LCK as a Potential Therapeutic Target for Acute Rejection after Kidney Transplantation: A Bioinformatics Clue

Characterizing hub biomarkers for post-transplant renal fibrosis and unveiling their immunological functions through RNA sequencing and advanced machine learning techniques

BACKGROUND

Kidney transplantation stands out as the most effective renal replacement therapy for patients grappling with end-stage renal disease. However, post-transplant renal fibrosis is a prevalent and irreversible consequence, imposing a substantial clinical burden. Unfortunately, the clinical landscape remains devoid of reliable biological markers for diagnosing post-transplant renal interstitial fibrosis.

METHODS

We obtained transcriptome and single-cell sequencing datasets of patients with renal fibrosis from NCBI Gene Expression Omnibus (GEO). Subsequently, we employed Weighted Gene Co-Expression Network Analysis (WGCNA) to identify potential genes by integrating core modules and differential genes. Functional enrichment analysis was conducted to unveil the involvement of potential pathways. To identify key biomarkers for renal fibrosis, we utilized logistic analysis, a LASSO-based tenfold cross-validation approach, and gene topological analysis within Cytoscape. Furthermore, histological staining, Western blotting (WB), and quantitative PCR (qPCR) experiments were performed in a murine model of renal fibrosis to verify the identified hub genes. Moreover, molecular docking and molecular dynamics simulations were conducted to explore possible effective drugs.

RESULTS

Through WGCNA, the intersection of core modules and differential genes yielded a compendium of 92 potential genes. Logistic analysis, LASSO-based tenfold cross-validation, and gene topological analysis within Cytoscape identified four core genes (CD3G, CORO1A, FCGR2A, and GZMH) associated with renal fibrosis. The expression of these core genes was confirmed through single-cell data analysis and validated using various machine learning methods. Wet experiments also verified the upregulation of these core genes in the murine model of renal fibrosis. A positive correlation was observed between the core genes and immune cells, suggesting their potential role in bolstering immune system activity. Moreover, four potentially effective small molecules (ZINC000003830276-Tessalon, ZINC000003944422-Norvir, ZINC000008214629-Nonoxynol-9, and ZINC000085537014-Cobicistat) were identified through molecular docking and molecular dynamics simulations.

CONCLUSION

Four potential hub biomarkers most associated with post-transplant renal fibrosis, as well as four potentially effective small molecules, were identified, providing valuable insights for studying the molecular mechanisms underlying post-transplant renal fibrosis and exploring new targets.

Identification of Genes Associated with Decreasing Abundance of Monocytes in Long-Term Peritoneal Dialysis Patients

Purpose

Chronic kidney disease (CKD) will become an end-stage renal disease (ESRD) at stage 5. Peritoneal dialysis (PD) is required for renal replacement therapy. This study aims to identify monocytes-related genes in peritoneal cells from long-term PD (LPD) patients and short-term PD (SPD) patients.

Methods

Bulk RNA-seq data (GSE125498 dataset) and ScRNA-seq data (GSE130888) were downloaded to identify differentially expressed genes, monocytes-related genes, and monocytes marker genes in LPD patients. Immune infiltration was analyzed in the GSE125498 dataset. Core genes associated with monocytes changes were screened out, followed by functional analysis and expression validation using RT-PCR.

Results

Monocytes are the most abundant immune cell in PD. The number of monocytes was remarkably decreased in LPD compared with SPD. A total of 16 up-regulated core genes negatively correlated with the abundance of monocytes were obtained in LPD. The expression of 16 core genes was lower in monocyte clusters than that in other cell clusters. In addition, LCK, CD3G, CD3E, CD3D, and LAT were involved in the signaling pathways of Th1 and Th2 cell differentiation, T cell receptor signaling pathway, and Th17 cell differentiation. CD2 was involved in hematopoietic cell lineage signaling pathway.

Conclusion

Identification of monocytes related-genes and related signaling pathways could be helpful in understanding the molecular mechanism of monocytes changes during PD.

Deep latent space fusion for adaptive representation of heterogeneous multi-omics data

The integration of multi-omics data makes it possible to understand complex biological organisms at the system level. Numerous integration approaches have been developed by assuming a common underlying data space. Due to the noise and heterogeneity of biological data, the performance of these approaches is greatly affected. In this work, we propose a novel deep neural network architecture, named Deep Latent Space Fusion (DLSF), which integrates the multi-omics data by learning consistent manifold in the sample latent space for disease subtypes identification. DLSF is built upon a cycle autoencoder with a shared self-expressive layer, which can naturally and adaptively merge nonlinear features at each omics level into one unified sample manifold and produce adaptive representation of heterogeneous samples at the multi-omics level. We have assessed DLSF on various biological and biomedical datasets to validate its effectiveness. DLSF can efficiently and accurately capture the intrinsic manifold of the sample structures or sample clusters compared with other state-of-the-art methods, and DLSF yielded more significant outcomes for biological significance, survival prognosis and clinical relevance in application of cancer study in The Cancer Genome Atlas. Notably, as a deep case study, we determined a new molecular subtype of kidney renal clear cell carcinoma that may benefit immunotherapy in the viewpoint of multi-omics, and we further found potential subtype-specific biomarkers from multiple omics data, which were validated by independent datasets. In addition, we applied DLSF to identify potential therapeutic agents of different molecular subtypes of chronic lymphocytic leukemia, demonstrating the scalability of DLSF in diverse omics data types and application scenarios.

Integrative Analysis of Prognostic Biomarkers for Acute Rejection in Kidney Transplant Recipients

BACKGROUND

Noninvasive biomarkers may predict adverse events such as acute rejection after kidney transplantation and may be preferable to existing methods because of superior accuracy and convenience. It is uncertain how these biomarkers, often derived from a single study, perform across different cohorts of recipients.

METHODS

Using a cross-validation framework that evaluates the performance of biomarkers, the aim of this study was to devise an integrated gene signature set that predicts acute rejection in kidney transplant recipients. Inclusion criteria were publicly available datasets of gene signatures that reported acute rejection episodes after kidney transplantation. We tested the predictive probability for acute rejection using gene signatures within individual datasets and validated the set using other datasets. Eight eligible studies of 1454 participants, with a total of 512 acute rejections episodes were included.

RESULTS

All sets of gene signatures had good positive and negative predictive values (79%-96%) for acute rejection within their own cohorts, but the predictability reduced to <50% when tested in other independent datasets. By integrating signature sets with high specificity scores across all studies, a set of 150 genes (included CXCL6, CXCL11, OLFM4, and PSG9) which are known to be associated with immune responses, had reasonable predictive values (varied between 69% and 90%).

CONCLUSIONS

A set of gene signatures for acute rejection derived from a specific cohort of kidney transplant recipients do not appear to provide adequate prediction in an independent cohort of transplant recipients. However, the integration of gene signature sets with high specificity scores may improve the prediction performance of these markers.

New horizons in drug discovery of lymphocyte-specific protein tyrosine kinase (Lck) inhibitors: a decade review (2011-2021) focussing on structure-activity relationship (SAR) and docking insights

Lymphocyte-specific protein tyrosine kinase (Lck), a non-receptor Src family kinase, has a vital role in various cellular processes such as cell cycle control, cell adhesion, motility, proliferation, and differentiation. Lck is reported as a key factor regulating the functions of T-cell including the initiation of TCR signalling, T-cell development, in addition to T-cell homeostasis. Alteration in expression and activity of Lck results in numerous disorders such as cancer, asthma, diabetes, rheumatoid arthritis, atherosclerosis, and neuronal diseases. Accordingly, Lck has emerged as a novel target against different diseases. Herein, we amass the research efforts in literature and pharmaceutical patents during the last decade to develop new Lck inhibitors. Additionally, structure-activity relationship studies (SAR) and docking models of these new inhibitors within the active site of Lck were demonstrated offering deep insights into their different binding modes in a step towards the identification of more potent, selective, and safe Lck inhibitors.

Identification of Key Pathways and Genes in Obesity Using Bioinformatics Analysis and Molecular Docking Studies

Obesity is an excess accumulation of body fat. Its progression rate has remained high in recent years. Therefore, the aim of this study was to diagnose important differentially expressed genes (DEGs) associated in its development, which may be used as novel biomarkers or potential therapeutic targets for obesity. The gene expression profile of E-MTAB-6728 was downloaded from the database. After screening DEGs in each ArrayExpress dataset, we further used the robust rank aggregation method to diagnose 876 significant DEGs including 438 up regulated and 438 down regulated genes. Functional enrichment analysis was performed. These DEGs were shown to be significantly enriched in different obesity related pathways and GO functions. Then protein-protein interaction network, target genes - miRNA regulatory network and target genes - TF regulatory network were constructed and analyzed. The module analysis was performed based on the whole PPI network. We finally filtered out STAT3, CORO1C, SERPINH1, MVP, ITGB5, PCM1, SIRT1, EEF1G, PTEN and RPS2 hub genes. Hub genes were validated by ICH analysis, receiver operating curve (ROC) analysis and RT-PCR. Finally a molecular docking study was performed to find small drug molecules. The robust DEGs linked with the development of obesity were screened through the expression profile, and integrated bioinformatics analysis was conducted. Our study provides reliable molecular biomarkers for screening and diagnosis, prognosis as well as novel therapeutic targets for obesity.

Using Artificial Intelligence Resources in Dialysis and Kidney Transplant Patients: A Literature Review

BACKGROUND

The purpose of this review is to depict current research and impact of artificial intelligence/machine learning (AI/ML) algorithms on dialysis and kidney transplantation. Published studies were presented from two points of view: What medical aspects were covered? What AI/ML algorithms have been used?

METHODS

We searched four electronic databases or studies that used AI/ML in hemodialysis (HD), peritoneal dialysis (PD), and kidney transplantation (KT). Sixty-nine studies were split into three categories: AI/ML and HD, PD, and KT, respectively. We identified 43 trials in the first group, 8 in the second, and 18 in the third. Then, studies were classified according to the type of algorithm.

RESULTS

AI and HD trials covered: (a) dialysis service management, (b) dialysis procedure, (c) anemia management, (d) hormonal/dietary issues, and (e) arteriovenous fistula assessment. PD studies were divided into (a) peritoneal technique issues, (b) infections, and (c) cardiovascular event prediction. AI in transplantation studies were allocated into (a) management systems (ML used as pretransplant organ-matching tools), (b) predicting graft rejection, (c) tacrolimus therapy modulation, and (d) dietary issues.

CONCLUSIONS

Although guidelines are reluctant to recommend AI implementation in daily practice, there is plenty of evidence that AI/ML algorithms can predict better than nephrologists: volumes, Kt/V, and hypotension or cardiovascular events during dialysis. Altogether, these trials report a robust impact of AI/ML on quality of life and survival in G5D/T patients. In the coming years, one would probably witness the emergence of AI/ML devices that facilitate the management of dialysis patients, thus increasing the quality of life and survival.

Exploration of the therapeutic aspects of Lck: A kinase target in inflammatory mediated pathological conditions

Lck, a non-receptor src family kinase, plays a vital role in various cellular processes such as cell cycle control, cell adhesion, motility, proliferation and differentiation. As a 56 KDa protein, Lck phosphorylates tyrosine residues of various proteins such as ZAP-70, ITK and protein kinase C. The structure of Lck is comprised of three domains, one SH3 in tandem with a SH2 domain at the amino terminal and the kinase domain at the carboxy terminal. Physiologically, Lck is involved in the development, function and differentiation of T-cells. Additionally, Lck regulates neurite outgrowth and maintains long-term synaptic plasticity in neurons. Given a major role of Lck in cytokine production and T cell signaling, alteration in expression and activity of Lck may result in various diseased conditions like cancer, asthma, diabetes, rheumatoid arthritis, psoriasis, inflammatory bowel diseases such as Crohn's disease and ulcerative colitis, atherosclerosis etc. This article provides evidence and information establishing Lck as one of the therapeutic targets in various inflammation mediated pathophysiological conditions.