Polymicrobial infections constitute small ecosystems that accommodate several bacterial species. Commonly, these bacteria are investigated in isolation. However, it is unknown to what extent the isolates interact and whether their interactions alter bacterial growth and ecosystem resilience in the presence and absence of antibiotics. We quantified the complete ecological interaction network for 72 bacterial isolates collected from 23 individuals diagnosed with polymicrobial urinary tract infections and found that most interactions cluster based on evolutionary relatedness. Statistical network analysis revealed that competitive and cooperative reciprocal interactions are enriched in the global network, while cooperative interactions are depleted in the individual host community networks. A population dynamics model parameterized by our measurements suggests that interactions restrict community stability, explaining the observed species diversity of these communities. We further show that the clinical isolates frequently protect each other from clinically relevant antibiotics. Together, these results highlight that ecological interactions are crucial for the growth and survival of bacteria in polymicrobial infection communities and affect their assembly and resilience. microbiology | systems biology | antibiotics | infection

The pentose metabolism of Archaea is largely unknown. Here, we have employed an integrated genomics approach including DNA microarray and proteomics analyses to elucidate the catabolic pathway for D-arabinose in Sulfolobus solfataricus. During growth on this sugar, a small set of genes appeared to be differentially expressed compared with growth on D-glucose. These genes were heterologously overexpressed in Escherichia coli, and the recombinant proteins were purified and biochemically studied. This showed that D-arabinose is oxidized to 2-oxoglutarate by the consecutive action of a number of previously uncharacterized enzymes, including a D-arabinose dehydrogenase, a D-arabinonate dehydratase, a novel 2-keto-3-deoxy-D-arabinonate dehydratase, and a 2,5-dioxopentanoate dehydrogenase. Promoter analysis of these genes revealed a palindromic sequence upstream of the TATA box, which is likely to be involved in their concerted transcriptional control. Integration of the obtained biochemical data with genomic context analysis strongly suggests the occurrence of pentose oxidation pathways in both Archaea and Bacteria, and predicts the involvement of additional enzyme components. Moreover, it revealed striking genetic similarities between the catabolic pathways for pentoses, hexaric acids, and hydroxyproline degradation, which support the theory of metabolic pathway genesis by enzyme recruitment.Pentose sugars are a ubiquitous class of carbohydrates with diverse biological functions. Ribose and deoxyribose are major constituents of nucleic acids, whereas arabinose and xylose are building blocks of several plant cell wall polysaccharides. Many prokaryotes, as well as yeasts and fungi, are able to degrade these polysaccharides, and use the released five-carbon sugars as a sole carbon and energy source. At present, three main catabolic pathways have been described for pentoses. The first is present in Bacteria and uses isomerases, kinases, and epimerases to convert D-and L-arabinose (Ara) and D-xylose (Xyl) into D-xylulose 5-phosphate (Fig. 1A), which is further metabolized by the enzymes of the phosphoketolase or pentose phosphate pathway. The genes encoding the pentose-converting enzymes are often located in gene clusters in bacterial genomes, for example, the araBAD operon for L-Ara (1), the xylAB operon for D-Xyl (2), and the darK-fucPIK gene cluster for D-Ara (3). The second catabolic pathway for pentoses converts D-Xyl into D-xylulose 5-phosphate as well, but the conversions are catalyzed by reductases and dehydrogenases instead of isomerases and epimerases (Fig. 1B). This pathway is commonly found in yeasts, fungi, mammals, and plants, but also in some bacteria (4 -6). In a third pathway, pentoses such as L-Ara, D-Xyl, D-ribose, and D-Ara are metabolized non-phosphorylatively to either 2-oxoglutarate (2-OG) 4 or to pyruvate and glycolaldehyde (Fig. 1C). The conversion to 2-OG, which is a tricarboxylic acid cycle intermediate, proceeds via the subsequent action of a pentose dehydrogenase, a pentonolactonase, a pentoni...

Cellular regulation is believed to evolve in response to environmental variability. However, this has been difficult to test directly. Here, we show that a gene regulation system evolves to the optimal regulatory response when challenged with variable environments. We engineered a genetic module subject to regulation by the lac repressor (LacI) in E. coli, whose expression is beneficial in one environmental condition and detrimental in another. Measured tradeoffs in fitness between environments predict the competition between regulatory phenotypes. We show that regulatory evolution in adverse environments is delayed at specific boundaries in the phenotype space of the regulatory LacI protein. Once this constraint is relieved by mutation, adaptation proceeds toward the optimum, yielding LacI with an altered allosteric mechanism that enables an opposite response to its regulatory ligand IPTG. Our results indicate that regulatory evolution can be understood in terms of tradeoff optimization theory.

Here we describe a method for protein identification and quantification using stable isotopes via in vivo metabolic labeling of the hyperthermophilic crenarchaeon Sulfolobus solfataricus. Stable isotope labeling for quantitative proteomics is becoming increasingly popular; however, its usefulness in protein identification has not been fully exploited. We use both 15N and 13C labeling to create three different versions of the same peptide, corresponding to the unlabeled, 15N and 13C labeled versions. The peptide then appears as three different peaks in a TOF-MS scan and three corresponding sets of MS/MS spectra are obtained. With this information, the elemental carbon and nitrogen compositions for each peptide and each fragment can be calculated. When this is used as a constraint in database searching and/or de novo sequencing, the confidence of a match is increased (for an example intact peptide from 34 choices to 1). This makes the method a useful proteomic tool for both sequenced and unsequenced organisms. Furthermore, it allows for accurate protein quantitation (standard deviations over >4 peptides per protein were within 10%) of three phenotypes in one MS experiment. Abundances for each peptide are calculated by determining the relative areas of each of the three peaks in the TOF-MS spectrum.