mining bacterial genomes for laccases luka ausec , marko verce, miha Črnigoj, vesna jerman,

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Mining bacterial genomes for laccases Luka Ausec, Marko Verce, Miha Črnigoj, Vesna Jerman, Ines Mandić-Mulec University of Ljubljana, Biotechnical Faculty Dept. of Food Science and Technology, Chair of Microbiology The 2nd International Symposium “VERA JOHANIDES”, Zagreb, May 11 2013

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University of Ljubljana, Biotechnical Faculty Dept. of Food Science and Technology, Chair of Microbiology. Mining bacterial genomes for laccases Luka Ausec , Marko Verce, Miha Črnigoj, Vesna Jerman, Ines Mandić-Mulec. The 2nd International Symposium “VERA JOHANIDES”, Zagreb, May 11 2013. - PowerPoint PPT Presentation

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Page 1: Mining bacterial genomes for laccases Luka Ausec , Marko Verce, Miha Črnigoj, Vesna Jerman,

Mining bacterial genomes for laccases

Luka Ausec, Marko Verce, Miha Črnigoj, Vesna Jerman, Ines Mandić-Mulec

University of Ljubljana, Biotechnical FacultyDept. of Food Science and Technology, Chair of Microbiology

The 2nd International Symposium “VERA JOHANIDES”, Zagreb, May 11 2013

Page 2: Mining bacterial genomes for laccases Luka Ausec , Marko Verce, Miha Črnigoj, Vesna Jerman,

Why care about laccases

What do they do? How do they do it?

Page 3: Mining bacterial genomes for laccases Luka Ausec , Marko Verce, Miha Črnigoj, Vesna Jerman,

Why care about laccases

What do they do? How do they do it?

Environmentally friendly

Page 4: Mining bacterial genomes for laccases Luka Ausec , Marko Verce, Miha Črnigoj, Vesna Jerman,

Fungal vs. Bacterial Laccases

Ease of pruduction Substrate range pH and temperature optimum Salt tolerance

Page 5: Mining bacterial genomes for laccases Luka Ausec , Marko Verce, Miha Črnigoj, Vesna Jerman,

Sources of novel bacterial laccases

DNA potential Activty

related cultured strains unknown known

metagenomics unknown unknown

bioinformatics known unknown

Page 6: Mining bacterial genomes for laccases Luka Ausec , Marko Verce, Miha Črnigoj, Vesna Jerman,

From genomes to pool of potentials

Ausec, Zakrzewski et al., PLoS ONE 2011

6.5 %on plasmids

75% of genes encode signal peptides

2,200 draft and completed genomes

1,240 putative laccase genes

Page 7: Mining bacterial genomes for laccases Luka Ausec , Marko Verce, Miha Črnigoj, Vesna Jerman,

Laccase from Thioalkalivibrio sp.

Mining the pool, #1: extremophile

pH

substrate optimal pH

ABTS 5

K4Fe(CN)6 5

pyrocatechol 8

pyrogallol 7

2,6-DMP 9.5

syringaldehyde 8

syringic acid N.A.

syringaldazine 8

guaiacol N.A.

Vanillic acid N.A.

ferulic acid N.A.

tyrosine N.A.

substrates

pH optima

Page 8: Mining bacterial genomes for laccases Luka Ausec , Marko Verce, Miha Črnigoj, Vesna Jerman,

Mining the pool, #2: anaerobe

Laccase from Geobacter

metallireducens

pH optimum temperature optimum

Page 9: Mining bacterial genomes for laccases Luka Ausec , Marko Verce, Miha Črnigoj, Vesna Jerman,

Conclusions

Bacterial laccases are diverse Bioinformatic (HMM-based) approaches

successful Bacterial laccases have promissing traits for

biotechnological applications

Page 10: Mining bacterial genomes for laccases Luka Ausec , Marko Verce, Miha Črnigoj, Vesna Jerman,

Thank you.

[email protected]

Page 11: Mining bacterial genomes for laccases Luka Ausec , Marko Verce, Miha Črnigoj, Vesna Jerman,

Bioinformatic analysis of bacterial laccase-like genes

>2200 completed and draft bacterial genomes

METHODS: profile Hidden Markov Models (pHMM) 5 models constructed

Page 12: Mining bacterial genomes for laccases Luka Ausec , Marko Verce, Miha Črnigoj, Vesna Jerman,

2-domain laccases3-domain laccases

Page 13: Mining bacterial genomes for laccases Luka Ausec , Marko Verce, Miha Črnigoj, Vesna Jerman,

Figure 3 - List of species encoding laccase genes and possessing plasmids in their genomes. The bars represent the number of laccase genes in the genome (black) and the number of laccase genes on plasmids (gray). The length of the bar shows the total number of genes for each organism.

76 genes on plasmids of 46 organisms