Relating Metatranscriptomic Profiles to the Micropollutant Biotransformation Potential of Complex Microbial Communities.

Biotransformation of chemical contaminants is of importance in various natural and engineered systems. However, in complex microbial communities and with chemical contaminants at low concentrations, our current understanding of biotransformation at the level of enzyme-chemical interactions is limited. Here, we explored an approach to identify associations between micropollutant biotransformation and specific gene products in complex microbial communities, using association mining between chemical and metatranscriptomic data obtained from experiments with activated sludge grown at different solid retention times. We successfully demonstrate proportional relationships between the measured rate constants and associated gene transcripts for nitrification as a major community function, but also for the biotransformation of two nitrile-containing micropollutants (bromoxynil and acetamiprid) and transcripts of nitrile hydratases, a class of enzymes that we experimentally confirmed to produce the detected amide transformation products. As these results suggest that metatranscriptomic information can indeed be quantitatively correlated with low abundant community functions such as micropollutant biotransformation in complex microbial communities, we proceeded to explore the potential of association mining to highlight enzymes likely involved in catalyzing less well-understood micropollutant biotransformation reactions. Specifically, we use the cases of nitrile hydration and oxidative biotransformation reactions to show that the consideration of additional experimental evidence (such as information on biotransformation pathways) increases the likelihood of detecting plausible novel enzyme-chemical relationships. Finally, we identify a cluster of mono- and dioxygenase fourth-level enzyme classes that most strongly correlate with oxidative micropollutant biotransformation reactions in activated sludge.

Detection of Organohalide-Respiring Enzyme Biomarkers at a Bioaugmented TCE-Contaminated Field Site.

RNA-based biomarkers have been successfully detected at field sites undergoing in situ bioremediation, but the detection of expressed enzymes is a more direct way to prove activity for a particular biocatalytic process of interest since they provide evidence of potential in situ activity rather than simply confirming presence and abundance of genes in a given population by measurement of DNA copies using qPCR. Here we successfully applied shotgun proteomics to field samples from a trichloroethene (TCE)-contaminated industrial site in southern Ontario, Canada that had been bio-augmented with the commercially available KB-1TM microbial culture. The KB-1TM culture contains multiple strains of Dehalococcoides mccartyi (D. mccartyi) as well as an organohalide respiring Geobacter species. The relative abundances of specific enzymatic proteins were subsequently compared to corresponding qPCR-derived levels of DNA and RNA biomarkers in the same samples. Samples were obtained from two wells with high hydraulic connectivity to the KB-1TM-bioaugemented enhanced in situ bioremediation system, and two control wells that showed evidence of low levels of native organohalide respiring bacteria (OHRB), Dehalococcoides and Geobacter. Enzymes involved in organohalide respiration were detected in the metaproteomes of all four field samples, as were chaperonins of D. mccartyi, chemotaxis proteins, and ATPases. The most highly expressed RDase in the bioaugmentation culture (VcrA) was the most highly detected enzyme overall in the bioaugmented groundwater samples. In one background groundwater well, we found high expression of the Geobacter pceA RDase. The DNA and RNA biomarkers detected using qPCR-based assays were a set of orthologs of Dehalococcoides reductive dehalogenases (VcrA, TceA, BvcA, dehalogenase "DET1545"), and the Ni-Fe uptake hydrogenase, HupL. Within a sample, RNA levels for key enzymes correlated with relative protein abundance. These results indicate that laboratory observations of TCE-bioremediation biomarker protein expression are recapitulated in field environmental systems and that both RNA and protein biomarker monitoring hold promise for activity monitoring of in situ populations of OHRB.

Trends in Micropollutant Biotransformation along a Solids Retention Time Gradient.

For many polar organic micropollutants, biotransformation by activated sludge microorganisms is a major removal process during wastewater treatment. However, our current understanding of how wastewater treatment operations influence microbial communities and their micropollutant biotransformation potential is limited, leaving major parts of observed variability in biotransformation rates across treatment facilities unexplained. Here, we present biotransformation rate constants for 42 micropollutants belonging to different chemical classes along a gradient of solids retention time (SRT). The geometric mean of biomass-normalized first-order rate constants shows a clear increase between 3 and 15 d SRT by 160% and 87%, respectively, in two experiments. However, individual micropollutants show a variety of trends. Rate constants of oxidative biotransformation reactions mostly increased with SRT. Yet, nitrifying activity could be excluded as primary driver. For substances undergoing other than oxidative reactions, i.e., mostly substitution-type reactions, more diverse dependencies on SRT were observed. Most remarkably, characteristic trends were observed for groups of substances undergoing similar types of initial transformation reaction, suggesting that shared enzymes or enzyme systems that are conjointly regulated catalyze biotransformation reactions within such groups. These findings open up opportunities for correlating rate constants with measures of enzyme abundance such as genes or gene products, which in turn should help to identify enzymes associated with the respective biotransformation reactions.

Altered Microbiome Leads to Significant Phenotypic and Transcriptomic Differences in a Lipid Accumulating Chlorophyte.

Given the challenges facing the economically favorable production of products from microalgae, understanding factors that might impact productivity rates including growth rates and accumulation of desired products, for example, triacylglycerols (TAG) for biodiesel feedstock, remains critical. Although operational parameters such as media composition and reactor design can clearly effect growth rates, the role of microbe-microbe interactions is just beginning to be elucidated. In this study an oleaginous marine algae Chlorella spp. C596 culture is shown to be better described as a microbial community. Perturbations to this microbial community showed a significant impact on phenotypes including sustained differences in growth rate and TAG accumulation of 2.4 and 2.5 fold, respectively. Characterization of the associated community using Illumina 16S rRNA amplicon and random shotgun transcriptomic analyses showed that the fast growth rate correlated with two specific bacterial species ( Ruegeria and Rhodobacter spp). The transcriptomic response of the Chlorella species revealed that the slower growing algal consortium C596-S1 upregulated genes associated with photosynthesis and resource scavenging and decreased the expression of genes associated with transcription and translation relative to the initial C596-R1. Our studies advance the appreciation of the effects microbiomes can have on algal growth in bioreactors and suggest that symbiotic interactions are involved in a range of critical processes including nitrogen, carbon cycling, and oxidative stress.

Biotransformation of Sulfonamide Antibiotics in Activated Sludge: The Formation of Pterin-Conjugates Leads to Sustained Risk.

The presence of antibiotics in treated wastewater and consequently in surface and groundwater resources raises concerns about the formation and spread of antibiotic resistance. Improving the removal of antibiotics during wastewater treatment therefore is a prime objective of environmental engineering. Here we obtained a detailed picture of the fate of sulfonamide antibiotics during activated sludge treatment using a combination of analytical methods. We show that pterin-sulfonamide conjugates, which are formed when sulfonamides interact with their target enzyme to inhibit folic acid synthesis, represent a major biotransformation route for sulfonamides in laboratory batch experiments with activated sludge. The same major conjugates were also present in the effluents of nine Swiss wastewater treatment plants. The demonstration of this biotransformation route, which is related to bacterial growth, helps explain seemingly contradictory views on optimal conditions for sulfonamide removal. More importantly, since pterin-sulfonamide conjugates show retained antibiotic activity, our findings suggest that risk from exposure to sulfonamide antibiotics may be less reduced during wastewater treatment than previously assumed. Our results thus further emphasize the inadequacy of focusing on parent compound removal and the importance of investigating biotransformation pathways and removal of bioactivity to properly assess contaminant removal in both engineered and natural systems.

Biomarkers' Responses to Reductive Dechlorination Rates and Oxygen Stress in Bioaugmentation Culture KB-1TM.

Using mRNA transcript levels for key functional enzymes as proxies for the organohalide respiration (OHR) rate, is a promising approach for monitoring bioremediation populations in situ at chlorinated solvent-contaminated field sites. However, to date, no correlations have been empirically derived for chlorinated solvent respiring, Dehalococcoides mccartyi (DMC) containing, bioaugmentation cultures. In the current study, genome-wide transcriptome and proteome data were first used to confirm the most highly expressed OHR-related enzymes in the bioaugmentation culture, KB-1TM, including several reductive dehalogenases (RDases) and a Ni-Fe hydrogenase, Hup. Different KB-1™ DMC strains could be resolved at the RNA and protein level through differences in the sequence of a common RDase (DET1545-like homologs) and differences in expression of their vinyl chloride-respiring RDases. The dominant strain expresses VcrA, whereas the minor strain utilizes BvcA. We then used quantitative reverse-transcriptase PCR (qRT-PCR) as a targeted approach for quantifying transcript copies in the KB-1TM consortium operated under a range of TCE respiration rates in continuously-fed, pseudo-steady-state reactors. These candidate biomarkers from KB-1TM demonstrated a variety of trends in terms of transcript abundance as a function of respiration rate over the range: 7.7 × 10-12 to 5.9 × 10-10 microelectron equivalents per cell per hour (µeeq/cell∙h). Power law trends were observed between the respiration rate and transcript abundance for the main DMC RDase (VcrA) and the hydrogenase HupL (R² = 0.83 and 0.88, respectively), but not transcripts for 16S rRNA or three other RDases examined: TceA, BvcA or the RDase DET1545 homologs in KB1TM. Overall, HupL transcripts appear to be the most robust activity biomarker across multiple DMC strains and in mixed communities including DMC co-cultures such as KB1TM. The addition of oxygen induced cell stress that caused respiration rates to decline immediately (>95% decline within one hour). Although transcript levels did decline, they did so more slowly than the respiration rate observed (transcript decay rates between 0.02 and 0.03 per hour). Data from strain-specific probes on the pangenome array strains suggest that a minor DMC strain in KB-1™ that harbors a bvcA homolog preferentially recovered following oxygen stress relative to the dominant, vcrA-containing strain.

Inferring Gene Networks for Strains of Dehalococcoides Highlights Conserved Relationships between Genes Encoding Core Catabolic and Cell-Wall Structural Proteins.

The interpretation of high-throughput gene expression data for non-model microorganisms remains obscured because of the high fraction of hypothetical genes and the limited number of methods for the robust inference of gene networks. Therefore, to elucidate gene-gene and gene-condition linkages in the bioremediation-important genus Dehalococcoides, we applied a Bayesian inference strategy called Reverse Engineering/Forward Simulation (REFS™) on transcriptomic data collected from two organohalide-respiring communities containing different Dehalococcoides mccartyi strains: the Cornell University mixed community D2 and the commercially available KB-1® bioaugmentation culture. In total, 49 and 24 microarray datasets were included in the REFS™ analysis to generate an ensemble of 1,000 networks for the Dehalococcoides population in the Cornell D2 and KB-1® culture, respectively. Considering only linkages that appeared in the consensus network for each culture (exceeding the determined frequency cutoff of ≥ 60%), the resulting Cornell D2 and KB-1® consensus networks maintained 1,105 nodes (genes or conditions) with 974 edges and 1,714 nodes with 1,455 edges, respectively. These consensus networks captured multiple strong and biologically informative relationships. One of the main highlighted relationships shared between these two cultures was a direct edge between the transcript encoding for the major reductive dehalogenase (tceA (D2) or vcrA (KB-1®)) and the transcript for the putative S-layer cell wall protein (DET1407 (D2) or KB1_1396 (KB-1®)). Additionally, transcripts for two key oxidoreductases (a [Ni Fe] hydrogenase, Hup, and a protein with similarity to a formate dehydrogenase, "Fdh") were strongly linked, generalizing a strong relationship noted previously for Dehalococcoides mccartyi strain 195 to multiple strains of Dehalococcoides. Notably, the pangenome array utilized when monitoring the KB-1® culture was capable of resolving signals from multiple strains, and the network inference engine was able to reconstruct gene networks in the distinct strain populations.

Use of De Novo Transcriptome Libraries to Characterize a Novel Oleaginous Marine Chlorella Species during the Accumulation of Triacylglycerols.

Marine chlorophytes of the genus Chlorella are unicellular algae capable of accumulating a high proportion of cellular lipids that can be used for biodiesel production. In this study, we examined the broad physiological capabilities of a subtropical strain (C596) of Chlorella sp. "SAG-211-18" including its heterotrophic growth and tolerance to low salt. We found that the alga replicates more slowly at diluted salt concentrations and can grow on a wide range of carbon substrates in the dark. We then sequenced the RNA of Chlorella strain C596 to elucidate key metabolic genes and investigate the transcriptomic response of the organism when transitioning from a nutrient-replete to a nutrient-deficient condition when neutral lipids accumulate. Specific transcripts encoding for enzymes involved in both starch and lipid biosynthesis, among others, were up-regulated as the cultures transitioned into a lipid-accumulating state whereas photosynthesis-related genes were down-regulated. Transcripts encoding for two of the up-regulated enzymes-a galactoglycerolipid lipase and a diacylglyceride acyltransferase-were also monitored by reverse transcription quantitative polymerase chain reaction assays. The results of these assays confirmed the transcriptome-sequencing data. The present transcriptomic study will assist in the greater understanding, more effective application, and efficient design of Chlorella-based biofuel production systems.

SPINE: SParse eIgengene NEtwork linking gene expression clusters in Dehalococcoides mccartyi to perturbations in experimental conditions.

We present a statistical model designed to identify the effect of experimental perturbations on the aggregate behavior of the transcriptome expressed by the bacterium Dehalococcoides mccartyi strain 195. Strains of Dehalococcoides are used in sub-surface bioremediation applications because they organohalorespire tetrachloroethene and trichloroethene (common chlorinated solvents that contaminate the environment) to non-toxic ethene. However, the biochemical mechanism of this process remains incompletely described. Additionally, the response of Dehalococcoides to stress-inducing conditions that may be encountered at field-sites is not well understood. The constructed statistical model captured the aggregate behavior of gene expression phenotypes by modeling the distinct eigengenes of 100 transcript clusters, determining stable relationships among these clusters of gene transcripts with a sparse network-inference algorithm, and directly modeling the effect of changes in experimental conditions by constructing networks conditioned on the experimental state. Based on the model predictions, we discovered new response mechanisms for DMC, notably when the bacterium is exposed to solvent toxicity. The network identified a cluster containing thirteen gene transcripts directly connected to the solvent toxicity condition. Transcripts in this cluster include an iron-dependent regulator (DET0096-97) and a methylglyoxal synthase (DET0137). To validate these predictions, additional experiments were performed. Continuously fed cultures were exposed to saturating levels of tetrachloethene, thereby causing solvent toxicity, and transcripts that were predicted to be linked to solvent toxicity were monitored by quantitative reverse-transcription polymerase chain reaction. Twelve hours after being shocked with saturating levels of tetrachloroethene, the control transcripts (encoding for a key hydrogenase and the 16S rRNA) did not significantly change. By contrast, transcripts for DET0137 and DET0097 displayed a 46.8±11.5 and 14.6±9.3 fold up-regulation, respectively, supporting the model. This is the first study to identify transcripts in Dehalococcoides that potentially respond to tetrachloroethene solvent-toxicity conditions that may be encountered near contamination source zones in sub-surface environments.

Relating mRNA and protein biomarker levels in a Dehalococcoides and Methanospirillum-containing community.

To better understand the quantitative relationships between messenger RNA (mRNA) and protein biomarkers relevant to bioremediation, we quantified and compared respiration-associated gene products in an anaerobic syntrophic community. Respiration biomarkers for Dehalococcoides, an organohalide reducer, and Methanospirillum, a hydrogenotrophic methanogen, were quantified via qRT-PCR for mRNA and multiple reaction monitoring (MRM) of proteotypic peptides for protein. mRNA transcripts of the Dehalococcoides reductive dehalogenases PceA, TceA, and DMC1545, and hydrogenase HupL, as well as the Methanospirillum oxidoreductases MvrD and FrcA were shown to be similarly regulated with respect to their temporal responses to substrate addition. However, MvrD was two orders of magnitude lower in mRNA abundance. Per cell, Dehalococcoides protein biomarkers quantified were more abundant than Methanospirillum proteins. Comparing mRNA with protein abundance, poor correlations were observed between mRNA transcript levels and the net protein produced. For example, Dehalococcoides HupL and TceA transcripts were similarly abundant though TceA was far more abundant at the protein level (167 ± 121 vs. 1095 ± 337 proteins per cell, respectively). In Methanospirillum, MvrD maintained comparable per-cell protein abundance to FrcA (42 ± 14 vs. 60 ± 1 proteins per cell, respectively) despite the significantly lower transcript levels. Though no variability in protein decay rates was observed, the mRNA translation rate quantified for TceA was greater than the other Dehalococcoides targets monitored. These data suggest that there is considerable variation in the relationship between mRNA abundance and protein production both across transcripts within an organism and across organisms. This highlights the importance of empirically based studies for interpreting biomarker levels in environmentally relevant organisms.

Meta-analyses of Dehalococcoides mccartyi strain 195 transcriptomic profiles identify a respiration rate-related gene expression transition point and interoperon recruitment of a key oxidoreductase subunit.

A cDNA-microarray was designed and used to monitor the transcriptomic profile of Dehalococcoides mccartyi strain 195 (in a mixed community) respiring various chlorinated organics, including chloroethenes and 2,3-dichlorophenol. The cultures were continuously fed in order to establish steady-state respiration rates and substrate levels. The organization of array data into a clustered heat map revealed two major experimental partitions. This partitioning in the data set was further explored through principal component analysis. The first two principal components separated the experiments into those with slow (1.6±0.6 µM Cl-/h)- and fast (22.9±9.6 µM Cl-/h)-respiring cultures. Additionally, the transcripts with the highest loadings in these principal components were identified, suggesting that those transcripts were responsible for the partitioning of the experiments. By analyzing the transcriptomes (n=53) across experiments, relationships among transcripts were identified, and hypotheses about the relationships between electron transport chain members were proposed. One hypothesis, that the hydrogenases Hup and Hym and the formate dehydrogenase-like oxidoreductase (DET0186-DET0187) form a complex (as displayed by their tight clustering in the heat map analysis), was explored using a nondenaturing protein separation technique combined with proteomic sequencing. Although these proteins did not migrate as a single complex, DET0112 (an FdhB-like protein encoded in the Hup operon) was found to comigrate with DET0187 rather than with the catalytic Hup subunit DET0110. On closer inspection of the genome annotations of all Dehalococcoides strains, the DET0185-to-DET0187 operon was found to lack a key subunit, an FdhB-like protein. Therefore, on the basis of the transcriptomic, genomic, and proteomic evidence, the place of the missing subunit in the DET0185-to-DET0187 operon is likely filled by recruiting a subunit expressed from the Hup operon (DET0112).

Molecular biomarker-based biokinetic modeling of a PCE-dechlorinating and methanogenic mixed culture.

Bioremediation of chlorinated ethenes via anaerobic reductive dechlorination relies upon the activity of specific microbial populations--most notably Dehalococcoides (DHC) strains. In the lab and field Dehalococcoides grow most robustly in mixed communities which usually contain both fermenters and methanogens. Recently, researchers have been developing quantitative molecular biomarkers to aid in field site diagnostics and it is hoped that these biomarkers could aid in the modeling of anaerobic reductive dechlorination. A comprehensive biokinetic model of a community containing Dehalococcoides mccartyi (formerly D. ethenogenes) was updated to describe continuously fed reactors with specific biomass levels based on quantitative PCR (qPCR)-based population data (DNA and RNA). The model was calibrated and validated with subsets of chemical and molecular biological data from various continuous feed experiments (n = 24) with different loading rates of the electron acceptor (1.5 to 482 µeeq/L-h), types of electron acceptor (PCE, TCE, cis-DCE) and electron donor to electron acceptor ratios. The resulting model predicted the sum of dechlorination products vinyl chloride (VC) and ethene (ETH) well. However, VC alone was under-predicted and ETH was over predicted. Consequently, competitive inhibition among chlorinated ethenes was examined and then added to the model. Additionally, as 16S rRNA gene copy numbers did not provide accurate model fits in all cases, we examined whether an improved fit could be obtained if mRNA levels for key functional enzymes could be used to infer respiration rates. The resulting empirically derived mRNA "adjustment factors" were added to the model for both DHC and the main methanogen in the culture (a Methanosaeta species) to provide a more nuanced prediction of activity. Results of this study suggest that at higher feeding rates competitive inhibition is important and mRNA provides a more accurate indicator of a population's instantaneous activity than 16S rRNA gene copies alone as biomass estimates.

Methanospirillum respiratory mRNA biomarkers correlate with hydrogenotrophic methanogenesis rate during growth and competition for hydrogen in an organochlorine-respiring mixed culture.

Molecular biomarkers hold promise for inferring rates of key metabolic activities in complex microbial systems. However, few studies have assessed biomarker levels for simultaneously occurring (and potentially competing) respirations. In this study, methanogenesis biomarkers for Methanospirillum hungatei were developed, tested, and compared to Dehalococcoides mccartyi biomarkers in a well-characterized mixed culture. Proteomic analyses of mixed culture samples (n = 4) confirmed expression of many M. hungatei methanogenesis enzymes. The mRNAs for two oxidoreductases detected were explored as quantitative biomarkers of hydrogenotrophic methanogenesis: a coenzyme F(420)-reducing hydrogenase (FrcA) and an iron sulfur protein (MvrD). As shown previously in D. mccartyi, M. hungatei transcript levels correlated linearly with measured (R = 0.97 for FrcA, R = 0.91 for MvrD; n = 7) or calculated respiration rate (R = 0.81 for FrcA, R = 0.62 for MvrD; n = 35) across two orders of magnitude on a log-log scale. The average abundance of MvrD transcripts was consistently two orders of magnitude lower than FrcA, regardless of experimental condition. In experiments where M. hungatei was competing for hydrogen with D. mccartyi, transcripts for the key respiratory hydrogenase HupL were generally less abundant per mL than FrcA and more abundant than MvrD. With no chlorinated electron acceptor added, HupL transcripts fell below both targets. These biomarkers hold promise for the prediction of in situ rates of respiration for these microbes, even when growing in mixed culture and utilizing a shared substrate which has important implications for both engineered and environmental systems. However, the differences in overall biomarker abundances suggest that the strength of any particular mRNA biomarker relies upon empirically established quantitative trends under a range of pertinent conditions.

Relating chloroethene respiration rates in Dehalococcoides to protein and mRNA biomarkers.

Molecular biomarkers could provide critical insight into myriad in situ microbial activities. In this study we explore correlations of both mRNA and protein biomarkers with chloroethene respiration rate in Dehalococcoides. In a series of continuously fed dechlorinating mixed-culture microcosm experiments (n = 26), we varied respiratory substrates, substrate ratios and feeding rates. Transcript levels for most biomarkers were responsive down to 0.01× the culture's maximum respiration rate. The dehalogenase TceA and the Ni-Fe hydrogenase HupL transcripts were positively correlated (Pearson's r of 0.89 and 0.88, respectively) with respiration rates on log-log plots between 1.5 and 280 µeeq/L-hr for mRNA abundances of 10(7) to 10(10) transcripts/mL (0.07-230 transcripts/genome). These trends were independent of the types of chloroethene or electron donors fed. Other mRNA target levels plateaued or declined at respiration rates above 5 µeeq/L-hr. Using both relative and absolute protein quantification methods, we found that per-genome protein abundances of most targeted biomarkers did not statistically change over the experimental time frames. However, quantified enzyme levels allowed us to calculate in vivo enzyme-specific rate constants (k(cat)) for the dehalogenases PceA and TceA: 400 and 22 substrate molecules/enzyme-sec, respectively. Overall, these data support the promise of both mRNA and protein biomarkers for estimating process rates through either empirical (mRNA-based) or kinetic (protein-based) models, but they require follow-up studies in other cultures and at active remediation sites.