Haploinsufficiency of the Myc regulator Mtbp extends survival and delays tumor development in aging mice.

Alterations of specific genes can modulate aging. Myc, a transcription factor that regulates the expression of many genes involved in critical cellular functions was shown to have a role in controlling longevity. Decreased expression of Myc inhibited many of the deleterious effects of aging and increased lifespan in mice. Without altering Myc expression, reduced levels of Mtbp, a recently identified regulator of Myc, limit Myc transcriptional activity and proliferation, while increased levels promote Myc-mediated effects. To determine the contribution of Mtbp to the effects of Myc on aging, we studied a large cohort of Mtbp heterozygous mice and littermate matched wild-type controls. Mtbp haploinsufficiency significantly increased longevity and maximal survival in mice. Reduced levels of Mtbp did not alter locomotor activity, litter size, or body size, but Mtbp heterozygous mice did exhibit elevated markers of metabolism, particularly in the liver. Mtbp(+/-) mice also had a significant delay in spontaneous cancer development, which was most prominent in the hematopoietic system, and an altered tumor spectrum compared to Mtbp(+/+) mice. Therefore, the data suggest Mtbp is a regulator of longevity in mice that mimics some, but not all, of the properties of Myc in aging

Mdm2 Is Required for Survival and Growth of p53-Deficient Cancer Cells.

p53 deletion prevents the embryonic lethality of normal tissues lacking Mdm2, suggesting that cells can survive without Mdm2 if p53 is also absent. Here we report evidence challenging this view, with implications for therapeutically targeting Mdm2. Deletion of Mdm2 in T-cell lymphomas or sarcomas lacking p53 induced apoptosis and G2 cell-cycle arrest, prolonging survival of mice with these tumors. p53-/- fibroblasts showed similar results, indicating that the effects of Mdm2 loss extend to pre-malignant cells. Mdm2 deletion in p53-/- cells upregulated p53 transcriptional target genes that induce apoptosis and cell-cycle arrest. Mdm2 deletion also increased levels of p73, a p53 family member. RNAi-mediated attenuation of p73 rescued the transcriptional and biological effects of Mdm2 loss, indicating that p73 mediates the consequences of Mdm2 deletion. In addition, Mdm2 deletion differed from blocking Mdm2 interaction with p53 family members, as Nutlin-3 induced G1 arrest but did not activate apoptosis in p53-/- sarcoma cells. Our results indicate that, in contrast to current dogma, Mdm2 expression is required for cell survival even in the absence of p53. Moreover, our results suggest that p73 compensates for loss of p53 and that targeting Mdm2 in p53-deficient cancers has therapeutic potential. ©2017 AACR

Non-Hodgkin and Hodgkin Lymphomas Select for Overexpression of BCLW.

Purpose: B-cell lymphomas must acquire resistance to apoptosis during their development. We recently discovered BCLW, an antiapoptotic BCL2 family member thought only to contribute to spermatogenesis, was overexpressed in diffuse large B-cell lymphoma (DLBCL) and Burkitt lymphoma. To gain insight into the contribution of BCLW to B-cell lymphomas and its potential to confer resistance to BCL2 inhibitors, we investigated the expression of BCLW and the other antiapoptotic BCL2 family members in six different B-cell lymphomas. Experimental Design: We performed a large-scale gene expression analysis of datasets comprising approximately 2,300 lymphoma patient samples, including non-Hodgkin and Hodgkin lymphomas as well as indolent and aggressive lymphomas. Data were validated experimentally with qRT-PCR and IHC. Results: We report BCLW is significantly overexpressed in aggressive and indolent lymphomas, including DLBCL, Burkitt, follicular, mantle cell, marginal zone, and Hodgkin lymphomas. Notably, BCLW was preferentially overexpressed over that of BCL2 and negatively correlated with BCL2 in specific lymphomas. Unexpectedly, BCLW was overexpressed as frequently as BCL2 in follicular lymphoma. Evaluation of all five antiapoptotic BCL2 family members in six types of B-cell lymphoma revealed that BCL2, BCLW, and BCLX were consistently overexpressed, whereas MCL1 and A1 were not. In addition, individual lymphomas frequently overexpressed more than one antiapoptotic BCL2 family member. Conclusions: Our comprehensive analysis indicates B-cell lymphomas commonly select for BCLW overexpression in combination with or instead of other antiapoptotic BCL2 family members. Our results suggest BCLW may be equally as important in lymphomagenesis as BCL2 and that targeting BCLW in lymphomas should be considered. ©2017 AACR

MicroRNA and transcription factor co-regulatory networks and subtype classification of seminoma and non-seminoma in testicular germ cell tumors

Recent studies have revealed that feed-forward loops (FFLs) as regulatory motifs have synergistic roles in cellular systems and their disruption may cause diseases including cancer. FFLs may include two regulators such as transcription factors (TFs) and microRNAs (miRNAs). In this study, we extensively investigated TF and miRNA regulation pairs, their FFLs, and TF-miRNA mediated regulatory networks in two major types of testicular germ cell tumors (TGCT): seminoma (SE) and non-seminoma (NSE). Specifically, we identified differentially expressed mRNA genes and miRNAs in 103 tumors using the transcriptomic data from The Cancer Genome Atlas. Next, we determined significantly correlated TF-gene/miRNA and miRNA-gene/TF pairs with regulation direction. Subsequently, we determined 288 and 664 dysregulated TF-miRNA-gene FFLs in SE and NSE, respectively. By constructing dysregulated FFL networks, we found that many hub nodes (12 out of 30 for SE and 8 out of 32 for NSE) in the top ranked FFLs could predict subtype-classification (Random Forest classifier, average accuracy ≥90%). These hub molecules were validated by an independent dataset. Our network analysis pinpointed several SE-specific dysregulated miRNAs (miR-200c-3p, miR-25-3p, and miR-302a-3p) and genes (EPHA2, JUN, KLF4, PLXDC2, RND3, SPI1, and TIMP3) and NSE-specific dysregulated miRNAs (miR-367-3p, miR-519d-3p, and miR-96-5p) and genes (NR2F1 and NR2F2). This study is the first systematic investigation of TF and miRNA regulation and their co-regulation in two major TGCT subtypes

The Potential Roles of Long Noncoding RNAs (lncRNA) in Glioblastoma Development

Long noncoding RNA (lncRNA) may contribute to the initiation and progression of tumor. In this study, we first systematically compared lncRNA and mRNA expression between glioblastoma and paired normal brain tissues using microarray data. We found 27 lncRNA and 82 mRNA significantly upregulated in glioblastoma, as well as 198 lncRNA and 285 mRNA significantly downregulated in glioblastoma. We identified 138 coexpressed lncRNA–mRNA pairs from these differentially expressed lncRNA and genes. Subsequent pathway analysis of the lncRNA-paired genes indicated that EphrinB–EPHB, p75-mediated signaling, TNFα/NF-κB, and ErbB2/ErbB3 signaling pathways might be altered in glioblastoma. Specifically, lncRNA RAMP2-AS1 had significant decrease of expression in glioblastoma tissues and showed coexpressional relationship with NOTCH3, an important tumor promoter in many neoplastic diseases. Our follow up experiment indicated that (i) an overexpression of RAMP2-AS1 reduced glioblastoma cell proliferation in vitro and also reduced glioblastoma xenograft tumors in vivo; (ii) NOTCH3 and RAMP2-AS1 coexpression rescued the inhibitory action of RAMP2-AS1 in glioblastoma cells; and (iii) RNA pull-down assay revealed a direct interaction of RAMP2-AS1 with DHC10, which may consequently inhibit, as we hypothesize, the expression of NOTCH3 and its downstream signaling molecule HES1 in glioblastoma. Taken together, our data revealed that lncRNA expression profile in glioblastoma tissue was significantly altered; and RAMP2-AS1 might play a tumor suppressive role in glioblastoma through an indirect inhibition of NOTCH3. Our results provided some insights into understanding the key roles of lncRNA–mRNA coregulation in human glioblastoma and the mechanisms responsible for glioblastoma progression and pathogenesis. Mol Cancer Ther; 15(12); 2977–86. ©2016 AACR

Pan-cancer analysis reveals cooperativity of both strands of microRNA that regulate tumorigenesis and patient survival

Recently, both 5p and 3p miRNA strands are being recognized as functional instead of only one, leaving many miRNA strands uninvestigated. To determine whether both miRNA strands, which have different mRNA-targeting sequences, cooperate to regulate pathways/functions across cancer types, we evaluate genomic, epigenetic, and molecular profiles of \u3e5200 patient samples from 14 different cancers, and RNA interference and CRISPR screens in 290 cancer cell lines. We identify concordantly dysregulated miRNA 5p/3p pairs that coordinately modulate oncogenic pathways and/or cell survival/growth across cancers. Down-regulation of both strands of miR-30a and miR-145 recurrently increased cell cycle pathway genes and significantly reduced patient survival in multiple cancers. Forced expression of all four strands show cooperativity, reducing cell cycle pathways and inhibiting lung cancer cell proliferation and migration. Therefore, we identify miRNA whose 5p/3p strands function together to regulate core tumorigenic processes/pathways and reveal a previously unknown pan-cancer miRNA signature with patient prognostic power

BCL-W has a fundamental role in B cell survival and lymphomagenesis.

Compromised apoptotic signaling is a prerequisite for tumorigenesis. The design of effective therapies for cancer treatment depends on a comprehensive understanding of the mechanisms that govern cell survival. The antiapoptotic proteins of the BCL-2 family are key regulators of cell survival and are frequently overexpressed in malignancies, leading to increased cancer cell survival. Unlike BCL-2 and BCL-XL, the closest antiapoptotic relative BCL-W is required for spermatogenesis, but was considered dispensable for all other cell types. Here, however, we have exposed a critical role for BCL-W in B cell survival and lymphomagenesis. Loss of Bcl-w conferred sensitivity to growth factor deprivation-induced B cell apoptosis. Moreover, Bcl-w loss profoundly delayed MYC-mediated B cell lymphoma development due to increased MYC-induced B cell apoptosis. We also determined that MYC regulates BCL-W expression through its transcriptional regulation of specific miR. BCL-W expression was highly selected for in patient samples of Burkitt lymphoma (BL), with 88.5% expressing BCL-W. BCL-W knockdown in BL cell lines induced apoptosis, and its overexpression conferred resistance to BCL-2 family-targeting BH3 mimetics. Additionally, BCL-W was overexpressed in diffuse large B cell lymphoma and correlated with decreased patient survival. Collectively, our results reveal that BCL-W profoundly contributes to B cell lymphoma, and its expression could serve as a biomarker for diagnosis and aid in the development of better targeted therapies

CTpathway: A Crosstalk-Based Pathway Enrichment Analysis Method for Cancer Research

Background: Pathway enrichment analysis (PEA) is a common method for exploring functions of hundreds of genes and identifying disease-risk pathways. Moreover, different pathways exert their functions through crosstalk. However, existing PEA methods do not sufficiently integrate essential pathway features, including pathway crosstalk, molecular interactions, and network topologies, resulting in many risk pathways that remain uninvestigated. Methods: To overcome these limitations, we develop a new crosstalk-based PEA method, CTpathway, based on a global pathway crosstalk map (GPCM) with \u3e440,000 edges by combing pathways from eight resources, transcription factor-gene regulations, and large-scale protein-protein interactions. Integrating gene differential expression and crosstalk effects in GPCM, we assign a risk score to genes in the GPCM and identify risk pathways enriched with the risk genes. Results: Analysis of \u3e8300 expression profiles covering ten cancer tissues and blood samples indicates that CTpathway outperforms the current state-of-the-art methods in identifying risk pathways with higher accuracy, reproducibility, and speed. CTpathway recapitulates known risk pathways and exclusively identifies several previously unreported critical pathways for individual cancer types. CTpathway also outperforms other methods in identifying risk pathways across all cancer stages, including early-stage cancer with a small number of differentially expressed genes. Moreover, the robust design of CTpathway enables researchers to analyze both bulk and single-cell RNA-seq profiles to predict both cancer tissue and cell type-specific risk pathways with higher accuracy. Conclusions: Collectively, CTpathway is a fast, accurate, and stable pathway enrichment analysis method for cancer research that can be used to identify cancer risk pathways. The CTpathway interactive web server can be accessed here http://www.jianglab.cn/CTpathway/ . The stand-alone program can be accessed here https://github.com/Bioccjw/CTpathway

Initiating conservation agriculture shows reduced soil CO2 emissions and improved soil aggregate stability in the first season in rainfed cropping in India

The reported study was undertaken to determine which soil health indicators showed measurable signs of improvement, during the first year of the process of introducing a Conservation Agriculture (CA) cropping system in rainfed areas in Madhya Pradesh, India. Soil health indicators of soil aggregate stability, soil-atmosphere CO2 fluxes, water infiltration, soil moisture, potentially mineralisable nitrogen, soil organic content and bulk density were measured. Results demonstrate that generally, there were improvements in all measured soil health indicators in CA soils, with decrease in CO2 emissions and increase in soil aggregates being statistically significant

MultiMiTar: A Novel Multi Objective Optimization based miRNA-Target Prediction Method

BACKGROUND: Machine learning based miRNA-target prediction algorithms often fail to obtain a balanced prediction accuracy in terms of both sensitivity and specificity due to lack of the gold standard of negative examples, miRNA-targeting site context specific relevant features and efficient feature selection process. Moreover, all the sequence, structure and machine learning based algorithms are unable to distribute the true positive predictions preferentially at the top of the ranked list; hence the algorithms become unreliable to the biologists. In addition, these algorithms fail to obtain considerable combination of precision and recall for the target transcripts that are translationally repressed at protein level. METHODOLOGY/PRINCIPAL FINDING: In the proposed article, we introduce an efficient miRNA-target prediction system MultiMiTar, a Support Vector Machine (SVM) based classifier integrated with a multiobjective metaheuristic based feature selection technique. The robust performance of the proposed method is mainly the result of using high quality negative examples and selection of biologically relevant miRNA-targeting site context specific features. The features are selected by using a novel feature selection technique AMOSA-SVM, that integrates the multi objective optimization technique Archived Multi-Objective Simulated Annealing (AMOSA) and SVM. CONCLUSIONS/SIGNIFICANCE: MultiMiTar is found to achieve much higher Matthew's correlation coefficient (MCC) of 0.583 and average class-wise accuracy (ACA) of 0.8 compared to the others target prediction methods for a completely independent test data set. The obtained MCC and ACA values of these algorithms range from -0.269 to 0.155 and 0.321 to 0.582, respectively. Moreover, it shows a more balanced result in terms of precision and sensitivity (recall) for the translationally repressed data set as compared to all the other existing methods. An important aspect is that the true positive predictions are distributed preferentially at the top of the ranked list that makes MultiMiTar reliable for the biologists. MultiMiTar is now available as an online tool at www.isical.ac.in/~bioinfo_miu/multimitar.htm. MultiMiTar software can be downloaded from www.isical.ac.in/~bioinfo_miu/multimitar-download.htm