Polly Fordyce, Ph.D. - US grants

DESCRIPTION (provided by applicant): In a watershed achievement, the Human Genome Project (HGP) recently sequenced the entire human genome, providing a wealth of information about potential genes and regulatory sequences. Despite this success, exactly how genomic sequence specifies the behavior and development of complex organisms remains largely unknown. Gene expression within cells is tightly regulated, with many genes expressed only under certain environmental conditions or at stereotyped time points during development. The next great challenge lies in developing a mechanistic understanding of how regulatory sequences dictate gene expression, with the ultimate goal of being able to quantitatively predict expression levels from sequence. Solving this challenge would have far-reaching impacts in biology, elucidating how changes in regulatory sequence can lead to transcriptional dysfunction and disease and improving rational design of transgenes for gene therapy. Regulation of gene expression is accomplished primarily via binding of transcription factors at specific genomic loci. Once bound, transcription factors can either recruit or block the general transcription machinery, thereby activating or repressing transcription. Most leading models of transcriptional regulation are built upon thermodynamic principles, and require information about transcription factor concentrations in vivo and their affinities for different DNA sequences. Despite this central role for binding affinities, experiments to date have been forced to infer affinities from genome-wide occupancy and expression measurements due to a lack of biophysical data. Using a recently developed microfluidic system that permits the high-throughput measurement of interaction affinities, this proposal seeks to systematically investigate the thermodynamics of transcriptional regulation at multiple scales, from individual interactions between transcription factors and target sequences to the nucleation of assemblies of DNA binding proteins at regulatory loci. Experiments will focus on, in turn: (1) how particular contacts between protein residues and DNA bases determine interaction affinities; (2) how cell-specific signals modify these interactions to dictate tissue-specific expression patterns; (3) how evolutionary changes in both regulatory DNA sequences and transcription factors rewire transcriptional networks during evolution to drive phenotypic change; and (4) how cooperativity and competition between transcription factors affect binding patterns to influence gene expression. Data from these experiments will provide crucial information required to construct ground-up, quantitative models of transcriptional regulation and increase our ability to predict gene expression from regulatory sequence. The funding provided by this K99 award would provide crucial resources for the PI, Polly Fordyce, to receive 2 years of additional formal training in the biological sciences and ensure a successful transition to an independent career. PUBLIC HEALTH RELEVANCE: Although we now have comprehensive information about the sequence of the human genome, we know very little about how this sequence encodes instructions for producing individual cells, tissues, and organs. This work seeks to decipher the code by which genomic sequence specifies the location, timing, and levels of gene expression. These experiments would benefit public health by increasing our understanding of how genomic sequence variation between people can lead to human disease.

ABSTRACT AND PROJECT SUMMARY Understanding the fundamental mechanisms that drive cellular processes within the complexity of biological systems remains a formidable task. Addressing this challenge requires the development of new technologies that not only increase the number of analytes that can be measured in a single experiment, but also link measurements across multiple parameters to reveal insights into mechanism. Here, I build upon a spectral encoding platform we have recently developed to create two such technologies that will dramatically boost the information content of high-throughput measurements by increasing their dimensionality. First, I will combine tools from microfluidics, electrical engineering, and chemistry to enable code-directed synthesis of programmable peptide libraries directly on spectrally encoded beads. By using the code embedded within each bead to direct the chemical synthesis of a specific peptide on its surface, this scheme simultaneously increases potential library size while dramatically lowering synthesis costs. In addition, peptides of interest in downstream assays can be identified via imaging alone, increasing ease of readout. As a first biological application, I will create libraries to probe proteolytic signatures that correlate with disease, with the goal of identifying peptide sequences that can be used in the future as diagnostic and imaging probes. In future applications, these programmable, proteome-scale libraries would facilitate a wide range of additional applications that include enzyme-substrate profiling (e.g. kinases, phosphatases, proteases), immune repertoire profiling, and protein-protein interaction screening. Second, I will probe both the causes and effects of cellular heterogeneity by developing a new assay that enables the measurement of both transcriptome and phenotype for many single cells in parallel. Here, spectrally encoded beads with unique oligonucleotide barcodes conjugated to their surface provide the critical link, allowing sequencing reads measuring transcript levels to be traced back not only to a single cell, but to a particular single cell that has already been characterized phenotypically. As a proof of principle, I will use the assay to measure levels of 40 secreted proteins and hundreds of transcripts for > 1000 cells, with the goal of understanding whether functional diversity in secreted protein profiles is transcriptionally encoded. In future work, this same scheme could be used to probe the molecular drivers of heterogeneity for a wide number of additional phenotypes, including intracellular and secreted protein levels, motility, cytotoxicity, morphology, and proliferation. Such assays would have tremendous biological and translational applications, from understanding how genetically identical immune cells clonally expand to yield diverse functional phenotypes to investigating the mechanisms that drive stem cell differentiation.  

Project Summary Enzymes are the primary catalysts of biological transformations and have enormous value in medicine and industry. While decades of research have established that active site residues are essential for catalysis, we do not yet understand in detail the contributions of residues beyond the active site to efficiency and specificity. Of course a folded enzyme is required for catalysis, but beyond folding there is a complex functional interplay of residues throughout an enzyme: allosteric ligand binding and remote mutations alter and enhance catalysis, active site residues coevolve with remote residues and regions, and distal mutations arise in screens and selections for enzymes with enhanced functional properties. Given this inherent complexity, our central premise is that we need tools that extend the power of traditional mechanistic enzymology to systematically investigate residues throughout the entire protein and their interconnectivity. Our central technological innovation delivers the needed tools: High-throughput Microfluidics for Enzyme Kinetics (HT-MEK) and Stability (HT-MES) expresses, purifies, and quantitatively assays 1200 enzyme variants in parallel, rapidly and inexpensively, yielding accurate kinetic and thermodynamic constants for many substrates and ligands over many conditions. With these measurements we will map functional regions and linkages throughout proteins, allowing enzymology to address previously inaccessible challenges in mechanism, evolution, and biology. We first apply these tools to the Alkaline Phosphatase (AP) superfamily member E. meningoseptica PafA, leveraging extensive prior structural, mechanistic, and phylogenetic insights to guide assay development and test previously inaccessible models to deepen our understanding of enzyme catalysis. We will systematically and quantitatively determine kinetic parameters for cognate and promiscuous PafA substrates and affinities for ground and transition-state PafA inhibitors, and we will do so for multiple mutations of every PafA residue; these measurements will provide a comprehensive map of enzyme regions that contribute to specific components of catalytic function. Next, we will use multi-mutant cycles to determine the energetic and functional linkage of these regions to active site residues and specific catalytic features, as well as the connections within and between these regions. Extension to multiple AP superfamily members across evolutionary distances will identify the range and limits of generality of functional maps and identify the alterations that rewire functional connectivity. Expanding this approach to other targets, some of which are explored herein, will address fundamental and practical problems of broad interest. This carefully-reasoned, stepwise approach will usher in a new era of enzymology, in which the acquisition of multidimensional functional maps of enzymes addresses new questions in mechanism, evolution, and biology, and in which enzymology impacts biomedicine and engineering in new ways.

Project Summary The 3D spatial context of a cell determines which genes and RNA isoforms it expresses, enabling specialized cell functions fundamental to multicellular life. In typical single-cell RNA-seq (scRNA-seq), the first step of cell dissociation erases the spatial context of the cell. This flaw creates an urgent need for a technology that has the same throughput of scRNA-seq but also encodes the cells? spatial context. Although a new wave of spatial transcriptomic technologies based on sequencing has emerged recently, all suffer from severe limitations: low efficiency (~1-2% of the Drop-Seq efficiency), providing 2D resolution only, failure to discriminate cell boundaries and requiring specialized or expensive equipment. These limitations are intrinsic and result from their shared reliance on cDNA synthesis in situ by from a solid support. Imaging-based technologies have higher spatial resolution but require more equipment, time for protocol execution, have limited gene measurement throughput, and cannot profile RNA isoforms or other sequence variants. To overcome these limitations in state-of-the-art spatial transcriptomic methods, we propose to develop Orthocode, an innovative paradigm for statistically-driven spatial transcriptomics, grounded in proof-of-principle molecular experiments, and cutting-edge statistical theory. Orthocode achieves > 50x or higher sensitivity compared to current approaches by encoding and recovering spatial information from simple, inexpensive and efficient molecular biology protocols. The experimental Orthocode protocol has two steps: 1) a pool of two types of ?location-encoding oligos? (a) barcoded emitter oligos produce copies of themselves that diffuse locally and (b) ?receptors? record the barcodes of nearby emitters are coupled to cells; 2) cells coupled to location- encoding oligos that have together record the spatial position of the cell, are isolated and input into scRNA-seq workflows, eg. Drop-seq and sequenced. Orthocode then employs a rigorous statistical analysis of the barcode profiles of location encoding oligos to triangulate the location of each sequenced cell. This rigorously reasoned experimental design and prototype development builds Orthocode from the simplest test systems to prototypes that will allow unprecedented spatial transcriptomic resolution in tissues to address a critical unmet need in biomedicine. The Orthocode paradigm can be generalized beyond RNA profiling to spatial measurements of proteins, DNA and epigenetic modifications and is a potential breakthrough innovation in deep-sequencing based spatial ?omics.