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Researchers in the Columbia University Department of Systems Biology and Herbert Irving Comprehensive Cancer Center have determined that measuring the expression levels of three genes associated with aging can be used to predict the aggressiveness of seemingly low-risk prostate cancer. Use of this three-gene biomarker, in conjunction with existing cancer-staging tests, could help physicians better determine which men with early prostate cancer can be safely followed with “active surveillance” and spared the risks of prostate removal or other invasive treatment. The findings were published today in the online edition of Science Translational Medicine.

More than 200,000 new cases of prostate cancer are diagnosed each year in the U.S. “Most of these cancers are slow growing and will remain so, and thus they do not require treatment,” said study leader Cory Abate-Shen, Michael and Stella Chernow Professor of Urological Oncology at Columbia University Medical Center (CUMC). “The problem is that, with existing tests, we cannot identify the small percentage of slow-growing tumors that will eventually become aggressive and spread beyond the prostate. The three-gene biomarker could take much of the guesswork out of the diagnostic process and ensure that patients are neither overtreated nor undertreated.”

The cell-of-origin model in cancer biology suggests that some tumors are more aggressive than others because of differences in the cell lineages from which they arise. In the prostate gland, there are three types of epithelial stem cell — luminal cells, basal cells, and rare neuroendocrine cells. There has been some discrepancy in the scientific literature, however, about whether luminal cells, basal cells, or both can act as a cell of tumor origin.

In a paper published online in the journal Nature Cell Biology, researchers in the laboratories of Columbia University Department of Systems Biology members Michael Shen, Andrea Califano, and Cory Abate-Shen undertook a comprehensive analysis of prostate basal cell properties in mouse models. They used a technique called genetic linkage marking to study an identical cell population in multiple assays of stem cell function.

The studies showed that discrepancies in the published literature arise because basal stem cell properties can change when studied outside their endogenous tissue microenvironment; that is, in ex vivo cell culture and tissue grafting assays. To avoid this problem, they suggest, genetic lineage tracing in vivo should be considered the gold standard for identifying physiologically relevant stem cells.

Barry Honig

When Columbia University founded the Center for Multiscale Analysis of Genomic and Cellular Networks (MAGNet) in 2005, one of its goals was to integrate the methods of structural biology with those of systems biology. Considering protein structure within the context of computational models of cellular networks, researchers hoped, would not only improve the predictive value of their models by giving another layer of evidence, but also lead to new types of predictions that could not be made using other methods.

In a new paper published in Nature magazine, Barry Honig, Andrea Califano, and other members of the Columbia Initiative in Systems Biology, including first authors Qiangfeng Cliff Zhang and Donald Petrey, report that this goal has now been realized. For the first time, the researchers have shown that information about protein structure can be used to make predictions about protein-protein interactions on a genome-wide scale. Their approach capitalizes on innovative techniques in computational structural biology that the Honig lab has developed over the last 15 years, culminating in the development of a new algorithm called Predicting Protein-Protein Interactions (PrePPI). In this interview, Honig describes the evolution of this new approach, and what it could mean for the future of systems biology.

Figure

Tumor-induced mRNA expression changes for individual biochemical reactions in central metabolism. 

A large study analyzing gene expression data from 22 cancer types has identified a broad spectrum of metabolic expression changes associated with cancer. The analysis, led by Dennis Vitkup, first author Jie Hu, a postdoctoral research scientist in the Vitkup lab, with a multi-institutional group of collaborators, also identified hundreds of potential drug targets that could cut off a tumor’s fuel supply or interfere with its ability to synthesize essential elements necessary for tumor growth. The study has just been published in the online edition of Nature Biotechnology .

As Columbia University Medical Center reports:

The results should ramp up research into drugs that interfere with cancer metabolism, a field that dominated cancer research in the early 20th century and has recently undergone a renaissance.

Genes forming cluster I in the context of cellular signaling pathways

Genes forming cluster I in the context of cellular signaling pathways. Proteins encoded by cluster genes are shown in yellow, and those corresponding to other relevant genes that were present in the input data but not selected by the NETBAG+ algorithm are shown in cyan. 

In a new paper published in the journal Nature Neuroscience, Columbia University researchers report that many of the genes that are mutated in schizophrenia are organized into two main networks. Surprisingly, the study also found that a genetic network that leads to schizophrenia is very similar to a network that has been linked to autism. 

Using a computational approach called NETBAG+, Dennis Vitkup and colleagues performed network-based analyses of rare de novo mutations to map the gene networks that lead to schizophrenia. When they compared one schizophrenia network to an autism network described in a study he published last year, they discovered that different copy number variants in the same genes can lead to either schizophrenia or autism. The overlapping genes are important for processes such as axon guidance, synapse function, and cell migration — processes within the brain that have been shown to play a role in the development of these two diseases. These gene networks are particularly active during prenatal development, suggesting that the foundations for schizophrenia and autism are laid very early in life.

GLOBUS algorithm

 An overview of the GLOBUS algorithm.

A Columbia University team led by professor Dennis Vitkup and PhD student German Plata of the Center for Computational Biology and Bioinformatics has developed a novel genome-wide framework for making probabilistic annotations of metabolic networks. Their approach, called Global Biochemical Reconstruction Using Sampling (GLOBUS), combines information about sequence homology with context-specific information including phylogeny, gene clustering, and mRNA co-expression to predict the probability of biochemical interactions between specific genes. By integrating these different categories of information using a principled probabilistic framework, this approach overcomes limitations of considering only one functional category or one gene at a time, providing a global and accurate prediction of metabolic networks.

In a paper published in Nature Chemical Biology, the scientists write, "Currently, most publicly available biochemical databases do not provide quantitative probabilities or confidence measures for existing annotations. This makes it hard for the users of these valuable resources to distinguish between confident assignments and mere guesses... The GLOBUS approach, which is based on statistical sampling of possible biochemical assignments, provides a principled framework for such global probabilistic annotations. The method assigns annotation probabilities to each gene and suggests likely alternative functions."

An extensive microRNA-mediated network of RNA-RNA interactions

Genome-wide inference of sponge modulators identified a miR-program mediated post-transcriptional regulatory (mPR) network including ~248,000 interactions.

For decades, scientists have thought that the primary role of messenger RNA (mRNA) is to shuttle information from the DNA to the ribosomes, the sites of protein synthesis. However, new studies now suggest that the mRNA of one gene can control, and be controlled by, the mRNA of other genes via a large pool of microRNA molecules, with dozens to hundreds of genes working together in complex self-regulating sub-networks.

In work published in the journal Cell, Andrea Califano, José Silva, and colleagues analyzed gene expression data in glioblastoma in combination with matched microRNA profiles to uncover a posttranscriptional regulation layer of surprising magnitude, comprising more than 248,000 microRNA (miR)-mediated interactions. These include ∼7,000 genes whose transcripts act as miR “sponges.” When two genes share a set of microRNA regulators, changes in expression of one gene affects the other. If, for instance, one of those genes is highly expressed, the increase in its mRNA molecules will “sponge up” more of the available microRNAs. As a result, fewer microRNA molecules will be available to bind and repress the other gene’s mRNAs, leading to a corresponding increase in expression.

Although such an effect had been previously elucidated, the range and relevance of this kind of interaction had not been characterized.

Transcriptional Network for Mesenchymal Transformation of Brain Tumors

The mesenchymal signature of high-grade gliomas is controlled by six transcription factors. TFs involved in activation of MGES targets are shown in pink, those involved in repression are in purple.

High-grade gliomas, such as glioblastoma, are incurable partly because the tumor cells are widely disseminated throughout the brain. This capacity for invasive growth has been associated with the expression of genes more commonly transcribed in mesenchymal cells. In work published in the journal Nature, Antonio Iavarone, Andrea Califano, and colleagues have identified a small transcription factor network that is responsible for the mesenchymal behavior of glioma cells.

The authors applied a specific algorithm designed to infer causal transcription factor-target interactions to gene expression profiles from 176 samples of high-grade gliomas. They analyzed the resulting interactome with a new algorithm that enabled them to evaluate the transcription factor network in terms of a previously identified mesenchymal gene expression signature from high-grade gliomas. This identified the 53 transcription factors that are associated with regulating mesenchymal gene expression. Further analyses identified signal transducer and activator of transcription 3 (STAT3) and CAATT/enhancer binding protein-β (CEBPβ) as potential master regulators that control the expression of a substantial proportion of these mesenchymal genes. The authors conclude that systems biology approaches can be used to identify master transcription factors that are involved in malignant transformation, and such approaches could be applied to help dissect the complexity of other tumour phenotypes.

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