Integrated Genomic Analysis Identifies ANKRD36 Gene as a Novel and Common Biomarker of Disease Progression in Chronic Myeloid Leukemia

Amino acid variants in the HLA-DQA1 and HLA-DQB1 molecules explain the major association of variants with relapse status in pediatric patients with steroid-sensitive nephrotic syndrome

BACKGROUND

Management of patients with steroid-sensitive nephrotic syndrome (SSNS) is challenging because of frequent relapses. Causal variants in the human leukocyte antigen (HLA) class II region that are associated with relapse remain undetermined.

METHODS

We collected a cohort of East Asian individuals comprising 206 pediatric patients with SSNS and 435 healthy controls from Southwest China. Ninety children with steroid-sensitive nephrotic syndrome without relapse (SSNSWR) and 116 children with steroid-dependent and/or frequent relapse nephrotic syndrome (SDNS/FRNS) were genotyped using Sanger sequencing. We then measured the transcriptional level, allele expression imbalance (AEI) and functional proteins of HLA-DQA1 and HLA-DQB1 in different stages of SDNS/FRNS.

RESULTS

rs1464545187 in ANKRD36 was associated with an approximately 1.69-fold greater risk for SSNSWR (P = 0.04; 95% confidence interval [CI], 1.05-2.72). Clustered risk variants in HLA-DQA1 and HLA-DQB1 were significantly associated with SDNS/FRNS (rs1047989: P = 2.26E-07, odds ratio [OR] = 2.25, 1.65-3.05; rs9273471: P = 5.45E-05, OR = 1.84, 1.37-2.46; HLA-DQB1*06:02: P = 0.017, OR = 0.19, 0.04-0.77). The genotype distributions of rs1047989, 2:171713702, rs1049123, rs9273471, and HLA-DQB1*06:02 in patients with SSNS were significantly different from those in healthy controls. rs1047989 (HLA-DQA1) was significantly associated with a greater number of infections at relapse in SDNS/FRNS patients (P = 0.045, OR = 6.79, 95% CI: 1.29-168.52). Flow cytometry showed that the proportion of cells expressing HLA-DQA1+/DQB1+ (HLA-DQA1+, P = 0.0046; HLA-DQB1+, P = 0.0045) was lowest in the relapse stage. In addition, the mRNA levels of HLA-DQA1 and HLA-DQB1 were significantly greater in the relapse group than in the remission group (HLA-DQA1, P = 0.03; HLA-DQB1, P = 0.002). No significant AEIs were detected in the different stages of SDNS/FRNS. The rs1047989 variant is likely to affect the structure and stability of HLA-DQA1.

CONCLUSION

rs1464545187 is a risk locus for SSNSWR but not SDNS/FRNS in Chinese children. Functional variations in HLA-DQA1 and HLA-DQB1 are implicated in regulating the immune response of SSNS patients, which may explain the typical triggering of SDNS/FRNS onset by infections.

The impaired response of nasal epithelial cells to microplastic stimulation in asthma and COPD

Microplastic particles from the air are inhaled and accumulate in the lungs, potentially causing immunological reactions and airway tissue injury. This study aimed to evaluate the biological effects of polyamide fibres on nasal epithelium co-cultivated with macrophages in control, asthma, and COPD groups. Nasal epithelial cells alone or in co-culture with monocyte-derived macrophages were exposed to polyamide fibres for 48 h. We identified 8 differentially expressed genes (DEGs) in controls, 309 DEGs in asthma (including ANKRD36C, BCL2L15, FCGBP, and IL-19), and 22 DEGs in COPD (e.g., BCL2L15, IL-19, CAPN14, PGBD5, PTPRH), particularly in epithelial/moMφ co-cultures. Microplastic exposure induced inflammatory cytokine secretion only for IL-8 production in controls (epithelial/ moMφs co-culture) and asthmatic (monoculture) epithelial cells in contrast to PM2.5, which was a strong inflammatory inducer. Gene Ontology analysis revealed that microplastic exposure affected sterol and cholesterol biosynthesis, secondary alcohol metabolism, and acetyl-CoA metabolism in asthma, and cell motility, chemokine signaling, leukocyte migration, and chemotaxis in COPD. Microplastic stimulation altered the response of airway epithelial cells in obstructive lung diseases differently than in controls, linking to Th2 inflammation, stress response modulation, and carcinogenesis. Asthmatic and COPD epithelial cells are more susceptible to damage from microplastic fibre exposure.

DEMINING: A deep learning model embedded framework to distinguish RNA editing from DNA mutations in RNA sequencing data

Precise calling of promiscuous adenosine-to-inosine RNA editing sites from transcriptomic datasets is hindered by DNA mutations and sequencing/mapping errors. Here, we present a stepwise computational framework, called DEMINING, to distinguish RNA editing and DNA mutations directly from RNA sequencing datasets, with an embedded deep learning model named DeepDDR. After transfer learning, DEMINING can also classify RNA editing sites and DNA mutations from non-primate sequencing samples. When applied in samples from acute myeloid leukemia patients, DEMINING uncovers previously underappreciated DNA mutation and RNA editing sites; some associated with the upregulated expression of host genes or the production of neoantigens.

The benefit of a complete reference genome for cancer structural variant analysis

The complexities of cancer genomes are becoming more easily interpreted due to advancements in sequencing technologies and improved bioinformatic analysis. Structural variants (SVs) represent an important subset of somatic events in tumors. While detection of SVs has been markedly improved by the development of long-read sequencing, somatic variant identification and annotation remains challenging. We hypothesized that use of a completed human reference genome (CHM13-T2T) would improve somatic SV calling. Our findings in a tumour/normal matched benchmark sample and two patient samples show that the CHM13-T2T improves SV detection and prioritization accuracy compared to GRCh38, with a notable reduction in false positive calls. We also overcame the lack of annotation resources for CHM13-T2T by lifting over CHM13-T2T-aligned reads to the GRCh38 genome, therefore combining both improved alignment and advanced annotations. In this process, we assessed the current SV benchmark set for COLO829/COLO829BL across four replicates sequenced at different centers with different long-read technologies. We discovered instability of this cell line across these replicates; 346 SVs (1.13%) were only discoverable in a single replicate. We identify 49 somatic SVs, which appear to be stable as they are consistently present across the four replicates. As such, we propose this consensus set as an updated benchmark for somatic SV calling and include both GRCh38 and CHM13-T2T coordinates in our benchmark. The benchmark is available at: 10.5281/zenodo.10819636 Our work demonstrates new approaches to optimize somatic SV prioritization in cancer with potential improvements in other genetic diseases.

Crosstalk of RNA methylation writers defines tumor microenvironment and alisertib resistance in breast cancer

Background

The five major RNA methylation modifications (m6A, m1A, m6Am, m5C, and m7G) exert biological roles in tumorigenicity and immune response, mediated mainly by "writer" enzymes. Here, the prognostic values of the "writer" enzymes and the TCP1 role in drug resistance in breast cancer (BC) were explored for further therapeutic strategies.

Methods

We comprehensively characterized clinical, molecular, and genetic features of subtypes by consensus clustering. RNA methylation modification "Writers" and related genes_risk (RMW_risk) model for BC was constructed via a machine learning approach. Moreover, we performed a systematical analysis for characteristics of the tumor microenvironment (TME), alisertib sensitivity, and immunotherapy response. A series of experiments in vitro were carried out to assess the association of TCP1 with drug resistance.

Results

One "writer" (RBM15B) and two related genes (TCP1 and ANKRD36) were identified for prognostic model construction, validated by GSE1456, GSE7390, and GSE20685 cohorts and our follow-up data. Based on the patterns of the genes related to prognosis, patients were classified into RMW_risk-high and RMW_risk-low subtypes. Lower RMW_Score was associated with better overall survival and the infiltration of immune cells such as memory B cells. Further analysis revealed that RMW_Score presented potential values in predicting drug sensitivity and response for chemo- and immunotherapy. In addition, TCP1 was confirmed to promote BC alisertib-resistant cell proliferation and migration in vitro.

Conclusion

RMW_Score could function as a robust biomarker for predicting BC patient survival and therapeutic benefits. This research revealed a potential TCP1 role regarding alisertib resistance in BC, providing new sights into more effective therapeutic plans.

Development and Validation of a Diagnostic Model Based on Hypoxia-Related Genes in Myocardial Infarction

Purpose

Myocardial infarction (MI) is a common cardiovascular disease, and its underlying pathological mechanism remains unclear. We aimed to develop a diagnostic model to distinguish different subtypes of MI.

Patients and Methods

The gene expression profiles of MI from the GEO database and hypoxia-related genes (HRGs) from MSigDB were downloaded. Then, the different MI subtypes based on HRGs were identified with unsupervised clustering. The difference of expression patterns and hypoxic-immune status among different subtypes of MI were investigated. The diagnostic model to distinguish the different subtypes of MI was developed and validated.

Results

Based on HRGs, MI samples were divided into two subtypes, cluster A and cluster B. A total of 211 genes showed significant changes in expression between the two subtypes. Cluster A was characterized by high hypoxia status and low immunity status. Based on weighted gene co-expression network analysis, ROC analysis and LASSO regression algorithm, 5 genes were identified as potential diagnostic markers. Finally, a diagnostic model based on these 5 genes was established, which can distinguish the two subtypes well.

Conclusion

The five hub genes, including ANKRD36, HLTF, KIF3A, OXCT1 and VPS13A, may be associated with the different subtypes of MI.

CLINICAL VALIDATION OF ANKRD36 MUTATIONS AS A NOVEL BIOMARKER FOR MONITORING EARLY PROGRESSION AND TIMELY CLINICAL INTERVENTIONS IN BLAST CRISIS CML

Background

Chronic Myeloid Leukemia (CML) is initiated in the bone marrow due to the chromosomal translocation t(9;22), resulting in the fusion oncogene BCR-ABL. Tyrosine kinase inhibitors (TKIs) targeting BCR-ABL have transformed fatal CML into an almost curable disease. However, TKIs lose efficacy during disease progression, and the mechanism of CML progression remains to be fully understood. Additionally, common molecular biomarkers for CML progression are lacking. Our studies previously detected ANKRD36 (c.1183_1184 delGC and c.1187_1188 dupTT) associated exclusively with advanced phase CML. However, clinical validation of this finding was pending. Therefore, this study aimed to clinically validate mutated ANKRD36 as a novel biomarker of CML progression.

Materials and Methods

The study enrolled 124 patients in all phases of CML, recruited from Mayo Hospital and Hameed Latif Hospital in Lahore, Punjab, between January 2019 and August 2021. All response criteria were adopted from the European LeukemiaNet guideline 2020. Informed consent was obtained from all study subjects. The study was approved by scientific and ethical review committees of all participating centers.Sanger sequencing was employed to detect ANKRD36 mutations in CML patients in accelerated phase (AP) (n=11) and blast crisis (BC) (n=10), with chronic-phase CML (CP-CML) patients as controls (n=103). Samples were processed using Big Dye Terminator Cycle Sequencing Ready Reaction kits and sequenced using ABI Prism 3730 Genetic Analyzer, and sequencing using forward and reverse primers for ANKRD36.

Results

During our study, 17% of CML patients progressed to advanced phases AP-CML n=11 (8.9%) and BC-CML n=10 (8.1%). The chronic- and advanced-phase patients showed significant difference with respect to male-to-female ratio, hemoglobin level, WBC count, and platelet count. Sanger sequencing detected ANKRD36 mutations c. 1183 1184 delGC and c. 1187 1185 dupTT exclusively in all AP- and BC-CML patients but in none of the CP-CML patients. Nevertheless, mutations status was not associated with male-to-female ratio, hemoglobin level, WBC count, and platelet count, which makes ANKRD32 as an independent predictor of early and terminal disease progression in CML.

Conclusions

The study confirms ANKRD36 as a novel genomic biomarker for early and late CML progression. Further prospective studies should be carried out in this regard. ANKRD36, although fully uncharacterized in humans, shows the highest expression in bone marrow, particularly myeloid cells. Functional integrated genomic studies are recommended to further explore the role of ANKRD36 in the biology and pathogenesis of CML.

HAPLN1 Affects Cell Viability and Promotes the Pro-Inflammatory Phenotype of Fibroblast-Like Synoviocytes

HAPLN1 maintains aggregation and the binding activity of extracellular matrix (ECM) molecules (such as hyaluronic acid and proteoglycan) to stabilize the macromolecular structure of the ECM. An increase in HAPLN1 expression is observed in a few types of musculoskeletal diseases including rheumatoid arthritis (RA); however, its functions are obscure. This study examined the role of HAPLN1 in determining the viability, proliferation, mobility, and pro-inflammatory phenotype of RA- fibroblast-like synoviocytes (RA-FLSs) by using small interfering RNA (siHAPLN1), over-expression vector (HAPLN1^OE), and a recombinant HAPLN1 (rHAPLN1) protein. HAPLN1 was found to promote proliferation but inhibit RA-FLS migration. Metformin, an AMPK activator, was previously found by us to be able to inhibit FLS activation but promote HAPLN1 secretion. In this study, we confirmed the up-regulation of HAPLN1 in RA patients, and found the positive relationship between HAPLN1 expression and the AMPK level. Treatment with either si-HAPLN1 or HAPLN1^OE down-regulated the expression of AMPK-ɑ gene, although up-regulation of the level of p-AMPK-ɑ was observed in RA-FLSs. si-HAPLN1 down-regulated the expression of proinflammatory factors like TNF-ɑ, MMPs, and IL-6, while HAPLN1^OE up-regulated their levels. qPCR assay indicated that the levels of TGF-β, ACAN, fibronectin, collagen II, and Ki-67 were down-regulated upon si-HAPLN1 treatment, while HAPLN1^OE treatment led to up-regulation of ACAN and Ki-67 and down-regulation of cyclin-D1. Proteomics of si-HAPLN1, rHAPLN1, and mRNA-Seq analysis of rHAPLN1 confirmed the functions of HAPLN1 in the activation of inflammation, proliferation, cell adhesion, and strengthening of ECM functions. Our results for the first time demonstrate the function of HAPLN1 in promoting the proliferation and pro-inflammatory phenotype of RA-FLSs, thereby contributing to RA pathogenesis. Future in-depth studies are required for better understanding the role of HAPLN1 in RA.