VictorJuiz/thesis huggingface dataset:
Identification of genetic regulators of antibody-drug conjugate toxicity using CRISPR-Cas9 screens

Antibody-drug conjugates (ADCs) selectively deliver highly toxic chemotherapeutic agents to target antigen-expressing cells and have become an important cancer treatment in recent years. However, the molecular mechanisms by which ADCs are internalized and activated within cells remain unclear. Here we use CRISPR-Cas9 screens to identify genes that control the toxicity of ADCs targeting different cancer antigens and cell types. Our results demonstrate critical roles for a range of known and novel intracellular trafficking regulators in ADC toxicity. We identify and characterize C18orf8/RMC1 as a regulator of ADC toxicity through its role in endosomal maturation. Through comparative analysis of CRISPR screens with ADCs bearing a noncleavable linker versus a cleavable valine-citrulline (VC) linker, we show that a subset of late endo/lysosomal and retrograde trafficking regulators selectively influence toxicity of noncleavable linker ADCs. We further show that cleavable VC linkers are rapidly processed upon internalization and therefore surprisingly appear to bypass the requirement of lysosomal delivery. Lastly, we show that depletion of sialic acids sensitizes cells to anti-CD22 and anti-Her2 ADCs by increasing the rate of ADC lysosomal delivery. In contrast, deletion of sialic acid biosynthesis genes protects cells from anti-CD30 ADCs, suggesting that proper sialylation is required for CD30 cell surface-expression. Together, these results reveal novel regulators of endolysosomal trafficking, provide important insights to guide future ADC design, and identify candidate combination therapy targets as well as potential mechanisms of ADC resistance.

Keywords: Genetics.
Advisor: Bassik Michael 
Brunet Anne
Gitler Aaron D. 
Pfeffer Suzanne 
Wyss-Coray Anton
Identification of synergistic cancer drug combinations and host modifiers of legionella pneumophila pathogenesis using CRISPR-Cas9 screens

The recent development of CRISPR-Cas9 technology has enabled robust high-throughput gene deletion screening as a powerful tool for discovery in mammalian cells. In this work, I present two diverse applications of CRISPR-Cas9 pooled high-throughput functional screening. First, I describe the development of a CRISPR-based double knockout system, which we use to screen for synergistic cancer drug combinations. While combination therapies can be an effective way to reduce drug resistance to targeted single-drug therapies, conventional methods of testing drug combinations have poor scalability. Using the CRISPR double knockout system, we generate a large-scale mammalian genetic interaction map comprised of ~490,000 double-sgRNA combinations corresponding to 21,321 drug target pairs in a search for synergistically lethal drug combinations in leukemia cells. We discover combination drug therapy targeting BCL2L1 (BCL-XL) and MCL1 as a promising therapy for leukemia with highly synergistic cancer-specific effects. Next, I describe a second application of CRISPR screening towards exploring the biology of a unique intracellular bacterial pathogen Legionella pneumophila that is able to drastically disrupt host cell processes during infection. I perform a genome-wide single knockout screen and secondary targeted screens to discover hundreds of host factors that mediate the pathogenesis of L. pneumophila during infection of human monocyte and macrophage cells. In addition to identifying two previously uncharacterized genes, C1ORF43 and KIAA1109 as novel regulators of phagocytosis, we highlight the GTPase RAB10 and its chaperone RABIF as key regulators of L. pneumophila replication and vacuole maturation. Collectively, this work demonstrates the power of high-throughput genetic screens for discovering novel drug combinations for cancer and improving our understanding of L. pneumophila host-pathogen interactions.

Advisor: Bassik Michael 
Attardi Laura 
Jackson Peter K. (Peter Kent)
Sage Julien
Identifying cell types and cellular contexts relevant for complex traits

We now know of many thousand genetic mutations associated with human phenotypes, and yet the process of translating this information into an understanding of biology has lagged. A crucial step forward is to identify trait-relevant cellular states or contexts, which when disrupted have a significant effect on a phenotype. With knowledge of the specific pathological contexts, one can develop model systems required for in-depth mechanistic study and further biological insights. Thus, in this thesis I investigated approaches of aggregating the many effects of variants associated with gene expression regulatory networks to identify trait-relevant cell types and cellular contexts. Through this work I demonstrate that to realize the lofty -- albeit achievable -- goals of human genetics, we must rethink strategies of producing biological knowledge from genetic studies.

Keywords: Biomedical Informatics.
Advisor: Pritchard Jonathan (Jonathan K.) 
Greenleaf William James 
Kundaje Anshul
Identity and Self-Presentation in Computer Mediated Environments

Identity and self-presentation have been prominent areas of study in the fields of communication and psychology for decades. Despite this lengthy history of study, interpretations of the two concepts are varied. With the introduction of computer mediated environments such as social media, scholars’ understanding of these concepts have been put to the test. This paper analyzes, categorizes, and synthesizes leading research in the area of identity and self-presentation in computer mediated environments. This literature review is guided by two overarching research questions: (1) How does identity influence self presentation online? and (2) How does self presentation online influence identity? Similarities and differences between theories as well as gaps in the literature and societal implications are discussed.

Keywords:  Department of Communication
Communication
Media Studies
online identity
social media identity
digital media identity
online self-presentation
online self perception
Thesis
Advisor: Harari Gabriella
Image descriptor aggregation for efficient retrieval

As more and more visual content is created every day, it is critical to make sense of large image databases. Searching such databases using a query image is the goal of content-based image retrieval, or visual search. At the core of this task is the trade-off between search speed and accuracy. This work proposes to use aggregation to improve this trade-off. By aggregating information across images we can directly compare a query image with sets of images represented by a single descriptor. We show that this can make the search considerably faster with a very limited loss of accuracy. The main questions explored in this work relate to how to best perform this aggregation: how to choose which images should be aggregated, how to represent a set of images with a single descriptor, and finally how to index these descriptors so as to maximize the search speed. We show that it is beneficial to aggregate images that share similar characteristics, such as images captured from nearby viewpoints, and that the higher level of abstraction achieved by searching aggregated descriptors instead of original images allows for considerable speed gains by reducing the size of the database. Our next contribution is to show that improving the representation of a given set of image descriptors can lead to additional gains: the representation using generalized max pooling is much better for retrieval tasks. More complex parametric methods do not seem to show additional benefits compared to simple pooling of optimized image descriptors. Finally, we show the importance of indexing the aggregated descriptors into a well-chosen hierarchical structure that combines the benefits of a coarse database search at the higher levels of the hierarchy with the benefits of a fine database search at the lower levels. All these contributions jointly combine to drastically improve retrieval speed without degrading the accuracy. This study is rooted in a theoretical framework that we develop as a basis for aggregation, and we then show that the insights learned from this framework carry on to a wide range of real-world applications such as 3D object retrieval, indoor localization and person re-identification.

Keywords: Electrical Engineering.
Advisor: Girod Bernd 
Wandell Brian A. 
Wetzstein Gordon
Imaging diagnostics for in situ measurements within challenging environments

The development, validation, and deployment of imaging diagnostic systems and corresponding image processing algorithms are presented that enable in situ measurements within two challenging environments: aquatic environments and high temperature, high pressure reactive gas environments. Image processing algorithms for a single-camera defocusing digital particle image velocimetry (DDPIV) imaging system were developed to perform three-dimensional tracking of natural particulate and organisms in the water column within a 10 cm x 10 cm x 20 cm volume at 60 Hz. The developed underwater imaging system was deployed at the Marine Biological Laboratory (Woods Hole, MA), and measurements of Mnemiopsis leidyi ctene rows are presented to demonstrate the measurement capabilities of the developed system. An imaging diagnostic was developed to image the structure of combustion events behind reflected shock waves. An intensified high-repetition-rate imaging system was used to acquire images of OH* chemiluminescence (near 308 nm) through a fused quartz shock tube end-wall window at 10-33 kHz during the combustion of n-heptane. The imaging technique enabled observation (12 cm x 12 cm) of the main ignition event in the core of the shock tube, that corresponds to typical markers of ignition (e.g., pressure rise).

Keywords: Mechanical Engineering.
Advisor: Dabiri John O. (John Oluseun)
Koseff Jeffrey Russell 
Ouellette Nicholas (Nicholas Testroet)
The impact of environmental exposures on the human and mouse gut microbiome

Advances in sequencing technologies have enabled unprecedented opportunities to characterize the effects of the microbiome on host physiology and the development of disease. Understanding the factors that influence microbiome colonization and stability and that shape immune responses is critical to harness the microbiome as a therapeutic target. In this dissertation, I explore microbiome associations with aberrant immunological conditions. I present my research in human and mouse models characterizing the impact of microbiome altering exposures during critical immune development windows. Chapter 2 describes the first interventional study to assess the impact of common household antimicrobials on the developing gut microbiome during the first year of life. Chapter 3 evaluates the efficacy of prebiotics to enrich for microbes that produce immunomodulatory compounds to offset life-threatening gut inflammatory responses. Chapter 4 describes leveraging the known microbiome differences in mouse models to mine for putative protective species in colonic inflammation. Chapter 5 addresses the application of novel sequencing technologies to overcome the current limitations of measuring the gut microbiome. Overall these chapters provide a better understanding of measuring host-microbial interactions in the context of immune regulation.

Keywords: Genetics.
Advisor: Bhatt Ami (Ami Siddharth) 
Pritchard Jonathan (Jonathan K.) 
Montgomery Stephen
Negrin Robert S.
Implementation and Testing of Reduced-Order Models in a General Reservoir Simulation Setting

Reduced-order modeling can lead to computational savings in reservoir management applications when many related models must be simulated, as is the case, for example, in production optimization. In this study, we implement two reduced-order modeling procedures into our in-house simulator AD-GPRS (Automatic Differentiation General Purpose Research Simulator). The methods considered are a POD-Only (proper orthogonal decomposition) technique and a Gauss-Newton with approximated tensors (GNAT) procedure. We believe this to be the first implementation of these methods in a general-purpose reservoir simulation setting. Both approaches entail offline (training runs plus preprocessing) and online (runtime) computations. POD-Only involves expensive online matrix multiplications, which greatly limit the attainable speedup. GNAT, by contrast, entails much less costly runtime computations.
The methods are applied to a 2D oil-water model and to a 3D, four-component, oil-gas compositional case. These models contain 13,000 and 4800 grid blocks, respectively. Some amount of numerical experimentation is required to determine the appropriate POD-Only and GNAT parameters. We show that, using parameters that provide accurate reduced-order model results, POD-Only is actually slower than the full-order AD-GPRS simulations, but GNAT can provide speedup of around 2-3X for the cases considered. More substantial speedup is achieved in some compositional cases for which AD-GPRS encounters numerical difficulties. Speedup could be further enhanced by eliminating the AD-GPRS computations that are not required for GNAT. Application of POD-Only and GNAT on locally refined versions of the original models is, unfortunately, not very successful. This may be due to some of our detailed numerical treatments. This issue should be clarified and, if possible, rectified in future work.

Keywords:  Energy Resources Engineering
School of Earth Energy &amp; Environmental Sciences
Reduced Order Modeling
Reservoir Simulation
Simulation Acceleration
Thesis
Advisor: Durlofsky Louis J.
Tchelepi Hamdi
Improved drug development and disease diagnosis using tailored light-molecule interactions

Light-matter interactions are critical for many modern biomedical techniques, from fluorescence-based DNA sequencing to photoablation of tumors. Even so, existing optical diagnostic tools can be slow and existing drug treatments often have adverse side effects. Here, we explore two methods for improving drug purification and disease diagnosis based on tailored light-molecule interactions. First, I will discuss how the optical-frequency magnetic resonances of dielectric nanoparticles can be used to separate chiral molecules for enantiopure pharmaceuticals. Currently, half of pharmaceuticals on the market are chiral, but 90% of these are sold as mixtures since existing chemical methods are expensive and time consuming. Yet, the non-therapeutic enantiomer can reduce efficacy and lead to adverse side effects. Illumination with circularly polarized light provides a potentially cost-effective and versatile alternative but can due to the weak nature of chiral light-matter interactions. Using silicon nanospheres as a model system, I explore electromagnetic design parameters to enhance enantioselective light absorption in chiral molecules while maintaining total molecular absorption rates. With optimized particles, enhancements in the rates of enantioselective absorption can reach 7X, leading to a projected 50% increase in yield for the separation of the molecule camphor. Next, I will discuss how Raman spectroscopy can enable rapid identification of bacteria. I acquire Raman spectra from over 60,000 bacterial cells, spanning 30 strains from 22 species and covering 95% of all bacterial infections treated at Stanford Hospital. This large reference dataset allows us to apply machine learning techniques to accurately identify bacteria strains and antibiotic treatments. I show how this method translates to clinical patient samples, predicting treatment for 25 patient isolates with 99.0±1.9% accuracy. This work lays the foundation for a technique that could allow for accurate and targeted treatment of bacterial infections within hours, reducing healthcare costs and antibiotics misuse, limiting antimicrobial resistance, and improving patient outcomes.

Keywords: Applied Physics.
Advisor: Brongersma Mark L. 
Dionne Jennifer Anne 
Banaei Niaz
Improved Referral of Very Low Birthweight Infants to High-Risk Infant Follow-Up in California

Background
A statewide initiative was implemented in California in June 2013 to address disparities in referral of very low birthweight (VLBW, birthweight less than 1500 grams) infants to high-risk infant follow-up programs. We examined the change in high-risk infant follow-up referral rates and identified risk factors associated with non-referral in the pre- and post-initiative periods.
Methods
We included VLBW infants born 2010-2016 in the population-based California Perinatal Quality Care Collaborative who survived to discharge home. We used multivariable logistic regression to examine factors associated with referral and derive risk-adjusted referral rates by NICU and region.
Results
83% of VLBW infants were referred in the pre-initiative period (January 2010-June 2013); 95% were referred in the post-initiative period (July 2013-December 2016). Referral rates improved the most (greater than or equal to 15%) for infants born at greater than 33 weeks gestation, with birthweight 1251-1500 grams, and born in intermediate and lower-volume NICUs. Post-intervention, Hispanic race-ethnicity, small for gestational age status, congenital anomaly, and major morbidity were no longer associated with decreased odds of referral. Lower birthweight, outborn status and higher NICU volume were no longer associated with increased referral. African-American race-ethnicity was associated with lower odds of referral, and higher NICU level with higher odds, during both time periods. Referral improved in many previously poor-performing NICUs and regions.
Conclusion
High-risk infant follow-up referral of VLBW infants improved significantly and disparities decreased following the statewide improvement initiative. Our results demonstrate the benefit of a targeted initiative in California, which may be applicable to other quality collaboratives.

Keywords:  Department of Health Research & Policy
neonatology
Thesis
Advisor: Lee Henry
Hintz Susan
Carmichael Suzan
Shaw Gary
Improving outcomes for Trastuzumab-resistant HER2+ breast cancer using combination treatment with anti-CD47 mAb

Trastuzumab is a targeted anti-cancer monoclonal antibody that binds the HER2 molecule on HER2-overexpressing breast cancers. Trastuzumab is effective in a neoadjuvant setting when combined with chemotherapy for HER2+ breast cancers that are caught and treated early. However, the majority of patients with advanced HER2+ disease display Trastuzumab resistance: nearly 70% do not respond to initial treatment, and the remaining 30% initially respond yet acquire resistance within one year of treatment. Can anything be done to help patients who develop Trastuzumab resistance? Our lab has previously shown that blockade of CD47, an innate immune checkpoint molecule that cancer cells use to evade programmed cell removal works well in combination with targeted anti-cancer monoclonal antibodies via an Fc-receptor-mediated phagocytosis mechanism. I hypothesize that Trastuzumab may still achieve clinical efficacy in Trastuzumab-resistant HER2+ cancers when used in combination with monoclonal antibodies against CD47.

Advisor: Weissman Irving L. 
Attardi Laura 
Pegram Mark 
Shizuru Judith Anne
Impurity quantum phase transitions in quantum dot nanostructures

Electronic systems subject to competing interactions can end up in different phases as the balance between these interactions shifts. When a quantum critical point separates these phases, exotic electronic behavior often marks the vicinity of the transition. In this work, we construct nanopatterned devices to probe such critical phenomena. The basic element of our devices is the GaAs/AlGaAs quantum dot, an isolated region of electronic charge which is coupled to a two-dimensional gas of weakly interacting electrons. We use different designs of quantum dots to realize different models. The first device studied in this work realizes the spin two-channel Kondo ('spin 2CK') model. In this model, a single impurity (i.e. a single spin-degenerate dot) is coupled to two separate reservoirs. When the couplings to both reservoirs are unequal, the more strongly coupled reservoir screens the impurity spin degeneracy and forms a many-body singlet; this is known as the Kondo effect. When both reservoirs are coupled equally strongly, a non-Fermi liquid ground state arises as a result of the overscreening by both reservoirs. We probe the anomalous scaling properties of this state, and show how it transitions into a more conventional Fermi liquid under the influence of various perturbations. The second device is first operated as a single metallic quantum dot in the quantum Hall regime. Spin degeneracy is broken, but the device can be tuned such that there is now a charge degeneracy which can then be screened by coupling to a reservoir. We tune to and away from equal couplings to see the effect of the two-channel Kondo state. Finally, we operate the second device in its full form as a double-dot device, to explore the competition between dot-lead and interdot interactions.

Keywords: Physics.
Advisor: Goldhaber-Gordon David
Kastner Marc A.
Kivelson Steven
In depth study of in situ combustion kinetics and coupling to flow

This thesis furthers understanding of three topics that are critical to In Situ Combustion (ISC) processes: crude oil kinetics, the tools used to model accurately and practically complex kinetic behavior and how oil kinetics couples with flow, especifically in systems with complex permeability distributions. On kinetics, we study the effects of pressure on crude oil kinetics for a large range of pressures and heating rates (1.5, 2.0, 5.0, 7.5, 8.0, 10.0, 15.0, 20.0, and 30.0 °C/min. Our results show that the activation energy of global reactions is not affected by changes in both partial pressure (21, 42, 50, 84, 209, and 250 psi) and absolute pressure (100-2000 psi) with values ranging 50 and 60 kJ/mol for the LTO regime and 90-100 kJ/mol for the HTO regime. Three different crude oils were tested. For one of the crude oils, we observe an increasing trend in oxygen consumption as absolute pressure is increased up to a critical pressure (~500 psi), after which it remains constant. This is associated to light component evaporation at low pressures, decreasing the amount of oil available for fuel generation. Total oxygen consumption starts to decrease again at very large pressures (> 1500 psi) and we associate it to water phase behavior at these conditions. We then propose a workflow and construct the corresponding tools to match Arrhenius-based reaction models for lab-scale numerical simulation models. The workflow is validated by matching multiple reaction models to experimental data obtained for a large range of heating rates (1.5-30 °C/min). On flow, we study instabilities that have been observed during combustion tube experiments by analyzing simple analogous problems and performing perturbation analysis from which we derive a new stability parameter. Finally, we propose and test a new experimental approach to observe directly how oil kinetics evolve under flow conditions by using thermal imaging and combining images with ramped temperature oxidation (RTO) experimental data. Results show that the reaction zone in lab conditions is in the order of centimeters. Reaction zone thicknesses range between 10-30 cm for the LTO zone and 10-50 cm for the HTO zone. Furthermore, we show how the new approach can help investigate complex systems and behaviors, such as oscillations, that could not otherwise be studied with conventional experiments.

Keywords: Energy Resources Engineering.
Advisor: Kovscek Anthony R. (Anthony Robert) 
Castanier Louis M.
Gerritsen Margot (Margot G.)