No description available.

The scope and application of high throughput techniques has expanded from studying a single genome, transcriptome or proteome to understanding complex environments at a greater resolution with the help of novel computational frameworks. Comprehensive structural annotation i.e. description of all functional elements in the genome, is required for measuring genome response accurately, using high throughput methods. Annotation of genome sequences using high throughput data from RNA-seq and proteomics experiments complement computational methods for identifying functional elements and can help validate existing in silico annotation, correct annotation errors, and could potentially identify novel functional elements. Re-annotation studies in recent times have revealed shortcomings of automated methods and the necessity to validate existing annotations using experimental data. This dissertation elucidates re-annotation of Mannheimia haemolytica, Pasteurella multocida and Histophilus somni, bacterial pathogens associated with bovine respiratory disease in cattle. Experimental re-annotation of these bacterial genomes using RNA-seq and proteomics enabled the validation of existing annotation and discovery of novel functional elements that can be utilized in future functional genomics studies. We also addressed the need for developing an automated bioinformatics workflow that is broadly applicable for bacterial genome re-annotation, by developing open source Perl pipeline that can use RNA-seq and proteomics data as input. Simultaneous analysis of host and pathogen gene expression profiling using metatranscriptomics approaches is necessary to improve our understanding of infectious diseases. Traditional methods for analysis of RNA-seq data do not address the impact of cross-mapping of reads to multiple genomes for data originating from a metatranscriptomic study. Analysis of sequence conservation between species can help determine a metric for cross mapping to correct for signal vs. noise. We generated artificial RNA-seq data and evaluated the impact of read length and sequence conservation on cross-mapping. Comparative genomics was used to identify a core and pan-genome for quantifying gene expression. Our results show that cross mapping between genomes can directly be related to evolutionary distance between these genomes and that an increase in RNA-seq read length tends to negate cross mapping.

<p>Nitrogen and phosphorus are the two macronutrients which play important roles in the plant, both structurally and functionally, e.g., starting from being constituents of cellular integrity to being signal molecules in signal transduction. Since they are required by plants in higher concentrations, it becomes indispensable to replenish their pools in soils by the application of chemical fertilizers. However, this practice is not only costly, the sources of Phosphorus and Nitrogen are not renewable and the excessive application in the form of fertilizers is not environmentally sustainable. Therefore, it warrants a better understanding of the plant responses during the nutrient deficiency because such knowledge will help implement strategies for breeding crops with more efficient use of minerals.</p><p>Most prior efforts in studying the molecular and physiological responses to low minerals were focused on roots. However, recently it has been found that shoot-to-root long distance signaling plays an important role in the adaptation of roots to low nitrogen or phosphorus. Here, we measured different physiological and morphological parameters and used RNA-Seq to elucidate the physiological and molecular responses in the vascular tissues of <i>Plantago major</i>, a new model species established in our laboratory, to low nitrogen, low phosphate or combined nitrogen and phosphate starvation<i>. </i>In this study, <i>P major </i>showed reduced photosynthesis and Fv/Fm, increased catalase and ascorbate peroxidase activity, reduced phosphate and nitrate contents in respective treatments. In addition, assessment of root morphological parameters revealed that nutrient deficiencies could lead to higher root densities and increased root to shoot ratios.</p><p>For molecular analysis of transcriptome changes, 24 hours of nutrient starvation exhibited an alteration of 33, 221, and 329 genes for the deficiencies of phosphorus, nitrogen and combined nitrogen and phosphorus, respectively. Our study helped to dissect several novel pathways associated with the vascular system in response to the deficiencies of major macronutrients. </p>

Anti-CD19 CAR-T cell immunotherapy is a hopeful treatment option for patients with B cell lymphomas, however it copes with partly severe adverse effects like neurotoxicity. Single-cell resolved molecular data sets in combination with clinical parametrization allow for comprehensive characterization of cellular subpopulations, their transcriptomic states, and their relation to the adverse effects. We here present a re-analysis of single-cell RNA sequencing data of 24 patients comprising more than 130,000 cells with focus on cellular states and their association to immune cell related neurotoxicity. For this, we developed a single-cell data portraying workflow to disentangle the transcriptional state space with single-cell resolution and its analysis in terms of modularly-composed cellular programs. We demonstrated capabilities of single-cell data portraying to disentangle transcriptional states using intuitive visualization, functional mining, molecular cell stratification, and variability analyses. Our analysis revealed that the T cell composition of the patient’s infusion product as well as the spectrum of their transcriptional states of cells derived from patients with low ICANS grade do not markedly differ from those of cells from high ICANS patients, while the relative abundancies, particularly that of cycling cells, of LAG3-mediated exhaustion and of CAR positive cells, vary. Our study provides molecular details of the transcriptomic landscape with possible impact to overcome neurotoxicity.

Gene-expression profiling enables researchers to quantify transcription levels in cells, thus providing insight into functional mechanisms of diseases and other biological processes. However, because of the high dimensionality of these data and the sensitivity of measuring equipment, expression data often contains unwanted confounding effects that can skew analysis. For example, collecting data in multiple runs causes nontrivial differences in the data (known as batch effects), known covariates that are not of interest to the study may have strong effects, and there may be large systemic effects when integrating multiple expression datasets. Additionally, many of these confounding effects represent higher-order interactions that may not be removable using existing techniques that identify linear patterns. We created Confounded to remove these effects from expression data. Confounded is an adversarial variational autoencoder that removes confounding effects while minimizing the amount of change to the input data. We tested the model on artificially constructed data and commonly used gene expression datasets and compared against other common batch adjustment algorithms. We also applied the model to remove cancer-type-specific signal from a pan-cancer expression dataset. Our software is publicly available at https://github.com/jdayton3/Confounded.

No description available.

Marine mammals such as northern elephant seals (NES) routinely experience hypoxemia and ischemia-reperfusion events to many tissues during deep dives with no apparent adverse effects. Adaptations to diving include increased antioxidants and elevated oxygen storage capacity associated with high hemoprotein content in blood and muscle. Despite experiencing decreased oxygen tensions during diving, NES likely rely on the mobilization of large lipids stores and catabolism of fatty acids to provide energy to exercising muscle while diving. To identify potential regulatory mechanisms that may underly hypoxia and exercise tolerance in diving mammals, this study used system-wide approaches to characterize changes in genes and proteins in two metabolically active tissues (skeletal muscle and blubber) and whole blood of NES over development and in response to translocation. Specifically, this study profiled muscle and blood gene expression associated with regulation of oxidative stress and inflammatory pathways in weaned pups, juveniles, and adult NES as well as evaluated muscle and blubber transcriptomic and proteomic responses to swimming and diving in juvenile NES. I found that expression of genes associated with mitochondrial biogenesis (PGC1A, ESRRA, ESRRG), immune system activation (HMOX2, IL1B, NRF2, BVR, IL10), and protection from lipid peroxidation (GPX4, PRDX6, PRDX1, SIRT1) increased over postnatal development in muscle and whole blood of NES, providing a potential ontogenic mechanism for increasing diving capacity and hypoxia and ischemia-reperfusion tolerance. I also found that expression of genes and abundance of proteins associated with lipid transport (APOD, ABCA6, ABCA8, ABCA10, CD1E), lipid catabolism (ADIPOQ , ENPP6), and adipogenesis (DLK1, ADIRF,) increased, while those associated with insulin sensitivity and energy expenditure (APLN, VGF) decreased in response to swimming and diving in juvenile NES blubber and muscle, suggesting potential mechanisms for fuel provisioning to muscle during exercise in hypoxic conditions. Together, these data provide insights into gene activity in muscle, blubber, and blood cells that may provide hypoxia tolerance and regulate energy homeostasis and exercise performance during breath holds in diving mammals.

<p dir="ltr">Neurological and neuropsychological dysfunctions resulting from manganese (Mn) accumulation in the human brain are well acknowledged; however, the underlying mechanisms are not yet fully understood. Amongst currently proposed Mn neurotoxicity mechanisms, some were only detectable at concentrations that can lead to over 50% viability loss in acute insult which cannot represent human brain exposure scenarios. Meanwhile, epidemiological reports suggest that exposures over a timeframe of years to decades at Mn levels near or even lower than the regulatory workspace threshold can still lead to adverse outcomes in the central nervous system. Therefore, how to model environmentally relevant chronic Mn exposures at near-threshold levels in <i>in vitro</i> experimental settings and how neurotoxicity is developed under this exposure paradigm are central questions awaiting to be answered.</p><p dir="ltr">Considering the essentiality of Mn as a critical metallic co-factor of multiple enzymes, this study aims to test the hypothesis that interrupted homeostasis of cellular functions that utilize Mn under physiological conditions are the most sensitive respondents to Mn overload. To test this hypothesis, a wide range of Mn concentrations were exposed in cell-based neuronal models across multiple durations. The responses of Mn-dependent biological processes were evaluated by protein phosphorylation quantifications, transcriptomic analyses, and functional measurements. Findings from these assessments highlighted the sensitivity of insulin/PI3K/AKT/mTOR signaling, cAMP/PKA/CREB signaling, cell adhesion, axonal guidance, and homeostatic regulation of divalent metals to near-physiological-threshold Mn overload. Alterations of these pathways illustrate a network of cellular functions that relies on optimal intracellular Mn content and vitally contributes to the neurodegeneration risks induced by chronic Mn exposures.</p><p dir="ltr">In conclusion, this work contributes to a more nuanced understanding of Mn neurotoxicity mechanisms, emphasizing the importance of both concentration and duration of exposure in the context of neurodegenerative risk, and paving the way for future research into constructing adverse outcome pathways of Mn via advanced <i>in vitro</i> modeling.</p>

Les myopathies héréditaires sont des maladies affectant les fibres musculaires, résultant d’un variant génétique pathogénique transmis par un ou les deux parents. Le diagnostic de ces maladies est complexe en raison de la similarité de certains symptômes avec d’autres maladies neuromusculaires et de leur hétérogénéité. Malgré les progrès des connaissances sur les maladies musculaires héréditaires, de nombreux patients ne reçoivent toujours pas de diagnostic moléculaire malgré les divers tests et le dépistage génétique. Actuellement, 13 variants dans le gène MLIP ont été rapportés chez 15 patients atteints d’une myopathie récessive. Les symptômes, les atteintes et l’âge au début de la maladie varient grandement entre ces patients. Ces variant sont suspectés d’entraîner une perte de fonction ou une altération majeure de la fonction protéique de MLIP et/ou de provoquer des dysfonctionnements dans les voies biologiques associées avec MLIP. De plus, les variants sont localisés autour de l’exon 4 contenant la séquence de localisation nucléaire et autour de l’exon 9 contenant, avec l’exon 10, un crochet AT permettant une interaction avec la chromatine. Pour comprendre la fonction de MLIP dans le muscle et expliquer la variabilité phénotypique, trois objectifs ont été définis : 1) définir le patron d’expression des transcrits chez trois patients; 2) modéliser le variant du patient adulte Z46 par CRISPR-Cas9 dans des myoblastes humains immortalisés; 3) analyser l’impact fonctionnel et pathologique des variants des trois patients. Le séquençage à longue lecture a révélé une architecture transcriptomique différente selon le groupe d’âge et a permis de répertorier 11 nouveaux transcrits non connus chez MLIP. De plus, les techniques de qRT-PCR et de Western Blot ont montré qu’uniquement le patient adulte Z46 présente un mécanisme compensatoire des niveaux de LMNA lors d’une diminution ou une perte de MLIP dans le noyau. Ce mécanisme serait spécifique au tissu ou au type cellulaire, car il n’est retrouvé dans les myoblastes porteurs du variant du patient. En analysant tous les résultats, la variabilité phénotypique semble être liée à la perte de la séquence de localisation nucléaire, mais aussi à l’intensité et l’efficacité de l’interaction entre MLIP et la chromatine. / Hereditary myopathies are diseases affecting muscle fibers, resulting from a pathogenic genetic variant transmitted by one or both parents. The diagnosis of these diseases is complex due to the similarity of some symptoms with other neuromuscular diseases and their heterogeneity. Despite advances in knowledge of hereditary muscle diseases, many patients still do not receive molecular diagnosis despite various tests and genetic screening. Currently, 13 variants in the MLIP gene have been reported in 15 patients with recessive myopathy. Symptoms, conditions, and age at the beginning of the disease vary greatly between these patients. These variants are suspected to cause loss of function or major impairment of MLIP protein function and/or to cause dysfunctions in the biological pathways associated with MLIP. Moreover, the variants are located around exon 4 containing the nuclear localization sequence, and around exon 9 containing, with exon 10, an AT-hook allowing an interaction with chromatin. To understand the function of MLIP in muscle and explain phenotypic variability, three objectives were defined: 1) to define the pattern of expression of transcripts in three patients; 2) to model the variant of adult patient Z46 by CRISPR-Cas9 in immortalized human myoblasts; 3) analyze the functional and pathological impact of the variants of the three patients. Long-Read Sequencing revealed a different transcriptomic architecture according to the age group and identified 11 new transcripts not known in MLIP. In addition, qRT-PCR and Western Blot techniques have shown that only the adult Z46 patient has a compensatory mechanism for LMNA levels during a decrease or loss of MLIP in the nucleus. This mechanism would be specific to tissue or cell type because it is not found in myoblasts carrying the variant of the same patient. By analyzing all the results, phenotypic variability seems to be related to the loss of the nuclear localization sequence, but also to the intensity and efficiency of the interaction between MLIP and chromatin.

Räumliche Transkriptomik-Sequenzierungstechniken werden bei der Untersuchung von RNA in komplexen Geweben immer populärer. Mit diesen neuartigen Ansätze wird die Häufigkeit von Transkripten unter Beibehaltung ihrer räumlichen Lage gemessen, und ermöglichen so die Untersuchung der Genexpression in einem unvoreingenommen, raumzeitlichen Kontext. Angesichts der Vielfalt der zugrunde liegenden experimentellen Techniken, die Datensätze, die von verschiedenen Transkriptomik-Assays erstellt werden, variieren stark. Diese Datensätze werden von Pipelines verarbeitet und analysiert, die speziell für die jeweilige Methode entwickelt sind. Sie sind weder einfach modifizierbar, noch erweiterbar, dadurch sind sie nicht mit Inputs anderer Technologien kompatibel. Hier wird spacemake vorgestellt, eine bioinformatische Software, die darauf abzielt, die Lücke zwischen den verschiedenen räumlichen transkriptomischen Sequenzierungsansätzen zu schließen, durch sie einheitliches, schnelles, modulares, reproduzierbares und erweiterbares Rahmenwerk für die Verarbeitung und Analyse groß angelegter räumlicher transkriptomischer Daten bietet. Spacemake verarbeitet erfolgreich Daten aus den neuesten räumlichen Transkriptomik-Assays, unabhängig von ihrer Inputs. Spacemake ist parallel und läuft im Vergleich zu anderen vergleichbaren Techniken schneller. Spacemake ist modular entwickelt, und bietet verschiedene Module wie automatisiertes Clustering und Analyse, Quality Control, Saturation Analyse durch Downsampling, Zusammenführung technischer Replikate, Integration von scRNA-seq-Daten und Alignment von Mikroskopiebildern. Um ein Höchstmaß an Flexibilität zu bieten, ermöglicht spacemake benutzerkonfigurierbare Einstellungen\textit{run-mode} Einstellungen, wodurch die Unterstützung einer breiten Palette experimenteller Designs gewährleistet wird. Da spacemake in Python geschrieben ist, lässt es sich gut mit anderen Computational Biologie Methoden integrieren. Insgesamt hat spacemake das Potenzial, ein wichtiger Bestandteil der räumlichen Transkriptomik-Toolbox der Gegenwart und Zukunft zu sein. / Spatial transcriptomics sequencing techniques are increasingly popular when studying RNA in complex tissues. These novel approaches measure the abundance of transcripts while retaining their spatial location information, thus allowing the study of gene expression in an unbiased, spatiotemporal context. Given the variety of the underlying experimental techniques, the datasets which are produced by each spatial transcriptomic assay also vary greatly. These datasets are processed and analyzed by pipelines tailored specifically for each method, and are not easily modifiable nor extendable, thus making them incompatible to work with inputs from other technologies. Here spacemake is introduced, a bioinformatic software that aims to close the gap between the various spatial transcriptomic sequencing approaches, by providing a unified, fast, modular, reproducible, and extendable framework for large-scale spatial transcriptomic data processing and analysis. Spacemake successfully processes data from the latest spatial transcriptomics assays, regardless of their input data structure. Spacemake is parallel and runs faster when compared with other similar methods. It has a modular design and offers several modules such as automated clustering and analysis, quality control, saturation analysis through downsampling, technical replicate merging, scRNA-seq data integration, and microscopy image alignment. To offer maximum flexibility, spacemake allows for user-configurable \textit{run-mode} settings, ensuring support for a wide range of experimental designs. Written in Python, spacemake integrates well with other computational biology solutions. Overall spacemake has the potential to be an important part of the spatial transcriptomics toolbox of the present and future.