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Nielsen, L. K.

Publications and source records attributed to Nielsen, L. K..

30 records · Page 2Linked to original sources

An automated workflow for multi-omics screening of microbial model organisms

Multi-omics datasets are becoming of key importance to drive discovery in fundamental research as much as generating knowledge for applied biotechnology. However, the construction of such large datasets is usually time-consuming and expensive. Automation is needed to overcome these issues by streamlining workflows from sample generation to data analysis. Here, we describe the construction of a complex workflow for the generation of high-throughput microbial multi-omics datasets. The workflow comprises a custom-built platform for automated cultivation and sampling of microbes, sample preparation protocols, analytical methods for sample analysis and automated scripts for raw data processing. We demonstrate possibilities and limitations of such workflow in generating data for three biotechnologically relevant model organisms, namely Escherichia coli, Saccharomyces cerevisiae, and Pseudomonas putida.

systems biology↗

Genetic analysis of global faba bean germplasm maps agronomic traits and identifies strong selection signatures for geographical origin

Faba bean (Vicia faba L.) is a high-protein grain legume crop with great potential for further cultivation. However, little is known about the genetics underlying trait diversity. In this study, we use 21,345 high-quality SNP markers to genetically characterise 2,678 faba bean genotypes. We perform genome-wide association studies of key agronomic traits using a Seven-parent-MAGIC population and detect 238 significant marker-trait associations linked to 12 traits of agronomic importance, with 65 of these being stable across multiple environments. Using a non-redundant diversity panel of 685 accessions from 52 countries, we identify 3 subpopulations differentiated by geographical origin and 33 genomic regions subject to strong diversifying selection between subpopulations. We find that SNP markers associated with the differentiation of northern and southern accessions were able to explain a significant proportion of agronomic trait variance in the Seven-parent-MAGIC population, suggesting that some of these traits have played an important role in breeding. Altogether, our findings point to genomic regions associated with important agronomic traits and selection in faba bean, which can be used for breeding purposes. Key MessageWe identified marker-trait associations for key faba bean agronomic traits and genomic signatures of selection within a global germplasm collection.

genetics↗

RNA-seq sample preparation kits strongly affect transcriptome profiles of a gas-fermenting bacterium

Transcriptome analysis via RNA sequencing (RNA-seq) has become a standard technique employed across various biological fields of study. This rapid adoption of the RNA-seq approach has been mediated, in part, by the development of different commercial RNA-seq library preparation kits compatible with standard next-generation sequencing (NGS) platforms. Generally, the essential steps of library preparation such as ribosomal RNA (rRNA) depletion and first-strand cDNA synthesis are tailored to a specific group of organisms (e.g. eukaryotes vs. prokaryotes) or genomic GC content. Therefore, the selection of appropriate commercial products is of crucial importance to capture the transcriptome of interest as closely to the native state as possible without introduction of technical bias. However, researchers rarely have the resources and time to test various commercial RNA-seq kits for their samples. This work reports a side-by-side comparison of RNA-seq data from Clostridium autoethanogenum obtained using three commercial rRNA removal and strand-specific library construction products by NuGEN Technologies, Qiagen, and Zymo Research and assesses their performance relative to published data. While all three vendors advertise their products as suitable for prokaryotes, we found significant differences in their performance regarding rRNA removal, strand-specificity, and, most importantly, transcript abundance distribution profiles. Notably, RNA-seq data obtained with Qiagen products were most similar to published data and delivered the best results in terms of library strandedness and transcript abundance distribution range. Our results highlight the importance of finding appropriate organism-specific workflows and library preparation products for RNA-seq studies. ImportanceRNA-seq is a powerful technique for transcriptome profiling while involving elaborate sample processing before library sequencing. Our work is important as we show that RNA-seq library preparation kits can strongly affect the outcome of the RNA-seq experiment. Although library preparation benefits from the availability of various commercial kits, choosing appropriate products for the specific samples can be challenging for new users or for users working with unconventional organisms. Evaluating the performance of different commercial products requires significant financial and time investment infeasible to most researchers. Therefore, users are often guided in their choice of kits by published data involving similar input samples. We conclude that important consideration should be given to selecting of sample processing workflows for any given organism.

systems biology↗

Absolute proteome quantification in the gas-fermenting acetogen Clostridium autoethanogenum

Microbes that can recycle one-carbon (C1) greenhouse gases into fuels and chemicals are vital for the biosustainability of future industries. Acetogens are the most efficient known microbes for fixing carbon oxides CO2 and CO. Understanding proteome allocation is important for metabolic engineering as it dictates metabolic fitness. Here, we use absolute proteomics to quantify intracellular concentrations for >1,000 proteins in the model-acetogen Clostridium autoethanogenum grown on three gas mixtures. We detect prioritisation of proteome allocation for C1 fixation and significant expression of proteins involved in the production of acetate and ethanol as well as proteins with unclear functions. The data also revealed which isoenzymes are important. Integration of proteomic and metabolic flux data demonstrated that enzymes catalyse high fluxes with high concentrations and high in vivo catalytic rates. We show that flux adjustments were dominantly accompanied with changing enzyme catalytic rates rather than concentrations. Our work serves as a reference dataset and advances systems-level understanding and engineering of acetogens.

systems biology↗

multiTFA: a Python package for multi-variate Thermodynamics-based Flux Analysis

SummaryWe achieve a significant improvement in thermodynamic-based flux analysis (TFA) by introducing multivariate treatment of thermodynamic variables and leveraging component contribution, the state-of-the-art implementation of the group contribution methodology. Overall, the method greatly reduces the uncertainty of thermodynamic variables. ResultsWe present multiTFA, a Python implementation of our framework. We evaluated our application using the core E. coli model and achieved a median reduction of 6.8 kJ/mol in reaction Gibbs free energy ranges, while three out of 12 reactions in glycolysis changed from reversible to irreversible. Availability and implementationOur framework along with documentation is available on https://github.com/biosustain/multitfa.

bioinformatics↗

SmartPeak automates targeted and quantitative metabolomics data processing

SmartPeak is an application that encapsulates advanced algorithms to enable fast, accurate, and automated processing of CE-, GC- and LC-MS(/MS) data, and HPLC data for targeted and semi-targeted metabolomics, lipidomics, and fluxomics experiments. HighlightsO_LINovel algorithms for retention time alignment, calibration curve fitting, and peak integration C_LIO_LIEnables reproducibility by reducing operator bias and ensuring high QC/QA C_LIO_LIAutomated pipeline for high throughput targeted and/or quantitative metabolomics, lipidomics, and fluxomics data processing from multiple analytical instruments C_LIO_LIManually curated data set of LC-MS/MS, GC-MS, and HPLC integrated peaks for further algorithm development and benchmarking C_LI

bioinformatics↗

Enhancing CO2-valorization using Clostridium autoethanogenum for sustainable fuel and chemicals production

Acetogenic bacteria can convert waste gases into fuels and chemicals. Design of bioprocesses for waste carbon valorization requires quantification of steady-state carbon flows. Here, steady-state quantification of autotrophic chemostats containing Clostridium autoethanogenum grown on CO2 and H2 revealed that captured carbon (460 {+/-} 80 mmol/gDCW/day) had a significant distribution to ethanol (54 {+/-} 3 mol% with a 2.4 {+/-} 0.3 g/L titer). We were impressed with this initial result, but also observed limitations to biomass concentration and growth rate. Metabolic modelling predicted culture performance and indicated significant metabolic adjustments when compared to fermentation with CO as the carbon source. Moreover, modelling highlighted flux to pyruvate, and subsequently reduced ferredoxin, as a target for improving CO2 and H2 fermentation. Supplementation with a small amount of CO enabled co-utilisation with CO2, and enhanced CO2 fermentation performance significantly, while maintaining an industrially relevant product profile. Additionally, the highest specific flux through the Wood-Ljungdahl pathway was observed during co-utilization of CO2 and CO. Furthermore, the addition of CO led to superior CO2-valorizing characteristics (9.7 {+/-} 0.4 g/L ethanol with a 66 {+/-} 2 mol% distribution, and 540 {+/-} 20 mmol CO2/gDCW/day). Similar industrial processes are commercial or currently being scaled up, indicating CO-supplemented CO2 and H2 fermentation has high potential for sustainable fuel and chemical production. This work also provides a reference dataset to advance our understanding of CO2 gas fermentation, which can contribute to mitigating climate change.

bioengineering↗

Benchmarking kinetic models of Escherichia coli metabolism

Predicting phenotype from genotype is the holy grail of quantitative systems biology. Kinetic models of metabolism are among the most mechanistically detailed tools for phenotype prediction. Kinetic models describe changes in metabolite concentrations as a function of enzyme concentration, reaction rates, and concentrations of metabolic effectors uniquely enabling integration of multiple omics data types in a unifying mechanistic framework. While development of such models for Escherichia coli has been going on for almost twenty years, multiple separate models have been established and systematic independent benchmarking studies have not been performed on the full set of models available. In this study we compared systematically all recently published kinetic models of the central carbon metabolism of Escherichia coli. We assess the ease of use of the models, their ability to include omics data as input, and the accuracy of prediction of central carbon metabolic flux phenotypes. We conclude that there is no clear winner among the models when considering the resulting tradeoffs in performance and applicability to various scenarios. This study can help to guide further development of kinetic models, and to demonstrate how to apply such models in real-world setting, ultimately enabling the design of efficient cell factories. Author summaryKinetic modeling is a promising method to predict cell metabolism. Such models provide mechanistic description of how concentrations of metabolites change in the cell as a function of time, cellular environment and the genotype of the cell. In the past years there have been several kinetic models published for various organisms. We want to assess how reliably models of Escherichia coli metabolism could predict cellular metabolic state upon genetic or environmental perturbations. We test selected models in the ways that represent common metabolic engineering practices including deletion and overexpression of genes. Our results suggest that all published models have tradeoffs and the model to use should be chosen depending on the specific application. We show in which cases users could expect the best performance from published models. Our benchmarking study should help users to make a better informed choice and also provides systematic training and testing dataset for model developers.

systems biology↗

A TetR-family protein activates transcription from a new promoter motif associated with essential genes for autotrophic growth in acetogens

Acetogens can fix carbon (CO or CO2) into acetyl-CoA via the Wood-Ljungdahl pathway (WLP) that also makes them attractive cell factories for the production of fuels and chemicals from waste feedstocks. Although most biochemical details of the WLP are well understood and systems-level characterisation of acetogen metabolism has recently improved, key transcriptional features such as promoter motifs and transcriptional regulators are still unknown in acetogens. Here, we use differential RNA-sequencing to identify a previously undescribed promoter motif associated with essential genes for autotrophic growth of the model-acetogen Clostridium autoethanogenum. RNA polymerase was shown to bind to the new promoter motif using a DNA-binding protein assay and proteomics enabled the discovery of four candidates to potentially function directly in control of transcription of the WLP and other key genes of C1 fixation metabolism. Next, in vivo experiments showed that a TetR-family transcriptional regulator (CAETHG_0459) and the housekeeping sigma factor ({sigma}A) activate expression of a reporter protein (GFP) in-frame with the new promoter motif from a fusion vector in E. coli. Lastly, a protein-protein interaction assay with the RNA polymerase (RNAP) shows that CAETHG_0459 directly binds to the RNAP. Together, the data presented here advance the fundamental understanding of transcriptional regulation of C1 fixation in acetogens and provide a strategy for improving the performance of gas-fermenting bacteria by genetic engineering.

bioengineering↗

Revisiting the evolution and taxonomy of Clostridia, a phylogenomic update

Clostridium is a large genus of obligate anaerobes belonging to the Firmicutes phylum of bacteria, most of which have a Gram-positive cell wall structure. The genus includes significant human and animal pathogens, causative of potentially deadly diseases such as tetanus and botulism. Despite their relevance and many studies suggesting that they are not a monophyletic group, the taxonomy of the group has largely been neglected. Currently, species belonging to the genus are placed in the unnatural order defined as Clostridiales, which includes the class Clostridia. Here we used genomic data from 779 strains to study the taxonomy and evolution of the group. This analysis allowed us to; (i) confirm that the group is composed of more than one genus (ii), detect major differences between pathogens classified as a single species within the group of authentic Clostridium spp. (sensu stricto), identify inconsistencies between taxonomy and toxin evolution that reflect on the pervasive misclassification of strains and, (iv) identify differential traits within central metabolism of members of what has been defined earlier and confirmed by us as cluster I. Our analysis shows that the current taxonomic classification of Clostridium species hinders the prediction of functions and traits, suggests a new classification for this fascinating class of bacteria and highlights the importance of phylogenomics for taxonomic studies.

microbiology↗

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↗

Universal Alternative Splicing Of Noncoding Exons

The human transcriptome is so large, diverse and dynamic that, even after a decade of investigation by RNA sequencing (RNA-Seq), we are yet to resolve its true dimensions. RNA-Seq suffers from an expression-dependent bias that impedes characterization of low-abundance transcripts. We performed targeted single-molecule and short-read RNA-Seq to survey the transcriptional landscape of a single human chromosome (Hsa21) at unprecedented resolution. Our analysis reaches the lower limits of the transcriptome, identifying a fundamental distinction between protein-coding and noncoding gene content: almost every noncoding exon undergoes alternative splicing, producing a seemingly limitless variety of isoforms. Analysis of syntenic regions of the mouse genome shows that few noncoding exons are shared between human and mouse, yet human splicing profiles are recapitulated on Hsa21 in mouse cells, indicative of regulation by a deeply conserved splicing code. We propose that noncoding exons are functionally modular, with alternative splicing generating an enormous repertoire of potential regulatory RNAs and a rich transcriptional reservoir for gene evolution.

genomics↗