Doxorubicin induces prolonged DNA damage signal in cells overexpressing DEK isoform-2.

DEK has a short isoform (DEK isoform-2; DEK2) that lacks amino acid residues between 49-82. The full-length DEK (DEK isoform-1; DEK1) is ubiquitously expressed and plays a role in different cellular processes but whether DEK2 is involved in these processes remains elusive. We stably overexpressed DEK2 in human bone marrow stromal cell line HS-27A, in which endogenous DEKs were intact or suppressed via short hairpin RNA (sh-RNA). We have found that contrary to ectopic DEK1, DEK2 locates in the nucleus and nucleolus, causes persistent γH2AX signal upon doxorubicin treatment, and couldn't functionally compensate for the loss of DEK1. In addition, DEK2 overexpressing cells were more sensitive to doxorubicin than DEK1-cells. Expressions of DEK1 and DEK2 in cell lines and primary tumors exhibit tissue specificity. DEK1 is upregulated in cancers of the colon, liver, and lung compared to normal tissues while both DEK1 and DEK2 are downregulated in subsets of kidney, prostate, and thyroid carcinomas. Interestingly, only DEK2 was downregulated in a subset of breast tumors suggesting that DEK2 can be modulated differently than DEK1 in specific cancers. In summary, our findings show distinct expression patterns and subcellular location and suggest non-overlapping functions between the two DEK isoforms.

Functional analysis of co-expression networks of zebrafish ace2 reveals enrichment of pathways associated with development and disease.

Human Angiotensin I Converting Enzyme 2 (ACE2) plays an essential role in blood pressure regulation and SARS-CoV-2 entry. ACE2 has a highly conserved, one-to-one ortholog (ace2) in zebrafish, which is an important model for human diseases. However, the zebrafish ace2 expression profile has not yet been studied during early development, between genders, across different genotypes, or in disease. Moreover, a network-based meta-analysis for the extraction of functionally enriched pathways associated with differential ace2 expression is lacking in the literature. Herein, we first identified significant development-, tissue-, genotype-, and gender-specific modulations in ace2 expression via meta-analysis of zebrafish Affymetrix transcriptomics datasets (ndatasets = 107); and the correlation analysis of ace2 meta-differential expression profile revealed distinct positively and negatively correlated local functionally enriched gene networks. Moreover, we demonstrated that ace2 expression was significantly modulated under different physiological and pathological conditions related to development, tissue, gender, diet, infection, and inflammation using additional RNA-seq datasets. Our findings implicate a novel translational role for zebrafish ace2 in organ differentiation and pathologies observed in the intestines and liver.

Global miRNA expression of bone marrow mesenchymal stem/stromal cells derived from Fanconi anemia patients.

Fanconi anemia (FA) is a rare genetic disorder characterized by genomic instability, developmental defects, and bone marrow (BM) failure. Hematopoietic stem cells (HSCs) in BM interact with the mesenchymal stem/stromal cells (MSCs); and this partly sustains the tissue homeostasis. MicroRNAs (miRNAs) can play a critical role during these interactions possibly via paracrine mechanisms. This is the first study addressing the miRNA profile of FA BM-MSCs obtained before and after BM transplantation (preBMT and postBMT, respectively). Non-coding RNA expression profiling and quality control analyses were performed in Donors (n = 13), FA preBMT (n = 11), and FA postBMT (n = 6) BM-MSCs using GeneChip miRNA 2.0 Array. Six Donor-FA preBMT pairs were used to identify a differentially expressed miRNA expression signature containing 50 miRNAs, which exhibited a strong correlation with the signature obtained from unpaired samples. Five miRNAs (hsa-miR-146a-5p, hsa-miR-148b-3p, hsa-miR-187-3p, hsa-miR-196b-5p, and hsa-miR-25-3p) significantly downregulated in both the paired and unpaired analyses were used to generate the BM-MSCs' miRNA-BM mononuclear mRNA networks upon integration of a public dataset (GSE16334; studying Donor versus FA samples). Functionally enriched KEGG pathways included cellular senescence, miRNAs, and pathways in cancer. Here, we showed that hsa-miR-146a-5p and hsa-miR-874-3p were rescued upon BMT (n = 3 triplets). The decrease in miR-146a-5p was also validated using RT-qPCR and emerged as a strong candidate as a modulator of BM mRNAs in FA patients.

Discovery of Cancer-Specific and Independent Prognostic Gene Subsets of the Slit-Robo Family Using TCGA-PANCAN Datasets.

The Slit-Robo family of axon guidance molecules works in concert, playing important roles in organ development and cancer. Expressions of individual Slit-Robo genes have been used in calculating univariable hazard ratios (HRuni) for predicting cancer prognosis in the literature. However, Slit-Robo members do not act independently; hence, hazard ratios from multivariable Cox regression (HRmulti) on the whole gene set can further lead to identification of cancer-specific, novel, and independent prognostic gene pairs or modules. Herein, we obtained mRNA expressions of the Slit-Robo family consisting of four Robos (ROBO1/2/3/4) and three Slits (SLIT1/2/3), along with four types of survival outcome across cancers found in the Cancer Genome Atlas (TCGA). We used cluster heat maps to visualize closely associated pairs/modules of prognostic genes across 33 different cancers. We found a smaller number of significant genes in HRmulti than in HRuni, suggesting that the former analysis was less redundant. High ROBO4 expression emerged as relatively protective within the family, in both types of HR analyses. Multivariable Cox regression, on the other hand, revealed significantly more HR signatures containing Slit-Robo pairs acting in opposing directions than those containing Slit-Slit or Robo-Robo pairs for disease-specific survival. Furthermore, we discovered, through the online app SmulTCan's lasso regression, Slit-Robo gene subsets that significantly differentiated between high- versus low-risk prognosis patient groups, particularly for renal cancers and low-grade glioma. The statistical pipeline reported herein can help test independent and significant pairs/modules within a codependent gene family for cancer prognostication, and thus should also prove useful in personalized/precision medicine research.

SmulTCan: A Shiny application for multivariable survival analysis of TCGA data with gene sets.

BACKGROUND: Survival analysis is widely used in cancer research, and although several methods exist in R, there is the need for a more interactive, flexible, yet comprehensive online tool to analyze gene sets using Cox proportional hazards (CPH) models. The web-based Shiny application (app) SmulTCan extends existing tools to multivariable CPH models of gene sets-as exemplified using the netrins and their receptors (netrins-receptors). It can be used to identify survival gene signatures (GSs) and select the best subsets of input gene, microRNA, methylation level, and copy number variation sets from the Cancer Genome Atlas (TCGA). OBJECTIVES: To create a tool for CPH model building and best subset selection, using survival data from TCGA with input gene expression files from UCSC Xena. Furthermore, we aim to analyze the input TSV file of netrins-receptors in SmulTCan and discuss our findings. METHODS: SmulTCan uses Shiny's reactivity with built-in R functions from packages for CPH model analysis and best subset selection including "survminer", "riskRegression", "rms", "glmnet", and "BeSS". RESULTS: Results from the SmulTCan app with the netrins-receptors gene set indicated unique hazard ratio GSs in certain renal and neural cancers, while the best subsets for this gene set, obtained via the app, could differentiate between prognostic outcomes in these cancers. AVAILABILITY: SmulTCan is available at http://konulabapps.bilkent.edu.tr:3838/SmulTCan/. The input file for netrins-receptors is available in the online version of this paper. TCGA dataset folders containing survival files are available through https://github.com/aozh7/SmulTCan/. SUPPLEMENTARY INFORMATION: The supplementary information (SI) accompanies the online version of this article.