Biology Reference
In-Depth Information
The modules consist of different domains, each catalysing a particular reac-
tion that modifies the acetate unit leading to a ketone, an alcohol, an alkene
or a completely reduced carbon. The metabolite, tethered to the PKSs by a
thioester bond, is thus built on these megasynthases in a sequential manner
just like on an assembly line: each module adds one acetate unit and modifies
it using the same set of domains (
Staunton & Weissman, 2001
). This assembly
line enzymology has also been found in another class of synthases, the nonribo-
somal synthases (NRPSs), responsible for the biosynthesis of peptidic second-
ary metabolites (
Sieber & Marahiel, 2005
). In these NRPSs, the building blocks
are amino acids, either proteinogenic or not, and each module adds sequentially
a residue and forms a peptide bond to yield the final peptide. In both class of
enzymes, the PKSs and NRPSs, the different modules are usually arranged in
line and there is a co-linearity between the amino acid sequence of the syn-
thase, from the N-terminal to the C-terminal end, and the reaction sequence
that yields to the full metabolite, the so-called co-linearity rule. The reader is
referred to excellent and authoritative reviews covering this fascinating field of
enzymology (
Fischbach & Walsh, 2006
;
Staunton & Weissman, 2001
).
With the advent of huge amount of microbial genomic data (more than
a thousand of microbial genomes have so far been sequenced), it appears
that (1) the genes responsible for the biosynthesis of microbial secondary
metabolites are usually clustered in the genome of the microorganism; (2) the
domains of the PKSs and NRPSs are conserved and their function and speci-
ficity can be predicted using bioinformatic tools; and (3) because of the co-
linearity rule, though not always respected, it has been possible to predict, yet
with still some uncertainty, the structure of the metabolite produced by these
synthases. The reverse prediction is also possible in certain cases, again with
some uncertainty, that is, the possibility to predict the biosynthetic genes from
the structure of the metabolite. Thus, genome mining has become an exciting
new way that is complementary to the traditional extraction/identification
strategy, to discover novel secondary metabolites (
Challis, 2008
).
In this review, the authors have tried to give the reader an up-to-date
vision of the connections between secondary metabolites from cyanobacte-
ria and their biosynthetic genes in a growing postgenomic era.
2. THE CYANOBACTERIAL SECONDARY METABOLITES
Cyanobacteria represent a large group of bacteria that have evolved
for a very long period of time (3.5 × 10
9
years) and this is probably the
reason why these bacteria are very diverse in terms of morphology and
