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Background Fusarium head blight (FHB) is a devastating disease of wheat worldwide. Resistance to FHB is quantitatively controlled by the combined effects of many small to medium effect QTL. Flowering traits, especially the extent of extruded anthers, are strongly associated with FHB resistance. Results To characterize the genetic basis of FHB resistance, we generated and analyzed phenotypic and gene expression data on the response to Fusarium graminearum ( Fg ) infection in 96 European winter wheat genotypes, including several lines containing introgressions from the highly resistant Asian cultivar Sumai3. The 96 lines represented a broad range in FHB resistance and were assigned to sub-groups based on their phenotypic FHB severity score. Comparative analyses were conducted to connect sub-group-specific expression profiles in response to Fg infection with FHB resistance level. Collectively, over 12,300 wheat genes were Fusarium responsive. The core set of genes induced in response to Fg was common across different resistance groups, indicating that the activation of basal defense response mechanisms was largely independent of the resistance level of the wheat line. Fg -induced genes tended to have higher expression levels in more susceptible genotypes. Compared to the more susceptible non-Sumai3 lines, the Sumai3-derivatives demonstrated higher constitutive expression of genes associated with cell wall and plant-type secondary cell wall biogenesis and higher constitutive and Fg -induced expression of genes involved in terpene metabolism. Gene expression analysis of the FHB QTL Qfhs.ifa-5A identified a constitutively expressed gene encoding a stress response NST1-like protein (TraesCS5A01G211300LC) as a candidate gene for FHB resistance. NST1 genes are key regulators of secondary cell wall biosynthesis in anther endothecium cells. Whether the stress response NST1-like gene affects anther extrusion, thereby affecting FHB resistance, needs further investigation. Conclusion Induced and preexisting cell wall components and terpene metabolites contribute to resistance and limit fungal colonization early on. In contrast, excessive gene expression directs plant defense response towards programmed cell death which favors necrotrophic growth of the Fg pathogen and could thus lead to increased fungal colonization.

Lactic acid bacteria are Gram-positive bacteria used throughout the world in many industrial applications for their acidification, flavor and texture formation attributes. One of the species, Lactococcus lactis, is employed for the production of fermented milk products like cheese, buttermilk and quark. It ferments lactose to lactic acid and, thus, helps improve the shelf life of the products. Many physiological and transcriptome studies have been performed in L. lactis in order to comprehend and improve its biotechnological assets. Using large amounts of transcriptome data to understand and predict the behavior of biological processes in bacterial or other cell types is a complex task. Gene networks enable predicting gene behavior and function in the context of transcriptionally linked processes. We reconstruct and present the gene co-expression network (GCN) for the most widely studied L. lactis strain, MG1363, using publicly available transcriptome data. Several methods exist to generate and judge the quality of GCNs. Different reconstruction methods lead to networks with varying structural properties, consequently altering gene clusters. We compared the structural properties of the MG1363 GCNs generated by five methods, namely Pearson correlation, Spearman correlation, GeneNet, Weighted Gene Co-expression Network Analysis (WGCNA), and Sparse PArtial Correlation Estimation (SPACE). Using SPACE, we generated an L. lactis MG1363 GCN and assessed its quality using modularity and structural and biological criteria. The L. lactis MG1363 GCN has structural properties similar to those of the gold-standard networks of Escherichia coli K-12 and Bacillus subtilis 168. We showcase that the network can be used to mine for genes with similar expression profiles that are also generally linked to the same biological process.

Respiratory syncytial virus (RSV) is one of the major causes of acute lower respiratory tract infections (ALRTI) in children. Children younger than 1 year are the most susceptible to RSV infection. RSV infections occur seasonally in temperate climate regions. Based on RSV surveillance and climatic data, we developed statistical models that were assessed and compared to predict the relationship between weather and RSV incidence among refugee children younger than 5 years in Dadaab refugee camp in Kenya. Most time-series analyses rely on the assumption of Gaussian-distributed data. However, surveillance data often do not have a Gaussian distribution. We used a generalized linear model (GLM) with a sinusoidal component over time to account for seasonal variation and extended it to a generalized additive model (GAM) with smoothing cubic splines. Climatic factors were included as covariates in the models before and after timescale decompositions, and the results were compared. Models with decomposed covariates fit RSV incidence data better than those without. The Poisson GAM with decomposed covariates of climatic factors fit the data well and had a higher explanatory and predictive power than GLM. The best model predicted the relationship between atmospheric conditions and RSV infection incidence among children younger than 5 years. This knowledge helps public health officials to prepare for, and respond more effectively to increasing RSV incidence in low-resource regions or communities.

Background Human metapneumovirus (HMPV) have similar symptoms to those caused by the respiratory syncytial virus (RSV). The modes of transmission and dynamics of time series data still remain poorly understood. Climatic factors have long been suspected to be implicated in impacting on the number of cases for these epidemics. Currently, only a few models satisfactorily capture the dynamics of time series data of these two viruses. Our objective was to assess the presence of influence of high incidences between the viruses and to ascertain whether higher incidences of one virus are influenced by the other. Methods In this study, we used a negative binomial model to investigate the relationship between RSV and HMPV while adjusting for climatic factors. We specifically aimed at establishing the heterogeneity in the autoregressive effect to account for the influence between these viruses. Results In this study, our findings showed that RSV incidence contributed to the severity of HMPV incidence. This was achieved through comparison of 12 models with different structures, including those with and without interaction between climatic factors. The models with climatic factors out-performed those without. Conclusions The study has improved our understanding of the dynamics of RSV and HMPV in relation to climatic cofactors thereby setting a platform to devise better intervention measures to combat the epidemics. We conclude that preventing and controlling RSV infection subsequently reduces the incidence of HMPV.

Background There is currently a dramatic increase in the number of COVID-19 cases worldwide, and further drastic restrictions in our daily life will be necessary to contain this pandemic. The implications of restrictive measures like social-distancing and mouth-nose protection on patients with chronic respiratory diseases have hardly been investigated. Methods Our survey, was conducted within the All Age Asthma Cohort (ALLIANCE), a multicenter longitudinal observational study. We assessed the effects of COVID-19 imposed social isolation and use of facial masks, on asthma course and mental health in patients with asthma and wheezing. Results We observed a high rate of problems associated with using facemasks and a significant reduction in the use of routine medical care. In addition to unsettling impacts, such as an increase in depression symptoms in adults, an astonishing and pleasing effect was striking: preschool children experienced an improvement in disease condition during the lockdown. This improvement can be attributed to a significant reduction in exposure to viral infections. Conclusion Long-term observation of this side effect may help improve our understanding of the influence of viral infections on asthma in early childhood.

Introduction Following lockdown periods and restricting public health measures in response to the COVID-19 pandemic, respiratory tract infections (RTIs) rose significantly worldwide. This led to an increased burden on children’s hospitals compromising medical care of acutely and chronically ill children. We characterized changes in the epidemiological pattern of circulating respiratory viral infections. Methods We assessed the number of patients with RTIs and the annual distribution of virus detections between 2019 and 2022 based on 4809 clinical samples (4131 patients) from a German pediatric tertiary care-center. We investigated the impact of lockdown periods on spectra of circulating respiratory viruses, pattern of coinfections, age, and seasonality of infections. Results A fourfold increase in the number of respiratory virus detections was observed in 2022 vs 2019 with numbers doubling in 2022 (vs 2021). In 2022, seasonal patterns of circulating virus, particularly Adeno and seasonal Coronavirus were far less pronounced compared to previous years, in fact almost disappeared for Rhinoviruses.”. SARS-CoV-2, Parainfluenza- and human Metapneumovirus detections increased significantly in 2022 (2019 vs 2022, p < 0.01). Coinfections with multiple viruses occurred more frequently since 2021 compared to pre-pandemic years, especially in younger children (2019 vs 2022, p < 0.01). Conclusion Compared to pre-pandemic years, we observed a dramatic increase in pediatric RTIs with an incrementing spectrum of viruses and a predominance in Rhino/Enterovirus infections – leading to a high rate of hospital admissions, particularly in conjunction with other viruses. This caused an acute shortage in medical care and may also be followed by an increase of virus-triggered secondary chronic respiratory diseases like asthma—rendering a burden on the health system.

Background Transcription of genes coding for xylanolytic and cellulolytic enzymes in Aspergillus niger is controlled by the transactivator XlnR. In this work we analyse and model the transcription dynamics in the XlnR regulon from time-course data of the messenger RNA levels for some XlnR target genes, obtained by reverse transcription quantitative PCR (RT-qPCR). Induction of transcription was achieved using low (1 mM) and high (50 mM) concentrations of D-xylose (Xyl). We investigated the wild type strain (Wt) and a mutant strain with partial loss-of-function of the carbon catabolite repressor CreA (Mt) . Results An improved kinetic differential equation model based on two antagonistic Hill functions was proposed, and fitted to the time-course RT-qPCR data from the Wt and the Mt by numerical optimization of the parameters. We show that perturbing the XlnR regulon with Xyl in low and high concentrations results in different expression levels and transcription dynamics of the target genes. At least four distinct transcription profiles were observed, particularly for the usage of 50 mM Xyl. Higher transcript levels were observed for some genes after induction with 1 mM rather than 50 mM Xyl, especially in the Mt. Grouping the expression profiles of the investigated genes has improved our understanding of induction by Xyl and the according regulatory role of CreA. Conclusions The model explains for the higher expression levels at 1 mM versus 50 mM in both Wt and Mt. It does not yet fully encapsulate the effect of partial loss-of-function of CreA in the Mt. The model describes the dynamics in most of the data and elucidates the time-dynamics of the two major regulatory mechanisms: i) the activation by XlnR, and ii) the carbon catabolite repression by CreA.

In the past few decades there has been tremendous progress in the fields of Bioinformatics and Systems Biology, particularly in studying biological networks. The development of high through-put data acquisition techniques has been vital in achieving this progress. Generally, the underlying mechanisms of gene regulation constitute an important process in living cells. This process controls transmission of information encoded in the DNA sequence to proteins through the central dogma of molecular biology. The study of biological networks helps improve our understanding of complex molecular processes. The xylanolytic XlnR regulon of A. niger encodes several enzymes, which are responsible for various processes in the cell. For instance, some of these enzymes are involved in the degradation of the polysaccharide xylan and cellulose. This thesis provides a detailed look into modeling regulon dynamics and target gene regulation mechanisms. Here, the XlnR regulon of A. niger is used as a model sample system to address challenges, which are associated with the process of network dynamics and experimental design. The XlnR regulon dynamics are studied in Chapter 2, and the models are evaluated on real experimental data in Chapter 6. Primarily the models were based on qualitative prior information of the regulation mechanisms for the target genes. This thesis also highlights the need to take strategic experimental designs into consideration when studying biological networks by advocating the deployment of time course experiments. It is through integration of theory and experiments that interesting challenges on networks such as the XlnR regulon can be addressed. The application of optimal and strategic experimental design in time course experiments to study biological networks is still at its infancy. Only a few studies have paid attention to this issue in an attempt to improve the information content in their data sets. The benefits of using induction signals for studying biological networks are illustrated in this thesis. To obtain meaningful results, various aspects of experimental design were studied. There is much to gain from the use of sensitivity functions, the Fisher Information Matrix (FIM), and the E-modified criterion in the design of time course experiments using mathematical models (Chapter 3). For instance, the extent to which the individual kinetic parameters influence the measured mRNA levels (gene expression) can be assessed by using sensitivity functions. This underlines the need for smart network perturbation techniques and data sampling strategies, both of which are crucial for improving the confidence intervals on the parameters. A comparison of transcription in strains of A. niger was performed in Chapters 4 and 5. These studies provided information on both the qualitative and quantitative roles of the carbon catabolite repressor protein (CreA). The response of xylan backbone-degrading enzymes to different D-xylose induction concentrations was investigated. The expression of accessory enzyme-encoding genes was found to be favored by using a high D-xylose concentration. The extent to which transcription varies between the wild type and mutant strains after D-xylose induction was assessed. The potential of D-xylose to induce expression of cellulase-encoding genes (target genes) was assessed. High Dxylose concentrations are preferable for inducing the gene expression of enzymes in the pentose metabolic pathway. The absence of a functional CreA positively influences expression of genes encoding xylan degrading enzymes, independent of D-xylose concentration. The xlnR gene was found to be constitutively expressed. Without performing time course experiments with frequent sampling and high data resolution, there is a risk of missing out on the time dynamics of the expression of the target genes. The role of CreA in the regulation of transcription in the A. nidulans, A. oryzae and A. niger species of Aspergillus has been widely studied. It mediates repression of the target genes and it was also found that mutation of CreA significantly increases transcription in the target genes of the XlnR regulon following low D-xylose induction concentrations. Basing on the work in Chapter 6, we see that mechanistic modeling with differential equations is a powerful way to study networks such as the XlnR regulon. The XlnR regulon was perturbed to generate transcription data from both in silico and wet-lab qPCR experiments. Network induction is a good way to study the information content of genomic data sets. Compared to the low Dxylose induction, transcription was found to be lower following high D-xylose induction. Exceptions were found in a very few number of genes, which did not exhibit significant differences in transcription under the two conditions. Using nonlinear differential equations, the regulatory mechanisms of the XlnR regulon were modeled and validated on qPCR data. The strategic experimental designs led to new insights on how to maximize the transcription data information content. It was shown that differences exist both between the responses of the different target genes over time. In Chapter 6, the quantitative information of CreA proved very useful for modifying the earlier-on proposed models in Chapters 2 and 3, and to evaluate the new models. Induction with D-xylose, particularly at high concentrations, yields a diversity of transcription profiles. To understand such transcription dynamics, it is crucial to understand the roles of the individual network components e.g. genes, and transcription factors or any other signaling molecules. The explanatory power of the models was significantly improved by incorporating these components into the modeling. Getting reliable inferences from experimental data sets is essential for the study of biological networks. However, the validity of results depends on a number of processes, such as the proper planning and execution of the experimental procedure of data collection and analysis. Fortunately, numerous analytical tools are available to facilitate such studies. The work in this thesis provides a basis for future research with regards to network. This includes issues such as: i) optimal network perturbation and design of time course experiments, ii) analysis of time constants and transcription dynamic responses, iii) data sampling strategies, and iv) evaluation of candidate model structures and their appropriateness in describing the transcription dynamics from measured mRNA time course data.

Background In this paper the dynamics of the transcription-translation system for XlnR regulon in Aspergillus niger is modeled. The model is based on Hill regulation functions and uses ordinary differential equations. The network response to a trigger of D-xylose is considered and stability analysis is performed. The activating, repressive feedback, and the combined effect of the two feedbacks on the network behavior are analyzed. Results Simulation and systems analysis showed significant influence of activating and repressing feedback on metabolite expression profiles. The dynamics of the D-xylose input function has an important effect on the profiles of the individual metabolite concentrations. Variation of the time delay in the feedback loop has no significant effect on the pattern of the response. The stability and existence of oscillatory behavior depends on which proteins are involved in the feedback loop. Conclusions The dynamics in the regulation properties of the network are dictated mainly by the transcription and translation degradation rate parameters, and by the D-xylose consumption profile. This holds true with and without feedback in the network. Feedback was found to significantly influence the expression dynamics of genes and proteins. Feedback increases the metabolite abundance, changes the steady state values, alters the time trajectories and affects the response oscillatory behavior and stability conditions. The modeling approach provides insight into network behavioral dynamics particularly for small-sized networks. The analysis of the network dynamics has provided useful information for experimental design for future in vitro experimental work.

Wheat has been domesticated into a large number of agricultural environments and has a remarkable ability to adapt to diverse environments. To understand this process, we survey genotype, repeat content and DNA methylation across a bread wheat landrace collection representing global genetic diversity. We identify independent variation in methylation, genotype and transposon copy number. We show that these, so far unexploited, sources of variation have had a massive impact on the wheat genome and that ancestral methylation states become preferentially 'hard coded' as SNPs via 5-methylcytosine deamination. These mechanisms also drive local adaption, impacting important traits such as heading date and salt tolerance. Methylation and transposon diversity could therefore be used alongside single nucleotide polymorphism (SNP) based markers for breeding.