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Understanding and improving microbial cell factories through Large Scale Data-approaches

Brink, Daniel LU (2019)
Abstract
Since the advent of high-throughput genome sequencing methods in the mid-2000s, molecular biology has rapidly transitioned towards data-intensive science. Recent technological developments have increased the accessibility of omics experiments by decreasing the cost, while the concurrent design of new algorithms have improved the computational work-flow needed to analyse the large datasets generated. This has enabled the long standing idea of a systems approach to the cell, where molecular phenomena are no longer observed in isolation, but as parts of a tightly regulated cell-wide system. However, large data biology is not without its challenges, many of which are directly related to how to store, handle and analyse ome-wide... (More)
Since the advent of high-throughput genome sequencing methods in the mid-2000s, molecular biology has rapidly transitioned towards data-intensive science. Recent technological developments have increased the accessibility of omics experiments by decreasing the cost, while the concurrent design of new algorithms have improved the computational work-flow needed to analyse the large datasets generated. This has enabled the long standing idea of a systems approach to the cell, where molecular phenomena are no longer observed in isolation, but as parts of a tightly regulated cell-wide system. However, large data biology is not without its challenges, many of which are directly related to how to store, handle and analyse ome-wide datasets.
The present thesis examines large data microbiology from a middle ground between metabolic engineering and in silico data management. The work was performed in the context of applied microbial lignocellulose valorisation with the end goal of generating improved cell factories for the production of value-added chemicals from renewable plant biomass. Three different challenges related to this feedstock were investigated from a large data-point of view: bacterial catabolism of lignin and its derived aromatic compounds; tolerance of baker’s yeast Saccharomyces cerevisiae to inhibitory compounds in lignocellulose hydrolysate; and the non-fermentable response to xylose in S. cerevisiae engineered for growth on this pentose sugar.
The bibliome of microbial lignin catabolism is vast and consists of a long-standing cohort of fundamental microbiology, and a more recent cohort of applied lignin biovalorisation. Here, an online database was created with the long-term ambition of closing the gap between the two and make new connections that can fuel the generation of new knowledge. Whole-genome sequencing was used to investigate the genetic basis for observed phenotypes in bacterial isolates capable of growing on different kinds of lignin-derived aromatics. A whole-genome approach was also used to identify key sequence variants in the genotype of an industrial S. cerevisiae strain evolved for improved tolerance to inhibitors and high temperature. Finally, assessment of the sugar signalome of S. cerevisiae was enabled by the design and validation of a panel of in vivo fluorescent biosensors for single-cell cytometric analysis. It was found that xylose triggered a signal similar to that of low glucose in yeast cells engineered with xylose utilization pathways, and that introduction of deletions previously related to improved xylose utilization altered the signal towards that of high glucose.
Taken together, the present thesis illustrates how omics-approaches can aid design of laboratory experiments to increase the knowledge and understanding of microorganisms, and demonstrates the need for a combined knowledge of molecular and computational biology in large-scale data microbiology.
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author
supervisor
opponent
  • Dr Patil, Kiran, EMBL, Structural and Computational Biosustainability, Germany
organization
publishing date
type
Thesis
publication status
published
subject
keywords
Microbiology, lignocellulose, lignin, D-xylose, Saccharomyces cerevisiae, Pseudomonas putida, bioinformatics, genome assembly, flow cytometry, metabolic engineering, sugar sensing/signalling
pages
262 pages
publisher
Department of Chemistry, Lund University
defense location
Lecture hall KC:C, Kemicentrum, Naturvetarvägen 14, Lund University, Faculty of Engineering LTH, Lund
defense date
2019-11-07 10:15
ISBN
978-91-7422-684-3
978-91-7422-685-0
language
English
LU publication?
yes
id
b157cf68-68d6-4847-a2b6-adb8dc478493
date added to LUP
2019-09-30 11:51:29
date last changed
2019-10-14 12:40:50
@phdthesis{b157cf68-68d6-4847-a2b6-adb8dc478493,
  abstract     = {Since the advent of high-throughput genome sequencing methods in the mid-2000s, molecular biology has rapidly transitioned towards data-intensive science. Recent technological developments have increased the accessibility of omics experiments by decreasing the cost, while the concurrent design of new algorithms have improved the computational work-flow needed to analyse the large datasets generated. This has enabled the long standing idea of a <i>systems</i> approach to the cell, where molecular phenomena are no longer observed in isolation, but as parts of a tightly regulated cell-wide system. However, large data biology is not without its challenges, many of which are directly related to how to store, handle and analyse ome-wide datasets.<br/>The present thesis examines large data microbiology from a middle ground between metabolic engineering and <i>in silico</i> data management. The work was performed in the context of applied microbial lignocellulose valorisation with the end goal of generating improved cell factories for the production of value-added chemicals from renewable plant biomass. Three different challenges related to this feedstock were investigated from a large data-point of view: bacterial catabolism of lignin and its derived aromatic compounds; tolerance of baker’s yeast <i>Saccharomyces cerevisiae</i> to inhibitory compounds in lignocellulose hydrolysate; and the non-fermentable response to xylose in <i>S. cerevisiae</i> engineered for growth on this pentose sugar.<br/>The bibliome of microbial lignin catabolism is vast and consists of a long-standing cohort of fundamental microbiology, and a more recent cohort of applied lignin biovalorisation. Here, an online database was created with the long-term ambition of closing the gap between the two and make new connections that can fuel the generation of new knowledge. Whole-genome sequencing was used to investigate the genetic basis for observed phenotypes in bacterial isolates capable of growing on different kinds of lignin-derived aromatics. A whole-genome approach was also used to identify key sequence variants in the genotype of an industrial <i>S. cerevisiae</i> strain evolved for improved tolerance to inhibitors and high temperature. Finally, assessment of the sugar signalome of <i>S. cerevisiae</i> was enabled by the design and validation of a panel of <i>in vivo</i> fluorescent biosensors for single-cell cytometric analysis. It was found that xylose triggered a signal similar to that of low glucose in yeast cells engineered with xylose utilization pathways, and that introduction of deletions previously related to improved xylose utilization altered the signal towards that of high glucose.<br/>Taken together, the present thesis illustrates how omics-approaches can aid design of laboratory experiments to increase the knowledge and understanding of microorganisms, and demonstrates the need for a combined knowledge of molecular and computational biology in large-scale data microbiology.<br/>},
  author       = {Brink, Daniel},
  isbn         = {978-91-7422-684-3 },
  keyword      = {Microbiology,lignocellulose,lignin,D-xylose,Saccharomyces cerevisiae,Pseudomonas putida,bioinformatics,genome assembly,flow cytometry,metabolic engineering,sugar sensing/signalling},
  language     = {eng},
  month        = {10},
  pages        = {262},
  publisher    = {Department of Chemistry, Lund University},
  school       = {Lund University},
  title        = {Understanding and improving microbial cell factories through Large Scale Data-approaches},
  year         = {2019},
}