Biology subjects

Gonzales, S.

Publications and source records attributed to Gonzales, S..

4 recordsLinked to original sources

bioRxiv2026

Defining the RNA Modification Landscape of Multiple Myeloma Reveals METTL3-Dependent m6A Regulation of NEAT1

RNA modifications play critical roles in gene regulation. N6-methyladenosine (m6A) is the most abundant modification on mRNA and long noncoding RNA (lncRNA) and regulates RNA processing, stability, and translation. RNA modifications are not well characterized in multiple myeloma (MM), a plasma cell malignancy characterized by relapse and disease progression, and the contribution of m6A-modified lncRNAs to the disease remains unclear. Here, we define the RNA modification landscape of MM by combining mass spectrometry, Nanopore Direct RNA sequencing, and methylated RNA immunoprecipitation sequencing. We identify 20 RNA modification types and > 15,000 m6A sites, including sites on 2,398 lncRNAs. Among these, we validate m6A sites on the paraspeckle-associated lncRNA NEAT1. Functional studies reveal that NEAT1 expression is regulated by the methyltransferase METTL3 and site-specific demethylation of a NEAT1 m6A site reduces MM cell viability. Single-cell RNA sequencing shows consistent NEAT1 enrichment in malignant plasma cells but minimal expression in healthy cells. These findings identify m6A-modified lncRNAs as key regulators of MM biology and establish NEAT1 as an epitranscriptomically controlled driver of MM cell survival.

cancer biology↗

bioRxiv2024

LINC01432 binds to CELF2 in newly diagnosed multiple myeloma promoting short progression-free survival to standard therapy

Multiple Myeloma (MM) is a highly prevalent and incurable form of cancer that arises from malignant plasma cells, with over 35,000 new cases diagnosed annually in the United States. While there are a growing number of approved therapies, MM remains incurable and nearly all patients will relapse and exhaust all available treatment options. Mechanisms for disease progression are unclear and in particular, little is known regarding the role of long non-coding RNAs (lncRNA) in mediating disease progression and response to treatment. In this study, we used transcriptome sequencing to compare newly diagnosed MM patients who had short progression- free survival (PFS) to standard first-line treatment (PFS < 24 months) to patients who had prolonged PFS (PFS > 24 months). We identified 157 differentially upregulated lncRNAs with short PFS and focused our efforts on characterizing the most upregulated lncRNA, LINC01432. We investigated LINC01432 overexpression and CRISPR/Cas9 knockdown in MM cell lines to show that LINC01432 overexpression significantly increases cell viability and reduces apoptosis, while knockdown significantly reduces viability and increases apoptosis, supporting the clinical relevance of this lncRNA. Next, we used individual-nucleotide resolution cross-linking immunoprecipitation with RT-qPCR to show that LINC01432 directly interacts with the RNA binding protein, CELF2. Lastly, we showed that LINC01432-targeted locked nucleic acid antisense oligonucleotides reduce viability and increases apoptosis. In summary, this fundamental study identified lncRNAs associated with short PFS to standard NDMM treatment and further characterized LINC01432, which inhibits apoptosis. Key points: lncRNA expression was found to be dysregulated in patients with short PFS to standard multiple myeloma therapy. LINC01432-bound CELF2 inhibits apoptosis.

cancer biology↗

bioRxiv2023

SOX7: Novel Autistic Gene Identified by Analysis of Multi-Omics Data

Genome-wide association studies and next generation sequencing data analyses based on DNA information have identified thousands of mutations associated with autism spectrum disorder (ASD). However, more than 99% of identified mutations are non-coding. Thus, it is unclear which of these mutations might be functional and thus potentially causal variants. Transcriptomic profiling using total RNA-sequencing has been one of the most utilized approaches to link protein levels to genetic information at the molecular level. The transcriptome captures molecular genomic complexity that the DNA sequence solely does not. Some mutations alter a genes DNA sequence but do not necessarily change expression and/or protein function. To date, few common variants reliably associated with the diagnosis status of ASD despite consistently high estimates of heritability. In addition, reliable biomarkers used to diagnose ASD or molecular mechanisms to define the severity of ASD do not exist. Therefore, it is necessary to integrate DNA and RNA testing together to identify true causal genes and propose useful biomarkers for ASD. We performed gene-based association studies with adaptive test using genome-wide association studies (GWAS) summary statistics with two large GWAS datasets (ASD 2019 data: 18,382 ASD cases and 27,969 controls [discovery data]; ASD 2017 data: 6,197 ASD cases and 7,377 controls [replication data]) which were obtained from the Psychiatric Genomics Consortium (PGC). In addition, we investigated differential expression between ASD cases and controls for genes identified in gene-based GWAS with two RNA-seq datasets (GSE211154: 20 cases and 19 controls; GSE30573: 3 cases and 3 controls). We identified 5 genes significantly associated with ASD in ASD 2019 data (KIZ-AS1, p=8.67x10-10; KIZ, p=1.16x10-9; XRN2, p=7.73x10-9; SOX7, p=2.22x10-7; LOC101929229 also known as PINX1-DT, p=2.14x10-6). Among these 5 genes, gene SOX7 (p=0.00087) and LOC101929229 (p=0.009) were replicated in ASD 2017 data. KIZ-AS1 (p=0.059) and KIZ (p=0.06) were close to the boundary of replication in ASD 2017 data. Genes SOX7 (p=0.036 in all samples; p=0.044 in white samples) indicated significant expression differences between cases and controls in the GSE211154 RNA-seq data. Furthermore, gene SOX7 was upregulated in cases than in controls in the GSE30573 RNA-seq data (p=0.0017; Benjamini-Hochberg adjusted p=0.0085). SOX7 encodes a member of the SOX (SRY-related HMG-box) family of transcription factors pivotally contributing to determining of the cell fate and identity in many lineages. The encoded protein may act as a transcriptional regulator after forming a protein complex with other proteins leading to autism. Gene SOX7 in the transcription factor family could be associated with ASD. This finding may provide new diagnostic and therapeutic strategies for ASD.

bioinformatics↗

bioRxiv2019

An Optimally Weighted Combination Method to DetectNovel Disease Associated Genes Using Publicly Available GWAS Summary Data

Gene-based analyses offer a useful alternative and complement to the usual single nucleotide polymorphism (SNP) based analysis for genome-wide association studies (GWASs). Using appropriate weights (pre-specified or eQTL-derived) can boost statistical power, especially for detecting weak associations between a gene and a trait. Because the sparsity level or association directions of the underlying association patterns in real data are often unknown and access to individual-level data is limited, we propose an optimal weighted combination (OWC) test applicable to summary statistics from GWAS. This method includes burden tests, weighted sum of squared score (SSU), weighted sum statistic (WSS), and the score test as its special cases. We analytically prove that aggregating the variants in one gene is the same as using the weighted combination of Z-scores for each variant based on the score test method. We also numerically illustrate that our proposed test outperforms several existing comparable methods via simulation studies. Lastly, we utilize schizophrenia GWAS data and a fasting glucose GWAS meta-analysis data to demonstrate that our method outperforms the existing methods in real data analyses. Our proposed test is implemented in the R program OWC, which is freely and publicly available.

genetics↗