BACKGROUND: Acute kidney injury (AKI) is a life-threatening complication characterized by rapid decline in renal function, which frequently occurs after transplantation surgery. However, the molecular mechanism underlying the development of post-transplant (post-Tx) AKI still remains unknown. An increasing number of studies have demonstrated that certain microRNAs (miRNAs) exert crucial functions in AKI. The present study sought to elucidate the molecular mechanisms in post-Tx AKI by constructing a regulatory miRNA-mRNA network. RESULTS: Based on two datasets (GSE53771 and GSE53769), three key modules, which contained 55 mRNAs, 76 mRNAs, and 151 miRNAs, were identified by performing weighted gene co-expression network analysis (WGCNA). The miRDIP v4.1 was applied to predict the interactions of key module mRNAs and miRNAs, and the miRNA-mRNA pairs with confidence of more than 0.2 were selected to construct a regulatory miRNA-mRNA network by Cytoscape. The miRNA-mRNA network consisted of 82 nodes (48 mRNAs and 34 miRNAs) and 125 edges. Two miRNAs (miR-203a-3p and miR-205-5p) and ERBB4 with higher node degrees compared with other nodes might play a central role in post-Tx AKI. Additionally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated that this network was mainly involved in kidney-/renal-related functions and PI3K-Akt/HIF-1/Ras/MAPK signaling pathways. CONCLUSION: We constructed a regulatory miRNA-mRNA network to provide novel insights into post-Tx AKI development, which might help discover new biomarkers or therapeutic drugs for enhancing the ability for early prediction and intervention and decreasing mortality rate of AKI after transplantation.

BACKGROUND: The unfolded protein response (UPR) served as a vital role in the progression of tumors, but the molecule mechanisms of UPR in bladder cancer (BLCA) have been not fully investigated. METHODS: We identified differentially expressed unfolded protein response-related genes (UPRRGs) between BLCA samples and normal bladder samples in the Cancer Genome Atlas (TCGA) database. Univariate Cox analysis and the least absolute shrinkage and selection operator penalized Cox regression analysis were used to construct a prognostic signature in the TCGA set. We implemented the validation of the prognostic signature in GSE13507 from the Gene Expression Omnibus database. The ESTIMATE, CIBERSORT, and ssGSEA algorithms were used to explore the correlation between the prognostic signature and immune cells infiltration as well as key immune checkpoints (PD-1, PD-L1, CTLA-4, and HAVCR2). GDSC database analyses were conducted to investigate the chemotherapy sensitivity among different groups. GSEA analysis was used to explore the potential mechanisms of UPR-based signature. RESULTS: A prognostic signature comprising of seven genes (CALR, CRYAB, DNAJB4, KDELR3, CREB3L3, HSPB6, and FBXO6) was constructed to predict the outcome of BLCA. Based on the UPRRGs signature, the patients with BLCA could be classified into low-risk groups and high-risk groups. Patients with BLCA in the low-risk groups showed the more favorable outcomes than those in the high-risk groups, which was verified in GSE13507 set. This signature could serve as an autocephalous prognostic factor in BLCA. A nomogram based on risk score and clinical characteristics was established to predict the over survival of BLCA patients. Furthermore, the signature was closely related to immune checkpoints (PD-L1, CTLA-4, and HAVCR2) and immune cells infiltration including CD8+ T cells, follicular helper T cells, activated dendritic cells, and M2 macrophages. GSEA analysis indicated that immune and carcinogenic pathways were enriched in high-risk group. CONCLUSIONS: We identified a novel unfolded protein response-related gene signature which could predict the over survival, immune microenvironment, and chemotherapy response of patients with bladder cancer.

BACKGROUND: Due to the limitations of the current routine diagnostic methods, low-level somatic mosaicism with variant allele fraction (VAF) < 10% is often undetected in clinical settings. To date, only a few studies have attempted to analyze tissue distribution of low-level parental mosaicism in a large clinical exome sequencing (ES) cohort. METHODS: Using a customized bioinformatics pipeline, we analyzed apparent de novo single-nucleotide variants or indels identified in the affected probands in ES trio data at Baylor Genetics clinical laboratories. Clinically relevant variants with VAFs between 30 and 70% in probands and lower than 10% in one parent were studied. DNA samples extracted from saliva, buccal cells, redrawn peripheral blood, urine, hair follicles, and nail, representing all three germ layers, were tested using PCR amplicon next-generation sequencing (amplicon NGS) and droplet digital PCR (ddPCR). RESULTS: In a cohort of 592 clinical ES trios, we found 61 trios, each with one parent suspected of low-level mosaicism. In 21 parents, the variants were validated using amplicon NGS and seven of them by ddPCR in peripheral blood DNA samples. The parental VAFs in blood samples varied between 0.08 and 9%. The distribution of VAFs in additional tissues ranged from 0.03% in hair follicles to 9% in re-drawn peripheral blood. CONCLUSIONS: Our study illustrates the importance of analyzing ES data using sensitive computational and molecular methods for low-level parental somatic mosaicism for clinically relevant variants previously diagnosed in routine clinical diagnostics as apparent de novo.

BACKGROUND: Genetic variation databases provide invaluable information on the presence and frequency of genetic variants in the 'untargeted' human population, aggregated with the primary goal to facilitate the interpretation of clinically important variants. The presence of somatic variants in such databases can affect variant assessment in undiagnosed rare disease (RD) patients. Previously, the impact of somatic mosaicism was only considered in relation to two Mendelian disease-associated genes. Here, we expand the analyses to identify additional mosaicism-prone genes in blood-derived reference population databases. RESULTS: To identify additional mosaicism-prone genes relevant to RDs, we focused on known/previously established ClinVar pathogenic and likely pathogenic single-nucleotide variants, residing in genes associated with early onset, severe autosomal dominant diseases. We asked whether any of these variants are present in a higher-than-expected frequency in the reference population databases and whether there is evidence of somatic origin (i.e., allelic imbalance) rather than germline heterozygosity (~ half of the reads supporting alternative allele). The mosaicism-prone genes identified were further categorized according to the processes they are involved in. Beyond the previously reported ASXL1 and DNMT3A, we identified 7 additional autosomal dominant RD-associated genes with known pathogenic single-nucleotide variants present in the reference population databases and good evidence of allelic imbalance: BRAF, CBL, FGFR3, IDH2, KRAS, PTPN11 and SETBP1. From this group of 9 genes, the majority (n = 7) was important for hematopoiesis. In addition, 4 of these genes were involved in cell proliferation. Further assessment of the known 156 hematopoietic genes led to identification of 48 genes (21 not yet associated with RDs) with at least some evidence of mosaicism detectable in reference population databases. CONCLUSIONS: These results stress the importance of considering genes involved in hematopoiesis and cell proliferation when interpreting the presence and frequency of genetic variants in blood-derived reference population databases, both public and private. This is especially important when considering new variants of uncertain significance in known hematopoietic/cell proliferation RD genes and future novel gene-disease associations involving this class of genes.

The genetic basis of phenotypic variation across populations has not been well explained for most traits. Several factors may cause disparities, from variation in environments to divergent population genetic structure. We hypothesized that a population-level polygenic risk score (PRS) can explain phenotypic variation among geographic populations based solely on risk allele frequencies. We applied a population-specific PRS (psPRS) to 26 populations from the 1000 Genomes to four phenotypes: lactase persistence (LP), melanoma, multiple sclerosis (MS) and height. Our models assumed additive genetic architecture among the polymorphisms in the psPRSs, as is convention. Linear psPRSs explained a significant proportion of trait variance ranging from 0.32 for height in men to 0.88 for melanoma. The best models for LP and height were linear, while those for melanoma and MS were nonlinear. As not all variants in a PRS may confer similar, or even any, risk among diverse populations, we also filtered out SNPs to assess whether variance explained was improved using psPRSs with fewer SNPs. Variance explained usually improved with fewer SNPs in the psPRS and was as high as 0.99 for height in men using only 548 of the initial 4208 SNPs. That reducing SNPs improves psPRSs performance may indicate that missing heritability is partially due to complex architecture that does not mandate additivity, undiscovered variants or spurious associations in the databases. We demonstrated that PRS-based analyses can be used across diverse populations and phenotypes for population prediction and that these comparisons can identify the universal risk variants.