Secondary metabolite prediction in Dactylellina haptotyla
(2014) BINP30 20142Degree Projects in Bioinformatics
- Abstract
- Nematode control remains to be an important challenge in agricultural and health industries, thereby new, efficient and preferably natural ways to engage them are required. Dactylellina haptotyla, a nematode-trapping fungus, forms infection structures called knobs to capture, paralyze and subsequently kill nematodes. In this study, genes involved in the secondary metabolism (SM) were investigated to assess their importance for the parasitism. Two tools for the prediction of secondary metabolite gene clusters were used and revealed a very low number of SM gene clusters in the genome of D. haptotyla. In total 21 and 8 clusters were found by the antiSMASH and SMURF tools with total of 325 and 95 genes, respectively. Gene regulation was... (More)
- Nematode control remains to be an important challenge in agricultural and health industries, thereby new, efficient and preferably natural ways to engage them are required. Dactylellina haptotyla, a nematode-trapping fungus, forms infection structures called knobs to capture, paralyze and subsequently kill nematodes. In this study, genes involved in the secondary metabolism (SM) were investigated to assess their importance for the parasitism. Two tools for the prediction of secondary metabolite gene clusters were used and revealed a very low number of SM gene clusters in the genome of D. haptotyla. In total 21 and 8 clusters were found by the antiSMASH and SMURF tools with total of 325 and 95 genes, respectively. Gene regulation was analysed in two conditions – in the knob before and during the infection. Differential expression revealed up-regulation of 24.3% and 16.8% of genes and it was dominant for at least one condition in 71.4% and 50.0% of clusters predicted by respective tools. Among differentially up-regulated genes 22.8% and 37.5% were also highly expressed. The numbers of predicted clusters are low compared to filamentous fungi and comparable to those found in unicellular microorganisms with much smaller genomes. The findings suggest that nematode-trapping fungi have lost most SM gene clusters and that the parasitism involves only a few secondary metabolites. This loss may be related to the expansion of other genes (SSPs, peptidases) indicated by previous studies and imply alternative mechanism for parasitism (Less)
- Popular Abstract
- Secondary metabolite prediction in D. haptotyla
Nematode control remains to be an important challenge in agricultural and health industries, thereby new, efficient and preferably natural ways to engage them are required. Dactylellina haptotyla, a nematode-trapping fungus, forms infection structures, called knobs, to capture, paralyze and subsequently kill nematodes. We hypothesised that some kind of nematode-affecting toxins might be used in the process and thus a natural interest to look for possible secondary metabolites emerged.
Secondary metabolites are chemical substances not essential for growth, development and reproduction of an organism, they are involved in competitive activities arising from limited energy and food... (More) - Secondary metabolite prediction in D. haptotyla
Nematode control remains to be an important challenge in agricultural and health industries, thereby new, efficient and preferably natural ways to engage them are required. Dactylellina haptotyla, a nematode-trapping fungus, forms infection structures, called knobs, to capture, paralyze and subsequently kill nematodes. We hypothesised that some kind of nematode-affecting toxins might be used in the process and thus a natural interest to look for possible secondary metabolites emerged.
Secondary metabolites are chemical substances not essential for growth, development and reproduction of an organism, they are involved in competitive activities arising from limited energy and food sources, mating partners etc. The most importantly, these compounds can be used in pharmaceutical, agricultural and other industries as currently known examples of these materials range from antibiotics, pain-killers, heart disease drugs to nicotine and even rubber.
Highly complex and time consuming laboratory experiments have led to utilization of only a small fraction of fungal SM possible uses. Bioinformatics tools based on the latest theoretical knowledge can be used to optimize the detection of possible secondary metabolites that could be missed by experimental analyses. These methods take into account that fungal genes involved in biosynthesis of the same SM are localised together on the chromosome and form biosynthesis gene clusters and also include into algorithms already known associations of specific genes with certain secondary metabolites.
Search of possible secondary metabolites was performed on Dactylellina haptotyla (Monacrosporium haptotylum) from the Orbiliomycetes class. It is a nematode-trapping fungus that is able to catch, kill and start digesting a moving worm in a timeframe of hours.
Genomic analysis of D. haptotyla produced unexpectedly low numbers of secondary metabolite gene clusters, suggesting that either alternative parasitism mechanism or a process requiring very few secondary metabolites takes place. Further expression analysis distinguished several SM gene clusters of different types (terpene, pks-type-1, pks-type-3, NRPS, putative and other) with increased activities during infection, suggesting their participation in the processes under study, and indicating utilization of secondary metabolites. Although these results shed more light on secondary metabolite involvement into parasitic activities of nematode-trapping fungi, additional analysis of the resulting clusters and their products is essential for more comprehensive
interpretation of predicted cluster amounts in D. haptotyla, and to better understand the underlying mechanisms.
Advisor: Dag Ahrén, PhD
Master´s Degree Project 30 credits in Bioinformatics, 2014
Microbial ecology group, Department of Biology, Lund university (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/4933971
- author
- Feiza, Vidmantas
- supervisor
-
- Dag Ahrén LU
- organization
- course
- BINP30 20142
- year
- 2014
- type
- H2 - Master's Degree (Two Years)
- subject
- language
- English
- id
- 4933971
- date added to LUP
- 2015-01-13 09:21:17
- date last changed
- 2015-01-29 14:50:33
@misc{4933971,
abstract = {{Nematode control remains to be an important challenge in agricultural and health industries, thereby new, efficient and preferably natural ways to engage them are required. Dactylellina haptotyla, a nematode-trapping fungus, forms infection structures called knobs to capture, paralyze and subsequently kill nematodes. In this study, genes involved in the secondary metabolism (SM) were investigated to assess their importance for the parasitism. Two tools for the prediction of secondary metabolite gene clusters were used and revealed a very low number of SM gene clusters in the genome of D. haptotyla. In total 21 and 8 clusters were found by the antiSMASH and SMURF tools with total of 325 and 95 genes, respectively. Gene regulation was analysed in two conditions – in the knob before and during the infection. Differential expression revealed up-regulation of 24.3% and 16.8% of genes and it was dominant for at least one condition in 71.4% and 50.0% of clusters predicted by respective tools. Among differentially up-regulated genes 22.8% and 37.5% were also highly expressed. The numbers of predicted clusters are low compared to filamentous fungi and comparable to those found in unicellular microorganisms with much smaller genomes. The findings suggest that nematode-trapping fungi have lost most SM gene clusters and that the parasitism involves only a few secondary metabolites. This loss may be related to the expansion of other genes (SSPs, peptidases) indicated by previous studies and imply alternative mechanism for parasitism}},
author = {{Feiza, Vidmantas}},
language = {{eng}},
note = {{Student Paper}},
title = {{Secondary metabolite prediction in Dactylellina haptotyla}},
year = {{2014}},
}