Table of Contents
01 February 2017; volume 13, issue 2
Articles
- Transcription factor family‐specific DNA shape readout revealed by quantitative specificity models
- Lin Yang1,†,
- Yaron Orenstein2,4,†,
- Arttu Jolma3,
- Yimeng Yin3,
- Jussi Taipale3,
- Ron Shamir (rshamir{at}tau.ac.il)*,2 and
- Remo Rohs (rohs{at}usc.edu)*,1
- 1Molecular and Computational Biology Program, Departments of Biological Sciences, Chemistry, Physics & Astronomy, and Computer Science, University of Southern California, Los Angeles, CA, USA
- 2Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel
- 3Division of Functional Genomics and Systems Biology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
- 4Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
- ↵*
Corresponding author. Tel: +972 3 640 5383; E‐mail: rshamir{at}tau.ac.il
Corresponding author. Tel: +1 213 740 0552; E‐mail: rohs{at}usc.edu
↵† These authors contributed equally to this work
The role of DNA shape in transcription factor (TF)‐binding specificity is explored using a TF–DNA binding dataset covering more than 400 mammalian TFs. DNA shape readout is important for many TF families and improves binding specificity models.
Synopsis
The role of DNA shape in transcription factor (TF)‐binding specificity is explored using a TF–DNA binding dataset covering more than 400 mammalian TFs. DNA shape readout is important for many TF families and improves binding specificity models.
The largest protein–DNA binding dataset derived from HT‐SELEX experiments covering more than 400 mammalian TFs is analyzed.
DNA shape readout plays an important role in DNA‐binding specificities of TFs across many protein families.
DNA shape in regions immediately flanking the core‐binding site is generally recognized upon TF binding.
Feature selection based on DNA sequencing data alone can provide structural insights into TF–DNA readout mechanisms.
Mol Syst Biol. (2017) 13: 910
- Received August 4, 2016.
- Revision received January 4, 2017.
- Accepted January 5, 2017.
- © 2017 The Authors. Published under the terms of the CC BY 4.0 license
This is an open access article under the terms of the Creative Commons Attribution 4.0 License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
- Mathematical modeling links Wnt signaling to emergent patterns of metabolism in colon cancer
- Mary Lee1,†,
- George T Chen2,†,
- Eric Puttock1,
- Kehui Wang3,4,
- Robert A Edwards3,4,
- Marian L Waterman (marian.waterman{at}uci.edu)*,2,4,5 and
- John Lowengrub (jlowengr{at}uci.edu)*,1,4,5,6
- 1Department of Mathematics, University of California, Irvine, Irvine, CA, USA
- 2Department of Microbiology and Molecular Genetics, University of California, Irvine, Irvine, CA, USA
- 3Department of Pathology, School of Medicine, University of California, Irvine, Irvine, CA, USA
- 4Chao Family Comprehensive Cancer Center, University of California, Irvine, Irvine, CA, USA
- 5Center for Complex Biological Systems, University of California, Irvine, Irvine, CA, USA
- 6Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, USA
- ↵*
Corresponding author. Tel: +1 949 824 2885; E‐mail: marian.waterman{at}uci.edu
Corresponding author. Tel: +1 949 824 8456; E‐mail: jlowengr{at}uci.edu
↵† These authors contributed equally to this work
A self‐organizing spotted pattern of glycolysis is discovered in xenograft colon tumors and is consistent with a Turing reaction–diffusion system. Model predictions of gene expression and therapy are validated and reveal synergistic outcomes by targeting Wnt and the glycolytic cell population.
Synopsis
A self‐organizing spotted pattern of glycolysis is discovered in xenograft colon tumors and is consistent with a Turing reaction–diffusion system. Model predictions of gene expression and therapy are validated and reveal synergistic outcomes by targeting Wnt and the glycolytic cell population.
Clusters of cells (spots) with high pyruvate dehydrogenase kinase (PDK) activity are observed in colon xenografts indicating a heterogeneous pattern of glycolytic activity.
A 3D Turing reaction–diffusion model is developed that incorporates Wnt signaling, a pathway known to upregulate PDK1 and Warburg metabolism and recapitulates the spotted patterns.
The model predicts that partial inhibition of Wnt signaling increases the levels of regulators that enhance the range of Wnt ligand diffusion; this is validated in xenograft tumors. Similar effects are found in radiochemotherapy‐treated colorectal cancer patients.
Model predictions of synergism between inhibitors of glycolysis and Wnt signaling are validated in 3D colon tumor spheroids.
Mol Syst Biol. (2017) 13: 912
- Received October 17, 2016.
- Revision received January 5, 2017.
- Accepted January 12, 2017.
- © 2017 The Authors. Published under the terms of the CC BY 4.0 license
This is an open access article under the terms of the Creative Commons Attribution 4.0 License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
News & Views
- Metabolic pattern formation in the tumor microenvironment
Metabolic alterations including increased glycolysis are a common feature of many cancers. In their recent study, Lowengrub, Waterman, and colleagues (Lee et al, 2017) report a spatial pattern of glycolysis in solid tumors that occurs within the tumor microenvironment. This spatial organization is linked to gradients derived from Wnt signaling and nutrient availability that mediate a reaction‐diffusion mechanism and is consistent with a Turing‐type model of spatial localization.
See also: M Lee et al (February 2017)
Metabolic alterations are a prevalent feature of many cancers. In their recent study, Lowengrub, Waterman, and colleagues (Lee et al, 2017) report a spatial glycolysis pattern in tumors that is linked to gradients derived from Wnt signaling and nutrient availability.
Mol Syst Biol. (2017) 13: 915
- © 2017 The Authors. Published under the terms of the CC BY 4.0 license
This is an open access article under the terms of the Creative Commons Attribution 4.0 License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Articles
- A method for high‐throughput production of sequence‐verified DNA libraries and strain collections
- Justin D Smith1,2,†,
- Ulrich Schlecht1,3,†,
- Weihong Xu1,4,†,
- Sundari Suresh1,3,
- Joe Horecka1,3,
- Michael J Proctor1,3,
- Raeka S Aiyar1,3,
- Richard A O Bennett5,6,
- Angela Chu1,3,
- Yong Fuga Li1,
- Kevin Roy1,2,
- Ronald W Davis1,2,3,
- Lars M Steinmetz1,2,7,
- Richard W Hyman1,3,
- Sasha F Levy5,6 and
- Robert P St.Onge (bstonge{at}stanford.edu)*,1,3
- 1Stanford Genome Technology Center, Stanford University, Palo Alto, CA, USA
- 2Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- 3Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
- 4Department of Surgery, Harvard Medical School and Massachusetts General Hospital, Boston, MA, USA
- 5Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA
- 6Department of Biochemistry and Cellular Biology, Stony Brook University, Stony Brook, NY, USA
- 7European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany
- ↵*Corresponding author. Tel: +1 650 721 2976; E‐mail: bstonge{at}stanford.edu
↵† These authors contributed equally to this work
Recombinase Directed Indexing (REDI), a new method that facilitates parsing and purification of array‐synthesized oligonucleotide pools, is presented. REDI is applied to produce a high‐quality DNA probe library and an arrayed collection of CRISPRi strains for essential yeast genes.
Synopsis
Recombinase Directed Indexing (REDI), a new method that facilitates parsing and purification of array‐synthesized oligonucleotide pools, is presented. REDI is applied to produce a high‐quality DNA probe library and an arrayed collection of CRISPRi strains for essential yeast genes.
A protocol and requisite strain resources are developed for Recombinase Directed Indexing (REDI), a method enabling parsing and purifying complex DNA libraries in yeast.
The method is used to produce a highly uniform, sequence‐verified pool of ˜3,300 oligonucleotide probes for bacterial detection.
The method is also used to create an arrayed collection of ˜9,000 CRISPR interference strains for conditional repression of yeast genes essential for fermentative or respiratory growth.
Mol Syst Biol. (2017) 13: 913
- Received July 28, 2016.
- Revision received January 12, 2017.
- Accepted January 12, 2017.
- © 2017 The Authors. Published under the terms of the CC BY 4.0 license
This is an open access article under the terms of the Creative Commons Attribution 4.0 License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
- Recurrent patterns of DNA copy number alterations in tumors reflect metabolic selection pressures
- Nicholas A Graham1,2,3,†,
- Aspram Minasyan1,2,†,
- Anastasia Lomova1,2,
- Ashley Cass1,2,
- Nikolas G Balanis1,2,
- Michael Friedman1,2,
- Shawna Chan1,2,
- Sophie Zhao1,2,
- Adrian Delgado1,2,
- James Go1,2,
- Lillie Beck1,2,
- Christian Hurtz4,18,
- Carina Ng4,
- Rong Qiao2,
- Johanna ten Hoeve1,2,
- Nicolaos Palaskas1,2,
- Hong Wu2,5,6,
- Markus Müschen4,7,
- Asha S Multani8,
- Elisa Port9,
- Steven M Larson10,
- Nikolaus Schultz11,12,
- Daniel Braas1,2,13,
- Heather R Christofk2,5,13,‡,
- Ingo K Mellinghoff12,14,15,16,‡ and
- Thomas G Graeber (tgraeber{at}mednet.ucla.edu)*,1,2,5,13,17
- 1Crump Institute for Molecular Imaging, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- 2Department of Molecular & Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- 3Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA, USA
- 4Department of Laboratory Medicine, University of California, San Francisco, CA, USA
- 5Jonsson Comprehensive Cancer Center, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- 6School of Life Sciences & Peking‐Tsinghua Center for Life Sciences, Peking University, Beijing, China
- 7Department of Haematology, University of Cambridge, Cambridge, UK
- 8Department of Genetics, M. D. Anderson Cancer Center, The University of Texas, Houston, TX, USA
- 9Department of Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- 10Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- 11Marie‐Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- 12Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- 13UCLA Metabolomics Center, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- 14Department of Neurology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- 15Department of Pharmacology, Weill Cornell Medical College, New York, NY, USA
- 16Department of Neurology, Weill Cornell Medical College, New York, NY, USA
- 17California NanoSystems Institute, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- 18Division of Hematology and Oncology, Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- ↵*Corresponding author. Tel: +1 310 206 6122; Fax: +1 310 206 8975; E‐mail: tgraeber{at}mednet.ucla.edu
A pan‐cancer and cross‐species analysis of recurrent copy number alteration (CNA) patterns reveals that coordinated CNA of energy metabolism genes provides a selective advantage during tumorigenesis by promoting glycolytic metabolism.
Synopsis
A pan‐cancer and cross‐species analysis of recurrent copy number alteration (CNA) patterns reveals that coordinated CNA of energy metabolism genes provides a selective advantage during tumorigenesis by promoting glycolytic metabolism.
Amplifications of glycolysis‐associated genes are conserved and enriched in tumors with high genomic instability, with glycolysis thus functioning as an “oncopathway” in regard to shaping the cancer CNA genome.
Copy number signatures predict the glycolytic activity of primary breast cancers and breast cancer cell lines.
Genetic manipulation of glycolysis genes prior to the initiation of genomic instability alters the resulting genomic copy number landscape at the endogenous loci.
Complex but coordinated DNA copy number changes can contribute to cancer phenotypes in genomically unstable tumors.
Mol Syst Biol. (2017) 13: 914
- Received July 8, 2016.
- Revision received January 12, 2017.
- Accepted January 16, 2017.
- © 2017 The Authors. Published under the terms of the CC BY 4.0 license
This is an open access article under the terms of the Creative Commons Attribution 4.0 License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
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