A structured evaluation of genome-scale constraint-based modeling tools for microbial consortia.

Harnessing the power of microbial consortia is integral to a diverse range of sectors, from healthcare to biotechnology to environmental remediation. To fully realize this potential, it is critical to understand the mechanisms behind the interactions that structure microbial consortia and determine their functions. Constraint-based reconstruction and analysis (COBRA) approaches, employing genome-scale metabolic models (GEMs), have emerged as the state-of-the-art tool to simulate the behavior of microbial communities from their constituent genomes. In the last decade, many tools have been developed that use COBRA approaches to simulate multi-species consortia, under either steady-state, dynamic, or spatiotemporally varying scenarios. Yet, these tools have not been systematically evaluated regarding their software quality, most suitable application, and predictive power. Hence, it is uncertain which tools users should apply to their system and what are the most urgent directions that developers should take in the future to improve existing capacities. This study conducted a systematic evaluation of COBRA-based tools for microbial communities using datasets from two-member communities as test cases. First, we performed a qualitative assessment in which we evaluated 24 published tools based on a list of FAIR (Findability, Accessibility, Interoperability, and Reusability) features essential for software quality. Next, we quantitatively tested the predictions in a subset of 14 of these tools against experimental data from three different case studies: a) syngas fermentation by C. autoethanogenum and C. kluyveri for the static tools, b) glucose/xylose fermentation with engineered E. coli and S. cerevisiae for the dynamic tools, and c) a Petri dish of E. coli and S. enterica for tools incorporating spatiotemporal variation. Our results show varying performance levels of the best qualitatively assessed tools when examining the different categories of tools. The differences in the mathematical formulation of the approaches and their relation to the results were also discussed. Ultimately, we provide recommendations for refining future GEM microbial modeling tools.

Genes controlling hydrolysate toxin tolerance identified by QTL analysis of the natural Saccharomyces cerevisiae BCC39850.

Lignocellulosic material can be converted to valorized products such as fuels. Pretreatment is an essential step in conversion, which is needed to increase the digestibility of the raw material for microbial fermentation. However, pretreatment generates by-products (hydrolysate toxins) that are detrimental to microbial growth. In this study, natural Saccharomyces strains isolated from habitats in Thailand were screened for their tolerance to synthetic hydrolysate toxins (synHTs). The Saccharomyces cerevisiae natural strain BCC39850 (toxin-tolerant) was crossed with the laboratory strain CEN.PK2-1C (toxin-sensitive), and quantitative trait locus (QTL) analysis was performed on the segregants using phenotypic scores of growth (OD600) and glucose consumption. VMS1, DET1, KCS1, MRH1, YOS9, SYO1, and YDR042C were identified from QTLs as candidate genes associated with the tolerance trait. CEN.PK2-1C knockouts of the VMS1, YOS9, KCS1, and MRH1 genes exhibited significantly greater hydrolysate toxin sensitivity to growth, whereas CEN.PK2-1C knock-ins with replacement of VMS1 and MRH1 genes from the BCC39850 alleles showed significant increased ethanol production titers compared with the CEN.PK2-1C parental strain in the presence of synHTs. The discovery of VMS1, YOS9, MRH1, and KCS1 genes associated with hydrolysate toxin tolerance in S. cerevisiae indicates the roles of the endoplasmic-reticulum-associated protein degradation pathway, plasma membrane protein association, and the phosphatidylinositol signaling system in this trait. KEY POINTS: • QTL analysis was conducted using a hydrolysate toxin-tolerant S. cerevisiae natural strain • Deletion of VMS1, YOS9, MRH1, and KCS1 genes associated with hydrolysate toxin-sensitivity • Replacement of VMS1 and MRH1 with natural strain alleles increased ethanol production titers in the presence of hydrolysate toxins.

SALARECON connects the Atlantic salmon genome to growth and feed efficiency.

Atlantic salmon (Salmo salar) is the most valuable farmed fish globally and there is much interest in optimizing its genetics and rearing conditions for growth and feed efficiency. Marine feed ingredients must be replaced to meet global demand, with challenges for fish health and sustainability. Metabolic models can address this by connecting genomes to metabolism, which converts nutrients in the feed to energy and biomass, but such models are currently not available for major aquaculture species such as salmon. We present SALARECON, a model focusing on energy, amino acid, and nucleotide metabolism that links the Atlantic salmon genome to metabolic fluxes and growth. It performs well in standardized tests and captures expected metabolic (in)capabilities. We show that it can explain observed hypoxic growth in terms of metabolic fluxes and apply it to aquaculture by simulating growth with commercial feed ingredients. Predicted limiting amino acids and feed efficiencies agree with data, and the model suggests that marine feed efficiency can be achieved by supplementing a few amino acids to plant- and insect-based feeds. SALARECON is a high-quality model that makes it possible to simulate Atlantic salmon metabolism and growth. It can be used to explain Atlantic salmon physiology and address key challenges in aquaculture such as development of sustainable feeds.

Genome-scale metabolic modelling enables deciphering ethanol metabolism via the acrylate pathway in the propionate-producer Anaerotignum neopropionicum.

BACKGROUND: Microbial production of propionate from diluted streams of ethanol (e.g., deriving from syngas fermentation) is a sustainable alternative to the petrochemical production route. Yet, few ethanol-fermenting propionigenic bacteria are known, and understanding of their metabolism is limited. Anaerotignum neopropionicum is a propionate-producing bacterium that uses the acrylate pathway to ferment ethanol and CO2 to propionate and acetate. In this work, we used computational and experimental methods to study the metabolism of A. neopropionicum and, in particular, the pathway for conversion of ethanol into propionate. RESULTS: Our work describes iANEO_SB607, the first genome-scale metabolic model (GEM) of A. neopropionicum. The model was built combining the use of automatic tools with an extensive manual curation process, and it was validated with experimental data from this and published studies. The model predicted growth of A. neopropionicum on ethanol, lactate, sugars and amino acids, matching observed phenotypes. In addition, the model was used to implement a dynamic flux balance analysis (dFBA) approach that accurately predicted the fermentation profile of A. neopropionicum during batch growth on ethanol. A systematic analysis of the metabolism of A. neopropionicum combined with model simulations shed light into the mechanism of ethanol fermentation via the acrylate pathway, and revealed the presence of the electron-transferring complexes NADH-dependent reduced ferredoxin:NADP+ oxidoreductase (Nfn) and acryloyl-CoA reductase-EtfAB, identified for the first time in this bacterium. CONCLUSIONS: The realisation of the GEM iANEO_SB607 is a stepping stone towards the understanding of the metabolism of the propionate-producer A. neopropionicum. With it, we have gained insight into the functioning of the acrylate pathway and energetic aspects of the cell, with focus on the fermentation of ethanol. Overall, this study provides a basis to further exploit the potential of propionigenic bacteria as microbial cell factories.

RNA targeting by the type III-A CRISPR-Cas Csm complex of Thermus thermophilus.

CRISPR-Cas is a prokaryotic adaptive immune system that provides sequence-specific defense against foreign nucleic acids. Here we report the structure and function of the effector complex of the Type III-A CRISPR-Cas system of Thermus thermophilus: the Csm complex (TtCsm). TtCsm is composed of five different protein subunits (Csm1-Csm5) with an uneven stoichiometry and a single crRNA of variable size (35-53 nt). The TtCsm crRNA content is similar to the Type III-B Cmr complex, indicating that crRNAs are shared among different subtypes. A negative stain EM structure of the TtCsm complex exhibits the characteristic architecture of Type I and Type III CRISPR-associated ribonucleoprotein complexes. crRNA-protein crosslinking studies show extensive contacts between the Csm3 backbone and the bound crRNA. We show that, like TtCmr, TtCsm cleaves complementary target RNAs at multiple sites. Unlike Type I complexes, interference by TtCsm does not proceed via initial base pairing by a seed sequence.

A protocol for adding knowledge to Wikidata: aligning resources on human coronaviruses.

BACKGROUND: Pandemics, even more than other medical problems, require swift integration of knowledge. When caused by a new virus, understanding the underlying biology may help finding solutions. In a setting where there are a large number of loosely related projects and initiatives, we need common ground, also known as a "commons." Wikidata, a public knowledge graph aligned with Wikipedia, is such a commons and uses unique identifiers to link knowledge in other knowledge bases. However, Wikidata may not always have the right schema for the urgent questions. In this paper, we address this problem by showing how a data schema required for the integration can be modeled with entity schemas represented by Shape Expressions. RESULTS: As a telling example, we describe the process of aligning resources on the genomes and proteomes of the SARS-CoV-2 virus and related viruses as well as how Shape Expressions can be defined for Wikidata to model the knowledge, helping others studying the SARS-CoV-2 pandemic. How this model can be used to make data between various resources interoperable is demonstrated by integrating data from NCBI (National Center for Biotechnology Information) Taxonomy, NCBI Genes, UniProt, and WikiPathways. Based on that model, a set of automated applications or bots were written for regular updates of these sources in Wikidata and added to a platform for automatically running these updates. CONCLUSIONS: Although this workflow is developed and applied in the context of the COVID-19 pandemic, to demonstrate its broader applicability it was also applied to other human coronaviruses (MERS, SARS, human coronavirus NL63, human coronavirus 229E, human coronavirus HKU1, human coronavirus OC4).

Machine learning approaches to predict the Plant-associated phenotype of Xanthomonas strains.

BACKGROUND: The genus Xanthomonas has long been considered to consist predominantly of plant pathogens, but over the last decade there has been an increasing number of reports on non-pathogenic and endophytic members. As Xanthomonas species are prevalent pathogens on a wide variety of important crops around the world, there is a need to distinguish between these plant-associated phenotypes. To date a large number of Xanthomonas genomes have been sequenced, which enables the application of machine learning (ML) approaches on the genome content to predict this phenotype. Until now such approaches to the pathogenomics of Xanthomonas strains have been hampered by the fragmentation of information regarding pathogenicity of individual strains over many studies. Unification of this information into a single resource was therefore considered to be an essential step. RESULTS: Mining of 39 papers considering both plant-associated phenotypes, allowed for a phenotypic classification of 578 Xanthomonas strains. For 65 plant-pathogenic and 53 non-pathogenic strains the corresponding genomes were available and de novo annotated for the presence of Pfam protein domains used as features to train and compare three ML classification algorithms; CART, Lasso and Random Forest. CONCLUSION: The literature resource in combination with recursive feature extraction used in the ML classification algorithms provided further insights into the virulence enabling factors, but also highlighted domains linked to traits not present in pathogenic strains.

A metabolic and physiological design study of Pseudomonas putida KT2440 capable of anaerobic respiration.

BACKGROUND: Pseudomonas putida KT2440 is a metabolically versatile, HV1-certified, genetically accessible, and thus interesting microbial chassis for biotechnological applications. However, its obligate aerobic nature hampers production of oxygen sensitive products and drives up costs in large scale fermentation. The inability to perform anaerobic fermentation has been attributed to insufficient ATP production and an inability to produce pyrimidines under these conditions. Addressing these bottlenecks enabled growth under micro-oxic conditions but does not lead to growth or survival under anoxic conditions. RESULTS: Here, a data-driven approach was used to develop a rational design for a P. putida KT2440 derivative strain capable of anaerobic respiration. To come to the design, data derived from a genome comparison of 1628 Pseudomonas strains was combined with genome-scale metabolic modelling simulations and a transcriptome dataset of 47 samples representing 14 environmental conditions from the facultative anaerobe Pseudomonas aeruginosa. CONCLUSIONS: The results indicate that the implementation of anaerobic respiration in P. putida KT2440 would require at least 49 additional genes of known function, at least 8 genes encoding proteins of unknown function, and 3 externally added vitamins.

Genomic convergence between Akkermansia muciniphila in different mammalian hosts.

BACKGROUND: Akkermansia muciniphila is a member of the human gut microbiota where it resides in the mucus layer and uses mucin as the sole carbon, nitrogen and energy source. A. muciniphila is the only representative of the Verrucomicrobia phylum in the human gut. However, A. muciniphila 16S rRNA gene sequences have also been found in the intestines of many vertebrates. RESULTS: We detected A. muciniphila-like bacteria in the intestines of animals belonging to 15 out of 16 mammalian orders. In addition, other species belonging to the Verrucomicrobia phylum were detected in fecal samples. We isolated 10 new A. muciniphila strains from the feces of chimpanzee, siamang, mouse, pig, reindeer, horse and elephant. The physiology and genome of these strains were highly similar in comparison to the type strain A. muciniphila MucT. Overall, the genomes of the new strains showed high average nucleotide identity (93.9 to 99.7%). In these genomes, we detected considerable conservation of at least 75 of the 78 mucin degradation genes that were previously detected in the genome of the type strain MucT. CONCLUSIONS: The low genomic divergence observed in the new strains may indicate that A. muciniphila favors mucosal colonization independent of the differences in hosts. In addition, the conserved mucus degradation capability points towards a similar beneficial role of the new strains in regulating host metabolic health.

Structure and activity of the RNA-targeting Type III-B CRISPR-Cas complex of Thermus thermophilus.

The CRISPR-Cas system is a prokaryotic host defense system against genetic elements. The Type III-B CRISPR-Cas system of the bacterium Thermus thermophilus, the TtCmr complex, is composed of six different protein subunits (Cmr1-6) and one crRNA with a stoichiometry of Cmr112131445361:crRNA1. The TtCmr complex copurifies with crRNA species of 40 and 46 nt, originating from a distinct subset of CRISPR loci and spacers. The TtCmr complex cleaves the target RNA at multiple sites with 6 nt intervals via a 5' ruler mechanism. Electron microscopy revealed that the structure of TtCmr resembles a "sea worm" and is composed of a Cmr2-3 heterodimer "tail," a helical backbone of Cmr4 subunits capped by Cmr5 subunits, and a curled "head" containing Cmr1 and Cmr6. Despite having a backbone of only four Cmr4 subunits and being both longer and narrower, the overall architecture of TtCmr resembles that of Type I Cascade complexes.

Genome-guided analysis allows the identification of novel physiological traits in Trichococcus species.

BACKGROUND: The genus Trichococcus currently contains nine species: T. flocculiformis, T. pasteurii, T. palustris, T. collinsii, T. patagoniensis, T. ilyis, T. paludicola, T. alkaliphilus, and T. shcherbakoviae. In general, Trichococcus species can degrade a wide range of carbohydrates. However, only T. pasteurii and a non-characterized strain of Trichococcus, strain ES5, have the capacity of converting glycerol to mainly 1,3-propanediol. Comparative genomic analysis of Trichococcus species provides the opportunity to further explore the physiological potential and uncover novel properties of this genus. RESULTS: In this study, a genotype-phenotype comparative analysis of Trichococcus strains was performed. The genome of Trichococcus strain ES5 was sequenced and included in the comparison with the other nine type strains. Genes encoding functions related to e.g. the utilization of different carbon sources (glycerol, arabinan and alginate), antibiotic resistance, tolerance to low temperature and osmoregulation could be identified in all the sequences analysed. T. pasteurii and Trichococcus strain ES5 contain a operon with genes encoding necessary enzymes for 1,3-PDO production from glycerol. All the analysed genomes comprise genes encoding for cold shock domains, but only five of the Trichococcus species can grow at 0 °C. Protein domains associated to osmoregulation mechanisms are encoded in the genomes of all Trichococcus species, except in T. palustris, which had a lower resistance to salinity than the other nine studied Trichococcus strains. CONCLUSIONS: Genome analysis and comparison of ten Trichococcus strains allowed the identification of physiological traits related to substrate utilization and environmental stress resistance (e.g. to cold and salinity). Some substrates were used by single species, e.g. alginate by T. collinsii and arabinan by T. alkaliphilus. Strain ES5 may represent a subspecies of Trichococcus flocculiformis and contrary to the type strain (DSM 2094T), is able to grow on glycerol with the production of 1,3-propanediol.

SAPP: functional genome annotation and analysis through a semantic framework using FAIR principles.

Summary: To unlock the full potential of genome data and to enhance data interoperability and reusability of genome annotations we have developed SAPP, a Semantic Annotation Platform with Provenance. SAPP is designed as an infrastructure supporting FAIR de novo computational genomics but can also be used to process and analyze existing genome annotations. SAPP automatically predicts, tracks and stores structural and functional annotations and associated dataset- and element-wise provenance in a Linked Data format, thereby enabling information mining and retrieval with Semantic Web technologies. This greatly reduces the administrative burden of handling multiple analysis tools and versions thereof and facilitates multi-level large scale comparative analysis. Availability and implementation: SAPP is written in JAVA and freely available at https://gitlab.com/sapp and runs on Unix-like operating systems. The documentation, examples and a tutorial are available at https://sapp.gitlab.io. Contact: jasperkoehorst@gmail.com or peter.schaap@wur.nl.

In silico-guided engineering of Pseudomonas putida towards growth under micro-oxic conditions.

BACKGROUND: Pseudomonas putida is a metabolically versatile, genetically accessible, and stress-robust species with outstanding potential to be used as a workhorse for industrial applications. While industry recognises the importance of robustness under micro-oxic conditions for a stable production process, the obligate aerobic nature of P. putida, attributed to its inability to produce sufficient ATP and maintain its redox balance without molecular oxygen, severely limits its use for biotechnology applications. RESULTS: Here, a combination of genome-scale metabolic modelling and comparative genomics is used to pinpoint essential [Formula: see text]-dependent processes. These explain the inability of the strain to grow under anoxic conditions: a deficient ATP generation and an inability to synthesize essential metabolites. Based on this, several P. putida recombinant strains were constructed harbouring acetate kinase from Escherichia coli for ATP production, and a class I dihydroorotate dehydrogenase and a class III anaerobic ribonucleotide triphosphate reductase from Lactobacillus lactis for the synthesis of essential metabolites. Initial computational designs were fine-tuned by means of adaptive laboratory evolution. CONCLUSIONS: We demonstrated the value of combining in silico approaches, experimental validation and adaptive laboratory evolution for microbial design by making the strictly aerobic Pseudomonas putida able to grow under micro-oxic conditions.

Identification of a Novel L-rhamnose Uptake Transporter in the Filamentous Fungus Aspergillus niger.

The study of plant biomass utilization by fungi is a research field of great interest due to its many implications in ecology, agriculture and biotechnology. Most of the efforts done to increase the understanding of the use of plant cell walls by fungi have been focused on the degradation of cellulose and hemicellulose, and transport and metabolism of their constituent monosaccharides. Pectin is another important constituent of plant cell walls, but has received less attention. In relation to the uptake of pectic building blocks, fungal transporters for the uptake of galacturonic acid recently have been reported in Aspergillus niger and Neurospora crassa. However, not a single L-rhamnose (6-deoxy-L-mannose) transporter has been identified yet in fungi or in other eukaryotic organisms. L-rhamnose is a deoxy-sugar present in plant cell wall pectic polysaccharides (mainly rhamnogalacturonan I and rhamnogalacturonan II), but is also found in diverse plant secondary metabolites (e.g. anthocyanins, flavonoids and triterpenoids), in the green seaweed sulfated polysaccharide ulvan, and in glycan structures from viruses and bacteria. Here, a comparative plasmalemma proteomic analysis was used to identify candidate L-rhamnose transporters in A. niger. Further analysis was focused on protein ID 1119135 (RhtA) (JGI A. niger ATCC 1015 genome database). RhtA was classified as a Family 7 Fucose: H+ Symporter (FHS) within the Major Facilitator Superfamily. Family 7 currently includes exclusively bacterial transporters able to use different sugars. Strong indications for its role in L-rhamnose transport were obtained by functional complementation of the Saccharomyces cerevisiae EBY.VW.4000 strain in growth studies with a range of potential substrates. Biochemical analysis using L-[3H(G)]-rhamnose confirmed that RhtA is a L-rhamnose transporter. The RhtA gene is located in tandem with a hypothetical alpha-L-rhamnosidase gene (rhaB). Transcriptional analysis of rhtA and rhaB confirmed that both genes have a coordinated expression, being strongly and specifically induced by L-rhamnose, and controlled by RhaR, a transcriptional regulator involved in the release and catabolism of the methyl-pentose. RhtA is the first eukaryotic L-rhamnose transporter identified and functionally validated to date.

Deciphering the trophic interaction between Akkermansia muciniphila and the butyrogenic gut commensal Anaerostipes caccae using a metatranscriptomic approach.

Host glycans are paramount in regulating the symbiotic relationship between humans and their gut bacteria. The constant flux of host-secreted mucin at the mucosal layer creates a steady niche for bacterial colonization. Mucin degradation by keystone species subsequently shapes the microbial community. This study investigated the transcriptional response during mucin-driven trophic interaction between the specialised mucin-degrader Akkermansia muciniphila and a butyrogenic gut commensal Anaerostipes caccae. A. muciniphila monocultures and co-cultures with non-mucolytic A. caccae from the Lachnospiraceae family were grown anaerobically in minimal media supplemented with mucin. We analysed for growth, metabolites (HPLC analysis), microbial composition (quantitative reverse transcription PCR), and transcriptional response (RNA-seq). Mucin degradation by A. muciniphila supported the growth of A. caccae and concomitant butyrate production predominantly via the acetyl-CoA pathway. Differential expression analysis (DESeq 2) showed the presence of A. caccae induced changes in the A. muciniphila transcriptional response with increased expression of mucin degradation genes and reduced expression of ribosomal genes. Two putative operons that encode for uncharacterised proteins and an efflux system, and several two-component systems were also differentially regulated. This indicated A. muciniphila changed its transcriptional regulation in response to A. caccae. This study provides insight to understand the mucin-driven microbial ecology using metatranscriptomics. Our findings show that the expression of mucolytic enzymes by A. muciniphila increases upon the presence of a community member. This could indicate its role as a keystone species that supports the microbial community in the mucosal environment by increasing the availability of mucin sugars.

Systems-level modeling of mycobacterial metabolism for the identification of new (multi-)drug targets.

Systems-level metabolic network reconstructions and the derived constraint-based (CB) mathematical models are efficient tools to explore bacterial metabolism. Approximately one-fourth of the Mycobacterium tuberculosis (Mtb) genome contains genes that encode proteins directly involved in its metabolism. These represent potential drug targets that can be systematically probed with CB models through the prediction of genes essential (or the combination thereof) for the pathogen to grow. However, gene essentiality depends on the growth conditions and, so far, no in vitro model precisely mimics the host at the different stages of mycobacterial infection, limiting model predictions. These limitations can be circumvented by combining expression data from in vivo samples with a validated CB model, creating an accurate description of pathogen metabolism in the host. To this end, we present here a thoroughly curated and extended genome-scale CB metabolic model of Mtb quantitatively validated using 13C measurements. We describe some of the efforts made in integrating CB models and high-throughput data to generate condition specific models, and we will discuss challenges ahead. This knowledge and the framework herein presented will enable to identify potential new drug targets, and will foster the development of optimal therapeutic strategies.

Regulation of Three Virulence Strategies of Mycobacterium tuberculosis: A Success Story.

Tuberculosis remains one of the deadliest diseases. Emergence of drug-resistant and multidrug-resistant M. tuberculosis strains makes treating tuberculosis increasingly challenging. In order to develop novel intervention strategies, detailed understanding of the molecular mechanisms behind the success of this pathogen is required. Here, we review recent literature to provide a systems level overview of the molecular and cellular components involved in divalent metal homeostasis and their role in regulating the three main virulence strategies of M. tuberculosis: immune modulation, dormancy and phagosomal rupture. We provide a visual and modular overview of these components and their regulation. Our analysis identified a single regulatory cascade for these three virulence strategies that respond to limited availability of divalent metals in the phagosome.

Monascus ruber as cell factory for lactic acid production at low pH.

A Monascus ruber strain was isolated that was able to grow on mineral medium at high sugar concentrations and 175g/l lactic acid at pH 2.8. Its genome and transcriptomes were sequenced and annotated. Genes encoding lactate dehydrogenase (LDH) were introduced to accomplish lactic acid production and two genes encoding pyruvate decarboxylase (PDC) were knocked out to subdue ethanol formation. The strain preferred lactic acid to glucose as carbon source, which hampered glucose consumption and therefore also lactic acid production. Lactic acid consumption was stopped by knocking out 4 cytochrome-dependent LDH (CLDH) genes, and evolutionary engineering was used to increase the glucose consumption rate. Application of this strain in a fed-batch fermentation resulted in a maximum lactic acid titer of 190g/l at pH 3.8 and 129g/l at pH 2.8, respectively 1.7 and 2.2 times higher than reported in literature before. Yield and productivity were on par with the best strains described in literature for lactic acid production at low pH.

Concurrent Haloalkanoate Degradation and Chlorate Reduction by Pseudomonas chloritidismutans AW-1T.

Haloalkanoates are environmental pollutants that can be degraded aerobically by microorganisms producing hydrolytic dehalogenases. However, there is a lack of information about the anaerobic degradation of haloalkanoates. Genome analysis of Pseudomonas chloritidismutans AW-1T, a facultative anaerobic chlorate-reducing bacterium, showed the presence of two putative haloacid dehalogenase genes, the l-DEX gene and dehI, encoding an l-2-haloacid dehalogenase (l-DEX) and a halocarboxylic acid dehydrogenase (DehI), respectively. Hence, we studied the concurrent degradation of haloalkanoates and chlorate as a yet-unexplored trait of strain AW-1T The deduced amino acid sequences of l-DEX and DehI revealed 33 to 37% and 26 to 86% identities with biochemically/structurally characterized l-DEX and the d- and dl-2-haloacid dehalogenase enzymes, respectively. Physiological experiments confirmed that strain AW-1T can grow on chloroacetate, bromoacetate, and both l- and d-α-halogenated propionates with chlorate as an electron acceptor. Interestingly, growth and haloalkanoate degradation were generally faster with chlorate as an electron acceptor than with oxygen as an electron acceptor. In line with this, analyses of l-DEX and DehI dehalogenase activities using cell-free extract (CFE) of strain AW-1T grown on dl-2-chloropropionate under chlorate-reducing conditions showed up to 3.5-fold higher dehalogenase activity than the CFE obtained from AW-1T cells grown on dl-2-chloropropionate under aerobic conditions. Reverse transcription-quantitative PCR showed that the l-DEX gene was expressed constitutively independently of the electron donor (haloalkanoates or acetate) or acceptor (chlorate or oxygen), whereas the expression of dehI was induced by haloalkanoates. Concurrent degradation of organic and inorganic halogenated compounds by strain AW-1T represents a unique metabolic capacity in a single bacterium, providing a new piece of the puzzle of the microbial halogen cycle.IMPORTANCE Halogenated organic and inorganic compounds are important environmental pollutants that have carcinogenic and genotoxic effects on both animals and humans. Previous research studied the degradation of organic and inorganic halogenated compounds separately but not concurrently. This study shows concurrent degradation of halogenated alkanoates and chlorate as an electron donor and acceptor, respectively, coupled to growth in a single bacterium, Pseudomonas chloritidismutans AW-1T Hence, besides biogenesis of molecular oxygen from chlorate reduction enabling a distinctive placement of strain AW-1T between aerobic and anaerobic microorganisms, we can now add another unique metabolic potential of this bacterium to the roster. The degradation of different halogenated compounds under anoxic conditions by a single bacterium is also of interest for the natural halogen cycle in different aquatic and terrestrial ecosystems where ample natural production of halogenated compounds has been documented.