Biology subjects

Nielsen, J.

Publications and source records attributed to Nielsen, J..

At least 91 records · Page 5Linked to original sources

bioRxiv2019

Compositional and functional differences of the mucosal microbiota along the intestine of healthy individuals

Gut mucosal microbes evolved closest to the host, developing specialized local communities. There is, however, insufficient knowledge of these communities as most studies have employed sequencing technologies to investigate faecal microbiota. This work used shotgun metagenomics of mucosal biopsies to explore the microbial communities compositions of terminal ileum and large intestine in 5 healthy individuals. Functional annotations and genome-scale metabolic modelling of selected species were then employed to identify local functional enrichments. While faecal metagenomics provided a good approximation of the average gut microbiome composition, mucosal biopsies allowed detecting the subtle variations of local microbial communities. Given their significant enrichment in the mucosal microbiota, we highlight the roles of Bacteroides species and describe the antimicrobial resistance biogeography along the intestine. We also detail which species, at which locations, are involved with the tryptophan/indole pathway, whose malfunctioning has been linked to pathologies including inflammatory bowel disease. Our study thus provides invaluable resources for investigating mechanisms connecting gut microbiota and host pathophysiology.

systems biology↗

bioRxiv2019

MOBN: an interactive database of multi-omics biological networks

It is essential to reveal the associations between different omics data for a comprehensive understanding of the altered biological process in human wellness and disease. To date, very few studies have focused on collecting and exhibiting multi-omics associations in a single database. Here, we present iNetModels, an interactive database and visualization platform of Multi-Omics Biological Networks (MOBNs). This platform describes the associations between the clinical chemistry, anthropometric parameters, plasma proteomics and metabolomics as well as metagenomics for oral and gut microbiome obtained from the same individuals. Moreover, iNetModels includes tissue- and cancer-specific Gene Co-expression Networks (GCNs) for exploring the connections between the specific genes. This platform allows the user to interactively explore a single feature's association with other omics data and customize its particular context (e.g. male/female specific). The users can also register their own data for sharing and visualization of the MOBNs and GCNs. Moreover, iNetModels allows users who do not have a bioinformatics background to facilitate human wellness and diseases research. iNetModels can be accessed freely at https://inetmodels.com without any limitation.

bioinformatics↗

bioRxiv2019

FALCONET: an R package to accelerate automatic visualisation of genome scale metabolic models

SummaryFALCONET (FAst visuaLisation of COmputational NETworks) enables the automatic for-mation and visualisation of metabolic maps from genome-scale models with R and CellDesigner, readily facilitating the visualisation of multi-layers omics datasets in the context of metabolic networks.\n\nMotivationUntil now, numerous GEMs have been reconstructed and used as scaffolds to conduct integrative omics analysis and in silico strain design. Due to the large network size of GEMs, it is challenging to produce and visualize these networks as metabolic maps for further in-depth analyses.\n\nResultsHere, we presented the R package - FALCONET, which facilitates drawing and visualizing metabolic maps in an automatic manner. This package will benefit the research community by allowing a wider use of GEMs in systems biology.\n\nAvailability and implementationFALCONET is available on https://github.com/SysBioChalmers/FALCONET and released under the MIT License.\n\nContactnielsenj@chalmers.se\n\nSupplementary informationSupplementary data are available online.

systems biology↗

bioRxiv2019

RMetD2: a tool for integration of relative transcriptomics data into Genome-scale metabolic models

Relative Metabolic Differences version 2 (RMetD2) is a tool for integration of differentially expressed (DE) genes into genome-scale metabolic models (GEMs) for revealing the altered metabolism between two biological conditions. This method provides a robust evaluation of the metabolism by using flux ranges instead of a single set of flux distributions. RMetD2 classifies reactions into three different groups, namely up-regulated, down-regulated and unchanged, which enables systematic interpretation of the metabolic differences between two different conditions. We employed this method in three different case studies using mice and human datasets, and compared it with state-of-the-art methods used for studying condition-specific metabolic differences using GEMs. We observed that RMetD2 is capable of capturing experimentally-observed features that are missed by other methods, highlighting its potential use in biotechnology and systems medicine applications. RMetD2 is implemented in Matlab and it is available without any limitation at https://sourceforge.net/projects/rmetd.

bioinformatics↗

bioRxiv2019

Young Genes are More Responsive to Environmental Stress than Ancient Genes in Budding Yeasts

The Saccharomycotina subphylum (budding yeasts) spans more than 400 million years of evolution and includes species that thrive in many of Earths harsh environments. Characterizing species that grow in harsh conditions could enable the design of more robust yeast strains for biotechnology. However, tolerance to stressful conditions is a multifactorial response, which is difficult to understand since many of the genes involved are as yet uncharacterized. In this work, three divergent yeast species were grown under multiple stressful conditions to identify stress-induced genes. For each condition, duplicated and non-conserved genes were significantly enriched for stress responsiveness compared to single-copy conserved genes. To understand this further, we developed a sorting method that considers evolutionary origin and duplication timing to assign an evolutionary age to each gene. Subsequent analysis of the sets of genes that changed expression revealed a relationship between stress-induced genes and the youngest gene set, regardless of the species or stress in question. These young genes are rarely essential for growth and evolve rapidly, which may facilitate their functionalization for stress tolerance and may explain their stress-induced expression. These findings show that systems-level analyses that consider gene age can expedite the identification of stress tolerance genes.

systems biology↗

bioRxiv2019

Transcriptomic networks implicate neuronal energetic abnormalities in three mouse models harboring autism and schizophrenia-associated mutations

Genetic risk for psychiatric illness is complex, so identification of shared molecular pathways where distinct forms of genetic risk might coincide is of substantial interest. A growing body of genetic and genomic studies suggest that such shared molecular pathways exist across disorders with different clinical presentations, such as schizophrenia and autism spectrum disorder (ASD). But how this relates to specific genetic risk factors is unknown. Further, whether some of the molecular changes identified in brain relate to potentially confounding antemortem or post-mortem factors is difficult to prove. We analyzed the transcriptome from the cortex and hippocampus of three mouse lines modeling human copy number variants (CNVs) associated with schizophrenia and ASD: Df(h15q13)/+, Df(h22q11)/+, and Df(h1q21)/+ which carry the 15q13.3 deletion, 22q11.2 deletion, and 1q21.1 deletion, respectively. Although we found very little overlap of differential expression at the level of individual genes, gene network analysis identified two modules of co-expressed genes that were dysregulated across all three mouse models. One module observed in both cortex and hippocampus was associated with neuronal energetics and firing rate, and overlapped with changes identified in post mortem human brain from SCZ and ASD patients. These data highlight aspects of convergent gene expression in mouse models harboring major risk alleles, and strengthen the connection between neuronal energetic dysfunction and neuropsychiatric disorders in humans.

bioinformatics↗

bioRxiv2019

Discovery of functional alternatively spliced PKM transcripts in human cancers

The association of pyruvate kinase muscle type (PKM) with survival of cancer patients is controversial. Here, we focus on different transcripts of PKM and investigate the association between their mRNA expression and the clinical survival of the patients in 25 different cancers. We find that the transcript encoding PKM2, and three other functional transcripts are prognostic in multiple cancers. Our integrative analysis shows that the functions of these four transcripts are highly conservative in different cancers. Next, we validate the prognostic effect of these transcripts in an independent kidney renal clear-cell carcinoma (KIRC) cohort and identify a prognostic signature which could distinguish high- and low-risk KIRC patients. Finally, we reveal the functional role of alternatively spliced PKM transcripts in KIRC, and discover the protein products of different transcripts of PKM. Our analysis demonstrated that alternatively spliced transcripts of not only PKM but also other genes should be considered in cancer studies, since it may enable the discovery and targeting of the right protein product for development of the efficient treatment strategies.

systems biology↗

bioRxiv2019

Proteome analysis of xylose metabolism in Rhodotorula toruloides during lipidproduction

BackgroundRhodotorula toruloides is a promising platform organism for production of lipids from lignocellulosic substrates. Little is known about the metabolic aspects of lipid production from the lignocellolosic sugar xylose by oleaginous yeasts in general and R. toruloides in particular. This study presents the first proteome analysis of the metabolism of R. toruloides during conversion of xylose to lipids.\n\nResultsR. toruloides cultivated on either glucose or xylose was subjected to comparative analysis of its growth dynamics, lipid composition, fatty acid profiles and proteome. The maximum growth and sugar uptake rate of glucose-grown R. toruloides cells were almost twice that of xylose-grown cells. Cultivation on xylose medium resulted in a lower final biomass yield although final cellular lipid content was similar between glucose- and xylose-grown cells. Analysis of lipid classes revealed the presence of monoacylglycerol in the early exponential growth phase as well as a high proportion of free fatty acids. Carbon source-specific changes in lipid profiles were only observed at early exponential growth phase, where C18 fatty acids were more saturated in xylose-grown cells. Proteins involved in sugar transport, initial steps of xylose assimilation and NADPH regeneration were among the proteins whose levels increased the most in xylose-grown cells across all time points. The levels of enzymes involved in the mevalonate pathway, phospholipid biosynthesis and amino acids biosynthesis differed in response to carbon source. In addition, xylose-grown cells contained higher levels of enzymes involved in peroxisomal beta-oxidation and oxidative stress response compared to cells cultivated on glucose.\n\nConclusionsThe results obtained in the present study suggest that sugar import is the limiting step during xylose conversion by R. toruloides into lipids. NADPH appeared to be regenerated primarily through pentose phosphate pathway although it may also involve malic enzyme as well as alcohol and aldehyde dehydrogenases. Increases in enzyme levels of both fatty acid biosynthesis and beta-oxidation in xylose-grown cells was predicted to result in a futile cycle. The results presented here are valuable for the development of lipid production processes employing R. toruloides on xylose-containing substrates.

microbiology↗

bioRxiv2019

Reconstruction and Analysis of a Kluyveromyces marxianus Genome-scale Metabolic Model

BackgroundKluyveromyces marxianus is a thermotolerant yeast with multiple biotechnological potentials for industrial applications, which can metabolize a broad range of carbon sources, including less conventional sugars like lactose, xylose, arabinose and inulin. These phenotypic traits are sustained even up to 45{degrees}C, what makes it a relevant candidate for industrial biotechnology applications, such as ethanol production. It is therefore of much interest to get more insight into the metabolism of this yeast. Recent studies suggested, that thermotolerance is achieved by reducing the number of growth-determining proteins or suppressing oxidative phosphorylation. Here we aimed to find related factors contributing to the thermotolerance of K. marxianus.\n\nResultsHere, we reported the first genome-scale metabolic model of Kluyveromyces marxianus, iSM996, using a publicly available Kluyveromyces lactis model as template. The model was manually curated and refined to include missing species-specific metabolic capabilities. The iSM996 model includes 1913 reactions, associated with 996 genes and 1531 metabolites. It performed well to predict the carbon source utilization and growth rates under different growth conditions. Moreover, the model was coupled with transcriptomics data and used to perform simulations at various growth temperatures.\n\nConclusionsK. marxianus iSM996 represents a well-annotated metabolic model of thermotolerant yeast, which provide new insight into theoretical metabolic profiles at different temperatures of K. marxianus. This could accelerate the integrative analysis of multi-omics data, leading to model-driven strain design and improvement.

systems biology↗

bioRxiv2019

Heart development in the lizards (Varanidae) with the greatest extent of ventricular septation

Among lizards, only monitor lizards (Varanidae) have a functionally divided cardiac ventricle. This enables them to sustain higher systemic blood pressures and higher metabolic rates than other reptiles of similar size. The division results from the concerted action of three partial septa, which may have homology to the full ventricular septum of mammals and archosaurs. Homology, however has only been inferred from anatomical comparisons of hearts of adult monitors whereas gene expression during heart development has not been studied. We show in developing monitors that the partial septa that separate the left and right ventricle, the muscular ridge and bulbuslamelle, express the evolutionary conserved transcription factors Tbx5, Irx1 and Irx2, orthologues of which mark the full ventricular septum. Compaction of embryonic trabeculae contributes to the formation of these septa. The septa are positioned, however, to the right of the atrioventricular junction and they do not partake in the separation of incoming atrial blood streams. Instead, the vertical septum within the left ventricle separates the atrial blood streams. It expresses Tbx3 and Tbx5, which orchestrate the formation of the electrical conduction axis of the full ventricular septum. These patterns of expression are more pronounced in monitors than in other lizards, and are associated with a deep electrical activation near the vertical septum, contrasting the primitive base-to-apex activation of other lizards. We conclude that current concepts of ventricular septum formation apply well to the monitor septa and that there is evolutionary conservation of ventricular septum formation among amniote vertebrates.

evolutionary biology↗

bioRxiv2019

Emergence of stable coexistence in a complex microbial community through metabolic cooperation and spatio-temporal niche partitioning

Microbial communities in nature often feature complex compositional dynamics yet also stable coexistence of diverse species. The mechanistic underpinnings of such dynamic stability remain unclear as system-wide studies have been limited to small engineered communities or synthetic assemblies. Here we show how kefir, a natural milk-fermenting community, realizes stable coexistence through spatio-temporal orchestration of species and metabolite dynamics. During milk fermentation, kefir grains (a polysaccharide matrix synthesized by kefir microbes) grow in mass but remain unchanged in composition. In contrast, the milk is colonized in a dynamic fashion with early members opening metabolic niches for the followers. Through large-scale mapping of metabolic preferences and inter-species interactions, we show how microbes poorly suited for milk survive in, and even dominate, the community through metabolic cooperation and uneven partitioning between the grain and the liquid phase. Overall, our findings reveal how spatio-temporal dynamics promote stable coexistence and have implications for deciphering and modulating complex microbial ecosystems.

microbiology↗

bioRxiv2019

Genome-scale model of Rhodotorula toruloides metabolism

The basidiomycete red yeast Rhodotorula toruloides is a promising platform organism for production of biooils. We present rhto-GEM, the first genome-scale model of R. toruloides metabolism, that was largely reconstructed using RAVEN toolbox. The model includes 4869 genes, 897 reactions, and 3334 metabolites. Lipid metabolism was described using the SLIMEr formalism, which allows direct integration of lipid class and acyl chain experimental distribution data. The simulation results confirmed that the R. toruloides model provides valid growth predictions on glucose, xylose and glycerol, while prediction of genetic engineering targets to increase production of linolenic acid and triacylglycerols highlighted genes that have previously been successfully used to increase lipid production. This renders rtho-GEM useful for future studies to improve the production of other industrially important oleochemicals including both value-added fatty acids and carotenoids, while it will also be valuable tool for system-wide omics-data analysis in R. toruloides. Expanding the portfolio of GEMs for lipid accumulating fungi contributes to both understanding of metabolic mechanisms of the oleaginous phenotype but also uncover particularities of lipid production machinery in R. toruloides.

systems biology↗

bioRxiv2019

Machine learning applied to predicting microorganism growth temperatures and enzyme catalytic optima

The optimal growth temperature (OGT) of organisms is an important index to estimate the stability of enzymes encoded in their genomes. However, experimental determination of OGT for microorganisms that cannot be cultivated is difficult. Here, we report on the development of a machine learning model that can accurately predict OGT directly from proteome-wide 2-mer amino acid composition. We make use of this model to predict OGTs for 1,438 microorganisms. In a subsequent step we combine OGT data with amino acid composition of individual enzymes to develop a second machine learning model for prediction of enzyme temperature optima (Topt). The resulting model is far superior to using OGT alone for estimating Topt in a dataset of 2,609 enzymes. Finally, we predict Topt for 6.5 million enzymes, covering 4,447 EC numbers, and make the resulting dataset available for researchers, enabling simple identification of enzymes that are potentially functional at extreme temperatures.

bioinformatics↗

bioRxiv2018

The human secretome - the proteins secreted from human cells

The proteins secreted by human tissues (the secretome) are important for the basic understanding of human biology, but also for identification of potential targets for future diagnosis and therapy. Here, we present an annotation of all predicted secreted proteins (n=2,623) with information about their spatial distribution in the human body. A high-throughput mammalian cell factory was established to create a resource of recombinant full-length proteins. This resource was used for phenotypic assays involving {beta}-cell dedifferentiation and for development of targeted proteomics assays. A comparison between host cells, including omics analysis, shows that many of the proteins that failed to be generated in CHO cells could be rescued in human HEK293 cells. In conclusion, the human secretome has been mapped and characterized to facilitate further exploration of the human secretome.

molecular biology↗

bioRxiv2018

Investigation of putative regulatory acetylation sites in Fas2p of Saccharomyces cerevisiae

Yeast metabolism is highly regulated, in part via coordinated reprogramming of metabolism on a transcriptional level, for example in response to environmental changes. Furthermore, regulation occurs on the protein level via posttranslational modifications directly affecting enzymatic activity - a mode of regulation that has the benefit of being very fast in response to environmental changes. One group of posttranslational modification that has been suggested to have a high impact on regulation of metabolism are acetylations. Around 4000 distinct protein acetylation sites have been found in Saccharomyces cerevisiae, many of which are located in central metabolic enzymes. However, reports on the verification of regulatory roles of specific acetylation sites on these metabolic enzymes have yet to emerge. This study investigates putative regulatory acetylation sites on Fas2p, which in concert with Fas1p is responsible for cytosolic fatty acid (FA) biosynthesis in S. cerevisiae. Fas2p stands out as one of the most highly acetylated proteins in yeast and is located at a branchpoint of acetyl-CoA metabolism. The amino acids (AAs) glutamine (Q) and arginine (R) were introduced to mimic a constitutively acetylated or non-acetylatable state at three separate lysine sites (K) (K83, K173 and K1551) confirmed to be acetylated in two independent studies, either separately or simultaneously. The results suggest that the residue replacement system in the specific case interferes with the enzymatic function of the fatty acid synthase (FAS), as QQQ and RRR triple mutants both reduce the amount of secreted free fatty acids (FFAs) in a faa1{Delta} faa4{Delta} yeast deletion mutant. The K173Q substitution significantly decreased C16 FA species at the expense of C18 FAs, while no such change could be observed for the corresponding K173R modification.

bioengineering↗

bioRxiv2018

Saccharomyces cerevisiae displays a stable transcription start site landscape in multiple conditions

One of the fundamental processes that determine cellular fate is regulation of gene transcription. Understanding these regulatory processes is therefore essential for understanding cellular responses to changes in environmental conditions. At the core promoter, the regulatory region containing the transcription start site (TSS), all inputs regulating transcription are integrated. Here, we used Cap Analysis of Gene Expression (CAGE) to analyze the pattern of transcription start sites at four different environmental conditions (limited in ethanol, limited in nitrogen, limited in glucose and limited in glucose under anaerobic conditions) using the Saccharomyces cerevisiae strain CEN.PK113-7D. With this experimental setup we were able to show that the TSS landscape in yeast is stable at different metabolic states of the cell. We also show that the shape index, a characteristic feature of each TSS describing the spatial distribution of transcription initiation events, has a surprisingly strong negative correlation with the measured expression levels. Our analysis supplies a set of high quality TSS annotations useful for metabolic engineering and synthetic biology approaches in the industrially relevant laboratory strain CEN.PK113-7D, and provides novel insights into yeast TSS dynamics and gene regulation.

systems biology↗

bioRxiv2018

Memote: A community-driven effort towards a standardized genome-scale metabolic model test suite

Several studies have shown that neither the formal representation nor the functional requirements of genome-scale metabolic models (GEMs) are precisely defined. Without a consistent standard, comparability, reproducibility, and interoperability of models across groups and software tools cannot be guaranteed.\n\nHere, we present memote (https://github.com/opencobra/memote) an open-source software containing a community-maintained, standardized set of metabolic model tests. The tests cover a range of aspects from annotations to conceptual integrity and can be extended to include experimental datasets for automatic model validation. In addition to testing a model once, memote can be configured to do so automatically, i.e., while building a GEM. A comprehensive report displays the models performance parameters, which supports informed model development and facilitates error detection.\n\nMemote provides a measure for model quality that is consistent across reconstruction platforms and analysis software and simplifies collaboration within the community by establishing workflows for publicly hosted and version controlled models.

systems biology↗

bioRxiv2018

Genome-scale reconstructions of the mammalian secretory pathway predict metabolic costs and limitations of protein secretion

In mammalian cells, >25% of synthesized proteins are exported through the secretory pathway. The pathway complexity, however, obfuscates its impact on the secretion of different proteins. Unraveling its impact on diverse proteins is particularly important for biopharmaceutical production. Here we delineate the core secretory pathway functions and integrate them with genome-scale metabolic reconstructions of human, mouse, and Chinese hamster cells. The resulting reconstructions enable the computation of energetic costs and machinery demands of each secreted protein. By integrating additional omics data, we find that highly secretory cells have adapted to reduce expression and secretion of other expensive host cell proteins. Furthermore, we predict metabolic costs and maximum productivities of biotherapeutic proteins and identify protein features that most significantly impact protein secretion. Finally, the model successfully predicts the increase in secretion of a monoclonal antibody after silencing a highly expressed selection marker. This work represents a knowledgebase of the mammalian secretory pathway that serves as a novel tool for systems biotechnology.

systems biology↗