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PPARγ is a nuclear receptor transcription factor that regulates adipogenic and insulin sensitizing gene programs via two activation function (AF) regulatory domains: a ligand-dependent AF-2 coregulator interaction surface within the C-terminal ligand-binding domain (LBD) and an N-terminal disordered AF-1 domain (NTD or A/B region). Here, we show the AF-1 contains an evolutionary conserved Trp-Pro motif that populates two long-lived AF-1 conformations via proline cis/trans isomerization. The Trp-Pro motif participates in transient intradomain AF-1 contacts and interdomain contacts with two surfaces of the LBD (β-sheet and AF-2). Mutagenesis indicates the Pro residue negatively regulates PPARγ transcriptional output, suggesting a potential regulatory mechanism for AF-1 isomerization. Our findings provide a structural rationale to explain previous in vitro and cellular studies that reported interdomain functional communication between the PPARγ AF-1 and LBD. Our study also illuminates a structural biology platform to study how disordered domains in nuclear receptors influence their structure and function.

Nuclear receptor (NR) transcription factors use a conserved activation function-2 (AF-2) helix 12 mechanism for agonist-induced coactivator interaction and NR transcriptional activation. In contrast, ligand-induced corepressor-dependent NR repression appears to occur through structurally diverse mechanisms. We report two crystal structures of peroxisome proliferator-activated receptor gamma (PPARγ) in an inverse agonist/corepressor-bound transcriptionally repressive conformation. Helix 12 is displaced from the solvent-exposed active conformation and occupies the orthosteric ligand-binding pocket enabled by a conformational change that doubles the pocket volume. Paramagnetic relaxation enhancement (PRE) NMR and chemical crosslinking mass spectrometry confirm the repressive helix 12 conformation. PRE NMR also defines the mechanism of action of the corepressor-selective inverse agonist T0070907, and reveals that apo-helix 12 exchanges between transcriptionally active and repressive conformations—supporting a fundamental hypothesis in the NR field that helix 12 exchanges between transcriptionally active and repressive conformations., Structural studies of nuclear receptor transcription factors revealed that nearly all nuclear receptors share a conserved helix 12 dependent transcriptional activation mechanism. Here the authors present two crystal structures of peroxisome proliferator-activated receptor gamma (PPARγ) in an inverse agonist/corepressor-bound transcriptionally repressive conformation, where helix 12 is located within the orthosteric ligand-binding pocket instead, and discuss mechanistic implications.

Small chemical modifications can have significant effects on ligand efficacy and receptor activity, but the underlying structural mechanisms can be difficult to predict from static crystal structures alone. Here we show how a simple phenyl-to-pyridyl substitution between two common covalent orthosteric ligands targeting peroxisome proliferator-activated receptor (PPAR) gamma converts a transcriptionally neutral antagonist (GW9662) into a repressive inverse agonist (T0070907) relative to basal cellular activity. X-ray crystallography, molecular dynamics simulations, and mutagenesis coupled to activity assays reveal a water-mediated hydrogen bond network linking the T0070907 pyridyl group to Arg288 that is essential for corepressor-selective inverse agonism. NMR spectroscopy reveals that PPARγ exchanges between two long-lived conformations when bound to T0070907 but not GW9662, including a conformation that prepopulates a corepressor-bound state, priming PPARγ for high affinity corepressor binding. Our findings demonstrate that ligand engagement of Arg288 may provide routes for developing corepressor-selective repressive PPARγ ligands., Peroxisome proliferator-activated receptor gamma (PPARγ) is a target for insulin sensitizing drugs. Here the authors combine NMR, X-ray crystallography and MD simulations and report a structural mechanism for eliciting PPARγ inverse agonism, where coactivator binding is inhibited and corepressor binding promoted, which causes PPARγ repression.

Crystal structures of peroxisome proliferator-activated receptor gamma (PPARγ) have revealed overlapping binding modes for synthetic and natural/endogenous ligands, indicating competition for the orthosteric pocket. Here we show that cobinding of a synthetic ligand to the orthosteric pocket can push natural and endogenous PPARγ ligands (fatty acids) out of the orthosteric pocket towards an alternate ligand-binding site near the functionally important omega (Ω)-loop. X-ray crystallography, NMR spectroscopy, all-atom molecular dynamics simulations, and mutagenesis coupled to quantitative biochemical functional and cellular assays reveal that synthetic ligand and fatty acid cobinding can form a ‘ligand link’ to the Ω-loop and synergistically affect the structure and function of PPARγ. These findings contribute to a growing body of evidence indicating ligand binding to nuclear receptors can be more complex than the classical one-for-one orthosteric exchange of a natural or endogenous ligand with a synthetic ligand.

Small chemical modifications can have significant effects on ligand efficacy and receptor activity, but the underlying structural mechanisms can be difficult to predict from static crystal structures alone. Here we show how a simple phenyl-to-pyridyl substitution between two common covalent orthosteric ligands targeting peroxisome proliferator-activated receptor gamma (PPARγ) converts a transcriptionally neutral antagonist (GW9662) into an inverse agonist (T0070907). X-ray crystallography, molecular dynamics simulations, and mutagenesis coupled to activity assays reveal a water-mediated hydrogen bond network linking the T0070907 pyridyl group to Arg288 that is essential for inverse agonism. NMR spectroscopy reveals that PPARγ exchanges between two long-lived conformations when bound to T0070907 but not GW9662, including a conformation that prepopulates a corepressor-bound state, priming PPARγ for high affinity corepressor binding. Our findings demonstrate that ligand engagement of Arg288 may provide new routes for developing PPARγ inverse agonist.

Let G be a group of order m. Define s ( G ) to be the smallest value of t such that out of any t elements in G, there are m with product 1. The Erdős–Ginzburg–Ziv theorem gives the upper bound s ( G ) ⩽ 2 m − 1 , and a lower bound is given by s ( G ) ⩾ D ( G ) + m − 1 , where D ( G ) is Davenport's constant. A conjecture by Zhuang and Gao [J.J. Zhuang, W.D. Gao, Erdős–Ginzburg–Ziv theorem for dihedral groups of large prime index, European J. Combin. 26 (2005) 1053–1059] asserts that s ( G ) = D ( G ) + m − 1 , and Gao [W.D. Gao, A combinatorial problem on finite abelian groups, J. Number Theory 58 (1996) 100–103] has proven this for all abelian G. In this paper we verify the conjecture for a few classes of non-abelian groups: dihedral and dicyclic groups, and all non-abelian groups of order pq for p and q prime.

It is widely held that major breakthroughs in cancer treatment will result from properly amassing, analyzing and utilizing existing and emerging “big data.” To date there is no single vehicle that integrates the data available, and of those being developed, none that put patient needs and outcomes as primary foci. CancerBase was released as part of the Cancer Moonshot launch in June 2016. It is a global, real-time data collection tool designed by and for patients. Patients were recruited through social media to share their information, including but not limited to, demographic details, personal cancer data, and history of treatment. Combining open text boxes and drop down preselected response boxes allow for the capture of information in a way that patients understand. In addition to cancer data, Facebook comments demonstrated immediate engagement with the tool and captured the organic community of patients who eagerly shared their stories with others. Personal interviews and a communal Twitter chat have recorded the interests and priorities from patients who were not necessarily familiar with CancerBase. In addition to medical data, general information and questions CancerBase participants wanted to add to the system have been collected. Incorporation of patient-driven deliverables is unique in the space of big data and cancer in that other tools, both existing and still in development, are geared toward the needs of researchers, payors, policy-makers and clinicians. CancerBase is unique in that it adapts to growth. Patient feedback drives improvements via ongoing communication between developers, PIs and participants. Wherever possible, participant recommendations and questions are rapidly incorporated into the website design. The remaining patient recommendations are cataloged and will be incorporated in the forthcoming releases of CancerBase. The initial launch of CancerBase demonstrated patient willingness to share personal, anonymized data and to recruit others. Patients were eager to engage and manipulate the limited data collected in the first release. Moving forward, patient priorities include: How do my treatment decisions compare with others and what can I learn from those who have come before me; What is the likelihood that I will have a recurrence; and, in the case of metastatic patients, how long might I have between progressions, to which organ(s) is progression most likely, and what are my likely survival outcomes. CancerBase is a database tool that resonates with the patient community and is driven by patient needs and interests. As the tool becomes increasingly robust it will grow to support the decision-making needs of clinicians and guide the investigations of researchers. The relaunch of CancerBase in Spring 2017 will address emerging patient concerns, integrate collected data, and utilize existing forecasting databases to add value to patients. Citation Format: Lori Marx-Rubiner, AnneMarie Ciccarella, Vincent An, Jared Bass, Will Berman, Jackson Berry, Chloe Chan, Christopher Han, Louis Harboe, Joshua Lurie, Sara Ma, Parker Malachowsky, Naylee Nagda, Simon Schneider, E. Bircan Çopur, Jorge J. Nieva, Jan Liphardt, Peter Kuhn, Jeremy M. Mason. A patient driven cancer database to collect information, analyze data, and predict outcomes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2603. doi:10.1158/1538-7445.AM2017-2603