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NumberNameLabTitleAbstract
1Sailendharan SudakaranMicrobiome HubMicrobiome Hub - Enabling a biological revolution at UWMicrobiome hub is a joint venture between the UW Biotechnology Center and the Wisconsin Institute for Discovery, making use of current prowess at UWBC in sequencing and bioinformatics and WID’s strength in data storage and management. Microbiome research is one of the rapidly growing areas of science, encompassing a broad range of systems from human body, animals, soil, plants, lakes, food, wastewater treatment systems, and test tubes. Continuous advancements in sequencing technologies have enabled researchers in utilizing various omics methodology to gain a better insight in to the complex relationships between organisms, genes and their environment and in turn this has led to the generation of vast amounts of data. Therefore, there is a need to ensure that adequate expertise and infrastructure is in place to meet the challenge of storing, analyzing and distributing the data generated as well as a robust and reliable framework for interpreting the data. Microbiome hub will serve as a campus wide resource to address these challenges at UW Madison. It will support researchers interested in tackling a broad spectrum of microbiome related studies from investigating fundamental questions, developing new techniques and methodologies to understanding the impact of microbiome across different fields. The hub will provide a broad spectrum of services such as technical expertise, consultation, coordinating collaborative research, data management and conducting outreach activities. Overall, by harnessing the significant research already taking place at UW, world class expertise and facilities we aim to develop the Microbiome Hub as a prominent center for microbiome research at UW.
3Sarah MillerTiny EarthTiny Earth: Engaging students in addressing antibiotic resistance, a global public health crisisAntibiotic resistance is a pressing public health crisis. Tiny Earth is a course-based undergraduate research experience (CURE) that equips diverse students with the tools to discover antibiotic-producing bacteria from soil and persist in science. Tiny Earth represents a worldwide collaboration of 700+ instructors in 30 countries who teach the Tiny Earth research course to an estimated 14,000+ students per year. The students engage in discovering new antibiotic-producing bacteria from soil, designing experiments, and sharing their results through the Tiny Earth database. Students also have the opportunity to send their isolates to the Tiny Earth Chemistry HubÐhoused at WIDÐfor further genomic and metabolomic processing. This poster will describe how Tiny Earth engages instructors and students in a community of practice to (1) incorporate content around antiracism, justice, equity, diversity, and inclusion (AJEDI), (2) pivot a hands-on, wet lab course to online and hybrid learning, and (3) expand chemistry, genomics, and bioprospecting content. The poster will also provide a general overview of the Tiny Earth network and recent updates.
5Erika Da-Inn LeeRoy LabDetecting higher-order structural changes in 3D genome organization with multi-task matrix factorizationThree-dimensional (3D) genome organization, which determines how the DNA is packaged inside the nucleus, has emerged as a key regulatory mechanism of cellular processes. High-throughput chromosomal conformation capture (Hi-C) technologies have enabled the study of 3D genome organization by experimentally measuring interactions among genomic regions in 3D space. Analysis of Hi-C data has revealed higher-order organizational units such as topologically associating domains (TADs). Changes or disruptions to such units have been associated with disease, development, and evolution. Therefore, a key problem is to systematically detect higher-order structural changes across Hi-C datasets from multiple conditions. Existing computational methods either do not model higher-order structural units or only compare pairs of Hi-C datasets. We address these limitations with Tree-Guided Integrated Factorization (TGIF), a new multi-task Non-negative Matrix Factorization (NMF) approach. TGIF models complex relationships among multiple Hi-C datasets as a tree such that closely related Hi-C datasets have similar lower-dimensional representation. TGIF provides a statistically significant set of differential TAD boundaries with higher precision than existing approaches. Application to a cardiomyocyte differentiation time-course dataset identified time-point specific TAD boundaries overlapping a retrotransposon element previously shown to be important for cell fate specification in humans and apes.
7Ross TredinnickPonto LabWID Virtual Environments Group Current ProjectsThe Wisconsin Institute for DiscoveryÕs Virtual Environments Group, located in the Lower Level of the Discovery Building, continues to explore emerging technologies such as Virtual Reality, Augmented Reality, 3D Scanning, and Immersive Visualization through federally funded and internally funded research projects and collaborations. Below is a summary of currently active projects and how we see each of them fitting in to WIDÕs current themes of Adaptability, Resilience, and Robustness.
9Hayley BoigenzahnYin LabMathematical models of prebiotic peptide formationHow large organic polymers like proteins could have formed on the early Earth during the origin of life is not well understood. Short polymers known as peptides, composed of the same amino acid Ôbuilding blocksÕ as proteins, can form in a variety of prebiotically plausible conditions. Peptides may develop intermolecular interactions that allow them to behave in ways that support the formation of other molecules or the emergence of protein-like functions. We reacted pairs of amino acids (glycine+alanine, glycine+serine, alanine+serine) using only prebiotically plausible materials and measured the concentrations of the peptides that formed. We proposed a simplified model describing peptide formation and attempted to fit parameters to the model based on our experimental results. Overall, the simplified model successfully captured the behavior seen in the experiments. This work helps us prepare to study more complex systems with more amino acid varieties, which will help us understand what peptides form favorably in prebiotic conditions and how complex intermolecular behaviors could emerge in a system of gradually growing peptides.
11Tymofii SokolskyiBaum LabSelection for compartmentalization in the origins of life
13Martel DenHartogTiny EarthTiny Earth Chemistry Hub: Advancing the Antibiotic Discovery PipelineThe Tiny Earth Chemistry Hub (TECH) brings student-based discovery to new heights. TECH was established in 2018 at the Wisconsin Institute for Discovery at UW-Madison as part of the ÒScience and DiscoveryÓ core of Tiny Earth. Multidisciplinary approaches allow TECH to go beyond what can be accomplished in a typical semester-long Tiny Earth course and moves the network closer to discovering the next antibiotic. TECHÕs drug discovery pipeline is an iterative process of bioactivity screening against safe-relatives of multi-drug resistant pathogens, genomic analysis, and metabolomic characterization.

Since its genesis, TECH has analyzed complete genomes from 125 bacterial isolates and selected 22 as high priority for chemical characterization based on biosynthetic gene cluster (BGC) novelty. Out of the prioritized isolates, 10 known antibiotic structures havebeen identified and TECH is rapidly increasing these numbers. TECH also hosts the Tiny Earth Summer Research Course (TESRC) for students from two-year and tribal colleges. To learn more about the Tiny Earth network, visit tinyearth.wisc.edu.
15Em CraftWID AdminDiscoverIT, UW-Madison, and WID Provide Your IT ServicesDiscoverIT, UW-Madison, and WID work together to provide an expansive set of IT resources. Whether you need general help desk support, focused research software, or server support, this chart will point you at who can help you the most.
17Chris ThomasHandelsman LabTiny Earth Chemistry Hub: Genomes to CompoundsThe mission of Tiny Earth (TE) is to discover novel antimicrobial compounds from strains procured through the TE network of courses held at universities across the United States and abroad. Strains isolated by students taking these courses are sent to the Tiny Earth Chemistry Hub (TECH) in order to study their bioactive compounds. With over 3,000 individual isolates currently at TECH, it is impossible to isolate the bioactive compounds from each and every strain. Instead, we apply whole genome sequencing and analysis to pick out the best candidates for study, using tools such as AntiSMASH and BiG-SCAPE. Extracts of the top candidates are made and rapidly fractionated using HPLC for screening. The collaborative exchange of information between these data help facilitate rapid compound identification, and hopefully will lead to novel structures in the near future.
19Spencer Halberg-SpencerRoy LabNetwork-based Data Integration and Visualization Provides a Global Understanding of Regulatory Mechanisms in Aspergillus fumigatusInvasive Aspergillosis (IA), a fungal infection of the lungs caused by the pathogen Aspergillus fumigatus, is the most common invasive fungal infection in immunosuppressed individuals. Recent studies have recognized IA as a secondary infection that complicates COVID-19 increasing mortality. Despite the high clinical relevance of A. fumigatus, the molecular mechanisms that underlie IA and co-morbid conditions remain poorly characterized. We present a network-based analysis pipeline that combines gene regulatory network (GRN) inference and network-based interpretation of regulatory modules to characterize A. fumigatus transcriptional response. Our GRN inference approach incorporates latent transcription factor activity (TFA) estimation to elucidate transcription factors that are post-transcriptionally regulated for which gene expression may not be informative. We provide an interactive network visualization framework that incorporates statistical and topological tools used to investigate context specific roles of regulators within the network. Our framework can be used to interpret input gene lists to predict associated biological pathways, prioritize regulators based on kernel diffusion and identify novel subnetwork components using a Steiner tree approximation. Application of our framework to A. fumigatus predicted known and novel regulators of multiple secondary metabolite regulatory pathways. Our approach and resource are broadly applicable for network-based interpretation of clinically significant fungal species.
21Jessica HanDenu LabDecoding the Epigenetic Language of Gut MicrobiomeSubtitle: Elucidate the mechanism of microbial TMAO modulating host transcription through epigenetic regulation
Trimethylamine-N-Oxide (TMAO), a metabolite generated by gut microbial consumption of choline, has been associated with the exacerbation of many inflammatory and metabolic diseases. More recently, TMAO has been linked to various neuronal diseases from mild cognitive impairment, Alzheimer’s Disease, to autism spectrum, suggesting that microbial TMAO may be involved in regulating Gut-Brain-Axis in the host. While the underlying mechanisms remain elusive, our preliminary data suggest that the microbially derived TMAO alters protein homeostasis in the host cells by placing reversible epigenetic modifications and modifying the chromatin states. To understand how TMAO modifies chromatin states in the host on a molecular level, we use germ-free mice and cell culture models treated with pathophysiological concentrations of TMAO and leverage multi-omics approaches to examine key epigenetic changes.
23Khagani EynullazadaSridharan LabIdentifying gene regulatory networks driving cell fateOne of the fundamental mysteries in biology is the determination of cell fate, which is the process of transitioning from one cell type (e.g., an embryonic stem cell (ESC)) io another (e.g., a skin or a muscle cell type). Cell state is specified by expression profile, which is maintained by gene regulatory networks (GRNs) that define interactions between regulatory proteins (e.g., transcription factors) and target genes. However, the identity of GRNs (how many connections, the strength of connections and the wiring between connections) that are responsible for cell fate determination are poorly studied. In order to elucidate how GRNs dictate cell fate, we used mouse ESC cells as a model system. We used expression-based network inference tool, MERLIN, on publicly available RNA-seq data from mESCs to infer a global GRN. We prioritized the regulators that are most likely to be essential for establishing ESC state. We tested the top 53 regulators inferred by MERLIN, using a high-throughput approach, Perturb-seq, which combines pooled CRISPR-Cas9 mediated deletion of regulators with an output of single cell RNA-seq. Perturb-seq allowed us to test our predictions for a large number of regulators in an efficient and cost-effective manner. Nearly all of the gRNAs (52) got efficiently transduced and were represented on average in about 150 cells. Sequencing recovered about 5708 cells, 107 being control (transduced with empty vectors). We integrated the Perturb-seq data with previously published mESC scRNA-seq dataset and found good overlap in the datasets. For several of the regulators, predicted targets exhibited a lower proportion of expressed cells providing support for our computational predictions. Taken together our integrated experimental and computational approach is well-positioned to examine GRNs driving a specific cell fate that we will extend to understand the impact of GRN transitions in cell type specification.
25Ahmet AlacaogluWright GroupOn the Complexity of a Practical Primal-Dual Coordinate MethodWe prove complexity bounds for the \emph{primal-dual algorithm with random extrapolation and coordinate descent (PURE-CD)}, which has been shown to obtain good practical performance for solving convex-concave min-max problems with bilinear coupling. Such problems arise in many machine learning contexts, including linear empirical risk minimization, matrix games, and image processing. Our results either match or improve the best-known complexities for dense and sparse (strongly)-convex-(strongly)-concave problems with bilinear coupling.
27Marianne BjornerSol’s-Lemus GroupComparison of genome-scale hybridization detection methodsPhylogenetics studies the evolutionary history between organisms, and with the advances in sequencing technology, more genetic information has become available to infer gene flow events. These gene flow events can lead to hybrid speciation and transform the well known ÒTree of LifeÓ into a network. Analogous to phylogenetic tree inference, there are also methods to compute these network structures. However, these are often computationally expensive. As a more efficient alternative, hybrid methods compare triplets or quartets of species to identify any hybrids between them. Still, these hybrid methods have their own strengths and pitfalls, and their accuracy is impacted by factors such as depth of gene flow, branch lengths, and amount of genetic mixing. In this poster I will compare the performance of summary methods designed to detect hybridization events: HyDe, Patterson's D-Statistic, TICR, and MSCquartets. The ability of these methods to accurately detect various hybridization events will be compared using data from simulated networks with variability in introgression depths, numbers of available genes, and network sizes.
29Dan CappabiancaSaha LabTowards and Optimized, GMP-Compatible, Virus-Free CRISPR CAR T Cell Manufacturing ProcessChimeric antigen receptor (CAR) T-cells are engineered immune cells that can be taken from a patient and redirected to fight cancer. Recently, we've developed a process to create anti-GD2 CAR T cells using CRISPR Cas9 that endows cells with a superior stem cell memory and less exhausted phenotype that improves their ability to fight GD2 positive tumors. To improve this process, we've looked at modifications to media conditions, timing, and small molecules to improve these already favorable phenotypes and boost the number of CAR positive cells. Two small molecules (PGA and M3814) have shown the largest improvement to editing and initial experiments suggest IL-2 helps preserve the memory-like phenotype.
31Madeline SmerchanskySaha LabPredicting Ligands That Direct T cell MaturationThere is great interest in using induced pluripotent stem cell (iPSC) derived T cells for off-the-shelf immunotherapy1. Methods for differentiating T cells from pluripotent stem cells rely primarily on undefined mouse cocultures that leave cells mostly arrested at an immature stage in vitro2. The unique cues that are important for T cell development in vitro are largely unexplored beyond the reliance on Notch signaling. Notch ligands are typically presented by surrounding stromal cells as delta-like ligands 1 and 4 (DLL1 and DLL4), forming the basis for in vitro differentiation methods such as the OP9-DLL4 coculture2. Quantitative analysis of T cell development in the thymus could shed light on what signals are presented to thymocytes during development. Comparing thymic signals to those in in vitro differentiation systems can help to inform the engineering of the in vitro differentiation niche to generate more mature iPSC derived T cells.
Gene expression of the postnatal human thymus3,4 and the top differentially expressed genes between na•ve and in vitro derived CD8 T cells5,6 were used to model cell-cell communication7. Ligands that impact the differences in gene expression between in vitro derived and na•ve T cells were predicted. The regulatory potential of prioritized ligands over pseudotime8 was plotted to achieve a temporal prediction of cell-cell signaling the thymus (Fig 1). Based on this analysis, we predict that knocking out ligands such as BMP4 and BMP7 in later stages of directed differentiation, while adding IL6, IL7, and CXCL12 in the later stages may improve maturation of T cells in vitro. Interestingly, Notch ligands are not in the top hits, perhaps due to the wide presence of these ligands in vitro. The next steps are to perform a screen to knock out or overexpress these ligands in the OP9-DLL4 platform to improve T cell maturation.
References: [1] Iriguchi, S. Cancer Sci. (2019) 110, 16-22. [2] Mohtashami, M. J. Immunol. (2010) 185, 867. [3] Park, J.-E. Science (2020) 367. [4] Stuart T, Cell (2019), 177:1888-1902. [5] Montel-Hagen, A. Cell Stem Cell (2019) 24, 376-389. [6] Robinson, M.D, Bioinformatics (2010) 26, 139-140. [7] Browaeys, R., Nat Methods, (2019). [8] Trapnell C, Nat Biotechnol (2014), 32:381-386
33Lauren SarkoSaha LabGenome-Edited, Chimeric Antigen Receptor (CAR) T Cells to Target Neurodegeneration and SenescenceCellular senescence is a biological process that reduces proliferation and helps to prevent propagation of damaged cells. Controlling senescence within the body can be critical, as senescent cells can also play an important role in the pathophysiology of various disorders, including AlzheimerÕs disease (AD). Abnormally high numbers of senescent cells have been found in diseased tissues, and removal of these high senescent cell populations in animal studies by lysing senescent cells (i.e., ÒsenolyticÓ approaches) can prevent neurodegeneration from occurring. Targeting the brain with therapeutic agents, however, has been a key challenge in treating these disorders as the blood-brain barrier (BBB) has evolved to restrict the transport of large molecules. Our research focuses on the use of T cells as a ÒTrojan horseÓ to deliver therapeutic molecules as they naturally surveil the brain. We exploit new abilities to program the specificity of T cell activation using synthetic receptors, called chimeric antigen receptors (CARs). Recent work indicates that these engineered T cells can be utilized to target senolytic agents for senescence-associated diseases, like liver fibrosis, by targeting the cell surface protein urokinase plasminogen activator receptor (uPAR). We have developed a new platform to genetically edit T cells to target and eliminate senescent cells while co-expressing additional factors. We leverage CRISPR-Cas9 genome editing and CAR T cell (CART) technology that redirects T cell specificity and effector functions to attack the desired target. We have successfully generated genome-edited uPAR-CART cells to recognize and specifically kill uPAR-positive senescent fibroblasts in vitro. We plan to adapt this strategy to a murine model of AD by using mouse T cells to evaluate efficacy of this senolytic strategy in vivo. We anticipate that genome-edited senolytic CART cells could provide biological insights into targeting and enhancing the brainÕs immune defense against neurodegenerative disease.
35Lauren HannaTiny EarthDo two Erwinia species produce different antibiotics?

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