DNA replication licensing in stem cells: Gatekeeping the commitment to proliferation.

Carroll et al. (2018. J. Cell Biol. https://doi.org/10.1083/jcb.201708023) developed a method to assess DNA replication licensing in tissues. They show that intestinal stem cells within wild-type crypts, but not in crypts with cancer-causing mutations, are largely unlicensed, suggesting that licensing may represent a rate-limiting step in the commitment to proliferation

Is Cancer a Metabolic Disease?

Although cancer has historically been viewed as a disorder of proliferation, recent evidence has suggested that it should also be considered a metabolic disease. Growing tumors rewire their metabolic programs to meet and even exceed the bioenergetic and biosynthetic demands of continuous cell growth. The metabolic profile observed in cancer cells often includes increased consumption of glucose and glutamine, increased glycolysis, changes in the use of metabolic enzyme isoforms, and increased secretion of lactate. Oncogenes and tumor suppressors have been discovered to have roles in cancer-associated changes in metabolism as well. The metabolic profile of tumor cells has been suggested to reflect the rapid proliferative rate. Cancer-associated metabolic changes may also reveal the importance of protection against reactive oxygen species or a role for secreted lactate in the tumor microenvironment. This article reviews recent research in the field of cancer metabolism, raising the following questions: Why do cancer cells shift their metabolism in this way? Are the changes in metabolism in cancer cells a consequence of the changes in proliferation or a driver of cancer progression? Can cancer metabolism be targeted to benefit patients

Integrative analysis of the inter-tumoral heterogeneity of triple-negative breast cancer.

Triple-negative breast cancers (TNBC) lack estrogen and progesterone receptors and HER2 amplification, and are resistant to therapies that target these receptors. Tumors from TNBC patients are heterogeneous based on genetic variations, tumor histology, and clinical outcomes. We used high throughput genomic data for TNBC patients (n = 137) from TCGA to characterize inter-tumor heterogeneity. Similarity network fusion (SNF)-based integrative clustering combining gene expression, miRNA expression, and copy number variation, revealed three distinct patient clusters. Integrating multiple types of data resulted in more distinct clusters than analyses with a single datatype. Whereas most TNBCs are classified by PAM50 as basal subtype, one of the clusters was enriched in the non-basal PAM50 subtypes, exhibited more aggressive clinical features and had a distinctive signature of oncogenic mutations, miRNAs and expressed genes. Our analyses provide a new classification scheme for TNBC based on multiple omics datasets and provide insight into molecular features that underlie TNBC heterogeneity

Adenovirus type 5 exerts genome-wide control over cellular programs governing proliferation, quiescence, and survival

The effects of the adenovirus Ad5 on basic host cell programs, such as cell-cycle regulation, were studied in a microarray analysis of human fibroblasts. About 2,000 genes were up- or down-regulated after Ad5 infection and Ad5 infection was shown to induce reversal of the quiescence program and recapitulation of the core serum response

Nearest Neighbor Networks: clustering expression data based on gene neighborhoods

<p>Abstract</p> <p>Background</p> <p>The availability of microarrays measuring thousands of genes simultaneously across hundreds of biological conditions represents an opportunity to understand both individual biological pathways and the integrated workings of the cell. However, translating this amount of data into biological insight remains a daunting task. An important initial step in the analysis of microarray data is clustering of genes with similar behavior. A number of classical techniques are commonly used to perform this task, particularly hierarchical and K-means clustering, and many novel approaches have been suggested recently. While these approaches are useful, they are not without drawbacks; these methods can find clusters in purely random data, and even clusters enriched for biological functions can be skewed towards a small number of processes (e.g. ribosomes).</p> <p>Results</p> <p>We developed Nearest Neighbor Networks (NNN), a graph-based algorithm to generate clusters of genes with similar expression profiles. This method produces clusters based on overlapping cliques within an interaction network generated from mutual nearest neighborhoods. This focus on nearest neighbors rather than on absolute distance measures allows us to capture clusters with high connectivity even when they are spatially separated, and requiring mutual nearest neighbors allows genes with no sufficiently similar partners to remain unclustered. We compared the clusters generated by NNN with those generated by eight other clustering methods. NNN was particularly successful at generating functionally coherent clusters with high precision, and these clusters generally represented a much broader selection of biological processes than those recovered by other methods.</p> <p>Conclusion</p> <p>The Nearest Neighbor Networks algorithm is a valuable clustering method that effectively groups genes that are likely to be functionally related. It is particularly attractive due to its simplicity, its success in the analysis of large datasets, and its ability to span a wide range of biological functions with high precision.</p

Rrp1b, a New Candidate Susceptibility Gene for Breast Cancer Progression and Metastasis

A novel candidate metastasis modifier, ribosomal RNA processing 1 homolog B (Rrp1b), was identified through two independent approaches. First, yeast two-hybrid, immunoprecipitation, and functional assays demonstrated a physical and functional interaction between Rrp1b and the previous identified metastasis modifier Sipa1. In parallel, using mouse and human metastasis gene expression data it was observed that extracellular matrix (ECM) genes are common components of metastasis predictive signatures, suggesting that ECM genes are either important markers or causal factors in metastasis. To investigate the relationship between ECM genes and poor prognosis in breast cancer, expression quantitative trait locus analysis of polyoma middle-T transgene-induced mammary tumor was performed. ECM gene expression was found to be consistently associated with Rrp1b expression. In vitro expression of Rrp1b significantly altered ECM gene expression, tumor growth, and dissemination in metastasis assays. Furthermore, a gene signature induced by ectopic expression of Rrp1b in tumor cells predicted survival in a human breast cancer gene expression dataset. Finally, constitutional polymorphism within RRP1B was found to be significantly associated with tumor progression in two independent breast cancer cohorts. These data suggest that RRP1B may be a novel susceptibility gene for breast cancer progression and metastasis

Regulation of the let-7a-3 Promoter by NF-κB

Changes in microRNA expression have been linked to a wide array of pathological states. However, little is known about the regulation of microRNA expression. The let-7 microRNA is a tumor suppressor that inhibits cellular proliferation and promotes differentiation, and is frequently lost in tumors. We investigated the transcriptional regulation of two let-7 family members, let-7a-3 and let-7b, which form a microRNA cluster and are located 864 bp apart on chromosome 22q13.31. Previous reports present conflicting data on the role of the NF-κB transcription factor in regulating let-7. We cloned three fragments upstream of the let-7a-3/let-7b miRNA genomic region into a plasmid containing a luciferase reporter gene. Ectopic expression of subunits of NF-κB (p50 or p65/RelA) significantly increased luciferase activity in HeLa, 293, 293T and 3T3 cells, indicating that the let-7a-3/let-7b promoter is highly responsive to NF-κB. Mutation of a putative NF-κB binding site at bp −833 reduced basal promoter activity and decreased promoter activity in the presence of p50 or p65 overexpression. Mutation of a second putative binding site, at bp −947 also decreased promoter activity basally and in response to p65 induction, indicating that both sites contribute to NF-κB responsiveness. While the levels of the endogenous primary let-7a and let-7b transcript were induced in response to NF-κB overexpression in 293T cells, the levels of fully processed, mature let-7a and let-7b miRNAs did not increase. Instead, levels of Lin-28B, a protein that blocks let-7 maturation, were induced by NF-κB. Increased Lin-28B levels could contribute to the lack of an increase in mature let-7a and let-7b. Our results suggest that the final biological outcome of NF-κB activation on let-7 expression may vary depending upon the cellular context. We discuss our results in the context of NF-κB activity in repressing self-renewal and promoting differentiation