C&SP28: Dynamic modulation of interferon binding affinity as a mechanism to regulate interferon receptor signaling

A. Collaborating Investigators: Gideon Schreiber,¹ Ivet Bahar,² James R Faeder²

B. Institutions: ¹Weizmann Institute and ²University of Pittsburgh

C. Funding Status of Project: ISF-I-Core- Center for Integrated Structural Cell Biology 2013-2018; ISF grant: Protein binding and catalysis in crowded environments: from in vitro to in vivo (2014-2018)

D. Biomedical Research Problem:

Type I interferons (IFNs) are multifunctional cytokines that mediate/induce diverse cellular responses, including both innate and adaptive immune responses, stimulation of antiviral responses, and cancer surveillance, upon forming a ternary complex with two surface receptors, IFNAR1 and IFNAR2 (Fig VIII.8a).^95-97 The activities of IFN-a subtypes correlate with their affinities to bind to IFNAR1 and IFNAR2.⁹⁸ While the Schreiber lab made seminal contributions to understanding the molecular basis of IFNARs,^95,96,98-105 the mechanism of regulation of differential IFN activities through interactions with IFNAR1 and 2,¹⁰² remains unclear. Our integrated computational (TR&D1) and experimental preliminary studies described below point to the significance of the intrinsic dynamics in modulating binding affinity.

E. Methods and Procedures: We adopted a closely integrated computational/experimental strategy that yielded promising results, which we are currently further pursuing: (i) we retrieved and analyzed existing structures, including a model constructed for human IFNAR1 ectodomain (EC) in the unbound state using data from two PDB structures,^105,106 (ii) we determined, using the GNM core function, the hinge sites that control the relative movements of IFNAR1 EC four subdomains SD1-SD4, (iii) we identified 5 residue pairs near those regions, which exhibit large fluctuations in their inter-residue distances, <(DR_ij)²>, during global motions; we hypothesized that those at the interface between subdomains SD3 and SD4 would be critical to enabling subdomain rearrangements that modulate, if not optimize, IFN binding (Fig VIII.8b), and obstructing their adaptability by locking the distance between those pairs, and in particular the pair with the largest moment arm (e.g. G162-F267) with respective to the hinge site, could impair the function, (iv) in vitro binding experiments and gene induction activity assays by the Schreiber lab confirmed that hindering the conformational flexibility of IFNAR1 at those regions (by cysteine-trapping) decreased binding affinity, and suppressed activity, and the relative strengths of these effects matched our predicted rank-ordered list, (v) ANM-predicted global (energetically softest) mode of motion also showed a highly cooperative flexing movement of the entire IFNAR1 EC (Fig VIII.8c), with end-to-end distance changes in accord with FRET experiments¹⁰⁷, (vi) ANM examination of the murine IFNAR1 structure resolved¹⁰⁶ in IFNb-bound state, demonstrated that this structure has the intrinsic ability to readily reconfigure toward the human counterpart. This result is important: it shows that the seemingly different human and murine structures are simply alternative easily exchangeable conformers, stabilized by the different sequences and/or bound substrates, and that IFNAR1 EC favors such structural shifts as required for function, consistent with ProDy predictions. This is another illustration of the adaptability of proteins to different bound states or to sequence variations/mutations via their softest modes of motion.¹⁰⁸ Further cross-linking, fluorescence quenching and gene induction experiments will be conducted in the Schreiber lab, in close coordination with TR&D1 computational studies at the Bahar lab.

C&SP27: Circuit reconstruction of association cortex

A. Collaborating Investigators: Wei-Chung Allen Lee,¹ Art Wetzel,² Greg Hood²

B. Institutions: ¹Harvard Medical School (HMS) and ²Pittsburgh Supercomputing Center

C. Funding Status: NIDCD 5R01-DC013622-03" Network Anatomy of Olfactory Processing (Lee) 8/9/2013 - 8/31/2016; DP2 OD022472-01 'Network anatomy of behavioral choice' (PI: Lee) Pending

D. Biomedical Research Problem:

This C&SP with Wei Chung Allen Lee at Harvard Medical School builds upon the successful DBP5 with Clay Reid of Harvard and the Allen Brain Institute during the 2012-2017 funding period. Lee is a former member of Reid's lab, and has continued this line of connectomics research as an independent researcher at HMS using both the original TEMCA microscope,⁸⁷ and an improved successor, TEMCA-GT. Our previous collaborative work^87,88with Reid and Lee has focused on studying the pattern of anatomical connections among functionally characterized neurons within mouse visual cortex. Lee plans to study next a new cortical area in the mouse. This region of the cortex is hypothesized to be involved in key cognitive functions including decision-making, movement planning, attention, and reward.^89-93 Recent work has demonstrated that groups of neurons in this brain region are activated sequentially.⁹⁴ Neurons fire selectively during discrete epochs of a navigational task, and the resulting sequences of neuronal activity accurately predict the behavioral choice of the animal.⁹⁴ Two of the unique aspects of this project are that we will, for the first time, be able to compare an association area with a sensory area and identify conserved and distinguishing features of connectivity motifs underlying their different network dynamics.

E. Methods and Procedures: We will trace a portion of the neural connectome within a region of tissue about 1mm³ in size. To do this will require ~1Petabyte of raw image data comprising ~25,000 sections. This volume will encompass 4 cortical columns, which are hypothesized to be modular, canonical, local circuits. Prior datasets have been up to Terabyte in size, so this will necessitate an order-of-magnitude improvement in our ability to acquire EM images and analyze them. Several technical hurdles must be overcome, and we focus here on the issues affecting alignment of these raw images into a form which can then be traced, either manually or in a semi-automated way. Specifically, we must reduce the amount of manual intervention so that the effort is much less per gigapixel than on prior datasets. Most of the manual intervention that has been necessary in aligning prior datasets has been associated with artifacts introduced during sectioning and handling (e.g., tissue folds and tears), so an important step in reducing labor has been the development of an improved automated pipeline for sample handling. This new workflow is centered around a novel substrate that allows automated collection of 1000s of serial thin sections from the microtome, and imaging them in the vacuum chamber of the TEMCA-GT transmission electron microscope. TEMCA-GT's improved stage allows for more accurate positioning of the samples, and this information can be fed into our AlignTK pipeline to constrain the model’s initial conditions and reduce the probability of registration errors (Fig VIII.7). This new approach will allow 10 times more data (5-10 TB) to be imaged per day, resulting in an increased workload for the alignment software. AlignTK is being converted to use GPUs for its most computationally-intensive tasks (TR&D2 Subaim 2.1). With current Harvard Medical School and MMBioS facilities, we estimate we will be able to register 64 Mpixels/sec of image data, with the ability to process a ~1PB dataset in roughly 6 months. The main concern will be to increase the robustness of the software to deal well with atypical image content (e.g. voids within capillaries), and to make it easy to incrementally process the data as it is generated. To increase robustness and throughput, we may also perform registration using MMBioS' SWiFT-IR software, which has heretofore been used primarily on SEM images, but should also perform well with TEM data.

C&SP24: Spatio-temporal cell biology

A. Collaborating Investigators: Alan Horwitz,¹ Gregory Johnson,¹ and Robert Murphy²

B. Institutions: ¹Allen Institute for Cell Science and ²Carnegie Mellon University

C. Funding Status of Project: Data generation fully funded by $100 million gift from the Allen Foundation

D. Biomedical Research Problem:

The goal of the Allen Institute for Cell Science (AICS) is to develop predictive models of cell behavior. The initial goal is to assemble a map of the shapes and positions of all major molecular machines and signaling complexes in human induced Pluripotent Stem (hiPS) cells and capture how they change during their differentiation into cardiomyocytes. hiPS cells will be genome-edited to express multiple fluorescent-tagged markers, and 3D images and short movies will be collected for 1000s of cells at various time points after initiation of differentiation, using a large fluorescence microscopy pipeline. We will combine images with various combinations of markers into a comprehensive 3D model of the undifferentiated stem cell and its changes during differentiation. To achieve this goal, several computational tools must be developed:

1) Models of causal spatio-temporal relationships of subcellular structures. Understanding how cell organization depends on the location and structure of other cellular components is an unknown and critical to understanding cell behavior and organization. These representations will allow others to model and understand the mechanisms underlying the ordered changes in cellular organization during differentiation. These models will be constructed from single images and short time series, and identify cellular spatio-temporal relationships at the mesoscale.

2) Integrated multi-protein subcellular organization models from images where only a small subset of markers are visible. Generating images of many labeled proteins in single cells is not feasible due to the limited number of non perturbing XFP colors. Therefore, it is necessary to build models of subcellular organization by combining information from images containing different, but overlapping, subsets of labeled structures.

3) Population modeling. Modeling the origin of the variation among cells in a population and how it may be based on position in cell cycle, partial early differentiation, and unknown effects of neighboring cells is necessary to understand how large populations of cells develop and mature. To parse out these relationships, we will need samples of large numbers of "similar" cells in the context of their differentiation, as well as how neighboring cells are related to cell organization.

E. Methods and Procedures:

This project takes advantage of the new CellOrganizer capabilities that will be developed in TR&D4, especially Aim 1 on point process models and causal inference. It will also draw on the existing capabilities for learning point process models^84,85 and for constructing models for a full differentiation process from short movies of parts of that process that were previously developed in conjunction with completed C&SP11 and similar to those in our recent study.⁸⁶ The collaboration will involve both large-scale testing of CellOrganizer in the context of the AICS images and joint development and refinement of methods.