Local adaptation and phenotypic plasticity are main mechanisms of organisms’ resilience in changing environments. Both are affected by gene flow and are expected to be weak in zooplankton populations inhabiting large continuous water bodies and strongly affected by currents. Lake Baikal, the deepest and one of the coldest lakes on Earth, experienced epilimnion temperature increase during the last 100 years, exposing Baikal’s zooplankton to novel selective pressures. We obtained a partial transcriptome of Epischura baikalensis (Copepoda: Calanoida), the dominant component of Baikal’s zooplankton, and estimated SNP allele frequencies and transcript abundances in samples from regions of Baikal that differ in multiyear average surface temperatures. The strongest signal in both SNP and transcript abundance differentiation is the SW-NE gradient along the 600+ km long axis of the lake, suggesting isolation by distance. SNP differentiation is stronger for nonsynonymous than synonymous SNPs and is paralleled by differential survival during a laboratory exposure to increased temperature, indicating directional selection operating on the temperature gradient. Transcript abundance, generally collinear with the SNP differentiation, shows samples from the warmest, less deep location clustering together with the southernmost samples. Differential expression is more frequent among transcripts orthologous to candidate thermal response genes previously identified in model arthropods, including genes encoding cytoskeleton proteins, heat-shock proteins, proteases, enzymes of central energy metabolism, lipid and antioxidant pathways. We conclude that the pivotal endemic zooplankton species in Lake Baikal exists under temperature-mediated selection and possesses both genetic variation and plasticity to respond to novel temperature-related environmental pressures.

(1,3;1,4)-β- D-glucan (β-glucan) is a plant cell wall hemicellulose and a main component of endosperm cell walls. The Cellulose Synthase F family of genes is involved in the synthesis of β-glucan. In this study full-length genomic sequences of CslF6 were obtained from multiple Avena species. Three unique alleles were found in each A. sativa line. Comparisons of these alleles to diploid Avena species allowed for identification of the genomic origin of each allele. The A and D genome alleles had identical amino acid sequences while the C-genome had 13 different amino acids. Global expression of CslF6 was completed at three developmental time point and three tissue types. RNAseq technology was utilized to determine genome specific expression patterns. Differential expression of genome specific-copies of CslF6 was found at all time points tested. Lower levels of C-genome expression of CslF6 were associated with increased levels of B-glucan.

Understanding how biodiversity evolves is a major goal in evolutionary developmental biology because changes in developmental processes are tightly linked with evolutionary diversification. Heterochrony--alteration to the rate or timing of development--can significantly alter the appearance of descendant species. Heterochronic shifts in gene expression and associated morphological change may explain the morphological divergence between the threatened June sucker (Chasmistes liorus) and the sympatric Utah sucker (Catostomus ardens) in Utah Lake, UT, USA. June sucker are endemic to Utah Lake and have subterminal mouths adapted for pelagic feeding, while Utah sucker (Catostomus ardens) have ventral mouths adapted for benthic feeding. As larvae, both June sucker and Utah sucker have terminal mouths. However, within the first 14 weeks of development, the Utah sucker's mouth shifts to a ventral position, whereas the June sucker's mouth only shifts to a subterminal position. Using geometric morphometrics and RNA-seq time courses, we document a difference in the timing of shape development and a corresponding change in the timing of gene expression between June sucker and two Utah sucker lineages. Our results suggest that the distinctive mouth morphology in June sucker may be the result of paedomorphosis in which adult June sucker exhibit an intermediate mouth morphology between that of the larval (terminal) and ancestral (ventral) states. On a broader scale, additional Chasmistes / Catostomus pairs exist in the Intermountain West that are also morphologically divergent, but genetically similar. These pairs could be the result of repeated convergent evolution driven by differential expression of genes in response to environmental cues.

Recent advances in high-throughput technique enables the generation of a large amount of omic data such as genomics, transcriptomics, proteomics, metabolomics, glycomics etc. Typically, differential expression analysis (e.g., student's t-test, ANOVA) is performed to identify biomolecules (e.g., genes, proteins, metabolites, glycans) with significant changes on individual level between biologically disparate groups (disease cases vs. healthy controls) for cancer biomarker discovery. However, differential expression analysis on independent studies for the same clinical types of patients often led to different sets of significant biomolecules and had only few in common. This may be attributed to the fact that biomolecules are members of strongly intertwined biological pathways and highly interactive with each other. Without considering these interactions, differential expression analysis could lead to biased results. Network-based methods provide a natural framework to study the interactions between biomolecules. Commonly used data-driven network models include relevance network, Bayesian network and Gaussian graphical models. In addition to data-driven network models, there are many publicly available databases such as STRING, KEGG, Reactome, and ConsensusPathDB, where one can extract various types of interactions to build knowledge-driven networks. While both data- and knowledge-driven networks have their pros and cons, an appropriate approach to incorporate the prior biological knowledge from publicly available databases into data-driven network model is desirable for more robust and biologically relevant network reconstruction. Recently, there has been a growing interest in differential network analysis, where the connection in the network represents a statistically significant change in the pairwise interaction between two biomolecules in different groups. From the rewiring interactions shown in differential networks, biomolecules that have strongly altered connectivity between distinct biological groups can be identified. These biomolecules might play an important role in the disease under study. In fact, differential expression and differential network analyses investigate omic data from two complementary perspectives: the former focuses on the change in individual biomolecule level between different groups while the latter concentrates on the change in pairwise biomolecules level. Therefore, an approach that can integrate differential expression and differential network analyses is likely to discover more reliable and powerful biomarkers. To achieve these goals, we start by proposing a novel data-driven network model (i.e., LOPC) to reconstruct sparse biological networks. The sparse networks only contains direct interactions between biomolecules which can help researchers to focus on the more informative connections. Then we propose a novel method (i.e., dwgLASSO) to incorporate prior biological knowledge into data-driven network model to build biologically relevant networks. Differential network analysis is applied based on the networks constructed for biologically disparate groups to identify cancer biomarker candidates. Finally, we propose a novel network-based approach (i.e., INDEED) to integrate differential expression and differential network analyses to identify more reliable and powerful cancer biomarker candidates. INDEED is further expanded as INDEED-M to utilize omic data at different levels of human biological system (e.g., transcriptomics, proteomics, metabolomics), which we believe is promising to increase our understanding of cancer. Matlab and R packages for the proposed methods are developed and available at Github (https://github.com/Hurricaner1989) to share with the research community. / Ph. D.

Massively parallel sequencing has rapidly revolutionized DNA and RNA research. Sample preparations are steadfastly advancing, sequencing costs have plummeted and throughput is ever growing. This progress has resulted in exponential growth in data generation with a corresponding demand for bioinformatic solutions. This thesis addresses methodological aspects of this sequencing revolution and applies it to selected biological topics. Papers I and II are technical in nature and concern sample preparation and data anal- ysis of RNA sequencing data. Paper I is focused on RNA degradation and paper II on generating strand specific RNA-seq libraries. Paper III and IV deal with current biological issues. In paper III, whole exomes of cancer patients undergoing chemotherapy are sequenced and their genetic variants associ- ated to their toxicity induced adverse drug reactions. In paper IV a comprehensive view of the gene expression of the endometrium is assessed from two time points of the menstrual cycle. Together these papers show relevant aspects of contemporary sequencing technologies and how it can be applied to diverse biological topics. / <p>QC 20160329</p>

The goal of many human disease-oriented studies is to detect molecular mechanisms different between healthy controls and patients. Yet, commonly used gene expression measurements from any tissues suffer from variability of cell composition. This variability hinders the detection of differentially expressed genes and is often ignored. However, this variability may actually be advantageous, as heterogeneous gene expression measurements coupled with cell counts may provide deeper insights into the gene expression differences on the cell type-specific level. Published computational methods use linear regression to estimate cell type-specific differential expression. Yet, they do not consider many artifacts hidden in high-dimensional gene expression data that may negatively affect the performance of linear regression. In this dissertation we specifically address the parameter space involved in the most rigorous use of linear regression to estimate cell type-specific differential expression and report under which conditions significant detection is probable. We define parameters affecting the sensitivity of cell type-specific differential expression estimation as follows: sample size, cell type-specific proportion variability, mean squared error (spread of observations around linear regression line), conditioning of the cell proportions predictor matrix, and the size of actual cell type-specific differential expression. Each parameter, with the exception of cell type-specific differential expression (effect size), affects the variability of cell type-specific differential expression estimates. We have developed a power-analysis approach to cell type by cell type and genomic site by site differential expression detection which relies upon Welch’s two-sample t-test and factors in differences in cell type-specific expression estimate variability and reduces false discovery. To this end we have published an R package, LRCDE, available in GitHub (http://www.github.com/ERGlass/lrcde.dev) which outputs observed statistics of cell type-specific differential expression, including two-sample t- statistic, t-statistic p-value, and power calculated from two-sample t-statistic on a genomic site- by-site basis.

Facioscapulohumeral muscular dystrophy(FSHD) is a disorder characterized by muscle weakness and wasting (atrophy). This disease is typically inherited as autosomal dominant and has a complex genetic and epigenetic etiology. Our collaborator had differentiated healthy human pluripotent stem cells(iPSC) into skeletal muscles and exploited ISO-Seq to explore cell gene expression and transcript alternative splicing usage profile during 8 differentiation stages. Later, stage specific gene differential expression, transcript alternative splicing, gene ontology and novel gene/transcript were analysed to characterize the feature of each stage during the differentiation. In terms of expressed genes with more than or equal to 5 transcripts, each stage had shown their own stage specific features. About transcripts, iPS, S1, ADM.D0, ADM.D4 have about 30% to 40% more total transcripts than the rest 4 stages. 4 kinds of alternative splicing events are generally distributed and S2 stage has the least alternative splicing events potentially due to technical reasons. As for gene differential expressions, ADM.D4 has considerable amount of differential expressed genes with 5 other stages and it has minor difference with ISM.D4 and S3 stages(they are all myotubes cells). The gene ontology analysis is performed according to the results of previous step, stage specific GO terms are revealed.

Facioscapulohumeral muscular dystrophy(FSHD) is a disorder characterized by muscle weakness and wasting (atrophy). This disease is typically inherited as autosomal dominant and has a complex genetic and epigenetic etiology. Our collaborator had differentiated healthy human pluripotent stem cells(iPSC) into skeletal muscles and exploited ISO-Seq to explore cell gene expression and transcript alternative splicing usage profile during 8 differentiation stages. Later, stage specific gene differential expression, transcript alternative splicing, gene ontology and novel gene/transcript were analysed to characterize the feature of each stage during the differentiation. In terms of expressed genes with more than or equal to 5 transcripts, each stage had shown their own stage specific features. About transcripts, iPS, S1, ADM.D0, ADM.D4 have about 30% to 40% more total transcripts than the rest 4 stages. 4 kinds of alternative splicing events are generally distributed and S2 stage has the least alternative splicing events potentially due to technical reasons. As for gene differential expressions, ADM.D4 has considerable amount of differential expressed genes with 5 other stages and it has minor difference with ISM.D4 and S3 stages(they are all myotubes cells). The gene ontology analysis is performed according to the results of previous step, stage specific GO terms are revealed.

Recent Next Generation Sequencing methods provide a count of RNA molecules in the form of short reads, yielding discrete, often highly non-normally distributed gene expression measurements. Due to this feature of RNA sequencing (RNA-seq) data, appropriate statistical inference methods are required. Although Negative Binomial (NB) regression has been generally accepted in the analysis of RNA-seq data, its appropriateness in the application to genetic studies has not been exhaustively evaluated. Additionally, adjusting for covariates that have an unknown relationship with expression of a gene has not been extensively evaluated in RNA-seq studies using the NB framework. Finally, the dependent structures in RNA-Seq data may violate the assumptions of some multiple testing correction methods. In this dissertation, we suggest an alternative regression method, evaluate the effect of covariates, and compare various multiple testing correction methods. We conduct simulation studies and apply these methods to a real data set. First, we suggest Firth’s logistic regression for detecting differentially expressed genes in RNA-seq data. We also recommend the data adaptive method that estimates a recalibrated distribution of test statistics. Firth’ logistic regression exhibits an appropriately controlled Type-I error rate using the data adaptive method and shows comparable power to NB regression in simulation studies. Next, we evaluate the effect of disease-associated covariates where the relationship between the covariate and gene expression is unknown. Although the power of NB and Firth’s logistic regression is decreased as disease-associated covariates are added in a model, Type-I error rates are well controlled in Firth’ logistic regression if the relationship between a covariate and disease is not strong. Finally, we compare multiple testing correction methods that control family-wise error rates and impose false discovery rates. The evaluation reveals that an understanding of study designs, RNA-seq data, and the consequences of applying specific regression and multiple testing correction methods are very important factors to control family-wise error rates or false discovery rates. We believe our statistical investigations will enrich gene expression studies and influence related statistical methods.

Gramíneas cultivadas, como trigo, cevada e milho são produtos agrícolas de fundamental importância no Brasil. Entre os fatores causadores de perdas na produção de grãos dessas espécies estão os estresses causados por fitopatógenos como Fusarium graminearum Schwabe (teleomorfo Gibberella zeae Schw.), agente causador da fusariose e de difícil controle químico, biológico ou mesmo genético. Uma estratégia que tem se mostrado eficiente no controle de doenças é a utilização de microrganismos antagonistas a diferentes fitopatógenos, dentre os quais destaca-se a bactéria P. agglomerans. O presente trabalho teve como objetivo identificar genes diferencialmente expressos em interações fungo fitopatogênico-microrganismo antagonista, considerando como modelo o sistema F. graminearum-P. agglomerans. A construção de uma biblioteca de cDNA de F. graminearum cultivado in vitro proporcionou a geração de 1.983 seqüências válidas, resultando em 1.283 unigenes. As categorias de maior representatividade desta biblioteca foram aquelas constituídas por proteínas envolvidas em vias da informação genética - DNA-RNA-proteína (26 %); proteínas hipotéticas (24 %) e proteínas do metabolismo (16 %). Tanto a categoria de proteínas envolvidas nos processos de desenvolvimento como as envolvidas na percepção a estímulos externos constituíram 10 % dos unigenes. Dentre os genes presumivelmente anotados, foram identificados aqueles codificadores de enzimas de importantes rotas metabólicas como gliceraldeído-3-fosfato-desidrogenase, fosfoglicerato quinases e fosfoenolpiruvato carboxilases, como também componentes produzidos pelo metabolismo secundário como micotoxinas e outras proteínas associadas a estresse e patogenicidade de fungos. Neste trabalho também foi verificado o potencial de antagonismo in vitro da bactéria P. agglomerans frente a três fitopatógenos de trigo: Drechslera tritici-repentis (Died.) Shoem e Bipolaris sorokiniana (Sacc. in Sorok.) e F. graminearum. Foi verificado que a inibição do crescimento destes fungos está associada à liberação de compostos solúveis e voláteis pela bactéria, que foram responsáveis por cerca de 50 % e 40 % de inibição, respectivamente. O perfil da expressão gênica de F. graminearum na interação com a bactéria P. agglomerans foi avaliado via macroarranjo. Dos 1.014 genes avaliados, 29 genes de F. graminearum foram diferencialmente expressos (p < 0,05) durante a interação com a bactéria antagonista, sendo 19 genes induzidos e 10 genes reprimidos. Entre os transcritos induzidos foram identificadas proteínas envolvidas nos processos de defesa e/ou virulência de fungos, cuja expressão foi induzida em resposta a estresses tanto abióticos como bióticos. Dos genes que foram reprimidos, destacaram-se: um transcrito com similaridade a uma proteína com um domínio do tipo dedo de zinco ?zinc finger? que é um fator de transcrição importante no processo de divisão celular, bem como proteínas envolvidas na cadeia respiratória, na modulação protéica e sinalização celular. Os dados do macroarranjo foram validados via transcrição reversa seguida de PCR quantitativo em tempo real (RT-PCRq), metodologia que se mostrou adequada para complementar a análise transcricional obtida por macroarranjo. As informações geradas na análise de antagonismo in vitro, bem como a análise e seqüenciamento dos transcritos, juntamente com a quantificação do nível de expressão na interação, foram fundamentais para compreender o padrão de resposta do fungo F. graminearum na interação com a bactéria P. agglomerans. / Cultivated grasses such as wheat, barley and maize are agricultural products of fundamental economic and social importance in Brazil. Among causing factors of important grain production losses in these species are diseases caused by phytopathogenic fungi such as Fusarium graminearum Schwabe (teleomorfo Gibberella zeae Schw.), the causal agent of fusariosis, a disease of difficult chemical, biological or even genetic control. An efficient and promising strategy to be adopted in order to protect cultivated plants against such diseases is the selection of antagonist microorganisms, amongst them the bacteria Pantoea agglomerans. This microbiota might have an important impact in scab control, isolated or in an integrated management program with chemical treatment. The present work aimed at identifying differentially expressed sequences in pathogenic fungi-antagonistic microorganisms interactions, considering the F. graminearum ? P. agglomerans model. The construction of a cDNA library for F. graminearum grown in PDA medium generated 1,983 valid sequences and provided 1,283 unigenes. The most representative categories in this library were proteins involved in genetic information pathways, DNA-RNA-protein (26 %); hypothetical proteins (24 %); and proteins involved in metabolism (16 %). The protein category involved in developmental processes as well as those related to external stimuli perception comprised 10 % of the obtained unigenes. Among putatively annotated genes, some coding for enzymes of important metabolic routes were identified, such as glyceraldehyde-3-phosphate dehydrogenase, phosphoglycerate kinase and phophoenolpyruvate carboxylase. Also secondary metabolism compounds, specially micotoxins and proteins related to fungi stresses and pathogenicity were identified. In the present work, the control of three wheat phytopathogens, Drechslera tritici-repentis (Died.) Shoem, Bipolaris sorokiniana (Sacc.in Sorok.) and F. graminearum, using specific isolates of P. agglomerans was demonstrated. It was observed that the 50 % and 40 % growth inhibition of these fungi is associated to the bacteria release of soluble and volatile compounds, respectively. The gene expression profile of F. graminearum during interaction with the bacteria P. agglomerans was evaluated via macroarray. Among the 1,014 analysed genes, 29 F. graminearum genes were differentially expressed (p < 0,05) during its interaction with the antagonist bacteria: 19 genes were induced while 10 genes were repressed. Among the induced transcripts, proteins involved in fungi defense and/or virulence processes were identified, whose expression was induced in reponse to abiotic or biotic stresses. Among the identified repressed genes, a transcript similar to a protein containing a zinc finger-type domain, a transcription factor relevant in cell division, deserves special attention, as well as proteins involved in respiratory chain, in protein modulation and in cell signaling. Additionally, the macroarray data were validated by reverse transcription followed by real-time quantitative PCR (RT-PCRq), a suitable method for complementing transcriptional analysis through macroarray. Finally, the information generated in in vitro pathogenic fungi-antagonistic microorganisms interactions analysis, as well as in the analysis and sequencing of the obtained transcripts, together with the determination of the level of expression during the evaluated interactions were essential for better understanding the response pattern of the fungus F. graminearum in interaction with the bacteria P. agglomerans