Bioinformatics Prediction Research Articles

Novel compound heterozygous MYO15A splicing variants in autosomal recessive non-syndromic hearing loss.

Hereditary hearing loss is a highly heterogeneous disorder. This study aimed to identify the genetic cause of a Chinese family with autosomal recessive non-syndromic sensorineural hearing loss (ARNSHL). Clinical information and peripheral blood samples were collected from the proband and its parents. Two-step high-throughput next-generation sequencing on the Ion Torrent platform was applied to detect variants as follows. First, long-range PCR was performed to amplify all the regions of the GJB2, GJB3, SLC26A4, and MT-RNR1 genes, followed by next-generation sequencing. If no candidate pathogenetic variants were found, the targeted exon sequencing with AmpliSeq technology was employed to examine another 64 deafness-associated genes. Sanger sequencing was used to identify variants and the lineage co-segregation. The splicing of the MYO15A gene was assessed by in silico bioinformatics prediction and minigene assays. Two candidate MYO15A gene (OMIM, #602,666) heterozygous splicing variants, NG_011634.2 (NM_016239.3): c.6177 + 1G > T and c.9690 + 1G > A, were identified in the proband, and these two variants were both annotated as pathogenic according to the American College of Medical Genetics and Genomics (ACMG) guidelines. Further bioinformatic analysis predicted that the c.6177 + 1G > T variant might cause exon skipping and that the c.9690 + 1G > A variant might activate a cryptic splicing donor site in the downstream intronic region. An in vitro minigene assay confirmed the above predictions. We identified a compound heterozygous splicing variant in the MYO15A gene in a Han Chinese family with ARNSHL. Our results broaden the spectrum of MYO15A variants, potentially benefiting the early diagnosis, prevention, and treatment of the disease.

Low-Level miR-199 Contribute to Neuropathic Low Back Pain via TRPV1 by Regulating the Production of Pro-Inflammatory Cytokines on Macrophage.

To explore the post-translational regulation of TRPV1, which plays an important role in neuropathic low back pain (NLBP). qPCR was used to examine the gene mRNA levels. Western blot was used to examine the protein level. NLBP rat model was established for confirming what we observed in clinical samples. Dual-luciferase assay was used to verify the miR-199 targets on the 3'UTR of TRPV1. Cell coculture was used to explore the interaction between macrophages and nerve cells. We found the mRNA level of TRVP1 decreased in the sinuvertebral nerve biopsy of NLBP. With bioinformatics prediction, miR199 would involve the post-transcription regulation of TRPV1. As the prediction, the miR199 level decreased in the clinical samples. Correlation regression analysis showed a negative correlation between miR-199 and TRPV1. The same phenomenon was confirmed in the rat NLBP model. With dual-luciferase assay, we confirmed that miR199 directly binds to the 3'UTR of TRPV1. Through co-culture of macrophage (THP1) and sNF96.2, we found that up or down-regulates miR-199 in macrophage and sNF96.2 could relieve or aggravate the injury of nerve cells strain. These results suggest that the occurrence of NLBP may be caused by the lower expression of miR-199 in macrophages and nerve via TRPV1.

Identification of novel biomarkers related to neutrophilic inflammation in COPD.

Chronic obstructive pulmonary disease (COPD) is one of the most prevalent chronic respiratory diseases and the fourth cause of mortality globally. Neutrophilic inflammation has a vital role in the occurrence and progression of COPD. This study aimed to identify the novel hub genes involved in neutrophilic inflammation in COPD through bioinformatic prediction and experimental validation. Both the single-cell RNA sequencing (scRNA-seq) dataset (GSE173896) and the RNA sequencing (RNA-seq) dataset (GSE57148) were downloaded from the Gene Expression Omnibus (GEO) database. The Seurat package was used for quality control, dimensions reduction, and cell identification of scRNA-seq. The irGSEA package was used for scoring individual cells. The Monocle2 package was used for the trajectory analysis of neutrophils. The CIBERSORT algorithm was used for analysis of immune cell infiltration in the lungs of COPD patients and controls in RNA-seq dataset, and weighted gene co-expression network analysis (WGCNA) correlated gene modules with neutrophil infiltration. The Mendelian randomization (MR) analysis explored the causal relationship between feature DEGs and COPD. The protein-protein interaction (PPI) network of novel hub genes was constructed, and real-time quantitative polymerase chain reaction (qRT-PCR) was used to validate novel hub genes in clinical specimens. In scRNA-seq, the gene sets upregulated in COPD samples were related to the neutrophilic inflammatory response and TNF-α activation of the NF-κB signaling pathway. In RNA-seq, immune infiltration analysis showed neutrophils were upregulated in COPD lung tissue. We combined data from differential and modular genes and identified 51 differential genes associated with neutrophilic inflammation. Using MR analysis, 6 genes were explored to be causally associated with COPD. Meanwhile, 11 hub genes were identified by PPI network analysis, and all of them were upregulated. qRT-PCR experiments validated 9 out of 11 genes in peripheral blood leukocytes of COPD patients. Furthermore, 5 genes negatively correlated with lung function in COPD patients. Finally, a network of transcription factors for NAMPT and PTGS2 was constructed. This study identified nine novel hub genes related to the neutrophilic inflammation in COPD, and two genes were risk factors of COPD, which may serve as potential biomarkers for the clinical severity of COPD.

Screening of potential biomarkers in propofol-induced neurotoxicity via bioinformatics prediction and experimental verification.

To identify hub genes and biological processes of propofol-induced neurotoxicity and promote the development of pediatric anesthesiology. We downloaded the GSE106799 dataset from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were screened, then Kyoto Encyclopedia of Genes and Genomes, Gene Ontology and Gene Set Enrichment analyses were performed on all DEGs. We identified potential ferroptosis genes in the pathogenesis of propofol-induced neurotoxicity. A key module was obtained after performing weighted gene co-expression network analysis (WGCNA) on the GSE106799 dataset. Hub genes were identified after the least absolute shrinkage and selection operator (LASSO) regression analysis of the intersection of DEGs and genes from the key module. We established a competing endogenous RNA network and predicted potential drugs according to the hub genes. Total RNA and proteins were extracted for real-time quantitative polymerase chain reaction and Western blotting, respectively. A total of 112 DEGs, including 76 upregulated and 36 downregulated ones were screened out. Propofol-induced neurotoxicity involved processes such as nervous system development, activation of JAK/STAT and MAPK signaling pathways, vascular regeneration, and oxidative stress. The results of WGCNA suggested that the tan module was the most strongly associated with propofol-induced neurotoxicity. We identified 4 hub genes (EGR4, HAO1, ITK and GM14446) after LASSO regression analysis. Animal experiments demonstrated that propofol caused overexpression of the protein levels of HAO1, ITK and inflammatory factors in the brain, as well as the mRNA levels of HAO1, ITK and GM14446. Propofol inhibited expression of EGR4 at mRNA and protein levels. Previous studies have demonstrated that EGR4, HAO1, ITK and GM14446 play a role in intellectual development, neuroinflammation and neuronal differentiation. These hub genes may help us to find new preventive and therapeutic targets for propofol-induced neurotoxicity.

Effect of transglutaminase-catalyzed glycosylation on the allergenicity of tropomyosin in the Perna viridis food matrix.

Food allergy is one of the hot issues in the field of food safety, and there have been a lot of concerns on how to reduce the allergenicity of food allergens. Food processing can change the allergenicity of allergens in the food matrix. In this study, ten IgE linear epitopes of the major allergen tropomyosin (TM) in Perna viridis were identified by bioinformatics prediction and serological experiments. The transglutaminase-catalyzed glycosylation modification sites glutamine, lysine and arginine were highly represented in the IgE linear epitopes of TM. The Perna viridis food matrix was treated with transglutaminase-catalyzed glycosylation. This reaction changed the secondary structure of protein in the food matrix, increased the content of β-sheets and decreased the content of β-turns. The intensity of intrinsic fluorescence and surface hydrophobicity were reduced. The IgE-binding activity of TM in the food matrix was reduced by modifying seven amino acid residues on six IgE linear epitopes. Transglutaminase-catalyzed glycosylation products decreased allergic symptoms in allergic mice, reduced the proportion of CD4+IL-4+ Th2 cells, and increased the proportion of CD4+IFN-γ+ Th1 cells and Treg cells. Mouse serum levels of IgE and IgG1 antibodies in the food matrix and TM were reduced. Therefore, this study provided a theoretical basis for the development of hypoallergenic Perna viridis products.

A Bioinformatically Initiated Approach to Evaluate GATA1 Regulatory Regions in Samples with Weak D, Del, or D– Phenotypes Despite Normal RHD Exons

Introduction: With over 360 blood group antigens in systems recognized, there are antigens, such as RhD, which demonstrate a quantitative reduction in antigen expression due to nucleotide variants in the non-coding region of the gene that result in aberrant splicing or a regulatory mechanism. This study aimed to evaluate bioinformatically predicted GATA1-binding regulatory motifs in the RHD gene for samples presenting with weak or apparently negative RhD antigen expression but showing normal RHD exons. Methods: Publicly available open chromatin region data were overlayed with GATA1 motif candidates in RHD. Genomic DNA from weak D, Del or D– samples with normal RHD exons (n = 13) was used to confirm RHD zygosity by quantitative PCR. Then, RHD promoter, intron 1, and intron 2 regions were amplified for Sanger sequencing to detect potential disruptions in the GATA1 motif candidates. Electrophoretic mobility shift assay (EMSA) was performed to assess GATA1-binding. Luciferase assays were used to assess transcriptional activity. Results: Bioinformatic analysis identified five of six GATA1 motif candidates in the promoter, intron 1 and intron 2 for investigation in the samples. Luciferase assays showed an enhancement in transcription for GATA1 motifs in intron 1 and for intron 2 only when the R2 haplotype variant (rs675072G>A) was present. GATA1 motifs were intact in 12 of 13 samples. For one sample with a Del phenotype, a novel RHD c.1–110A>C variant disrupted the GATA1 motif in the promoter which was supported by a lack of a GATA1 supershift in the EMSA and 73% transcriptional activity in the luciferase assay. Two samples were D+/D– chimeras. Conclusion: The bioinformatic predictions enabled the identification of a novel DEL allele, RHD c.1–110A>C, which disrupted the GATA1 motif in the proximal promoter. Although the majority of the samples investigated here remain unexplained, we provide GATA1 targets which may benefit future RHD regulatory investigations.

FEN1 Promotes Hepatocellular Carcinoma Progression by Activating Cell Cycle Transition from G2 To M Phase.

Flap endonuclease 1 (FEN1) is a structure-specific nuclease that is involved in the occurrence and development of various types of tumors. Previous studies have shown that FEN1 plays an important role in the development of hepatocellular carcinoma, however, the molecular mechanisms remain fully elucidated, especially its effect on the cell cycle of hepatocellular carcinoma has not been investigated. In this study, via bioinformatics prediction and clinical specimen verification, we confirmed that FEN1 was highly expressed in HCC and correlated with poor prognosis. The knockdown or overexpression of FEN1 could inhibit or promote the proliferation and invasion of HCC cells. Importantly, cell cycle and functional experiments showed that FEN1 could promote cell proliferation by inducing cell cycle transition from G2 to M phase. Further studies indicated that FEN1 regulated the G2/M transition by modulating cell division cycle 25C (Cdc25C), cyclin-dependent kinase 1 (CDK1) and Cyclin B1 expressions. To sum up, our research suggested that FEN1 could promote the proliferation, migration and invasion of HCC cells via activating cell cycle progression from G2 to M phase, indicating that FEN1 may be a potential target for the treatment of HCC.

The mir-199b-5p encapsulated in adipocyte-derived exosomes mediates radioresistance of colorectal cancer cells by targeting JAG1

Radiotherapy is a key treatment option for colorectal cancer, but its efficacy varies among patients. Our previous studies suggested that adipose tissue may confer the radioresistance of several abdominal tumors, such as pancreatic cancer, biliary cancer, and others. In the present work, the effects of adipocytes in regulating the radiosensitivity of colorectal cancer are explored for the first time. It was found that colony formation was increased and radiation-induced apoptosis decreased in colorectal cancer cells HCT8 and HCT116 co-cultured with adipocytes, which verified the mediation of adipocyte-driven radioresistance in colorectal cancer in vitro. Next, the colorectal cancer cells were incubated with adipocyte-derived exosomes, and a perceptible reduction in radiosensitivity was detected. Furthermore, to investigate the possible mechanisms involved, the exosomes were isolated, the encapsulated microRNAs were extracted and analyzed by small RNA sequencing. Based on bioinformatics analysis and qRT-PCR verification, miR-199b-5p was chosen for functional annotation. It was shown that miR-199b-5p expression was significantly upregulated after 6 Gy irradiation, and overexpressed miR-199b-5p significantly suppressed the radiosensitivity of HCT8 and HCT116 cells. In addition, jagged canonical Notch ligand 1(JAG1) was identified as the target gene of miR-199b-5p by using bioinformatics prediction and dual luciferase reporter gene assay. It was demonstrated that JAG1 conferred the radioresistance of colorectal cancer cells both in vivo and in vitro. Taken together, the present study demonstrates that adipocytes trigger the radioresistance of colorectal cancer cells, probably by targeting JAG1 through an adipocyte-derived exosomal miR-199b-5p.

Methods for Bioinformatic Prediction of Genuine sRNAs from Outer Membrane Vesicles.

The molecular pathogenesis of Gram-negative bacteria remains a complex and incompletely understood phenomenon. Various factors are believed to contribute to the pathogenicity of these bacteria. One key mechanism utilized by Gram-negative bacteria is the production of outer membrane vesicles (OMVs), which are small spherical particles derived from the bacterial outer membrane. These OMVs are crucial in delivering virulence factors to the host, facilitating host-pathogen interactions. Within these OMVs, small regulatory RNAs (sRNAs) have been identified as important players in modulating the host immune response. One of the main challenges in studying OMVs and their cargo of sRNAs is the difficulty in isolating and purifying sufficient quantities of OMVs, as well as accurately predicting genuine sRNAs computationally. In this chapter, we present protocols aimed at overcoming these obstacles.

DeepHLApan: A Deep Learning Approach for the Prediction of Peptide-HLA Binding and Immunogenicity.

Neoantigens are crucial in distinguishing cancer cells from normal ones and play a significant role in cancer immunotherapy. The field of bioinformatics prediction for tumor neoantigens has rapidly developed, focusing on the prediction of peptide-HLA binding affinity. In this chapter, we introduce a user-friendly tool named DeepHLApan, which utilizes deep learning techniques to predict neoantigens by considering both peptide-HLA binding affinity and immunogenicity. We provide the application of DeepHLApan, along with the source code, docker version, and web-server. These resources are freely available at https://github.com/zjupgx/deephlapan and http://pgx.zju.edu.cn/deephlapan/ .

Mining raw plant transcriptomic data for new cyclopeptide alkaloids.

In recent years, genome and transcriptome mining have dramatically expanded the rate of discovering diverse natural products from bacteria and fungi. In plants, this approach is often more limited due to the lack of available annotated genomes and transcriptomes combined with a less consistent clustering of biosynthetic genes. The recently identified burpitide class of ribosomally synthesized and post-translationally modified peptide (RiPP) natural products offer a valuable opportunity for bioinformatics-guided discovery in plants due to their short biosynthetic pathways and gene encoded substrates. Using a high-throughput approach to assemble and analyze 700 publicly available raw transcriptomic data sets, we uncover the potential distribution of split burpitide precursor peptides in Streptophyta. Metabolomic analysis of target plants confirms our bioinformatic predictions of new cyclopeptide alkaloids from both known and new sources.

The role of miR-10a-5p in LPS-induced inhibition of progesterone synthesis in goose granulosa cells by down-regulating CYP11A1.

The poultry ovary is a preferred target for E. coli and Salmonella infection of tissues, and lipopolysaccharide (LPS) is a critical molecule in infecting the organism and interfering with cell function, invading the ovaries through the cloaca and interfering with progesterone (P4) secretion by follicular granulosa cells (GCs), seriously affecting the health of breeding geese. miRNAs are small, non-coding RNAs with a variety of important regulatory roles. To investigate the mechanism of miR-10a-5p mediated LPS inhibition of progesterone synthesis in goose granulosa cells, Yangzhou geese at peak laying period were selected as experimental animals to verify the expression levels of genes and transcription factors related to progesterone synthesis. In this study, bioinformatic predictions identified miR-10a-5p target gene CYP11A1, and genes and transcription factors related to the sex steroid hormone secretion pathway were screened. We detected that LPS inhibited CYP11A1 expression while increasing miR-10a-5p expression in vivo. Progesterone decreased significantly in goose granulosa cells treatment with 1 μg/mL LPS for 24 h, while progesterone-related genes and regulatory factors were also suppressed. We also determined that the downregulation of miR-10a-5p led to CYP11A1 expression. Overexpression of miR-10a-5p suppressed LPS-induced CYP11A1 expression, resulting in decreased progesterone secretion. Our findings indicated that miR-10a-5p was up-regulated by LPS and inhibited progesterone synthesis by down-regulating CYP11A1. This study provides insight into the molecular mechanisms regulating geese reproduction and ovulation.

DeepO-GlcNAc: a web server for prediction of protein O-GlcNAcylation sites using deep learning combined with attention mechanism.

Protein O-GlcNAcylation is a dynamic post-translational modification involved in major cellular processes and associated with many human diseases. Bioinformatic prediction of O-GlcNAc sites before experimental validation is a challenge task in O-GlcNAc research. Recent advancements in deep learning algorithms and the availability of O-GlcNAc proteomics data present an opportunity to improve O-GlcNAc site prediction. This study aims to develop a deep learning-based tool to improve O-GlcNAcylation site prediction. We construct an annotated unbalanced O-GlcNAcylation data set and propose a new deep learning framework, DeepO-GlcNAc, using Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) combined with attention mechanism. The ablation study confirms that the additional model components in DeepO-GlcNAc, such as attention mechanisms and LSTM, contribute positively to improving prediction performance. Our model demonstrates strong robustness across five cross-species datasets, excluding humans. We also compare our model with three external predictors using an independent dataset. Our results demonstrated that DeepO-GlcNAc outperforms the external predictors, achieving an accuracy of 92%, an average precision of 72%, a MCC of 0.60, and an AUC of 92% in ROC analysis. Moreover, we have implemented DeepO-GlcNAc as a web server to facilitate further investigation and usage by the scientific community. Our work demonstrates the feasibility of utilizing deep learning for O-GlcNAc site prediction and provides a novel tool for O-GlcNAc investigation.

Expression Analysis of miRNAs Targeting PIK3CA and AKT1 Genes of PI3K Signaling Pathway in Breast Cancer Cells.

Background: MicroRNAs (miRNAs) are key gene regulators that are involved in many bi­ological and also pathological processes, including breast cancer. Breast cancer is the most common form of malignancies in women and requires new therapies and biomarkers. Different signaling pathways, such as Phosphoinositide 3-kinase (PI3K) signaling pathway are involved in breast cancer and can be new candidates for targeted therapies based on miRNAs. The aim of this study was to predict miRNAs targeting Phosphatidylinositol-4, 5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA) and serine/threonine kinase 1 (AKT1) genes of PI3K cascade bioinformatically and to analyze their expression in breast cancer. Materials and Methods: Bioinformatic software and tools were used to predict miRNAs targeting PIK3CA and AKT1 genes. MCF-7 and MCF-10A cell lines were cultured as breast cancer and control cells respec­tively. RNA extraction, cDNA synthesis, and quantitative real-time PCR were performed. REST 2009® was utilized to analyze the expression of miRNAs and their target genes. Results: The results of our bioinformatic predictions indicated that miR-576-5p, miR-501-3p, and miR-3143 can be the first candidate miRNAs targeting PI3K signaling pathway. Data analyses demon­strated that PIK3CA and AKT1 genes were up-regulated while all bioinformatically predicted miRNAs were down-regulated in MCF-7 cell line compared to the normal cells. Conclusion: The results of our study demonstrated that PIK3CA and AKT1 can be targeted by miR-576-5p and miR-501-3p respectively. Furthermore, miR-3143 can target both mRNAs. Since these miRNAs target oncogenes, they can be proposed for new complementary targeted therapies in breast cancer patients. [GMJ.2017;6(4):338-45] DOI: 10.22086/gmj.v6i4.925

MiR-23b-3p regulates the differentiation of goat intramuscular preadipocytes by targeting the PDE4B gene

This study aimed to explore the effect of miR-23b-3p on the differentiation of goat intramuscular preadipocytes, and to confirm whether miR-23b-3p plays its roles via targeting the PDE4B gene. Based on the pre-transcriptome sequencing data obtained previously, the miR-23b-3p, which was differentially expressed in goat intramuscular adipocytes before and after differentiation, was used as an entry point. real-time quantitative-polymerase chain reaction (qPCR) was used to detect the expression pattern of miR-23b-3p during the differentiation of goat intramuscular preadipocytes. The effects of miR-23b-3p on adipose differentiation and adipose differentiation marker genes were determined at the morphological and molecular levels. The downstream target genes of miR-23b-3p were determined using bioinformatics prediction as well as dual luciferase reporter assay to clarify the targeting relationship between miR-23b-3p and the predicted target genes. The results indicated that overexpression of miR-23b-3p reduced lipid droplet accumulation in goat intramuscular adipocytes, significantly down-regulated the expression levels of adipogenic marker genes AP2, C/EBPα, FASN, and LPL (P < 0.01). In addition, the expressions of C/EBPβ, DGAT2, GLUT4 and PPARγ were significantly downregulated (P < 0.05). After interfering with the expression of miR-23b-3p, lipid droplet accumulation was increased in goat intramuscular adipocytes. The expression levels of ACC, ATGL, AP2, DGAT2, GLUT4, FASN and SREBP1 were extremely significantly up-regulated (P < 0.01), and the expression levels of C/EBPβ, LPL and PPARγ were significantly up-regulated (P < 0.05). It was predicted that PDE4B might be a target gene of miR-23b-3p. The mRNA expression level of PDE4B was significantly decreased after overexpression of miR-23b-3p (P < 0.01), and the interference with miR-23b-3p significantly increased the mRNA level of PDE4B (P < 0.05). The dual luciferase reporter assay indicated that miR-23b-3p had a targeting relationship with PDE4B gene. MiR-23b-3p regulates the differentiation of goat intramuscular preadipocytes by targeting the PDE4B gene.

Circ-0000953 deficiency exacerbates podocyte injury and autophagy disorder by targeting Mir665-3p-Atg4b in diabetic nephropathy

ABSTRACT Circular RNAs (circRNAs) are special non-coding RNA (ncRNA) molecules that play a significant role in many diseases. However, the biogenesis and regulation of circRNAs in diabetic nephropathy (DN) are largely unknown. Here, we investigated the expression profile of circRNAs in kidney of DN mice through circular RNA sequencing (circRNA-seq). The renal biopsy samples of patients with DN had low circ -0,000,953 expression, which was significantly associated with renal function. Furthermore, loss-of-function and gain-of-function experiments were carried out to prove the role of circ -0,000,953 in DN. Podocyte conditional knockin (cKI) or systemic overexpression of circ -0,000,953 alleviated albuminuria and restored macroautophagy/autophagy in kidney of diabetic mice. However, circ -0,000,953 knockdown exacerbated albuminuria and podocyte injury. Mechanistically, we found circ -0,000,953 directly binds to Mir665-3p-Atg4b to perform its function. Silencing of Mir665-3p or overexpression of Atg4b recovered podocyte autophagy both in vitro and in vivo. To examine the cause of circ -0,000,953 downregulation in DN, bioinformatics prediction found that circ -0,000,953 sequence has a high possibility of containing an m6A methylation site. Additionally, METTL3 was proved to regulate the expression and methylation level of circ -0,000,953 through YTHDF2 (YTH N6-methyladenosine RNA binding protein 2). In conclusion, this study revealed that circ -0,000,953 regulates podocyte autophagy by targeting Mir665-3p-Atg4b in DN. Therefore, circ -0,000,953 is a potential biomarker for prevention and cure of DN. Abbreviation: CCL2/MCP-1: C-C motif chemokine ligand 2; ceRNA: competing endogenous RNA; circRNA: circular RNA; cKI: conditional knockin; cKO: conditional knockout; CRE: creatinine; DM: diabetes mellitus; DN: diabetic nephropathy; ESRD: end-stage renal disease; HG: high glucose; IF: immunofluorescence; MAP1LC3/LC3B: microtubule-associated protein 1 light chain 3 beta; MPC5: mouse podocyte clone 5; MTECs: mouse tubular epithelial cells; MTOR: mechanistic target of rapamycin kinase; NC: normal control; ncRNA: non-coding RNA; NPHS1: nephrosis 1, nephrin; NPHS2: nephrosis 2, podocin; PAS: periodic acid-Schiff; RELA/p65: v-rel reticuloendotheliosis viral oncogene homolog A (avian); SDs: slit diaphragm proteins; Seq: sequencing; STZ: streptozotocin; SV40: SV40-MES13-cells, mouse mesangial cell line; T1D: type 1 diabetes mellitus; T2D: type 2 diabetes mellitus; TEM: transmission electron microscopy; TNF/TNF-α: tumor necrosis factor; VECs: vascular endothelial cells; WT1: WT1 transcription factor; YTHDF2: YTH N6-methyladenosine RNA binding protein 2.

Benchmarking splice variant prediction algorithms using massively parallel splicing assays

BackgroundVariants that disrupt mRNA splicing account for a sizable fraction of the pathogenic burden in many genetic disorders, but identifying splice-disruptive variants (SDVs) beyond the essential splice site dinucleotides remains difficult. Computational predictors are often discordant, compounding the challenge of variant interpretation. Because they are primarily validated using clinical variant sets heavily biased to known canonical splice site mutations, it remains unclear how well their performance generalizes.ResultsWe benchmark eight widely used splicing effect prediction algorithms, leveraging massively parallel splicing assays (MPSAs) as a source of experimentally determined ground-truth. MPSAs simultaneously assay many variants to nominate candidate SDVs. We compare experimentally measured splicing outcomes with bioinformatic predictions for 3,616 variants in five genes. Algorithms’ concordance with MPSA measurements, and with each other, is lower for exonic than intronic variants, underscoring the difficulty of identifying missense or synonymous SDVs. Deep learning-based predictors trained on gene model annotations achieve the best overall performance at distinguishing disruptive and neutral variants, and controlling for overall call rate genome-wide, SpliceAI and Pangolin have superior sensitivity. Finally, our results highlight two practical considerations when scoring variants genome-wide: finding an optimal score cutoff, and the substantial variability introduced by differences in gene model annotation, and we suggest strategies for optimal splice effect prediction in the face of these issues.ConclusionSpliceAI and Pangolin show the best overall performance among predictors tested, however, improvements in splice effect prediction are still needed especially within exons.

Bioinformatics prediction and screening of viral mimicry candidates through integrating known and predicted DMI data.

Domain-motif interactions (DMIs) represent transient bonds formed when a Short Linear Motif (SLiM) engages a globular domain via a compact contact interface. Understanding the mechanics of DMIs is critical for maintaining diverse regulatory processes and deciphering how various viruses hijack host cellular machinery. However, identifying DMIs through traditional in vitro and in vivo experiments is challenging due to their degenerate nature and small contact areas. Predictions often carry a high rate of false positives, necessitating rigorous in-silico validation before embarking on experimental work. This study assessed the binding energy changes in predicted SLiM instances through in-silico peptide exchange experiment, elucidating how they interact with known 3D DMI complexes. We identified a subset of potential mimicry candidates that exhibited effective binding affinities with native DMI structures, suggesting their potential to be true mimicry candidates. The identified viral SLiMs can be potential targets in developing therapeutics, opening new opportunities for innovative treatments that can be finely tuned to address the complex molecular underpinnings of various diseases. To gain a comprehensive understanding of identified DMIs, it is imperative to conduct further validation through experimental approaches.

ITGA9-AS1 up-regulates ITGA9 by targeting miR-4765 and recruiting HNRNPU to affect the proliferation and apoptosis of non-small cell lung cancer cells.

Long non-coding RNAs (lncRNAs) have been regarded as promising biomarkers in the regulation of various biological and pathological processes of non-small cell lung cancer (NSCLC). LncRNAITGA9-AS1 has been reported to be down-regulated in elderly patients with lung cancer, but how it may influence NSCLC remains to be identified. Therefore, we aim to explore the specific mechanism involving ITGA9-AS1 and ITGA9 in NSCLC. A functional assay was conducted to verify ITGA9-AS1's proliferative effects on NSCLC cells. Mechanism experiments with bioinformatics predictions were performed to explore the interaction of ITGA9-AS1 and ITGA9 in NSCLC cells. ITGA9-AS1 inhibited NSCLC cell proliferation while enhancing cell apoptosis. It up-regulated ITGA9 by competitively sponging miR-4765, and it stabilizedITGA9 mRNA by recruiting a RNA-binding protein (RBP)-HNRNPU (heterogeneous nuclear ribonucleoprotein U) in NSCLC cells. ITGA9-AS1 suppressed NSCLC progression by the up-regulation of ITGA9 via targeting miR-4765 and recruiting HNRNPU.

Lithocholic acid decreases mRNA stability of nuclear receptor PPARα by upregulating miR-21 expression in hepatoma HepG2 cells

To investigate the regulatory effects of lithocholic acid (LCA) on nuclear receptor peroxisome proliferatoractivated receptor-alpha (PPARα) mRNA stability at the post-transcriptional level. PPARα 3'UTR luciferase reporter gene vectors were constructed and transfected into HepG2 cells to observe the changes in cellular luciferase activity in response to LCA treatments. Bioinformatic prediction and miRNA PCR array technique were used to identify the differentially expressed miRNAs induced by LCA and their potential binding sites on the 3'UTR. The binding sites (Mut1, Mut2 and Mut1+Mut2) were mutated to compare the changes in cellular luciferase activity following LCA treatment. Western blotting and RTqPCR were used to detect the activated signaling pathway and the expression levels of its downstream transcription factors in LCA-treated cells. The changes in PPARα protein expression level were detected in the cells following overexpression of the transcription factors. Treatment with 100 μmol/L LCA significantly reduced luciferase activity of PPARα 3'UTR1 and 3'UTR2 in HepG2 cells by more than 50% (P<0.01) and induced significant upregulation of miR-21 and miR-22, especially the former (by 2.35 folds, P<0.05). Two predicted miR-21-binding sites in the 3'UTR1 were mutated to construct Mut1, Mut2 and Mut1+Mut2 reporter gene vectors. LCA treatment down-regulated 3'UTR1 luciferase activity by 51%, while Mut1, Mut2, and Mut1+Mut2 were down-regulated by 37%, 39%, and 13%, respectively. LCA caused ERK1/2 phosphorylation and activation of the ERK1/2 signaling pathway, and treatment with 100 μmol/L LCA upregulated the expression of transcription factor early growth response 1 (EGR1) by 5.83 folds (P<0.01). Transient overexpression of EGR1 significantly decreased cellular PPARα protein levels (P<0.05). LCA reduces PPARα mRNA stability and thus decreases PPARα mRNA and protein expressions in hepatocytes by activating the ERK1/2 signaling pathway and upregulating EGR1 and miR-21, which targets 3'UTR regulatory region of PPARα mRNA.