Neurons are highly polarized cells with high energy demands. The mitochondria meet those demands by generating 90% of energy in neurons, thus, it is unsurprising that mitochondrial dysfunction has been linked to age-related neurodegenerative disorders. However, the mitochondrial molecular cascade leading to age-dependent energy failure remains to be elucidated. Prominent aging theories hypothesize that accumulation of mitochondrial DNA (mtDNA) mutations plays a role in age-dependent decline in neuronal metabolism. Heteroplasmy of pathogenic mtDNA variants has been shown to cause alterations in ETC protein function, TCA metabolite levels, and mitochondrial dynamics. Still, the mechanism through which mtDNA mutations instigate changes in neuronal metabolism remains to be explored. Due to the importance of mtDNA in producing key mitochondrial proteins required for neuronal energy homeostasis, we hypothesize that accumulated mtDNA mutations alter neuronal metabolism locally, which then leads to age-dependent neuronal dysfunction. By systematically inducing mtDNA mutations through a Cre-loxP system in Tfamfl/fl mouse neurons we propose to accomplish three aims that will (1) characterize mtDNA distribution and motility throughout wild-type and TFAM-mutant neurons, (2) investigate the neuronal metabolic decline pathways in TFAM-mutant neurons, and (3) elucidate the impact of accumulating mtDNA mutations in neuronal metabolism and identify compensatory metabolic pathways. This work will reveal subcellular mechanisms that contribute to the mitochondrial molecular cascade that leads to neurodegeneration and provide insights into age-dependent energy failure.

Nitrogenase is the only known enzyme that can catalyze the reduction of dinitrogen (N₂) to ammonia (NH₃), a vital precursor to nucleic and amino acids as well as countless commodity chemicals. Despite decades of research, the mechanism by which nitrogenase fixes nitrogen into ammonia within ambient conditions is still not entirely known. Understanding how nitrogenase can sustainably fix nitrogen under such benign conditions would revolutionize how we can produce some of the basic building blocks of life. Through single-particle cryogenic electron microscopy (cryoEM) we can start to answer these outstanding questions with atomic-resolution snapshots of nitrogenase, from reaction intermediates during catalysis to the formation of large scale dynamic nitrogenase complexes with interacting partners. However, traditional cryoEM grid preparation workflows are not tailored towards oxygen-sensitive proteins, like nitrogenase. Current practices call for anaerobic grid vitrification devices to be placed in anoxic chambers, which presents numerous hurdles, including temperature and humidity control, optimization of freezing conditions, costs for purchase and operation as well as accessibility. I will present a streamlined approach that allows for the vitrification of oxygen-sensitive proteins using an automated aerobic blot-free grid vitrification device. This establishes a workflow that not only leads to high-resolution structure determination of nitrogenase but has the potential to unlock a subset class of proteins that continue to evade structural study.

In this talk, we will demonstrate a novel method for safely modeling or controlling high-dimensional, nonlinear systems arising from interconnected or multi-scale problems. Namely, we will present a reachability-based solution using the Hopf formula, "antagonistic error" and state augmented (SA) linearizations. SA systems are well-known for their ability to outperform standard linear methods in capturing nonlinear dynamics by lifting the system to a high-dimensional space, approximating the Koopman operator. We show through a series of inequalities that it is possible to define a differential game in the SA space whose projected solutions are conservative. It follows that predictions and the optimal controller of this special SA linear game with error are valid in the true system despite any bounded lifting error or disturbance. We demonstrate this in the simple slow-manifold system for clarity and in the Van der Pol system to observe the use of different lifting functions.

The video of this presentation is available here.

Superior labrum anterior to posterior (SLAP) lesions are a common injury in athletes who participate in overhead throwing sports such as baseball, softball, water polo, and football. Existing research on SLAP tears has primarily focused on treatment methods and repair techniques, necessitating further exploration of mechanisms of injury and preventive measures. The objective of my research is to use multiple modalities at various scales of biological organization to assess the impact of repetitive motions associated with high athlete training volumes on SLAP lesion formation. Imaging has traditionally not been performed until an athlete is already experiencing symptoms and surgery is possibly necessary, but existing bioengineering models have posited that tears experience higher strains with similar loads as tear sizes increase, meaning that imaging, early diagnosis, and treatment is key in preventing injury progression. By investigating the biomechanical factors at the tissue and whole-body level that contribute to labral tears, the study findings will have important implications for injury prevention, optimization of training regimens, and the development of effective rehabilitation protocols specifically tailored for athletes engaged in overhead-throwing sports. Ultimately, this research has the potential to significantly improve the long-term health and performance outcomes of overhead-throwing athletes by providing evidence-based insights into the underlying mechanisms of SLAP tears and facilitating the implementation of targeted preventive measures.

The video of this presentation is available here.

Mitochondria assume the form of a three-dimensional temporal network in the cell. There is a spectrum of mitochondrial network morphologies and dynamics that ranges from static, organized single mitochondria to branched, motile networks of connected mitochondria. This spectrum is sensitive to cell state, cell type, and organ system. We hypothesize that there are shared, but also unique, biophysical parameters that govern the mitochondrial temporal network dynamics in cells and tissues. We investigate two model systems, intestinal organoids and branching lung organoids, to extract and understand these biophysical parameters. We used adaptive optics-lattice light sheet microscopy (AO-LLSM) to capture 4D (x, y, z, time) mitochondria data in tissues with low photo-toxicity and high spatio-temporal resolution. Automated image processing and computational temporal network tracking was performed using the MitoTNT software package to quantify fission/fusion events, morphological parameters, motility, and graph temporal network properties. We found network feature quantifications unique to each organoid type and have demonstrated the ability to separate the data along principle components, suggesting the ability to predict organoid (and cell) type based on mitochondrial dynamics.

During Drosophila cellularization, 6000 nuclei migrate to the plasma membrane to form an epithelial sheet. To simultaneously invaginate the new cell membranes, the embryonic membrane must expand 25x from a reservoir of microvilli. Exocytosed, Golgi-derived vesicles resupply the reservoir during the first 30 minutes of membrane invagination. Concomitantly with the completion of exocytosis, velocity jumps ~6x, and elastic and viscous properties drop. The governing mechanisms are experimentally occluded by the redundancy of motor proteins, microtubules and F-actin. A mathematical model can disentangle the complexity of this process and shed light on possible mechanisms. In this work, we propose a model of furrow invagination by representing the membrane-cytoskeleton composite with a viscoelastic model, constrained by conservation of area, and dynamic cytoskeletal properties. We observe that our continuum model is capable of governing the slow-to-fast transition and the final furrow length. We expect the model will lead to new insights into the biophysical invagination mechanisms. While our current focus is on Drosophila cellularization, this model may be extended to understand other multinucleated systems, such as T-tubule formation in muscle cells.

Dr. Rockne will present several examples of translating mathematical models into clinical situations, from inception to final implementation. He will focus on models of cancer immunotherapy and combination therapies, including CAR T-cells and targeted radionuclide radiation. The talk will show how clinical challenges are identified to motivate the modeling, what data is needed to parameterize and validate the models, and how unexpected turns that might initially seem like failures end up being insightful advances. The talk is targeted at students interested in translational cancer research at the interface of clinical data and computational analysis/modeling.

The video of the presentation is available here.

Signal transduction carried out by chemical messengers and various kinases plays an essential role in regulating all biological processes. Visualizing the spatial compartmentation of these signaling events using genetically encodable biosensors can provide invaluable insights into how individual signaling agents are able to execute multiple cell functions with high specificity. However, current biosensors are limited in both multiplexing capability and tissue imaging compatibility. In this study, we engineered an array of far-red chemigenetic biosensors to probe protein kinase A (PKA) signaling by using a HaloTag7 self-labeling protein tag as the reporting unit. We find that these chemigenetic PKA sensors achieve up to 1250% dynamic range, drastically outperforming any other far-red PKA sensors currently available. We then show that this far-red chemigenetic design is generalizable to visualize the activity of other signaling molecules with high sensitivity. We also demonstrate that these far-red chemigenetic sensors are able to provide spatiotemporal information of PKA signaling via 4D imaging of PKA activity in HEK293T spheroids and murine pancreatic beta cell islets. Thus, the engineered chemigenetic sensors enable highly sensitive imaging of cell signaling activity in a previously unoccupied far-red optical window. 

Human genomic DNA is packaged into chromatin, in which the basic repeating unit is the nucleosome, an assembly of ~147 DNA base pairs wound around a histone octamer core. Access to genetic information is tightly regulated by a group of proteins known as chromatin remodelers. We focus on the ATP-dependent Brg/Brahma Associated Factors (BAF) chromatin remodeling complex, composed of ~10-15 subunits and regulates diverse transcriptional cascades. Mutations in genes encoding subunits of the BAF complex are encountered in over 20% of human cancers, highlighting their relevance in human health. However, how the BAF complex recognizes distinct chromatin states, including histone post-translational modifications (PTMs), and the effects of cancer-associated mutations on BAF function remain poorly understood. Our goal is to functionally probe the mechanism by which the BAF complex dynamically moves on nucleosomal DNA and generate high-resolution structures of the human BAF complex bound to multiple distinct nucleosomal substrates. Understanding these interactions is crucial for comprehending BAF's role in regulating gene expression and cellular functions, potentially leading to novel therapeutic strategies for chromatin remodeling-related diseases.

In contrast to the dietary patterns of most primates, human populations obtain a significant, though variable, portion of their calorie intake from the consumption of other vertebrates. Within this category, mammalian red meat is considered by some as the highest quality source of nutrition available. However, there is controversy regarding the evolutionary significance of the relative roles of hunting versus scavenging for mammalian meat, as well as its means of processing and preparation. Paleoanthropological data, such as butchery tools in the fossil record, indicate that access to red meat and other animal tissues likely predated the emergence of the genus Homo, and that its consumption likely increased during successful expansion of the lineage. Given the high nutritive value of red meat for present-day humans, especially during specific life history stages (e.g., pregnancy, lactation, weaning, and childhood development), it is reasonable to suggest that access to red meat contributed to overall survival and, ultimately, the evolution and emergence of our species. Today, however, increased red meat intake is linked to an epidemic of chronic diseases, such as cancer, atherosclerosis, obesity, and type 2 diabetes, and contributes to substantial environmental degradation and climate change. Chronic red meat consumption is a non-trivial dilemma, given that it's increasingly becoming a prominent dietary component in many low- and middle-income countries. Here we discuss relevant information spanning from ~3 million years ago to the present, highlighting how throughout human evolution red meat may have transformed from a “blessing” to a “curse.”

The video of this presentation is available here.

To perform their function, biological systems need to operate across multiple scales. Current techniques in structural and cellular biology lack either the resolution or the context to observe the structure of individual biomolecules in their natural environment, and are often hindered by artifacts. Our goal is to build tools that can reveal molecular structures in their native cellular environment using the power of cryo-electron tomography to image biomolecules at molecular resolution in situ. I will show how we used these techniques to discover how jumbo phage build a proteinaceous compartment that separates its replicating genome from the rest of the cell to avoid host defenses that acts akin to a eukaryotic nucleus. 

The video of this presentation is available here.

Matthias G. von Herrath, MD discussed updates and treatment options for Type 1 diabetes, observations regarding publishing scientific data, robustness and reproducibility in biomedical sciences, and more. Slides used during the presentation are available here. The video of this presentation is available here. 

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a heritable condition that predisposes affected individuals to ventricular arrhythmia and sudden cardiac death (SCD). Through genomic screening, it may be possible to identify at-risk individuals and family members prior to the onset of any symptoms, thereby reducing the morbidity and mortality associated with ARVC. However, several unknowns remain that limit the application of this precision medicine approach, including genotype prevalence in a general population, disease penetrance and its modifiers, and the phenotypic characteristics associated with incidentally identified, ARVC-associated genetic variants. In this talk, Interfaces alumnus Dr. Eric Carruth discusses the research being done through Geisinger’s MyCode Community Health Initiative to answer these pressing questions and pioneer a genome-first approach to ARVC identification and diagnosis. The video of this presentation is available here.

AUTHORS: Dani Gonzalez, Zachary Weatherford, Nicholas Bettencourt, Tyler Wilps, Hannah
Wellington, Emmanuel Elijah, Allison Pastor, Amy Leu, Samuel Galloway, Matthew Kritz,
Andrew McCulloch, Samuel Ward
University of California, San Diego, La Jolla, CA

PURPOSE: To compare the incidence of injury/illness between genders and across collegiate
sports.
METHODS: Injury reports from the 2012-2013 academic year to the 2021-2022 academic year
were compiled for 23 collegiate sports over a 10-year period (12 men’s and 11 women’s teams).
Injuries were included if: 1) they were a new event, and 2) required intervention from athletic
trainers or medical staff. Repeated interventions for the same injury were excluded. Data were
subset by gender, sport, and general anatomic location. Incidence was calculated by
normalizing absolute injury numbers by the number of athletes per team, and these values were
also compared to reported training and competition hours for each sport. 
RESULTS: A total of 5296 participants were included in this study, with a total of 6,160 injuries
reported. Of these reported injuries, 3082 (50.03%) were sustained by female athletes and 3078
(49.97%) by male athletes. When injury numbers were normalized by the number of athletes on
the team, no statistical difference was seen between injury rates on men’s and women’s teams
of like sports. Across all sports, injury rates were between 0.38-1.61 injuries per athlete per
year, with both Women’s (0.61 ± 0.38) and Men’s Cross Country (0.38 ± 0.23) having the lowest
injury rates and Women’s Basketball (1.58 ± 0.33) and Men’s Volleyball (1.61 ± 0.29) having the
highest. Across all sports, increases in injury incidence occurred consistently at the start of the
academic year (August-October) and at the start of competition which varies by team. Similar
breakdowns of injuries by body area were seen in the men’s and women’s sports with Lower
Extremity injuries being the most prevalent in both groups (W: 44.0 ± 5.7%, M: 39.4 ± 4.3%),
followed by Trunk (W: 22.5 ± 2.8%, M: 23.2 ± 3.2%), Upper Extremity (W: 19.5 ± 3.7%, M: 23.8
± 2.4%), Head (W: 8.4 ± 1.9%, M: 8.7 ± 1.6%), Spine (W: 4.5 ± 1.2%, M: 4.1 ± 1.3%), and Other
(W: 1.0 ± 0.8%, M: 0.8 ± 0.5%).
CONCLUSION: Across all sports, injury incidence spikes occurred at the beginning of the
training season and competition season. Injury rates and locations were remarkably similar
between the men’s and women’s teams of corresponding sports. Future interventions focusing
on modulating training volumes and intensities at the beginning of the academic year could
have a dramatic effect on overall injury rates.

The video of this presentation is available here. 

Inflammatory Bowel Diseases (IBD) are chronic, life-altering conditions with the average age of diagnosis at the early age of 21. Through collaborative research in the Ghosh lab (UCSD School of Medicine) and Yang lab (UCSD Chemistry & Biochemistry), it has been shown that targeting two isoforms of Peroxisome Proliferator-Activated Receptor (PPARα and PPARγ) can alleviate IBD in cell and mouse-model based assays. Using Boolean implication networks to analyze gene clusters, the continuum states of IBD-related diseases were more easily differentiated between disease and healthy states of patients. The genes PPARA and PPARG from these gene clusters were identified as suitable druggable targets. My approach is to design and synthesize novel PPARα/γ dual agonists which are balanced in activity, based on the only known balanced dual agonist in the literature currently, PAR5359. This will involve computational modeling experiments to design the molecules, synthetic organic chemistry, as well as in vitro binding assays to test the binding of novel molecules, in order to validate in silico docking experiments.

The video of this presentation is available here. 

Pelvic floor disorders negatively impact ~30% of women in the United States. Pelvic organ prolapse, a type of pelvic floor disorder characterized by the descent of abdominal organs from their normal positions, is caused by damage to pelvic floor muscles (PFMs). PFM injury contributing to prolapse is often due to childbirth, with computational studies estimating a greater than 300% strain of a PFM during vaginal fetal delivery. Previous studies on PFM damage have been done in non-pregnant rats, where long-term PFM dysfunction was shown to be triggered by sustained inflammation, long term muscle atrophy, and fibrosis. However, these studies fail to incorporate lactation as a variable, with studies suggesting that lactating individuals have a decreased regenerative capacity due to low estrogen levels. The interplay between immune response, lactation, and regeneration following maternal birth injury is not completely understood, requiring further research to design better regenerative therapies. I hypothesize that simulated birth injury in a biologically relevant pregnant rat model will depict a pro-inflammatory postpartum environment that delays regeneration and decreases muscle function in lactating rats compared to non-lactating rats. This talk will characterize long-term PFM pathophysiology in a simulated birth injury pregnant rat model. Future directions discussed will detail a multi-scale approach utilizing biomechanical testing and the potential for a minimally invasive extracellular matrix hydrogel. 

Cardiovascular diseases affect millions of people each year with myocardial infarction as one of the
leading causes for the progression of heart failure and death. Currently, there is a lack of therapeutics that
prevent left ventricular remodeling while also promoting tissue repair and regeneration. Here in the
Christman lab, we have developed a porcine left ventricle-derived decellularized myocardial matrix (MM)
hydrogel that has been shown to mitigate left ventricular remodeling and has completed a Phase 1 clinical trial.
Previous characterization consisted of scanning electron microscopy to observe the material on the
microscale, rheological measurements to ensure viability via catheter injection and proteomic analysis
showing that the MM hydrogel is predominantly composed of type I collagen – a material known to form
a hydrogel when purified – alongside other fibrillar collagens and extracellular matrix proteins such as
laminin. However, the fundamental mechanisms of hydrogel formation for the heterogeneous MM
hydrogel have yet to be uncovered. I employ a combination of microscopy and bond analysis techniques,
such as cryogenic transmission electron microscopy, two-photon microscopy and Fourier transform
infrared spectroscopy to elucidate the structure of MM at various levels. Using this multi-scale approach,
I can then probe the fundamental material relationships between composition, structure, and function to
better understand its intrinsic hydrogel-forming mechanisms.

Model uncertainty due to differing assumptions or unknown system mechanisms is often overlooked when applying mathematical models in biology and medicine. In diabetes diagnostics, mathematical models have long been used to make inferences about a patient’s metabolic health using available clinical data such as blood glucose measurements over time. These approaches often rely on a phenomenological model to approximate the physiological system, ignoring possible uncertainty in the model structure. However, there are usually several possible phenomenological models, each of which uses different formulations to represent the same biological processes. Given a family of phenomenological models, one typically chooses a single model based on a priori assumptions. In this work, we instead focus on leveraging the whole family of models to develop robust predictors in the face of uncertainty in the models describing the biological process. This talk outlines several approaches to average the predictions from all available models, including Bayesian model averaging and probability distribution fusion. As a test case, we chose the prediction of beta cell insulin regulation and associated diagnostic metrics from blood glucose measurements. Our results show that working with a family of models instead of a single model improves the certainty of modeling-based predictions, reduces biases associated with selecting one model, and explicitly accounts for model uncertainty. 

The video of this presentation is available here. 

Life in the deep ocean and continental subsurface is estimated to make up more than half of all biomass on the planet, and must withstand the crushing pressures up to 1000 times that at the surface. Lipid membranes encapsulating cells and organelles are sensitive to their physical environments, especially pressure. To understand the mechanisms by which membrane structure adapts to high pressure environments, we have employed lipidomic and structural analyses of extracted membranes from pressure-adapted deep-sea invertebrates (ctenophores), allowing us to identify key chemical and physical signatures of depth adaptation. To validate these findings, I employed yeast and bacterial model organisms, engineered with tuned lipid biosynthesis pathways, and grown under varying pressures in the lab. This multi-scale, comparative approach has revealed that lipid properties such as headgroup stoichiometry, ether vs. ester chain linkages, chain unsaturation, and chain length length provide biophysical advantages to buffer against physiologically-incompatible membrane states and preserve membrane plasticity at high pressure. Understanding lipid adaptations to pressure could allow for the identification of fundamental lipid biophysics and mechanisms by which cells sense the global membrane environment to facilitate homeostatic membrane responses.

The video of this presentation is available here. 

It is well-established that long-term consumption of processed and unprocessed red meat (beef, pork, lamb, and veal) increases the risk of several chronic diseases currently burdening the U.S. healthcare system. One of the strongest links between red meat consumption and disease is to cancer, particularly of the colon and rectum (i.e. colorectal cancer, CRC). While myriad factors contribute to CRC development and progression in vivo, one theory involves incorporation of a non-human sugar enriched in red meat called N-glycolylneuraminic acid (Neu5Gc). Neu5Gc, once consumed, is metabolically incorporated into endogenous sialoglycoproteins. These N-glycolyl-containing glycoconjugates are recognized by the immune system as foreign entities, and antibodies (Abs) are produced against a plethora of glycans terminating in Neu5Gc, resulting in a form of chronic inflammation known as xenosialitis. Xenosialitis is consistent with all known facts and is supported by much experimental evidence. It is the only theory that can explain the human-specific nature of CRC risk. This study will take advantage of recent discoveries at the biochemical level to propose methods and approaches to address some of the present limitations for obtaining definitive proof of the mechanism in human populations, as well as exploring ways to measure individual risk. Our solution is to assess the stable and long-lived metabolite of Neu5Gc, whereby the N-glycolyl group is transferred to chondroitin sulfate (Gc-CS), an abundant glycosaminoglycan (GAG) found in the extracellular matrix and in the serum of both omnivorous humans and “human-like” (Cmah–/–) mice fed a Neu5Gc-rich diet. A non-sulfated chondroitin metabolite (Gc-CN) can also be detected and analyzed. We hypothesize that Gc-CS and Gc-CN may serve as new biomarkers for red meat consumption and CRC risk. Moreover, we believe Abs against Gc-CS/CN exist, which, if established, will be the second known class of xeno-autoantigens and the first-known class of xeno- autoantigenic metabolites. To validate this, we will (1) generate and characterize Abs specific for Gc-CS/CN by immunizing Neu5Gc-deficient, Cmah–/– mice with Gc-CN or Gc-CS coupled to a carrier protein that recruits T cell help, screen for serum Ab response, and generate monoclonal antibodies (MAbs) not cross-reacting with N-acetylated-CS/CN (Ac-CS/CN). Using these MAbs, we will (2) determine if either Gc-CS or Gc-CN can be detected in human tumors and normal tissues. Lastly, we will (3) evaluate Gc-CS/CN and anti-Gc-CS/CN Abs in plasma from participants of three large prospective cohorts, including the Nurses' Health Study (NHS) to examine correlations, risk, and prognosis of inflammation-driven progression of tumor growth. With these archived samples, we will be able to correlate levels with red meat intake and CRC risk and prognosis during follow-up of more than 30 years.

The video of this presentation is available here. 

The Hopf formula for Hamilton-Jacobi Reachability analysis has been proposed for controlling stochastic, nonlinear, high-dimensional systems as a space-parallelizable method. In exchange, however, a complex, potentially non-convex optimization problem must be solved, limiting its application to linear time-varying systems. With the intent of solving Hamilton-Jacobi backwards reachable sets (BRS) and their corresponding controllers, we pair the Hopf solution with Koopman theory, which can linearize high-dimensional nonlinear systems. We find that this is a viable method for approximating the BRS and performs better than naive, local linearizations. Furthermore, we construct a Koopman-Hopf controller for robustly driving a 10-dimensional, nonlinear, stochastic, glycolysis model and find that it significantly out-competes both stochastic and game-theoretic Koopman-based model predictive controllers against stochastic disturbance. Although the demonstrations are limited, we believe the proposed method has implications for engineering efforts in diverse domains, particularly in medicine, finance and other large systems where control has remained intractable.

The video of the presentation is available here. 

Sam Jones, PhD, is a biomedical scientist turned science journalist based in Washington, DC. Over the years she has held a range of titles, including scientist, editor, and science video host, but today she is the executive producer & co-host of the science podcast Tiny Matters. She's also a freelance journalist and fact-checker—writing for outlets like The New York Times, Scientific American, Popular Science, and Nature—and an occasional freelancer for biotech companies, writing and editing case studies, website materials, and social media content. You can find her work at sjoneswriting.com.

The video of this presentation can be found here.

Systems that evolve over time in dynamic and nonlinear ways range from robotics to systems biology. These systems may enter undesirable states (e.g. collision with obstacles, population levels of harmful bacteria, etc.). Reachability analysis allows us to analyze when and how systems will become unsafe, and how to apply inputs to the system to maintain safety and reach desired states.  I will discuss applications in various areas, and current plans to extend our work for analysis of multi-scale biological systems.

The video of the presentation is available here. 

Proteases as molecular machines are known to drive a large array of fundamental processes in the body. While the presence of proteases can be probed with conventional techniques to measure genes and proteins, the ability to measure protease cleavage activity in living tissue is limited. Yet, we know proteases are regulated by a multitude of factors in their environment, including their concentration, spatial localization, availability of cofactors, and presence of regulators. In order to create a tool to measure proteases in the living tissue of the injured brain, we created a nanosensor that detects the protease calpain.

The treatment of antibiotic-resistant bacterial infections is an unmet therapeutic need, due to the rapid emergence of resistant strains and the low level of investment in new therapeutics. The use of nanoparticles to enhance activity of current therapeutics, and to enable new approaches based on nucleic acid therapeutics, will be described.  Our effort focuses on the use of porous silicon nanoparticles, and systems that deliver small molecule antibiotics or siRNA against inflammatory macrophages will be used as examples.  Attachment of functional peptides imparts specific targeting and cell penetration properties to the constructs that show improved gene silencing and therapeutic outcomes in vivo.

Shivani Shukla, Bioengineering (BENG) PhD Program, UC San Diego
Co-mentors: Zeinab Jahed and Lingyan Shi

Electrical signaling governs muscle contraction, brain function, and insulin secretion. While clinical EEG/ECG
techniques signify aberrant electrical activity during disease, it is unclear how single cell action potentials (APs)
are affected by disease or drugs. Gold standard electrophysiology tools such as patch clamp and extracellular
microelectrode arrays are insufficient tools for recording APs from many cells for hours, weeks, or months. The
goal of my project is to develop a higher throughput and tunable platform for obtaining electrophysiological
signals from single adherent cells with millisecond resolution. In this seminar, I will present our newly developed
platform which contains arrays of nanopillar (NP) electrodes made using maskless photolithography and a two-
step dry and wet etching technique. We optimized various surface functionalization techniques to improve
adhesion of diverse cell types on our platforms. We demonstrate electrical recording from electrogenic 2D cardiac
monolayers, 3D printed cardiomyocytes, neuron-like PC12 adherent cells, and bacterial biofilms, demonstrating
multi-scale, multi-kingdom electrophysiological capabilities using a single device. We observed intracellular
action potentials for several days in 2D monolayers and 3D-printed cardiomyocytes with altered waveforms,
indicating differences in cytoarchitecture for drug screening. 3D cultures grown on paired-electrode platforms
showed simultaneous APs and extracellular spiking within a 10 um distance, suggesting the possibility of
extrapolating APs from in vivo extracellular signals. In differentiated PC12 cells, intracellular APs were recorded
over several days for the first time, and morphologies of neurites atop nanopillars were characterized using
fluorescence microscopy and electron microscopy. Our platform was redesigned to house bacterial biofilms,
which show membrane potential oscillations in response to high potassium flow. Biofilm electrophysiology
directly influences antibiotic resistance and gut microbiome heterogeneity. The versatility of our nanopillar
electrode arrays to record electrical signals from cells with a range sizes from 2 um - 30 um in diameter offers a
powerful tool for understanding how single cell electrical activity affects function and disease.

The video of this presentation is available here. 

Kristen Garcia, Bioengineering Ph.D. Program, UC San Diego

Co-Mentors: Daniela Valdez-Jasso, Bioengineering

Pulmonary arterial hypertension (PAH) is a life-threatening disease characterized by pulmonary vascular remodeling and is represented by the narrowing of blood vessels in the lungs. I am interested in gaining a deeper understanding of the remodeling process of the right ventricle (RV) from the cellular-level up to the organ-level in PAH, and to determine how estrogen or other sex-differences affects this process. PAH is four times more likely to affect females over males, specifically females between the ages of 30-60 years old. I am working with an animal model to look across the multiple scales of the heart. On the organ-level, I use cardiac magnetic resonance imaging (cMRI) and in vivo pressure-volume relationships to quantitatively analyze the systolic/diastolic function of the heart. Diffusion-tensor MRI imaging (DTI) is also used to get the geometry and fiber orientation of the organ as a whole. These two imaging techniques are used to create a biventricular mesh of the heart. Next, mechanical tests on the planar biaxial device are used to determine stress-strain measurements on the tissue level, which can help determine how the stiffness changes in relation to the progression of PAH. Using tissue engineering applications, the tissue is decellularized to look at the effect of the extracellular matrix on the stress-strain measurements, and mechanobiology of the cellular level is determined to conclude how cells change over disease stages. Combining the knowledge of all these experiments will allow for us to use and adapt a very useful three-dimensional model that can help predict how structural and functional changes in the RV lead to RV failure, and how this is affected by the sex of the animal. I am currently in the process of using cMRI and DTI data to determine the myofiber directions throughout the heart and looking into levels of estrogen and its relation to the hemodynamic data results. 

The video of this presentation can be found here. 

Andrew Nguyen, Mechanical and Aerospace Engineering Ph.D. Program, UC San Diego

Advisor: Professor Padmini Rangamani, Mechanical and Aerospace Engineering

Synaptic plasticity is important for learning and memory. With increasing evidence linking sleep states to changes in synaptic strength, an emerging view is that sleep promotes learning and memory, by facilitating experience-induced synaptic plasticity. Adenosine, a neuromodulator often associated with the regulation of sleep, and its receptors (A1R and A2AR) have been found to be responsible for the potentiation or inhibition of synaptic transmission in the context of calcium signaling in neurons. In my work, I hypothesize that the interactions between adenosine and various calcium-mediated signaling pathways, such as glutamate-mediated calcium signaling, will give rise to the modulation of synaptic plasticity in neurons. To investigate this hypothesis I have been developing a quantitative model of signaling crosstalk between adenosine receptors and Glutamate receptors (mGluR5) to capture the connection between the molecular machinery associated with circadian adenosine to the molecular machinery associated with synaptic plasticity.

The video of this presentation is available here. 

The goal of our lab at the University of California, San Diego (called the Bio-Nano-Electronics or BioNE Lab) is to “Engineer nanoscale electronics for higher throughput cellular- and molecular-scale sensing, to accelerate scientific discoveries related to human health and disease”.  To design more intelligent nano-electronics we first carefully characterize the complex nano-bio interface. Next, we use this knowledge to design our nano-electronics to be higher throughput, accurate, sensitive, with minimal perturbations to the biological system. Finally, with confidence in our biological read-outs from these high-throughput tools, there is a need to develop algorithms that can “learn” from our big dataset to answer fundamental biological questions. In this seminar I will present our recent advances using the above approach to design a nanoscale platform for recording electrical signals in parallel from hundreds to thousands of cardiac cells for cardiotoxicity screening. I will also briefly discuss ongoing efforts in our lab to utilize our platform for neuronal and biofilm electrophysiology. 

The video of this presentation is available here. 

Metaproteomics is a mass spectrometry method to unbiasedly analyze the proteins expressed by a community of organisms. Stool metaproteomics is an emerging technique for the analysis of microbial protein abundance in human feces. Up to half of the dry solids in stool are bacterial biomass, almost exclusively from the digestive tract, making stool ideal biomaterial to analyze gut microbial proteins, as well as host and dietary proteins and other biomolecules. A recent study from the Gonzalez Lab employed stool metaproteomics to study inflammatory bowel disease, revealing that Bacteroides spp. proteases are elevated in disease and colitis phenotypes can be rescued with protease inhibitor treatment. In this talk, I will be discussing recent advances in stool metaproteomics, including high-throughput workflows, database development, and bioinformatic pipelines. I will also describe how I am addressing persistent limitations in stool metaproteomics and how I am applying this methodology to uncover protein effectors in the gut-brain axis in health and disease. 

The video of this presentation is available here.

Heart failure remains a significant cause of morbidity, mortality, and medical costs. 2-deoxy-ATP (dATP), a novel heart failure therapeutic, has been shown to improve contractile function without impairing relaxation. Previous studies in our group have shown that elevated dATP increases the rate of crossbridge binding cycling and the rate of calcium transient decay. However, the precise molecular mechanisms of these effects and how therapeutic responses are achieved when dATP is only a small fraction of the total ATP pool are unknown. We used multiscale computational modeling to integrate a filament-scale model of acto-myosin interactions, whole myocyte model, organ scale model of biventricular mechanoenergetics, and whole body circulatory model to predict the effects of dATP on ventricular mechanics in normal and heart failure conditions. We discovered synergistic interactions between calcium dynamics and force development that leads to improvements in myocyte contractility and lusitropy. Simulations also predicted that increased transition out of the super-relaxed state of myosin with elevated dATP can explain the large increases in force observed at low percentages of dATP. We further showed that this effect likely depends on both nearest neighbor cooperativity and thick filament mechanosensing. We predicted improvements in ventricular function consistent with experimental data in both healthy and failing conditions, with no additional impairment of metabolic state. Ventricular function was improved to a greater degree in failure than in healthy conditions, due at least in part to improved energy efficiency with elevated dATP.

The video of this presentation is available here. 

AMPAR, alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor, is a receptor concentrated at the postsynaptic density (PSD) of dendritic spines. AMPAR is often used as a readout for synaptic plasticity since increased AMPAR density at the PSD leads to a stronger synaptic response to a stimulus while decreased AMPAR density leads to a weaker response. Changes in AMPAR density during synaptic plasticity involve numerous complex signaling pathways, many of which have been studied both experimentally and computationally. However, there are still open questions regarding how the dynamics of several key components upstream of AMPAR, including CaMKII and phosphatase dynamics, influence the mechanisms and dynamics of AMPAR trafficking. Here we construct both a deterministic compartmental ordinary differential equations model and a deterministic reaction-diffusion model of the simplified signaling network underlying AMPAR dynamics during synaptic plasticity to probe how upstream kinase and phosphatase dynamics influence AMPAR dynamics and trafficking. We find that key signaling components and trafficking mechanisms act over different timescales to modulate AMPAR density at the PSD. In particular, AMPAR temporal dynamics show two timescales associated with endo/exocytosis and total available AMPAR sources, while there is a strong relationship between total AMPAR number at the PSD and the spine volume to surface area ratio.

The video of this presentation is available here. 

The monitoring of intracranial pressure (ICP) fluctuations, which is needed in the context of a number of neurological diseases, requires the insertion of pressure sensors, an invasive procedure with considerable risk factors. ICP fluctuations drive the wave-like pulsatile motion  of  cerebrospinal  fluid  (CSF)  along  the  compliant  spinal  canal.  Systematically derived simplified models relating the ICP fluctuations with the resulting CSF flow rate can be useful in enabling indirect evaluations of the former from non-invasive magnetic resonance imaging (MRI) measurements of the latter. As a preliminary step in enabling these  predictive  efforts,  a  model  is  developed for  the  pulsating  viscous  motion of  CSF  in  the  spinal  canal,  assumed  to  be  a  linearly  elastic  compliant  tube  of  slowly varying section, with a Darcy pressure-loss term included to model the fluid resistance introduced  by  the  trabeculae,  which  are  thin  collagen-reinforced  columns  that  forma  weblike  structure  stretching  across  the  subarachnoid  space  (SSAS).  Use  of  Fourier-series  expansions  enables  predictions  of  CSF  flow  rate  for  realistic  anharmonic  ICP fluctuations. The flow rate predicted using a representative ICP waveform together with a realistic canal anatomy is seen to compare favorably with in-vivo phase-contrast MRI measurements at multiple sections along the spinal canal. The results indicate that the proposed model, involving a limited number of parameters, can serve as a basis for future quantitative analyses targeting predictions of ICP temporal fluctuations based on MRI measurements of spinal-canal anatomy and CSF flow rate.

The video of this presentation is available here. 

Mounting evidence suggests cerebrovascular dysfunction plays an important mechanistic role in the development of Alzheimer’s disease (AD), and that cerebral white matter (WM) is especially vulnerable to the effects of microvascular dysfunction. Arterial Spin Labeling (ASL), a class of Magnetic Resonance Imaging (MRI) techniques, is a popular method for assessing cerebral hemodynamics due to its non-invasive nature. However, inaccurate ASL methodology has prevented the systematic characterization of WM microvascular health and hemodynamics, even across healthy aging, thus hindering our understanding of cerebrovascular contributions to AD development. My project investigates whether velocity-selective ASL (VSASL) is a more robust measure of WM cerebral blood flow (CBF) than conventional techniques and can better capture age-related change in CBF.

The video of this presentation is available here. 

Mitochondrial transcription factor A (TFAM) plays important roles in mitochondrial DNA (mtDNA) compaction, transcription initiation, and regulation of processes like transcription and replication processivity. Possible modes of local regulation of TFAM function within the mitochondrial matrix include phosphorylation by protein kinase A (PKA) and non-enzymatic acetylation by acetyl-CoA. Here we present that DNA-bound TFAM is less susceptible to these modifications. We confirm that phosphorylated or acetylated TFAM compact circular double-stranded DNA just as well as unmodified TFAM and provide an in-depth analysis of acetylated sites on TFAM. We show that both modifications of TFAM increase the processivity of mitochondrial RNA polymerase during transcription through TFAM-imposed barriers on DNA, but that each version of modified TFAM retains its full activity in transcription initiation. We conclude that TFAM phosphorylation by PKA and non-enzymatic acetylation are unlikely to occur at the mtDNA and that modified free TFAM retains its vital functionalities like compaction and transcription initiation while allowing transcription processivity enhancement.

The video of this presentation is available here. 

I propose a Bayesian parameter estimation framework to facilitate model calibration and uncertainty analysis for predictive models of biological systems. Many of these models are nonlinear differential equations that characterize the dynamics of the relevant biochemical species. System biologists need to estimate many free parameters from sparse and noisy experimental data to calibrate these models. Despite the uncertainties associated with this data, systems biologists directly fit the free parameters, typically ignoring any uncertainty altogether. My work moves past this traditional model-fitting paradigm by adapting an approximate marginal Markov chain Monte Carlo (MCMC) method for Bayesian parameter estimation. I find that this method can recover the posterior parameter distribution from experimental data for a well-known biological system, the mitogen-activated protein kinase (MAPK) signaling pathway. This work introduces a new modeling paradigm that bridges the gap between standard systems biology modeling practices and rigorous uncertainty quantification.

The video of this presentation is available here. 

AMP-Kinase (AMPK) is the master sensor for the organism’s metabolism and energy, and has many diseases linked to both its activation and inhibition, including cancer, gastrointestinal, heart, and even neurodegenerative diseases. My main research project involves designing and synthesizing potent activators selective for α1β1γ1 and α2β1γ1 isoforms. The gene encoding the β1 subunit of AMPK, PRKAB1, was first discovered as a viable drug target for inflammatory bowel disease (IBD) by our collaborators, the Ghosh lab (UCSD School of Medicine). Using Boolean implication networks to analyze gene clusters, the continuum states of IBD-related diseases were more easily differentiated between disease and health states of patients. Recent efforts in medicinal chemistry in collaboration with the Ghosh lab has opened up some exciting new opportunities in drug design to target proteins such as APMK. Using in silico docking experiments, along with previously published ligand-protein pair co-crystal structures as references, we were able to create a robust model for predicting the binding affinity and selectivity for novel β1 selective agonists. I have established the chemistry and methodology for synthesizing these agonists, and intend to both synthesize and test (in vitro) these novel β1 selective agonists designed in silico.

The video of this presentation is available here. 

Dilated and hypertrophic cardiomyopathies (DCM and HCM) affect 1 in 500 Americans. Current treatments only slow disease progression without treating underlying mechanisms; therefore, novel therapeutics are needed to treat HCM and DCM. 2’-deoxy-adeninetripohsphate is a naturally occurring myosin activator, and proposed heart failure and DCM therapeutic. In this work, I use molecular simulations to understand the mechanism by which dATP increase cardiac contractile force and function. Specifically, I have used molecular dynamics simulations to build Markov models of protein kinetics, that can be used with Brownian dynamics simulations to understand actin-myosin kinetics. Through this work, have shown that dATP functions by increasing the association rate of myosin to actin in the pre-powerstroke state. The DCM mutations A223T and S532P found cardiac myosin heavy chain were also modeled with this framework and found to have diverging effects on the association rate of myosin to actin. Specifically, dATP, S532P and A223T have 5.3-, 3.0-, and 0.56-time fold changes to the association rate respectively. These simulations help to predict organ level function, and lead to improved mechanistic understandings of actin-myosin interactions.

The video of this presentation is available here. 

Pulmonary arterial hypertension (PAH) is a life-threatening progressive disease that is  characterized by pulmonary vascular remodeling and is represented by narrowing of blood  vessels in the lungs. PAH effects females four times more likely than males, specifically females  between the ages of 30-60 years old, leading to an interesting paradox known as the estrogen  puzzle. Past research has resulted in contradicting conclusions; some of which show a  protective effect of estrogen on PAH versus others showing a disease promoting effect. It is  known that right ventricular (RV) function and morphology are important indicators of the  severity of PAH. I am interested in determining the role that estrogen plays on the right  ventricle during the progression of PAH. I hypothesize that estrogen plays a major role in the  remodeling of the RV leading to an overall protective effect against right heart failure. I aim to  quantify the hemodynamics of the right ventricle throughout the progression of PAH using a rat  model and to identify the sex dependent effects of estrogen in the remodeling and mechanics  of the RV. After the induction of PAH using a Sugen-Hypoxia protocol, terminal procedures are  conducted, in which in vivo blood pressure-volume relationships are measured, with the goal of  investigating changes due to pressure overload. I am currently focusing on these pressure volume loops for each subset of animals with the goal of finding patterns to describe the  differences in the hearts function for males, non-ovariectomized females, and ovariectomized  females. I am looking at the PV loop as a whole, the slope of the isovolumic contraction and  relaxation phases, and am attempting to determine if we can expect to see a specific range of  values for the PV curves for a given group. My future plans are to dive more into the estrogen  component of RV remodeling in PAH and relate estrogen levels to PV loops and overall heart  function.

The video of this presentation is available here.