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Biology subjects

Lukan, C. J.

Publications and source records attributed to Lukan, C. J..

2 recordsLinked to original sources

Systematic fusion transcript discovery in mantle cell lymphoma using long-read sequencing

Fusion transcripts are composed of hybrid RNA consisting of transcripts from two distinct genes and can arise from physical linking of genes at the DNA level, splicing or read-through transcription. In addition, there are also fusion transcripts that can occur between a protein coding gene and long non-coding RNAs. Systemic detection of all fusion transcripts at the RNA-level is important in the identification of potential therapeutic drug targets as well as biomarkers for detection, classification, and subtyping of cancer. We used long-read third-generation sequencing of RNA, Iso Sequencing to identify fusion transcripts in Mantle Cell Lymphoma (MCL) cell lines. Our results revealed widespread transcript diversity in MCL. The majority of the long-read transcripts were novel. Some of the thousands of novel transcripts we identified were fusion transcripts. These fusion transcripts had some of the longest transcripts in the MCL transcriptome. We identified the fusion junction of several select fusion transcripts involving protein coding genes including the well-known and widely expressed CTBS::GNG5 and validated their presence using other techniques. Furthermore, we also identified and validated a novel fusion transcript between the multifunctional, m6A methylation writer, RBM15, and LAMTOR5:AS, a long noncoding RNA. Use of the chemical compound, JT-607, an inhibitor of CPSF73/CPSF3 which affects both alternative polyadenylation and read-through transcription resulted in increased expression of the RBM15::LAMTOR5:AS fusion transcript. Our analysis suggests that RBM15::LAMTOR5:AS and many fusion transcripts we identified are intrachromosomal. Since the origin, significance and impact of many fusion transcripts remain unknown, our results support using an unbiased approach to identify fusion transcripts. This will help us to fully comprehend the complexity of the human transcriptome in normal biology and in disease.

genetics↗

Gene expression signatures of response to fluoxetine treatment: systematic review and meta-analyses

BackgroundSelecting the best antidepressant for a patient with major depressive disorder (MDD) remains a challenge, and some have turned to genomic (and other omic) data to identify an optimal therapy. In this work, we synthesized gene expression data for fluoxetine treatment in both human patients and rodent models, to better understand biological pathways affected by treatment, as well as those that may distinguish clinical or behavioral response. MethodsFollowing the PRISMA guidelines, we searched the Gene Expression Omnibus (GEO) for studies profiling humans or rodent models with treatment of the antidepressant fluoxetine, excluding those not done in the context of depression or anxiety, in an irrelevant tissue type, or with fewer than three samples per group. Included studies were systematically reanalyzed by differential expression analysis and Gene Set Enrichment Analysis (GSEA). Individual pathway and gene statistics were synthesized across studies by three p-value combination methods, and then corrected for false discovery. ResultsOf the 74 data sets that were screened, 20 were included: 18 in rodents, and two in tissue from human patients. Studies were highly heterogeneous in the comparisons of both treated vs. control samples and responders vs. non-responders, with 737 and 356 pathways, respectively, identified as significantly different between groups in at least one study. However, 19 pathways were identified as consistently different in responders vs. non-responders, including toll-like receptor (TLR) and other immune pathways. Signal transduction pathways were identified as consistently affected by fluoxetine treatment in depressed patients and rodent models. DiscussionThese meta-analyses confirm known pathways and provide new hints toward antidepressant resistance, but more work is needed. Most included studies involved rodent models, and both patient studies had small cohorts. Additional large-cohort studies applying additional omics technologies are necessary to understand the intricacies and heterogeneity of antidepressant response.

genomics↗