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Halide perovskites are promising photovoltaic (PV) materials with the potential to lower the cost of electricity and greatly expand the penetration of PV if they can demonstrate long-term stability under illumination in the presence of moisture and oxygen. The solar cell service lifetime, as quantified by T80 (the time required for the power conversion efficiency to drop to 80% of its starting value), for utility, commercial, or residential PV systems needs to be several decades in order to yield low-cost electricity, and thus it is not practical to directly measure it. It would be useful if T80 could be predicted from the initial dynamics of a solar cell's performance, but until now no models have been developed to do so. In this work, we report the development of machine learning models to predict T80 of ITO/NiOx/CH3NH3PbI3/C60/BCP/Ag solar cells operating at maximum power point under 1-sun equivalent photon flux in air at varying temperatures and relative humidities. Efficiency losses are driven by short-circuit current and fill factor, indicating that photochemical reactions with O2 and H2O are a major contributor to degradation. Spatial patterns evident from in situ dark field optical microscopy also suggest that the electric field gradient at device edges plays a significant role in perovskite decomposition. Models are trained using a menu of features from three distinct categories: (i) measurements of the initial rates of change of device parameters, (ii) ambient conditions during operation (temperature & partial pressure of H2O), and (iii) features based on underlying physics and chemistry. We show that a theory-based physiochemical feature derived from a model of the chemical reaction kinetics of the rate of degradation of CH3NH3PbI3 is particularly valuable for prediction and was selected as the most dominant feature in the best performing models. With a dataset consisting of 45 degradation experiments with T80 values ranging over a factor of almost 30, the model predicts T80 with an average accuracy of about 40% on samples not used in training. This hybrid ML approach should be effective when applied to other compositions, device architectures, and advanced packaging schemes. [ABSTRACT FROM AUTHOR]

Stability of proteins at high temperature has been a topic of interest for many years, as this attribute is favourable for applications ranging from therapeutics to industrial chemical manufacturing. Our current understanding and methods for designing high-temperature stability into target proteins are inadequate. To drive innovation in this space, we have curated a large dataset, learn2thermDB, of protein-temperature examples, totalling 24 million instances, and paired proteins across temperatures based on homology, yielding 69 million protein pairs - orders of magnitude larger than the current largest. This important step of pairing allows for study of high-temperature stability in a sequence-dependent manner in the big data era. The data pipeline is parameterized and open, allowing it to be tuned by downstream users. We further show that the data contains signal for deep learning. This data offers a new doorway towards thermal stability design models. [ABSTRACT FROM AUTHOR]

Mass-Suite (MSS) is a Python-based, open-source software package designed to analyze high-resolution mass spectrometry (HRMS)-based non-targeted analysis (NTA) data, particularly for water quality assessment and other environmental applications. MSS provides flexible, user-defined workflows for HRMS data processing and analysis, including both basic functions (e.g., feature extraction, data reduction, feature annotation, data visualization, and statistical analyses) and advanced exploratory data mining and predictive modeling capabilities that are not provided by currently available open-source software (e.g., unsupervised clustering analyses, a machine learning-based source tracking and apportionment tool). As a key advance, most core MSS functions are supported by machine learning algorithms (e.g., clustering algorithms and predictive modeling algorithms) to facilitate function accuracy and/or efficiency. MSS reliability was validated with mixed chemical standards of known composition, with 99.5% feature extraction accuracy and ~ 52% overlap of extracted features relative to other open-source software tools. Example user cases of laboratory data evaluation are provided to illustrate MSS functionalities and demonstrate reliability. MSS expands available HRMS data analysis workflows for water quality evaluation and environmental forensics, and is readily integrated with existing capabilities. As an open-source package, we anticipate further development of improved data analysis capabilities in collaboration with interested users. [ABSTRACT FROM AUTHOR]

The rapid increase of the potent greenhouse gas methane in the atmosphere creates great urgency to develop and deploy technologies for methane mitigation. One approach to removing methane is to use bacteria for which methane is their carbon and energy source (methanotrophs). Such bacteria naturally convert methane to CO2 and biomass, a value-added product and a cobenefit of methane removal. Typically, methanotrophs grow best at around 5,000 to 10,000 ppm methane, but methane in the atmosphere is 1.9 ppm. Air above emission sites such as landfills, anaerobic digestor effluents, rice paddy effluents, and oil and gas wells contains elevated methane in the 500 ppm range. If such sites are targeted for methane removal, technology harnessing aerobic methanotroph metabolism has the potential to become economically and environmentally viable. The first step in developing such methane removal technology is to identify methanotrophs with enhanced ability to grow and consume methane at 500 ppm and lower. We report here that some existing methanotrophic strains grow well at 500 ppm methane, and one of them, Methylotuvimicrobium buryatense 5GB1C, consumes such low methane at enhanced rates compared to previously published values. Analyses of bioreactor-based performance and RNAseq-based transcriptomics suggest that this ability to utilize low methane is based at least in part on extremely low non-growth-associated maintenance energy and on high methane specific affinity. This bacterium is a candidate to develop technology for methane removal at emission sites. If appropriately scaled, such technology has the potential to slow global warming by 2050. [ABSTRACT FROM AUTHOR]

We describe experiments that follow species dynamics and gene expression patterns in synthetic bacterial communities including species that compete for the single carbon substrate supplied, methane, and species unable to consume methane, which could only succeed through cooperative interactions. We demonstrate that these communities mostly select for two functional guilds, methanotrophs of the family Methylococcaceae and non-methanotrophic methylotrophs of the family Methylophilaceae, these taxonomic guilds outcompeting all other species included in the synthetic mix. The metatranscriptomics analysis uncovered that in both Methylococcaceae and Methylophilaceae, some of the most highly transcribed genes were the ones encoding methanol dehydrogenases (MDH). Remarkably, expression of alternative MDH genes (mxaFI versus xoxF), previously shown to be subjects to the rare Earth element switch, was found to depend on environmental conditions such as nitrogen source and methane and O2 partial pressures, and also to be species- specific. Along with the xoxF genes, genes encoding divergent cytochromes were highly expressed in both Methylophilaceae and Methylococcaceae, suggesting their function in methanol metabolism, likely encoding proteins serving as electron acceptors from XoxF enzymes. The research presented tested a synthetic community model that is much simplified compared to natural communities consuming methane, but more complex than the previously utilized two-species model. The performance of this model identifies prominent species for future synthetic ecology experiments and highlights both advantages of this approach and the challenges that it presents. [ABSTRACT FROM AUTHOR]

Many bacterial infections involve polymicrobial communities in which constituent organisms are synergistically pathogenic. Periodontitis, a commonly occurring chronic inflammatory disorder, is induced by multispecies bacterial communities. The periodontal keystone pathogen Porphyromonas gingivalis and the accessory pathogen Streptococcus gordonii exhibit polymicrobial synergy in animal models of disease. Mechanisms of co-adhesion and community formation by P. gingivalis and S. gordonii are well-established; however, little is known regarding the basis for increased pathogenicity. In this study we used time-coursed RNA-Seq to comprehensively and quantitatively examine the dynamic transcriptional landscape of P. gingivalis in amodel consortiumwith S. gordonii. Genes encoding a number of potential virulence determinants had higher relative mRNA levels in the context of dual species model communities than P. gingivalis alone, including adhesins, the Type IX secretion apparatus, and tetratricopeptide repeat (TPR) motif proteins. In contrast, genes encoding conjugation systems and many of the stress responses showed lower levels of expression in P. gingivalis. A notable exception to reduced abundance of stress response transcripts was the genes encoding components of the oxidative stress-related OxyR regulon, indicating an adaptation of P. gingivalis to detoxify peroxide produced by the streptococcus. Collectively, the results are consistent with evolutionary adaptation of P. gingivalis to a polymicrobial oral environment, one outcome of which is increased pathogenic potential. [ABSTRACT FROM AUTHOR]

The utilization of methane, a potent greenhouse gas, is an important component of local and global carbon cycles that is characterized by tight linkages between methane-utilizing (methanotrophic) and nonmethanotrophic bacteria. It has been suggested that the methanotroph sustains these nonmethanotrophs by cross-feeding, because subsequent products of the methane oxidation pathway, such as methanol, represent alternative carbon sources. We established cocultures in a microcosm model system to determine the mechanism and substrate that underlay the observed cross-feeding in the environment. Lanthanum, a rare earth element, was applied because of its increasing importance in methylotrophy. We used co-occurring strains isolated from Lake Washington sediment that are involved in methane utilization: a methanotroph and two nonmethanotrophic methylotrophs. Gene-expression profiles and mutant analyses suggest that methanol is the dominant carbon and energy source the methanotroph provides to support growth of the nonmethanotrophs. However, in the presence of the nonmethanotroph, gene expression of the dominant methanol dehydrogenase (MDH) shifts from the lanthanide-dependent MDH (XoxF)-type, to the calcium-dependent MDH (MxaF)-type. Correspondingly, methanol is released into the medium only when the methanotroph expresses the MxaF-type MDH. These results suggest a cross-feeding mechanism in which the nonmethanotrophic partner induces a change in expression of methanotrophMDHs, resulting in release of methanol for its growth. This partner-induced change in gene expression that benefits the partner is a paradigm for microbial interactions that cannot be observed in studies of pure cultures, underscoring the importance of synthetic microbial community approaches to understand environmental microbiomes. [ABSTRACT FROM AUTHOR]

Background: Oral biofilm studies based on simplified experimental setups are difficult to interpret. Models are limited mostly by the number of bacterial species observed and the insufficiency of artificial media. Few studies have attempted to overcome these limitations and to cultivate native oral biofilm. Aims: This study aimed to grow oral biofilm in vivo before transfer to a biofilm reactor for ex situ incubation. The in vitro survival of this oral biofilm and the changes in bacterial composition over time were observed. Methods: Six human enamel-dentin slabs embedded buccally in dental splints were used as biofilm carriers. Fitted individually to the upper jaw of 25 non-smoking male volunteers, the splints were worn continuously for 48 h. During this time, tooth-brushing and alcohol-consumption were not permitted. The biofilm was then transferred on slabs into a biofilm reactor and incubated there for 48 h while being nourished in BHI medium. Live/dead staining and confocal laser scanning microscopy were used to observe bacterial survival over four points in time: directly after removal (T0) and after 1 (T1), 24 (T2), and 48 h (T3) of incubation. Bacterial diversity at T0 and T3 was compared with 454-pyrosequencing. Fluorescence in situ hybridization (FISH) was performed to show specific taxa. Survival curves were calculated with a specially designed MATLAB script. Acacia and QIIME 1.9.1 were used to process pyrosequencing data. SPSS 21.0 and R 3.3.1 were used for statistical analysis. Results: After initial fluctuations at T1, survival curves mostly showed approximation of the bacterial numbers to the initial level at T3. Pyrosequencing analysis resulted in 117 OTUs common to all samples. The genera Streptococcus and Veillonella (both Firmicutes) dominated at T0 and T3. They make up two thirds of the biofilm. Genera with lower relative abundance had grown significantly at T3. FISH analysis confirmed the pyrosequencing results, i.e., the predominant staining of Firmicutes. Conclusion: We demonstrate the in vitro survival of native primary oral biofilm in its natural complexity over 48 h. Our results offer a baseline for cultivation studies of native oral biofilms in (phyto-) pharmacological and dental materials research. Further investigations and validation of culturing conditions could also facilitate the study of biofilm-induced diseases. [ABSTRACT FROM AUTHOR]

Background: Two variants of Methylobacterium extorquens AM1 demonstrated a trade-off between growth rate and biomass yield. In addition, growth rate and biomass yield were also affected by supplementation of growth medium with different amounts of cobalt. The metabolism changes relating to these growth phenomena as well as the trade-off were investigated in this study. 13C metabolic flux analysis was used to generate a detailed central carbon metabolic flux map with both absolute and normalized flux values. Results: The major differences between the two variants occurred at the formate node as well as within C3-C4 inter-conversion pathways. Higher relative fluxes through formyltetrahydrofolate ligase, phosphoenolpyruvate carboxylase, and malic enzyme led to higher biomass yield, while higher relative fluxes through pyruvate kinase and pyruvate dehydrogenase led to higher growth rate. These results were then tested by phenotypic studies on three mutants (null pyk, null pck mutant and null dme mutant) in both variants, which agreed with the model prediction. Conclusions: In this study, 13C metabolic flux analysis for two strain variants of M. extorquens AM1 successfully identified metabolic pathways contributing to the trade-off between cell growth and biomass yield. Phenotypic analysis of mutants deficient in corresponding genes supported the conclusion that C3-C4 inter-conversion strategies were the major response to the trade-off. [ABSTRACT FROM AUTHOR]

In this perspective article, we question how well model organisms, the ones that are easy to cultivate in the laboratory and that show robust growth and biomass accumulation, reflect the dynamics and interactions of microbial communities observed in nature. Today's -omics toolbox allows assessing the genomic potential of microbes in natural environments in a high-throughput fashion and at a strain-level resolution. However, understanding of the details of microbial activities and of the mechanistic bases of community function still requires experimental validation in simplified and fully controlled systems such as synthetic communities. We have studied methane utilization in Lake Washington sediment for a few decades and have identified a number of species genetically equipped for this activity. We have also identified co-occurring satellite species that appear to form functional communities together with the methanotrophs. Here, we compare experimental findings from manipulation of natural communities involved in metabolism of methane in this niche with findings from manipulation of synthetic communities assembled in the laboratory of species originating from the same study site, from very simple (two-species) to rather complex (50-species) synthetic communities. We observe some common trends in community dynamics between the two types of communities, toward representation of specific functional guilds. However, we also identify strong discrepancies between the dominant methane oxidizers in synthetic communities compared to natural communities, under similar incubation conditions. These findings highlight the challenges that exist in using the synthetic community approach to modeling dynamics and species interactions in natural communities. [ABSTRACT FROM AUTHOR]

The authors provide an overview of core areas of data science, discuss application areas from within chemical engineering research and conclude on how data science principles can be included in their training. They cite an emerging set of tasks needed by previous chemical engineers to manage huge date set consisting of ensembles of spatiotemporal data, sensibly read data in a computationally scalable manner and extract knowledge with techniques whose statistical reliability can be quantified.

Background: The toxicity of alcohols is one of the major roadblocks of biological fermentation for biofuels production. Methylobacterium extorquens AM1, a facultative methylotrophic a-proteobacterium, has been engineered to generate 1-butanol from cheap carbon feedstocks through a synthetic metabolic pathway. However, M. extorquens AM1 is vulnerable to solvent stress, which impedes further development for 1-butanol production. Only a few studies have reported the general stress response of M. extorquens AM1 to solvent stress. Therefore, it is highly desirable to obtain a strain with ameliorated 1-butanol tolerance and elucidate the molecular mechanism of 1-butnaol tolerance in M. extorquens AM1 for future strain improvement. Results: In this work, adaptive laboratory evolution was used as a tool to isolate mutants with 1-butanol tolerance up to 0.5 %. The evolved strains, BHBT3 and BHBT5, demonstrated increased growth rates and higher survival rates with the existence of 1-butanol. Whole genome sequencing revealed a SNP mutation at kefB in BHBT5, which was confirmed to be responsible for increasing 1-butanol tolerance through an allelic exchange experiment. Global metabolomic analysis further discovered that the pools of multiple key metabolites, including fatty acids, amino acids, and disaccharides, were increased in BHBT5 in response to 1-butanol stress. Additionally, the carotenoid synthesis pathway was significantly down-regulated in BHBT5. Conclusions: We successfully screened mutants resistant to 1-butanol and provided insights into the molecular mechanism of 1-butanol tolerance in M. extorquens AM1. This research will be useful for uncovering the mechanism of cellular response of M. extorquens AM1 to solvent stress, and will provide the genetic blueprint for the rational design of a strain of M. extorquens AM1 with increased 1-butanol tolerance in the future. [ABSTRACT FROM AUTHOR]

Background: Methane-utilizing bacteria (methanotrophs) are capable of growth on methane and are attractive systems for bio-catalysis. However, the application of natural methanotrophic strains to large-scale production of value-added chemicals/biofuels requires a number of physiological and genetic alterations. An accurate metabolic model coupled with flux balance analysis can provide a solid interpretative framework for experimental data analyses and integration. Results: A stoichiometric flux balance model of Methylomicrobium buryatense strain 5G(B1) was constructed and used for evaluating metabolic engineering strategies for biofuels and chemical production with a methanotrophic bacterium as the catalytic platform. The initial metabolic reconstruction was based on whole-genome predictions. Each metabolic step was manually verified, gapfilled, and modified in accordance with genome-wide expression data. The final model incorporates a total of 841 reactions (in 167 metabolic pathways). Of these, up to 400 reactions were recruited to produce 118 intracellular metabolites. The flux balance simulations suggest that only the transfer of electrons from methanol oxidation to methane oxidation steps can support measured growth and methane/oxygen consumption parameters, while the scenario employing NADH as a possible source of electrons for particulate methane monooxygenase cannot. Direct coupling between methane oxidation and methanol oxidation accounts for most of the membrane-associated methane monooxygenase activity. However the best fit to experimental results is achieved only after assuming that the efficiency of direct coupling depends on growth conditions and additional NADH input (about 0.1-0.2 mol of incremental NADH per one mol of methane oxidized). The additional input is proposed to cover loss of electrons through inefficiency and to sustain methane oxidation at perturbations or support uphill electron transfer. Finally, the model was used for testing the carbon conversion efficiency of different pathways for C1-utilization, including different variants of the ribulose monophosphate pathway and the serine cycle. Conclusion: We demonstrate that the metabolic model can provide an effective tool for predicting metabolic parameters for different nutrients and genetic perturbations, and as such, should be valuable for metabolic engineering of the central metabolism of M. buryatense strains. [ABSTRACT FROM AUTHOR]

The ecophysiology of long-chain fatty acid-degrading syntrophic β-oxidizing bacteria has been poorly understood due to a lack of quantitative abundance data. Here, TaqMan quantitative PCR (qPCR) assays targeting the 16S rRNA gene of the known mesophilic syntrophic β-oxidizing bacterial genera Syntrophomonas and Syntrophus were developed and validated. Microbial community dynamics were followed using qPCR and Illumina-based high-throughput amplicon sequencing in triplicate methanogenic bioreactors subjected to five consecutive batch feedings of oleic acid. With repeated oleic acid feeding, the initial specific methane production rate significantly increased along with the relative abundances of Syntrophomonas and methanogenic archaea in the bioreactor communities. The novel qPCR assays showed that Syntrophomonas increased from 7 to 31% of the bacterial community 16S rRNA gene concentration, whereas that of Syntrophus decreased from 0.02 to less than 0.005%. High-throughput amplicon sequencing also revealed that Syntrophomonas became the dominant genus within the bioreactor microbiomes. These results suggest that increased specific mineralization rates of oleic acid were attributed to quantitative shifts within the microbial communities toward higher abundances of syntrophic β-oxidizing bacteria and methanogenic archaea. The novel qPCR assays targeting syntrophic β-oxidizing bacteria may thus serve as monitoring tools to indicate the fatty acid β-oxidization potential of anaerobic digester communities. [ABSTRACT FROM AUTHOR]

We sequenced the genomes of 19 methylotrophic isolates from Lake Washington, which belong to nine genera within eight families of the A lphaproteobacteria, two of the families being the newly proposed families. Comparative genomic analysis with a focus on methylotrophy metabolism classifies these strains into heterotrophic and obligately or facultatively autotrophic methylotrophs. The most persistent metabolic modules enabling methylotrophy within this group are the N-methylglutamate pathway, the two types of methanol dehydrogenase ( MxaFI and XoxF), the tetrahydromethanopterin pathway for formaldehyde oxidation, the serine cycle and the ethylmalonyl- CoA pathway. At the same time, a great potential for metabolic flexibility within this group is uncovered, with different combinations of these modules present. Phylogenetic analysis of key methylotrophy functions reveals that the serine cycle must have evolved independently in at least four lineages of A lphaproteobacteria and that all methylotrophy modules seem to be prone to lateral transfers as well as deletions. [ABSTRACT FROM AUTHOR]

We describe five novel Methylophilaceae ecotypes from a single ecological niche in Lake Washington, USA, and compare them to three previously described ecotypes, in terms of their phenotype and genome sequence divergence. Two of the ecotypes appear to represent novel genera within the Methylophilaceae. Genome-based metabolic reconstruction highlights metabolic versatility of Methylophilaceae with respect to methylotrophy and nitrogen metabolism, different ecotypes possessing different combinations of primary substrate oxidation systems (MxaFI-type methanol dehydrogenase versus XoxF-type methanol dehydrogenase; methylamine dehydrogenase versus N-methylglutamate pathway) and different potentials for denitrification (assimilatory versus respiratory nitrate reduction). By comparing pairs of closely related genomes, we uncover that site-specific recombination is the main means of genomic evolution and strain divergence, including lateral transfers of genes from both closely- and distantly related taxa. The new ecotypes and the new genomes contribute significantly to our understanding of the extent of genomic and metabolic diversity among organisms of the same family inhabiting the same ecological niche. These organisms also provide novel experimental models for studying the complexity and the function of the microbial communities active in methylotrophy. [ABSTRACT FROM AUTHOR]

Molecular dynamics simulations of protein folding or unfolding, unlike most in vitro experimental methods, are performed on a single molecule. The effects of neighboring molecules on the unfolding/ folding pathway are largely ignored experimentally and simply not modeled computationally. Here, we present two all-atom, explicit solvent molecular dynamics simulations of 32 copies of the Engrailed homeodomain (EnHD), an ultrafast-folding and -unfolding protein for which the folding/unfolding pathway is well-characterized. These multimolecule simulations, in comparison with single-mole-cule simulations and experimental data, show that intermolecular interactions have little effect on the folding/unfolding pathway. EnHD unfolded by the same mechanism whether it was simulated in only water or also in the presence of other EnHD molecules. It populated the same native state, transition state, and folding intermediate in both simulation systems, and was in good agree-ment with experimental data available for each of the three states. Unfolding was slowed slightly by interactions with neighboring proteins, which were mostly hydrophobic in nature and ultimately caused the proteins to aggregate. Protein-water hydrogen bonds were also replaced with protein-protein hydrogen bonds, addition-ally contributing to aggregation. Despite the increase in protein-pro-tein interactions, the protein aggregates formed in simulation did not do so at the total exclusion of water. These simulations support the use of single-molecule techniques to study protein unfolding and also provide insight into the types of interactions that occur as pro-teins aggregate at high temperature at an atomic level. [ABSTRACT FROM AUTHOR]

Porphyromonas gingivalis is a major etiological agent in chronic and aggressive forms of periodontal disease. The organism is an asaccharolytic anaerobe and is a constituent of mixed species biofilms in a variety of microenvironments in the oral cavity. P. gingivalis expresses a range of virulence factors over which it exerts tight control. High-throughput sequencing technologies provide the opportunity to relate functional genomics to basic biology. In this study we report qualitative and quantitative RNA-Seq analysis of the transcriptome of P. gingivalis. We have also applied RNA-Seq to the transcriptome of a ΔluxS mutant of P. gingivalis deficient in AI-2-mediated bacterial communication. The transcriptome analysis confirmed the expression of all predicted ORFs for strain ATCC 33277, including 854 hypothetical proteins, and allowed the identification of hitherto unknown transcriptional units. Twelve non-coding RNAs were identified, including 11 small RNAs and one cobalamin riboswitch. Fifty-seven genes were differentially regulated in the LuxS mutant. Addition of exogenous synthetic 4,5-dihydroxy-2,3-pentanedione (DPD, AI-2 precursor) to the ΔluxS mutant culture complemented expression of a subset of genes, indicating that LuxS is involved in both AI-2 signaling and non-signaling dependent systems in P. gingivalis. This work provides an important dataset for future study of P. gingivalis pathophysiology and further defines the LuxS regulon in this oral pathogen. [ABSTRACT FROM AUTHOR]

Porphyromonas gingivalis is a major etiological agent and chronic and aggressive forms of periodontal disease. The organism is an assacharolytic anaerobe and is a constituent of mixed species biofilms in a variety of microenvironments in the oral cavity. P. gingivalis expresses a range of virulence factors over which it exerts tight control. High-throughput sequencing technologies provide the opportunity to relate functional genomics to basic biology. In this study we report qualitative and quantitative RNA-Seq analysis of the transcriptome of P. gingivalis. We have also applied RNA-Seq to the transcriptome of a ΔluxS mutant of P. gingivalis deficient in AI-2-mediated bacterial communication. The transcriptome analysis confirmed the expression of all predicted ORFs for strain ATCC 33277, including 854 hypothetical proteins, and allowed the identification of hitherto unknown transcriptional units. Twelve noncoding RNAs were identified, including 11 small RNAs and one cobalamin riboswitch. Fifty seven genes were differentially regulated in the LuxS mutant. Addition of exogenous synthetic 4,5- dihydroxy-2,3-pentanedione (DPD, AI-2 precursor) to the ΔluxS mutant culture complemented expression of a subset of genes, indicating that LuxS is involved in both AI-2 signaling and non-signaling dependent systems in P. gingivalis. This work provides an important dataset for future study of P. gingivalis pathophysiology and further defines the LuxS regulon in this oral pathogen. [ABSTRACT FROM AUTHOR]

Methylotrophs, organisms able to gain energy and carbon from compounds containing no carbon–carbon bonds, such as methane, methanol and methylated amines, are widespread in nature. However, knowledge of their nutrient preference and their metabolism is mostly based on experiments with cultures grown in defined laboratory conditions. Here, we use transcriptomics to explore the activity of one methylotroph, Methyotenera mobilis in its natural environment, lake sediment from which it has been previously isolated. Cells encapsulated in incubation cassettes were exposed to sediment conditions, with or without supplementation with a carbon/energy source (methylamine), and gene-expression patterns were compared for those cells to patterns for cells incubated in a defined medium supplemented with methylamine. A few specific trends in gene expression were observed at in situ conditions that may be of environmental significance, as follows. Expression of genes for the linear formaldehyde oxidation pathway linked to tetrahydromethanopterin increased, suggesting an important role for this pathway in situ, in contrast to laboratory condition culture, in which the cyclic ribulose monophosphate pathway seemed to be the major route for formaldehyde oxidation. Along with the ribulose monophosphate cycle that is also a major pathway for assimilating C1 units, the methylcitric acid cycle seemd to be important in situ, suggesting that multicarbon compounds may be the natural carbon and/or energy substrates for M. mobilis, challenging the notion of an obligately methylotrophic lifestyle for this bacterium. We also detected a major switch in expression of genes responsible for the mode of motility between different conditions: from flagellum-enabled motility in defined medium to in situ expression of pili known to be involved in twitching motility and adherence. Overall, this study offers a novel approach for gaining insights into the lifestyle of individual microbes in their native environments. [ABSTRACT FROM AUTHOR]

Here, we compare the distributions of main chain (ΦΨ) angles (i.e., Ramachandran maps) of the 20 naturally occurring amino acids in three contexts: (i) molecular dynamics (MD) simulations of Gly-Gly-X-Gly-Gly pentapeptides in water at 298 K with exhaustive sampling, where X = the amino acid in question; (ii) 188 independent protein simulations in water at 298 K from our Dynameomics Project; and (iii) static crystal and NMR structures from the Protein Data Bank. The GGXGG peptide series is often used as a model of the unstructured denatured state of proteins. The sampling in the peptide MD simulations is neither random nor uniform. Instead, individual amino acids show preferences for particular conformations, but the peptide is dynamic, and interconversion between conformers is facile. For a given amino acid, the (ΦΨ) distributions in the protein simulations and the Protein Data Bank are very similar and often distinct from those in the peptide simulations. Comparison between the peptide and protein simulations shows that packing constraints, solvation. and the tendency for particular amino acids to be used for specific structural motifs can overwhelm the "intrinsic propensities" of amino acids for particular (4,'I') conformations. We also compare our helical propensities with experimental consensus values using the host-guest method, which appear to be determined largely by context and not necessarily the intrinsic conformational propensities of the guest residues. These simulations represent an improved coil library free from contextual effects to better model intrinsic conformational propensities and provide a detailed view of conformations making up the "random coil" state. [ABSTRACT FROM AUTHOR]