Absence of AIF1L contributes to cell migration and a poor prognosis of breast cancer

Identification of Single-Nucleotide Polymorphisms in Differentially Expressed Genes Favoring Soybean Meal Tolerance in Higher-Growth Zebrafish (Danio rerio)

Genetic variability within the same fish species could confer soybean meal (SBM) tolerance in some individuals, thus favoring growth. This study investigates the single-nucleotide polymorphisms (SNPs) in differentially expressed genes (DEGs) favoring SBM tolerance in higher-growth zebrafish (Danio rerio). In a previous work, nineteen families of zebrafish were fed a fish meal diet (100FM control diet) or SBM-based diets supplemented with saponin (50SBM + 2SPN-experimental diet), from juvenile to adult stages. Individuals were selected from families with a genotype-by-environment interaction higher (170 ± 18 mg) or lower (76 ± 10 mg) weight gain on 50SBM + 2SPN in relation to 100FM. Intestinal transcriptomic analysis using RNA-seq revealed six hundred and sixty-five differentially expressed genes in higher-growth fish fed 50SBM + 2SPN diet. In this work, using these results, 47 SNPs in DEGs were selected. These SNPs were genotyped by Sequenom in 340 zebrafish that were fed with a 50SBM + 2SPN diet or with 100FM diet. Marker-trait analysis revealed 4 SNPs associated with growth in 3 immunity-related genes (aif1l, arid3c, and cst14b.2) in response to the 50SBM + 2SPN diet (p-value < 0.05). Two SNPs belonging to aif1l y arid3c produce a positive (+19 mg) and negative (-26 mg) effect on fish growth, respectively. These SNPs can be used as markers to improve the early selection of tolerant fish to SBM diet or other plant-based diets. These genes can be used as biomarkers to identify SNPs in commercial fish, thus contributing to the aquaculture sustainability.

Identification and analysis of chemokine-related and NETosis-related genes in acute pancreatitis to develop a predictive model

Background: Chemokines and NETosis are significant contributors to the inflammatory response, yet there still needs to be a more comprehensive understanding regarding the specific molecular characteristics and interactions of NETosis and chemokines in the context of acute pancreatitis (AP) and severe AP (SAP). Methods: To address this gap, the mRNA expression profile dataset GSE194331 was utilized for analysis, comprising 87 AP samples (77 non-SAP and 10 SAP) and 32 healthy control samples. Enrichment analyses were conducted for differentially expressed chemokine-related genes (DECRGs) and NETosis-related genes (DENRGs). Three machine-learning algorithms were used for the identification of signature genes, which were subsequently utilized in the development and validation of nomogram diagnostic models for the prediction of AP and SAP. Furthermore, single-gene Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were performed. Lastly, an interaction network for the identified signature genes was constructed. Results: We identified 12 DECRGs and 7 DENRGs, and enrichment analyses indicated they were primarily enriched in cytokine-cytokine receptor interaction, chemokine signaling pathway, TNF signaling pathway, and T cell receptor signaling pathway. Moreover, these machine learning algorithms finally recognized three signature genes (S100A8, AIF1, and IL18). Utilizing the identified signature genes, we developed nomogram models with high predictive accuracy for AP and differentiation of SAP from non-SAP, as demonstrated by area under the curve (AUC) values of 0.968 (95% CI 0.937-0.990) and 0.862 (95% CI 0.742-0.955), respectively, in receiver operating characteristic (ROC) curve analysis. Subsequent single-gene GESA and GSVA indicated a significant positive correlation between these signature genes and the proteasome complex. At the same time, a negative association was observed with the Th1 and Th2 cell differentiation signaling pathways. Conclusion: We have identified three genes (S100A8, AIF1, and IL18) related to chemokines and NETosis, and have developed accurate diagnostic models that might provide a novel method for diagnosing AP and differentiating between severe and non-severe cases.

CircGLIS3 inhibits thyroid cancer invasion and metastasis through miR-146b-3p/AIF1L axis

PURPOSE

Studies have shown that circRNA is involved in the occurrence and development of human cancers. However, it remains unclear that the contribution of circRNA in thyroid carcinoma and its role in the process of tumorigenesis.

METHODS

The expression profile of circRNA-miRNA-mRNA in thyroid carcinoma was detected by RNA sequencing and verified by qRT-PCR. The characteristics of circGLIS3 were verified by RNase R and actinomycin assays, subcellular fractionation, and fluorescence in situ hybridization. The functions of circGLIS3 and AIF1L were detected by wound healing, transwell, 3D culture and Western blot. RNA Immunoprecipitation (RIP), RNA pulldown and dual-luciferase reporter assays were used to verify the target genes of circGLIS3 and downstream miRNAs. Functional rescue experiments were performed by transfecting miRNA mimics or siRNA of target genes. Finally, metastatic mouse models were used to investigate circGLIS3 function in vivo.

RESULTS

In this study, we discovered a novel circRNA (has_circ_0007368, named as circGLIS3) by RNA sequencing. CircGLIS3 was down-regulated in thyroid carcinoma tissues and cells line, and was negatively associated with malignant clinical features of thyroid carcinoma. Functional studies found that circGLIS3 could inhibit the migration and invasion of thyroid carcinoma cells, and was related to the EMT process. Mechanistically, circGLIS3 can upregulate the expression of the AIF1L gene by acting as a miR-146b-3p sponge to inhibit the progression of thyroid carcinoma.

CONCLUSION

Our study identified circGLIS3 as a novel tumor suppressor in thyroid cancer, indicating the potential of circGLIS3 as a promising diagnostic and prognostic marker for thyroid cancer.

Whole transcriptome sequencing and competitive endogenous RNA regulation network construction analysis in benzo[a]pyrene-treated breast cancer cells

Breast cancer is the most common malignant tumor in women worldwide, and environmental pollutants are considered to be risk factors. Currently, most studies into benzo[a]pyrene (B[a]P)-induced breast cancer focus on biological effects such as proliferation, invasion, and metastasis, DNA damage, estrogen receptor (ER)-related molecular mechanisms, oxidative damage, and other metabolic pathways. This study aims to provide insights into the role of B[a]P in breast cancer development through RNA-seq and bioinformatics analysis and construction of a competing endogenous RNA (ceRNA) regulatory network. By analyzing RNA-seq results, we identified 144 differentially-expressed circRNAs, 69 differentially-expressed lncRNAs, 20 differentially-expressed miRNAs, and 212 differentially-expressed mRNAs. Following on, we analyzed the gene ontology (GO) and KEGG enrichment functions of the differentially-expressed RNAs. In addition, the protein-protein interaction (PPI) network was mapped for differentially-expressed mRNAs. Subsequently, we constructed ceRNA networks, one of which consisted of 45 dysregulated circRNAs, 11 miRNAs, and 9 mRNAs, and a second consisted of 40 lncRNAs, 11 miRNAs, and 9 mRNAs. Finally, 6 circRNAs, 4 lncRNAs, 1 miRNA, and 4 mRNAs were randomly selected for quantitative real-time PCR verification. PCR results were further verified by Western blotting assays. These results show that the expression level of differentially-expressed RNA was consistent with the sequencing data, and the Western blotting results were highly consistent with the PCR results, confirming that the sequencing result was very reliable. This study systematically explores the ceRNA atlas of differentially-expressed genes related to B[a]P exposure in breast cancer cells, providing new insights into mechanisms of environmental pollutants in breast cancer.

Uncovering emergent phenotypes in endothelial cells by clustering of surrogates of cardiovascular risk factors

Endothelial dysfunction (ED) is a hallmark of atherosclerosis and is influenced by well-defined risk factors, including hypoxia, dyslipidemia, inflammation, and oscillatory flow. However, the individual and combined contributions to the molecular underpinnings of ED remain elusive. We used global gene expression in human coronary artery endothelial cells to identify gene pathways and cellular processes in response to chemical hypoxia, oxidized lipids, IL-1β induced inflammation, oscillatory flow, and these combined stimuli. We found that clustering of the surrogate risk factors differed from the sum of the individual insults that gave rise to emergent phenotypes such as cell proliferation. We validated these observations in samples of human coronary artery atherosclerotic plaques analyzed using single-cell RNA sequencing. Our findings suggest a hierarchical interaction between surrogates of CV risk factors and the advent of emergent phenotypes in response to combined stimulation in endothelial cells that may influence ED.

In-Depth Analysis of the Plasma Proteome in ME/CFS Exposes Disrupted Ephrin-Eph and Immune System Signaling

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a disabling disease with worldwide prevalence and limited therapies exclusively aimed at treating symptoms. To gain insights into the molecular disruptions in ME/CFS, we utilized an aptamer-based technology that quantified 4790 unique human proteins, allowing us to obtain the largest proteomics dataset yet available for this disease, detecting highly abundant proteins as well as rare proteins over a nine-log dynamic range. We report a pilot study of 20 ME/CFS patients and 20 controls, all females. Significant differences in the levels of 19 proteins between cohorts implicate pathways related to the extracellular matrix, the immune system and cell-cell communication. Outputs of pathway and cluster analyses robustly highlight the ephrin pathway, which is involved in cell-cell signaling and regulation of an expansive variety of biological processes, including axon guidance, angiogenesis, epithelial cell migration, and immune response. Receiver Operating Characteristic (ROC) curve analyses distinguish the plasma proteomes of ME/CFS patients from controls with a high degree of accuracy (Area Under the Curve (AUC) > 0.85), and even higher when using protein ratios (AUC up to 0.95), that include some protein pairs with established biological relevance. Our results illustrate the promise of plasma proteomics for diagnosing and deciphering the molecular basis of ME/CFS.

Allograft inflammatory factor-1-like is not essential for age dependent weight gain or HFD-induced obesity and glucose insensitivity

The allograft inflammatory factor (AIF) gene family consists of two identified paralogs – AIF1 and AIF1-like (AIF1L). The encoded proteins, AIF1 and AIF1L, are 80% similar in sequence and show conserved tertiary structure. While studies in human populations suggest links between AIF1 and metabolic diseases such as obesity and diabetes, such associations with AIF1L have not been reported. Drawing parallels based on structural similarity, we postulated that AIF1L might contribute to metabolic disorders, and studied it using mouse models. Here we report that AIF1L is expressed in major adipose depots and kidney but was not detectable in liver or skeletal muscle; in notable contrast to AIF1, AIF1L was also not found in spleen. Studies of AIF1L deficient mice showed no obvious postnatal developmental phenotype. In response to high fat diet (HFD) feeding for 6 or 18 weeks, WT and AIF1L deficient mice gained weight similarly, showed no differences in fat or lean mass accumulation, and displayed no changes in energy expenditure or systemic glucose handling. These findings indicate that AIF1L is not essential for the development of obesity or impaired glucose handling due to HFD, and advance understanding of this little-studied gene and its place in the AIF gene family.

Key Genes And Pathways Controlled By E2F1 In Human Castration-Resistant Prostate Cancer Cells

Background

Treatment of castration-resistant prostate cancer (CRPC) is an enormous challenge. As E2F transcription factor 1 (E2F1) is an essential factor in CRPC, this study investigated the genes and pathways controlled by E2F1 and their effects on cellular behavior in CRPC.

Methods

In vitro assays were used to evaluate cellular proliferation, apoptosis, and behavior. Cellular expression was quantified by RNA sequencing (RNA-seq). Gene co-expression was assessed using the GeneMANIA database, and correlations were analyzed with the GEPIA server. Altered pathways of differentially expressed genes (DEGs) were revealed by functional annotation. Module analysis was performed using the STRING database and hub genes were filtered with the Cytoscape software. Some DEGs were validated by real-time quantitative PCR (RT-qPCR).

Results

Knockdown of E2F1 significantly inhibited proliferation and accelerated apoptosis in PC3 cells but not in DU145 cells. Invasion and migration were reduced for both cell lines. A total of 1811 DEGs were identified in PC3 cells and 27 DEGs in DU145 cells exhibiting E2F1 knockdown. Ten overlapping DEGs, including TMOD2 and AIF1L, were identified in both knockdown cell lines and were significantly enriched for association with actin filament organization pathways. TMOD2 and KREMEN2 were genes co-expressed with E2F1; six overlapping DEGs were positively correlated with transcription factor E2F1. DEGs of the PC3 and DU145 groups were associated with multiple pathways. Five DEGs that overlapped between the two cell lines and three hub DEGs from PC3 cells were validated by RT-qPCR.

Conclusion

The results of this study suggest that E2F1 has a critical role in regulating actin filaments, as indicated by the change in expression level of several genes, including TMOD2 and AIF1L, in CRPC. This extends our understanding of the cellular responses affected by E2F1 in CRPC.