Comprehensive bioinformatics analytics and in vivo validation reveal SLC31A1 as an emerging diagnostic biomarker for acute myocardial infarction.

BACKGROUND: Globally, Acute Myocardial Infarction (AMI) is a common cause of heart failure (HF), which has been a leading cause of mortality resulting from non-communicable diseases. On the other hand, increasing evidence suggests that the role of energy production within the mitochondria strongly links to the development and progression of heart diseases, while Cuproptosis, a newly identified cell death mechanism, has not yet been comprehensively analyzed from the aspect of cardiovascular medicine. MATERIALS AND METHODS: 8 transcriptome profiles curated from the GEO database were integrated, from which a diagnostic model based on the Stacking algorithm was established. The efficacy of the model was evaluated in a multifaced manner (i.e., by Precision-Recall curve, Receiver Operative Characteristic curve, etc.). We also sequenced our animal models at the bulk RNA level and conducted qPCR and immunohistochemical staining, with which we further validated the expression of the key contributor gene to the model. Finally, we explored the immune implications of the key contributor gene. RESULTS: A merged machine learning model containing 4 Cuproptosis-related genes (i.e., PDHB, CDKN2A, GLS, and SLC31A1) for robust AMI diagnosis was developed, in which SLC31A1 served as the key contributor. Through in vivo modeling, we validated the aberrant overexpression of SLC31A1 in AMI. Besides, further transcriptome analysis revealed that its high expression was correlated with significant potential immunological implications in the infiltration of many immune cell types, especially monocyte. CONCLUSIONS: We constructed an AMI diagnostic model based on Cuproptosis-related genes and validated the key contributor gene in animal modeling. We also analyzed the effects on the immune system for its overexpression in AMI.

Screening and identification of lncRNAs in preadipocyte differentiation in sheep.

Studies of preadipocyte differentiation and fat deposition in sheep have mainly focused on functional genes, and with no emphasis placed on the role that long non-coding RNAs (lncRNAs) may have on the activity of those genes. Here, the expression profile of lncRNAs in ovine preadipocyte differentiation was investigated and the differentially expressed lncRNAs were screened on day 0 (D0), day 2(D2) and day 8(D8) of ovine preadipocyte differentiation, with their target genes being predicted. The competing endogenous RNA (ceRNA) regulatory network was constructed by GO and KEGG enrichment analysis for functional annotation, and some differentially expressed lncRNAs were randomly selected to verify the RNA-Seq results by RT-qPCR. In the study, a total of 2517 novel lncRNAs and 3943 known lncRNAs were identified from ovine preadipocytes at the three stages of differentiation, with the highest proportion being intergenic lncRNAs. A total of 3455 lncRNAs were expressed at all three stages of preadipocyte differentiation, while 214, 226 and 228 lncRNAs were uniquely expressed at day 0, day 2 and day 8, respectively. By comparing the expression of the lncRNAs between the three stages of differentiation stages, a total of 405, 272 and 359 differentially expressed lncRNAs were found in D0-vs-D2, D0-vs-D8, and D2-vs-D8, respectively. Functional analysis revealed that the differentially expressed lncRNAs were enriched in signaling pathways related to ovine preadipocyte differentiation, such as mitogen-activated protein kinase (MAPK) pathway, the phosphoinositide 3-kinase protein kinase B (PI3K-Akt) pathway, and the transforming growth factor beta (TGF-ß) pathway. In summary, lncRNAs from preadipocytes at different stages of differentiation in sheep were identified and screened using RNA-Seq technology, and the regulatory mechanisms of lncRNAs in preadipocyte differentiation and lipid deposition were explored. This study provides a theoretical reference for revealing the roles of lncRNAs in ovine preadipocyte differentiation and also offers a theoretical basis for further understanding the regulatory mechanisms of ovine preadipocyte differentiation.

Interplay of Sphingolipid Metabolism in Predicting Prognosis of GBM Patients: Towards Precision Immunotherapy.

Background: In spite of numerous existing bio-surveillance systems for predicting glioma (GBM) prognosis, enhancing the efficacy of immunotherapy remains an ongoing conundrum. The continual scrutiny of the dynamic interplay between the sphingolipid metabolic pathway and tumor immunophenotypes has unveiled potential implications. However, the intricate orchestration of functional and regulatory mechanisms by long non-coding RNAs (lncRNAs) in GBM, particularly in the context of sphingolipid metabolism, remains cryptic. Methods: We harnessed established R packages to intersect gene expression profiles of GBM patients within the The Cancer Genome Atlas (TCGA) database with the compilation of sphingolipid metabolism genes from GeneCards. This enabled us to discern markedly distinct lncRNAs, which were subsequently deployed to construct a robust prognostic model utilizing Lasso-Cox regression analysis. We then scrutinized the immune microenvironment across various risk strata using the ssGSEA and CIBERSORT algorithms. To evaluate mutation patterns and drug resistance profiles within patient subgroups, we devised the "Prophytic" and "Maftools" packages, respectively. Results: Our investigation scrutinized lncRNAs linked to sphingolipid metabolism, utilizing glioma specimens from TCGA. We meticulously curated 1224 sphingolipid-associated genes gleaned from GeneCards and pinpointed 272 differentially expressed mRNAs via transcriptomic analysis. Enrichment analyses underscored their significance in sphingolipid processes. A prognostic model founded on 17 meticulously selected lncRNAs was systematically constructed and validated. This model adeptly stratified GBM patients into high- and low-risk categories, yielding highly precise prognostic insights. We also discerned correlations between immune cell infiltration and genetic mutation discrepancies, along with distinct therapeutic responses through drug sensitivity analysis. Notably, computational findings were corroborated through experimental validation by RT-PCR. Conclusion: In summation, our exhaustive inquiry underscores the multifaceted utility of the sphingolipid metabolic pathway as an autonomous diagnostic and prognostic indicator for glioma patients. Furthermore, we amalgamate a profusion of substantiated evidence concerning immune infiltration and gene mutations, thereby reinforcing the proposition that sphingolipid metabolism may function as a pivotal determinant in the panorama of immunotherapeutic interventions.

Capsaicin alleviates doxorubicin-induced acute myocardial injury by regulating iron homeostasis and PI3K-Akt signaling pathway.

BACKGROUND: Capsaicin (CAP), a frequently occurring alkaloid component found in spicy peppers, has demonstrated therapeutic potential against tumors, metabolic disease, and cardiovascular disorders. Doxorubicin (DOX), a widely used anthracycline drug in chemotherapy, is notorious for its cardiotoxicity. This study aimed to investigate the potential of CAP in mitigating DOX toxicity in mouse hearts and H9C2 cells, as well as to explore the underlying mechanisms. METHODS: In our study, we conducted experiments on both mice and H9C2 cells. The mice were divided into four groups and treated with different substances: normal saline, CAP, DOX and CAP+DOX. We evaluated the induction of ferroptosis by DOX and the remission of ferroptosis by CAP using various methods, including echocardiography, Hematoxylin and Eosin (H&E) staining, Masson's trichrome staining, and determination of ferroptosis metabolites, genes and proteins. Additionally, we employed RNA-seq to identify the inhibitory effect of CAP on DOX-induced myocardial apoptosis, which was further confirmed through western blotting. Similar approaches were applied to H9C2 cells, yielding reliable results. RESULTS: Our study demonstrated that treatment with CAP improved the survival rate of DOX-treated mice and reduced myocardial injury. Mechanistically, CAP downregulated transferrin (Trf) and upregulated solute carrier family 40 member 1 (SLC40A1), which helped maintain iron levels in the cells and prevent ferroptosis. Furthermore, CAP inhibited DOX-induced apoptosis by modulating the phosphoinositide 3-kinase (PI3K)- protein kinase B (Akt) signaling pathway. Specifically, CAP activated the PI3K-Akt pathway and regulated downstream BCL2 and BAX to mitigate DOX-induced apoptosis. Therefore, our results suggest that CAP effectively alleviates acute myocardial injury induced by DOX. CONCLUSION: Our findings demonstrate that CAP has the potential to alleviate DOX-induced ferroptosis by regulating iron homeostasis. Additionally, it can inhibit DOX-induced apoptosis by activating PI3K-Akt signaling pathway.

GLS as a diagnostic biomarker in breast cancer: in-silico, in-situ, and in-vitro insights.

Background: Recently, a novel programmed cell death mechanism, Cuproptosis, has been discovered and found to play an important role in the development and progression of diverse tumors. In the present study, we comprehensively investigated the core gene of this mechanism, GLS, in breast cancer. Materials and methods: Bulk RNA sequencing data were curated from the TCGA repository to investigate the aberrant expression of GLS over diverse cancer types. Then, we examined its efficacy as a diagnostic biomarker in breast cancer by Area Under Curve (AUC) of the Receiver Operative Characteristic (ROC) curve. Furthermore, by applying siRNA technique, we knocked down the GLS expression level in cancerous cell lines, measuring the corresponding effects on cell proliferation and metastasis. Afterward, we explored the potential implications of GLS expression in the tumor immune microenvironment quantitatively by using several R packages and algorithms, including ESTIMATE, CIBERSORT, etc. Results: Pan-cancer analysis suggested that GLS was aberrantly over-expressed in many cancer types, with breast cancer being typical. More in-depth analyses revealed the expression of GLS exerted a high ROC-AUC value in breast cancer diagnosis. Through the knock-down of GLS expression, it was found that GLS expression was strongly relevant to the growth and metastasis of tumor. Furthermore, it was also found to be correlated with the immune tumor microenvironment. Conclusion: We highlighted that GLS expression might be applicable as a diagnostic biomarker in breast cancer and possess significant implications in the growth and metastasis of tumor and the immune tumor microenvironment, sharing new insights into ontological and personalized medicine.

Establishment of a novel lysosomal signature for the diagnosis of gastric cancer with in-vitro and in-situ validation.

Background: Gastric cancer (GC) represents a malignancy with a multi-factorial combination of genetic, environmental, and microbial factors. Targeting lysosomes presents significant potential in the treatment of numerous diseases, while lysosome-related genetic markers for early GC detection have not yet been established, despite implementing this process by assembling artificial intelligence algorithms would greatly break through its value in translational medicine, particularly for immunotherapy. Methods: To this end, this study, by utilizing the transcriptomic as well as single cell data and integrating 20 mainstream machine-learning (ML) algorithms. We optimized an AI-based predictor for GC diagnosis. Then, the reliability of the model was initially confirmed by the results of enrichment analyses currently in use. And the immunological implications of the genes comprising the predictor was explored and response of GC patients were evaluated to immunotherapy and chemotherapy. Further, we performed systematic laboratory work to evaluate the build-up of the central genes, both at the expression stage and at the functional aspect, by which we could also demonstrate the reliability of the model to guide cancer immunotherapy. Results: Eight lysosomal-related genes were selected for predictive model construction based on the inclusion of RMSE as a reference standard and RF algorithm for ranking, namely ADRB2, KCNE2, MYO7A, IFI30, LAMP3, TPP1, HPS4, and NEU4. Taking into account accuracy, precision, recall, and F1 measurements, a preliminary determination of our study was carried out by means of applying the extra tree and random forest algorithms, incorporating the ROC-AUC value as a consideration, the Extra Tree model seems to be the optimal option with the AUC value of 0.92. The superiority of diagnostic signature is also reflected in the analysis of immune features. Conclusion: In summary, this study is the first to integrate around 20 mainstream ML algorithms to construct an AI-based diagnostic predictor for gastric cancer based on lysosomal-related genes. This model will facilitate the accurate prediction of early gastric cancer incidence and the subsequent risk assessment or precise individualized immunotherapy, thus improving the survival prognosis of GC patients.

Machine learning approach combined with causal relationship inferring unlocks the shared pathomechanism between COVID-19 and acute myocardial infarction.

Background: Increasing evidence suggests that people with Coronavirus Disease 2019 (COVID-19) have a much higher prevalence of Acute Myocardial Infarction (AMI) than the general population. However, the underlying mechanism is not yet comprehended. Therefore, our study aims to explore the potential secret behind this complication. Materials and methods: The gene expression profiles of COVID-19 and AMI were acquired from the Gene Expression Omnibus (GEO) database. After identifying the differentially expressed genes (DEGs) shared by COVID-19 and AMI, we conducted a series of bioinformatics analytics to enhance our understanding of this issue. Results: Overall, 61 common DEGs were filtered out, based on which we established a powerful diagnostic predictor through 20 mainstream machine-learning algorithms, by utilizing which we could estimate if there is any risk in a specific COVID-19 patient to develop AMI. Moreover, we explored their shared implications of immunology. Most remarkably, through the Bayesian network, we inferred the causal relationships of the essential biological processes through which the underlying mechanism of co-pathogenesis between COVID-19 and AMI was identified. Conclusion: For the first time, the approach of causal relationship inferring was applied to analyzing shared pathomechanism between two relevant diseases, COVID-19 and AMI. Our findings showcase a novel mechanistic insight into COVID-19 and AMI, which may benefit future preventive, personalized, and precision medicine.Graphical abstract.

Integrated transcriptome analysis reveals roles of long non-coding RNAs (lncRNAs) in caprine skeletal muscle mass and meat quality.

Long non-coding RNAs (lncRNAs) play important roles in the growth and development of skeletal muscle. However, there is limited information on goats. In this study, expression profiles of lncRNAs in Longissimus dorsi muscle from Liaoning cashmere (LC) goats and Ziwuling black (ZB) goats with divergent meat yield and meat quality were compared using RNA-sequencing. Based on our previous microRNA (miRNA) and mRNA profiles obtained from the same tissues, the target genes and binding miRNAs of differentially expressed lncRNAs were obtained. Subsequently, lncRNA-mRNA interaction networks and a ceRNA network of lncRNA-miRNA-mRNA were constructed. A total of 136 differentially expressed lncRNAs were identified between the two breeds. Fifteen cis target genes and 143 trans target genes were found for differentially expressed lncRNAs, and they were enriched in muscle contraction, muscle system process, muscle cell differentiation, and p53 signaling pathway. A total of 69 lncRNA-trans target gene pairs were constructed, with close relationship with muscle development, intramuscular fat deposition, and meat tenderness. A total of 16 lncRNA-miRNA-mRNA ceRNA pairs were identified, of which some reportedly associated with skeletal muscle development and fat deposition were found. The study will provide an improved understanding of the roles of lncRNAs in caprine meat yield and meat quality.

SEC5 is involved in M2 polarization of macrophages via the STAT6 pathway, and its dysfunction in decidual macrophages is associated with recurrent spontaneous abortion.

Decidual macrophages (dMϕs) play critical roles in the establishment of microhomeostasis at the maternal-fetal interface during pregnancy. Impaired macrophage polarization during early pregnancy is associated with recurrent spontaneous abortion (RSA). In the present study, the SEC5 expression level was found to be significantly decreased in primary dMϕs of patients with RSA, and downregulation of SEC5 expression inhibited M2 polarization and STAT6 phosphorylation, whereas SEC5 overexpression in the Mϕs promoted M2 polarization and STAT6 phosphorylation in vitro. We subsequently found that SEC5 interacted with STAT6 in THP-1-derived Mϕs. The abundance of phosphorylated STAT6 (pSTAT6) protein was obviously increased, with a predominant distribution in the nucleus, after M2 polarization of Mϕs, and SEC5 protein was colocalized with pSTAT6. Moreover, a significantly reduced pSTAT6 expression level was observed in the dMϕs of patients with RSA. M2 polarization of Mϕs showed a stimulatory effect on the proliferation and invasion of human extravillous trophoblasts (EVTs) in vitro, and downregulation of SEC5 expression in Mϕs effectively reversed this effect. In a mouse model of LPS-induced early pregnancy loss, the uterine SEC5 expression level and the number of M2-Mϕs at the maternal-fetal interface were significantly reduced. More interestingly, heterozygous SEC5-deficient (SEC5-/+) pregnant mice were more sensitive to LPS-induced pregnancy loss. Taken together, these data indicate that SEC5 participates in the regulation of M2 polarization of Mϕs by interacting with STAT6 and that decreased SEC5 expression inhibits the M2 polarization of dMϕs and results in early pregnancy loss by interfering with the physical activities of EVTs and immunotolerance at the maternal-fetal interface.

Cuproptosis-Associated lncRNA Establishes New Prognostic Profile and Predicts Immunotherapy Response in Clear Cell Renal Cell Carcinoma.

Background: Clear cell renal cell carcinoma (ccRCC) accounts for 80% of all kidney cancers and has a poor prognosis. Recent studies have shown that copper-dependent, regulated cell death differs from previously known death mechanisms (apoptosis, ferroptosis, and necroptosis) and is dependent on mitochondrial respiration (Tsvetkov et al., Science, 2022, 375 (6586), 1254-1261). Studies also suggested that targeting cuproptosis may be a novel therapeutic strategy for cancer therapy. In ccRCC, both cuproptosis and lncRNA were critical, but the mechanisms were not fully understood. The aim of our study was to construct a prognostic profile based on cuproptosis-associated lncRNAs to predict the prognosis of ccRCC and to study the immune profile of clear cell renal cell carcinoma (ccRCC). Methods: We downloaded the transcriptional profile and clinical information of ccRCC from The Cancer Genome Atlas (TCGA). Co-expression network analysis, Cox regression method, and least absolute shrinkage and selection operator (LASSO) method were used to identify cuproptosis-associated lncRNAs and to construct a risk prognostic model. In addition, the predictive performance of the model was validated and recognized by an integrated approach. We then also constructed a nomogram to predict the prognosis of ccRCC patients. Differences in biological function were investigated by GO, KEGG, and immunoassay. Immunotherapy response was measured using tumor mutational burden (TMB) and tumor immune dysfunction and rejection (TIDE) scores. Results: We constructed a panel of 10 cuproptosis-associated lncRNAs (HHLA3, H1-10-AS1, PICSAR, LINC02027, SNHG15, SNHG8, LINC00471, EIF1B-AS1, LINC02154, and MINCR) to construct a prognostic prediction model. The Kaplan-Meier and ROC curves showed that the feature had acceptable predictive validity in the TCGA training, test, and complete groups. The cuproptosis-associated lncRNA model had higher diagnostic efficiency compared to other clinical features. The analysis of Immune cell infiltration and ssGSEA further confirmed that predictive features were significantly associated with the immune status of ccRCC patients. Notably, the superimposed effect of patients in the high-risk group and high TMB resulted in shorter survival. In addition, the higher TIDE scores in the high-risk group suggested a poorer outcome for immune checkpoint blockade response in these patients. Conclusion: The ten cuproptosis-related risk profiles for lncRNA may help assess the prognosis and molecular profile of ccRCC patients and improve treatment options, which can be further applied in the clinic.

Deep Small RNA Sequencing Reveals Important miRNAs Related to Muscle Development and Intramuscular Fat Deposition in Longissimus dorsi Muscle From Different Goat Breeds.

MicroRNAs (miRNAs) are a class of small non-coding RNAs that have been shown to play important post-transcriptional regulatory roles in the growth and development of skeletal muscle tissues. However, limited research into the effect of miRNAs on muscle development in goats has been reported. In this study, Liaoning cashmere (LC) goats and Ziwuling black (ZB) goats with significant phenotype difference in meat production performance were selected and the difference in Longissimus dorsi muscle tissue expression profile of miRNAs between the two goat breeds was then compared using small RNA sequencing. A total of 1,623 miRNAs were identified in Longissimus dorsi muscle tissues of the two goat breeds, including 410 known caprine miRNAs, 928 known species-conserved miRNAs and 285 novel miRNAs. Of these, 1,142 were co-expressed in both breeds, while 230 and 251 miRNAs were only expressed in LC and ZB goats, respectively. Compared with ZB goats, 24 up-regulated miRNAs and 135 miRNAs down-regulated were screened in LC goats. A miRNA-mRNA interaction network showed that the differentially expressed miRNAs would target important functional genes associated with muscle development and intramuscular fat deposition. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed that the target genes of differentially expressed miRNAs were significantly enriched in Ras, Rap 1, FoxO, and Hippo signaling pathways. This study suggested that these differentially expressed miRNAs may be responsible for the phenotype differences in meat production performance between the two goat breeds, thereby providing an improved understanding of the roles of miRNAs in muscle tissue of goats.

Improving Thermostability of Chimeric Enzymes Generated by Domain Shuffling Between Two Different Original Glucoamylases.

In order to improve enzymatic properties of glucoamylases, six recombinant genes GA1-GA6 were created by domain shuffling of glucoamylase genes GAA1 from Aspergillus niger Ld418AI and GATE from Talaromyces emersonii Ld418 TE using overlap extension PCR and were expressed in Saccharomyces cerevisiae W303-1B; only activities of GA1 and GA2 in the fermentation broth were higher than those of GAA1 but less than those of GATE. Further research results of GA1 and GA2 indicated that chimeric glucoamylases GA1 and GA2 revealed increased thermostability compared with GAA1 and GATE, although with a slight change in the activity and optimal temperature. However, GA1 had almost the same catalytic efficiency as GATE, whereas the catalytic efficiency of GA2 was slightly less than that of GATE, but still higher than that of GAA1. The structural analysis showed that the change of enzymatic properties could be caused by the increased and extended α-helix and ß-sheet, which change the secondary and tertiary structures of chimeric glucoamylases. These results demonstrated that domain shuffling was feasible to generate a chimeric enzyme with novel properties.