Antibiotic resistance is prevalent among the bacterial pathogens causing urinary tract infections. However, antimicrobial treatment is often prescribed "empirically", in the absence of antibiotic susceptibility testing, risking mismatched and therefore ineffective treatment. Here, linking a 10year longitudinal dataset of over 700,000 community-acquired UTIs with over 5,000,000 individually-resolved records of antibiotic purchases, we identify strong associations of antibiotic resistance with the demographics, records of past urine cultures and history of drug purchases of the patients. When combined together, these associations allow for machine learning-based personalized drug-specific predictions of antibiotic resistance, thereby enabling drug-prescribing algorithms that match antibiotic treatment recommendation to the expected resistance of each sample. Applying these algorithms retrospectively, over a one-year test period, we find that they much reduce the risk of mismatched treatment compared to the current standard-of-care. The clinical application of such algorithms may help improve the effectiveness of antimicrobial treatments. Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:

The presence and role of melatonin in plants are still under debate owing to difficulties of identification and quantification. Accordingly, although it has been frequently proposed that melatonin acts as an antioxidant in phototrophic organisms, experimental data on its physiological role are scarce. This study describes the use of a rapid and simple new method for quantification of melatonin in the marine macroalga Ulva sp., organisms routinely exposed to tide-related environmental stresses and known for their high tolerance to abiotic conditions. The method was used here to show that exposure to oxidative stress-inducing environmental conditions (elevated temperature and heavy metals) induced a rise in melatonin level in the algae. Addition of exogenous melatonin alleviated the algae from cadmium-induced stress. Interestingly, although the algae were taken from a culture growing free floating and kept under constant photoperiod and water level, they exhibited a semi-lunar rhythm of melatonin levels that correlated with predicted spring tides. The correlation can probably be interpreted as reflecting preparation for predicted low tides, when the algae are exposed to increasing temperature, desiccation, and salinity, all known to induce oxidative stress. Given the simplicity of the described method it can easily be adapted for the study of melatonin in many other phototrophic organisms. These results provide, for the first time, experimental data that support both an antioxidant role for melatonin and its semi-lunar rhythm in macroalgae.

In cyanobacteria, photoprotection from overexcitation of photochemical centers can be obtained by excitation energy dissipation at the level of the phycobilisome (PBS), the cyanobacterial antenna, induced by the orange carotenoid protein (OCP). A single photoactivated OCP bound to the core of the PBS affords almost total energy dissipation. The precise mechanism of OCP energy dissipation is yet to be fully determined, and one question is how the carotenoid can approach any core phycocyanobilin chromophore at a distance that can promote efficient energy quenching. We have performed intersubunit cross-linking using glutaraldehyde of the OCP and PBS followed by liquid chromatography coupled to tandem mass spectrometry (LC/MS-MS) to identify cross-linked residues. The only residues of the OCP that cross-link with the PBS are situated in the linker region, between the N-and C-terminal domains and a single C-terminal residue. These links have enabled us to construct a model of the site of OCP binding that differs from previous models. We suggest that the N-terminal domain of the OCP burrows tightly into the PBS while leaving the OCP C-terminal domain on the exterior of the complex. Further analysis shows that the position of the small core linker protein ApcC is shifted within the cylinder cavity, serving to stabilize the interaction between the OCP and the PBS. This is confirmed by a ΔApcC mutant. Penetration of the N-terminal domain can bring the OCP carotenoid to within 5-10 Å of core chromophores; however, alteration of the core structure may be the actual source of energy dissipation.photosynthesis | light harvesting | nonphotochemical quenching | cyanobacteria | cross-linking E xcess energy arriving at photochemical reaction centers (RCs) can be detrimental (1, 2), leading to loss of photosynthetic viability (photoinhibition; PI) (3, 4). Cyanobacteria have evolved several mechanisms for energy dissipation to deal with overexcitation (5, 6). The major light-harvesting complex (LHC) in cyanobacteria is the phycobilisome (PBS), a giant complex that can functionally transfer energy to two to four RCs (7-9). If overexcitation occurs rapidly, photoprotection can be achieved by decreasing the energy arriving at the RCs by increasing energy thermal dissipation (nonphotochemical quenching; NPQ) or by physical (or functional) disconnection of the PBS (10, 11). In most cyanobacterial species, NPQ is obtained by the presence a 35-kDa, water-soluble, orange carotenoid protein (OCP) (12, 13). OCP-dependent NPQ was shown to be induced (14, 15) by strong blue-green light. The OCP has two states-an inactive resting orange state (OCP O ) and an active red state (OCP R ). Upon illumination, the OCP undergoes carotenoid and protein conformational changes to yield the metastable OCP R (16). The OCP R binds to the PBS and significantly quenches excitation energy and PBS fluorescence (17). The OCP noncovalently binds a single keto-carotenoid chromophore, 3′-hydroxyechinenone (hECN). The high-resolution crystal structure of the OCP O [...

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Background:The phycobilisome is assembled from many subunits, but the entire structure has not been determined. Results: Coupled cross-linking/MS revealed neighboring residues within the interfaces between subunits. Conclusion: The rods completely cover the core cylinders and are not in a staggered assembly form. Significance: Energy transfer from rods to cores overcomes a jump of over 30 Å without loss of efficiency.

Metabolic conversions allow organisms to produce a set of essential metabolites from the available nutrients in an environment, frequently requiring metabolic exchanges among co-inhabiting organisms. Genomic-based metabolic simulations are being increasingly applied for exploring metabolic capacities, considering different environments and different combinations of microorganisms. NetMet is a web-based tool and a software package for predicting the metabolic performances of microorganisms and their corresponding combinations in user-defined environments. The algorithm takes, as input, lists of (i) species-specific enzymatic reactions (EC numbers), and (ii) relevant metabolic environments. The algorithm generates, as output, lists of (i) compounds that individual species can produce in each given environment, and (ii) compounds that are predicted to be produced through complementary interactions. The tool is demonstrated in two case studies. First, we compared the metabolic capacities of different haplotypes of the obligatory fruit and vegetable pathogen Candidatus Liberibacter solanacearum to those of their culturable taxonomic relative Liberibacter crescens. Second, we demonstrated the potential production of complementary metabolites by pairwise combinations of co-occurring endosymbionts of the plant phloem-feeding whitefly Bemisia tabaci.

The study of microbial activity can be viewed as a triangle with three sides: environment (dominant resources in a specific habitat), community (species dictating a repertoire of metabolic conversions) and function (production and/or utilization of resources and compounds). Advances in metagenomics enable a high-resolution description of complex microbial communities in their natural environments and support a systematic study of environment-community-function associations. NetCom is a web-tool for predicting metabolic activities of microbial communities based on network-based interpretation of assembled and annotated metagenomics data. The algorithm takes as an input, lists of differentially abundant enzymatic reactions and generates the following outputs: (i) pathway associations of differently abundant enzymes; (ii) prediction of environmental resources that are unique to each treatment, and their pathway associations; (iii) prediction of compounds that are produced by the microbial community, and pathway association of compounds that are treatment-specific; (iv) network visualization of enzymes, environmental resources and produced compounds, that are treatment specific (2 and 3D). The tool is demonstrated on metagenomic data from rhizosphere and bulk soil samples. By predicting root-specific activities, we illustrate the relevance of our framework for forecasting the impact of soil amendments on the corresponding microbial communities. NetCom is available online.

Background The design of ecologically sustainable and plant-beneficial soil systems is a key goal in actively manipulating root-associated microbiomes. Community engineering efforts commonly seek to harness the potential of the indigenous microbiome through substrate-mediated recruitment of beneficial members. In most sustainable practices, microbial recruitment mechanisms rely on the application of complex organic mixtures where the resources/metabolites that act as direct stimulants of beneficial groups are not characterized. Outcomes of such indirect amendments are unpredictable regarding engineering the microbiome and achieving a plant-beneficial environment. Results This study applied network analysis of metagenomics data to explore amendment-derived transformations in the soil microbiome, which lead to the suppression of pathogens affecting apple root systems. Shotgun metagenomic analysis was conducted with data from ‘sick’ vs ‘healthy/recovered’ rhizosphere soil microbiomes. The data was then converted into community-level metabolic networks. Simulations examined the functional contribution of treatment-associated taxonomic groups and linked them with specific amendment-induced metabolites. This analysis enabled the selection of specific metabolites that were predicted to amplify or diminish the abundance of targeted microbes functional in the healthy soil system. Many of these predictions were corroborated by experimental evidence from the literature. The potential of two of these metabolites (dopamine and vitamin B12) to either stimulate or suppress targeted microbial groups was evaluated in a follow-up set of soil microcosm experiments. The results corroborated the stimulant’s potential (but not the suppressor) to act as a modulator of plant beneficial bacteria, paving the way for future development of knowledge-based (rather than trial and error) metabolic-defined amendments. Our pipeline for generating predictions for the selective targeting of microbial groups based on processing assembled and annotated metagenomics data is available at https://github.com/ot483/NetCom2. Conclusions This research demonstrates how genomic-based algorithms can be used to formulate testable hypotheses for strategically engineering the rhizosphere microbiome by identifying specific compounds, which may act as selective modulators of microbial communities. Applying this framework to reduce unpredictable elements in amendment-based solutions promotes the development of ecologically-sound methods for re-establishing a functional microbiome in agro and other ecosystems.