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BackgroundThe establishment of the gut microbiota in early life is a critical process that influences the development and fitness of vertebrates. However, the relative influence of transmission from the early social environment and host selection throughout host ontogeny remains understudied, particularly in avian species. We conducted conspecific and heterospecific cross-fostering experiments in zebra finches (Taeniopygia guttata) and Bengalese finches (Lonchura striata domestica) under controlled conditions and repeatedly sampled the faecal microbiota of these birds over the first 3 months of life. We thus documented the development of the gut microbiota and characterised the relative impacts of the early social environment and host selection due to species-specific characteristics and individual genetic backgrounds across ontogeny by using 16S ribosomal RNA gene sequencing.ResultsThe taxonomic composition and community structure of the gut microbiota changed across ontogenetic stages; juvenile zebra finches exhibited higher alpha diversity than adults at the post-breeding stage. Furthermore, in early development, the microbial communities of juveniles raised by conspecific and heterospecific foster parents resembled those of their foster family, emphasising the importance of the social environment. In later stages, the social environment continued to influence the gut microbiota, but host selection increased in importance.ConclusionsWe provided a baseline description of the developmental succession of gut microbiota in zebra finches and Bengalese finches, which is a necessary first step for understanding the impact of the early gut microbiota on host fitness. Furthermore, for the first time in avian species, we showed that the relative strengths of the two forces that shape the establishment and maintenance of the gut microbiota (i.e. host selection and dispersal from the social environment) change during development, with host selection increasing in importance. This finding should be considered when experimentally manipulating the early-life gut microbiota. Our findings also provide new insights into the mechanisms of host selection.

The species of theChaetosphaeriaceae(Chaetosphaeriales,Sordariomycetes, Ascomycota) are microscopic asexually reproducing fungi, some of which belong to the life cycle of sexual morphs with macroscopic fruit bodies (~300μmdiam) perceptible as “black dots” on the surface of decaying plants. They are invisible to most of the people and difficult to find even by a trained eye. In addition, they are usually hard to cultivate and slow growing, what make them difficult to study. Traditionally, they are reported from the decaying plant matter in the forest habitat and some exhibitendophyticlifestyle . Surprisingly, analysis ofmetabarcodingdata showed that they are also common soil fungi, dwelling in a bulk soil in grasslands and croplands. There are about 44 genera classified into this family with monographs available for several genera, but with very little knowledge about their biology. Studies showed thatChaetosphaeriaceaemembers are important degraders of lignocellulose in wood, litter and soil and have rich structural diversity of natural compounds (e.g.terpenoids, cyclic peptides,naphthoquinones,lactones,lipopeptides,xanthones). Three unpublished and unannotated draft genomes ofChaetosphaeriaceaeare available (1000 fungal genome project). Nevertheless, the information about the genetic background of the basic pathways is missing in this fungal lineage. The same is true for the wholeChaetosphaeriales. Our study is filling this gap using analysis of genomes from 16 species.&nbsp

Additional file 2. LMM investigating alpha diversity in zebra finches across ontogenetic stages. P-values ≤ 0.05 are shown in bold.

Additional file 7. The mean abundance and the standard deviation of the microbial families identified in the Bengalese finch.

Additional file 3. LMM investigating alpha diversity in Bengalese finches across ontogenetic stages. P-values ≤ 0.05 are shown in bold.

Additional file 3: Figure S3. Comparison between promoter motives of TSS located within (inMAI) and outside (exMAI) of the magnetosome island, cultivated under anoxic (0% dO2) and oxic (95% dO2) conditions. The motif logos were created with Weblogo [58].

Additional file 6. The mean abundance and the standard deviation of the microbial families identified in the zebra finches.

Background One of the most complex prokaryotic organelles are magnetosomes, which are formed by magnetotactic bacteria as sensors for navigation in the Earth’s magnetic field. In the alphaproteobacterium Magnetospirillum gryphiswaldense magnetosomes consist of chains of magnetite crystals (Fe3O4) that under microoxic to anoxic conditions are biomineralized within membrane vesicles. To form such an intricate structure, the transcription of > 30 specific structural genes clustered within the genomic magnetosome island (MAI) has to be coordinated with the expression of an as-yet unknown number of auxiliary genes encoding several generic metabolic functions. However, their global regulation and transcriptional organization in response to anoxic conditions most favorable for magnetite biomineralization are still unclear. Results Here, we compared transcriptional profiles of anaerobically grown magnetosome forming cells with those in which magnetosome biosynthesis has been suppressed by aerobic condition. Using whole transcriptome shotgun sequencing, we found that transcription of about 300 of the > 4300 genes was significantly enhanced during magnetosome formation. About 40 of the top upregulated genes are directly or indirectly linked to aerobic and anaerobic respiration (denitrification) or unknown functions. The mam and mms gene clusters, specifically controlling magnetosome biosynthesis, were highly transcribed, but constitutively expressed irrespective of the growth condition. By Cappable-sequencing, we show that the transcriptional complexity of both the MAI and the entire genome decreased under anaerobic conditions optimal for magnetosome formation. In addition, predominant promoter structures were highly similar to sigma factor σ70 dependent promoters in other Alphaproteobacteria. Conclusions Our transcriptome-wide analysis revealed that magnetite biomineralization relies on a complex interplay between generic metabolic processes such as aerobic and anaerobic respiration, cellular redox control, and the biosynthesis of specific magnetosome structures. In addition, we provide insights into global regulatory features that have remained uncharacterized in the widely studied model organism M. gryphiswaldense, including a comprehensive dataset of newly annotated transcription start sites and genome-wide operon detection as a community resource (GEO Series accession number GSE197098).

Additional file 5. nMDS plots of the dissimilarities of the gut microbiota across Bengalese finch Ontogeny based on (A) Bray-Curtis dissimilarities, (B) Weighted UniFrac distances.

Additional file 10. Indicator OTUs for sample types at each sampling time. IndVal index is calculated as the product of specificity (A) and fidelity (B) values ranging between 0 and 1 (1 indicates the taxon exclusively occurs in the given group).

Additional file 9. Alpha diversity of different sample types based on (A) Shannon's diversity index, and (B) Faith's phylogenetic diversity at 5, 10 and 35 dph. The lines within the box plots indicate the medians, and the lower and upper boundaries of the boxes indicate the 25th and 75th percentiles, respectively. Whiskers above and below the boxes correspond to 1.5 times the interquartile range (IQR) above and below the 25th and 75th percentiles, respectively.

Additional file 1. Sample numbers used in the study.

Additional file 8. LMM investigatigating alpha diversity in different sample types across time.

Additional file 1: Figure S1. Cell growth and magnetic response (Cmag) under A) anoxic (dO2 0%, 10 mM nitrate), B) oxic (dO2 95%, 4 mM ammonium), C) microoxic (dO2 1%, 4 mM nitrate), D) oxic with nitrate (dO2 95%, 4 mM nitrate) conditions. (Scale bar 1 μm). Growth (black and grey lines) and Cmag (colored lines) were depicted for each replicate (circles, diamonds and triangles). The black arrow indicates the sampling timepoint for the RNA-seq experiments.

Additional file 4: Figure S2. Distribution of classified transcription start sites (TSS) under anoxic (0% dO2) and oxic (95% dO2) conditions in the whole genome (pTSS, primary TSS; asTSS, antisense TSS; iTSS, intragenic TSS; oTSS; other TSS).

Additional file 4. nMDS plots of the dissimilarities of the gut microbiota across Zebra finch Ontogeny based on (A) Bray-Curtis dissimilarities, (B) Weighted UniFrac distances.

The microbial biogas network is complex and intertwined, and therefore relatively stable in its overall functionality. However, if key functional groups of microorganisms are affected by biotic or abiotic factors, the entire efficacy may be impaired. Bacteriophages are hypothesized to alter the steering process of the microbial network. In this study, an enriched fraction of virus-like particles was extracted from a mesophilic biogas reactor and sequenced on the Illumina MiSeq and Nanopore GridION sequencing platforms. Metagenome data analysis resulted in identifying 375 metagenome-assembled viral genomes (MAVGs). Two-thirds of the classified sequences were only assigned to the superkingdom Viruses and the remaining third to the family Siphoviridae, followed by Myoviridae, Podoviridae, Tectiviridae, and Inoviridae. The metavirome showed a close relationship to the phage genomes that infect members of the classes Clostridia and Bacilli. Using publicly available biogas metagenomic data, a fragment recruitment approach showed the widespread distribution of the MAVGs studied in other biogas microbiomes. In particular, phage sequences from mesophilic microbiomes were highly similar to the phage sequences of this study. Accordingly, the virus particle enrichment approach and metavirome sequencing provided additional genome sequence information for novel virome members, thus expanding the current knowledge of viral genetic diversity in biogas reactors.

Additional file 1.

Additional file 1: Figure S1. (A) DNA region of the plasmid pEC-XT99A-gabTDP134L with the 21 bp sequence deleted in ALE strain GluA T7 (boxed in red). (B) Determination of the minimum inhibitory concentration for tetracycline for strain GluA T0 with the orginal plasmid pEC-XT99A-gabTDP134L and GluA RG1 carrying this plasmid with the 21 bp deletion. Cells were grown in Duetz cultivation system using BHIS supplemented with increasing concentrations of tetracycline (0–80 µg µL−1). The ΔOD600 values were determined after 48 hours and are shown as means with standard deviations (n = 3 cultivations). Figure S2. Dependence of the specific acitvity of GOGAT on the glutamine concentration determined for GRLys1ΔgltB overexpressing the native version of gltBD (A) or the mutated gltBE686QD (B). Crude extracts of GRLys1ΔgltB (pEKEx3-gltBD) and GRLys1ΔgltB (pEKEx3-gltBE686QD) were assayed for specific activity of the glutamate synthase (GOGAT) in the presence of increasing glutamine concentrations (0 mM, 0.1 mM, 0.25 mM, 0.5 mM, 0.75 mM, 1 mM, 2 mM, 5 mM, 10 mM). Values represent means and standard deviations of triplicate measurements. Figure S3. Glutarate production by GluA T7 in chloride-free medium. Cells were grown in Duetz cultivation system using either standard CGXII minimal medium or CGXII minimal medium without chloride supplemented with 40 g L−1 glucose, induced with 1 mM IPTG, and harvested after 48 h. Values and error bars represent means and standard deviations of glutarate (green) and 5AVA concentrations (red) in the culture supernatants (n = 3 cultivations).

Additional file 1 of “Towards a ‘chassis’ for bacterial magnetosome biosynthesis: genome streamlining of Magnetospirillum gryphiswaldense by multiple deletions”: Table S1. Overview of all single deletion mutants which were also combined in strains ∆TZ-16 and ∆TZ-17. Table S2. Overview of primers used in this study. UF = upstream forward; UR = upstream reverse; DF = downstream forward; DR = downstream reverse. Figure S1. Molecular organization of nif operon in M. gryphiswaldense. The deleted nitrogen fixation cluster comprises 16 genes necessary for nitrogen fixation (shown in red): nifWABZTHDK, fixABC, draGT and three ferredoxins (MSR1_18560; MSR1_18600; MSR1_18640). Black arrows represent other genes encoding a putative rubrerythrin protein (MSR1_18580), a SIR2-like domain containing protein (MSR1_18630), a GAF domain-containing protein (MSR1_18650), a biliverdin-producing heme oxygenase (MSR1_18660) and a tRNA (MSR1_18670). Figure S2. Phenotypic characterization of multiple deletion mutants. Electron micrographs of combinatorial deletion mutants ∆TZ-01–∆TZ-15. Scale bars: left columns 500 nm; right columns 100 nm. Cell growth of strains ∆TZ-01–∆TZ-15 under aerobic and anaerobic conditions as well as oxidative stress (H2O2) and moderate heat stress (33 °C). Each strain was analyzed in triplicates and each curve shows the average. Figure S3. Genetic organization of the Kmr, false positive mutant ∆TZ-15∆M04 K752 Kmr. The targeted M04 had not been deleted but was still maintained in the genome. A large part (~ 9.1 kb) of the 10.2 kb deletion vector pORFM-GalK-M04 harboring the Kmr gene was found to be inserted at the intended site, but harboring a spontaneous duplication of both the upstream and downstream homologous regions intended for targeted insertion of the deletion construct by homologous recombination. In addition, the galK gene was inactivated by insertion of a copy of the IS element ISMgr2 into the central region. Figure S4. Growth profiles of these strains induced with MMC with concentrations between 0.2 and 0.3 µg/ml MMC, induced 8 h. Cells were washed twice, adjusted to initial OD and growth experiments started at 28 °C under aerobic conditions and each strain was analyzed in triplicates while each curve shows its average (standard deviation

Additional file 2: Fig. S1. Serendipita indica (Si) colonization has an effect on Brachypodium distachyon (Bd) root structure. Fig. S2. Progress of Serendipita indica (Si) spore proliferation during colonization of Brachypodium distachyon Bd21-3. Fig. S3. Comparison of the resequenced Serendipita indica (Si) genome to the 2011 assembly. Fig.S4. Comparison of the resequenced Serendipita indica (Si) genome to Serendipita vermifera and Laccaria bicolor. Fig. S5. qRT-PCR confirmation of DEGs identified during mRNA sequencing. Fig. S6. Filtering pipelines applied in the analysis. Fig. S7. Size distribution of total and unique putative ck (cross-kingdom) -sRNAs. Fig. S8. Percentage distribution of the 5′ terminal nucleotide in unique putative endogenous sRNAs. Fig. S9. Percentage distribution of the 5′ terminal nucleotide in unique putative ck-sRNAs. Fig. S10. Percentage distribution of the 5′ terminal nucleotide in total putative endogenous sRNAs. Fig. S11. Percentage distribution of the 5′ terminal nucleotide in total putative ck-sRNAs. Fig. S12. Stem-loop PCR (gel electrophoresis) of some Si and Bd21-3 sRNAs expressed in the Bd-Si sample. Table S1. Quantification of identified features in the resequenced genome of Serendipita indica (Si). Table S2. Total reads from the Bd-C (mock-treated), Bd-Si (colonized root) and Si-ax (axenic culture) samples and their alignment rate (HISAT2) to the corresponding genomes. Table S3. Significant gene ontology terms of molecular function in the differentially expressed genes (DEGs) datasets. Table S4. Predicted protein effectors identified in the resequenced Serendipita indica (Si) genome. Table S5. Candidate RNAi machinery proteins predicted from the resequenced Serendipita indica (Si) genome. Table S6. Total and unique reads for filtered sRNAs from Bd-C (mock-treated), Bd-Si (colonized root) and Si-ax (axenic culture). Table S7. Sequences of putative sRNAs in Tables 4 and 5. Table S8. sRNA sequences after stem-loop PCR amplification. Table S9. Primers used for qRT-PCR (Figure S5). Table S10. Primers used for stem-loop PCR and sequencing (Figure S12, Table S8).

Actinobacteria are among the most prolific sources of medically and agriculturally important compounds, derived from their biosynthetic gene clusters (BGCs) for specialized (secondary) pathways of metabolism. Genomics witnesses that the majority of actinobacterial BGCs are silent, most likely due to their low or zero transcription. Much effort is put into the search for approaches towards activation of silent BGCs, as this is believed to revitalize the discovery of novel natural products. We hypothesized that the global transcriptional factor AdpA, due to its highly degenerate operator sequence, could be used to upregulate the expression of silent BGCs. Using Streptomyces cyanogenus S136 as a test case, we showed that plasmids expressing either full-length adpA or its DNA-binding domain led to significant changes in the metabolome. These were evident as changes in the accumulation of colored compounds, bioactivity, as well as the emergence of a new pattern of secondary metabolites as revealed by HPLC-ESI-mass spectrometry. We further focused on the most abundant secondary metabolite and identified it as the polyene antibiotic lucensomycin. Finally, we uncovered the entire gene cluster for lucensomycin biosynthesis (lcm), that remained elusive for five decades until now, and outlined an evidence-based scenario for its adpA-mediated activation.

Additional file 1: Figure S1. Growth of C. necator H16 wild type and CNCA15 (ΔprpRBCMD) mutant strains on 3-HP and propionate. Strains were cultivated in MM supplemented with 50 mM 3-HP (a) or propionate (b) as the sole source of carbon and energy. Blue circles represent H16 wild type, red squares the CNCA15 (ΔprpRBCMD) mutant. Error bars represent the standard deviation of the mean for three independent experiments.

Additional file 5: Figure S4. Growth (a) and 3-HP consumption (b) of C. necator strains CNCA07 (ΔhpdH) and CNCA16 (ΔhbdH). Strains H16, CNCA07 (ΔhpdH) and CNCA16 (ΔhbdH) were cultivated in MM supplemented with 50 mM 3-HP as the sole source of carbon and energy. Blue circles, H16 wild type; inverted purple triangles, CNCA07; pink diamonds, CNCA16. Error bars represent the standard deviation of the mean for three independent experiments.

Additional file 9: Table S2. Strains used and generated in this study.

Additional file 10: Table S3. Plasmids used and generated in this study.

Additional file 2: Figure S2. Proposed valine degradation pathway in C. necator H16. (a) Valine degradation pathway with associated C. necator enzymes for each reaction. (b) Putative C. necator valine degradation operon and upstream regulatory gene, together with associated locus tags. Genes are proposed to encode the following enzymes: AraC family transcriptional regulator (araC, H16_B1193), branched-chain acyl-CoA dehydrogenase (acaD, HB161192), (methyl)malonate semialdehyde dehydrogenase (mmsA3, H16_B1191), 3-hydroxyisobutyrate dehydrogenase (hbdH, H16_B1190), enoyl-CoA dehydratase (crt, H16_B1189) and 3-hydroxyisobutyryl-CoA hydrolase (hibH; note: the gene is currently annotated to encode an enoyl-CoA hydratase/isomerase, H16_B1188). Glu, glutamate; α-KG, α-ketoglutarate. The first two steps of valine degradation are carried out by branched-chain amino acid aminotransferase and 3-methyl-2-oxobutanoate dehydrogenase (2-oxoisovalerate dehydrogenase), respectively; their encoding genes are not part of this operon. Blue colours indicate reactions and genes associated with the proposed mmsA3 operon. Sizes of genes and intergenic regions not drawn to scale.

Additional file 3: Table S1. Genes of interest differentially expressed on 3-HP compared to fructose.

Additional file 6: Figure S5. Cell growth (a) and 3-HP consumption (b) of C. necator CNCA13 complementation strains. The different CNCA13 (ΔmmsA1ΔmmsA2ΔmmsA3) complementation strains carrying plasmids pBBR1MCS-2-PphaC-mmsA1 (green triangles), pBBR1MCS-2-PphaC-mmsA2 (inverted purple triangles), and pBBR1MCS-2-PphaC-mmsA3 (orange diamonds), respectively, were cultivated in MM supplemented with 50 mM 3-HP as the sole source of carbon and energy. Controls included H16 wild type (blue circles) and CNCA13 (brown squares) carrying the empty pBBR1MCS-2-PphaC vector. Error bars indicate the standard deviation of the mean for three independent experiments.

Additional file 6. Growth, metabolite, and protein secretion concentration profiles of 13C-MFA experiments.

Additional file 1. Description of the network model reconstruction for 13C-based metabolic flux analysis, and amino acid fragments used in 13C-MFA.

Additional file 2. Local clustering procedure.

Reproducibility of differential expression analysis with varying cDNA library replicates. (PDF 1397Â kb)

Gram-positive

Table S1. Bacterial strains, plasmids and oligonucleotides used in this work. Bacterial strains, plasmids and oligonucleotides used in this work are listed. (DOCX 17Â kb)

Additional file 1. Additional tables.

Table S2. RNA-seq analysis of genes differentially transcribed upon sigD overexpression or sigD disruption. The name of genes which expression levels increased under sigD overexpression (M-value > 1.0) are listed. (DOCX 18 kb)

The opportunistic pathogen Pseudomonas aeruginosa controls the production of virulence factors by quorum sensing (QS). Besides cell density, QS in P. aeruginosa is co-regulated by metabolic influences, especially nutrient limitation. Previously, a co-culture model system was established consisting of P. aeruginosa and the chitinolytic bacterium Aeromonas hydrophila, in which parasitic growth of P. aeruginosa is strictly dependent on the QS-controlled production of pyocyanin in response to nutrient limitation (Jagmann et al., 2010). In this study, the co-culture was employed to identify novel genes involved in the regulation of pyocyanin production. Via transposon mutagenesis, the gene gbuA encoding a guanidinobutyrase was identified, deletion of which led to a loss of pyocyanin production in co-cultures and to a reduced pyocyanin production in single cultures. Addition of the natural substrate of GbuA to the mutant strain enhanced the negative effect on pyocyanin production in single cultures. The gbuA mutant showed a reduced transcription of the pqsABCDE operon and could be complemented by PqsE overexpression and addition of alkylquinolone signal molecules. The strong effect of gbuA deletion on the QS-controlled pyocyanin production in co-cultures showed the value of this approach for the discovery of novel gene functions linking metabolism and QS in P. aeruginosa.

Additional file 8: Figure S7. Growth of C. necator H16 and CNCA15 (ΔprpRBCMD) mutant strains on fructose and valine. Strains H16 (A) and CNCA15 (B) were grown on MM agar plates containing 25 mM fructose (a, left panel) and 30 mM valine (b, right panel). Agar plates were incubated at 30 °C for 5 days.

Additional file 4: Figure S3. Differential expression of key metabolic genes in C. necator H16 during exponential phase growth on 3-HP and fructose, respectively. Red and green boxes indicate genes significantly up- and downregulated respectively, when cells were grown on 3-HP (padj

Additional file 8: Figure S7. Growth of C. necator H16 and CNCA15 (ΔprpRBCMD) mutant strains on fructose and valine. Strains H16 (A) and CNCA15 (B) were grown on MM agar plates containing 25 mM fructose (a, left panel) and 30 mM valine (b, right panel). Agar plates were incubated at 30 °C for 5 days.

Staphylococcus aureus has to cope with oxidative and electrophile stress during host-pathogen interactions. The TetR-family repressor GbaA was shown to sense electrophiles, such as N-ethylmaleimide (NEM) via monothiol mechanisms of the two conserved Cys55 or Cys104 residues in vitro. In this study, we further investigated the regulation and function of the GbaA repressor and its Cys residues in S. aureus COL. The GbaA-controlled gbaAB-SACOL2595-97 and SACOL2592-nmrA-2590 operons were shown to respond only weakly 3-10-fold to oxidants, electrophiles or antibiotics in S. aureus COL, but are 57-734-fold derepressed in the gbaA deletion mutant, indicating that the physiological inducer is still unknown. Moreover, the gbaA mutant remained responsive to disulfide and electrophile stress, pointing to additional redox control mechanisms of both operons. Thiol-stress induction of the GbaA regulon was strongly diminished in both single Cys mutants, supporting that both Cys residues are required for redox-sensing in vivo. While GbaA and the single Cys mutants are reversible oxidized under diamide and allicin stress, these thiol switches did not affect the DNA binding activity. The repressor activity of GbaA could be only partially inhibited with NEM in vitro. Survival assays revealed that the gbaA mutant confers resistance under diamide, allicin, NEM and methylglyoxal stress, which was mediated by the SACOL2592-90 operon encoding for a putative glyoxalase and oxidoreductase. Altogether, our results support that the GbaA repressor functions in the defense against oxidative and electrophile stress in S. aureus. GbaA represents a 2-Cys-type redox sensor, which requires another redox-sensing regulator and an unknown thiol-reactive ligand for full derepression of the GbaA regulon genes.

Additional file 11: Table S4. Primers used in this study.

Additional file 11: Table S4. Primers used in this study.

Additional file 7: Figure S6. Genetic complementation of CNCA16 (Δhbdh), CNCA05 (ΔmmsA3) and CNCA13 (ΔmmsA1ΔmmsA2ΔmmsA3) mutant strains restores growth on valine. (a) Growth of C. necator H16 wild type, CNCA16 (ΔhbdH) mutant and complemented CNCA16 (ΔhbdH) mutant. Strain H16 (A) and the CNCA16 mutant control (B) contained empty pMTL71301 vector, the complemented CNCA16 mutant (C) contained plasmid pMTL71301-PacaD-hbdh. (b) Growth of C. necator H16 wild type, CNCA05 (ΔmmsA3) mutant and complemented CNCA05 (ΔmmsA3) mutant. Strain H16 (A) and the CNCA05 mutant control (B) contained empty pBBR1MCS-2-PphaC-eyfp vector, the complemented CNCA05 mutant (C) contained plasmid pBBR1MCS-2-PphaC-mmsA3. (c) Growth of C. necator H16 CNCA13 (ΔmmsA1ΔmmsA2ΔmmsA3) complemented with either mmsA1, mmsA2 and mmsA3 genes. Strain H16 (A) and the CNCA13 mutant control (B) contained empty pBBR1MCS-2-PphaC-1-eyfp vector, whereas the complemented CNCA13 mutants contained pBBR1MCS-2-PphaC-mmsA1 (C), pBBR1MCS-2-PphaC-mmsA2 (D) and pBBR1MCS-2-PphaC-mmsA3 (E), respectively. All strains were grown on MM agar plates containing 25 mM fructose (i) and 30 mM valine (ii). Agar plates were incubated at 30 °C for 5 days.

Additional file 4: Figure S3. Differential expression of key metabolic genes in C. necator H16 during exponential phase growth on 3-HP and fructose, respectively. Red and green boxes indicate genes significantly up- and downregulated respectively, when cells were grown on 3-HP (padj

Additional file 7: Figure S6. Genetic complementation of CNCA16 (Δhbdh), CNCA05 (ΔmmsA3) and CNCA13 (ΔmmsA1ΔmmsA2ΔmmsA3) mutant strains restores growth on valine. (a) Growth of C. necator H16 wild type, CNCA16 (ΔhbdH) mutant and complemented CNCA16 (ΔhbdH) mutant. Strain H16 (A) and the CNCA16 mutant control (B) contained empty pMTL71301 vector, the complemented CNCA16 mutant (C) contained plasmid pMTL71301-PacaD-hbdh. (b) Growth of C. necator H16 wild type, CNCA05 (ΔmmsA3) mutant and complemented CNCA05 (ΔmmsA3) mutant. Strain H16 (A) and the CNCA05 mutant control (B) contained empty pBBR1MCS-2-PphaC-eyfp vector, the complemented CNCA05 mutant (C) contained plasmid pBBR1MCS-2-PphaC-mmsA3. (c) Growth of C. necator H16 CNCA13 (ΔmmsA1ΔmmsA2ΔmmsA3) complemented with either mmsA1, mmsA2 and mmsA3 genes. Strain H16 (A) and the CNCA13 mutant control (B) contained empty pBBR1MCS-2-PphaC-1-eyfp vector, whereas the complemented CNCA13 mutants contained pBBR1MCS-2-PphaC-mmsA1 (C), pBBR1MCS-2-PphaC-mmsA2 (D) and pBBR1MCS-2-PphaC-mmsA3 (E), respectively. All strains were grown on MM agar plates containing 25 mM fructose (i) and 30 mM valine (ii). Agar plates were incubated at 30 °C for 5 days.