چکیده (انگلیسی):

Secondary metabolites of fungi are receiving an increasing amount of interest due to their
prolific bioactivities and the fact that fungal biosynthesis of secondary metabolites often occurs from
co-regulated and co-located gene clusters. This makes the gene clusters attractive for synthetic biology
and industrial biotechnology applications. We have previously published a method for accurate prediction
of clusters from genome and transcriptome data, which could also suggest cross-chemistry, however,
this method was limited both in the number of parameters which could be adjusted as well as in userfriendliness.
Furthermore, sensitivity to the transcriptome data required manual curation of the predictions.
In the present work, we have aimed at improving these features.
Results: FunGeneClusterS is an improved implementation of our previous method with a graphical user
interface for off- and on-line use. The new method adds options to adjust the size of the gene cluster(s)
being sought as well as an option for the algorithm to be flexible with genes in the cluster which may
not seem to be co-regulated with the remainder of the cluster.We have benchmarked the method using
data from the well-studied Aspergillus nidulans and found that the method is an improvement over the
previous one. In particular, it makes it possible to predict clusters with more than 10 genes more accurately,
and allows identification of co-regulated gene clusters irrespective of the function of the genes.
It also greatly reduces the need for manual curation of the prediction results. We furthermore applied
the method to transcriptome data from A. niger. Using the identified best set of parameters, we were able
to identify clusters for 31 out of 76 previously predicted secondary metabolite synthases/synthetases.
Furthermore, we identified additional putative secondary metabolite gene clusters. In total, we predicted
432 co-transcribed gene clusters in A. niger (spanning 1.323 genes, 12% of the genome). Some of
these had functions related to primary metabolism, e.g. we have identified a cluster for biosynthesis of
biotin, as well as several for degradation of aromatic compounds. The data identifies that suggests that
larger parts of the fungal genome than previously anticipated operates as gene clusters. This includes
both primary and secondary metabolism as well as other cellular maintenance functions.
Conclusion: We have developed FunGeneClusterS in a graphical implementation and made the method
capable of adjustments to different datasets and target clusters. The method is versatile in that it can
predict co-regulated clusters not limited to secondary metabolism. Our analysis of data has shown not
only the validity of the method, but also strongly suggests that large parts of fungal primary metabolism
and cellular functions are both co-regulated and co-located.