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Abstract The interpretation of complex biological datasets requires the identification of representative variables that describe the data without critical information loss. This is particularly important in the analysis of large phenotypic datasets (phenomics). Here we introduce Multi-Attribute Subset Selection (MASS), an algorithm which separates a matrix of phenotypes (e.g., yield across microbial species and environmental conditions) into predictor and response sets of conditions. Using mixed integer linear programming, MASS expresses the response conditions as a linear combination of the predictor conditions, while simultaneously searching for the optimally descriptive set of predictors. We apply the algorithm to three microbial datasets and identify environmental conditions that predict phenotypes under other conditions, providing biologically interpretable axes for strain discrimination. MASS could be used to reduce the number of experiments needed to identify species or to map their metabolic capabilities. The generality of the algorithm allows addressing subset selection problems in areas beyond biology. 

ABSTRACT Heterotrophic marine bacteria utilize and recycle dissolved organic matter (DOM), impacting biogeochemical cycles. It is currently unclear to what extent distinct DOM components can be used by different heterotrophic clades. Here, we ask how a natural microbial community from the Eastern Mediterranean Sea (EMS) responds to different molecular classes of DOM (peptides, amino acids, amino sugars, disaccharides, monosaccharides, and organic acids) comprising much of the biomass of living organisms. Bulk bacterial activity increased after 24 h for all treatments relative to the control, while glucose and ATP uptake decreased or remained unchanged. Moreover, while the per-cell uptake rate of glucose and ATP decreased, that of Leucin significantly increased for amino acids, reflecting their importance as common metabolic currencies in the marine environment.Pseudoalteromonadaceaedominated the peptides treatment, while differentVibrionaceaestrains became dominant in response to amino acids and amino sugars.Marinomonadaceaegrew well on organic acids, andAlteromonadaseaeon disaccharides. A comparison with a recent laboratory-based study reveals similar peptide preferences forPseudoalteromonadaceae, whileAlteromonadaceae, for example, grew well in the lab on many substrates but dominated in seawater samples only when disaccharides were added. We further demonstrate a potential correlation between the genetic capacity for degrading amino sugars and the dominance of specific clades in these treatments. These results highlight the diversity in DOM utilization among heterotrophic bacteria and complexities in the response of natural communities. IMPORTANCEA major goal of microbial ecology is to predict the dynamics of natural communities based on the identity of the organisms, their physiological traits, and their genomes. Our results show that several clades of heterotrophic bacteria each grow in response to one or more specific classes of organic matter. For some clades, but not others, growth in a complex community is similar to that of isolated strains in laboratory monoculture. Additionally, by measuring how the entire community responds to various classes of organic matter, we show that these results are ecologically relevant, and propose that some of these resources are utilized through common uptake pathways. Tracing the path between different resources to the specific microbes that utilize them, and identifying commonalities and differences between different natural communities and between them and lab cultures, is an important step toward understanding microbial community dynamics and predicting how communities will respond to perturbations. 

Half of the Earth’s annual primary production is carried out by phytoplankton in the surface ocean. However, this metabolic activity is heavily impacted by heterotrophic bacteria, which dominate the transformation of organic matter released from phytoplankton.