<p>  </p> <p>Plant complex traits are controlled by multi-layer of dynamic and complicated gene networks regulated at different levels. To better inform crop breeding to promote desired traits, a comprehensive and fundamental understanding of their genetic basis is much needed. With the rapid developments of <em>omics</em> planforms and next generation sequencing technology, we now have large-scale data from genome, epigenome, transcriptome, metabolome, and others for the crop plants. Integration of those multiple <em>omics</em> data together with computational approaches led to the establishment of a novel science known as system biology. Research described in this thesis used system biology approaches to dissect complex crop traits such as disease response of tomato (Chapter2 and Chapter3) and the heterosis of nitrogen use efficiency of maize (Chapter4).</p> <p>Plant disease response is an elaborate, multilayered complex trait involving several lines of defense signaling. In the past decades, progress in molecular analyses of plant immune system has revealed key elements of a complex response network in Arabidopsis, a model species. Histone modifications, a type of epigenetic regulation, have emerged as key modulators that regulate defense responses, while our understanding of the role of histone-modifying enzymes in this process is still in its infancy. Here, we described the immune function of two histone methyltransferases SDG33 and SDG34 in tomato. We found the single mutants in <em>sdg33</em> and <em>sdg34</em> showed increased susceptibility to hemibiotrophic bacterial pathogen <em>Pseudomonas syringae</em> whereas the double mutant <em>sdg33sdg34</em> is comparable to wild type. Using RNA-seq and histone ChIP-seq approaches, we investigated the possible underlying mechanisms and found that the expression of a set of immune-related genes is misregulated by <em>P. syringae</em> only in the single mutants but not in the double mutant. Integrating with epigenomic data, we found that the misexpression of those SDG33/SDG34 dependent immune-response genes was associated with altered histone methylation status in the single mutant. Intriguingly, the double mutant also showed altered histone methylation but unaffected gene expression, suggesting a compensating regulatory mechanism at play. The function of SDG33 and SDG34 in immune response seems to be specific for the pathogen, as the double mutants exhibited enhanced resistance the single mutants showed no altered responses when treated with necrotrophic fungal pathogen <em>Botrytis cinerea</em>. Network analysis found the most regulatory gene by <em>B. cinerea</em> in a SDG33/SDG34 dependent manner have been implicated in biotic stress response such as <em>ERF4, TOPLESS, PUB23 </em>and<em> RCD1</em>. Comparing the immune response of double mutant against <em>P. syringae</em> and <em>B. cinerea</em>, we found that the disease related genes are only mis-regulated in the interaction of <em>B. cinerea</em> treatment not in the <em>P. syringae</em> treatment, which could be the reason of enhanced resistance to <em>B. cinerea</em> but not for <em>P. syringae</em> in the double mutants. In summary, we found the histone methyltransferases SDG33 and SDG34 has different functions in the immune response against <em>P. syringae</em> and <em>B. cinerea</em>, which might be direct or indirect relevant to the histone methylation level of the expression of downstream immune related gene.</p> <p>In addition to biotic stress, another complex trait studied in this thesis is the heterosis of nitrogen use efficiency (NUE) in Maize. NUE is another complex trait associated with multiple physiological processes including N sensing, uptake, assimilation, transport, and storage. Heterosis refers to a phenomenon where the progeny generated by crossing two different cultivars of the same species exhibit superior fitness than the inbred parents. Even though, heterosis has been exploited to improve complex traits including NUE, the underlying molecular mechanisms is not completely understood. Here, we analyzed N-responsive transcriptomes and physiological traits of a panel of six maize hybrids and their corresponding inbreds grown in the field at two different N levels. We observed diverse levels of trait heterosis that are dependent on the N conditions and organ types. We discovered dramatic pattern shift of beyond-parental-range gene expression in hybrids in response to varying N levels. We identified through integrative analyses a set of genes whose expression heterosis are quantitatively correlated to trait heterosis. These genes are involved in response to stimulus, photosynthesis, and N metabolism, and likely mediate the heterosis phenotype of N-use and growth traits in maize. In summary, our integrated analysis provided insights into the mechanistic basis of the heterosis of NUE. </p> <p>Together, applying systems and functional genomics approaches to investigate important agricultural traits could lead to a comprehensive understanding of plant complex traits to inform future engineering and breeding for better crops.</p>

My dissertation comprises three related topics of evolutionary and computational biology, which correspond to the three Chapters. Chapter 1 focuses on tempo and mode of evolution in visual genes, namely opsins, via duplication events and subsequent molecular adaptation in Odonata (dragonflies and damselflies). Gene duplication plays a central role in adaptation to novel environments by providing new genetic material for functional divergence and evolution of biological complexity. Odonata have the largest opsin repertoire of any insect currently known. In particular our results suggest that both the blue sensitive (BS) and long-wave sensitive (LWS) opsin classes were subjected to strong positive selection that greatly weakens after multiple duplication events, a pattern that is consistent with the permanent heterozygote model. Due to the immense interspecific variation and duplicability potential of opsin genes among odonates, they represent a unique model system to test hypotheses regarding opsin gene duplication and diversification at the molecular level. Chapter 2 primarily focuses on reconstruction of the phylogenetic backbone of Odonata using RNA-seq data. In order to reconstruct the evolutionary history of Odonata, we performed comprehensive phylotranscriptomic analyses of 83 species covering 75% of all extant odonate families. Using maximum likelihood, Bayesian, coalescent-based and alignment free tree inference frameworks we were able to test, refine and resolve previously controversial relationships within the order. In particular, we confirmed the monophyly of Zygoptera, recovered Gomphidae and Petaluridae as sister groups with high confidence and identified Calopterygoidea as monophyletic. Fossil calibration coupled with diversification analyses provided insight into key events that influenced the evolution of Odonata. Specifically, we determined that there was a possible mass extinction of ancient odonate diversity during the P-Tr crisis and a single odonate lineage persisted following this extinction event. Lastly, Chapter 3 focuses on identification of erroneously assigned sequence homology using the intelligent agents of machine learning techniques. Accurate detection of homologous relationships of biological sequences (DNA or amino acid) amongst organisms is an important and often difficult task that is essential to various evolutionary studies, ranging from building phylogenies to predicting functional gene annotations. We developed biologically informative features that can be extracted from multiple sequence alignments of putative homologous genes (orthologs and paralogs) and further utilized in context of guided experimentation to verify false positive outcomes.

The emergence of moving learning activities online, due to Covid-19 pandemic preventive measures and fast growing technology, forced many educational institutions to design and prepare their distance learning systems on time, without adopting the best practices for an effective and holistic digital transformation. This fact highlights the need of reconsidering the models in online learning and redesigning the learning processes to meet students e-learning needs and receive a more qualitative educational experience. This study intends to show which are the perspectives of students on the next generation learning platform, post-covid, and how this can contribute to a more effective learning in HE. To serve this purpose, an internet-based survey was designed. The questionnaire was distributed via the DSV iLearn platform of the program to all participants-users of the platform, and the method of data analysis chosen was Quantitative analysis. The results of this study showed an overall satisfaction with the platform capabilities and a satisfactory level of students’ skills in the use of ICT technologies and a digital learning platform. Some important conclusions about dependencies of students’ characteristics that affect their engagement with an elearning platform also arose, while areas of improvement were identified regarding the feeling of safety using a digital platform, alternative assessment techniques, new platform technological features and interaction with peers and instructors. Under this study limitations and the potential of a future and more extensive research, the aim of these results is their incorporation in the design, implementation and optimization processes of the next generation learning platform, contributing to the Higher Education e-learning systems performance and responsiveness flexibility, taking also into consideration qualitative factors related to students’ characteristics and needs.

Malgré l’usage de médicaments antipsychotiques depuis plusieurs décennies, leur mécanisme d’action précis, autre que leur interaction avec les récepteurs dopaminergiques et sérotoninergiques, demeure peu connu. Nur77 (Nr4a1 ou NGFI-B) est un facteur de transcription de la famille des récepteurs nucléaires associé aux effets des antipsychotiques. Ceci étant dit, le mécanisme d’action de Nur77 est également peu connu. Afin de mieux comprendre les éléments impliqués avec les antipsychotiques et l’activité de Nur77, nous avons comparé les niveaux de transcrits dans le striatum suite à un traitement avec l’halopéridol chez des rats sauvages et déficients en Nur77 à l’aide de la technique de séquençage à haut débit (RNAseq) et d’une analyse bio-informatique. L’halopéridol et Nur77 ont modulé d’importants groupes de gènes associés avec la signalisation des récepteurs dopaminergiques et la synapse glutamatergique. L’analyse a révélé des modulations de gènes clés reliés à la signalisation des protéines G. Parmi les transcrits modulés significativement chez les rats traités avec halopéridol et ceux déficients en Nur77, la dual specificity phosphatase 5 (Dusp5) représente un nouveau candidat d’intérêt. En effet, nous avons confirmé que les niveaux d’ARNm et protéiques de Dusp5 dans le striatum sont associés aux mouvements involontaires anormaux (dyskinésie) dans un modèle de primates non-humains traités chroniquement avec halopéridol. Cette analyse transcriptomique a démontré des altérations rapides et importantes d’éléments impliqués dans la signalisation des protéines G par l’halopéridol, et a permis d’identifier, pour la première fois, une expression de Dusp5 dépendante de Nur77 en tant que nouvelle composante reliée avec la dyskinésie tardive. / Despite antipsychotic drugs being used for several decades, their precise mechanism of action remains elusive. Nur77 (Nr4a1 or NGFI-B) is a transcription factor of the nuclear receptor family associated with antipsychotic drug effects. However, the mechanism of action of Nur77 is also not well understood. To better understand the signaling components implicated with antipsychotic drug use and Nur77 activity, we compared striatal gene transcripts following haloperidol in wild-type and Nur77-deficient rats using Next Generation RNA Sequencing (RNAseq) and a bioinformatics analysis. Haloperidol and Nur77 modulated important subsets of striatal genes associated with dopamine receptor signaling and glutamate synapses. The analysis revealed modulations of key components of G protein signaling that are consistent with a rapid adaptation of striatal cells that may partially explain long-term haloperidol-induced dopamine D2 receptor upregulation. Amongst significantly modulated transcripts in rats treated with haloperidol and rats deficient in Nur77, dual specificity phosphatase 5 (Dusp5) represents a new and very interesting candidate. Indeed, we confirmed that striatal Dusp5 mRNA and protein levels were associated with abnormal involuntary movements (dyskinesia) in non-human primates chronically exposed to haloperidol. This transcriptomic analysis showed important haloperidol-induced G protein-coupled receptor signaling alterations that may support a regulatory role of Nur77 in dopamine D2 receptor signaling pathways and identified, for the first time, a putative Nur77-dependent expression of Dusp5 as a new signaling component for antipsychotic drug-induced tardive dyskinesia.

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Affinity ligands are important molecules used in affinity chromatography for purification of significant substances from complex mixtures. To find affinity ligands specific to important target molecules could be a challenging process. Cytiva uses the powerful phage display technique to find new promising affinity ligands. The phage display technique is a method run in several enrichment cycles. When developing new affinity ligands, a protein scaffold library with a diversity of up to 1010-1011 different protein scaffold variants is run through the enrichment cycles.  The result from the phage display rounds is screened for target molecule binding followed by sequencing, usually with one of the conventional screening methods ELISA or Biacore followed by Sanger sequencing. However, the throughput of these analyses are unfortunately very low, often with only a few hundred screened clones. Therefore, Next Generation Sequencing or NGS, has become an increasingly popular screening method for phage display libraries which generates millions of sequences from each phage display round. This creates a need for a robust data analysis pipeline to be able to interpret the large amounts of data.  In this project, a pipeline for analysis of NGS data of phage displayed libraries has been developed at Cytiva. Cytiva uses NGS as one of their screening methods of phage displayed protein libraries because of the high throughput compared to the conventional screening methods. The purpose is to find new affinity ligands for purification of essential substances used in drugs.  The pipeline has been created using the object-oriented programming language R and consists of several analyses covering the most important steps to be able to find promising results from the NGS data. With the developed pipeline the user can analyze the data on both DNA and protein sequence level and per position residue breakdown, as well as filter the data based on specific amino acids and positions. This gives a robust and thorough analysis which can lead to promising results that can be used in the development of novel affinity ligands for future purification products.

Droplet-based microfluidics is a powerful tool permitting massive-scale single-cell analysis in pico-/nano-liter water-in-oil droplets. It has been integrated into various library preparation techniques to accomplish high-throughput scRNA-seq, scDNA-seq, scATAC-seq, scChIP-seq, as well as scMulti-omics-seq. These advanced technologies have been providing unique and novel insights into both normal differentiation and disease development at single-cell level. In this thesis, we develop four new droplet-based tools for single-cell omics profiling. First, the developed Drop-BS is the first droplet-based platform to construct single-cell bisulfite sequencing libraries for DNA methylome profiling and allows production of BS library of 2,000-10,000 single cells within 2 d. We applied the technology to separately profile mixed cell lines, mouse brain tissues, and human brain tissues to reveal cell type heterogeneity. Second, the new Drop-ChIP platform only requires two steps of droplet generation to achieve multiple steps of reactions in droplets such as single-cell lysis, chromatin fragmentation, ChIP, and barcoding. Third, we aim to establish a droplet-based platform to accomplish high-throughput full-length RNA-seq (Drop-full-seq), which both current tube-based and droplet-based methods cannot realize. Last, we constructed an in-house droplet-based tool to assist single-cell ATAC-seq library preparation (Drop-ATAC), which provided a low-cost and facile protocol to conduct scATAC-seq in laboratories without the expensive instrument. / Doctor of Philosophy / Microfluidics is a collection of techniques to manipulate fluids in the micrometer scale. One of microfluidic techniques is called "droplet-based microfluidics". It can manipulate (i.e., generate, merge, sort, split, etc) pico-/nano-liter of water-in-oil droplets. First, since the water phase is separated by the continuous oil phase, these droplets are discrete and individual reactors. Second, droplet-based microfluidics can achieve highly parallel manipulation of thousands to millions of droplets. These two advantages make droplet-based microfluidics an ideal tool to perform single-cell assays. Over the past 10 years, various droplet-based platforms have been developed to study single-cell transcriptome, genome, epigenome, as well as multi-ome. To expand droplet-based tools for single-cell analysis, we aim to develop four novel platforms in this thesis. First, Drop-BS, by integrating droplet generation and droplet fusion techniques, can achieve high-throughput single-cell bisulfite sequencing library preparation. It can generate 10,000 single-cell BS libraries within 2 days which is difficult to achieve for conventional library preparation in tubes/microwells. Second, we developed a novel and facile Drop-ChIP platform to prepare single-cell ChIP-seq library. It is easy to operate since it only requires two steps of droplet generation. It also generates higher quality of data compared to previous work. In addition, we are working on the development and characterization of the other two droplet-based tools to achieve full-length single-cell RNA-seq and single-cell ATAC-seq.