Jennifer Fung - US grants

DESCRIPTION (provided by applicant): The long-term goal of this research is to determine how chromosome synapsis functions to promote proper chromosome segregation during meiosis. Chromosome missegregation during meiosis is directly tied to human infertility and is also the leading known genetic cause for mental retardation and developmental disabilities. Elucidating the basic mechanisms underlying proper chromosome segregation during meiosis will enable greater understanding of the intricate pathways that contribute to normal gametogenesis and fertility. During prophase I, homologous chromosomes pair and then synapse. Synapsis occurs via the assembly of a proteinaceous structure known as the synaptonemal complex that forms between homologous chromosomes. Successful assembly of the synaptonemal complex is a key prerequisite to proper chromosome segregation during meiosis. However, many basic questions about the kinetics of assembly of these structures remain unanswered. Our objective for this proposal is to determine how the process of synaptonemal complex assembly contributes towards its dual function of 1) maintaining a tight association between homologs and 2) promoting crossing over and its regulation. Our first aim uses fast, live, 3-D fluorescence imaging and quantitative image analysis to determine the kinetics of synaptonemal complex assembly in budding yeast to answer several important questions. What is the rate of synapsis polymerization? Is it bidirectional or unidirectional? How far can synapsis extend from one initiation site? In the past, the answers of these questions have eluded investigation, due to the fact that in most organisms, multiple moving chromosomes are synapsing from a large number of sites, over a long time frame, in a highly compacted nucleus. To reduce the complexity of the problem, we propose to introduce a zip3 mutation that 1) limits the number of synapsing chromosomes to as low as one and 2) changes nucleation from multiple sites to one, or at most two sites, along the chromosome. Synapsis will be followed by imaging the Zip1 protein that has been previously coupled to GFP and used successfully to image the motion of fully synapsed chromosomes but not synapsis formation. Our second aim will be to characterize the process of nucleation. To accomplish this task, we will couple components of the initiation complex to a ligand binding domain of the estrogen receptor that keeps the fused protein inactive until introduction of estrogen. We then can investigate how the introduction and timing of various known components of the initiation complex influences the progression of synapsis. For our last aim, we will determine whether changes in synapsis nucleation and polymerization rates affect crossing over and its regulation. Using a genome-wide approach developed in my lab for looking at crossover control in a single cell that has undergone meiosis, we will assess how particular changes in synaptonemal complex assembly and nucleation can affect crossover distribution and thus chromosome segregation.) PUBLIC HEALTH RELEVANCE: Chromosome missegregation during meiosis is directly tied to human infertility and is also the leading known genetic cause for mental retardation and developmental disabilities. This work investigates the mechanisms in place to ensure faithful chromosome segregation by elucidating how the assembly of the synaptonemal complex contributes to this process. Such research may lead to new ideas for treatment of infertility or to development of diagnostic tests to detect potential problems of chromosome segregation early on before expensive medical and surgical treatments are attempted.

DESCRIPTION (provided by applicant): Fluorescence microscopy has the unique capacity to probe both static and live processes with great specificity to link the dynamics and/or localization of molecular and cellular components with their function. Recently, a new microscope platform, OMX, was designed to acquire sub-second four-dimensional (4D) multi-color in vivo data with a dual functionality to attain sub-diffraction structured illumination (SI) imaging of fixed samples. Over the last two years, this first generation OMX microscope at UCSF has been converted from a dedicated development microscope to a production microscope open to projects from within UCSF and from the outside academic community. As a result of more general use, users identified several major desired improvements which, if they could be made to the OMX microscope, would vastly expand their ability to attain their research goals. The first update is to increase the time over which biological processes can be observed in their unperturbed natural state. Phototoxicity is a major limitation in live microscopy, inducin morphological changes, delays in cell progression and cell death. Introduction of pulsed lasers to reduce the excitation light to microsecond exposures rather than the current millisecond limits will permit far lower photon doses to be achieved, allowing cells to be imaged for much longer periods of time. The second update is to use recent improvements in camera and stage technology to increase the speed and stability of 3D structural illumination data acquisition. This update will have the added benefit of permitting in vivo 3D SI. Currently the quality and throughput of 3D SI microscopy is severely compromised by drift between consecutive sections in a 3D image stack. In this application, we seek funds to revolutionize the technological base of the OMX microscope for far faster and more stable data acquisition for both live and SI imaging. The proposed enhancements include 1) upgrading our lasers to pulsed lasers to achieve microsecond exposure times; 2) incorporating a sCMOS camera with faster acquisition rates, which in combination with the pulsed lasers will permit a 3D SI data stack to be acquired in 3 seconds rather than the current 13 minutes; 3) replacing our current xyz stage that introduces thermally induced drifts with a more modern stage to improve the stability, speed and depth of SI data acquisitions; and 4) upgrading the computer that will control the new stage motors to one with PCI/PCI(e) slots. These technological advances will benefit a great many biomedical research projects funded by NIH and will be of vital importance in elucidating the basic biological processes underlying many human diseases. PUBLIC HEALTH RELEVANCE: Studies of the dynamic processes occurring in living organisms in a non-perturbed setting is of vital importance in understanding the basic mechanisms of the biological processes underlying many human diseases. Rapid three-dimensional in vivo and super-resolution structured illumination imaging have become powerful new techniques in monitoring the changes that occur in the cell. This application will 1) greatly extend the ability of this technology to follow a biological process through its entire course without perturbation of its natural state and 2) enable super-resolution microscopy on live samples heretofore could only be examined in non-living specimens.

Cells are complex machines filled with molecules that can perform simple logic functions like the circuits inside a computer. Many cells can perform complex behaviors such as engulfing other cells, movement towards a source of food, and remembering past events. Even though most cellular components are believed to be discovered, it is still unclear how the molecules in a cell work together to perform relatively complex actions. This project seeks to solve the problem by applying concepts from theoretical computer science to understand how the cell switches from one activity to another. This will open up new possibilities for re-engineering cells by reprogramming their internal controls. This research program will create novel educational and outreach opportunities based on exposing students from a variety of scientific backgrounds, as well as members of the public with interests in electronics and computer hobbies, to the idea that cells can be viewed as computing machines; therefore awareness of quantitative cell biology among the next generation of engineering students will increase.

While general understanding of the molecular biology of cells is constantly increasing, it has proven difficult to integrate this molecular scale information into a global view of how cells make decisions and perform complex behaviors like migration, phagocytosis, and division. The goal of this project is to connect the huge gap in complexity and detail from the molecular scale to the level of cell behavior by using concepts from computer science. In computer science, the highly complex details of electronic circuitry can often be understood, analyzed, and designed by using abstract models in which a complex system is represented by a finite set of states, allowing behavior to be represented by transitions between states. Such models are called finite state automata and they are the most fundamental representation of a computing device. In this project cells are described as finite state automata, by using organelle size measurements to identify and define distinct states. It is hypothesized that an organelle-level state description will allow for the reduction of the dimensionality of the state space from millions of dimensions corresponding to individual molecules in the cell down to a much smaller number of dimensions based on organelle morphological measurements that are readily observable in living cells. Large numbers of cells will be imaged at high resolution, numerical descriptors of each organelle will be described, and a state space for cellular organization will be defined using statistically rigorous methods to define states and state transitions. The investigators will explore how chemical and mechanical inputs to the cell drive transitions within this state space, thus providing a way to view the cell as a type of finite-state automaton. Such a representation will also provide a framework for synthetic biology applications in which the regulatory pathways that determine state transitions could be re-wired to produce different behaviors, essentially turning a single cell into a programmable microdevice. The conceptual framework of the cell as a decision-making computational device will be harnessed to present outreach exhibits at Maker Faires, which are an ongoing series of events that bring together electronics, computer, and crafts hobbyists.

This project is co-funded by the program in Cellular Dynamics and Function in the division of Molecular and Cellular Biosciences in the Directorate of Biological Sciences and the programs in Statistics and Mathematical Biology in the division of Mathematical Sciences in the Directorate of Mathematics and Physical Sciences.

Project Summary Pairing of homologous chromosomes is a key biological phenomenon that underlies Mendelian inheritance but also occurs outside of meiosis in diverse contexts including DNA repair, transvection, and X- chromosome inactivation. But while many of the molecules have been identified that mediate homolog recognition, the fact that homolog pairing requires the chromosomes to physically align with each other poses a challenge from a polymer dynamics perspective. How can individual chromosomes locate and pair with their homologs in the densely packed interior of a nucleus? Cytoskeletal motors attach to telomeres via nuclear envelope spanning proteins, thus dragging chromosomes around in the nucleus by their ends, but this motion appears to be randomly directed, and does not serve to pull homologs directly together. We hypothesize that these random active forces serve to increase chromosome mobility, causing chromosomes to undergo anomalous superdiffusion, a type of motion predicted to facilitate search and capture. We have developed a Brownian dynamics simulation of meiotic chromosome pairing that predicts super-diffusion and zippering, a processive association driven by successive pairing of neighboring loci. Our model predicts that active forces can have a large effect on pairing rates even in comparison with non-random chromosome positioning effects such as nuclear envelope attachment or meiotic bouquet formation. We propose to test the predictions of this model using live cell imaging and quantitative image analysis, combined with yeast genetics to alter key elements of the process including force generation, nuclear envelope attachment, pairing site density, and nonrandom chromosome organization. Our results should impact not only the understanding of meiotic homolog pairing as a physical process, but also the physical biology of chromosome motion in general.

PROJECT SUMMARY/ABSTRACT The Multidimensional Quantitative Imaging Core will provide quantitative image, genomic and statistical analysis for microscopy and sequencing projects generated by the Center's research investigators. Formerly, the Core operated as a Computational Biology Core and focused only on providing expertise for genomic and statistical analysis for the P50 investigators' projects. We have expanded the scope of the Core to include image analysis since many of the P50 investigators' projects will examine cellular and subcellular morphological changes that accompany changes in differentiation, maturation and cell lineage specification. One of the goals of the core is to apply new advances in deep learning to image recognition of cellular structures. Since applying deep learning techniques requires significant computational resources as well as expertise in image acquisition and analysis, the Core facility is better suited and equipped to handle these types of analyses than individual investigators or teams. The Core is partnering with UCSF's Center of Cellular Construction to develop general deep learning algorithms for image analysis of cell biology. The Core will also integrate ?omics? data with imaging data by aiding in the development of de novo mapping analyses to integrate the disparate datasets. Finally, the Core will participate in training and educating P50 students, interns and fellows in the best practices of bioinformatics and image analysis.

ABSTRACT - BIOASSAY FACILITY CORE The EaRTH Center Bioassay Facility Core will provide the enabling technology and expertise needed to advance cross-disciplinary environmental studies across the UCSF EaRTH Center research community. The EaRTH Center Bioassay Facility Core will offer several facilities to cover the majority of the EaRTH Center researchers needs: 1) an extensive biomonitoring facility providing chemical detection and measurement in many different media including air, water, soil, hazardous waste streams, consumer products and biological or human tissues; 2) state of the art SWATH mass spectrometry as well as traditional proteomics for assessing a chemical's effect on biological processes; 3) access to an extensive chemical library combined with ability to perform high- throughput in vivo chemical screening in a model organism and tissue culture; 4) novel xenografting of human tissue into mice to more realistically model chemical exposures. The Core therefore will enable EaRTH Center researchers to push the boundaries of environmental research by providing high-throughput, efficient and cost- effective chemical evaluation technologies.

Project Summary Homologous chromosome pairing is a central process underlying Mendelian inheritance, but while many genetic studies have revealed genes involved in homology recognition and recombination, the physical process by which the chromosomes come together inside the densely packed nucleus remains poorly understood. Telomeres of meiotic chromosomes are anchored on the nuclear envelope and attached to the cytoskeleton, which exerts randomly directed pulling forces. A key question is how randomly directed forces can facilitate the homology search process. Using a series of computational models, we have shown that randomly directed telomere forces can in theory promote search in several ways: driving superdiffusive motion of chromatin, overcoming entanglement, unpairing incorrectly paired regions to improve fidelity, and opposing entropic de-mixing of chromosomes. We propose to test these distinct predicted functions using live cell imaging and quantitative image analysis, combined with yeast genetics to alter the forces applied to the chromosomes. Our results should impact not only the understanding of meiotic homolog pairing as a physical process, but also the physical biology of chromosome motion in general as well as the broad concept of active random motion in biology.

Learning is assumed to require a brain, but even very simple animals are capable of learning. Even single cells have been shown to display primitive types of learning, but how such learning takes place, without a nervous system, is currently not understood. In this project, a giant single cell organism, Stentor, will be used to explore how a single cell can learn. Stentor cells are preyed upon in their natural habitat but can escape from attack by contracting into a ball when touched, but this contraction burns up energy. For a Stentor cell sitting on a pond plant, it will often get tapped by pond plants or small algae that are not threatening. In deciding whether or not to contract when touched, the Stentor cell relies on past experience. The cells learn to ignore light, non-threatening touches, and only contract when hit with a larger aggressive force. In an analogous way, humans living by a railroad track get used to the train and they don?t jump when they hear it go by. This kind of learning is seen in all animals, but it is usually displayed in those with a nervous system. Can single cells learn? If so, how? Single Stentor cells grown in the lab will be videotaped as they contract in response to a mechanical force, and the response will be measured when different genes are shut down. This will reveal how the cell learns at a molecular level. At the same time, a simple mathematical model of behavior will be used to: a) predict genes that are involved in sensing when touched; b) identify genes that are involved in driving the contraction; and c) identify how the cell decides whether or not to contract. This project will show, for the first time, how a single cell is able to learn. Broader Impact activities will include the interdisciplinary training of students along with public outreach activities.

Cells integrate multiple inputs and select between different behavioral responses, in some cases seeming to learn from experience. The computational processes by which cells process information to generate appropriate behaviors remain poorly understood. Learning is usually considered to be a feature of multicellular animals with some form of neuronal network, but the seeming ability of single cells to learn suggests it is a more general feature of life. One of the most tractable systems for studying learning by a single cell is Stentor coeruleus, a giant cell that shows quantifiable behaviors in response to mechanical stimulation. Repeated stimulation leads to habituation, in which the cell learns to ignore a stimulus of a particular magnitude. Habituation in Stentor has been well documented, but the mechanistic basis is unknown. In this project, an expert on the biology of Stentor coeruleus will team up with an expert on computational biology, to develop a quantitative understanding of how learning takes place in a single cell. The project will combine quantitative measurements of cell responses with a simple two-state mathematical model for cellular learning and molecular perturbations of gene function, to ask fundamental questions about how learning takes place, identify key molecular pathways that underlie learning and memory in a cell, and probe the computational complexity of cellular decision-making. Investigation of gene function will exploit proteomic and phosphoproteomic information to identify the sensory and effector molecules along with the signaling connections that link the stimulus to the response. Once these elements are known, it will then be possible to determine which aspects of the system (sensory, effector, or signaling) are modulated during the learning process.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.