Using low volume eDNA methods to sample pelagic marine animal assemblages.

Environmental DNA (eDNA) is an increasingly useful method for detecting pelagic animals in the ocean but typically requires large water volumes to sample diverse assemblages. Ship-based pelagic sampling programs that could implement eDNA methods generally have restrictive water budgets. Studies that quantify how eDNA methods perform on low water volumes in the ocean are limited, especially in deep-sea habitats with low animal biomass and poorly described species assemblages. Using 12S rRNA and COI gene primers, we quantified assemblages comprised of micronekton, coastal forage fishes, and zooplankton from low volume eDNA seawater samples (n = 436, 380-1800 mL) collected at depths of 0-2200 m in the southern California Current. We compared diversity in eDNA samples to concurrently collected pelagic trawl samples (n = 27), detecting a higher diversity of vertebrate and invertebrate groups in the eDNA samples. Differences in assemblage composition could be explained by variability in size-selectivity among methods and DNA primer suitability across taxonomic groups. The number of reads and amplicon sequences variants (ASVs) did not vary substantially among shallow (<200 m) and deep samples (>600 m), but the proportion of invertebrate ASVs that could be assigned a species-level identification decreased with sampling depth. Using hierarchical clustering, we resolved horizontal and vertical variability in marine animal assemblages from samples characterized by a relatively low diversity of ecologically important species. Low volume eDNA samples will quantify greater taxonomic diversity as reference libraries, especially for deep-dwelling invertebrate species, continue to expand.

Single-cell analysis in hypersaline brines predicts a water-activity limit of microbial anabolic activity.

Hypersaline brines provide excellent opportunities to study extreme microbial life. Here, we investigated anabolic activity in nearly 6000 individual cells from solar saltern sites with water activities (aw) ranging from 0.982 to 0.409 (seawater to extreme brine). Average anabolic activity decreased exponentially with aw, with nuanced trends evident at the single-cell level: The proportion of active cells remained high (>50%) even after NaCl saturation, and subsets of cells spiked in activity as aw decreased. Intracommunity heterogeneity in activity increased as seawater transitioned to brine, suggesting increased phenotypic heterogeneity with increased physiological stress. No microbial activity was detected in the 0.409-aw brine (an MgCl2-dominated site) despite the presence of cell-like structures. Extrapolating our data, we predict an aw limit for detectable anabolic activity of 0.540, which is beyond the currently accepted limit of life based on cell division. This work demonstrates the utility of single-cell, metabolism-based techniques for detecting active life and expands the potential habitable space on Earth and beyond.

Depth drives the distribution of microbial ecological functions in the coastal western Antarctic Peninsula.

The Antarctic marine environment is a dynamic ecosystem where microorganisms play an important role in key biogeochemical cycles. Despite the role that microbes play in this ecosystem, little is known about the genetic and metabolic diversity of Antarctic marine microbes. In this study we leveraged DNA samples collected by the Palmer Long Term Ecological Research (LTER) project to sequence shotgun metagenomes of 48 key samples collected across the marine ecosystem of the western Antarctic Peninsula (wAP). We developed an in silico metagenomics pipeline (iMAGine) for processing metagenomic data and constructing metagenome-assembled genomes (MAGs), identifying a diverse genomic repertoire related to the carbon, sulfur, and nitrogen cycles. A novel analytical approach based on gene coverage was used to understand the differences in microbial community functions across depth and region. Our results showed that microbial community functions were partitioned based on depth. Bacterial members harbored diverse genes for carbohydrate transformation, indicating the availability of processes to convert complex carbons into simpler bioavailable forms. We generated 137 dereplicated MAGs giving us a new perspective on the role of prokaryotes in the coastal wAP. In particular, the presence of mixotrophic prokaryotes capable of autotrophic and heterotrophic lifestyles indicated a metabolically flexible community, which we hypothesize enables survival under rapidly changing conditions. Overall, the study identified key microbial community functions and created a valuable sequence library collection for future Antarctic genomics research.

Harnessing the microbiome to prevent global biodiversity loss.

Global biodiversity loss and mass extinction of species are two of the most critical environmental issues the world is currently facing, resulting in the disruption of various ecosystems central to environmental functions and human health. Microbiome-targeted interventions, such as probiotics and microbiome transplants, are emerging as potential options to reverse deterioration of biodiversity and increase the resilience of wildlife and ecosystems. However, the implementation of these interventions is urgently needed. We summarize the current concepts, bottlenecks and ethical aspects encompassing the careful and responsible management of ecosystem resources using the microbiome (termed microbiome stewardship) to rehabilitate organisms and ecosystem functions. We propose a real-world application framework to guide environmental and wildlife probiotic applications. This framework details steps that must be taken in the upscaling process while weighing risks against the high toll of inaction. In doing so, we draw parallels with other aspects of contemporary science moving swiftly in the face of urgent global challenges.

Machine Learning Predicts Biogeochemistry from Microbial Community Structure in a Complex Model System.

Microbial community structure is influenced by the environment and in turn exerts control on many environmental parameters. We applied this concept in a bioreactor study to test whether microbial community structure contains information sufficient to predict the concentration of H2S as the product of sulfate reduction. Microbial sulfate reduction is a major source of H2S in many industrial and environmental systems and is often influenced by the existing physicochemical conditions. Production of H2S in industrial systems leads to occupational hazards and adversely affects the quality of products. A long-term (148 days) experiment was conducted in upflow bioreactors to mimic sulfidogenesis, followed by inhibition with nitrate salts and a resumption of H2S generation when inhibition was released. We determined microbial community structure in 731 samples across 20 bioreactors using 16S rRNA gene sequencing and applied a random forest algorithm to successfully predict different phases of sulfidogenesis and mitigation (accuracy = 93.17%) and sessile and effluent microbial communities (accuracy = 100%). Similarly derived regression models that also included cell abundances were able to predict H2S concentration with remarkably high fidelity (R2 > 0.82). Metabolic profiles based on microbial community structure were also found to be reliable predictors for H2S concentration (R2 = 0.78). These results suggest that microbial community structure contains information sufficient to predict sulfidogenesis in a closed system, with anticipated applications to microbially driven processes in open environments. IMPORTANCE Microbial communities control many biogeochemical processes. Many of these processes are impractical or expensive to measure directly. Because the taxonomic structure of the microbial community is indicative of its function, it encodes information that can be used to predict biogeochemistry. Here, we demonstrate how a machine learning technique can be used to predict sulfidogenesis, a key biogeochemical process in a model system. A distinction of this research was the ability to predict H2S production in a bioreactor from the effluent bacterial community structure without direct observations of the sessile community or other environmental conditions. This study establishes the ability to use machine learning approaches in predicting sulfide concentrations in a closed system, which can be further developed as a valuable tool for predicting biogeochemical processes in open environments. As machine learning algorithms continue to improve, we anticipate increased applications of microbial community structure to predict key environmental and industrial processes.

From microbial community structure to metabolic inference using paprica.

Microbial taxonomic marker gene studies using 16S rRNA gene amplicon sequencing provide an understanding of microbial community structure and diversity; however, it can be difficult to infer the functionality of microbes in the ecosystem from these data. Here, we show how to predict metabolism from phylogeny using the paprica pipeline. This approach allows resolution at the strain and species level for select regions on the prokaryotic phylogenetic tree and provides an estimate of gene and metabolic pathway abundance. For complete details on the use and execution of this protocol, please refer to Erazo and Bowman (2021).

Detection of Sulfate-Reducing Bacteria as an Indicator for Successful Mitigation of Sulfide Production.

Sulfate-reducing bacteria (SRBs) are one of the main sources of biogenic H2S generation in oil reservoirs. Excess H2S production in these systems leads to oil biosouring, which causes operational risks and health hazards and can increase the cost of refining crude oil. Nitrate salts are often added to the system to suppress sulfidogenesis. Because SRB populations can persist in biofilms even after nitrate treatment, identifying shifts in the sessile community is crucial for successful mitigation. However, sampling the sessile community is hampered by its inaccessibility. Here, we use the results of a long-term (148 days) ex situ experiment to identify particular sessile community members from observations of the sample waste stream. Microbial community structure was determined for 731 samples across 20 bioreactors using 16S rRNA gene sequencing. By associating microbial community structure with specific steps in the mitigation process, we could distinguish between taxa associated with H2S production and mitigation. After initiation of nitrate treatment, certain SRB populations increased in the planktonic community during critical time points, indicating the dissociation of SRBs from the biofilm. Predicted relative abundances of the dissimilatory sulfate reduction pathway also increased during the critical time points. Here, by analyzing the planktonic community structure, we describe a general method that uses high-throughput amplicon sequencing, metabolic inferences, and cell abundance data to identify successful biofilm mitigation. We anticipate that our approach is also applicable to other systems where biofilms must be mitigated but cannot be sampled easily. IMPORTANCE Microbial biofilms are commonly present in many industrial processes and can negatively impact performance and safety. Within the oil industry, subterranean biofilms cause biosouring with implications for oil quality, cost, occupational health, and the environment. Because these biofilms cannot be sampled directly, methods are needed to indirectly assess the success of mitigation measures. This study demonstrates how the planktonic microbial community can be used to assess the dissociation of sulfate-reducing bacterium (SRB)-containing biofilms. We found that an increase in the abundance of a specific SRB population in the effluent after nitrate treatment can be used as a potential indicator for the successful mitigation of biofilm-forming SRBs. Moreover, a method for determining critical time points for detecting potential indicators is suggested. This study expands our knowledge of improving mitigation strategies for biosouring and could have broader implications in other systems where biofilms lead to adverse consequences.

Making Sense of a Scent-Sensing Metaphor for Microbes and Environmental Predictions.

Microbes serve as sensitive indicators of ecosystem change due to their vast diversity and tendency to change in abundance in response to environmental conditions. Although we most frequently observe these changes to study the microbial community itself, it is increasingly common to use them to understand the surrounding environment. In this way microbial communities can be thought of as powerful sensors capable of reporting shifts in chemical or physical conditions with high fidelity. In this commentary, I further explore this idea by drawing a comparison to the olfactory system, where populations of sensory neurons respond to the presence of specific odorants. The possible combinations of sensory neurons that can transduce a signal are virtually limitless. Yet, the brain can deconvolute the signal into recognizable and actionable data. The further development of machine learning techniques and its application hold great promise for our ability to interpret microbes to detect environmental change.

Recurrent microbial community types driven by nearshore and seasonal processes in coastal Southern California.

A multitude of concurrent biological and physical processes contribute to microbial community turnover, especially in highly dynamic coastal environments. Characterizing what factors contribute most to shifts in microbial community structure and the specific organisms that correlate with changes in the products of photosynthesis improves our understanding of nearshore microbial ecosystem functions. We conducted high frequency sampling in nearshore Southern California in order to capture sub-weekly microbial community dynamics. Microbial communities were characterized by flow cytometry and 16S rRNA gene sequencing, and placed in the context of physicochemical parameters. Within our time-series, season and nutrient availability corresponded to changes in dominant microbial community members. Concurrent aseasonal drivers with overlapping scales of variability were also apparent when we used network analysis to assess the microbial community as subsets of the whole. Our analyses revealed the microbial community as a mosaic, with overlapping groups of taxa that varied on different timescales and correlated with unique abiotic and biotic factors. Specifically, a subnetwork associated with chlorophyll a exhibited rapid turnover, indicating that ecologically important subsets of the microbial community can change on timescales different than and in response to factors other than those that govern turnover of most members of the assemblage.

Sensitivity of the mangrove-estuarine microbial community to aquaculture effluent.

Mangrove-dominated estuaries host a diverse microbial assemblage that facilitates nutrient and carbon conversions and could play a vital role in maintaining ecosystem health. In this study, we used 16S rRNA gene analysis, metabolic inference, nutrient concentrations, and δ13C and δ15N isotopes to evaluate the impact of land use change on near-shore biogeochemical cycles and microbial community structures within mangrove-dominated estuaries. Samples in close proximity to active shrimp aquaculture were high in NH4 +, NO3 - NO2 -, and PO4 3-; lower in microbial community and metabolic diversity; and dominated by putative nitrifiers, denitrifies, and sulfur-oxidizing bacteria. Near intact mangrove forests we observed the presence of potential nitrogen fixers of the genus Calothrix and order Rhizobiales. We identified possible indicators of aquaculture effluents such as Pseudomonas balearica, Ponitmonas salivi brio , family Chromatiaceae, and genus Arcobacter. These results highlight the sensitivity of the estuarine-mangrove microbial community, and their ecosystem functions, to land use changes.

Microbial diversity and activity in Southern California salterns and bitterns: analogues for remnant ocean worlds.

Concurrent osmotic and chaotropic stress make MgCl2 -rich brines extremely inhospitable environments. Understanding the limits of life in these brines is essential to the search for extraterrestrial life on contemporary and relict ocean worlds, like Mars, which could host similar environments. We sequenced environmental 16S rRNA genes and quantified microbial activity across a broad range of salinity and chaotropicity at a Mars-analogue salt harvesting facility in Southern California, where seawater is evaporated in a series of ponds ranging from kosmotropic NaCl brines to highly chaotropic MgCl2 brines. Within NaCl brines, we observed a proliferation of specialized halophilic Euryarchaeota, which corresponded closely with the dominant taxa found in salterns around the world. These communities were characterized by very slow growth rates and high biomass accumulation. As salinity and chaotropicity increased, we found that the MgCl2 -rich brines eventually exceeded the limits of microbial activity. We found evidence that exogenous genetic material is preserved in these chaotropic brines, producing an unexpected increase in diversity in the presumably sterile MgCl2 -saturated brines. Because of their high potential for biomarker preservation, chaotropic brines could therefore serve as repositories of genetic biomarkers from nearby environments (both on Earth and beyond) making them prime targets for future life-detection missions.

Understanding Microbial Community Dynamics in Up-Flow Bioreactors to Improve Mitigation Strategies for Oil Souring.

Oil souring occurs when H2S is generated in oil reservoirs. This not only leads to operational risks and health hazards but also increases the cost of refining crude oil. Sulfate-reducing microorganisms are considered to be the main source of the H2S that leads to oil souring. Substrate competition between nitrate-reducing and sulfate-reducing microorganisms makes biosouring mitigation via the addition of nitrate salts a viable strategy. This study explores the shift in microbial community across different phases of biosouring and mitigation. Anaerobic sand-filled columns wetted with seawater and/or oil were used to initiate the processes of sulfidogenesis, followed by mitigation with nitrate, rebound sulfidogenesis, and rebound control phases (via nitrate and low salinity treatment). Shifts in microbial community structure and function were observed across different phases of seawater and oil setups. Marine bacterial taxa (Marinobacter, Marinobacterium, Thalassolituus, Alteromonas, and Cycloclasticus) were found to be the initial responders to the application of nitrate during mitigation of sulfidogenesis in both seawater- and oil- wetted columns. Autotrophic groups (Sulfurimonas and Desulfatibacillum) were found to be higher in seawater-wetted columns compared to oil-wetted columns, suggesting the potential for autotrophic volatile fatty acid (VFA) production in oil-field aquifers when seawater is introduced. Results indicate that fermentative (such as Bacteroidetes) and oil-degrading bacteria (such as Desulfobacula toluolica) play an important role in generating electron donors in the system, which may sustain biosouring and nitrate reduction. Persistence of certain microorganisms (Desulfobacula) across different phases was observed, which may be due to a shift in metabolic lifestyle of the microorganisms across phases, or zonation based on nutrient availability in the columns. Overall results suggest mitigation strategies for biosouring can be improved by monitoring VFA concentrations and microbial community dynamics in the oil reservoirs during secondary recovery of oil.

The NASA Roadmap to Ocean Worlds.

In this article, we summarize the work of the NASA Outer Planets Assessment Group (OPAG) Roadmaps to Ocean Worlds (ROW) group. The aim of this group is to assemble the scientific framework that will guide the exploration of ocean worlds, and to identify and prioritize science objectives for ocean worlds over the next several decades. The overarching goal of an Ocean Worlds exploration program as defined by ROW is to "identify ocean worlds, characterize their oceans, evaluate their habitability, search for life, and ultimately understand any life we find." The ROW team supports the creation of an exploration program that studies the full spectrum of ocean worlds, that is, not just the exploration of known ocean worlds such as Europa but candidate ocean worlds such as Triton as well. The ROW team finds that the confirmed ocean worlds Enceladus, Titan, and Europa are the highest priority bodies to target in the near term to address ROW goals. Triton is the highest priority candidate ocean world to target in the near term. A major finding of this study is that, to map out a coherent Ocean Worlds Program, significant input is required from studies here on Earth; rigorous Research and Analysis studies are called for to enable some future ocean worlds missions to be thoughtfully planned and undertaken. A second finding is that progress needs to be made in the area of collaborations between Earth ocean scientists and extraterrestrial ocean scientists.

Recurrent seascape units identify key ecological processes along the western Antarctic Peninsula.

The western Antarctic Peninsula (WAP) is a bellwether of global climate change and natural laboratory for identifying interactions between climate and ecosystems. The Palmer Long-Term Ecological Research (LTER) project has collected data on key ecological and environmental processes along the WAP since 1993. To better understand how key ecological parameters are changing across space and time, we developed a novel seascape classification approach based on in situ temperature, salinity, chlorophyll a, nitrate + nitrite, phosphate, and silicate. We anticipate that this approach will be broadly applicable to other geographical areas. Through the application of self-organizing maps (SOMs), we identified eight recurrent seascape units (SUs) in these data. These SUs have strong fidelity to known regional water masses but with an additional layer of biogeochemical detail, allowing us to identify multiple distinct nutrient profiles in several water masses. To identify the temporal and spatial distribution of these SUs, we mapped them across the Palmer LTER sampling grid via objective mapping of the original parameters. Analysis of the abundance and distribution of SUs since 1993 suggests two year types characterized by the partitioning of chlorophyll a into SUs with different spatial characteristics. By developing generalized linear models for correlated, time-lagged external drivers, we conclude that early spring sea ice conditions exert a strong influence on the distribution of chlorophyll a and nutrients along the WAP, but not necessarily the total chlorophyll a inventory. Because the distribution and density of phytoplankton biomass can have an impact on biomass transfer to the upper trophic levels, these results highlight anticipated links between the WAP marine ecosystem and climate.

Identification of Microbial Dark Matter in Antarctic Environments.

Numerous studies have applied molecular techniques to understand the diversity, evolution, and ecological function of Antarctic bacteria and archaea. One common technique is sequencing of the 16S rRNA gene, which produces a nearly quantitative profile of community membership. However, the utility of this and similar approaches is limited by what is known about the evolution, physiology, and ecology of surveyed taxa. When representative genomes are available in public databases some of this information can be gleaned from genomic studies, and automated pipelines exist to carry out this task. Here the paprica metabolic inference pipeline was used to assess how well Antarctic microbial communities are represented by the available completed genomes. The NCBI's Sequence Read Archive (SRA) was searched for Antarctic datasets that used one of the Illumina platforms to sequence the 16S rRNA gene. These data were quality controlled and denoised to identify unique reads, then analyzed with paprica to determine the degree of overlap with the closest phylogenetic neighbor with a completely sequenced genome. While some unique reads had perfect mapping to 16S rRNA genes from completed genomes, the mean percent overlap for all mapped reads was 86.6%. When samples were grouped by environment, some environments appeared more or less well represented by the available genomes. For the domain Bacteria, seawater was particularly poorly represented with a mean overlap of 80.2%, while for the domain Archaea glacial ice was particularly poorly represented with an overlap of only 48.0% for a single sample. These findings suggest that a considerable effort is needed to improve the representation of Antarctic microbes in genome sequence databases.

Denitrification potential of the eastern oyster microbiome using a 16S rRNA gene based metabolic inference approach.

The eastern oyster (Crassostrea virginica) is a foundation species providing significant ecosystem services. However, the roles of oyster microbiomes have not been integrated into any of the services, particularly nitrogen removal through denitrification. We investigated the composition and denitrification potential of oyster microbiomes with an approach that combined 16S rRNA gene analysis, metabolic inference, qPCR of the nitrous oxide reductase gene (nosZ), and N2 flux measurements. Microbiomes of the oyster digestive gland, the oyster shell, and sediments adjacent to the oyster reef were examined based on next generation sequencing (NGS) of 16S rRNA gene amplicons. Denitrification potentials of the microbiomes were determined by metabolic inferences using a customized denitrification gene and genome database with the paprica (PAthway PRediction by phylogenetIC plAcement) bioinformatics pipeline. Denitrification genes examined included nitrite reductase (nirS and nirK) and nitrous oxide reductase (nosZ), which was further subdivided by genotype into clade I (nosZI) or clade II (nosZII). Continuous flow through experiments measuring N2 fluxes were conducted with the oysters, shells, and sediments to compare denitrification activities. Paprica properly classified the composition of microbiomes, showing similar classification results from Silva, Greengenes and RDP databases. Microbiomes of the oyster digestive glands and shells were quite different from each other and from the sediments. The relative abundance of denitrifying bacteria inferred by paprica was higher in oysters and shells than in sediments suggesting that oysters act as hotspots for denitrification in the marine environment. Similarly, the inferred nosZI gene abundances were also higher in the oyster and shell microbiomes than in the sediment microbiome. Gene abundances for nosZI were verified with qPCR of nosZI genes, which showed a significant positive correlation (F1,7 = 14.7, p = 6.0x10-3, R2 = 0.68). N2 flux rates were significantly higher in the oyster (364.4 ± 23.5 µmol N-N2 m-2 h-1) and oyster shell (355.3 ± 6.4 µmol N-N2 m-2 h-1) compared to the sediment (270.5 ± 20.1 µmol N-N2 m-2 h-1). Thus, bacteria carrying nosZI genes were found to be an important denitrifier, facilitating nitrogen removal in oyster reefs. In addition, this is the first study to validate the use of 16S gene based metabolic inference as a method for determining microbiome function, such as denitrification, by comparing inference results with qPCR gene quantification and rate measurements.

Bacterial community segmentation facilitates the prediction of ecosystem function along the coast of the western Antarctic Peninsula.

Bacterial community structure can be combined with observations of ecophysiological data to build predictive models of microbial ecosystem function. These models are useful for understanding how function might change in response to a changing environment. Here we use five spring-summer seasons of bacterial community structure and flow cytometry data from a productive coastal site along the western Antarctic Peninsula to construct models of bacterial production (BP), an ecosystem function that heterotrophic bacteria provide. Through a novel application of emergent self-organizing maps we identified eight recurrent modes in the structure of the bacterial community. A model that combined bacterial abundance, mode and the fraction of cells belonging to the high nucleic acid population (fHNA; R2=0.730, P<0.001) best described BP. Abrupt transitions between modes during the 2013-2014 spring-summer season corresponded to rapid shifts in fHNA. We conclude that parameterizing community structure data via segmentation can yield useful insights into microbial ecosystem function and ecosystem processes.

Microbial Communities Can Be Described by Metabolic Structure: A General Framework and Application to a Seasonally Variable, Depth-Stratified Microbial Community from the Coastal West Antarctic Peninsula.

Taxonomic marker gene studies, such as the 16S rRNA gene, have been used to successfully explore microbial diversity in a variety of marine, terrestrial, and host environments. For some of these environments long term sampling programs are beginning to build a historical record of microbial community structure. Although these 16S rRNA gene datasets do not intrinsically provide information on microbial metabolism or ecosystem function, this information can be developed by identifying metabolisms associated with related, phenotyped strains. Here we introduce the concept of metabolic inference; the systematic prediction of metabolism from phylogeny, and describe a complete pipeline for predicting the metabolic pathways likely to be found in a collection of 16S rRNA gene phylotypes. This framework includes a mechanism for assigning confidence to each metabolic inference that is based on a novel method for evaluating genomic plasticity. We applied this framework to 16S rRNA gene libraries from the West Antarctic Peninsula marine environment, including surface and deep summer samples and surface winter samples. Using statistical methods commonly applied to community ecology data we found that metabolic structure differed between summer surface and winter and deep samples, comparable to an analysis of community structure by 16S rRNA gene phylotypes. While taxonomic variance between samples was primarily driven by low abundance taxa, metabolic variance was attributable to both high and low abundance pathways. This suggests that clades with a high degree of functional redundancy can occupy distinct adjacent niches. Overall our findings demonstrate that inferred metabolism can be used in place of taxonomy to describe the structure of microbial communities. Coupling metabolic inference with targeted metagenomics and an improved collection of completed genomes could be a powerful way to analyze microbial communities in a high-throughput manner that provides direct access to metabolic and ecosystem function.

Alkane hydroxylase genes in psychrophile genomes and the potential for cold active catalysis.

BACKGROUND: Psychrophiles are presumed to play a large role in the catabolism of alkanes and other components of crude oil in natural low temperature environments. In this study we analyzed the functional diversity of genes for alkane hydroxylases, the enzymes responsible for converting alkanes to more labile alcohols, as found in the genomes of nineteen psychrophiles for which alkane degradation has not been reported. To identify possible mechanisms of low temperature optimization we compared putative alkane hydroxylases from these psychrophiles with homologues from nineteen taxonomically related mesophilic strains. RESULTS: Seven of the analyzed psychrophile genomes contained a total of 27 candidate alkane hydroxylase genes, only two of which are currently annotated as alkane hydroxylase. These candidates were mostly related to the AlkB and cytochrome p450 alkane hydroxylases, but several homologues of the LadA and AlmA enzymes, significant for their ability to degrade long-chain alkanes, were also detected. These putative alkane hydroxylases showed significant differences in primary structure from their mesophile homologues, with preferences for specific amino acids and increased flexibility on loops, bends, and α-helices. CONCLUSION: A focused analysis on psychrophile genomes led to discovery of numerous candidate alkane hydroxylase genes not currently annotated as alkane hydroxylase. Gene products show signs of optimization to low temperature, including regions of increased flexibility and amino acid preferences typical of psychrophilic proteins. These findings are consistent with observations of microbial degradation of crude oil in cold environments and identify proteins that can be targeted in rate studies and in the design of molecular tools for low temperature bioremediation.