-D-xylulose-5-phosphate synthase (<span class="html-italic">DXS</span>) and 3-hydroxyl-3-methyglutaryl CoA synthase (<span class="html-italic">HMGS</span>).</p>
Full article ">Figure 6
<p>Validation of selected lncRNAs through quantitative RT-PCR in <span class="html-italic">Artemisia annua</span> HAP (high-artemisinin-producing type) and LAP (low-artemisinin-producing type) genotypes. (<b>A</b>–<b>H</b>) Comparative relative expression of both lncRNAs and corresponding target coding genes. *, ** refer to significant difference at <span class="html-italic">p</span> &lt; 0.05 and 0.01, respectively.</p>
Full article ">
23 pages, 19767 KiB  
Article
Small and Long Non-Coding RNA Analysis for Human Trophoblast-Derived Extracellular Vesicles and Their Effect on the Transcriptome Profile of Human Neural Progenitor Cells
by Jessica A. Kinkade, Pallav Singh, Mohit Verma, Teka Khan, Toshihiko Ezashi, Nathan J. Bivens, R. Michael Roberts, Trupti Joshi and Cheryl S. Rosenfeld
Cells 2024, 13(22), 1867; https://doi.org/10.3390/cells13221867 - 11 Nov 2024
Viewed by 622
Abstract
In mice, the fetal brain is dependent upon the placenta for factors that guide its early development. This linkage between the two organs has given rise to the term, the placenta–brain axis. A similar interrelationship between the two organs may exist in humans. [...] Read more.
In mice, the fetal brain is dependent upon the placenta for factors that guide its early development. This linkage between the two organs has given rise to the term, the placenta–brain axis. A similar interrelationship between the two organs may exist in humans. We hypothesize that extracellular vesicles (EVs) released from placental trophoblast (TB) cells transport small RNA and other informational biomolecules from the placenta to the brain where their contents have pleiotropic effects. Here, EVs were isolated from the medium in which human trophoblasts (TBs) had been differentiated in vitro from induced pluripotent stem cells (iPSC) and from cultured iPSC themselves, and their small RNA content analyzed by bulk RNA-seq. EVs derived from human TB cells possess unique profiles of miRs, including hsa-miR-0149-3p, hsa-302a-5p, and many long non-coding RNAs (lncRNAs) relative to EVs isolated from parental iPSC. These miRs and their mRNA targets are enriched in neural tissue. Human neural progenitor cells (NPCs), generated from the same iPSC, were exposed to EVs from either TB or iPSC controls. Both sets of EVs were readily internalized. EVs from TB cells upregulate several transcripts in NPCs associated with forebrain formation and neurogenesis; those from control iPSC upregulated a transcriptional phenotype that resembled glial cells more closely than neurons. These results shed light on the possible workings of the placenta–brain axis. Understanding how the contents of small RNA within TB-derived EVs affect NPCs might yield new insights, possible biomarkers, and potential treatment strategies for neurobehavioral disorders that originate in utero, such as autism spectrum disorders (ASDs). Full article
(This article belongs to the Section Reproductive Cells and Development)
Show Figures

Figure 1

Figure 1
<p>Volcano plots for miRs and lncRNAs within EVs from TB vs. iPSC. Gray dots are miRs or lncRNAs that are not differentially expressed. (<b>A</b>) Volcano plot depicting the differential expression of microRNAs (miRs) within extracellular vesicles (EVs) from TB versus iPSC groups. Gray dots represent miRs that are not differentially expressed. Green dots indicate miRs with a log2 fold change difference between TB and iPSC groups, while red dots highlight miRs with both a significant log10 <span class="html-italic">p</span>-value and log2 fold change difference. (<b>B</b>) Volcano plot for long non-coding RNAs (lncRNAs) within EVs from TB versus iPSC groups. Gray dots denote non-differentially expressed lncRNAs. Green dots show lncRNAs with a log2 fold change between the two groups, and red dots represent lncRNAs with a significant log10 <span class="html-italic">p</span>-adjusted (<span class="html-italic">Q</span>)-value and log2 fold change difference.</p>
Full article ">Figure 2
<p>The analysis of individual miRs with the miRsTissueAtlas2 program [<a href="#B45-cells-13-01867" class="html-bibr">45</a>]. The diagram shows that hsa-miR0149-3p is predominantly expressed in the brain, hsa-miR-302a-5p is abundantly expressed in the heart, followed by the brain and nerve tissues, and hsa-miR-935 is almost exclusively expressed in the brain.</p>
Full article ">Figure 3
<p>TissueEnrich program [<a href="#B46-cells-13-01867" class="html-bibr">46</a>] analysis to determine which human organs and tissues have an abundance of transcripts that might be recognized by differentially expressed miRs shown in <a href="#cells-13-01867-f002" class="html-fig">Figure 2</a>. The primary mRNA targets for hsa-miR-0149-3p are enriched almost exclusively in the cerebral cortex. The mRNA targets for hsa-302a-5p are enriched in the cerebral cortex, followed by the prostate and thyroid gland. Primary mRNA targets for hsa-miR-395 are surprisingly abundant in the cervix and uterus.</p>
Full article ">Figure 4
<p>Extracellular vesicles (EVs) derived from human trophoblasts (TBs) and iPSCs and their internalization by human neural progenitor cells (NPCs). (<b>A</b>) A transmission electron microscopy (TEM) image of EVs derived from human TB cells. (<b>B</b>) A TEM image of EVs derived from human iPSCs.</p>
Full article ">Figure 5
<p>The internalization of EVs from TBs and iPSCs in human NPCs. (A) Fluorescence image of the internalization of EVs from human iPSCs. Red punctate material represents fluorescently tagged EVs (white arrows); the nuclei of NPCs are stained with DAPI (blue); and NPC fibers are labeled in green. (B) Fluorescence image of the internalization of EVs from human TB cells. Red punctate material represents fluorescently tagged EVs (white arrows); the nuclei of NPCs are stained with DAPI (blue); and NPC fibers are labeled in green.</p>
Full article ">Figure 6
<p>Transcriptome results of NPCs treated with TB EVs, iPSC EVs, and control NPCs. (<b>A</b>) A 2D PCA plot of NPCs treated with TB EVs (blue circles), iPSC EVs (green circles), and control NPCs (red circles). Clear separation is evident between control NPCs and those treated with TB EVs or iPSC EVs. (<b>B</b>) Heatmap analysis of NPCs treated with TB EVs, iPSC EVs, and control NPCs. The control NPC formed one cluster, whereas those treated with TB EVs and iPSC EVs showed some overlap between samples. (<b>C</b>) The volcano plot analysis of control NPCs vs. TB EVs treated with NPCs demonstrates several genes that show an increase of a more than 1.5-fold change (FC, shown in green), those few genes that have a −Log<sub>10</sub> <span class="html-italic">Q</span>-value (equivalent to <span class="html-italic">q</span> value ≤ 0.05, shown in light blue), and those that qualified both a −Log<sub>10</sub> <span class="html-italic">Q</span>-value and log<sub>2</sub> FC (shown in red). (<b>D</b>) The volcano plot analysis of control NPCs vs. iPSC EVs treated with NPCs demonstrates several genes that show an increase of more than 1.5-fold change (FC, shown in green), those few genes that have a −Log<sub>10</sub> <span class="html-italic">Q</span>-value (shown in light blue), and those that have qualified both a −Log<sub>10</sub> <span class="html-italic">Q</span>-value and log<sub>2</sub> FC (shown in red). Four independent replicates were assessed for each of the groups.</p>
Full article ">Figure 7
<p>STRING and hub gene analyses for proteins differentially expressed between control NPCs vs. TB EVs treated with NPCs. (<b>A</b>) Protein–protein interactions (PPI) were determined by STRING analysis. (<b>B</b>) The PPI files generated with STRING were imported into the cytoHubba app [<a href="#B67-cells-13-01867" class="html-bibr">67</a>] in Cytoscape [<a href="#B59-cells-13-01867" class="html-bibr">59</a>] to determine the top 10 hub proteins. Within this program, hub proteins were determined with MCC analysis as recommended [<a href="#B67-cells-13-01867" class="html-bibr">67</a>].</p>
Full article ">Figure 8
<p>STRING and hub gene analyses for proteins differentially expressed between control NPCs vs. iPSC EVs treated with NPCs. (<b>A</b>) Protein–protein interactions (PPI) were determined by STRING analysis. (<b>B</b>) The PPI.files generated with STRING were imported into the cytoHubba (Version 0.1) app [<a href="#B67-cells-13-01867" class="html-bibr">67</a>] in Cytoscape [<a href="#B59-cells-13-01867" class="html-bibr">59</a>] to determine the top 10 hub proteins. Within this program, hub proteins were determined with MCC analysis as recommended [<a href="#B67-cells-13-01867" class="html-bibr">67</a>].</p>
Full article ">Figure 9
<p>Gene ontology biological process (GO BP) and molecular function (GO MF) pathways are predicted to be affected based on differentially expressed genes. This was determined by using the WEB-based GEne SeT AnaLysis Toolkit (WebGestalt) 2019 version online program. (<b>A</b>) Control NPCs vs. TB EVs treated with NPCs. (<b>B</b>) Control NPCs vs. iPSC EVs.</p>
Full article ">Figure 10
<p>Brain-specific gene enrichment analysis for differentially expressed genes was determined by the GTEx Portal (API V2) [<a href="#B69-cells-13-01867" class="html-bibr">69</a>]. This was performed for the top 50 differentially expressed genes in each of the comparisons and by searching all brain regions in this database. (<b>A</b>) Control NPCs vs. TB EVs treated with NPCs. (<b>B</b>) Control NPCs vs. iPSCs treated with NPCs.</p>
Full article ">Figure 11
<p>Tissue enrichment analysis based on the TissueEnrich program [<a href="#B46-cells-13-01867" class="html-bibr">46</a>] with the 185 differentially expressed transcripts in human NPCs treated with TB-derived EVs that intersect with miR and lncRNA changes within EVs. (<b>A</b>) These transcripts are primarily associated with the placenta, followed by seminal vesicles, long, adipose tissue, the cerebral cortex, endometrium, ovary, gallbladder, cervix/uterine, and thyroid gland. (<b>B</b>) Heat map analysis reveals that the transcripts that are abundant in the placenta include <span class="html-italic">TMEM100</span>, <span class="html-italic">SVEP1</span>, <span class="html-italic">PTGES</span>, <span class="html-italic">PDGFB</span>, <span class="html-italic">PABPC4L</span>, <span class="html-italic">NRK</span>, <span class="html-italic">MSX2</span>, <span class="html-italic">MEOX2</span>, <span class="html-italic">HGF</span>, <span class="html-italic">DUSP9</span>, <span class="html-italic">CYTL1</span>, <span class="html-italic">CDKN1C</span>, and <span class="html-italic">APLN</span>.</p>
Full article ">
18 pages, 603 KiB  
Review
Biological Insights and Recent Advances in Plant Long Non-Coding RNA
by Zhihao Zhao, Yaodong Yang, Amjad Iqbal, Qiufei Wu and Lixia Zhou
Int. J. Mol. Sci. 2024, 25(22), 11964; https://doi.org/10.3390/ijms252211964 - 7 Nov 2024
Viewed by 349
Abstract
Long non-coding RNA (lncRNA) refers to an RNA molecule longer than 200 nucleotides (nt) that plays a significant role in regulating essential molecular and biological processes. It is commonly found in animals, plants, and viruses, and is characterized by features such as epigenetic [...] Read more.
Long non-coding RNA (lncRNA) refers to an RNA molecule longer than 200 nucleotides (nt) that plays a significant role in regulating essential molecular and biological processes. It is commonly found in animals, plants, and viruses, and is characterized by features such as epigenetic markers, developmental stage-specific expression, and tissue-specific expression. Research has shown that lncRNA participates in anatomical processes like plant progression, while also playing a crucial role in plant disease resistance and adaptation mechanisms. In this review, we provide a concise overview of the formation mechanism, structural characteristics, and databases related to lncRNA in recent years. We primarily discuss the biological roles of lncRNA in plant progression as well as its involvement in response to biotic and abiotic stresses. Additionally, we examine the current challenges associated with lncRNA and explore its potential application in crop production and breeding. Studying plant lncRNAs is highly significant for multiple reasons: It reveals the regulatory mechanisms of plant growth and development, promotes agricultural production and food security, and drives research in plant genomics and epigenetics. Additionally, it facilitates ecological protection and biodiversity conservation. Full article
(This article belongs to the Special Issue Molecular Research in Plant Adaptation to Abiotic Stress)
Show Figures

Figure 1

Figure 1
<p>The mechanisms of lncRNAs.</p>
Full article ">
15 pages, 2466 KiB  
Article
Profiling Plasma Extracellular Vesicle Metabotypes and miRNAs: An Unobserved Clue for Predicting Relapse in Patients with Early-Stage NSCLC
by Vivi Bafiti, Eleni Thanou, Sotiris Ouzounis, Athanasios Kotsakis, Vasilis Georgoulias, Evi Lianidou, Theodora Katsila and Athina Markou
Cancers 2024, 16(22), 3729; https://doi.org/10.3390/cancers16223729 - 5 Nov 2024
Viewed by 470
Abstract
Background and Objective: Lung cancer, the second most prevalent cancer globally, poses significant challenges in early detection and prognostic assessment. Despite advancements in targeted therapies and immunotherapy, the timely identification of relapse remains elusive. Blood-based liquid biopsy biomarkers, including circulating tumor cells (CTCs), [...] Read more.
Background and Objective: Lung cancer, the second most prevalent cancer globally, poses significant challenges in early detection and prognostic assessment. Despite advancements in targeted therapies and immunotherapy, the timely identification of relapse remains elusive. Blood-based liquid biopsy biomarkers, including circulating tumor cells (CTCs), cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), circulating-free RNAs (cfRNAs), and extracellular vesicles (EVs)/exosomes, offer promise for non-invasive monitoring. Methods: We employ a comprehensive approach integrating miRNA/lncRNA/metabolomic datasets, following a mixed-methods content analysis, to identify candidate biomarkers in NSCLC. NSCLC-associated miRNA/gene/lncRNA associations were linked to in silico-derived molecular pathways. Results: For data validation, mass spectrometry-based untargeted metabolomics of plasma EVs highlighted miRNA/lncRNA/metabotypes, linking “glycerophospholipid metabolism” to lncRNA H19 and “alanine, aspartate and glutamate metabolism” to miR-29a-3p. Prognostic significance was established for miR-29a-3p, showing lower expression in NSCLC patients with disease progression compared to stable disease (p = 0.004). Kaplan–Meier survival analysis indicated that patients with miR-29a-3p under-expression had significantly shorter overall survival (OS) (p = 0.038). Despite the expression of lncRNA H19 in plasma EVs being undetected, its expression in plasma cfRNAs correlated significantly with disease progression (p = 0.035). Conclusions: Herein, we showcase the potential of plasma EV-derived miR-29a-3p as a prognostic biomarker and underscore the intricate interplay of miRNAs, lncRNAs, and metabolites in NSCLC biology. Our findings offer new insights and avenues for further exploration, contributing to the ongoing quest for effective biomarkers in early-stage NSCLC. Full article
(This article belongs to the Special Issue RNA in Non-Small-Cell Lung Cancer)
Show Figures

Figure 1

Figure 1
<p>A graphical representation of the in silico pipeline designed and employed to identify miRNAs and lncRNAs that can serve as candidate biomarkers for NSCLC. Transcriptomics datasets were retrieved from the GEO database and the DGE analysis was performed with the GEO2R tool. NSCLC lncRNAs were mined through LncTarD2.0 and then cross-linked with miRNAs-DEGs. Pathway analysis was conducted based on the identified lncRNAs and miRNAs. Next, cross-omics data integration between RNA and EV metabotypes was implemented to reveal candidate biomarkers in NSCLC.</p>
Full article ">Figure 2
<p>Network representation of the NSCLC-related gene, miRNA, and lncRNA associations. (<b>A</b>) miRNA–gene associations (a directed graph); red nodes: up-regulated genes; blue nodes: down-regulated genes; purple nodes: miRNAs. The nodes are interconnected with arrowed edges indicating the direction of the association. (<b>B</b>) miRNA/gene/lncRNA associations; two types of nodes are depicted, also in different shapes. Rectangular nodes: target elements; circular nodes: regulatory elements; blue nodes: down-regulated lncRNAs; red notes: up-regulated lncRNAs; yellow nodes: transcription factors; purple nodes: miRNAs; green nodes: protein-coding genes. Three edge types indicate the relationships among the nodes in question; solid edges: regulatory relationships; dotted edges: binding or interaction; double-dashed edges: associations; edge colors: regulation direction; black edges: an increase in expression (positively E); purple edges: a decrease in expression (negatively E); orange edges: a decrease in function (negatively F); blue edges: a positive function (positively F); and red edges: an interaction between nodes (interact). Nodes with bold outlines: candidate biomarkers.</p>
Full article ">Figure 3
<p>Untargeted metabolomics in plasma EVs from NSCLC patients reveals key perturbed metabolic pathways. Metabolite Set Enrichment Analysis (MSEA) was performed using Metaboanalyst v.6. The enriched pathways were ranked by significance, as indicated by the color scale (top ten pathways with <span class="html-italic">p</span>-values &lt; 0.05).</p>
Full article ">Figure 4
<p>(<b>A</b>) Relative-fold change (2<sup>−ΔΔCq</sup>) of <span class="html-italic">miR-29a-3p</span> in EVs from early-stage NSCLC patient samples in terms of relapse and survival, (<b>B</b>) Kaplan–Meier estimates of OS for NSCLC patients with respect to <span class="html-italic">miR-29a-3p</span> expression. ** <span class="html-italic">p</span> ≤ 0.01.</p>
Full article ">
19 pages, 1428 KiB  
Review
Extracellular Vesicle lncRNAs as Key Biomolecules for Cell-to-Cell Communication and Circulating Cancer Biomarkers
by Panagiotis Papoutsoglou and Antonin Morillon
Non-Coding RNA 2024, 10(6), 54; https://doi.org/10.3390/ncrna10060054 - 5 Nov 2024
Viewed by 491
Abstract
Extracellular vesicles (EVs) are secreted by almost every cell type and are considered carriers of active biomolecules, such as nucleic acids, proteins, and lipids. Their content can be uptaken and released into the cytoplasm of recipient cells, thereby inducing gene reprogramming and phenotypic [...] Read more.
Extracellular vesicles (EVs) are secreted by almost every cell type and are considered carriers of active biomolecules, such as nucleic acids, proteins, and lipids. Their content can be uptaken and released into the cytoplasm of recipient cells, thereby inducing gene reprogramming and phenotypic changes in the acceptor cells. Whether the effects of EVs on the physiology of recipient cells are mediated by individual biomolecules or the collective outcome of the total transferred EV content is still under debate. The EV RNA content consists of several types of RNA, such as messenger RNA (mRNA), microRNA (miRNA), and long non-coding RNA (lncRNA), the latter defined as transcripts longer than 200 nucleotides that do not code for proteins but have important established biological functions. This review aims to update our insights on the functional roles of EV and their cargo non-coding RNA during cancer progression, to highlight the utility of EV RNA as novel diagnostic or prognostic biomarkers in cancer, and to tackle the technological advances and limitations for EV RNA identification, integrity assessment, and preservation of its functionality. Full article
(This article belongs to the Special Issue Extracellular Vesicles and ncRNA)
Show Figures

Figure 1

Figure 1
<p>Different subtypes of lncRNAs and circRNA biogenesis. (<b>a</b>) Classification of lncRNAs based on their genomic location relative to protein-coding genes (PCG); (<b>b</b>) generation of circRNA from back-splicing of a precursor mRNA (pre-mRNA). Arrows in panel (<b>a</b>) show the direction of transcription and in panel (<b>b</b>) splicing events.</p>
Full article ">Figure 2
<p>Biological effects of cancer-derived EVs on diverse cell types within TME. Cancer cells produce heterogenous EV populations consisting of different RNA cargo compositions and sizes, which are secreted in the extracellular space and shape the physiological responses of recipient non-tumorigenic cells of the TME, such as monocytes, dendritic cells, and fibroblasts. MVB: multivesicular body.</p>
Full article ">Figure 3
<p>Circulating lncRNAs within EVs as cancer biomarkers. Examples of lncRNAs demonstrated to be highly enriched in EVs from human biofluids derived from patients with breast, prostate, pancreatic, liver, bladder, and kidney cancer.</p>
Full article ">
12 pages, 2735 KiB  
Article
Identification of Genes and Long Non-Coding RNAs Putatively Related to Portunus trituberculatus Sex Determination and Differentiation Using Oxford Nanopore Technology Full-Length Transcriptome Sequencing
by Shaoting Jia, Guang Li, Yuchao Huang, Yashi Hou, Baoquan Gao and Jianjian Lv
Int. J. Mol. Sci. 2024, 25(21), 11845; https://doi.org/10.3390/ijms252111845 - 4 Nov 2024
Viewed by 453
Abstract
The swimming crab (Portunus trituberculatus) is an economically important species in China, and its growth traits show obvious sexual dimorphism. Thus, it is important to study the mechanism of sex determination and differentiation in this species. Herein, we identified 2138 differentially [...] Read more.
The swimming crab (Portunus trituberculatus) is an economically important species in China, and its growth traits show obvious sexual dimorphism. Thus, it is important to study the mechanism of sex determination and differentiation in this species. Herein, we identified 2138 differentially expressed genes and 132 differentially expressed long non-coding RNAs (lncRNAs) using Oxford Nanopore Technology full-length transcriptome sequencing. We predicted 561 target genes of the differentially expressed lncRNAs according to their location and base pair complimentary principles. Furthermore, pathways related to sex determination, differentiation, and reproduction were enriched for lncRNAs according to gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses. This indicated that lncRNAs might play regulatory roles in these pathways. Our results could form the basis for future studies of sex determination and differentiation in P. trituberculatus. Full article
Show Figures

Figure 1

Figure 1
<p>Analysis of the ONT full-length transcriptome sequence of the testis and ovary in <span class="html-italic">P. trituberculatus</span>. (<b>A</b>) Heatmap of the expression relationship between samples. (<b>B</b>) Venn diagram of DEGs between testis and ovary; T: testis, O: ovary. (<b>C</b>) MA plots of DEGs. (<b>D</b>) Volcano plot of DEGs. ONT, Oxford Nanopore Technology; DEG, differentially expressed gene; MA, M-versus-A; FC, fold change; CPM, counts per million.</p>
Full article ">Figure 2
<p>DEGs related to testis and ovary development. (<b>A</b>) Heatmap of the top 16 highly expressed DEGs in the testis. (<b>B</b>) Heatmap of the top 18 highly expressed DEGs in the ovary. (<b>C</b>) Scatter plots of the top 20 enriched biological process gene ontology (GO) terms. (<b>D</b>) Column diagram of the top 20 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways.</p>
Full article ">Figure 3
<p>Analysis of lncRNAs from testis and ovary transcriptome sequencing. (<b>A</b>) Prediction of lncRNAs by four methods (CPC, CNCI, CPAT, and Pfam). (<b>B</b>) Venn diagram of lncRNA expression mode in the testis and ovary; T: testis, O: ovary. (<b>C</b>) Four classifications of lncRNAs. (<b>D</b>) Volcano plots of differentially expressed lncRNAs created using DESeq2. (<b>E</b>) Prediction of lncRNA target genes by two methods. lncRNA, long non-coding RNA; CPC, coding potential calculator; CNCI, coding-non-coding index; CPAT, coding potential assessment tool; Pfam, protein families.</p>
Full article ">Figure 4
<p>Analysis of the differentially expressed lncRNA target genes. (<b>A</b>) Heatmap of the top 20 highly expressed genes in the ovary. (<b>B</b>) Heatmap of the top 20 highly expressed genes in the testis. (<b>C</b>) Scatter plots of the top 20 enriched biological process GO terms. (<b>D</b>) Column diagram of the top 20 KEGG pathways.</p>
Full article ">Figure 5
<p>Verification of six differentially expressed lncRNAs. (<b>A</b>) Heatmap of six differentially expressed lncRNAs from transcriptome sequencing. (<b>B</b>) Validation of the six differentially expressed lncRNAs by qRT-PCR (**** <span class="html-italic">p</span> &lt; 0.0001). The data analysis was conducted by the 2<sup>−ΔΔCT</sup> method, and <span class="html-italic">β-actin</span> was chosen as a reference gene.</p>
Full article ">
32 pages, 9671 KiB  
Article
Ten Hypermethylated lncRNA Genes Are Specifically Involved in the Initiation, Progression, and Lymphatic and Peritoneal Metastasis of Epithelial Ovarian Cancer
by Eleonora A. Braga, Alexey M. Burdennyy, Leonid A. Uroshlev, Danila M. Zaichenko, Elena A. Filippova, Svetlana S. Lukina, Irina V. Pronina, Iana R. Astafeva, Marina V. Fridman, Tatiana P. Kazubskaya, Vitaly I. Loginov, Alexey A. Dmitriev, Aleksey A. Moskovtsev and Nikolay E. Kushlinskii
Int. J. Mol. Sci. 2024, 25(21), 11843; https://doi.org/10.3390/ijms252111843 - 4 Nov 2024
Viewed by 644
Abstract
Abstract: Our work aimed to evaluate and differentiate the role of ten lncRNA genes (GAS5, HAND2-AS1, KCNK15-AS1, MAGI2-AS3, MEG3, SEMA3B-AS1, SNHG6, SSTR5-AS1, ZEB1-AS1, and ZNF667-AS1) in the development and progression of epithelial [...] Read more.
Abstract: Our work aimed to evaluate and differentiate the role of ten lncRNA genes (GAS5, HAND2-AS1, KCNK15-AS1, MAGI2-AS3, MEG3, SEMA3B-AS1, SNHG6, SSTR5-AS1, ZEB1-AS1, and ZNF667-AS1) in the development and progression of epithelial ovarian cancer (EOC). A representative set of clinical samples was used: 140 primary tumors from patients without and with metastases and 59 peritoneal metastases. Using MS-qPCR, we demonstrated an increase in methylation levels of all ten lncRNA genes in tumors compared to normal tissues (p < 0.001). Using RT-qPCR, we showed downregulation and an inverse relationship between methylation and expression levels for ten lncRNAs (rs < −0.5). We further identified lncRNA genes that were specifically hypermethylated in tumors from patients with metastases to lymph nodes (HAND2-AS1), peritoneum (KCNK15-AS1, MEG3, and SEMA3B-AS1), and greater omentum (MEG3, SEMA3B-AS1, and ZNF667-AS1). The same four lncRNA genes involved in peritoneal spread were associated with clinical stage and tumor extent (p < 0.001). Interestingly, we found a reversion from increase to decrease in the hypermethylation level of five metastasis-related lncRNA genes (MEG3, SEMA3B-AS1, SSTR5-AS1, ZEB1-AS1, and ZNF667-AS1) in 59 peritoneal metastases. This reversion may be associated with partial epithelial–mesenchymal transition (EMT) in metastatic cells, as indicated by a decrease in the level of the EMT marker, CDH1 mRNA (p < 0.01). Furthermore, novel mRNA targets and regulated miRNAs were predicted for a number of the studied lncRNAs using the NCBI GEO datasets and analyzed by RT-qPCR and transfection of SKOV3 and OVCAR3 cells. In addition, hypermethylation of SEMA3B-AS1, SSTR5-AS1, and ZNF667-AS1 genes was proposed as a marker for overall survival in patients with EOC. Full article
(This article belongs to the Special Issue Molecular Diagnostics and Genomics of Tumors)
Show Figures

Figure 1

Figure 1
<p>(<b>a</b>) Methylation levels of ten lncRNA genes in 18 samples from donors (D), 123 histologically normal ovarian tissues from EOC patients (N), 140 primary ovarian tumors (T), and 59 peritoneal macroscopic metastases (PM); (<b>b</b>) methylation levels of ten lncRNA genes in 43 primary ovarian tumors from patients without metastases (T) and 43 matched histologically normal ovarian tissues (N). * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, # <span class="html-italic">p</span> &lt; 0.0001.</p>
Full article ">Figure 2
<p>(<b>a</b>) Hypermethylated lncRNA genes associated with advanced clinical stages of EOC; 47 samples of stages I + II and 93 samples of stages III + IV; (<b>b</b>) hypermethylated lncRNA genes associated with advanced histological grade of EOC; 72 G1–G2 samples and 68 G3–G4 samples. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, # <span class="html-italic">p</span> &lt; 0.0001.</p>
Full article ">Figure 3
<p>Hypermethylated lncRNA genes associated with different types of EOC metastases: (<b>a</b>) metastases to the great omentum (70 patients—without, 70 patients—with); (<b>b</b>) dissemination through the peritoneum (70 patients—without, 70 patients—with); (<b>c</b>) metastases to the lymph nodes (110 patients—N0, 30 patients—N1–N3); (<b>d</b>) samples from patients with metastases of any type were considered (44 patients—without, 96 patients—with). ** <span class="html-italic">p</span> &lt; 0.01, # <span class="html-italic">p</span> &lt; 0.0001.</p>
Full article ">Figure 4
<p>Methylation levels of six lncRNA genes in 59 peritoneal metastases (PM) vs. 59 primary tumors from the same EOC patients. ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, # <span class="html-italic">p</span> &lt; 0.0001.</p>
Full article ">Figure 5
<p>Changes in the levels of ten lncRNAs in primary tumors compared to matched histologically normal tissues. The lncRNAs HAND2-AS1, MEG3, and ZEB1-AS1 were tested in the subset of 73 paired (T/N) samples, GAS5 in 68 samples, and the remaining six lncRNAs in 56 samples. ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, # <span class="html-italic">p</span> &lt; 0.0001.</p>
Full article ">Figure 6
<p>Changes in expression levels of four lncRNAs (GAS5, HAND2-AS1, MAGI2-AS3, MEG3) in serous ovarian cystadenocarcinoma according to the GEPIA 2.0 data (red—tumor, gray—normal); 426 tumor samples, 88 normal tissues; red asterisk corresponds to <span class="html-italic">p</span> &lt; 0.01; TPM—transcripts per million.</p>
Full article ">Figure 7
<p>Statistically significant negative correlation between the changes in methylation and expression levels of ten lncRNA genes in the subset of 90 paired (T/N) samples of EOC. The lncRNAs HAND2-AS1, MEG3, and ZEB1-AS1 were tested in the subset of 73 samples, GAS5 in 68 samples, and the other six lncRNAs in 56 samples. Spearman’s correlation coefficients (<span class="html-italic">r<sub>s</sub></span>) are given.</p>
Full article ">Figure 8
<p>(<b>a</b>) Decreased relative expression level of lncRNA HAND2-AS1 in primary ovarian tumors from patients with lymphatic metastases (21 samples, T/N) compared to primary tumors without lymphatic metastases (52 samples, T/N); (<b>b</b>) decreased relative expression level of lncRNA HAND2-AS1 in primary ovarian tumors from patients with any metastases (50 samples, T/N) compared to primary tumors without any metastases (23 samples, T/N); (<b>c</b>) increased relative expression level of lncRNAs HAND2-AS1 and MEG3 in peritoneal metastases compared to primary tumors from the same EOC patients (31 PM samples vs. 31 tumor samples).</p>
Full article ">Figure 9
<p>Changes in mRNA levels of five EMT markers (CDH1, SNAI2/SLUG, ZEB1, ZEB2, VIM mRNAs) in 30 peritoneal metastases (PM) compared to 46 primary ovarian tumors (T).</p>
Full article ">Figure 10
<p>(<b>a</b>) Relative expression levels of FKBP14 and SERPINF1 mRNAs in the subset of 44 EOC samples (27 T/N +17 PM/N); (<b>b</b>–<b>d</b>) positive correlations of expression levels of lncRNAs MAGI2-AS3 and HAND2-AS1 with expression levels of FKBP14 and SERPINF1 mRNAs in 44 EOC samples (27 T/N +17 PM/N).</p>
Full article ">Figure 11
<p>(<b>a</b>) Relative expression levels of four miRNAs (miR-124-3p, miR-124-5p, miR-137-3p, miR-33b-5p); (<b>b</b>) correlation plot to analyze possible correlations between four miRNAs and ten lncRNAs in the subset of 41 paired (T/N) EOC samples.</p>
Full article ">Figure 12
<p>Changes in the levels of lncRNA GAS5 in (<b>a</b>) SKOV3 and (<b>b</b>) OVCAR3 cells transfected with miRNA mimics: c.el-67—cel-miR-67-3p, hsa-124—hsa-miR-124-3p, hsa-137—hsa-miR-137-3p. SKOV3: <span class="html-italic">p</span> (hsa-124 vs. mock) = 0.13, <span class="html-italic">p</span> (hsa-137 vs. mock) = 0.32, <span class="html-italic">p</span> (hsa-221 vs. mock) = 0.99; OVCAR3: <span class="html-italic">p</span> (hsa-124 vs. mock) = 0.99, <span class="html-italic">p</span> (hsa-137 vs. mock) = 0.99, <span class="html-italic">p</span> (hsa-221 vs. mock) = 0.99. Data are presented as the median and 25–75% percentiles (n = 4).</p>
Full article ">Figure 13
<p>Changes in the levels of lncRNA ZNF667-AS1 in (<b>a</b>) SKOV3 and (<b>b</b>) OVCAR3 cells transfected with miRNA mimics: c.el-67—cel-miR-67-3p, hsa-124—hsa-miR-124-3p, hsa-137—hsa-miR-137-3p. SKOV3: <span class="html-italic">p</span> (hsa-124 vs. mock) = 0.99, <span class="html-italic">p</span> (hsa-137 vs. mock) = 0.25, <span class="html-italic">p</span> (hsa-221 vs. mock) = 0.55; OVCAR3: <span class="html-italic">p</span> (hsa-124 vs. mock) = 0.057, <span class="html-italic">p</span> (hsa-137 vs. mock) = 0.99, <span class="html-italic">p</span> (hsa-221 vs. mock) = 0.99. Data are presented as the median and 25–75% percentiles (n = 4).</p>
Full article ">Figure 14
<p>Changes in the levels of lncRNA ZEB1-AS1 in (<b>a</b>) SKOV3 and (<b>b</b>) OVCAR3 cells transfected with miRNA mimics: c.el-67—cel-miR-67-3p, hsa-124—hsa-miR-124-3p, hsa-137—hsa-miR-137-3p. SKOV3: <span class="html-italic">p</span> (hsa-124 vs. mock) = 0.99, <span class="html-italic">p</span> (hsa-137 vs. mock) = 0.99, <span class="html-italic">p</span> (hsa-221 vs. mock) = 0.72, OVCAR3: <span class="html-italic">p</span> (hsa-124 vs. mock) = 0.99, <span class="html-italic">p</span> (hsa-137 vs. mock) = 0.99, <span class="html-italic">p</span> (hsa-221 vs. mock) = 0.99. Data are presented as the median and 25–75% percentiles (n = 4).</p>
Full article ">Figure 15
<p>Changes in the levels of lncRNA KCNK15-AS1 in (<b>a</b>) SKOV3 and (<b>b</b>) OVCAR3 cells transfected with miRNA mimics: c.el-67—cel-miR-67-3p, hsa-124—hsa-miR-124-3p, hsa-137—hsa-miR-137-3p. SKOV3: <span class="html-italic">p</span> (hsa-124 vs. mock) = 0.99, <span class="html-italic">p</span> (hsa-137 vs. mock) = 0.78, <span class="html-italic">p</span> (hsa-221 vs. mock) = 0.10; OVCAR3: <span class="html-italic">p</span> (hsa-124 vs. mock) = 0.69, <span class="html-italic">p</span> (hsa-137 vs. mock) = 0.99, <span class="html-italic">p</span> (hsa-221 vs. mock) = 0.99. Data are presented as the median and 25–75% percentiles (n = 4).</p>
Full article ">Figure 16
<p>Analysis of overall survival and overall hazard in OC patients based on methylation status of (<b>a</b>) <span class="html-italic">ZNF667-AS1</span>, (<b>b</b>) <span class="html-italic">SEMA3B-AS1</span>, and (<b>c</b>) <span class="html-italic">SSTR5-AS1</span> genes.</p>
Full article ">Figure 17
<p>Comparison of the functional significance of the studied lncRNAs; (<b>a</b>) potential mRNA targets of nine lncRNAs according to the analysis of co-expressed mRNAs at <span class="html-italic">r<sub>s</sub></span> &gt; 0.4 in the GSE211669 dataset; KCNK15-AS1 had 41 target mRNAs; (<b>b</b>) EMT-associated genes among the identified mRNA targets according to GeneCards; 8 of 41 target mRNAs for KCNK15-AS1 were EMT-associated.</p>
Full article ">
15 pages, 5395 KiB  
Article
Transcriptome and Expression Analysis of Glycerol Biosynthesis-Related Genes in Glenea cantor Fabricius (Cerambycidae: Lamiinae)
by Taihui Lan, Ranran Su, Zishu Dong, Xin Tong, Xialin Zheng and Xiaoyun Wang
Int. J. Mol. Sci. 2024, 25(21), 11834; https://doi.org/10.3390/ijms252111834 - 4 Nov 2024
Viewed by 418
Abstract
Glenea cantor Fabricius (Cerambycidae: Lamiinae) is an important pest that damages kapok trees in Southeast Asia with a wide adaptability to temperature. Glycerol is a protectant and energy source for insects in low-temperature environments. However, glycerol biosynthesis-related genes at the molecular level are [...] Read more.
Glenea cantor Fabricius (Cerambycidae: Lamiinae) is an important pest that damages kapok trees in Southeast Asia with a wide adaptability to temperature. Glycerol is a protectant and energy source for insects in low-temperature environments. However, glycerol biosynthesis-related genes at the molecular level are limited in G. cantor. In this study, the supercooling points and freezing points at different stages were measured, and the cold hardiness of male and female pupae significantly differed. Moreover, a full-length transcriptome of G. cantor was established; glycerol kinase (GK) and glycerol-3-phosphate dehydrogenase (GPDH) genes, which are related to glycerol metabolism, were identified, with a special focus on their expression profiles. A total of 24,476 isoforms stemmed from the full-length transcriptome, along with 568 lncRNAs, 56 transcription factor (TF) families, and 1467 alternative splicing (AS) events. The KEGG pathway enrichment analysis revealed that the isoforms associated with AS were enriched primarily in glycerolipid and glycerophospholipid metabolism. In total, three GK genes and one GPDH gene were identified, and GcGK1 and GcGK3 presented differential sex expression during the pupal stage, which may play a role in thermal adaptability. This study provides a valuable transcriptional database of G. cantor and helps to elucidate the function of glycerol in the thermal adaptation mechanism of longhorn beetles. Full article
Show Figures

Figure 1

Figure 1
<p>Venn diagram showing the statistical annotation results of the four public databases.</p>
Full article ">Figure 2
<p>Bar plot showing homologous species distribution of transcripts in the Nr database.</p>
Full article ">Figure 3
<p>LncRNA prediction and transcription factor classification in <span class="html-italic">G. cantor</span>. (<b>A</b>) The number of lncRNA transcripts was predicted using CNCI and CPC software. (<b>B</b>) The numbers and families of the top 10 transcription factors were predicted using PacBio.</p>
Full article ">Figure 4
<p>AS events in the full-length transcriptome of <span class="html-italic">G. cantor</span>. (<b>A</b>) Types of AS events identified in the full-length transcriptome. (<b>B</b>) The number of genes containing AS isoforms was counted. The vertical coordinate indicates the number of isoforms, and the horizontal coordinate indicates the number and percentage of genes containing the corresponding number of isoforms.</p>
Full article ">Figure 5
<p>GO and KEGG pathway functional enrichment analysis of isoform AS events. The horizontal coordinate is the Rich factor, which is the ratio of the number of isoforms in the metabolic pathway or GO term to the number of corresponding background isoforms, with larger values indicating greater enrichment. The vertical coordinate indicates the metabolic pathway or GO term with the highest level of enrichment. The color of the dot represents the q value, which is the <span class="html-italic">p</span> value after checking. The red color represents a lower value, and a lower value indicates more significant enrichment. The size of the dots represents the number of genes in the pathway. (<b>A</b>) Bubble plot of the top 20 enriched KEGG pathways. (<b>B</b>) Bubble plot of enriched BPs according to GO terms. (<b>C</b>) Bubble plot of enriched MFs of GO terms. (<b>D</b>) Bubble plot of enriched CCs of GO terms.</p>
Full article ">Figure 6
<p>Neighbor-joining (NJ) phylogenetic tree of GK genes from <span class="html-italic">G. cantor</span> and other insect species.</p>
Full article ">Figure 7
<p>Neighbor-joining (NJ) phylogenetic tree of the GPDH gene from <span class="html-italic">G. cantor</span> and other insect species.</p>
Full article ">Figure 8
<p>Relative expression levels of the GcGK and GcGPDH genes in different development stages of <span class="html-italic">G. cantor</span>. (<b>A</b>) GK1; (<b>B</b>) GK2; (<b>C</b>) GK3; (<b>D</b>) GPDH. Note: E: egg, L4: fourth-instar larvae, PM: male pupae, PF: female pupae, AM: male adult, AF: female adult. Significant differences in relative expression levels at different developmental stages are indicated by distinct letters, <span class="html-italic">p</span> &lt; 0.05.</p>
Full article ">
17 pages, 10591 KiB  
Article
LncRNA Taurine Up-Regulated 1 Knockout Provides Neuroprotection in Ischemic Stroke Rats by Inhibiting Nuclear-Cytoplasmic Shuttling of HuR
by Xiaocheng Shi, Sha Liu, Yichun Zou, Hengping Wu, Jinyang Ma, Junbin Lin and Xin Zhang
Biomedicines 2024, 12(11), 2520; https://doi.org/10.3390/biomedicines12112520 - 4 Nov 2024
Viewed by 558
Abstract
Background: Long non-coding RNA taurine-upregulated gene 1 (TUG1) is involved in various cellular processes, but its role in cerebral ischemia–reperfusion injury remains unclear. This study investigated TUG1’s role in regulating the nucleocytoplasmic shuttling of human antigen R (HuR), a key apoptosis regulator [...] Read more.
Background: Long non-coding RNA taurine-upregulated gene 1 (TUG1) is involved in various cellular processes, but its role in cerebral ischemia–reperfusion injury remains unclear. This study investigated TUG1’s role in regulating the nucleocytoplasmic shuttling of human antigen R (HuR), a key apoptosis regulator under ischemic conditions. Methods: CRISPR-Cas9 technology was used to generate TUG1 knockout Sprague Dawley rats to assess TUG1’s impact on ischemic injury. The infarct area and neuronal apoptosis were evaluated using TUNEL, hematoxylin and eosin (HE), and TTC staining, while behavioral functions were assessed. Immunofluorescence staining with confocal microscopy was employed to examine TUG1-mediated HuR translocation and expression changes in the apoptosis-related proteins COX-2 and Bax. Results: TUG1 knockout rats showed significantly reduced cerebral infarct areas, decreased neuronal apoptosis, and improved neurological functions compared to controls. Immunofluorescence staining revealed that HuR translocation from the nucleus to the cytoplasm was inhibited, leading to decreased COX-2 and Bax expression levels. Conclusions: TUG1 knockout reduces ischemic damage and neuronal apoptosis by inhibiting HuR nucleocytoplasmic shuttling, making TUG1 a potential therapeutic target for ischemic stroke. Full article
Show Figures

Figure 1

Figure 1
<p>Generation and validation of the TUG1 knockout (TUG1<sup>KO</sup>) rat model. (<b>A</b>) Schematic representation of the rat TUG1 gene locus, including PCR primer locations and gRNA target sites (the gene orientation is from left to right; the total size is 7.04 kb). Solid boxes indicate open reading frames (ORFs), and open boxes represent untranslated regions (UTRs). (<b>B</b>) Sequencing verification of the TUG1<sup>KO</sup> rats (−/−). Using gRNA1 and gRNA2, Cas9-mediated cleavage generated a 9468 bp deletion, encompassing all exons of the TUG1 gene. (<b>C</b>–<b>F</b>) Genotyping of TUG1<sup>KO</sup> rats (−/−) by DNA gel electrophoresis of PCR-amplified products. Lane M: DNA size marker; TUG1−/−: homozygous knockout (−/−); lane WT: wild type (+/+). Primers used: (<b>C</b>) Tug1-F and Tug1-R; (<b>D</b>) Tug1-F and Tug1-R with Tug1-He/Wt-R; (<b>E</b>) Tug1-F with Tug1-He/Wt-F and Tug1-R; and (<b>F</b>) Tug1-F1 and Tug1-R1. (<b>G</b>) Quantitative real-time PCR analysis of TUG1 expression in TUG1<sup>KO</sup> rats (TUG1+/−) showed a 73.7% reduction compared to wild-type (WT) controls. (*** <span class="html-italic">p</span> &lt; 0.001).</p>
Full article ">Figure 2
<p>Knockout TUG1 reduced cerebral infarction and improved neurological outcome after MCAO. (<b>A</b>) Comparative analysis of mNSS scores for neurological deficits in rats 24 h after MCAO surgery. *** <span class="html-italic">p</span> &lt; 0.001 compared to WT + MCAO, n = 8/group. (<b>B</b>,<b>C</b>) TTC staining of brain tissue sections after MCAO. The area of the infarct zone on representative images of coronal sections of the brain shown in white-stained areas and quantification of brain infarct volume is provided. ** <span class="html-italic">p</span> &lt; 0.01 compared to WT + MCAO, n = 3/group. (<b>D</b>,<b>E</b>) Neuronal apoptosis revealed with TUNEL staining (×400) in the ischemic brain of rats and quantification of the number of positive apoptotic cells (red). The number of positive TUNEL cells was significantly lower in TUG1+/− heterozygous rats than in WT rats.(**** <span class="html-italic">p</span> &lt; 0.0001) (<b>F</b>,<b>G</b>) HE staining (×100) was used to show changes in neurons, and more normal neurons were observed in TUG1+/− heterozygous rats than in WT rats, indicating a milder ischemic brain injury.</p>
Full article ">Figure 3
<p>TUG1 promotes HuR cytoplasmic translocation. (<b>A</b>) Hippocampal CA1 neurons were imaged using wide-field microscopy and confocal microscopy. The red fluorescent region, indicated by arrows in the image, represents cytoplasmic HuR staining. The scale bar corresponds to 20 µm. (<b>B</b>) Immunofluorescence staining revealed that in WT rats after MCAO, the proportion of cytoplasmic HuR increased significantly compared to the overall cell proportion. In contrast, TUG1+/− heterozygous rats showed no significant difference in this proportion in comparison to the control group (** <span class="html-italic">p</span> &lt; 0.01). (<b>C</b>) Confocal microscopy provided consistent results with wide-field microscopy (<span class="html-italic">n</span> = 5) (** <span class="html-italic">p</span> &lt; 0.01). (<b>D</b>) Following 6 h of OGD treatment, HT22 cells were subjected to TUG1-FISH staining on paraffin-embedded sections. In these images, TUG1 appears in green, while the cell nuclei are stained with DAPI in blue. The green fluorescent region indicated by arrows in the control group denotes TUG1 staining in the cell nucleus. Conversely, the green fluorescent region indicated by arrows in the OGD group depicts TUG1 staining in the cytoplasm outside the cell nucleus. The scale bar represents 20 µm. (<b>E</b>,<b>F</b>) In HT22 cells, after 6 h of OGD treatment, the proportion of TUG1 in the cytoplasm decreased significantly compared to the control group from 76.19% to 53.43% (* <span class="html-italic">p</span> &lt; 0.05), while the proportion of TUG1 in the cell nucleus increased significantly from 23.82% to 46.57% (* <span class="html-italic">p</span> &lt; 0.05). Values for all panels are means ± standard deviations.</p>
Full article ">Figure 4
<p>TUG1 improves COX-2 mRNA and Bax mRNA stability through HuR. (<b>A</b>) Immunofluorescence staining of COX-2 was conducted on neurons in the CA1 region of the hippocampus. (<b>B</b>) The fluorescent intensity of COX-2 was quantitatively analyzed. (** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001) (<b>C</b>) Immunofluorescence staining of Bax was conducted on neurons in the CA1 region of the hippocampus. (<b>D</b>) The fluorescent intensity of Bax was quantitatively analyzed. Cell nuclei were stained with DAPI (blue). (** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001).</p>
Full article ">
Back to TopTop