Supplemental Information
Metatranscriptomic insights on gene expression and regulatory controls in Candidatus
Accumulibacter phosphatis
Ben O. Oyserman1,2, Daniel R. Noguera1, Tijana Glavana del Rio3, Susannah G. Tringe3,
Katherine D. McMahon1,2,†
1
Department of Civil and Environmental Engineering, University of Wisconsin at Madison,
Madison, WI, 53706, USA; 2Department of Bacteriology, University of Wisconsin at Madison,
Madison, WI, 53706, USA; 3US Department of Energy Joint Genome Institute, Walnut Creek,
CA 94598, USA.
†
corresponding author
Address: 5525 Microbial Science Building, 1550 Linden Dr., Madison, WI 53706
Tel: (608) 890-2836
Fax: (608) 262-5199
Email: kdmcmahon@wisc.edu
Supplemental Introduction
The development and implementation of molecular methods has greatly increased our
understanding of PAOs (He & McMahon, 2011). RNA-seq provides an opportunity to explore
the expression of pathways that have previously been conjectured to provide an important
contribution as well as those which have been over looked. Additionally, the availability of
abundant transcriptional data allows for the exploration into mechanisms that control transcript
abundance as well as conjecture about other mechanisms of regulation. Below we discuss the
transcript abundance of relevant genes involved in nitrogen, sulfur and energy metabolism,
explore potential allosteric regulatory mechanisms, and conduct an analysis to determine if
codon bias may be used as a good proxy for high expression of genes in uncultivated organisms.
Differential transcript abundances across COG categories in a single EBPR cycle
For highly expressed genes, the COG categories corresponding to Amino Acid Transport and
Metabolism (p-value 2.1 e-02), Lipid Transport and Metabolism (p-value 3.2 e-09), and
Carbohydrate Transport and Metabolism (p-value 4.2 e-08) also demonstrated a statistically
significant enrichment (Figure 3A). Trend Category Q demonstrated statistically significant
enrichment (p-value 4.7 e-02) for the COG category Posttranslational Modification, Protein
Turnover and, Chaperones. Trend Category DD demonstrated statistically significant enrichment
in Coenzyme Transport and Metabolism (p-value 2.7 e-02) and Carbohydrate Transport and
Metabolism (p-value 2.8 e-02).
Nitrogen Metabolism in CAP
Genes involved in nitrate reduction (NapAB), (CAP2UW1_3906-CAP2UW1_3909), nitrite
reduction to nitric-oxide, (NirS) (CAP2UW1_2489) and nitrous oxide reductase NosZ
(CAP2UW1_3419) all clustered within the AAC, with the exception of napD (CAP2UW1_3905)
and NorBC (CAP2UW1_2326), which clustered within the RT. Interestingly, Accumulibacter
clade IIA has been shown to be incapable of respiratory nitrate reduction (Flowers et al., 2009;
Oehmen et al., 2010) Furthermore, the lack of nitrite/nitrate in the media and the occurrence of
acetate uptake, PHB synthesis and P release preclude anoxic respiration by Accumulibacter in
this study. However, nitrite and nitrate measurements were not taken therefore the biological
relevance of these expression patterns are not known. Accumulibacter may be simply responding
to the lack of external electron acceptor by turning on the machinery to scavenge any available
oxidized nitrogen compounds. Alternatively, it may be that the expression of these genes is for a
purpose other than anaerobic respiration, such as anaerobic redox balance and the generation of
PMF (Berks et al., 1995; Richardson & Watmough, 1999; Moreno-vivia et al., 1999; Skennerton
et al., 2014).
Sulfur Metabolism During Anaerobic Acetate Contact:
Little previous work on EBPR has addressed Accumulibacter sulfur metabolism, though
strain UW-1 was found to have genes needed for assimilatory sulfate reduction. Indeed, the high
expression of genes involved in assimilatory sulfate reduction anaerobically was recently noted
(Mao et al., 2014) and further demonstrated in this analysis by the high expression of an
assimilatory sulfate reduction operon within the AAC (CAP2UW1_1967- CAP2UW1_1973).
Further supporting an important role for anaerobic sulfur metabolism was the identification of a
cysteine synthase (CAP2UW1_0445) that also clustered within the AAC and was one of the
most highly expressed and dynamic genes. It may be that the demand for sulfur is especially high
anaerobically, for example the highly expressed and dynamic Ni-Fe hydrogenases contain four
cysteine thiolate ligands in the active site (Volbeda et al., 1995). Interestingly, one of the
products of cysteine synthase is acetate (Becker et al., 1969). Two additional cysteine synthase
homologs are found in Accumulibacter but did not show dynamic expression (CAP2UW1_2893,
CAP2UW1_4020).
Energy Metabolism
Several sets of genes involved in direct ATP generation were also up regulated under
anaerobic conditions. An operon encoding F0F1-type ATP synthase (CAP2UW1_4351CAP2UW1_4359) was also among the genes in the RT. The high up regulation of F0F1-type
ATPase anaerobically demonstrates the importance of anaerobic ATP production, corroborating
findings showing that when F0F1-type ATPase is inhibited using DCCD N-N’ –
dicyclohexycarboimide, additional glycogen is likely degraded to produce the required ATP
(Saunders et al., 2007). One currently unexplainable finding was the anaerobic expression of
complex I, II, III and IV of the oxidative phosphorylation complex (See Supplemental Figure 10
for locus tags). Both complex III and IV, require oxygen to function, thus the expression of these
genes may perhaps indicate that CAP is scavenging for terminal electron acceptors. Targeted
RT-qCPR on a subset of these locus tags should be conducted to further investigate their
anaerobic expression patterns. The enrichment of the diverse array of energy production and
conversion related genes identified in this analysis during early anaerobic acetate contact
suggests that anaerobic redox balance in Accumulibacter requires distinct machinery from the
aerobic phase, and that this aspect of the metabolism likely contributes to the novel anaerobic
operation and combination of phylogenetically widespread capabilities of glycolysis, PHA
storage, and polyP accumulation.
Allosteric regulations
In addition to transcriptional regulation, posttranslational regulation such as through allosteric
inhibition and activation, likely play an important role contributing to the Accumulibacter
phenotype. Once acetate enters the cell, transcriptional evidence suggests that it is activated to
acetyl-CoA through both low or high affinity pathways (Figure 4). The relative acetate flux
through these two pathways should influence the energy budget because while both low and high
affinity pathways require ATP, the low affinity pathway converts ATP into ADP while the high
affinity pathways converts ATP into AMP. Acetate flowing through the high affinity pathway
would cause AMP levels to rise, which has been shown to allosterically activate phosphofructose
kinase 1, the committing step in glycolysis (Evans et al., 1981). The activation of glycogen
degradation would produce an abundance of reducing equivalents increasing the NADH/NAD
ratios and causing allosteric inhibition of citrate synthase and the TCA cycle (Weitzman &
Jones, 1968) shunting carbon towards PHB synthesis. Conversely, when glycogen levels are
limiting, low NADH/NAD+ ratios would release inhibition of the TCA cycle allowing acetate
flux through these pathways. Acetate flowing through the low affinity pathway would cause a
sharp rise in cellular concentration of acetyl-P, which has been shown to allosterically activate
PhaC (Miyake et al 1997). Additionally, the high demand for CoA for activation of acetate
through both the high and low affinity pathways would result in a release of the allosteric
inhibition of β-ketothioloase and PHB synthesis (Mothes et al., 1997). While many of these
allosteric interactions have been demonstrated in model systems, determining how they
contribute to the ecophysiology of uncultivated organisms remains a grand challenge. A first step
may be to use targeted metabolite investigations targeting CoA, acetyl-P, NADH/NAD,
NADPH/NADP and other key molecules to determine if their intracellular concentrations agree
with what the EBPR model predicts.
Codon bias and expression:
Codon bias for each gene was calculated using the codon adaptive index using a custom perl
script (Sharp & Li, 1987). Genes were sorted on their max RPKM transcript abundance and
plotted against the calculated CAI value (see supplemental table).
We explored whether codon bias observed in different genes was a good predictor of
expression levels, since the ability to predict relative expression levels in uncultured organisms
based only on genome sequence would be extremely powerful. The genetic code contains
redundancies in codons for most amino acids. Although these synonymous codons are
interchangeable in the amino acid for which they code, these codons are generally not used
synonymously, presumably due to the increase in efficiency and accuracy of biased codon usage
and its influence on expression rates (Hershberg & Petrov, 2008, 2009; Jansen, 2003; Supek et
al., 2010). Many investigations on isolated model organisms have demonstrated the relationship
between high expression patterns and codon bias, however few studies on uncultivated
organisms have used metatranscriptomics to investigate the relationship of codon bias and
expression in the genome (Vezzi et al., 2005). Despite the lack of expression data of many
uncultivated organisms, environmental studies cite codon bias as an indicator of high expression
(Moreira et al., 2004; Ghylin et al., 2014). Here we demonstrate the link between codon bias and
high expression in an uncultivated organism (Supplemental Figure 9). We found that the average
codon bias is statistically higher for the most highly expressed genes (Supplemental Figure 9) in
this experiment. However, we also found that the reciprocal (codon bias predicting high
expression) did not show a strong relationship and caution against predicting high expression
from codon bias alone (e.g. predicting that a gene with high CAI value is more likely to be lowly
expressed than highly expressed). However, these are not extensive experiments and it is likely
that some genes with high codon bias may be highly expressed under other conditions not tested.
Supplementary References
Becker MA, Kredich NM, Tomkins GM. (1969). The Purification and Characterization of O
Salmonella typhimurium The Purification and Characterization of 0-Acetylserine from
Salmonella typhimurium*. J Biol Chem 244:2418–2427.
Berks BC, Ferguson SJ, Moir JWB, Richardson DJ. (1995). Enzymes and associated electron
transport systems that catalyse the respiratory reduction of nitrogen oxides and oxyanions.
Biochim Biophys Acta - Bioenerg 1232:97–173.
http://linkinghub.elsevier.com/retrieve/pii/0005272895000925.
Evans PR, Farrants GW, Hudson JJ, G. BH. (1981). Phosphofructokinase: Structure and
Control. Philos Trans R Soc Lond B Biol Sci 293:53–62.
Flowers JJ, He S, Yilmaz S, Noguera DR, McMahon KD. (2009). Denitrification capabilities of
two biological phosphorus removal sludges dominated by different “Candidatus
Accumulibacter” clades. Environ Microbiol Rep 1:583–588.
http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2929836&tool=pmcentrez&r
endertype=abstract (Accessed July 4, 2011).
Ghylin TW, Garcia SL, Moya F, Oyserman BO, Schwientek P, Forest KT, et al. (2014).
Comparative single-cell genomics reveals potential ecological niches for the freshwater acI
Actinobacteria lineage. ISME J 1–14. http://www.ncbi.nlm.nih.gov/pubmed/25093637
(Accessed October 25, 2014).
He S, McMahon KD. (2011). Microbiology of “Candidatus Accumulibacter” in activated
sludge. Microb Biotechnol 4:603–19.
http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3819010&tool=pmcentrez&r
endertype=abstract (Accessed July 27, 2014).
Hershberg R, Petrov D a. (2009). General rules for optimal codon choice. PLoS Genet
5:e1000556.
http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2700274&tool=pmcentrez&r
endertype=abstract (Accessed January 22, 2014).
Hershberg R, Petrov D a. (2008). Selection on codon bias. Annu Rev Genet 42:287–99.
http://www.ncbi.nlm.nih.gov/pubmed/18983258 (Accessed February 21, 2014).
Jansen R. (2003). Revisiting the codon adaptation index from a whole-genome perspective:
analyzing the relationship between gene expression and codon occurrence in yeast using a
variety of models. Nucleic Acids Res 31:2242–2251.
http://nar.oxfordjournals.org/lookup/doi/10.1093/nar/gkg306 (Accessed February 23,
2014).
Mao Y, Yu K, Xia Y, Chao Y, Zhang T. (2014). Genome Reconstruction and Gene Expression
of “Candidatus Accumulibacter phosphatis” Clade IB Performing Biological Phosphorus
Removal. Environ Sci Technol 48:10363–10371.
http://www.ncbi.nlm.nih.gov/pubmed/25089581.
Moreira D, Rodríguez-Valera F, López-García P. (2004). Analysis of a genome fragment of a
deep-sea uncultivated Group II euryarchaeote containing 16S rDNA, a spectinomycin-like
operon and several energy metabolism genes. Environ Microbiol 6:959–69.
http://www.ncbi.nlm.nih.gov/pubmed/15305921 (Accessed March 24, 2014).
Moreno-vivia C, Cabello P, Martinez-Luque M, Blasco R, Castillo F. (1999). MINIREVIEW
Prokaryotic Nitrate Reduction : Molecular Properties and Functional Distinction among
Bacterial Nitrate Reductases. J Bacteriol 181:6573–6584.
Mothes G, Rivera IS, Babel W. (1997). Competition between β-ketothiolase and citrate
synthase during poly(β-hydroxybutyrate) synthesis inMethylobacterium rhodesianum.
Arch Microbiol 405–410.
Oehmen A, Carvalho G, Lopez-Vazquez CM, van Loosdrecht MCM, Reis M a M. (2010).
Incorporating microbial ecology into the metabolic modelling of polyphosphate
accumulating organisms and glycogen accumulating organisms. Water Res 44:4992–5004.
http://www.ncbi.nlm.nih.gov/pubmed/20650504 (Accessed October 6, 2014).
Richardson DJ, Watmough NJ. (1999). Inorganic nitrogen metabolism in bacteria. Curreny
Opin Chem Biol 3:207–219.
Saunders AM, Mabbett AN, McEwan AG, Blackall LL. (2007). Proton motive force generation
from stored polymers for the uptake of acetate under anaerobic conditions. FEMS Microbiol
Lett 274:245–51. http://www.ncbi.nlm.nih.gov/pubmed/17610509 (Accessed September
1, 2011).
Sharp PM, Li W-H. (1987). The codon adaptation index - a measure of directional
synonymous codon usage bias, and its potential applications. Nucleic Acids Res 15:1281–
1295.
Skennerton CT, Barr JJ, Slater FR, Bond PL, Tyson GW. (2014). Expanding our view of
genomic diversity in Candidatus Accumulibacter clades. Environ Microbiol.
http://www.ncbi.nlm.nih.gov/pubmed/25088527 (Accessed October 25, 2014).
Supek F, Skunca N, Repar J, Vlahovicek K, Smuc T. (2010). Translational selection is
ubiquitous in prokaryotes. PLoS Genet 6:e1001004.
http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2891978&tool=pmcentrez&r
endertype=abstract (Accessed February 23, 2014).
Vezzi a, Campanaro S, D’Angelo M, Simonato F, Vitulo N, Lauro FM, et al. (2005). Life at
depth: Photobacterium profundum genome sequence and expression analysis. Science
307:1459–61. http://www.ncbi.nlm.nih.gov/pubmed/15746425 (Accessed September 14,
2014).
Volbeda A, Charon MH, Piras C, Hatchikian EC, Frey M, Fontecilla-Camps JC. (1995). Crystal
structure of the nickel-iron hydrogenase from Desulfovibrio gigas. Nature 373:580–7.
http://www.ncbi.nlm.nih.gov/pubmed/7854413.
Weitzman P, Jones D. (1968). Regulation of citrate synthase and microbial taxonomy.
Nature 219:270–272.