71446 research outputs found
Sort by
Evolution of Cell Types in Tunicate Blood
No full textMajor evolutionary innovations have occurred at the level of cell types, from the emergence of neurons in early metazoans to the specialization of adaptive immune cells in vertebrates. To study the developmental and genetic mechanisms driving cell type diversification, we rely on comparative analyses of present-day organisms. Single cell genomics have revolutionized the field, enabling quantitative comparisons of diverse species with little prior knowledge required. With this detailed molecular view of cell states, it remains unclear which species and tissues will be fruitful to study. Species-of-focus should ideally have enough divergence that there is variation to study, while also having enough similarities that we can identify homologous components.
Innate immunity is a promising system, with highly conserved molecular pathways alongside constant diversification driven by rapidly evolving pathogens. We chose to study immune cells in tunicates, a group of chordate invertebrates. Tunicates are our closest relatives lacking adaptive immunity, placing them at a key position in the evolution of immune systems. Their blood cells are comprised primarily of innate immune cells, though these cells have been poorly characterized at a molecular level.
In this thesis, I present modernized characterizations of tunicate blood and demonstrate its potential as a system for studying cell type evolution. Chapter 2 documents a technical problem for working with marine organisms and introduces a solution to overcome it. In Chapter 3, this solution lets us generate a high-quality blood cell atlas from the well studied tunicate species, Ciona robusta. By using this atlas to make quantitative comparisons to vertebrate blood cells, we find that tunicate and vertebrate immune cells have diverged significantly. This divergence suggests that immune systems might be highly variable across Metazoa, but it is difficult to learn how diversity arises at such large distances. In Chapter 4, we instead look within tunicates, and single-cell RNA sequencing data from eleven species reveals a mix of highly conserved and rapidly diverging blood cell states. We use this data to study how gene regulatory relationships change at varying evolutionary distance. Altogether, this work reveals the enormous diversity of cell types even within chordates, and it establishes tunicate blood as a promising system for studying cell type evolution.Systems Biolog
Meningeal regulatory T Cells: Gatekeepers of brain homeostasis and mediators of neuro-immunological dysfunction
No full textOur understanding of the meningeal immune system has expanded considerably in recent years,
particularly in relation to how innate and adaptive effector immunocytes are mobilized in response to central
nervous system (CNS) challenges. However, much less is known about how these cells contribute to brain
homeostasis under steady-state conditions. My PhD work focuses on the heterogeneous and polyfunctional
regulatory T cell (Treg) compartment in the meninges, with particular attention to its role in maintaining CNS
integrity. Using a combination of flow cytometry, transcriptomic profiling, imaging, and behavioral assays,
we identified functionally distinct meningeal Treg subsets, including one specialized in controlling interferon-
gamma (IFN-γ) responses and another involved in the regulation of follicular B cell activity. Acute Treg
ablation resulted in rapid IFN-γ production by meningeal lymphocytes, altered meningeal B cell profiles,
and enabled increased immunocyte access to the brain parenchyma. These local immune changes were
accompanied by reactive gliosis and transcriptional reprogramming in the hippocampus. Within the dentate
gyrus, neural stem cells underwent increased cell death and failed to progress through differentiation,
coinciding with deficits in short-term spatial-reference memory. Together, these findings demonstrate that
meningeal Tregs act as critical, multi-functional regulators of CNS homeostasis under physiological
conditions. Building on these insights, our current work investigates how meningeal Tregs respond to and
potentially shape neurodegenerative disease, with a focus on Alzheimer’s disease (AD). Two parallel efforts
are underway. First, we are examining how AD pathology influences the phenotype and function of
meningeal Tregs in mice, and whether these cells can be modulated to alter disease progression. Second,
we are characterizing the meningeal Treg compartment in human dura, comparing it to its murine
counterpart and assessing how it is affected in the context of AD. These studies aim to advance our
understanding of neuroimmune regulation in neurodegeneration and to explore the therapeutic potential of
targeting meningeal Tregs in chronic CNS disease.Immunolog
Correcting Biases in Satellite Methane Observations: Applications to Landfill Emissions in the United States and Continental-Scale Emissions in Africa
No full textSatellite observations of dry-column methane mixing ratios (XCH4) from shortwave infrared (SWIR) solar backscatter radiation provide a powerful resource to quantify methane emissions in service of climate action. The TROPOspheric Monitoring Instrument (TROPOMI), launched in October 2017, provides global daily coverage at a 5.5 × 7 km2 (nadir) pixel resolution, but its methane retrievals can suffer from biases associated with SWIR surface albedo, scattering from aerosols and cirrus clouds, and across-track variability (striping). The Greenhouse gases Observing SATellite (GOSAT) instrument, launched in 2009, has better spectral characteristics and its methane retrieval is much less subject to biases, but its data density is 250× sparser than TROPOMI. Here, we present a blended TROPOMI+GOSAT methane product obtained by training a machine learning (ML) model to predict the difference between TROPOMI and GOSAT co-located measurements, using only predictor variables included in the TROPOMI retrieval, and then applying the correction to the complete TROPOMI record from April 2018 to present. We find that the largest corrections are associated with coarse aerosol particles, high SWIR surface albedo, and across-track pixel index. Our blended product corrects a systematic difference between TROPOMI and GOSAT over water, and it features corrections exceeding 10 ppb over arid land, persistently cloudy regions, and high northern latitudes. It reduces the TROPOMI spatially variable bias over land (referenced to GOSAT data) from 14.3 to 10.4 ppb at a 0.25° × 0.3125° resolution. Validation with Total Carbon Column Observing Network (TCCON) ground-based column measurements shows reductions in variable bias compared with the original TROPOMI data from 4.7 to 4.4 ppb and in single-retrieval precision from 14.5 to 11.9 ppb. TCCON data are all in locations with a SWIR surface albedo below 0.4 (where TROPOMI biases tend to be relatively low), but they confirm the dependence of TROPOMI biases on SWIR surface albedo and coarse aerosol particles, as well as the reduction of these biases in the blended product. Fine-scale inspection of the Arabian Peninsula shows that a number of hotspots in the original TROPOMI data are removed as artifacts in the blended product. The blended product also corrects striping and aerosol/cloud biases in single-orbit TROPOMI data, enabling better detection and quantification of ultra-emitters. Residual coastal biases can be removed by applying additional filters. The ML method presented here can be applied more generally to validate and correct data from any new satellite instrument by reference to a more established instrument (chapter 1).
We use satellite observations of atmospheric methane from the TROPOMI instrument to estimate total annual methane emissions for 2019–2023 from four large Southeast US landfills with gas collection and control systems. The emissions are on average 6× higher than the values reported by the landfills to the US Greenhouse Gas Reporting Program (GHGRP) which are used by the US Environmental Protection Agency for its national Greenhouse Gas Inventory (GHGI). We find increasing emissions over the 2019–2023 period whereas the GHGRP reports a decrease. The GHGRP requires gas-collecting landfills to estimate their annual emissions either with a recovery-first model (estimating emissions as a function of methane recovered) or a generation-first model (estimating emissions from a first-order decay applied to waste-in-place). All four landfills choose to use the recovery-first model, which yields emissions that are one-quarter of those from the generation- first model and decreasing over 2019–2023, in contrast with the TROPOMI observations. Our TROPOMI estimates for two of the landfills agree with the generation-first model, with increasing emissions over 2019–2023 due to increasing waste-in-place or decreasing methane recovery, and are still higher than the generation-first model for the other two landfills. Further examination of the GHGRP emissions from all reporting landfills in the US shows that the 19% decrease in landfill emissions reported by the GHGI over 2005–2022 reflects an increasing preference for the recovery-first model by the reporting landfills, rather than an actual emission decrease. The generation-first model would imply an increase in landfill emissions over 2013–2022, and this is more consistent with atmospheric observations (chapter 2).
Africa has been recognized as a major driver of the recent rise in atmospheric methane, but the causes are not well understood. Here we use TROPOMI satellite observations of methane to quantify and attribute African emission trends over the August 2018–December 2024 period. We do this with monthly analytical inversions, optimizing surface fluxes at 50 km resolution on the continental scale and using two alternative bottom-up wetland emission models (WetCHARTs-CYGNSS and LPJ-EOSIM-MERRA2) as prior estimates. Our best estimate of total surface fluxes from Africa over the 2019–2024 period is 72 Tg a-1, including 32 Tg a-1 from wetlands and 23 Tg a-1 from livestock as the dominant sources. We find that the bottom-up models greatly underestimate wet- land emissions in South Sudan and Lake Chad and greatly overestimate emissions in the Congo Basin. Annual methane surface fluxes from Africa increased by 19–21 Tg a-1 over the 2019–2024 period, contributing 27% of the global emission increase in 2019–2021 and continuing to increase after 2021 even as global emissions decreased. The 2019–2024 increase in African emissions included 11 Tg a-1 from livestock, 4.3–5.7 Tg a-1 from wetlands, and 2.5–2.8 Tg a-1 from waste. The increase in livestock emissions was steady over the period while wetland emissions surged in 2020 and 2024. Previous studies attributed uncertainties in bottom-up wetland data to poor inundation data, but we find that the CYGNSS satellite inundation data match the spatial, seasonal, and interannual patterns of our optimized wetland emissions. We find instead large differences with the bottom-up emission intensities (emissions per unit inundated area), suggesting that emissions intensities are a large source of error in bottom-up wetland emission models (chapter 3).Engineering and Applied Sciences - Engineering Science
Is a linguistic model needed to build abstract event representations?
No full textA central question in cognitive development is whether language simply expresses pre-existing event concepts or plays a critical role in their construction and use. Recent findings from studies with infants, preschoolers and adults have raised the possibility that generic two-place relations (e.g., cats push rabbits) can only be represented when people have access to the transitive sentences that express them. This suggests that these concepts could be constructed as we acquire a pre-existing, external language that expresses them. To explore this hypothesis, we tested whether adult homesigners—individuals without exposure to a pre-existing language—could construct such concepts in a nonverbal imitation task. Participants viewed three instances of a given generic event (with either one or two participants), then they were given new exemplars of the same kinds (e.g., new rabbit and cat) and prompted to act. Their performance was compared to English-speaking five-year-olds. Both groups performed well in the critical two-participant condition, consistently mapping figurines of the right kind to each role. There were no group or event-type differences. Thus, homesigners have the representational resources needed to support role binding. These findings demonstrate that abstract representations of generic two-place relations can emerge without exposure to a language that models these constructions or a set of shared linguistic conventions.PsychologyAccepted Manuscrip
Engineering Vitrification Methods for Nanoscale Visualization of Dynamic Cell Processes
No full textBiological visualization is a critical tool for understanding and communicating biological processes, mechanisms, and interactions. A key gap in current visualization technology is the simultaneous imaging of processes that take place in nanoscale spatial regimes and millisecond temporal regimes. This work addresses these gaps through the development of a new tool for preparing biological samples, in addition to improvements in the sample preparation itself to allow access to a range of previously inaccessible thicker samples. Additionally, we address the development of biological cell lines expressing proteins crucial to human health and physiology, and the subsequent imaging of fluorescently tagged protein constructs we express in these cells. The data analysis to process these images is nontrivial, and we therefore develop new algorithmic techniques for analyzing the colocalization of two protein species in high density and noisy experimental contexts.Engineering and Applied Sciences - Applied Physic
Accelerating the development and application of genetic medicines
No full textPrecision gene editing offers the promise of permanently correcting alleles that cause disease in the genomes of living individuals with one-time treatments. Making this promise a reality has been a major objective of molecular biology and genetics since the descriptions of the mutations that cause cystic fibrosis—the first for any genetic disease—were reported in 1989. My doctoral work has focused on developing and applying precision gene editing technologies to correct cystic fibrosis-causing pathogenic mutations at ever increasing scales.
In Chapter 1, I discuss cystic fibrosis, an autosomal recessive disease arising from loss-of-function mutations in the CFTR gene. I summarize the pathogenesis and genetic epidemiology of the disease, and I describe the profound need for highly effective medicines that correct cystic fibrosis-causing alleles in the age of highly effective CFTR modulator therapies. I also illustrate how recent advances in gene editing technologies enable us to explore the development of genetic medicines at scale in different ways.
In Chapter 2, I describe the systematic optimization of prime editing to efficiently correct CFTR.F508∆, a three-nucleotide deletion that is the predominant cause of cystic fibrosis. By combining six recent advances in prime editing—epegRNAs, the PEmax architecture, MLH1dn, strategic silent edits, PE6, and dsgRNAs—we increased CFTR.F508∆ correction efficiency from .5% in model HEK293T cell lines to 58% in therapeutically relevant immortalized bronchial epithelial cells. In primary airway epithelial cells derived from people with cystic fibrosis, the optimized prime editing strategy enabled 25% precise correction of CFTR.F508∆, a 140-fold improvement over initial prime editing systems, with a 3.4-fold higher edit-to-indel ratio than nuclease-mediated homology directed repair approaches, and minimal off-target editing. Editing primary airway cell cultures restored CFTR ion channel function to >50% of wild-type levels, comparable to treatment with the modulator drug combination elexacaftor/tezacaftor/ivacaftor. Direct and efficient correction of CFTR.F508∆ suggests a durable one-time treatment for cystic fibrosis and provides a blueprint for optimizing prime editing to correct other pathogenic gene variants, including the >1000 other cystic fibrosis-causing alleles that have been described to date.
In Chapter 3, I present work that explores a variety of therapeutically viable viral and nonviral strategies for the efficient delivery of CFTR-correcting prime editors to the airway. Building on the high-potency CFTR.F508∆ gene editing strategies we devised in Chapter 2, we formulate helper-dependent adenoviral vectors (HD-Ad), engineered virus-like particles (eVLPs), and lipid nanoparticles (LNPs) for the delivery of prime editors to both differentiated and undifferentiated primary airway cells as well as to human bronchial epithelial cell lines. We demonstrate functional restoration of CFTR-mediated channel currents that approaches the efficacy of existing small molecule modulator drugs by delivering CFTR.F508∆-correcting prime editors via HD-Ad and eVLPs. We also develop a prime editing formulation for the modulator-ineligible allele CFTR.G542X that achieves 40% correction efficiency when delivered to 16HBEge cells via LNPs. To enable efficient packaging of CFTR-correcting prime editors into dual adeno-associated viral (AAV) vectors, we assess the ability of size-minimized SpCas9 domains to support prime editing. Our work provides insight into the relationship between editing efficiency and functional correction for CFTR, suggesting that modest (~3%) editing efficiencies can restore CFTR function to therapeutic levels but that 15-20% correction is likely needed to match the functional levels of current standard-of-care modulators. These proof-of-concept in vitro experiments lay a foundation for the evaluation of diverse delivery modalities to correct CFTR via prime editing in the airways of animal models.
In Chapter 4, I summarize efforts to leverage self-targeting lentiviral screens to accelerate the optimization of prime editing formulations for therapeutic applications. We describe LVPrime, a computational toolkit that facilitates the design and analysis of self-targeting lentiviral screens for optimizing therapeutic pegRNAs. We use these tools to screen thousands of pegRNAs for 17 cystic fibrosis-causing alleles that do not respond to modulator therapies in multiple cell types, including ~3200 pegRNAs to correct W1282X, ~2300 pegRNAs to correct c.489+1G>T, and ~1100 pegRNAs to correct R1162X. We also describe adaptive triaging, an active learning method that enables iterative optimization of pegRNA silent edit strategies in an allele- and cell type-agnostic manner. Adaptive triaging facilitates identification of high efficiency pegRNAs without the need to comprehensively screen multiple pegRNA parameters simultaneously and shows efficacy across a diversity of experimental scales. The tools and findings from this work could be applied in future translational studies that endeavor to correct disease-causing alleles via prime editing in preclinical settings.
In Chapter 5, I describe a series of studies to evolve and characterize botulinum neurotoxin (BoNT) proteases to cleave therapeutically relevant protein targets. We used phage-assisted evolution to reprogram BoNTs to cleave procaspase-1, a mediator of programmed inflammatory cell death, and NaV1.7, a voltage-gated sodium channel implicated in the sensation of acute pain. We also developed an efficient platform to broadly characterize the substrate specificity of both wild-type and evolved BoNT protease variants. Substrate profiling of evolved procaspase-1 cleaving BoNT/X variants demonstrated that evolved proteases acquired enhanced substrate sequence specificity over their wild-type BoNT/X starting point and enabled the nomination of proteome-wide off-target cleavage activity. We leveraged substrate profiling to inform evolutionary trajectories for a NaV1.7-cleaving BoNT/E, demonstrating that information on the substrate preferences of proteases can be used for the forward design of reprogrammed proteases. Together, these results expand the repertoire of experimental tools available for therapeutic protease development.Biophysic
Economic Security in Blockchain Systems
No full textA key scientific question underlying the blockchain ecosystem is to what extent the core
security properties of the protocols hold when assuming rational validators in the presence of
capable economic attackers. To what degree and at what cost can these systems be disrupted?
In this thesis, I analyze the underlying economic security properties of three of the most
fundamental decentralization consensus algorithms: proof of work (PoW), proof of stake (PoS),
and oracle information aggregation.
In Chapter 2 of this work, I counter a prominent narrative that PoW is inherently flawed in
an environment in which double-spend attacks are possible. By considering counterattacks, I
recover PoW robustness against reorganization attacks through a game-theoretic model. In
particular, I consider hashrate markets as a potential vector of attack and show that PoW
remains robust in this case.
In Chapter 3 of this work, I show novel chain reorganization and finality-delay attacks
on the PoS mechanism of Ethereum. These attacks are deviations from the ’honest’ staking
strategy, and I show that for participants staking a substantial percentage of the network’s
staked assets, these attacks can be cheap and destructive to the network.
In Chapter 4 of this work, I design an incentive mechanism for the information aggregation
of noisy signals that is highly resilient to bribery. I establish the asymptotic strength and
limitations of this mechanism against various classes of bribery including an attacker able to
condition bribes on individual reports and on the outcome of the information aggregation. I
achieve strong protection even in the latter case. To do this, I assume the presence of a source
of truth (SoT) that is prohibitively expensive for typical use but can be invoked infrequently.
This robustness to bribes is achieved even while in equilibrium there is no invocation of the
SoT.Engineering and Applied Sciences - Computer Scienc
The Politics of Purity: Democratic Transformations in Nepal's Southern Borderland
No full textThis dissertation considers the relationship between endurance and change within political life in Nepal. Over the preceding decades, the country has been part of a regional phenomenon that scholars have called the “democratization of democracy”—a process in which groups historically marginalized in formal democratic politics have mobilized to assert their presence, demand social justice, and claim political power. In Nepal, these struggles were institutionally realized in the 2008 abolition of the monarchy and the establishment of a federal system, developments that many Nepalis heralded at the time as the beginning of “New Nepal,” one no longer based in the inequalities and hierarchies of history. Drawing on data collected from two years of ethnographic fieldwork in the city of Birgunj in the country’s southern plains, the dissertation examines this transformation through an analysis of political life in Nepal over the longue durée. The southern plains region, known locally as the Tarai or Madhesh, has a history of political marginalization, and activists there were at the forefront of struggles for a federal system. The dissertation considers why, in the years after the establishment of a federal republic, there is nostalgia for the king in some circles in Birgunj. The chapters illustrate the ways in which older social and political forms of space, community, legitimacy, and rule have inflected newer political formations and dynamics, shaping how things like democracy, political belonging, accountability, and nationalism are imagined and practiced in contemporary Nepal. In doing so, the dissertation contends that historical orders and imaginaries have a continued resonance in contemporary political life even as they are changed through processes of modernization and democratization.Anthropolog
Mapping determinants of protein specificity with high-throughput mutagenesis and probabilistic modeling
No full textProteins evolve by iteratively varying their sequences to traverse fitness landscapes for different functions, driving the emergence of novelty at larger scales. Understanding the design principles of proteins requires a quantitative understanding of the map between a given protein sequence and an array of different fitness landscapes—for example, for different substrates or ligands. Scientists have studied this question using patterns found in natural sequences and by collecting their own maps of how variation in protein sequence affects different cellular functions. In this thesis, I will begin by broadly reviewing the problem of understanding and predicting protein sequence-function landscapes and then introduce the particular system most of interest to this thesis: membrane transporter proteins. Transporters’ sequences must encode functional rules for highly tuned specificities across chemical space, as well as for carefully energetically balanced transitions between structurally distinct conformations.
First, in Chapter 2, I briefly discuss my work on the evolutionary history of one such family of transporters, the Natural resistance associated macrophage transporters (or Nramps). In Chapter 3, I then use high-throughput mutagenesis of a library guided by structure and evolution to systematically dissect the determinants of metal import and specificity in a model homolog of this family, DraNramp. I find that a key set of core residues in the first and second shells is essential to allow for import of the typically excluded substrate of Mg2+. A wide range of surrounding residues throughout the protein’s core additionally act as hotspots for modulating both epistatic interactions between mutations within one fitness landscape and specificity modulation between fitness landscapes. I then propose a theoretical model in which residues modulating the protein’s conformational equilibrium could underlie both effects.
In Chapter 4, I build a new database of results from multiplexed functional assays of specificity, including my own, to ask if and how probabilistic models trained on natural sequence information can be used to guide our search for protein variants that alter substrate specificity. I find that many popular machine learning-based approaches systematically bias their predictions against variants that alter specificity by conditioning on local sequence context. To address this, I propose a simple weighted difference between models that can guide the sampling of sequence libraries to enrich for variants with altered specificity. Finally, in Chapter 5, I discuss work done collaboratively with Jacob Licht and Rachelle Gaudet in which we systematically analyzed the commonalities and differences between conformational transitions in the superfamily of membrane transporters containing DraNramp, identifying a common core “rocking” mechanism with additional protein-specific variations, which we categorize.
In sum, I have done several projects analyzing protein specificity and evolution from several angles: experimental mutagenesis, machine learning, and structural analysis. These results demonstrate advances in our ability to predict and understand the determinants of protein specificity but additionally suggest that a properly predictive understanding of the design rules of protein specificity remains out of reach. In Chapter 6, I discuss prospects for this field.Biophysic
Engineering Cancer Vaccines with Ionic Liquids
No full textCancer remains one of the leading causes of death worldwide, and despite advances in detection and treatment, achieving durable immune protection against recurrence remains a major challenge. Immunotherapy harnesses the body’s own immune system to recognize and eliminate cancer cells and has revolutionized cancer treatment. However, many patients fail to respond due to tumor heterogeneity and immune suppression. Cancer vaccines that utilize whole tumor cells offer a promising route to generate antitumor immunity by presenting a broad set of highly matched antigens as they use tumors as the source of antigen. Despite their rich antigen repertoire and high personalization, conventional whole tumor cell cancer vaccines have demonstrated limited clinical success due to insufficient immunogenicity. Traditional approaches, such as genetic engineering or cell surface modification, can enhance immune stimulation but are often labor-intensive, technically complex, and limited in scalability. Therefore, a simple, modular, and effective method to integrate potent immunostimulatory signals directly into whole tumor cell vaccines could substantially enhance efficacy, manufacturability, and scalability.
Ionic liquids (ILs) are bulky salts comprised of organic cations and anions with a melting point below 100°C. They are highly tunable and have previously been demonstrated to have abilities in crossing biological barriers. In my dissertation research, I present a facile method to load immunostimulatory CpG oligodeoxynucleotides (ODN) into tumor cells undergoing immunogenic cell death using an ionic liquid-containing cocktail, permitting cell modification in a single step for cancer vaccination. Results indicate that these adjuvant-loaded tumor cells (called IL-Vax) enhance the uptake by dendritic cells (DCs) and their subsequent activation. IL-Vax offers strong protection against tumor growth in a prophylactic setting with 100% survival in vaccinated mice 60 days post-challenge. Furthermore, IL-Vax combined with immune checkpoint blockade leads to slowed tumor growth and a 60% increase in median survival in B16F10 melanoma tumor-bearing mice. Our strategy enables modularity to load different cargos or cargo combinations which may be conducive for additional tailoring of such vaccines and potentially other cell loading applications.Engineering and Applied Sciences - Engineering Science