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- Herrgård, Markus J, et al.
(författare)
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A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology
- 2008
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Ingår i: Nature Biotechnology. ; 26:10, s. 1155-1160
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Tidskriftsartikel (refereegranskat)abstract
- Genomic data allow the large-scale manual or semi-automated assembly of metabolic network reconstructions, which provide highly curated organism-specific knowledge bases. Although several genome-scale network reconstructions describe Saccharomyces cerevisiae metabolism, they differ in scope and content, and use different terminologies to describe the same chemical entities. This make comparisons between them difficult and underscores the desirability of a consolidated metabolic network that collects and formalizes the 'community knowledge' of yeast metabolism. We describe how we have produced a consensus metabolic network reconstruction for S. cerevisiae. In drafting it, we placed special emphasis on referencing molecules to persistent databases or using database-independent forms, such as SMILES or InChl strings, as this permits their chemical structure to be represented unambiguously and in a manner that permits automated reasoning. The reconstruction is readily available via a publicly accessible database and in the Systems Biology Markup Language (http://www.comp-sys-bio.org/yeastnet). It can be maintained as a resource that serves as a common denominator for studying the systems biology of yeast. Similar strategies should benefit communities studying genome-scale metabolic networks of other organisms.
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| 2. |
- Tarbier, Marcel, 1990-
(författare)
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Into the Single-Verse : Subtle gene expression differences between virtually identical single cells are informative of gene regulation
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The ability to profile transcriptomes and proteomes in a high-throughput fashion in single cells has truly revolutionized functional genomics, and countless functional and regulatory insights have been based on these technologies. While major applications include the discovery of new cell types and the a posteriori sorting of cell populations, studies of gene expression noise and gene co-expression have made use of this inter-cellular heterogeneity in a genuine quantitative fashion. Yet, there are still major limitations to overcome.First, strong dynamic processes, such as cell cycle or differentiation axes, tend to overshadow more subtle underlying regulatory processes. While this has sparked the development of tools that can identify and correct these biases at large, few insights into the subtleties of gene regulation have been published thus far. The majority of studies still focus on drastic changes such as differentiation or disease. We address this issue in paper I and to a limited extend in paper II and paper III through the elimination of major confounders during experimental design. In these papers, we show that variation and covariation of miRNAs, mRNAs and proteins between individual cells of a homogeneous non-dynamic population are informative of gene regulation.Second, while single-cell technologies are booming, with new technologies being published every day, the co-profiling of RNA and protein in the same single cells still remains a major challenge. All current technologies are limited either by protein location or throughput, or require invasive cell fixation that can compromise mRNA stability. We overcome these limitations in paper II through the combination of quantitative single-cell RNA sequencing with proximity extension assays for protein detection. Using this technology, SPARC, we show that transcription factor protein, but not transcription factor RNA, covaries with the RNA expression of its targets. We also show that translation is a major mediator of the shift in variation from the RNA to the protein level.Third, some technologies still suffer from limited sensitivity. While, for instance, the first single-cell miRNA detection already succeeded in 2006 and the first single-cell small RNA sequencing technique was published in 2016, few insights into miRNA dynamics or function have been gained from single-cell data since. Using an optimized single-cell small RNA sequencing protocol, we quantify the miRNA transcriptome of close to 200 single cells in paper III. We show that variation and covariation can be linked to miRNA transcription and turnover. Integrating miRNA and miRNA target data from all three papers, we present evidence that the induction of variation on the RNA level and the buffering of protein expression noise are naturally occurring for many miRNAs.In summary, we present new strategies and new protocols that overcome existing limitations in the field, and we present regulatory insights that were enabled by quantitative measurements of single-cell gene expression variation and covariation.
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