Human Gene Research Articles

Identification and analysis of oncogenic non-synonymous single nucleotide polymorphisms in the human NRAS gene: An exclusive in silico study

BackgroundN-ras protein is encoded by the NRAS gene and operates as GDP-GTP-controlled on/off switching. N-ras interacts with cellular signaling networks that regulate various cellular activities including cell proliferation and survival. The nonsynonymous single nucleotide polymorphism (nsSNPs)-mediated alteration can substantially disrupt the structure and activity of the corresponding protein. N-ras has been reported to be associated with numerous diseases including cancers due to the nsSNPs. A comprehensive study on the NRAS gene to unveil the potentially damaging and oncogenic nsSNPs is yet to be accomplished. Hence, this extensive in silico study is intended to identify the disease-associated, specifically oncogenic nsSNPs of the NRAS gene. ResultsOut of 140 missense variants, 7 nsSNPs (I55R, G60E, G60R, Y64D, L79F, D119G, and V152F) were identified to be damaging utilizing 10 computational tools that works based on different algorithms with high accuracy. Among those, G60E, G60R, and D119G variants were further filtered considering their location in the highly conserved region and later identified as oncogenic variants. Interestingly, G60E and G60R variants were revealed to be particularly associated with lung adenocarcinoma, rhabdomyosarcoma, and prostate adenocarcinoma. Therefore, D119G could be subjected to detailed investigation for identifying its association with specific cancer. ConclusionThis in silico study identified the deleterious and oncogenic missense variants of the human NRAS gene that could be utilized for designing further experimental investigation. The outcomes of this study would be worthwhile in future research for developing personalized medicine.

Testing the Significance of Ranked Gene Sets in Genome-wide Transcriptome Profiling Data Using Weighted Rank Correlation Statistics

Background: Popular gene set enrichment analysis approaches assumed that genes in the gene set contributed to the statistics equally. However, the genes in the transcription factors (TFs) derived gene sets, or gene sets constructed by TF targets identified by the ChIP-Seq experiment, have a rank attribute, as each of these genes have been assigned with a p-value which indicates the true or false possibilities of the ownerships of the genes belong to the gene sets. Objective: Popular gene set enrichment analysis approaches assumed that genes in the gene set contributed to the statistics equally. However, the genes in the transcription factors (TFs) derived gene sets, or gene sets constructed by TF targets identified by the ChIP-Seq experiment, have a rank attribute, as each of these genes have been assigned with a p-value which indicates the true or false possibilities of the ownerships of the genes belong to the gene sets. Methods: A method was proposed by first creating ranked gene sets and gene lists and then applying weighted Kendall's tau rank correlation statistics to the test. After introducing top-down weights to the genes in the gene set, a new software called "Flaver" was developed. Results: Theoretical properties of the proposed method were established, and its differences over the GSEA approach were demonstrated when analyzing the transcriptome profiling data across 55 human tissues and 176 human cell-lines. The results indicated that the TFs identified by our method have higher tendency to be differentially expressed across the tissues analyzed than its competitors. It significantly outperforms the well-known gene set enrichment analyzing tools, GOStats (9%) and GSEA (17%), in analyzing well-documented human RNA transcriptome datasets. Conclusions: The method is outstanding in detecting gene sets of which the gene ranks were correlated with the expression levels of the genes in the transcriptome data.

TMErisk score: a tumor microenvironment-based model for predicting prognosis and immunotherapy in patients with head and neck squamous cell carcinoma

Tumor microenvironment (TME) is closely associated with the progression and prognosis of head and neck squamous cell carcinoma (HNSCC). To investigate potential biomarkers for predicting therapeutic outcomes in HNSCC, we analyzed the immune and stromal status of HNSCC based on the genes associated with TME using the ESTIMATE algorithm. Immune and stromal genes were identified with differential gene expression and weighted gene co-expression network analysis (WGCNA). From these genes, 118 were initially selected through Cox univariate regression and then further input into least absolute shrinkage and selection operator (LASSO) regression analysis. As a result, 11 genes were screened out for the TME-related risk (TMErisk) score model which presented promising overall survival predictive potential. The TMErisk score was negatively associated with immune and stromal scores but positively associated with tumor purity. Individuals with high TMErisk scores exhibited decreased expression of most immune checkpoints and all human leukocyte antigen family genes, and reduced abundance of infiltrating immune cells. Divergent genes were mutated in HNSCC. In both high and low TMErisk score groups, the tumor protein P53 exhibited the highest mutation frequency. A higher TMErisk score was found to be associated with reduced overall survival probability and worse outcomes of immunotherapy. Therefore, the TMErisk score could serve as a valuable model for the outcome prediction of HNSCC in clinic.

Spinach genomes reveal migration history and candidate genes for important crop traits.

Spinach (Spinacia oleracea) is an important leafy crop possessing notable economic value and health benefits. Current genomic resources include reference genomes and genome-wide association studies. However, the worldwide genetic relationships and the migration history of the crop remained uncertain, and genome-wide association studies have produced extensive gene lists related to agronomic traits. Here, we re-analysed the sequenced genomes of 305 cultivated and wild spinach accessions to unveil the phylogeny and history of cultivated spinach and to explore genetic variation in relation to phenotypes. In contrast to previous studies, we employed machine learning methods (based on Extreme Gradient Boosting, XGBoost) to detect variants that are collectively associated with agronomic traits. Variant-based cluster analyses revealed three primary spinach groups in the Middle East, Asiaand Europe/US. Combining admixture analysis and allele-sharing statistics, migration routes of spinach from the Middle East to Europe and Asia are presented. Using XGBoost machine learning models we predict genomic variants influencing bolting time, flowering time, petiole color, and leaf surface texture and propose candidate genes for each trait. This study enhances our understanding of the history and phylogeny of domesticated spinach and provides valuable information on candidate genes for future genetic improvement of the crop.

Alleviation of Angiotensin II-Induced Vascular Endothelial Cell Injury through Long Non-coding RNA TUG1 Inhibition.

Hypertension damages endothelial cells, causing vascular remodelling. It is caused by Ang II-induced endothelial cell (EC) destruction. The long noncoding RNA (lncRNAs) are emerging regulators of endothelium homeostasis. Injured endothelium expresses lncRNA taurine-upregulated gene 1 (TUG1), which may mediate endothelial cell damage, proliferation, apoptosis, and autophagy and contribute to cardiovascular disease. However, uncertainty surrounds the function of lncRNA TUG1, on arterial endothelium cell damage. This research aimed to investigate the role and mechanism of lncRNA TUG1 in vascular endothelial cell injury. A microarray analysis of lncRNA human gene expression was used to identify differentially expressed lncRNAs in human umbilical vein endothelial cell (HUVEC) cultures. The viability, apoptosis, and migration of Ang II-treated HUVECs were then evaluated. In order to investigate the role of lncRNA TUG1 in hypertension, qRT-PCR, western blotting, and RNA-FISH were used to examine the expression of TUG1 in SHR mice. Ang II-activated HUVECs and SHR rats' abdominal aortas highly express the lncRNA TUG1. LncRNA TUG1 knockdown in HUVECs could increase cell viability, reduce apoptosis, and produce inflammatory factors. In SHR rat abdominal aortas, lncRNA TUG1 knockdown promoted proliferation and inhibited apoptosis. HE spotting showed that lncRNA TUG1 knockdown improved SHR rats' abdominal aorta shape. lncRNA TUG1 knockdown promotes miR-9- 5p, which inhibits CXCR4 following transcription. The lncRNA TUG1/miR-9-5p/CXCR4 axis and vascular cell injury were also examined. MiR-9-5p silencing or CXCR4 overexpression lowered cell survival, apoptosis, and lncRNA TUG1-induced IL-6 and NO expression. lncRNA TUG1 suppression could reduce Ang II-induced endothelial cell damage by regulating and targeting miR-9-5p to limit CXCR4 expression and open new vascular disease research pathways.

How MicroRNAs Command the Battle against Cancer

MicroRNAs (miRNAs) are small RNA molecules that regulate more than 30% of genes in humans. Recent studies have revealed that miRNAs play a crucial role in tumorigenesis. Large sets of miRNAs in human tumors are under-expressed compared to normal tissues. Furthermore, experiments have shown that interference with miRNA processing enhances tumorigenesis. Multiple studies have documented the causal role of miRNAs in cancer, and miRNA-based anticancer therapies are currently being developed. This review primarily focuses on two key points: (1) miRNAs and their role in human cancer and (2) the regulation of tumor suppressors by miRNAs. The review discusses (a) the regulation of the tumor suppressor p53 by miRNA, (b) the critical role of the miR-144/451 cluster in regulating the Itch-p63-Ago2 pathway, and (c) the regulation of PTEN by miRNAs. Future research and the perspectives of miRNA in cancer are also discussed. Understanding these pathways will open avenues for therapeutic interventions targeting miRNA regulation.

Cell envelope and stress-responsive pathways underlie an evolved oleaginous Rhodotorula toruloides strain multi-stress tolerance

BackgroundThe red oleaginous yeast Rhodotorula toruloides is a promising cell factory to produce microbial oils and carotenoids from lignocellulosic hydrolysates (LCH). A multi-stress tolerant strain towards four major inhibitory compounds present in LCH and methanol, was derived in our laboratory from strain IST536 (PYCC 5615) through adaptive laboratory evolution (ALE) under methanol and high glycerol selective pressure.ResultsComparative genomic analysis suggested the reduction of the original strain ploidy from triploid to diploid, the occurrence of 21,489 mutations, and 242 genes displaying copy number variants in the evolved strain. Transcriptomic analysis identified 634 genes with altered transcript levels (465 up, 178 down) in the multi-stress tolerant strain. Genes associated with cell surface biogenesis, integrity, and remodelling and involved in stress-responsive pathways exhibit the most substantial alterations at the genome and transcriptome levels. Guided by the suggested stress responses, the multi-stress tolerance phenotype was extended to osmotic, salt, ethanol, oxidative, genotoxic, and medium-chain fatty acid-induced stresses.ConclusionsThe comprehensive analysis of this evolved strain provided the opportunity to get mechanistic insights into the acquisition of multi-stress tolerance and a list of promising genes, pathways, and regulatory networks, as targets for synthetic biology approaches applied to promising cell factories, toward more robust and superior industrial strains. This study lays the foundations for understanding the mechanisms underlying tolerance to multiple stresses in R. toruloides, underscoring the potential of ALE for enhancing the robustness of industrial yeast strains.

Multiple institutions’ research findings using the National Mesothelioma Virtual Bank

Mesothelioma remains an under-researched cancerous disease due to the lack of high-quality patient samples and clinical information especially outcomes and asbestos exposure data. The National Mesothelioma Virtual Bank (NMVB) is a biobank in which mesothelioma annotated biospecimens can be made widely available to the research community. Here, we summarized the significant research findings from 20 publications that utilized the NMVB samples for novel biomarker and therapeutic discoveries. The results showed that the use of the NMVB resource was dispersed among a variety of basic science topics including, but not limited to, biomarkers, abnormal gene expression, and potential therapeutic targets. Positive biomarkers included several miRNAs and antibodies, HMGB1, ATG5, PIAS3, pancytokeratin and GATA3. Genes that had mutations or high/low levels of expression were BAP1, a human control gene of importance in this disease, as well as various cytokines, and checkpoint inhibitors TM4SF1, PKM2, ARHGDIA, COBLL1, WT1, FOXM1, and CD30. Treatments investigated include thiostrepton, interferon-β gene, and Brentuximab. Publications reviewed indicated a significant impact of the NMVB resource utilized in significant studies focusing on biomarker and therapeutic discoveries, which can act as a model for rare diseases, especially in oncology.

Microbiota and Gut-Liver Axis: An Unbreakable Bond?

The gut microbiota, amounting to approximately 100 trillion (1014) microbes represents a genetic repertoire that is bigger than the human genome itself. Evidence on bidirectional interplay between human and microbial genes is mounting. Microbiota probably play vital roles in diverse aspects of normal human metabolism, such as digestion, immune modulation, and gut endocrine function, as well as in the genesis and progression of many human diseases. Indeed, the gut microbiota has been most closely linked to various chronic ailments affecting the liver, although concrete scientific data are sparse. In this narrative review, we initially discuss the basic epidemiology of gut microbiota and the factors influencing their initial formation in the gut. Subsequently, we delve into the gut-liver axis and the evidence regarding the link between gut microbiota and the genesis or progression of various liver diseases. Finally, we summarise the recent research on plausible ways to modulate the gut microbiota to alter the natural history of liver disease.

Diverse functions and pathogenetic role of Crumbs in retinopathy.

The Crumbs protein (CRB) family plays a crucial role in maintaining the apical-basal polarity and integrity of embryonic epithelia. The family comprises different isoforms in different animals and possesses diverse structural, localization, and functional characteristics. Mutations in the human CRB1 or CRB2 gene may lead to a broad spectrum of retinal dystrophies. Various CRB-associated experimental models have recently provided mechanistic insights into human CRB-associated retinopathies. The knowledge obtained from these models corroborates the importance of CRB in retinal development and maintenance. Therefore, complete elucidation of these models can provide excellent therapeutic prospects for human CRB-associated retinopathies. In this review, we summarize the current animal models and human-derived models of different CRB family members and describe the main characteristics of their retinal phenotypes.

Network pharmacology, molecular docking, and dynamics analyses to predict the antiviral activity of ginger constituents against coronavirus infection.

COVID-19 is a global pandemic that caused a dramatic loss of human life worldwide, leading to accelerated research for antiviral drug discovery. Herbal medicine is one of the most commonly used alternative medicine for the prevention and treatment of many conditions including respiratory system diseases. In this study, a computational pipeline was employed, including network pharmacology, molecular docking simulations, and molecular dynamics simulations, to analyze the common phytochemicals of ginger rhizomes and identify candidate constituents as viral inhibitors. Furthermore, experimental assays were performed to analyze the volatile and non-volatile compounds of ginger and to assess the antiviral activity of ginger oil and hydroalcoholic extract. Network pharmacology analysis showed that ginger compounds target human genes that are involved in related cellular processes to the viral infection. Docking analysis highlighted five pungent compounds and zingiberenol as potential inhibitors for the main protease (Mpro), spike receptor-binding domain (RBD), and human angiotensin-converting enzyme 2 (ACE2). Then, (6)-gingerdiacetate was selected for molecular dynamics (MD) simulations as it exhibited the best binding interactions and free energies over the three target proteins. Trajectories analysis of the three complexes showed that RBD and ACE2 complexes with the ligand preserved similar patterns of root mean square deviation (RMSD) and radius of gyration (Rg) values to their respective native structures. Finally, experimental validation of the ginger hydroalcoholic extract confirmed the existence of (6)-gingerdiacetate and revealed the strong antiviral activity of the hydroalcoholic extract with IC of 2.727 . Our study provides insights into the potential antiviral activity of (6)-gingerdiacetate that may enhance the host immune response and block RBD binding to ACE2, thereby, inhibiting SARS-CoV-2 infection.

Human circBOULE RNAs as potential biomarkers for sperm quality and male infertility.

Reliable molecular biomarkers to predict fertility remain scarce. The current study explored the potential of testis-specific circBOULE RNAs as biomarkers for male infertility and sperm quality. Using RT-PCR and RT-qPCR assays, we identified seven circular RNAs from the human BOULE gene in human sperm. We found that sperm circEx3-6 RNA exhibited a significantly decreased expression in asthenozoospermia while circEx2-6 and circEx2-7 expression decreased in teratozoospermia, compared with the controls. Furthermore, circEx2-6 expression exhibited a negative correlation with sperm DNA Fragmentation Index (DFI), and circEx2-7 levels were correlated with both fertilization and cleavage rates involving assisted reproductive technologies. Further functional analyses in a transgenic fly model lent support for the roles of circBOULE RNAs in sperm development and human fertility. Collectively, our findings support that sperm circBOULE RNAs may serve as diagnostic biomarkers for assessing sperm motility and DNA quality. Hence clinical application and significance of sperm circular RNAs in assisted reproductive technologies warrant further investigation.

In Silico Analysis of Non-Synonymous Snps in the Human WWOX Gene

In Silico Analysis of Non-Synonymous Snps in the Human WWOX Gene

Validation of human telomere length multi-ancestry meta-analysis association signals identifies POP5 and KBTBD6 as human telomere length regulation genes

Genome-wide association studies (GWAS) have become well-powered to detect loci associated with telomere length. However, no prior work has validated genes nominated by GWAS to examine their role in telomere length regulation. We conducted a multi-ancestry meta-analysis of 211,369 individuals and identified five novel association signals. Enrichment analyses of chromatin state and cell-type heritability suggested that blood/immune cells are the most relevant cell type to examine telomere length association signals. We validated specific GWAS associations by overexpressing KBTBD6 or POP5 and demonstrated that both lengthened telomeres. CRISPR/Cas9 deletion of the predicted causal regions in K562 blood cells reduced expression of these genes, demonstrating that these loci are related to transcriptional regulation of KBTBD6 and POP5. Our results demonstrate the utility of telomere length GWAS in the identification of telomere length regulation mechanisms and validate KBTBD6 and POP5 as genes affecting telomere length regulation.

Exome-wide association study for replication of rare variants affecting the severity of COVID-19 in the Russian population

BACKGROUND: Human genetics is one of the factors determining the severity of COVID-19 disease. Previously, a large-scale whole-genome association study of the COVID-19 HG project (COVID-19 Host Genetics Initiative, 2021) investigated the association of genetic variants at multiple loci with COVID-19 severity. The genetic variants that have the greatest impact on COVID-19 severity are expected to have a low frequency in the population. Therefore, the study of rare variants may provide additional insights into the pathogenesis of the disease and thus help in the development of prevention and treatment options. AIM: The aim is to perform a replication analysis in search of genes with enrichment for rare genetic variants in relation to the severity of COVID-19 disease. METHODS: In this study, the clinical exome of a Russian cohort of patients was sequenced based on the St. Petersburg State Budgetary Institution "City Hospital No. 40" and St. Petersburg State University. The study used biomaterial from patients hospitalised at "City Hospital No. 40" with a diagnosis of COVID-19 and healthy individuals included in the population control group. The severity of the course of COVID-19 was determined according to the results of lung computed tomography. The list of genes for subsequent replication was generated by a literature review. Replication analysis of genes associated with COVID-19 severity was performed using burden test methods. RESULTS: In total, 701 clinical exomes were sequenced: 263 severe COVID-19 and 438 healthy individuals. A literature review identified 18 genes associated with severe COVID-19 that were included in the replication analysis. The replication analysis did not identify any genes whose association with severe COVID-19 was confirmed in the study cohort. CONCLUSION: Replication analysis did not identify any genes for which a significant association between functional variant enrichment and COVID-19 severity was found. However, the results demonstrated that the direction of the correlation was consistent with findings from previous studies. The small size of the sample analysed is an obvious limitation of our study. Expanding the study cohort would increase the power of the tests and allow us to detect additional rare variants that influence the severity of COVID-19 progression

MousiPLIER: A Mouse Pathway-Level Information Extractor Model.

High throughput gene expression profiling measures individual gene expression across conditions. However, genes are regulated in complex networks, not as individual entities, limiting the interpretability of gene expression data. Machine learning models that incorporate prior biological knowledge are a powerful tool to extract meaningful biology from gene expression data. Pathway-level information extractor (PLIER) is an unsupervised machine learning method that defines biological pathways by leveraging the vast amount of published transcriptomic data. PLIER converts gene expression data into known pathway gene sets, termed latent variables (LVs), to substantially reduce data dimensionality and improve interpretability. In the current study, we trained the first mouse PLIER model on 190,111 mouse brain RNA-sequencing samples, the greatest amount of training data ever used by PLIER. We then validated the mousiPLIER approach in a study of microglia and astrocyte gene expression across mouse brain aging. mousiPLIER identified biological pathways that are significantly associated with aging, including one latent variable (LV41) corresponding to striatal signal. To gain further insight into the genes contained in LV41, we performed k-means clustering on the training data to identify studies that respond strongly to LV41. We found that the variable was relevant to striatum and aging across the scientific literature. Finally, we built a web server (http://mousiplier.greenelab.com/) for users to easily explore the learned latent variables. Taken together this study defines mousiPLIER as a method to uncover meaningful biological processes in mouse brain transcriptomic studies.Significance statement RNA-sequencing studies define differential expression of individual genes across conditions. However, genes are regulated in complex networks, not as individual entities. Machine learning models that incorporate biological pathway information are a powerful tool to analyze human gene expression. However, such models are lacking for mouse, despite the vast number of mouse RNA-seq datasets. We trained a mouse pathway-level information extractor model (mousiPLIER) to reduce data dimensionality from over 10,000 genes to 196 'latent variables' that map to known biological pathways. To validate this approach, we applied mousiPLIER to differential expression across mouse brain aging. We identified 26 functional pathways (latent variables) that varied across aging. Finally, we developed a web server to facilitate use of mousiPLIER by the scientific community.

TMX2 potentiates cell viability of hepatocellular carcinoma by promoting autophagy and mitophagy

ABSTRACT The dysregulation of membrane protein expression has been implicated in tumorigenesis and progression, including hepatocellular carcinoma (HCC). In this study, we aimed to identify membrane proteins that modulate HCC viability. To achieve this, we performed a CRISPR activation screen targeting human genes encoding membrane-associated proteins, revealing TMX2 as a potential driver of HCC cell viability. Gain- and loss-of-function experiments demonstrated that TMX2 promoted growth and tumorigenesis of HCC. Clinically, TMX2 was an independent prognostic factor for HCC patients. It was significantly upregulated in HCC tissues and associated with poor prognosis of HCC patients. Mechanistically, TMX2 was demonstrated to promote macroautophagy/autophagy by facilitating KPNB1 nuclear export and TFEB nuclear import. In addition, TMX2 interacted with VDAC2 and VADC3, assisting in the recruitment of PRKN to defective mitochondria to promote cytoprotective mitophagy during oxidative stress. Most interestingly, HCC cells responded to oxidative stress by upregulating TMX2 expression and cell autophagy. Knockdown of TMX2 enhanced the anti-tumor effect of lenvatinib. In conclusion, our findings emphasize the pivotal role of TMX2 in driving the HCC cell viability by promoting both autophagy and mitophagy. These results suggest that TMX2 May serve as a prognostic marker and promising therapeutic target for HCC treatment.

#787 Kidney genetics—a reproducible and curated database of kidney-disease associated genes

Abstract Background and Aims Genetic insights are becoming increasingly influential in the understanding and treatment of various kidney diseases (KD). Hundreds of genes associated with monogenic kidney disease have been identified, providing valuable insights into their diagnosis, management, and monitoring. However, the lack of a unified and standardized database of genes assigned to kidney diseases has led to diagnostic blind spots and comparability issues among current studies of kidney genetics. To address this gap, we created “Kidney-Genetics”, a regularly updated, automated and publicly accessible database, which aims to provide a comprehensive list of all relevant genes associated with kidney disease. Methods Kidney disease-associated gene information was compiled from various sources, including: (1) Genomics England and Australia PanelApp [1], (2) a comprehensive literature review of published gene lists, (3) clinical diagnostic panels for kidney disease, (4) a Human Phenotype Ontology (HPO)-based [2] search in rare disease databases (OMIM, Orphanet), and (5) a PubTator [3] API-based automated literature extraction from PubMed. An evidence-scoring system was developed to distinguish high- evidence genes from low evidence genes and candidate genes. High-evidence genes were defined as those present in two or more of the five resources and were manually curated based on predetermined criteria or, in the case of existing ClinGen [4] curation, their data and scores were used. Genes with a score of one or less were accordingly classified as candidate genes. Additionally, genes were grouped into different categories for later genotype-phenotype correlation matching (see Fig. 1). Results The Kidney-Genetics database currently contains detailed information on 3025 kidney-associated genes with detailed annotations on gene expression, kidney phenotype, inheritance mode, disease onset, possible syndromic disease mode and genetic variation. The number of genes extracted from the five analyzed sources of information are as follows: (1) 534, (2) 822, (3) 956, (4) 789, and (5) 2158 (see Fig. 2). Notably, 598 genes (19.8%) of the total 3025 genes are present in two or more of the analyzed information sources, thus meeting our evidence criteria, indicating high confidence and their potential for diagnostic use. Of these high-evidence genes, 526 (88.0%) are present in at least one, and 56 (9.4%) are present in all 10 comprehensive diagnostic laboratory panels. To ensure currency, Kidney-Genetics will be updated regularly and automatically on a monthly basis. We will also provide phenotypic and functional clustering results to facilitate gene grouping. Conclusion By utilizing Kidney-Genetics, clinicians, geneticists, and researchers can examine genomic data and improve their understanding of the genetic components of diverse KDs. The code and results are freely available on GitHub (kidney-genetics.org) [5]. A standardized pipeline and automated system keep our database on the cutting edge of kidney research and diagnostics. Screening efforts toward manual curation (such as through the ClinGen initiative) and assignment of diagnostic genes to kidney disease groups (e.g., syndromic vs. isolated; adult vs. pediatric; cystic, nephrotic, etc.) are currently under development.

Characterising 24-h skeletal muscle gene expression alongside metabolic & endocrine responses under diurnal conditions.

Skeletal muscle plays a central role in the storage, synthesis, and breakdown of nutrients, yet little research has explored temporal responses of this human tissue, especially with concurrent measures of systemic biomarkers of metabolism. To characterise temporal profiles in skeletal muscle expression of genes involved in carbohydrate metabolism, lipid metabolism, circadian clocks, and autophagy and descriptively relate them to systemic metabolites and hormones during a controlled laboratory protocol. Ten healthy adults (9M/1F, mean ± SD: age: 30 ± 10 y; BMI: 24.1 ± 2.7 kg·m-2) rested in the laboratory for 37 hours with all data collected during the final 24 hours of this period (i.e., 0800-0800 h). Participants ingested hourly isocaloric liquid meal replacements alongside appetite assessments during waking before a sleep opportunity from 2200-0700 h. Blood samples were collected hourly for endocrine and metabolite analyses, with muscle biopsies occurring every 4 h from 1200 h to 0800 h the following day to quantify gene expression. Plasma insulin displayed diurnal rhythmicity peaking at 1804 h. Expression of skeletal muscle genes involved in carbohydrate metabolism (Name - Acrophase; GLUT4 - 1440 h; PPARGC1A -1613 h; HK2 - 1824 h) and lipid metabolism (FABP3 - 1237 h; PDK4 - 0530 h; CPT1B - 1258 h) displayed 24 h rhythmicity that reflected the temporal rhythm of insulin. Equally, circulating glucose (0019 h), NEFA (0456 h), glycerol (0432 h), triglyceride (2314 h), urea (0046 h), CTX (0507 h) and cortisol concentrations (2250 h) also all displayed diurnal rhythmicity. Diurnal rhythms were present in human skeletal muscle gene expression as well systemic metabolites and hormones under controlled diurnal conditions. The temporal patterns of genes relating to carbohydrate and lipid metabolism alongside circulating insulin are consistent with diurnal rhythms being driven in part by the diurnal influence of cyclic feeding and fasting.

#1945 Comparative analysis of kidney transplantation modeled using precision-cut kidney slices and kidney transplantation in pigs

Abstract Background and Aims Kidney transplants are at risk for so far unavoidable ischemia-reperfusion injury. Several experimental kidney transplantation models are available to study this injury, but all have their own limitations. Here, we describe precision-cut kidney slices (PCKS) as a novel model of kidney ischemia-reperfusion injury in comparison with pig and human kidney transplantation. Method Following bilateral nephrectomy in pigs, we applied warm ischemia (1 h), cold ischemia (20 h) and a reperfusion period (4 h) to one whole kidney undergoing transplantation to a recipient pig and, in parallel, established PCKS undergoing ischemia and modeled reperfusion. Tissues were analyzed using standard histopathology assessment by two blinded pathologists, and bulk RNAseq with gene set enrichment analysis with comparison to human kidney biopsy gene sets obtained from Gene Expression Omnibus. Results Histopathological assessment revealed the presence of some but not all morphological features of tubular injury in PCKS as seen in pig kidney transplantation. RNAseq demonstrated that the majority of changes occurred after reperfusion only, with a partial overlap between PCKS and kidney transplantation, with some differences in transcriptional response attributable to systemic inflammatory responses and immune cell migration. Comparison of PCKS and pig kidney transplantation with RNAseq data from human kidney biopsies by gene set enrichment analysis revealed that both PCKS and pig kidney transplantation reproduced the post-reperfusion pattern of human kidney transplantation. In contrast, only post-cold ischemia PCKS and pig kidney partially resembled the gene set of human acute kidney injury. Conclusion Overall, the present study established that a PCKS protocol can model kidney transplantation and its reperfusion-related damage on a histological and a transcriptomic level. PCKS may thus expand the toolbox for developing novel therapeutic strategies against ischemia-reperfusion injury.