Photosynthesis Research

, Volume 126, Issue 1, pp 99–109 | Cite as

Bioinformatic analysis of the distribution of inorganic carbon transporters and prospective targets for bioengineering to increase Ci uptake by cyanobacteria

  • Sandeep B. Gaudana
  • Jan Zarzycki
  • Vamsi K. Moparthi
  • Cheryl A. KerfeldEmail author


Cyanobacteria have evolved a carbon-concentrating mechanism (CCM) which has enabled them to inhabit diverse environments encompassing a range of inorganic carbon (Ci: \({\text{HCO}}_{3}^{ - }\) and CO2) concentrations. Several uptake systems facilitate inorganic carbon accumulation in the cell, which can in turn be fixed by ribulose 1,5-bisphosphate carboxylase/oxygenase. Here we survey the distribution of genes encoding known Ci uptake systems in cyanobacterial genomes and, using a pfam- and gene context-based approach, identify in the marine (alpha) cyanobacteria a heretofore unrecognized number of putative counterparts to the well-known Ci transporters of beta cyanobacteria. In addition, our analysis shows that there is a huge repertoire of transport systems in cyanobacteria of unknown function, many with homology to characterized Ci transporters. These can be viewed as prospective targets for conversion into ancillary Ci transporters through bioengineering. Increasing intracellular Ci concentration coupled with efforts to increase carbon fixation will be beneficial for the downstream conversion of fixed carbon into value-added products including biofuels. In addition to CCM transporter homologs, we also survey the occurrence of rhodopsin homologs in cyanobacteria, including bacteriorhodopsin, a class of retinal-binding, light-activated proton pumps. Because they are light driven and because of the apparent ease of altering their ion selectivity, we use this as an example of re-purposing an endogenous transporter for the augmentation of Ci uptake by cyanobacteria and potentially chloroplasts.


pfam Rhodopsin Inorganic carbon transport Cyanobacteria Carbon fixation Carbon-concentrating mechanism Genomic context Synthetic biology Bioinformatics 



The authors thank Ryan L. Leverenz, Onur Erbilgin, and Seth D. Axen for helpful discussions. This work was supported by the NSF (EF1105892 and MCB0851094) and by the US DOE contract no. DE-AC02 05CH11231.

Supplementary material

11120_2014_59_MOESM1_ESM.pdf (101 kb)
Supplementary material 1 (PDF 100 kb)


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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Sandeep B. Gaudana
    • 1
  • Jan Zarzycki
    • 1
    • 2
  • Vamsi K. Moparthi
    • 3
  • Cheryl A. Kerfeld
    • 1
    • 2
    • 4
    Email author
  1. 1.DOE Plant Research Laboratories, Department of Biochemistry and Molecular BiologyMichigan State UniversityEast LansingUSA
  2. 2.Physical Biosciences DivisionLawrence Berkeley National LaboratoryBerkeleyUSA
  3. 3.Department of Energy Joint Genome InstituteWalnut CreekUSA
  4. 4.Department of Plant and Microbial BiologyUniversity of CaliforniaBerkeleyUSA

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