Genetic basis of maize whole kernel, embryo, and endosperm oil

Maize hybrids with elevated oil content have potential value as livestock feed directly and as a source of oil for human consumption. Selection on embryo and/or endosperm oil content could supplement selection at the whole kernel level and enable the development of hybrids with tissue-specific oil accumulation. The genetic basis of embryo and endosperm oil content was investigated in two separate populations: a set of elite commercial inbreds which varied for whole kernel oil content and in a segregating set of lines derived from a cross between high and low whole kernel oil parents. The traits were repeatable across environments and the impact of genotype by environment was small relative to the effect of genotype. The phenotypic data suggested that there was a relationship between embryo and endosperm oil content, but that relationship was germplasm dependent. Regions of the genome associated with the traits were detected in the inbred population using association mapping and in the segregating population with composite interval mapping. The results of the genetic mapping suggested that embryo and endosperm oil content have some common controlling loci but that the traits are under partially independent genetic control

ELUCIDATING THE MECHANISM OF LIPL: A NON-HEME FE(II), α -KETOGLUTARATE: URIDINE-5’-MONOPHOSPHATE DIOXYGENASE

Several nucleoside natural product antibiotics from Streptomyces sp. and actinomycetes have recently been shown to target bacterial peptidoglycan cell wall biosynthesis by inhibiting the bacterial translocase I (MraY). The biosynthetic gene clusters for A-90289, liposidomycins and caprazamycins revealed a protein with sequence similarity to proteins annotated as α-KG:taurine dioxygenases (TauD). This enzyme (LipL) is a mononuclear, non-heme, Fe(II) dependent α-keto glutarate (α-KG) :uridine monophosphate (UMP) dioxygenase responsible for the net dephosphorylation and two electron oxidation of UMP to uridine-5’-aldehyde. The postulated reaction coordinates involving the activation of the C-5’ center in UMP and the corresponding formation of uridine-5’-aldehyde are modeled on extensive spectroscopic and structural characterizations of TauD. In this dissertation, the postulated radical mechanism for LipL involving the formation of an unstable hydroxylated intermediate is investigated via the characterization of a key product obtained from the reaction of LipL (and its homolog Cpr19) with a synthetically modified surrogate substrate where the bridging phosphoester oxygen in UMP is replaced with a 5’ C-P bond. We further validate our hypothesis by analyzing the reactions of both LipL and Cpr19 with specifically 2H1 – labeled UMP substrate and confirming the expected products via mass spectrometry. In addition, we explore substrate promiscuity of the enzymes and utilize a set of site specific mutants of Cpr19 as means of gaining better insight into the active site residues. Predictive models for Cpr19 and LipL structures are developed by the combination of experimental results and chemical logic

Regulation of Cardiomyocyte Proliferation by microRNAs and Small Molecules

Understanding the molecular mechanisms regulating cardiac cell proliferation during the embryonic, fetal and adult life holds a paramount importance in view of developing innovative strategies aimed at inducing myocardial regeneration after cardiac damage. Previous high throughput screening studies in our laboratory identified a series of microRNAs able to trigger cardiomyocyte proliferation and stimulate cardiac regeneration after myocardial infarction. In the first part of this project, we investigated the mechanism of action of the top ten most effective of these miRNAs, revealing an involvement of the Hippo-YAP pathway in their action. We found that all the investigated miRNAs activated YAP-mediated transcription, nuclear localization of active YAP and increased expression of YAP responsive genes. Of notice, miR-199a-3p, one of the most effective miRNAs exerted its direct effect on two mRNA targets impinging on the Hippo pathway, the inhibitory kinase TAOK1 and the E3 ubiquitin ligase, β−TrCP. Most of the miRNAs inducing proliferation (including miR-199a-3p) also modulated the dynamics of the actin cytoskeleton in the treated cardiomyocytes, which displayed a rounded shape and gross bundles of actin fibers at the cytoplasm periphery. Consistent with these observations, we found that the Cofilin2 mRNA was a direct target of four of the investigated miRNAs and that downregulation of Cofilin2 itself was sufficient to promote cardiomyocyte proliferation, activate nuclear translocation of YAP and stimulate transcription of TEAD-responsive genes. The second part of the project was aimed at identifying small molecules exerting a mitogenic effect on neonatal cardiomyocytes through an unbiased high-throughput screening (HTS) of a library of 780 FDA-registered drugs. The neuroactive alkaloid harmine was identified as the most powerful molecule at inducing cardiomyocyte proliferation in vitro and heart regeneration after myocardial infarction in vivo. Harmine exerted its activity through the inhibition of the dual specificity phosphorylation-regulated tyrosine kinase, Dyrk1a and, again, the activation of YAP nuclear translocation. Collectively, these results identify both YAP activation and actin cytoskeleton remodelling as major determinants of cardiomyocyte proliferation and establish the molecular basis for the development of pharmacological therapies to promote heart regeneration through the stimulation of the endogenous capacity of cardiomyocytes to proliferate

Cohort Identification Using Semantic Web Technologies: Ontologies and Triplestores as Engines for Complex Computable Phenotyping

Electronic health record (EHR)-based computable phenotypes are algorithms used to identify individuals or populations with clinical conditions or events of interest within a clinical data repository. Due to a lack of EHR data standardization, computable phenotypes can be semantically ambiguous and difficult to share across institutions. In this research, I propose a new computable phenotyping methodological framework based on semantic web technologies, specifically ontologies, the Resource Description Framework (RDF) data format, triplestores, and Web Ontology Language (OWL) reasoning. My hypothesis is that storing and analyzing clinical data using these technologies can begin to address the critical issues of semantic ambiguity and lack of interoperability in the context of computable phenotyping. To test this hypothesis, I compared the performance of two variants of two computable phenotypes (for depression and rheumatoid arthritis, respectively). The first variant of each phenotype used a list of ICD-10-CM codes to define the condition; the second variant used ontology concepts from SNOMED and the Human Phenotype Ontology (HPO). After executing each variant of each phenotype against a clinical data repository, I compared the patients matched in each case to see where the different variants overlapped and diverged. Both the ontologies and the clinical data were stored in an RDF triplestore to allow me to assess the interoperability advantages of the RDF format for clinical data. All tested methods successfully identified cohorts in the data store, with differing rates of overlap and divergence between variants. Depending on the phenotyping use case, SNOMED and HPO’s ability to more broadly define many conditions due to complex relationships between their concepts may be seen as an advantage or a disadvantage. I also found that RDF triplestores do indeed provide interoperability advantages, despite being far less commonly used in clinical data applications than relational databases. Despite the fact that these methods and technologies are not “one-size-fits-all,” the experimental results are encouraging enough for them to (1) be put into practice in combination with existing phenotyping methods or (2) be used on their own for particularly well-suited use cases.Doctor of Philosoph

Air Force Institute of Technology Research Report 2010

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, Mathematics, Statistics and Engineering Physic

심층학습을 이용한 액체계의 성질 예측

학위논문(박사)--서울대학교 대학원 :자연과학대학 화학부,2020. 2. 정연준.최근 기계학습 기술의 급격한 발전과 이의 화학 분야에 대한 적용은 다양한 화학적 성질에 대한 구조-성질 정량 관계를 기반으로 한 예측 모형의 개발을 가속하고 있다. 용매화 자유 에너지는 그러한 기계학습의 적용 예중 하나이며 다양한 용매 내의 화학반응에서 중요한 역할을 하는 근본적 성질 중 하나이다. 본 연구에서 우리는 목표로 하는 용매화 자유 에너지를 원자간의 상호작용으로부터 구할 수 있는 새로운 심층학습 기반 용매화 모형을 소개한다. 제안된 심층학습 모형의 계산 과정은 용매와 용질 분자에 대한 부호화 함수가 각 원자와 분자들의 구조적 성질에 대한 벡터 표현을 추출하며, 이를 토대로 원자간 상호작용을 복잡한 퍼셉트론 신경망 대신 벡터간의 간단한 내적으로 구할 수 있다. 952가지의 유기용질과 147가지의 유기용매를 포함하는 6,493가지의 실험치를 토대로 기계학습 모형의 교차 검증 시험을 실시한 결과, 평균 절대 오차 기준 0.2 kcal/mol 수준으로 매우 높은 정확도를 가진다. 스캐폴드-기반 교차 검증의 결과 역시 0.6 kcal/mol 수준으로, 외삽으로 분류할 수 있는 비교적 새로운 분자 구조에 대한 예측에 대해서도 우수한 정확도를 보인다. 또한, 제안된 특정 기계학습 모형은 그 구조 상 특정 용매에 특화되지 않았기 때문에 높은 양도성을 가지며 학습에 이용할 데이터의 수를 늘이는 데 용이하다. 원자간 상호작용에 대한 분석을 통해 제안된 심층학습 모형 용매화 자유 에너지에 대한 그룹-기여도를 잘 재현할 수 있음을 알 수 있으며, 기계학습을 통해 단순히 목표로 하는 성질만을 예측하는 것을 넘어 더욱 상세한 물리화학적 이해를 하는 것이 가능할 것이라 기대할 수 있다.Recent advances in machine learning technologies and their chemical applications lead to the developments of diverse structure-property relationship based prediction models for various chemical properties; the free energy of solvation is one of them and plays a dominant role as a fundamental measure of solvation chemistry. Here, we introduce a novel machine learning-based solvation model, which calculates the target solvation free energy from pairwise atomistic interactions. The novelty of our proposed solvation model involves rather simple architecture: two encoding function extracts vector representations of the atomic and the molecular features from the given chemical structure, while the inner product between two atomistic features calculates their interactions, instead of black-boxed perceptron networks. The cross-validation result on 6,493 experimental measurements for 952 organic solutes and 147 organic solvents achieves an outstanding performance, which is 0.2 kcal/mol in MUE. The scaffold-based split method exhibits 0.6 kcal/mol, which shows that the proposed model guarantees reasonable accuracy even for extrapolated cases. Moreover, the proposed model shows an excellent transferability for enlarging training data due to its solvent-non-specific nature. Analysis of the atomistic interaction map shows there is a great potential that our proposed model reproduces group contributions on the solvation energy, which makes us believe that the proposed model not only provides the predicted target property, but also gives us more detailed physicochemical insights.1. Introduction 1 2. Delfos: Deep Learning Model for Prediction of Solvation Free Energies in Generic Organic Solvents 7 2.1. Methods 7 2.1.1. Embedding of Chemical Contexts 7 2.1.2. Encoder-Predictor Network 9 2.2. Results and Discussions 13 2.2.1. Computational Setup and Results 13 2.2.2. Transferability of the Model for New Compounds 17 2.2.3. Visualization of Attention Mechanism 26 3. Group Contribution Method for the Solvation Energy Estimation with Vector Representations of Atom 29 3.1. Model Description 29 3.1.1. Word Embedding 29 3.1.2. Network Architecture 33 3.2. Results and Discussions 39 3.2.1. Computational Details 39 3.2.2. Prediction Accuracy 42 3.2.3. Model Transferability 44 3.2.4. Group Contributions of Solvation Energy 49 4. Empirical Structure-Property Relationship Model for Liquid Transport Properties 55 5. Concluding Remarks 61 A. Analyzing Kinetic Trapping as a First-Order Dynamical Phase Transition in the Ensemble of Stochastic Trajectories 65 A1. Introduction 65 A2. Theory 68 A3. Lattice Gas Model 70 A4. Mathematical Model 73 A5. Dynamical Phase Transitions 75 A6. Conclusion 82 B. Reaction-Path Thermodynamics of the Michaelis-Menten Kinetics 85 B1. Introduction 85 B2. Reaction Path Thermodynamics 88 B3. Fixed Observation Time 94 B4. Conclusions 101Docto

Diversification Across a Dynamic Landscape: Phylogeography and Riverscape Genetics of Speckled Dace (Rhinichthys osculus) in Western North America

Evolution occurs at various spatial and temporal scales. For example, speciation may occur in historic time, whereas localized adaptation is more contemporary. Each is required to identify and manage biodiversity. However, the relative abundance of Speckled Dace (Rhinichthys osculus), a small cyprinid fish in western North America (WNA) and the study species for this dissertation, establishes it an atypical conservation target, particularly when contrasted with the profusion of narrowly endemic forms it displays. Yet, the juxtaposition of ubiquity versus endemism provides an ideal model against which to test hypotheses regarding the geomorphic evolution of WNA. More specifically, it also allows the evolutionary history of Speckled Dace to be contrasted at multiple spatial and temporal scales, and interpreted in the context of contemporary anthropogenic pressures and climatic uncertainty. Chapter II dissects the broad distribution of Speckled Dace and quantifies how its evolution has been driven by hybridization/ introgression. Chapter III narrows the geographic focus by interpreting Speckled Dace distribution within two markedly different watersheds: The Colorado River and the Great Basin. The former is a broad riverine habitat whereas the latter is an endorheic basin. Two biogeographic models compare and contrast the tempo and mode of evolution within these geologically disparate habitats. Chapter IV employs a molecular clock to determine origin of Speckled Dace lineages in Death Valley (CA/NV), and to contrast these against estimates for a second endemic species, Devil’s Hole Pupfish (Cyprinodon diabolis). While palaeohydrology served to diversify Rhinichthys, its among-population connectivity occurred contemporaneously. These data also provide guidance for assessing the origin of the Devil’s Hole Pupfish, a topic of considerable contention. The final two chapters present bioinformatic software that facilitates the analysis of single-nucleotide-polymorphism (SNP) DNA data (used herein). Chapter V describes COMP-D, a program designed to assess introgression among lineages, whereas Chapter VI presents programmatic modifications to BAYESASS that allow migration to be quantified from SNP datasets. These five studies provide an in-depth understanding of contemporary and historical processes that shape aquatic biodiversity in environments prone to anthropogenic disturbance. They also highlight the complexities of evolutionary mechanisms and their implications for conservation in a changing world

Essential Genes And Their Role In Autism Spectrum Disorder

Essential genes (EGs) play central roles in fundamental cellular processes and are required for the survival of an organism. EGs are enriched for human disease genes and are under strong purifying selection. This intolerance to deleterious mutations, commonly observed haploinsufficiency and the importance of EGs in pre- and postnatal development suggests a possible cumulative effect of deleterious variants in EGs on complex neurodevelopmental disorders. Autism spectrum disorder (ASD) is a heterogeneous, highly heritable neurodevelopmental syndrome characterized by impaired social interaction, communication and repetitive behavior. More and more genetic evidence points to a polygenic model of ASD and it is estimated that hundreds of genes contribute to ASD. The central question addressed in this dissertation is whether genes with a strong effect on survival and fitness (i.e. EGs) play a specific role in ASD risk. I compiled a comprehensive catalog of 3,915 mammalian EGs by combining human orthologs of lethal genes in knockout mice and genes responsible for cell-based essentiality. With an updated set of EGs, I characterized the genetic and functional properties of EGs and demonstrated the association between EGs and human diseases. Next I provided evidence for a stronger contribution of EGs to ASD risk, compared to non-essential genes (NEGs). By examining the exonic de novo and inherited variants from 1,781 ASD quartet families, I demonstrated a significantly higher burden of damaging mutations in EGs in ASD probands compared to their non-ASD siblings. Analysis of EGs in the developing brain identified clusters of co-expressed EGs implicated in ASD, among which I proposed a priority list of 29 EGs with potential ASD risk as targets for future functional and behavioral studies. Finally, I developed the essentiality burden score (EBS), which captures the burden of rare mutations in EGs as a novel polygenic predictor of individual ASD risk and a useful addition to the current tools for understanding the polygenic architecture of ASD. Overall, I show that large-scale studies of gene function in model organisms and human cell lines provide a powerful approach for prioritization of genes and pathogenic variants identified by sequencing studies of complex human disease