Organic acid production in Aspergillus niger and other filamentous fungi

The aim of the thesis was to increase the understanding of organic acid production in Aspergillus niger and other filamentous fungi, with the ultimate purpose to improve A. niger as biotechnological production host.

In Chapter 1, the use of microbial cell-factories for the production of various compounds of interest, with a focus on organic acid production in A. niger, is introduced.

To convert A. niger into a cell-factory for the production of fumarate, an organic acid that this fungus does not naturally accumulate extracellularly, we need to know the key components that lead to high extracellular fumarate accumulation. This can be achieved by studying a natural fumarate producer, in our case the filamentous fungus Rhizopus delemar. To increase both the understanding of R. delemar fumarate production, and identify a possible candidate fumarate exporter protein for heterologuous expression in A. niger, we studied differences in the transcriptional and proteomic responses of R. delemar under high and low fumarate producing conditions, described in Chapter 2. Based on our analyses, we propose that a substantial part of the fumarate accumulated in R. delemar during nitrogen starvation results from the urea cycle due to amino acid catabolism. Thus, although we failed to identify the correct fumarte exporter (discussed in Chapter 8), the results of these analyses lead to a broader understanding of the mechanism underlying fumarate accumulation in R. delemar.

In order to make A. niger a suitable production host for other organic acids, we also delved deeper into the understanding of why A. niger has an innate ability to secrete various organic acids, especially citrate, described in Chapter 3. We show that an increase in citrate secretion under iron limited conditions is a physiological response consistent with a role of citrate as A. niger iron siderophore. We found that A. niger citrate secretion increases with decreasing amounts of iron added to the culture medium and, in contrast to previous findings, this response is independent of the nitrogen source. Differential transcriptomics analyses of the two A. niger mutants NW305 (gluconate non-producer) and NW186 (gluconate and oxalate non-producer) revealed up-regulation of the citrate biosynthesis gene citA under iron limited conditions compared to iron replete conditions. In addition, we show that A. niger can utilise Fe(III) citrate as iron source. Finally, we discuss our findings in the general context of the pH-dependency of A. niger organic acid production, offering an explanation, besides competition, for why A. niger organic acid production is a sequential process influenced by the external pH of the culture medium.

In Chapter 4, we further unravel the various different mechanisms underlying extracellular A. niger citrate accumulation. We show that the phenotype of increased extracellular citrate accumulation can have fundamentally different underlying mechanisms, depending on how this response was triggered. We found that varying the amount and supplement of an arginine auxotrophic A. niger strain induces increased citrate productivity. Transcriptomics  analysis shows down-regulation of citrate metabolising enzymes in the conditions in which more citrate is accumulated extracellularly. This contrasts with the transcriptional adaptations triggered by iron limited conditions, described in Chapter 3. By combining data obtained from both experimental setups described in Chapters 3 and 4, we compiled a list of likely citrate transporter candidates. Two promising citrate exporter candidates were tested in the yeast Saccharomyces cerevisiae, of which one was successfully identified as citrate exporter. Our findings provide the first steps in untangling the complex interplay of different mechanisms underlying A. niger citrate accumulation, and we pinpoint, for the first time, a promising A. niger citrate exporter candidate, offering a valuable tool for improvement of A. niger as biotechnological cell-factory for citrate production.

For the identification of different A. niger substrate importers, we combined in silico and in vivo approaches, and established a reliable pipeline to identify and test candidate transport proteins. The in silico approach, in which likely glucose transporter candidates are inferred from good matches with a glucose transporter specific Hidden Markov model (HMMgluT), and the in vivo approach, in which a sub-cellular proteomics approach is applied to isolate plasmalemmal glucose transporters, is described in Chapter 5. In the presented research work, a hidden Markov model (HMM), that shows a good performance in the identification and segmentation of functionally validated glucose transporters, was constructed. The model (HMMgluT) was used to analyse the A. niger membrane-associated proteome response to high and low glucose concentrations at a low pH. By combining the abundance patterns of the proteins found in the A. niger plasmalemma proteome with their HMMgluT scores, two new putative high affinity glucose transporters, denoted MstG and MstH, were identified. MstG and MstH were functionally validated and biochemically characterised by heterologous expression in a S. cerevisiae glucose transport null mutant. They were shown to be a high affinity glucose transporter (Km = 0.6 ± 0.1 mM) and a very high affinity glucose transporter (Km = 0.06 ± 0.005 mM) respectively.

The concepts developed in Chapter 5 were applied in Chapter 6 to identify further substrate importer proteins in both A. niger and another filamentous fungus, Trichoderma reesei. Again a hidden Markov model, this time for the identification of xylose transporters, was constructed and used to analyse the A. niger and T. reesei in silico proteomes, yielding a list of candidate xylose transporters. From this list, three A. niger (XltA, XltB and XltC) and three T. reesei (Str1, Str2 and Str3) transporters were selected, functionally validated and biochemically characterised through their expression in a S. cerevisiae hexose transport null mutant, engineered to be able to metabolise xylose, but unable to transport this sugar. All six transporters were able to support growth of the engineered yeast on xylose, but varied in affinities and efficiencies in the uptake of the pentose. Amino acid sequence analysis of the selected transporters showed the presence of specific residues and motifs associated to xylose transporters. Transcriptional analysis of A. niger and T. reesei showed that XltA and Str1 were specifically induced by xylose and dependent on the XlnR/Xyr1 regulators, implying a biological role for these transporters in xylose utilisation. Thus, our findings show that our approach using HMMs is a robust pipeline to identify different substrate importer candidates.

In Chapter 7, comparative plasmalemma proteomic analysis was used to identify candidate L-rhamnose transporters in A. niger. Further analysis was focused on protein ID 1119135 (RhtA) (JGI A. niger ATCC 1015 genome database). RhtA was classified as a Family 7 Fucose:H+ Symporter (FHS) within the Major Facilitator Superfamily. Family 7 currently includes exclusively bacterial transporters able to use different sugars. Strong indications for its role in L-rhamnose transport were obtained by functional complementation of the Saccharomyces cerevisiae EBY.VW.4000 strain in growth studies with a range of potential substrates. Biochemical analysis using L-[3H(G)]-rhamnose confirmed that RhtA is a L-rhamnose transporter. The RhtA gene is located in tandem with a hypothetical alpha-L-rhamnosidase gene (rhaB). Transcriptional analysis of rhtA and rhaB confirmed that both genes have a coordinated expression, being strongly and specifically induced by L-rhamnose, and controlled by RhaR, a transcriptional regulator involved in the release and catabolism of the methyl-pentose. RhtA is the first eukaryotic L-rhamnose transporter identified and functionally validated to date.

In Chapter 8, the findings presented in this thesis with regards to our attempts at improving A. niger as biotechnological production host are summarised, and further implications for metabolic engineering approaches based on the conclusions drawn are discussed.