Summary Pyridine nucleotide transhydrogenase (PntAB) is an integral membrane protein complex participating in the regulation of NAD(P)+:NAD(P)H redox homeostasis in various prokaryotic and eukaryotic organisms. In the present study we addressed the function and biological role of PntAB in oxygenic photosynthetic cyanobacteria capable of both autotrophic and heterotrophic growth, with support from structural three‐dimensional (3D)‐modeling. The pntA gene encoding the α subunit of heteromultimeric PntAB in Synechocystis sp. PCC 6803 was inactivated, followed by phenotypic and biophysical characterization of the ΔpntA mutant under autotrophic and mixotrophic conditions. Disruption of pntA resulted in phenotypic growth defects observed under low light intensities in the presence of glucose, whereas under autotrophic conditions the mutant did not differ from the wild‐type strain. Biophysical characterization and protein‐level analysis of the ΔpntA mutant revealed that the phenotypic defects were accompanied by significant malfunction and damage of the photosynthetic machinery. Our observations link the activity of PntAB in Synechocystis directly to mixotrophic growth, implicating that under these conditions PntAB functions to balance the NADH: NADPH equilibrium specifically in the direction of NADPH. The results also emphasize the importance of NAD(P)+:NAD(P)H redox homeostasis and associated ATP:ADP equilibrium for maintaining the integrity of the photosynthetic apparatus under low‐light glycolytic metabolism.

Proteomes of an oxygenic photosynthetic cyanobacterium, Synechocystis sp. PCC 6803, were analyzed under photoautotrophic (low and high CO2, assigned as ATLC and ATHC), photomixotrophic (MT), and light-activated heterotrophic (LAH) conditions. Allocation of proteome mass fraction to seven sub-proteomes and differential expression of individual proteins were analyzed, paying particular attention to photosynthesis and carbon metabolism–centered sub-proteomes affected by the quality and quantity of the carbon source and light regime upon growth. A distinct common feature of the ATHC, MT, and LAH cultures was low abundance of inducible carbon-concentrating mechanisms and photorespiration-related enzymes, independent of the inorganic or organic carbon source. On the other hand, these cells accumulated a respiratory NAD(P)H dehydrogenase I (NDH-11) complex in the thylakoid membrane (TM). Additionally, in glucose-supplemented cultures, a distinct NDH-2 protein, NdbA, accumulated in the TM, while the plasma membrane-localized NdbC and terminal oxidase decreased in abundance in comparison to both AT conditions. Photosynthetic complexes were uniquely depleted under the LAH condition but accumulated under the ATHC condition. The MT proteome displayed several heterotrophic features typical of the LAH proteome, particularly including the high abundance of ribosome as well as amino acid and protein biosynthesis machinery-related components. It is also noteworthy that the two equally light-exposed ATHC and MT cultures allocated similar mass fractions of the total proteome to the seven distinct sub-proteomes. Unique trophic condition-specific expression patterns were likewise observed among individual proteins, including the accumulation of phosphate transporters and polyphosphate polymers storing energy surplus in highly energetic bonds under the MT condition and accumulation under the LAH condition of an enzyme catalyzing cyanophycin biosynthesis. It is concluded that the rigor of cell growth in the MT condition results, to a great extent, by combining photosynthetic activity with high intracellular inorganic carbon conditions created upon glucose breakdown and release of CO2, besides the direct utilization of glucose-derived carbon skeletons for growth. This combination provides the MT cultures with excellent conditions for growth that often exceeds that of mere ATHC.

The increasing move towards open access full-text scientific literature enhances our ability to utilize advanced text-mining methods to construct information-rich networks that no human will be able to grasp simply from 'reading the literature'. The utility of text-mining for well-studied species is obvious though the utility for less studied species, or those with no prior track-record at all, is not clear. Here we present a concept for how advanced textmining can be used to create information-rich networks even for less well studied species and apply it to generate an open-access gene-gene association network resource for Synechocystis sp. PCC 6803, a representative model organism for cyanobacteria and first case-study for the methodology. By merging the text-mining network with networks generated from species-specific experimental data, network integration was used to enhance the accuracy of predicting novel interactions that are biologically relevant. A rulebased algorithm was constructed in order to automate the search for novel candidate genes with a high degree of likely association to known target genes by (1) ignoring established relationships from the existing literature, as they are already 'known', and (2) demanding multiple independent evidences for every novel and potentially relevant relationship. Using selected case studies, we demonstrate the utility of the network resource and rule-based algorithm to (i) discover novel candidate associations between different genes or proteins in the network, and (ii) rapidly evaluate the potential role of ABSTRACTThe increasing move towards open access full-text scientific literature enhances our ability to utilize advanced text-mining methods to construct information-rich networks that no human will be able to grasp simply from 'reading the literature'. The utility of text-mining for well-studied species is obvious though the utility for less studied species, or those with no prior track-record at all, is not clear. Here we present a concept for how advanced text-mining can be used to create information-rich networks even for less well studied species and apply it to generate an openaccess gene-gene association network resource for Synechocystis sp. PCC 6803, a representative model organism for cyanobacteria and first case-study for the methodology. uy merging the textmining network with networks generated from species-specific experimental data, network integration was used to enhance the accuracy of predicting novel interactions that are biologically relevant. A rule-based algorithm was constructed in order to automate the search for novel candidate genes with a high degree of likely association to known target genes by (1) ignoring established relationships from the existing literature, as they are already 'known', and (2) demanding multiple independent evidences for every novel and potentially relevant relationship. Using selected case studies, we demonstrate the utility of the network resource and rule-based algorithm to (i) discover novel candidate ass...

The increasing move towards open access full-text scientific literature enhances our ability to utilize advanced text-mining methods to construct information-rich networks that no human will be able to grasp simply from ‘reading the literature’. The utility of text-mining for well-studied species is obvious though the utility for less studied species, or those with no prior track-record at all, is not clear. Here we present a concept for how advanced text-mining can be used to create information-rich networks even for less well studied species and apply it to generate an open-access gene-gene association network resource for Synechocystis sp. PCC 6803, a representative model organism for cyanobacteria and first case-study for the methodology. By merging the text-mining network with networks generated from species-specific experimental data, network integration was used to enhance the accuracy of predicting novel interactions that are biologically relevant. A rule-based algorithm (filter) was constructed in order to automate the search for novel candidate genes with a high degree of likely association to known target genes by (1) ignoring established relationships from the existing literature, as they are already ‘known’, and (2) demanding multiple independent evidences for every novel and potentially relevant relationship. Using selected case studies, we demonstrate the utility of the network resource and filter to (i) discover novel candidate associations between different genes or proteins in the network, and (ii) rapidly evaluate the potential role of any one particular gene or protein. The full network is provided as an open-source resource.

The increasing move towards open access full-text scientific literature enhances our ability to utilize advanced text-mining methods to construct information-rich networks that no human will be able to grasp simply from 'reading the literature'. The utility of text-mining for well-studied species is obvious though the utility for less studied species, or those with no prior track-record at all, is not clear. Here we present a concept for how advanced textmining can be used to create information-rich networks even for less well studied species and apply it to generate an open-access gene-gene association network resource for Synechocystis sp. PCC 6803, a representative model organism for cyanobacteria and first case-study for the methodology. By merging the text-mining network with networks generated from species-specific experimental data, network integration was used to enhance the accuracy of predicting novel interactions that are biologically relevant. A rulebased algorithm was constructed in order to automate the search for novel candidate genes with a high degree of likely association to known target genes by (1) ignoring established relationships from the existing literature, as they are already 'known', and (2) demanding multiple independent evidences for every novel and potentially relevant relationship. Using selected case studies, we demonstrate the utility of the network resource and rule-based algorithm to (i) discover novel candidate associations between different genes or proteins in the network, and (ii) rapidly evaluate the potential role of ABSTRACTThe increasing move towards open access full-text scientific literature enhances our ability to utilize advanced text-mining methods to construct information-rich networks that no human will be able to grasp simply from 'reading the literature'. The utility of text-mining for well-studied species is obvious though the utility for less studied species, or those with no prior track-record at all, is not clear. Here we present a concept for how advanced text-mining can be used to create information-rich networks even for less well studied species and apply it to generate an openaccess gene-gene association network resource for Synechocystis sp. PCC 6803, a representative model organism for cyanobacteria and first case-study for the methodology. uy merging the textmining network with networks generated from species-specific experimental data, network integration was used to enhance the accuracy of predicting novel interactions that are biologically relevant. A rule-based algorithm was constructed in order to automate the search for novel candidate genes with a high degree of likely association to known target genes by (1) ignoring established relationships from the existing literature, as they are already 'known', and (2) demanding multiple independent evidences for every novel and potentially relevant relationship. Using selected case studies, we demonstrate the utility of the network resource and rule-based algorithm to (i) discover novel candidate ass...