Interpreting Human Genetic Variation through Genomics and In Vivo Models

Improvements in genomic technology, both in the increased speed and reduced cost of sequencing, have expanded the appreciation of the abundance of human genetic variation. However the sheer amount of variation, as well as the varying type and genomic content of variation, poses a challenge in understanding the clinical consequence of a single mutation. This work uses several methodologies to interpret the observed variation in the human genome, and presents novel strategies for the prediction of allele pathogenicity.

Using the zebrafish model system as an in vivo assay of allele function, we identified a novel driver of Bardet-Biedl Syndrome (BBS) in CEP76. A combination of targeted sequencing of 785 cilia-associated genes in a cohort of BBS patients and subsequent in vivo functional assays recapitulating the human phenotype gave strong evidence for the role of CEP76 mutations in the pathology of an affected family. This portion of the work demonstrated the necessity of functional testing in validating disease-associated mutations, and added to the catalogue of known BBS disease genes.

Further study into the role of copy-number variations (CNVs) in a cohort of BBS patients showed the significant contribution of CNVs to disease pathology. Using high-density array comparative genomic hybridization (aCGH) we were able to identify pathogenic CNVs as small as several hundred bp. Dissection of constituent gene and in vivo experiments investigating epistatic interactions between affected genes allowed for an appreciation of several paradigms by which CNVs can contribute to disease. This study revealed that the contribution of CNVs to disease in BBS patients is much higher than previously expected, and demonstrated the necessity of consideration of CNV contribution in future (and retrospective) investigations of human genetic disease.

Finally, we used a combination of comparative genomics and in vivo complementation assays to identify second-site compensatory modification of pathogenic alleles. These pathogenic alleles, which are found compensated in other species (termed compensated pathogenic deviations [CPDs]), represent a significant fraction (from 3 – 10%) of human disease-associated alleles. In silico pathogenicity prediction algorithms, a valuable method of allele prioritization, often misrepresent these alleles as benign, leading to omission of possibly informative variants in studies of human genetic disease. We created a mathematical model that was able to predict CPDs and putative compensatory sites, and functionally showed in vivo that second-site mutation can mitigate the pathogenicity of disease alleles. Additionally, we made publically available an in silico module for the prediction of CPDs and modifier sites.

These studies have advanced the ability to interpret the pathogenicity of multiple types of human variation, as well as made available tools for others to do so as well.

Genetic and Molecular Basis of Encapsulation and Capsule Diversity in Kingella kingae

Kingella kingae is a bacterial pathogen that is increasingly recognized as an etiology of septic arthritis, osteomyelitis, bacteremia, and endocarditis in young children. The pathogenesis of K. kingae disease starts with bacterial adherence to the respiratory epithelium of the posterior pharynx. Previous work has identified type IV pili and a trimeric autotransporter protein called Knh (Kingella NhhA homolog) as critical factors for adherence to human epithelial cells. Additional studies established that the presence of a polysaccharide capsule interferes with Knh-mediated adherence. Given the inhibitory role of capsule during adherence we sought to uncover the genes involved in capsule expression to understand how capsule is elaborated on the cell surface. Additionally, this work aimed to further characterize capsule diversity among K. kingae clinical isolates and to investigate the relationship between capsule type and site of isolation.

We first set out to identify the carbohydrates present in the K. kingae capsule present in the prototype strain 269-492. Glycosyl composition and NMR analysis of surface extractable polysaccharides demonstrated two distinct polysaccharides, one consisting of GalNAc and Kdo with the structure →3)-β-GalpNAc-(1→5)-β-Kdop-(2→ and the other containing galactose alone with the structure →5)-β-Galf-(1→.

To discern the two polysaccharides we disrupted the ctrA gene required for surface localization of the K. kingae polysaccharide capsule and observed a loss of GalNAc and Kdo but no effect on the presence of Gal in bacterial surface extracts. In contrast, deletion of the pamABCDE locus involved in production of a reported galactan exopolysaccharide eliminated Gal but had no effect on the presence of GalNAc and Kdo in surface extracts. These results established that K. kingae strain KK01 produces a polysaccharide capsule with the structure →3)-β-GalpNAc-(1→5)-β-Kdop-(2→ and a separate exopolysaccharide with the structure →5)-β-Galf-(1→.

Having established that K. kingae produces a capsule comprised of GalNAc and Kdo, we next set out to identify the genetic determinants of capsule through a transposon mutagenesis screen. In addition to the previously identified ctrABCD operon, lipA, lipB, and a putative glycosyltransferase termed csaA (capsule synthesis region A gene A) were found to be essential for the production of surface-localized capsule. The ctr operon, lipA, lipB, and csaA were found to be present at unlinked locations throughout the genome, which is atypical for gram-negative organisms that elaborate a capsule dependent on an ABC-type transporter for surface localization. Through examining capsule localization in the ctrA, lipA, lipB, and csaA mutant strains, we determined that the ctrABCD, lipA/lipB, and csaA gene products respectively function in capsule export, assembly, and synthesis, respectively. The GalNAc transferase and Kdo transferase domains found in CsaA further support its role in catalyzing the synthesis of the GalNAc-Kdo capsule in the K. kingae prototype strain.

To investigate the capsule diversity that exists in K. kingae we screened a panel of strains isolated from patients with invasive disease or healthy carriers for the csaA capsule synthesis locus. We discovered that Kingella kingae expresses one of 4 capsule synthesis loci (csa, csb, csc, or csd) associated with a capsule consisting of Kdo and GalNAc (type a), Kdo and GlcNAc (type b), Kdo and ribose (type c), and GlcNAc and galactose (type d), respectively. Cloning of the csa, csb, csc, or csd locus into the empty flanking gene region in a non-encapsulated mutant (creation of an isogenic capsule swap) was sufficient to produce either the type a, type b, or type c capsule, respectively, further supporting the role of these loci in expression of a specific polysaccharide linkage. Capsule type a and capsule type b accounted for 96% of invasive strains. Conversely, capsule type c and capsule type d were found disproportionately among carrier isolates, suggesting that capsule type is important in promoting invasion and dissemination.

In conclusion, we discovered that Kingella kingae expresses a polysaccharide capsule and an exopolysaccharide on its surface that require distinct genetic loci for surface localization. Further investigation into genetic determinants of encapsulation revealed the loci ctrABCD, lipA/lipB, and a putative glycosyltransferase are required for capsule expression, with the gene products having roles in capsule export, assembly, and synthesis, respectively. The putative glycosyltransferase CsaA was determined to be a bifunctional enzyme with both GalNAc-transferase and Kdo-transferase activity. Furthermore, we discovered a total of 4 capsule types expressed in clinical isolates of K. kingae, each with a distinct capsule synthesis locus. The variation in the proportion of capsule types found between invasive strains and carriage strains suggest that capsule type is important in promoting invasion and dissemination. Taken together, this work expands our knowledge of the capsule types expressed among K. kingae carrier and invasive isolates and provides insights into the common genetic determinants of capsule expression. These contributions may lead to selecting clinically relevant capsule types to develop into a capsule based vaccine to prevent K. kingae colonization.

Advanced Techniques for Image Quality Assessment of Modern X-ray Computed Tomography Systems

X-ray computed tomography (CT) imaging constitutes one of the most widely used diagnostic tools in radiology today with nearly 85 million CT examinations performed in the U.S in 2011. CT imparts a relatively high amount of radiation dose to the patient compared to other x-ray imaging modalities and as a result of this fact, coupled with its popularity, CT is currently the single largest source of medical radiation exposure to the U.S. population. For this reason, there is a critical need to optimize CT examinations such that the dose is minimized while the quality of the CT images is not degraded. This optimization can be difficult to achieve due to the relationship between dose and image quality. All things being held equal, reducing the dose degrades image quality and can impact the diagnostic value of the CT examination.

A recent push from the medical and scientific community towards using lower doses has spawned new dose reduction technologies such as automatic exposure control (i.e., tube current modulation) and iterative reconstruction algorithms. In theory, these technologies could allow for scanning at reduced doses while maintaining the image quality of the exam at an acceptable level. Therefore, there is a scientific need to establish the dose reduction potential of these new technologies in an objective and rigorous manner. Establishing these dose reduction potentials requires precise and clinically relevant metrics of CT image quality, as well as practical and efficient methodologies to measure such metrics on real CT systems. The currently established methodologies for assessing CT image quality are not appropriate to assess modern CT scanners that have implemented those aforementioned dose reduction technologies.

Thus the purpose of this doctoral project was to develop, assess, and implement new phantoms, image quality metrics, analysis techniques, and modeling tools that are appropriate for image quality assessment of modern clinical CT systems. The project developed image quality assessment methods in the context of three distinct paradigms, (a) uniform phantoms, (b) textured phantoms, and (c) clinical images.

The work in this dissertation used the “task-based” definition of image quality. That is, image quality was broadly defined as the effectiveness by which an image can be used for its intended task. Under this definition, any assessment of image quality requires three components: (1) A well defined imaging task (e.g., detection of subtle lesions), (2) an “observer” to perform the task (e.g., a radiologists or a detection algorithm), and (3) a way to measure the observer’s performance in completing the task at hand (e.g., detection sensitivity/specificity).

First, this task-based image quality paradigm was implemented using a novel multi-sized phantom platform (with uniform background) developed specifically to assess modern CT systems (Mercury Phantom, v3.0, Duke University). A comprehensive evaluation was performed on a state-of-the-art CT system (SOMATOM Definition Force, Siemens Healthcare) in terms of noise, resolution, and detectability as a function of patient size, dose, tube energy (i.e., kVp), automatic exposure control, and reconstruction algorithm (i.e., Filtered Back-Projection– FPB vs Advanced Modeled Iterative Reconstruction– ADMIRE). A mathematical observer model (i.e., computer detection algorithm) was implemented and used as the basis of image quality comparisons. It was found that image quality increased with increasing dose and decreasing phantom size. The CT system exhibited nonlinear noise and resolution properties, especially at very low-doses, large phantom sizes, and for low-contrast objects. Objective image quality metrics generally increased with increasing dose and ADMIRE strength, and with decreasing phantom size. The ADMIRE algorithm could offer comparable image quality at reduced doses or improved image quality at the same dose (increase in detectability index by up to 163% depending on iterative strength). The use of automatic exposure control resulted in more consistent image quality with changing phantom size.

Based on those results, the dose reduction potential of ADMIRE was further assessed specifically for the task of detecting small (<=6 mm) low-contrast (<=20 HU) lesions. A new low-contrast detectability phantom (with uniform background) was designed and fabricated using a multi-material 3D printer. The phantom was imaged at multiple dose levels and images were reconstructed with FBP and ADMIRE. Human perception experiments were performed to measure the detection accuracy from FBP and ADMIRE images. It was found that ADMIRE had equivalent performance to FBP at 56% less dose.

Using the same image data as the previous study, a number of different mathematical observer models were implemented to assess which models would result in image quality metrics that best correlated with human detection performance. The models included naïve simple metrics of image quality such as contrast-to-noise ratio (CNR) and more sophisticated observer models such as the non-prewhitening matched filter observer model family and the channelized Hotelling observer model family. It was found that non-prewhitening matched filter observers and the channelized Hotelling observers both correlated strongly with human performance. Conversely, CNR was found to not correlate strongly with human performance, especially when comparing different reconstruction algorithms.

The uniform background phantoms used in the previous studies provided a good first-order approximation of image quality. However, due to their simplicity and due to the complexity of iterative reconstruction algorithms, it is possible that such phantoms are not fully adequate to assess the clinical impact of iterative algorithms because patient images obviously do not have smooth uniform backgrounds. To test this hypothesis, two textured phantoms (classified as gross texture and fine texture) and a uniform phantom of similar size were built and imaged on a SOMATOM Flash scanner (Siemens Healthcare). Images were reconstructed using FBP and a Sinogram Affirmed Iterative Reconstruction (SAFIRE). Using an image subtraction technique, quantum noise was measured in all images of each phantom. It was found that in FBP, the noise was independent of the background (textured vs uniform). However, for SAFIRE, noise increased by up to 44% in the textured phantoms compared to the uniform phantom. As a result, the noise reduction from SAFIRE was found to be up to 66% in the uniform phantom but as low as 29% in the textured phantoms. Based on this result, it clear that further investigation was needed into to understand the impact that background texture has on image quality when iterative reconstruction algorithms are used.

To further investigate this phenomenon with more realistic textures, two anthropomorphic textured phantoms were designed to mimic lung vasculature and fatty soft tissue texture. The phantoms (along with a corresponding uniform phantom) were fabricated with a multi-material 3D printer and imaged on the SOMATOM Flash scanner. Scans were repeated a total of 50 times in order to get ensemble statistics of the noise. A novel method of estimating the noise power spectrum (NPS) from irregularly shaped ROIs was developed. It was found that SAFIRE images had highly locally non-stationary noise patterns with pixels near edges having higher noise than pixels in more uniform regions. Compared to FBP, SAFIRE images had 60% less noise on average in uniform regions for edge pixels, noise was between 20% higher and 40% lower. The noise texture (i.e., NPS) was also highly dependent on the background texture for SAFIRE. Therefore, it was concluded that quantum noise properties in the uniform phantoms are not representative of those in patients for iterative reconstruction algorithms and texture should be considered when assessing image quality of iterative algorithms.

The move beyond just assessing noise properties in textured phantoms towards assessing detectability, a series of new phantoms were designed specifically to measure low-contrast detectability in the presence of background texture. The textures used were optimized to match the texture in the liver regions actual patient CT images using a genetic algorithm. The so called “Clustured Lumpy Background” texture synthesis framework was used to generate the modeled texture. Three textured phantoms and a corresponding uniform phantom were fabricated with a multi-material 3D printer and imaged on the SOMATOM Flash scanner. Images were reconstructed with FBP and SAFIRE and analyzed using a multi-slice channelized Hotelling observer to measure detectability and the dose reduction potential of SAFIRE based on the uniform and textured phantoms. It was found that at the same dose, the improvement in detectability from SAFIRE (compared to FBP) was higher when measured in a uniform phantom compared to textured phantoms.

The final trajectory of this project aimed at developing methods to mathematically model lesions, as a means to help assess image quality directly from patient images. The mathematical modeling framework is first presented. The models describe a lesion’s morphology in terms of size, shape, contrast, and edge profile as an analytical equation. The models can be voxelized and inserted into patient images to create so-called “hybrid” images. These hybrid images can then be used to assess detectability or estimability with the advantage that the ground truth of the lesion morphology and location is known exactly. Based on this framework, a series of liver lesions, lung nodules, and kidney stones were modeled based on images of real lesions. The lesion models were virtually inserted into patient images to create a database of hybrid images to go along with the original database of real lesion images. ROI images from each database were assessed by radiologists in a blinded fashion to determine the realism of the hybrid images. It was found that the radiologists could not readily distinguish between real and virtual lesion images (area under the ROC curve was 0.55). This study provided evidence that the proposed mathematical lesion modeling framework could produce reasonably realistic lesion images.

Based on that result, two studies were conducted which demonstrated the utility of the lesion models. The first study used the modeling framework as a measurement tool to determine how dose and reconstruction algorithm affected the quantitative analysis of liver lesions, lung nodules, and renal stones in terms of their size, shape, attenuation, edge profile, and texture features. The same database of real lesion images used in the previous study was used for this study. That database contained images of the same patient at 2 dose levels (50% and 100%) along with 3 reconstruction algorithms from a GE 750HD CT system (GE Healthcare). The algorithms in question were FBP, Adaptive Statistical Iterative Reconstruction (ASiR), and Model-Based Iterative Reconstruction (MBIR). A total of 23 quantitative features were extracted from the lesions under each condition. It was found that both dose and reconstruction algorithm had a statistically significant effect on the feature measurements. In particular, radiation dose affected five, three, and four of the 23 features (related to lesion size, conspicuity, and pixel-value distribution) for liver lesions, lung nodules, and renal stones, respectively. MBIR significantly affected 9, 11, and 15 of the 23 features (including size, attenuation, and texture features) for liver lesions, lung nodules, and renal stones, respectively. Lesion texture was not significantly affected by radiation dose.

The second study demonstrating the utility of the lesion modeling framework focused on assessing detectability of very low-contrast liver lesions in abdominal imaging. Specifically, detectability was assessed as a function of dose and reconstruction algorithm. As part of a parallel clinical trial, images from 21 patients were collected at 6 dose levels per patient on a SOMATOM Flash scanner. Subtle liver lesion models (contrast = -15 HU) were inserted into the raw projection data from the patient scans. The projections were then reconstructed with FBP and SAFIRE (strength 5). Also, lesion-less images were reconstructed. Noise, contrast, CNR, and detectability index of an observer model (non-prewhitening matched filter) were assessed. It was found that SAFIRE reduced noise by 52%, reduced contrast by 12%, increased CNR by 87%. and increased detectability index by 65% compared to FBP. Further, a 2AFC human perception experiment was performed to assess the dose reduction potential of SAFIRE, which was found to be 22% compared to the standard of care dose.

In conclusion, this dissertation provides to the scientific community a series of new methodologies, phantoms, analysis techniques, and modeling tools that can be used to rigorously assess image quality from modern CT systems. Specifically, methods to properly evaluate iterative reconstruction have been developed and are expected to aid in the safe clinical implementation of dose reduction technologies.

Metabolism Regulates the Fate and Function of T Lymphocytes

Proper balancing of the activities of metabolic pathways to meet the challenge of providing necessary products for biosynthetic and energy demands of the cell is a key requirement for maintaining cell viability and allowing for cell proliferation. Cell metabolism has been found to play a crucial role in numerous cell settings, including in the cells of the immune system, where a successful immune response requires rapid proliferation and successful clearance of dangerous pathogens followed by resolution of the immune response. Additionally, it is now well known that cell metabolism is markedly altered from normal cells in the setting of cancer, where tumor cells rapidly and persistently proliferate. In both settings, alterations to the metabolic profile of the cells play important roles in promoting cell proliferation and survival.

It has long been known that many types of tumor cells and actively proliferating immune cells adopt a metabolic phenotype of aerobic glycolysis, whereby the cell, even under normoxic conditions, imports large amounts of glucose and fluxes it through the glycolytic pathway and produces lactate. However, the metabolic programs utilized by various immune cell subsets have only recently begun to be explored in detail, and the metabolic features and pathways influencing cell metabolism in tumor cells in vivo have not been studied in detail. The work presented here examines the role of metabolism in regulating the function of an important subset of the immune system, the regulatory T cell (Treg) and the role and regulation of metabolism in the context of malignant T cell acute lymphoblastic leukemia (T-ALL). We show that Treg cells, in order to properly function to suppress auto-inflammatory disease, adopt a metabolic program that is characterized by oxidative metabolism and active suppression of anabolic signaling and metabolic pathways. We found that the transcription factor FoxP3, which is highly expressed in Treg cells, drives this phenotype. Perturbing the metabolic phenotype of Treg cells by enforcing increased glycolysis or driving proliferation and anabolic signaling through inflammatory signaling pathways results in a reduction in suppressive function of Tregs.

In our studies focused on the metabolism of T-ALL, we observed that while T-ALL cells use and require aerobic glycolysis, the glycolytic metabolism of T-ALL is restrained compared to that of an antigen activated T cell. The metabolism of T-ALL is instead balanced, with mitochondrial metabolism also being increased. We observed that the pro-anabolic growth mTORC1 signaling pathway was limited in primary T-ALL cells as a result of AMPK pathway activity. AMPK pathway signaling was elevated as a result of oncogene induced metabolic stress. AMPK played a key role in the regulation of T-ALL cell metabolism, as genetic deletion of AMPK in an in vivo murine model of T-ALL resulted in increased glycolysis and anabolic metabolism, yet paradoxically increased cell death and increased mouse survival time. AMPK acts to promote mitochondrial oxidative metabolism in T-ALL through the regulation of Complex I activity, and loss of AMPK reduced mitochondrial oxidative metabolism and resulted in increased metabolic stress. Confirming a role for mitochondrial metabolism in T-ALL, we observed that the direct pharmacological inhibition of Complex I also resulted in a rapid loss of T-ALL cell viability in vitro and in vivo. Taken together, this work establishes an important role for AMPK to both balance the metabolic pathways utilized by T-ALL to allow for cell proliferation and to also promote tumor cell viability by controlling metabolic stress.

Overall, this work demonstrates the importance of the proper coupling of metabolic pathway activity with the function needs of particular types of immune cells. We show that Treg cells, which mainly act to keep immune responses well regulated, adopt a metabolic program where glycolytic metabolism is actively repressed, while oxidative metabolism is promoted. In the setting of malignant T-ALL cells, metabolic activity is surprisingly balanced, with both glycolysis and mitochondrial oxidative metabolism being utilized. In both cases, altering the metabolic balance towards glycolytic metabolism results in negative outcomes for the cell, with decreased Treg functionality and increased metabolic stress in T-ALL. In both cases, this work has generated a new understanding of how metabolism couples to immune cell function, and may allow for selective targeting of immune cell subsets by the specific targeting of metabolic pathways.

Molecular Insights of CD4+ T Cell Differentiation, Effector Formation and Helper Function

CD4+ T cells play a crucial in the adaptive immune system. They function as the central hub to orchestrate the rest of immunity: CD4+ T cells are essential governing machinery in antibacterial and antiviral responses by facilitating B cell affinity maturation and coordinating the innate and adaptive immune systems to boost the overall immune outcome; on the contrary, hyperactivation of the inflammatory lineages of CD4+ T cells, as well as the impairments of suppressive CD4+ regulatory T cells, are the etiology of various autoimmunity and inflammatory diseases. The broad role of CD4+ T cells in both physiological and pathological contexts prompted me to explore the modulation of CD4+ T cells on the molecular level.

microRNAs (miRNAs) are small RNA molecules capable of regulating gene expression post-transcriptionally. miRNAs have been shown to exert substantial regulatory effects on CD4+ T cell activation, differentiation and helper function. Specifically, my lab has previously established the function of the miR-17-92 cluster in Th1 differentiation and anti-tumor responses. Here, I further analyzed the role of this miRNA cluster in Th17 differentiation, specifically, in the context of autoimmune diseases. Using both gain- and loss-of-function approaches, I demonstrated that miRNAs in miR-17-92, specifically, miR-17 and miR-19b in this cluster, is a crucial promoter of Th17 differentiation. Consequently, loss of miR-17-92 expression in T cells mitigated the progression of experimental autoimmune encephalomyelitis and T cell-induced colitis. In combination with my previous data, the molecular dissection of this cluster establishes that miR-19b and miR-17 play a comprehensive role in promoting multiple aspects of inflammatory T cell responses, which underscore them as potential targets for oligonucleotide-based therapy in treating autoimmune diseases.

To systematically study miRNA regulation in effector CD4+ T cells, I devised a large-scale miRNAome profiling to track in vivo miRNA changes in antigen-specific CD4+ T cells activated by Listeria challenge. From this screening, I identified that miR-23a expression tightly correlates with CD4+ effector expansion. Ectopic expression and genetic deletion strategies validated that miR-23a was required for antigen-stimulated effector CD4+ T cell survival in vitro and in vivo. I further determined that miR-23a targets Ppif, a gatekeeper of mitochondrial reactive oxygen species (ROS) release that protects CD4+ T cells from necrosis. Necrosis is a type of cell death that provokes inflammation, and it is prominently triggered by ROS release and its consequent oxidative stress. My finding that miR-23a curbs ROS-mediated necrosis highlights the essential role of this miRNA in maintaining immune homeostasis.

A key feature of miRNAs is their ability to modulate different biological aspects in different cell populations. Previously, my lab found that miR-23a potently suppresses CD8+ T cell cytotoxicity by restricting BLIMP1 expression. Since BLIMP1 has been found to inhibit T follicular helper (Tfh) differentiation by antagonizing the master transcription factor BCL6, I investigated whether miR-23a is also involved in Tfh differentiation. However, I found that miR-23a does not target BLIMP1 in CD4+ T cells and loss of miR-23a even fostered Tfh differentiation. This data indicate that miR-23a may target other pathways in CD4+ T cells regarding the Tfh differentiation pathway.

Although the lineage identity and regulatory networks for Tfh cells have been defined, the differentiation path of Tfh cells remains elusive. Two models have been proposed to explain the differentiation process of Tfh cells: in the parallel differentiation model, the Tfh lineage is segregated from other effector lineages at the early stage of antigen activation; alternatively, the sequential differentiation model suggests that naïve CD4+ T cells first differentiate into various effector lineages, then further program into Tfh cells. To address this question, I developed a novel in vitro co-culture system that employed antigen-specific CD4+ T cells, naïve B cells presenting cognate T cell antigen and BAFF-producing feeder cells to mimic germinal center. Using this system, I were able to robustly generate GC-like B cells. Notably, well-differentiated Th1 or Th2 effector cells also quickly acquired Tfh phenotype and function during in vitro co-culture, which suggested a sequential differentiation path for Tfh cells. To examine this path in vivo, under conditions of classical Th1- or Th2-type immunizations, I employed a TCRβ repertoire sequencing technique to track the clonotype origin of Tfh cells. Under both Th1- and Th2- immunization conditions, I observed profound repertoire overlaps between the Teff and Tfh populations, which strongly supports the proposed sequential differentiation model. Therefore, my studies establish a new platform to conveniently study Tfh-GC B cell interactions and provide insights into Tfh differentiation processes.

Occurrence and Function of Hoogsteen Base Pairs in Nucleic Acids

Nucleic acids (DNA and RNA) play essential roles in the central dogma of biology for the storage and transfer of genetic information. The unique chemical and conformational structures of nucleic acids – the double helix composed of complementary Watson-Crick base pairs, provide the structural basis to carry out their biological functions. DNA double helix can dynamically accommodate Watson-Crick and Hoogsteen base-pairing, in which the purine base is flipped by ~180° degrees to adopt syn rather than anti conformation as in Watson-Crick base pairs. There is growing evidence that Hoogsteen base pairs play important roles in DNA replication, recognition, damage or mispair accommodation and repair. Here, we constructed a database for existing Hoogsteen base pairs in DNA duplexes by a structure-based survey from the Protein Data Bank, and structural analyses based on the resulted Hoogsteen structures revealed that Hoogsteen base pairs occur in a wide variety of biological contexts and can induce DNA kinking towards the major groove. As there were documented difficulties in modeling Hoogsteen or Watson-Crick by crystallography, we collaborated with the Richardsons’ lab and identified potential Hoogsteen base pairs that were mis-modeled as Watson-Crick base pairs which suggested that Hoogsteen can be more prevalent than it was thought to be. We developed solution NMR method combined with the site-specific isotope labeling to characterize the formation of, or conformational exchange with Hoogsteen base pairs in large DNA-protein complexes under solution conditions, in the absence of the crystal packing force. We showed that there are enhanced chemical exchange, potentially between Watson-Crick and Hoogsteen, at a sharp kink site in the complex formed by DNA and the Integration Host Factor protein. In stark contrast to B-form DNA, we found that Hoogsteen base pairs are strongly disfavored in A-form RNA duplex. Chemical modifications N1-methyl adenosine and N1-methyl guanosine that block Watson-Crick base-pairing, can be absorbed as Hoogsteen base pairs in DNA, but rather potently destabilized A-form RNA and caused helix melting. The intrinsic instability of Hoogsteen base pairs in A-form RNA endows the N1-methylation as a functioning post-transcriptional modification that was known to facilitate RNA folding, translation and potentially play roles in the epitranscriptome. On the other hand, the dynamic property of DNA that can accommodate Hoogsteen base pairs could be critical to maintaining the genome stability.

Reward circuitry dysfunction in psychiatric and neurodevelopmental disorders and genetic syndromes: animal models and clinical findings.

This review summarizes evidence of dysregulated reward circuitry function in a range of neurodevelopmental and psychiatric disorders and genetic syndromes. First, the contribution of identifying a core mechanistic process across disparate disorders to disease classification is discussed, followed by a review of the neurobiology of reward circuitry. We next consider preclinical animal models and clinical evidence of reward-pathway dysfunction in a range of disorders, including psychiatric disorders (i.e., substance-use disorders, affective disorders, eating disorders, and obsessive compulsive disorders), neurodevelopmental disorders (i.e., schizophrenia, attention-deficit/hyperactivity disorder, autism spectrum disorders, Tourette's syndrome, conduct disorder/oppositional defiant disorder), and genetic syndromes (i.e., Fragile X syndrome, Prader-Willi syndrome, Williams syndrome, Angelman syndrome, and Rett syndrome). We also provide brief overviews of effective psychopharmacologic agents that have an effect on the dopamine system in these disorders. This review concludes with methodological considerations for future research designed to more clearly probe reward-circuitry dysfunction, with the ultimate goal of improved intervention strategies.