Genomic analysis of human and mouse TCL1 loci reveals a complex of tightly clustered genes

TCL1 and TCL1b genes on human chromosome 14q23.1 are activated in T cell leukemias by translocations and inversions at 14q32.1, juxtaposing them to regulatory elements of T cell receptor genes. In this report we present the cloning, mapping, and expression analysis of the human and murine TCL1/Tcl1 locus. In addition to TCL1 and TCL1b, the human locus contains two additional genes, TCL1-neighboring genes (TNG) 1 and 2, encoding proteins of 141 and 110 aa, respectively. Both genes show no homology to any known genes, but their expression profiles are very similar to those of TCL1 and TCL1b. TNG1 and TNG2 also are activated in T cell leukemias with rearrangements at 14q32.1. To aid in the development of a mouse model we also have characterized the murine Tcl1 locus and found five genes homologous to human TCL1b. Tcl1b1- Tcl1b5 proteins range from 117 to 123 aa and are 65-80% similar, but they show only a 30-40% similarity to human TCL1b. All five mouse Tcl1b and murine Tcl1 mRNAs are abundant in mouse oocytes and two-cell embryos but rare in various adult tissues and lymphoid cell lines. These data suggest a similar or complementary function of these proteins in early embryogenesis

Expression of the long non-coding RNA TCL6 is associated with clinical outcome in pediatric B-cell acute lymphoblastic leukemia

The authors would like to thank the Deutsche José Carreras Leukämie-Stiftung, Inocente Inocente Foundation, the Ministry of Economy of Spain (SAF2015- 67919-R), Consejería de Salud de la Junta de Andalucía (Pl-0245-2017, CS2016- 3), BBVA Foundation, Francisco-Cobos Foundation, Fero Foundation and AECC Foundation for funding Pedro P. Medinas’s lab. Álvaro Andrades is supported by an FPU17/00067 PhD fellowship, Alberto M. Arenas is supported by an FPU17/01258 PhD fellowship, Paola Peinado is supported by a La Caixa Foundation PhD Fellowship (LCF/BQ/DE15/10360019), Isabel F. Coira was supported by a PhD FPI-fellowship (BES-2013-064596), Daniel J. García is supported by a Fundación Benéfica Anticáncer Santa Cándida y San Francisco Javier PhD fellowship and Juan Carlos Álvarez-Pérez is supported by a Marie Sklodowska Curie action (H2020-MSCA-IF-2018). The funding agencies had no role in study design, data collection, and analysis, decision to publish, or preparation of the manuscript. The authors would also like to thank the Biobanc de l’Hospital Infantil Sant Joan de Déu per a la Investigació, integrated in the Spanish Biobank Network of ISCIII, as well as Asociación Malagueña para la Investigación en Leucemias (AMPILE), for the sample and data procurement

Fhit modulation of the Akt-survivin pathway in lung cancer cells: Fhit-tyrosine 114 (Y114) is essential

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Telomerase activity, apoptosis and cell cycle progression in ataxia telangiectasia lymphocytes expressing TCL1

Individuals affected by ataxia telangiectasia (AT) have a marked susceptibility to cancer. Ataxia telangiectasia cells, in addition to defects in cell cycle checkpoints, show dysfunction of apoptosis and of telomeres, which are both thought to have a role in the progression of malignancy. In 1-5% of patients with AT, clonal expansion of T lymphocytes carrying t(14;14) chromosomal translocation, deregulating TCL1 gene(s), has been described. While it is known that these cells can progress with time to a frank leukaemia, the molecular pathway leading to tumorigenesis has not yet been fully investigated. In this study, we compared AT clonal cells, representing 88% of the entire T lymphocytes (AT94-1) and expressing TCL1 oncogene (ATM- TCL1 +), cell cycle progression to T lymphocytes of AT patients without TCL1 expression (ATM- TCL1-) by analysing their spontaneous apoptosis rate, spontaneous telomerase activity and telomere instability. We show that in ATM- TCL1+ lymphocytes, apoptosis rate and cell cycle progression are restored back to a rate comparable with that observed in normal lymphocytes while telomere dysfunction is maintained. © 2003 Cancer Research UK

Gene expression and splicing alterations analyzed by high throughput RNA sequencing of chronic lymphocytic leukemia specimens.

BackgroundTo determine differentially expressed and spliced RNA transcripts in chronic lymphocytic leukemia specimens a high throughput RNA-sequencing (HTS RNA-seq) analysis was performed.MethodsTen CLL specimens and five normal peripheral blood CD19+ B cells were analyzed by HTS RNA-seq. The library preparation was performed with Illumina TrueSeq RNA kit and analyzed by Illumina HiSeq 2000 sequencing system.ResultsAn average of 48.5 million reads for B cells, and 50.6 million reads for CLL specimens were obtained with 10396 and 10448 assembled transcripts for normal B cells and primary CLL specimens respectively. With the Cuffdiff analysis, 2091 differentially expressed genes (DEG) between B cells and CLL specimens based on FPKM (fragments per kilobase of transcript per million reads and false discovery rate, FDR q < 0.05, fold change >2) were identified. Expression of selected DEGs (n = 32) with up regulated and down regulated expression in CLL from RNA-seq data were also analyzed by qRT-PCR in a test cohort of CLL specimens. Even though there was a variation in fold expression of DEG genes between RNA-seq and qRT-PCR; more than 90 % of analyzed genes were validated by qRT-PCR analysis. Analysis of RNA-seq data for splicing alterations in CLL and B cells was performed by Multivariate Analysis of Transcript Splicing (MATS analysis). Skipped exon was the most frequent splicing alteration in CLL specimens with 128 significant events (P-value <0.05, minimum inclusion level difference >0.1).ConclusionThe RNA-seq analysis of CLL specimens identifies novel DEG and alternatively spliced genes that are potential prognostic markers and therapeutic targets. High level of validation by qRT-PCR for a number of DEG genes supports the accuracy of this analysis. Global comparison of transcriptomes of B cells, IGVH non-mutated CLL (U-CLL) and mutated CLL specimens (M-CLL) with multidimensional scaling analysis was able to segregate CLL and B cell transcriptomes but the M-CLL and U-CLL transcriptomes were indistinguishable. The analysis of HTS RNA-seq data to identify alternative splicing events and other genetic abnormalities specific to CLL is an added advantage of RNA-seq that is not feasible with other genome wide analysis

The expression of FHIT, PCNA and EGFR in benign and malignant breast lesions

Immunohistochemical staining for FHIT and PCNA proteins was carried out in 451 breast lesions showing nonproliferative benign breast disease (BBD) (n=263), proliferative BBD without atypia (n=128), proliferative BBD with atypia (n=11), carcinoma in situ (n=15) or invasive carcinoma (n=34) and for EGFR protein in a subset of 71 of these cases. FHIT underexpression was not detected in nonproliferative lesions, but occurred in 2% of proliferative BBD without atypia, 10% proliferative BBD with atypia, 27% of carcinoma in situ and 41% of invasive carcinoma, which suggests that it could be useful in assessing those carcinoma in situ lesions (ductal, DCIS and lobular, LCIS) that are more likely to progress to malignancy. Preliminary microarray comparisons on DCIS and invasive carcinoma samples dissected from formalin-fixed paraffin sections showed a consistent downregulation of two previously identified FHIT-related genes, caspase 1 and BRCA1 in lesions underexpressing FHIT

The nuclear orphan receptor Nr4a2 induces Foxp3 and regulates differentiation of CD4+ T cells

Regulatory T cells (Tregs) have a central role in maintaining immune homoeostasis through various mechanisms. Although the Forkhead transcription factor Foxp3 defines the Treg cell lineage and functions, the molecular mechanisms of Foxp3 induction and maintenance remain elusive. Here we show that Foxp3 is one of the direct targets of Nr4a2. Nr4a2 binds to regulatory regions of Foxp3, where it mediates permissive histone modifications. Ectopic expression of Nr4a2 imparts Treg-like suppressive activity to naïve CD4+ T cells by inducing Foxp3 and by repressing cytokine production, including interferon-γ and interleukin-2. Deletion of Nr4a2 in T cells attenuates induction of Tregs and causes aberrant induction of Th1, leading to the exacerbation of colitis. Nr4a2-deficeint Tregs are prone to lose Foxp3 expression and have attenuated suppressive ability both in vitro and in vivo. Thus, Nr4a2 has the ability to maintain T-cell homoeostasis by regulating induction, maintenance and suppressor functions of Tregs, and by repression of aberrant Th1 induction

Accurate molecular classification of cancer using simple rules

<p>Abstract</p> <p>Background</p> <p>One intractable problem with using microarray data analysis for cancer classification is how to reduce the extremely high-dimensionality gene feature data to remove the effects of noise. Feature selection is often used to address this problem by selecting informative genes from among thousands or tens of thousands of genes. However, most of the existing methods of microarray-based cancer classification utilize too many genes to achieve accurate classification, which often hampers the interpretability of the models. For a better understanding of the classification results, it is desirable to develop simpler rule-based models with as few marker genes as possible.</p> <p>Methods</p> <p>We screened a small number of informative single genes and gene pairs on the basis of their depended degrees proposed in rough sets. Applying the decision rules induced by the selected genes or gene pairs, we constructed cancer classifiers. We tested the efficacy of the classifiers by leave-one-out cross-validation (LOOCV) of training sets and classification of independent test sets.</p> <p>Results</p> <p>We applied our methods to five cancerous gene expression datasets: leukemia (acute lymphoblastic leukemia [ALL] vs. acute myeloid leukemia [AML]), lung cancer, prostate cancer, breast cancer, and leukemia (ALL vs. mixed-lineage leukemia [MLL] vs. AML). Accurate classification outcomes were obtained by utilizing just one or two genes. Some genes that correlated closely with the pathogenesis of relevant cancers were identified. In terms of both classification performance and algorithm simplicity, our approach outperformed or at least matched existing methods.</p> <p>Conclusion</p> <p>In cancerous gene expression datasets, a small number of genes, even one or two if selected correctly, is capable of achieving an ideal cancer classification effect. This finding also means that very simple rules may perform well for cancerous class prediction.</p