Exploring the molecular biology of ischemic cardiomyopathy based on ferroptosis­related genes.

Ischemic cardiomyopathy (ICM) is a serious cardiac disease with a very high mortality rate worldwide, which causes myocardial ischemia and hypoxia as the main damage. Further understanding of the underlying pathological processes of cardiomyocyte injury is key to the development of cardioprotective strategies. Ferroptosis is an iron-dependent form of regulated cell death characterized by the accumulation of lipid hydroperoxides to lethal levels, resulting in oxidative damage to the cell membrane. The current understanding of the role and regulation of ferroptosis in ICM is still limited, especially in the absence of evidence from large-scale transcriptomic data. Through comprehensive bioinformatics analysis of human ICM transcriptome data obtained from the Gene Expression Omnibus database, the present study identified differentially expressed ferroptosis-related genes (DEFRGs) in ICM. Subsequently, their potential biological mechanisms and cross-talk were analyzed, and hub genes were identified by constructing protein-protein interaction networks. Ferroptosis features such as reactive oxygen species generation, changes in ferroptosis marker proteins, iron ion aggregation and lipid oxidation, were identified in the H9c2 anoxic reoxygenation injury model. Finally, the diagnostic ability of Gap junction alpha-1 (GJA1), Solute carrier family 40 member 1 (SLC40A1), Alpha-synuclein (SNCA) were identified through receiver operating characteristic curves and the expression of DEFRGs was verified in an in vitro model. Furthermore, potential drugs (retinoic acid) that could regulate ICM ferroptosis were predicted based on key DEFRGs. The present article presents new insights into the role of ferroptosis in ICM, investigating the regulatory role of ferroptosis in the pathological process of ICM and advocating for ferroptosis as a potential novel therapeutic target for ICM based on evidence from the ICM transcriptome.

Dynamic Changes of Regulatory T Cells/CD4⁺ T Cells in Peripheral Blood of Adult Kidney Transplant Recipients: A Comparison of Pediatric and Adult Kidney Donors.

BACKGROUND Inducing transplantation tolerance and monitoring the recipient's immune status to improve allograft survival remains the main goal for kidney transplantation (KTx). MATERIAL AND METHODS A total of 53 renal transplantation patients and 20 healthy individuals were assigned to the post-transplantation and healthy groups, respectively; 10 recipients with stable renal function for 2 years after kidney transplantation were assigned to Group C. Eleven kidney transplantation recipients were hospitalized due to lung infection. Flow cytometry was used to measure levels of Tregs/CD4⁺ T cells. RESULTS The Tregs/CD4⁺ T cells ratio reached homeostasis 6 months after KTx, with no significant difference between Group D (healthy control group) and pre-surgery or Group C (2 years after KTx group). The pediatric donor group and the adult donor group reached immune homeostasis 3 months after the operation. Immune homeostasis is maintaining a balance between immune tolerance and immunogenicity. There was no significant difference in graft function between the pediatric and adult donor groups before surgery, 1 day after surgery, 1 week after surgery, 2 weeks after surgery, and 1 month after surgery; however, graft function was significantly better in the pediatric donor group compared with the adult donor group at 3 mouths (eGFR: 51.7 (40.4-66.2) vs 73.0 (55.7-90.2), P=0.008<0.05) and 6 months (eGFR: 52.2 (37.5-62.8) vs 80.5 (64.1-90.4), P<0.001) after surgery. Pediatric donor kidneys reached immune homeostasis 3 months after surgery, with better graft function at this time compared with adult donor kidneys. The proportion of Tregs/CD4⁺ T cells in recipients with a pulmonary infection after KTx was lower than in those with infection recovery. CONCLUSIONS Expanding the use of pediatric kidneys should be further explored by the transplantation community. The proportion of Tregs/CD4⁺ T cells in recipients with a pulmonary infection after KTx was lower than in those with infection recovery.

A Multiparametric Nomogram for Predicting Delayed Graft Function in Adult Recipients of Pediatric Donor Kidneys.

BACKGROUND: Delayed graft function (DGF) is one of the most common postoperative complications after kidney transplantation. The ability to predict DGF after transplantation can greatly aid clinical decision-making. Several models have been proposed to predict DGF in adult recipients of adult donor kidneys, but there is currently no model to predict DGF in adult recipients of pediatric donor transplants. Therefore, based on our medical records, we retrospectively investigated the pretransplant risk factors of DGF in transplants from pediatric donors to adult recipients. METHODS: Our center is in compliance with national laws, the Declaration of Istanbul, and the Helsinki Congress. The donors used by us were organs donated after the death of citizens and the participants were neither paid nor coerced. A retrospective review of 84 adult patients who received pediatric donor kidneys at a single center from April 4, 2015 to November 17, 2021 was conducted to investigate the pretransplant risk factors for the development of DGF. RESULTS: DGF was observed in 45 of 68 patients (66.17%) in the training group and 9 of 16 patients (56.25%) in the validation group. Multivariate logistic analysis showed that kidney donor profile index, cold ischemia time, number of human leukocyte antigen mismatches, and pretransplant dialysis duration were significant independent risk factors for DGF. By integrating these 4 factors, we constructed a nomogram model to predict DGF. According to the prediction model, the area under the curve of DGF of the training group and validation group was 0.899 and 0.905, respectively. CONCLUSION: We have constructed a novel, reliable, and accurate visual nomogram that provides a practical tool for predicting DGF in adult recipients of pediatric donor kidneys.

Prediction model of delayed graft function based on clinical characteristics combined with serum IL-2 levels.

BACKGROUND: Kidney transplantation is an effective treatment for end-stage renal disease (ESRD). Delayed graft function (DGF) is a common complication after kidney transplantation and exerts substantial effects on graft function and long-term graft survival. Therefore, the construction of an effective model to predict the occurrence of DGF is particularly important. METHODS: Seventy-one patients receiving their first kidney transplant at the First Affiliated Hospital of Nanchang University from October 2020 to October 2021 were enrolled in the discovery cohort. Based on clinical characteristics and serum markers, a logistic regression model was used to simulate the risk of DGF in the discovery cohort. The DGF prediction model was named the prediction system and was composed of risk factors related to DGF. Thirty-two patients receiving a kidney transplant at the First Affiliated Hospital of Nanchang University from October 2021 to February 2022 were enrolled in the validation cohort. The validation cohort was used to verify the accuracy and reliability of the prediction model. RESULTS: Cold ischemia time (CIT), donor history of diabetes mellitus, donor interleukin-2 (IL-2) level and donor terminal creatinine level constitute the prediction system. In the validation test, the area under the receiver operating characteristic curve (AUC) was 0.867 for the prediction system, and good calibration of the model was confirmed in the validation cohort. CONCLUSIONS: This study constructed a reliable and highly accurate prediction model that provides a practical tool for predicting DGF. Additionally, IL-2 participates in the kidney injury process and may be a potential marker of kidney injury.

Multi-resBind: a residual network-based multi-label classifier for in vivo RNA binding prediction and preference visualization.

BACKGROUND: Protein-RNA interactions play key roles in many processes regulating gene expression. To understand the underlying binding preference, ultraviolet cross-linking and immunoprecipitation (CLIP)-based methods have been used to identify the binding sites for hundreds of RNA-binding proteins (RBPs) in vivo. Using these large-scale experimental data to infer RNA binding preference and predict missing binding sites has become a great challenge. Some existing deep-learning models have demonstrated high prediction accuracy for individual RBPs. However, it remains difficult to avoid significant bias due to the experimental protocol. The DeepRiPe method was recently developed to solve this problem via introducing multi-task or multi-label learning into this field. However, this method has not reached an ideal level of prediction power due to the weak neural network architecture. RESULTS: Compared to the DeepRiPe approach, our Multi-resBind method demonstrated substantial improvements using the same large-scale PAR-CLIP dataset with respect to an increase in the area under the receiver operating characteristic curve and average precision. We conducted extensive experiments to evaluate the impact of various types of input data on the final prediction accuracy. The same approach was used to evaluate the effect of loss functions. Finally, a modified integrated gradient was employed to generate attribution maps. The patterns disentangled from relative contributions according to context offer biological insights into the underlying mechanism of protein-RNA interactions. CONCLUSIONS: Here, we propose Multi-resBind as a new multi-label deep-learning approach to infer protein-RNA binding preferences and predict novel interactions. The results clearly demonstrate that Multi-resBind is a promising tool to predict unknown binding sites in vivo and gain biology insights into why the neural network makes a given prediction.

Representation learning applications in biological sequence analysis.

Although remarkable advances have been reported in high-throughput sequencing, the ability to aptly analyze a substantial amount of rapidly generated biological (DNA/RNA/protein) sequencing data remains a critical hurdle. To tackle this issue, the application of natural language processing (NLP) to biological sequence analysis has received increased attention. In this method, biological sequences are regarded as sentences while the single nucleic acids/amino acids or k-mers in these sequences represent the words. Embedding is an essential step in NLP, which performs the conversion of these words into vectors. Specifically, representation learning is an approach used for this transformation process, which can be applied to biological sequences. Vectorized biological sequences can then be applied for function and structure estimation, or as input for other probabilistic models. Considering the importance and growing trend for the application of representation learning to biological research, in the present study, we have reviewed the existing knowledge in representation learning for biological sequence analysis.

Ornithine and breast cancer: a matched case-control study.

In vivo and vitro evidence indicates that ornithine and its related metabolic products play a role in tumor development. Whether ornithine is associated with breast cancer in humans is still unclear. We examined the association between circulating ornithine levels and breast cancer in females. This 1:1 age-matched case-control study identified 735 female breast cancer cases and 735 female controls without breast cancer. All cases had a pathological test to ascertain a breast cancer diagnosis. The controls were ascertained using pathologic testing, clinical examinations, and/or other tests. Fasting blood samples were used to measure ornithine levels. The average age for cases and controls were 49.6 years (standard deviation [SD] 8.7 years) and 48.9 years (SD 8.7 years), respectively. Each SD increase in ornithine levels was associated with a 12% reduction of breast cancer risk (adjusted odds ratio [OR] 0.88; 95% confidence interval [CI] 0.79-0.97). The association between ornithine and breast cancer did not differ by pathological stages of diagnosis or tumor grades (all P for trend > 0.1). We observed no effect measure modification by molecular subtypes (P for interaction = 0.889). In conclusion, higher ornithine levels were associated with lower breast cancer risk in females.

Rapid detection of the neonicotinoid insecticide imidacloprid using a quenchbody assay.

Contamination of the land and water by neonicotinoid insecticide residues is currently a severe environmental problem. However, the traditional methods for pesticide residue analysis are time consuming and laborious. To tackle this problem, here we describe a novel quenchbody (Q-body) immunoassay reagent that allows the rapid and sensitive detection of imidacloprid, one of the most frequently used neonicotinoid pesticides, in aqueous solution. A Q-body comprises an antibody Fab fragment that is site-specifically labeled with a fluorescent dye. The Fab fragment quenches the dye with its internal tryptophan residues via photoinduced electron transfer. The subsequent addition of imidacloprid stabilizes the antibody structure and displaces the quenched dye to the outside of the protein, resulting in increased fluorescence. The constructed Q-body assay exhibited a high dynamic range and a low limit of detection (10 ng mL-1), and the entire assay procedure could be completed in a few minutes. The assay showed a low cross-reactivity with possible interfering analogous compounds, indicating that it has a good selectivity. Hence, the developed Q-body assay has excellent potential as a universal technology for monitoring neonicotinoid residues in environmental and food samples. Graphical abstract A novel quenchbody (Q-body) immunoassay reagent that allows the rapid and sensitive detection of imidacloprid, one of the most frequently used neonicotinoid pesticides, in aqueous solution was developed. The addition of imidacloprid stabilizes the Q-body structure and displaces the quenched dye to the outside of the protein, resulting in increased fluorescence. The constructed Q-body assay exhibited a high dynamic range and a low limit of detection (10 ng mL-1), and completed in a few minutes.

Silhouette Scores for Arbitrary Defined Groups in Gene Expression Data and Insights into Differential Expression Results.

BACKGROUND: Hierarchical Sample clustering (HSC) is widely performed to examine associations within expression data obtained from microarrays and RNA sequencing (RNA-seq). Researchers have investigated the HSC results with several possible criteria for grouping (e.g., sex, age, and disease types). However, the evaluation of arbitrary defined groups still counts in subjective visual inspection. RESULTS: To objectively evaluate the degree of separation between groups of interest in the HSC dendrogram, we propose to use Silhouette scores. Silhouettes was originally developed as a graphical aid for the validation of data clusters. It provides a measure of how well a sample is classified when it was assigned to a cluster by according to both the tightness of the clusters and the separation between them. It ranges from 1.0 to - 1.0, and a larger value for the average silhouette (AS) over all samples to be analyzed indicates a higher degree of cluster separation. The basic idea to use an AS is to replace the term cluster by group when calculating the scores. We investigated the validity of this score using simulated and real data designed for differential expression (DE) analysis. We found that larger (or smaller) AS values agreed well with both higher (or lower) degrees of separation between different groups and higher percentages of differentially expressed genes (PDEG). We also found that the AS values were generally independent on the number of replicates (Nrep). Although the PDEG values depended on Nrep, we confirmed that both AS and PDEG values were close to zero when samples in the data showed an intermingled nature between the groups in the HSC dendrogram. CONCLUSION: Silhouettes is useful for exploring data with predefined group labels. It would help provide both an objective evaluation of HSC dendrograms and insights into the DE results with regard to the compared groups.

Sphingosine-1-phosphate induces the migration of thyroid follicular carcinoma cells through the microRNA-17/PTK6/ERK1/2 pathway.

Sphingosine-1-phosphate (S1P) is a bioactive lipid known to play a role in tumorigenesis and cancer progression. However, the molecular mechanisms of S1P regulated migration of papillary thyroid cancer cells are still unknown. In this study, we showed that S1P induced PTK6 mRNA and protein expression in two thyroid follicular cancer cell lines (ML-1 and FTC-133). Further studies demonstrated that induced PTK6 and its downstream signal component (ERK1/2) are involved in S1P-induced migration. Upon investigating the mechanisms behind this event, we found that miR-17 inhibited the expression of PTK6 through direct binding to its 3'-UTR. Through overexpression and knockdown studies, we found that miR-17 can significantly inhibit S1P-induced migration in thyroid follicular cancer cells. Interestingly, overexpression or knockdown of PTK6 or ERK1/2 effectively removed the inhibition of S1P-induced migration by miR-17. Furthermore, we showed that S1P decreased miR-17 expression levels. Meanwhile, in papillary thyroid cancers, miR-17 is downregulated and negatively associated with clinical staging, whereas PTK6 is upregulated and positively associated with clinical stages. Collectively, our work defines a novel signaling pathway implicated in the control of thyroid cancer migration.