Mutation produces new variation in populations, and in each generation these variants are copied from parents to offspring. While almost all variants of genes are lost, they may remain in the population for many generations. We use branching process models to analyze counts of gene copies. In a population of constant size, on average, a gene copy produces one offspring copy at the next generation. An advantageous mutant will have a mean greater than 1, and a deleterious one will have a mean less than 1. It is thought that most mutations are slightly deleterious, and with high probability those variants become extinct rapidly. Nonetheless, the few deleterious mutants that are not yet extinct may achieve high numbers. Thus, we have a particular interest in those with a mean slightly less than 1. We use different probability models for the offspring distribution and consider the mutant’s survival about: the extinction probability over k generations, the expected copy count conditional on survival, and the probability of survival additional k generations conditional on surviving k already. We find that variances in addition to means of offspring distributions closely relate these statistics. By adjusting parameters of distributions, we let the mean and variance be approximately the same across distributions. Based on our simulations, when k is large and the mean and variance of offspring are the same, the mutant’s survival condition is uniform throughout. In other words, those statistics above can be estimated by the mean and variance exclusively, and the specific distribution does not affect much the conditional population dynamics. However, at the first few generations, these statistics are different for each distribution. Thus, if we know the mean and variance of a mutant, we can predict the long-term population behaviors conditional on survival without knowing the true distribution of the mutant.

By their second birthday, toddlers help others. Moreover, infants as young as 9 months of age can detect others’ needs. Yet it is unclear if early helping behavior is based on and motivated by an underlying assessment of others’ needs, or motivated by other factors, like the desire to socially interact or affiliate with others. Here, we investigate whether toddlers utilize an experimenter’s needs when helping. We presented N=24, 24-month-old toddlers with two helping tasks: a puzzle and hunger-or-thirst task. In the puzzle task, toddlers watched an actor fail to complete a puzzle due to a missing puzzle-piece. Toddlers then had the opportunity to help the actor by either bringing her a puzzle-piece that fit the puzzle (need-fulfilling) or a puzzle-piece that did not fit the puzzle (not need fulfilling). Overall, toddlers were more likely to help with the puzzle-piece that fulfilled the actor’s need than the puzzle-piece that did not fulfill the actor’s need, p=.008. In the hunger-or-thirst task, the actor told toddlers that she was either hungry or thirsty. Toddlers could help the actor by giving her one of three different items: (a) cereal, (b) water, or (c) a shoe. Here, toddlers brought the needed item (e.g. cereal if she was hungry) at chance, p=.11. However, toddlers were more likely to bring the needed or the thematically related item (e.g. water if she was hungry) significantly more than chance, p<.001. Our findings provide initial evidence that 24-month-old toddlers are motivated to help based on others’ concrete needs: toddlers brought the needed puzzle-piece. However, toddlers have a harder time helping appropriately when the actor’s need is internal and therefore more abstract; hence, the equal likeliness to help with the needed and related item during the hunger-or-thirst task.

As the global burden of neurological diseases continues to grow each year, there exists a need for drug delivery vehicles that can overcome barriers specific to the brain. The heterogeneous brain extracellular matrix (ECM) is an understudied barrier to effective therapeutic delivery, particularly in the presence of disease states. We utilize a novel multiple particle tracking (MPT) approach to characterize microstructural changes in perineuronal nets (PNNs), a key structural mediator of plasticity, in the developing brain. Our overarching goal is to develop a combined approach of MPT, statistical analysis using Python-based software packages, immunohistochemistry (IHC), and mRNA expression profiles to monitor changes in PNN structure and function through development and in the presence of neuroinflammation and oxidative stress processes. For this, we cultured 300 μm-thick organotypic whole hemisphere (OWH) brain slices prepared from postnatal day 35 (P35) rats. We treated slices with 100 ng/mL lipopolysaccharide (LPS, induced-inflammation model) or 100mM glutamate (MSG, induced-excitotoxicity model) for 3 h, removed the toxin and fixed slices at 1 h, 6 h, and 24 h after toxin removal. Fixed slices were stained using Wisteria floribunda agglutinin (WFA) and PNN counts were quantified using the ImageJ software package. In LPS and MSG-treated slices, 100-nm particles were added to live slices stained with WFA and MPT was performed in WFA+ regions, followed by statistical analysis of individual nanoparticle trajectories. From the IHC imaging and PNN count quantification, we observed differences in the number and morphology of PNNs present in the MSG and LPS models when compared to healthy controls. Characterizing structural changes in PNNs in living tissue furthers our understanding of the impact of neuroinflammation and oxidative stress on neuronal plasticity, and the subsequent impact on progression of neuropsychiatric diseases.

Proteins are essential to life, but many structural methods fail to capture the dynamic nature of proteins. This means researchers are left with an incomplete view of how protein function and structure relate. The Stoll lab uses electron paramagnetic resonance (EPR) to study the dynamic structure of proteins. The project I am working on is investigating the way in which the measurement conditions in EPR affect the determined protein structure. We use maltose binding protein (MBP) as a model system, since its structure has been previously well-characterized. My primary role has been to create new mutants for site-directed spin labeling, in which a radical spin label is attached to the protein. This radical is measured by placing the sample in a magnetic field and results in a probability distribution of distances. Common spin labels used in EPR, however, have many rotatable bonds, andd can cause uncertainty in the extracted distance distribution. Our project is investigating the contribution of spin labels to the EPR experiment. I use polymerase chain reaction to create new mutants of MBP by swapping a native residue with a cysteine residue in the mutants. Each mutant has one or two amino acids replaced with a cysteine. After mutating the DNA, I then transform the mutant DNA into wild type cells to grow the mutant protein and purify the protein. The spin label forms a disulfide bond with the cysteine residue in the mutant, providing an unpaired electron for measurement. By using various commonly used spin labels on many different site pairs in MBP, we hope to develop a model in which the contribution of spin labels to the probability distribution of distances is accounted for. This will ensure more accurate results in determining protein structure and will aid in structural characterization of many proteins.

Developing treatments for complex disease is hindered by a variety of obstacles including clearance by the reticuloendothelial system, degradative proteases of blood and tissue microenvironments, and overall limited tissue penetration. Nanoparticles hold potential as drug delivery platforms for overcoming these problems, where they can be used to encapsulate and protect enzyme therapeutics and improve delivery to specific organs of the body. Using poly(lactic-co-glycolic acid) (PLGA) polymeric nanoparticles with a poly(ethylene glycol) (PEG) coating and catalase as a model enzyme, we compared two methods of nanoparticle formulations for enzyme encapsulation: nanoprecipitation and double emulsion. We tested the effects of F127 (Pluronic F127), P80 (polysorbate 80), and PVA (poly(vinyl alcohol)) surfactants on the size, polydispersity index (PdI), zeta potential, and enzymatic activity of nanoparticles formulated via nanoprecipitation. We determined PVA to be the ideal surfactant with the greatest enzyme loading for nanoprecipitation (~3.6%), similar to the activity obtained from double emulsion (~3.7%). For double emulsion, we determined similarly that PVA worked the best out of the surfactants tested. However, the nanoprecipitation method provided little to no enzyme protection in the presence of a protease with complete deactivation within 4 hours, while double emulsion nanoparticles extended activity through 24 hours (~6.6% of original activity). We further optimized the double emulsion method by altering the sonication times, determining that a sonication time of 15 seconds yielded particles with an activity of ~3.5% while maintaining similar size, zeta potential, and PdI. We also found that sonication conditions significantly affected enzyme deactivation. With a modified bicinchoninic acid assay, we measured total protein concentration within the particles, allowing us to calculate enzyme percent deactivation from formulation processes. Altogether, our work shows that enzyme-loaded nanoparticles made via the double emulsion method achieve high encapsulation and protection of enzymatic cargo for drug delivery.

Computer vision models are used to help analyze biomedical images for diagnosis and treatment through looking for differences between images by a comparison to a template image. For instance, optical coherence tomography (OCT) is used to diagnose and treat retinal issues. When looking at the brain, injury, cellular uptake and characteristic features vary across regions, therefore images are often segmented into established brain regions to determine how the brain is impacted in a particular study. Current models fail to work in segmenting brain regions because each brain has variation in local microstructure, making it difficult to compare one brain to another. Furthermore, when brains are sliced, the exact location within the brain can be difficult to pinpoint, particularly in regard to depth, because the regions vary slice to slice. Therefore, my research addresses the increasing need for a method of analysis to align and compare images from brain regions across slices from a single brain, and from brain to brain. Using scikit-image analysis tools, I extracted information from cell images and videos of nanoparticles obtained in brain slices and determined trends within various regions. My program extracted cell density, shape, and death, then analyzed the uptake of nanoparticles to determine where a small segment of an image is most likely located within the brain. Iterating over the entire image generated a rough map of the regions within the brain which is refined using mapping descriptions detailed in literature. This research resulted in a systematic program that uses image analysis tools to extract features of defined brain regions. This program allows for quick, accurate and consistent analysis of regional differences of cellular features, nanoparticle distribution, toxicity, and other important measures.

Giardia lamblia, a microscopic flagellated parasite that causes giardiasis, is a highly divergent eukaryote in which conventional Golgi, endosomes, lysosomes, and mitochondria are absent. Similar to other parasites of medical importance, Giardia lamblia has two life cycle stages - proliferative trophozoite form and water-resistant, nonmotile, infectious cyst form. During encystation when Giardia trophozoites transform into infectious cysts, they secrete cyst wall proteins (CWP1-3) that are trafficked and processed in Encystation Specific Vesicles (ESVs). These vesicles are thought to be stage-induced Golgi in Giardia. Previous work in the lab has shown that the signaling activities of G. lamblia’s single Rho family GTPase, GIRac play an important role in regulating this encystation process. The aim is to characterize proteins in Giardia lamblia that potentially interact with GIRac, currently focusing on homologs of known players in membrane trafficking by examining their order of arrival using morphology of the ESVs based on CWP1 staining. Since this is subjective, there is a need for stage-specific molecular markers. In other eukaryotes, Rab GTPases have been established as markers of membrane identity and directionality of trafficking. Only two out of nine Giardia’s Rab GTPases have been localized and reportedly found at ESVs and based on published images, they appear to be recruited at different stages of ESV maturation. By tagging the N-terminus of all 9 Giardia Rab GTPases with fluorescent tags, we can screen them for their localization to ESVs and perform multi-color imaging to determine the order of arrival of these markers. Ultimately, this finding of stage-specific molecular markers could be a powerful tool to further suggests its potential as a novel target for drug development to treat giardiasis.

A positron emission tomography scan (PET scan) can be used to map areas of potential cancer in the body. Cancer cells multiply at an incredibly fast rate, and in doing so, use a greater amount of glucose to fuel their high-energy needs compared to normal tissues. PET scans use radiolabeled fluorodeoxyglucose to quantify cellular metabolic activity, producing visual hotspots that are usually overlayed on computed tomography (CT) images and correlate to cancer sites (“PET/CT”). PET/CTs are often used to follow patients with acute myeloid leukemia (AML) after hematopoetic cell transplantation (HCT). In the lab’s database of over 300 consecutive patients whose AML relapsed after HCT, we found 15 patients who were followed with PET/CT because of extramedullary disease. These patients received a variety of treatments for their AML relapses, including induction chemotherapy, radiation, and/or hypomethylating therapy. We sought to assess if a negative post-treatment PET was helpful in predicting a cure. Through detailed chart review, we found PET scan negativitity is hard to achieve, a negative PET is not reassuring (50% still relapsed) and even a low SUV-positive PET is prognostic of disease progression. Moreover, if any extramedullary disease is present, systemic therapy prolongs life more than local radiation alone, but no treatments are likely to be curative.

In the central nervous system, insulin acts as an anorexigenic hormone, regulating the desire for fatty and sugary foods. It also plays an important role in learning and memory. Thus, a malfunctioning insulin transport system across the blood-brain barrier (BBB) could be linked to obesity, the development of type 2 diabetes, and cognitive impairments as occur in Alzheimer’s disease. Studies have shown that insulin binds to insulin receptors (IR) located on the endothelial cells which make up the BBB; the activated insulin-IR complex is then taken up into the cell, where it sets off a signal cascade. Additionally, insulin binds a protein responsible for transport from the luminal to abluminal side of the brain endothelial cell. However, the mechanism by which this occurs is still not understood. Thus, we set out to elucidate the difference between insulin receptor uptake and insulin transcytosis in vivo, focusing on clathrin and caveolin, the two proteins primarily responsible for mediating endocytosis. To do this, we use radiolabeled insulin, the IR antagonist S961 (which binds to IR but is not taken up into the cell), and three pharmacological agents: phorbol 12-myristate 13-actetate (PMA) to promote endocytosis, monensin to disrupt clathrin-mediated endocytosis, and filipin to disrupt caveolin. To eliminate serum factors, cardiac perfusion in male CD-1 mice will be performed. One group will be the vehicle control group, and the other will receive radiolabeled insulin, S961, in addition to one of the three pharmacological agents. Perfusions will last from 1-10 minutes, in which brains are collected and dissected into brain regions. Radioactivity is measured in the hypothalamus and hippocampus as well as whole brain. The data collected will help us better understand the differences between insulin transcytosis versus endocytosis and how insulin transport may go awry in Alzheimer’s disease, diabetes, and obesity conditions.

As anthropogenic climate change progresses and alters our marine ecosystems, it is important to monitor the changes it creates in order to inform plans for mitigation or management. This project, completed as part of the Puget Sound Foraminifera Research Project at The Burke Museum, uses sediment samples collected by the Washington State Department of Ecology and extracts benthic foraminifera from those samples to learn vital information about the health of the Puget Sound. Benthic foraminifera are single-celled organisms that live on or in marine sediment and form shells of calcium carbonate or agglutinated sand grains. This research looks at the dissolution of shells of a species of calcareous foraminifera called Elphidiella hannai in order to establish whether different populations across Puget Sound are affected differently and if dissolution primarily happens before or after the death of individuals. For this research, samples collected from a variety of Puget Sound embayments over four years starting in 2015 were processed and stained with Rose Bengal to ascertain whether the foraminifera were alive at the time they were collected. Following this, foraminifera were sorted and species were grouped. Finally Elphidiella hannai were assessed for dissolution on a 0-3 scale, 0 meaning pristine and 3 heavily damaged. Preliminary results show higher levels of damage in stained individuals and vastly differing amounts of damage in different Puget Sound embayments, suggesting most damage happened while foraminifera were still alive and that changes in acidity of water are impacting populations at different rates.

Many psychiatric disorders, including substance abuse, have been linked to risky decision-making, but the mechanisms underlying these pathologies remain unclear. Cortical intratelencephalic and pyramidal tract (PT) neurons have distinct projections, morphology, and firing properties, but their role in behavioral regulation remains unknown. PT neurons have been identified in the orbitofrontal cortex (OFC), which is known to be heavily involved in the cognitive process of decision making. Based on previous studies in our lab demonstrating that inactivation of PT neurons increases reward preference, we hypothesized that PT neuron inhibition would increase risky decision-making. To test this hypothesis, rats were trained on a risky-decision task (RDT) in which they were trained to lever press for a food pellet reward. In order to target PT neurons in the OFC, CAV2-CRE virus was bilaterally injected into the pontine reticular nucleus (PnC), and an inhibitory Designer Receptor Exclusively-Activated by Designer Drugs (DREADD; DIO-hM4Di) was injected bilaterally into the OFC; this strategy allows selective expression of DREADDs in PT neurons. In the RDT, two options were presented: a “risky” choice associated with descending probability of administration (100%, 50%, 25%, and 12.5%) for delivery of four food pellets, or a “safe” lever that always delivers one food pellet. To evaluate response flexibility, rats underwent a reversal task whereby five consecutive responses on the active lever switched the “active” lever to the previously inactive one. In both tasks, rats underwent two sessions: in the test session, animals received an injection clozapine-N-oxide (CNO) to activate DREADDs, and in the control session, animals received an injection of vehicle (DMSO). Interestingly, we found that inhibiting PT neurons did not significantly alter decision-making or reversal-learning. Future studies will monitor activity of PT neurons using in vivo calcium imaging to determine the contribution of this cell population to decision-making tasks.

Risk taking is strongly associated with many disordered behaviors. Many studies implicate the orbitofrontal cortex (OFC) in such behaviors, though its modulation yields inconsistent results. This could be due to possible contrasting effects of intratelencephalic (IT) and pyramidal tract (PT) neurons, which have distinct morphologies and projections. Despite this, specific functionality differences are unknown. Given that 5-HT2A and D1 receptors have predominant expression in IT neurons over PT neurons, and antagonism of these receptors decreases risk-seeking, it was hypothesized that inhibition of all IT neuron activity in the OFC would also result in risk aversion. IT neurons in rats were inhibited during a risky-decision making task (RDT). To modulate IT neurons, an inhibitory designer receptor exclusively activated by designer drugs (DREADD) was injected into lateral OFC bilaterally, conjugated to CRE and FLP drivers injected into contralateral nucleus accumbens. In the full RDT, animals were presented with two levers: The “safe” lever delivered one pellet reward per press, while the “risky” lever delivered four pellets, with descending probability of reward administration (100%, 50%, 25%, 12.5%). Two test sessions were conducted whereby animals received intraperitoneal injections of clozapine-N-oxide (CNO) or a vehicle (DMSO) 30 minutes prior to task onset. Animals receiving CNO the first time received vehicle the second time. To determine if IT neurons were involved in perseverative responding, animals underwent a reversal task. They were presented with an active lever -which administered a food pellet per press -, and an inactive lever. Five consecutive responses on the active lever switched the inactive lever to the “active” lever. Results showed no significant changes in decision-making or reversal learning upon receiving CNO or vehicle. IT neuron modulation may need to be more temporally specific; future studies will focus on monitoring IT activity during decision-making paradigms to further elucidate their contributions within OFC.

As climate change progresses, predictions of long lasting and severe droughts are projected to affect many communities. These communities are encouraged to utilize food resources that produce high yields for sustainability. Learning the key physiological differences between drought tolerant and sensitive plants may help communities like this adapt. A key difference between tolerant and sensitive plants is the efficiency of sugar mobilization. Drought sensitive plants tend to hinder mobilization of sugars while drought tolerant plants maintain this sugar flow. Here, our objective is to locate and understand where the mobilization of sugars is bottle-necked in the sensitive plants, and how that differs for tolerant plants. We hypothesize that more drought-tolerant plants display an increase in sugar mobilization resulting in higher fruit yield, faster rate of seed filling/fruit formation, and an overall greater rate of plant growth as compared to drought-sensitive plants. Our experimental protocol consists of eight different genetic lines ranging in sensitivity to drought. By reducing soil moisture content to 50%, we will induce drought and start measuring growth rates of the middle leaflet of the third trifoliate, pod elongation, seed length, and biomass of different plant tissues to determine the bottleneck of carbon and sugar movement in all lines. By contrasting this net movement of sugars across lines, we hope to observe differences indicating physiological mechanisms distinguishing tolerant from sensitive lines. Understanding these differences would help educate and improve communities who are affected by drought due to increasing climate change and overall help improve sustainability of crop production.