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.

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.

Representative microphotographs of hematoxylin and eosin staining of advanced FGFR3-TACC3-shp53–generated tumors show histological features of high-grade glioma.

A new paper published by Columbia University Medical Center researchers in the journal Science has determined that some cases of glioblastoma, the most aggressive form of primary brain cancer, result from the fusion of the genes FGFR and TACC. Raul Rabadan, a co-senior author on the study, led efforts to identify these genes by using quantitative methods to analyze the glioblastoma genome from nine patients, and then compare these results with more than 300 genomes from the Cancer Genome Atlas project.

The collaboration with cancer genomics expert Antonio Iavarone and co-senior author Anna Lasorella found that the protein produced by the FGFR-TACC fusion disrupts the mitotic spindle (the cellular structure that guides mitosis) and causes aneuploidy, an uneven distribution of chromosomes that causes tumorigenesis. The researchers also found that drugs that target this aberration can dramatically slow the growth of tumors in mice, suggesting a potential therapeutic target.