Evaluating the performance of random forest and iterative random forest based methods when applied to gene expression data.

Gene-to-gene networks, such as Gene Regulatory Networks (GRN) and Predictive Expression Networks (PEN) capture relationships between genes and are beneficial for use in downstream biological analyses. There exists multiple network inference tools to produce these gene-to-gene networks from matrices of gene expression data. Random Forest-Leave One Out Prediction (RF-LOOP) is a method that has been shown to be efficient at producing these gene-to-gene networks, frequently known as GEne Network Inference with Ensemble of trees (GENIE3). Random Forest can be replaced in this process by iterative Random Forest (iRF), which performs variable selection and boosting. Here we validate that iterative Random Forest-Leave One Out Prediction (iRF-LOOP) produces higher quality networks than GENIE3 (RF-LOOP). We use both synthetic and empirical networks from the Dialogue for Reverse Engineering Assessment and Methods (DREAM) Challenges by Sage Bionetworks, as well as two additional empirical networks created from Arabidopsis thaliana and Populus trichocarpa expression data.

Antiviral Strategies Against SARS-CoV-2: A Systems Biology Approach.

The unprecedented scientific achievements in combating the COVID-19 pandemic reflect a global response informed by unprecedented access to data. We now have the ability to rapidly generate a diversity of information on an emerging pathogen and, by using high-performance computing and a systems biology approach, we can mine this wealth of information to understand the complexities of viral pathogenesis and contagion like never before. These efforts will aid in the development of vaccines, antiviral medications, and inform policymakers and clinicians. Here we detail computational protocols developed as SARS-CoV-2 began to spread across the globe. They include pathogen detection, comparative structural proteomics, evolutionary adaptation analysis via network and artificial intelligence methodologies, and multiomic integration. These protocols constitute a core framework on which to build a systems-level infrastructure that can be quickly brought to bear on future pathogens before they evolve into pandemic proportions.

Potentially adaptive SARS-CoV-2 mutations discovered with novel spatiotemporal and explainable AI models.

BACKGROUND: A mechanistic understanding of the spread of SARS-CoV-2 and diligent tracking of ongoing mutagenesis are of key importance to plan robust strategies for confining its transmission. Large numbers of available sequences and their dates of transmission provide an unprecedented opportunity to analyze evolutionary adaptation in novel ways. Addition of high-resolution structural information can reveal the functional basis of these processes at the molecular level. Integrated systems biology-directed analyses of these data layers afford valuable insights to build a global understanding of the COVID-19 pandemic. RESULTS: Here we identify globally distributed haplotypes from 15,789 SARS-CoV-2 genomes and model their success based on their duration, dispersal, and frequency in the host population. Our models identify mutations that are likely compensatory adaptive changes that allowed for rapid expansion of the virus. Functional predictions from structural analyses indicate that, contrary to previous reports, the Asp614Gly mutation in the spike glycoprotein (S) likely reduced transmission and the subsequent Pro323Leu mutation in the RNA-dependent RNA polymerase led to the precipitous spread of the virus. Our model also suggests that two mutations in the nsp13 helicase allowed for the adaptation of the virus to the Pacific Northwest of the USA. Finally, our explainable artificial intelligence algorithm identified a mutational hotspot in the sequence of S that also displays a signature of positive selection and may have implications for tissue or cell-specific expression of the virus. CONCLUSIONS: These results provide valuable insights for the development of drugs and surveillance strategies to combat the current and future pandemics.

Improved growth rate in Clostridium thermocellum hydrogenase mutant via perturbed sulfur metabolism.

BACKGROUND: Metabolic engineering is a commonly used approach to develop organisms for an industrial function, but engineering aimed at improving one phenotype can negatively impact other phenotypes. This lack of robustness can prove problematic. Cellulolytic bacterium Clostridium thermocellum is able to rapidly ferment cellulose to ethanol and other products. Recently, genes involved in H2 production, including the hydrogenase maturase hydG and NiFe hydrogenase ech, were deleted from the chromosome of C. thermocellum. While ethanol yield increased, the growth rate of ΔhydG decreased substantially compared to wild type. RESULTS: Addition of 5 mM acetate to the growth medium improved the growth rate in C. thermocellum ∆hydG, whereas wild type remained unaffected. Transcriptomic analysis of the wild type showed essentially no response to the addition of acetate. However, in C. thermocellum ΔhydG, 204 and 56 genes were significantly differentially regulated relative to wild type in the absence and presence of acetate, respectively. Genes, Clo1313_0108-0125, which are predicted to encode a sulfate transport system and sulfate assimilatory pathway, were drastically upregulated in C. thermocellum ΔhydG in the presence of added acetate. A similar pattern was seen with proteomics. Further physiological characterization demonstrated an increase in sulfide synthesis and elimination of cysteine consumption in C. thermocellum ΔhydG. Clostridium thermocellum ΔhydGΔech had a higher growth rate than ΔhydG in the absence of added acetate, and a similar but less pronounced transcriptional and physiological effect was seen in this strain upon addition of acetate. CONCLUSIONS: Sulfur metabolism is perturbed in C. thermocellum ΔhydG strains, likely to increase flux through sulfate reduction to act either as an electron sink to balance redox reactions or to offset an unknown deficiency in sulfur assimilation.

Multi-omics of permafrost, active layer and thermokarst bog soil microbiomes.

Over 20% of Earth's terrestrial surface is underlain by permafrost with vast stores of carbon that, once thawed, may represent the largest future transfer of carbon from the biosphere to the atmosphere. This process is largely dependent on microbial responses, but we know little about microbial activity in intact, let alone in thawing, permafrost. Molecular approaches have recently revealed the identities and functional gene composition of microorganisms in some permafrost soils and a rapid shift in functional gene composition during short-term thaw experiments. However, the fate of permafrost carbon depends on climatic, hydrological and microbial responses to thaw at decadal scales. Here we use the combination of several molecular 'omics' approaches to determine the phylogenetic composition of the microbial communities, including several draft genomes of novel species, their functional potential and activity in soils representing different states of thaw: intact permafrost, seasonally thawed active layer and thermokarst bog. The multi-omics strategy reveals a good correlation of process rates to omics data for dominant processes, such as methanogenesis in the bog, as well as novel survival strategies for potentially active microbes in permafrost.

New group in the Leptospirillum clade: cultivation-independent community genomics, proteomics, and transcriptomics of the new species "Leptospirillum group IV UBA BS".

Leptospirillum spp. are widespread members of acidophilic microbial communities that catalyze ferrous iron oxidation, thereby increasing sulfide mineral dissolution rates. These bacteria play important roles in environmental acidification and are harnessed for bioleaching-based metal recovery. Known members of the Leptospirillum clade of the Nitrospira phylum are Leptospirillum ferrooxidans (group I), Leptospirillum ferriphilum and "Leptospirillum rubarum" (group II), and Leptospirillum ferrodiazotrophum (group III). In the Richmond Mine acid mine drainage (AMD) system, biofilm formation is initiated by L. rubarum; L. ferrodiazotrophum appears in later developmental stages. Here we used community metagenomic data from unusual, thick floating biofilms to identify distinguishing metabolic traits in a rare and uncultivated community member, the new species "Leptospirillum group IV UBA BS." These biofilms typically also contain a variety of Archaea, Actinobacteria, and a few other Leptospirillum spp. The Leptospirillum group IV UBA BS species shares 98% 16S rRNA sequence identity and 70% average amino acid identity between orthologs with its closest relative, L. ferrodiazotrophum. The presence of nitrogen fixation and reverse tricarboxylic acid (TCA) cycle proteins suggest an autotrophic metabolism similar to that of L. ferrodiazotrophum, while hydrogenase proteins suggest anaerobic metabolism. Community transcriptomic and proteomic analyses demonstrate expression of a multicopper oxidase unique to this species, as well as hydrogenases and core metabolic genes. Results suggest that the Leptospirillum group IV UBA BS species might play important roles in carbon fixation, nitrogen fixation, hydrogen metabolism, and iron oxidation in some acidic environments.

Insights into the structure and metabolic function of microbes that shape pelagic iron-rich aggregates ("iron snow").

Microbial ferrous iron [Fe(II)] oxidation leads to the formation of iron-rich macroscopic aggregates ("iron snow") at the redoxcline in a stratified lignite mine lake in east-central Germany. We aimed to identify the abundant Fe-oxidizing and Fe-reducing microorganisms likely to be involved in the formation and transformation of iron snow present in the redoxcline in two basins of the lake that differ in their pH values. Nucleic acid- and lipid-stained microbial cells of various morphologies detected by confocal laser scanning microscopy were homogeneously distributed in all iron snow samples. The dominant iron mineral appeared to be schwertmannite, with shorter needles in the northern than in the central basin samples. Total bacterial 16S rRNA gene copies ranged from 5.0 × 10(8) copies g (dry weight)(-1) in the acidic central lake basin (pH 3.3) to 4.0 × 10(10) copies g (dry weight)(-1) in the less acidic (pH 5.9) northern basin. Total RNA-based quantitative PCR assigned up to 61% of metabolically active microbial communities to Fe-oxidizing- and Fe-reducing-related bacteria, indicating that iron metabolism was an important metabolic strategy. Molecular identification of abundant groups suggested that iron snow surfaces were formed by chemoautotrophic iron oxidizers, such as Acidimicrobium, Ferrovum, Acidithiobacillus, Thiobacillus, and Chlorobium, in the redoxcline and were rapidly colonized by heterotrophic iron reducers, such as Acidiphilium, Albidiferax-like, and Geobacter-like groups. Metaproteomics yielded 283 different proteins from northern basin iron snow samples, and protein identification provided a glimpse into some of their in situ metabolic processes, such as primary production (CO2 fixation), respiration, motility, and survival strategies.

Biostimulation induces syntrophic interactions that impact C, S and N cycling in a sediment microbial community.

Stimulation of subsurface microorganisms to induce reductive immobilization of metals is a promising approach for bioremediation, yet the overall microbial community response is typically poorly understood. Here we used proteogenomics to test the hypothesis that excess input of acetate activates complex community functioning and syntrophic interactions among autotrophs and heterotrophs. A flow-through sediment column was incubated in a groundwater well of an acetate-amended aquifer and recovered during microbial sulfate reduction. De novo reconstruction of community sequences yielded near-complete genomes of Desulfobacter (Deltaproteobacteria), Sulfurovum- and Sulfurimonas-like Epsilonproteobacteria and Bacteroidetes. Partial genomes were obtained for Clostridiales (Firmicutes) and Desulfuromonadales-like Deltaproteobacteria. The majority of proteins identified by mass spectrometry corresponded to Desulfobacter-like species, and demonstrate the role of this organism in sulfate reduction (Dsr and APS), nitrogen fixation and acetate oxidation to CO2 during amendment. Results indicate less abundant Desulfuromonadales, and possibly Bacteroidetes, also actively contributed to CO2 production via the tricarboxylic acid (TCA) cycle. Proteomic data indicate that sulfide was partially re-oxidized by Epsilonproteobacteria through nitrate-dependent sulfide oxidation (using Nap, Nir, Nos, SQR and Sox), with CO2 fixed using the reverse TCA cycle. We infer that high acetate concentrations, aimed at stimulating anaerobic heterotrophy, led to the co-enrichment of, and carbon fixation in Epsilonproteobacteria. Results give an insight into ecosystem behavior following addition of simple organic carbon to the subsurface, and demonstrate a range of biological processes and community interactions were stimulated.

The temporal analysis of yeast exponential phase using shotgun proteomics as a fermentation monitoring technique.

System biology and bioprocess technology can be better understood using shotgun proteomics as a monitoring system during the fermentation. We demonstrated a shotgun proteomic method to monitor the temporal yeast proteome in early, middle and late exponential phases. Our study identified a total of 1389 proteins combining all 2D-LC-MS/MS runs. The temporal Saccharomyces cerevisiae proteome was enriched with proteolysis, radical detoxification, translation, one-carbon metabolism, glycolysis and TCA cycle. Heat shock proteins and proteins associated with oxidative stress response were found throughout the exponential phase. The most abundant proteins observed were translation elongation factors, ribosomal proteins, chaperones and glycolytic enzymes. The high abundance of the H-protein of the glycine decarboxylase complex (Gcv3p) indicated the availability of glycine in the environment. We observed differentially expressed proteins and the induced proteins at mid-exponential phase were involved in ribosome biogenesis, mitochondria DNA binding/replication and transcriptional activator. Induction of tryptophan synthase (Trp5p) indicated the abundance of tryptophan during the fermentation. As fermentation progressed toward late exponential phase, a decrease in cell proliferation was implied from the repression of ribosomal proteins, transcription coactivators, methionine aminopeptidase and translation-associated proteins.

Sustainable syntrophic growth of Dehalococcoides ethenogenes strain 195 with Desulfovibrio vulgaris Hildenborough and Methanobacterium congolense: global transcriptomic and proteomic analyses.

Dehalococcoides ethenogenes strain 195 (DE195) was grown in a sustainable syntrophic association with Desulfovibrio vulgaris Hildenborough (DVH) as a co-culture, as well as with DVH and the hydrogenotrophic methanogen Methanobacterium congolense (MC) as a tri-culture using lactate as the sole energy and carbon source. In the co- and tri-cultures, maximum dechlorination rates of DE195 were enhanced by approximately three times (11.0±0.01 µmol per day for the co-culture and 10.1±0.3 µmol per day for the tri-culture) compared with DE195 grown alone (3.8±0.1 µmol per day). Cell yield of DE195 was enhanced in the co-culture (9.0±0.5 × 10(7) cells per µmol Cl(-) released, compared with 6.8±0.9 × 10(7) cells per µmol Cl(-) released for the pure culture), whereas no further enhancement was observed in the tri-culture (7.3±1.8 × 10(7) cells per µmol Cl(-) released). The transcriptome of DE195 grown in the co-culture was analyzed using a whole-genome microarray targeting DE195, which detected 102 significantly up- or down-regulated genes compared with DE195 grown in isolation, whereas no significant transcriptomic difference was observed between co- and tri-cultures. Proteomic analysis showed that 120 proteins were differentially expressed in the co-culture compared with DE195 grown in isolation. Physiological, transcriptomic and proteomic results indicate that the robust growth of DE195 in co- and tri-cultures is because of the advantages associated with the capabilities of DVH to ferment lactate to provide H(2) and acetate for growth, along with potential benefits from proton translocation, cobalamin-salvaging and amino acid biosynthesis, whereas MC in the tri-culture provided no significant additional benefits beyond those of DVH.

Use of label-free quantitative proteomics to distinguish the secreted cellulolytic systems of Caldicellulosiruptor bescii and Caldicellulosiruptor obsidiansis.

The extremely thermophilic, Gram-positive bacteria Caldicellulosiruptor bescii and Caldicellulosiruptor obsidiansis efficiently degrade both cellulose and hemicellulose, which makes them relevant models for lignocellulosic biomass deconstruction to produce sustainable biofuels. To identify the shared and unique features of secreted cellulolytic apparatuses from C. bescii and C. obsidiansis, label-free quantitative proteomics was used to analyze protein abundance over the course of fermentative growth on crystalline cellulose. Both organisms' secretomes consisted of more than 400 proteins, of which the most abundant were multidomain glycosidases, extracellular solute-binding proteins, flagellin, putative pectate lyases, and uncharacterized proteins with predicted secretion signals. Among the identified proteins, 53 to 57 significantly changed in abundance during cellulose fermentation in favor of glycosidases and extracellular binding proteins. Mass spectrometric characterizations, together with cellulase activity measurements, revealed a substantial abundance increase of a few bifunctional multidomain glycosidases composed of glycosidase (GH) domain family 5, 9, 10, 44, or 48 and family 3 carbohydrate binding (CBM3) modules. In addition to their orthologous cellulases, the organisms expressed unique glycosidases with different domain organizations: C. obsidiansis expressed the COB47_1671 protein with GH10/5 domains, while C. bescii expressed the Athe_1857 (GH10/48) and Athe_1859 (GH5/44) proteins. Glycosidases containing CBM3 domains were selectively enriched via binding to amorphous cellulose. Preparations from both bacteria contained highly thermostable enzymes with optimal cellulase activities at 85°C and pH 5. The C. obsidiansis preparation, however, had higher cellulase specific activity and greater thermostability. The C. bescii culture produced more extracellular protein and additional SDS-PAGE bands that demonstrated glycosidase activity.

Discovery and annotation of small proteins using genomics, proteomics, and computational approaches.

Small proteins (10-200 amino acids [aa] in length) encoded by short open reading frames (sORF) play important regulatory roles in various biological processes, including tumor progression, stress response, flowering, and hormone signaling. However, ab initio discovery of small proteins has been relatively overlooked. Recent advances in deep transcriptome sequencing make it possible to efficiently identify sORFs at the genome level. In this study, we obtained ~2.6 million expressed sequence tag (EST) reads from Populus deltoides leaf transcriptome and reconstructed full-length transcripts from the EST sequences. We identified an initial set of 12,852 sORFs encoding proteins of 10-200 aa in length. Three computational approaches were then used to enrich for bona fide protein-coding sORFs from the initial sORF set: (1) coding-potential prediction, (2) evolutionary conservation between P. deltoides and other plant species, and (3) gene family clustering within P. deltoides. As a result, a high-confidence sORF candidate set containing 1469 genes was obtained. Analysis of the protein domains, non-protein-coding RNA motifs, sequence length distribution, and protein mass spectrometry data supported this high-confidence sORF set. In the high-confidence sORF candidate set, known protein domains were identified in 1282 genes (higher-confidence sORF candidate set), out of which 611 genes, designated as highest-confidence candidate sORF set, were supported by proteomics data. Of the 611 highest-confidence candidate sORF genes, 56 were new to the current Populus genome annotation. This study not only demonstrates that there are potential sORF candidates to be annotated in sequenced genomes, but also presents an efficient strategy for discovery of sORFs in species with no genome annotation yet available.

Ecological distribution and population physiology defined by proteomics in a natural microbial community.

An important challenge in microbial ecology is developing methods that simultaneously examine the physiology of organisms at the molecular level and their ecosystem level interactions in complex natural systems. We integrated extensive proteomic, geochemical, and biological information from 28 microbial communities collected from an acid mine drainage environment and representing a range of biofilm development stages and geochemical conditions to evaluate how the physiologies of the dominant and less abundant organisms change along environmental gradients. The initial colonist dominates across all environments, but its proteome changes between two stable states as communities diversify, implying that interspecies interactions affect this organism's metabolism. Its overall physiology is robust to abiotic environmental factors, but strong correlations exist between these factors and certain subsets of proteins, possibly accounting for its wide environmental distribution. Lower abundance populations are patchier in their distribution, and proteomic data indicate that their environmental niches may be constrained by specific sets of abiotic environmental factors. This research establishes an effective strategy to investigate ecological relationships between microbial physiology and the environment for whole communities in situ.

Posttranslational modification and sequence variation of redox-active proteins correlate with biofilm life cycle in natural microbial communities.

Characterizing proteins recovered from natural microbial communities affords the opportunity to correlate protein expression and modification with environmental factors, including species composition and successional stage. Proteogenomic and biochemical studies of pellicle biofilms from subsurface acid mine drainage streams have shown abundant cytochromes from the dominant organism, Leptospirillum Group II. These cytochromes are proposed to be key proteins in aerobic Fe(II) oxidation, the dominant mode of cellular energy generation by the biofilms. In this study, we determined that posttranslational modification and expression of amino-acid sequence variants change as a function of biofilm maturation. For Cytochrome579 (Cyt579), the most abundant cytochrome in the biofilms, late developmental-stage biofilms differed from early-stage biofilms in N-terminal truncations and decreased redox potentials. Expression of sequence variants of two monoheme c-type cytochromes also depended on biofilm development. For Cyt(572), an abundant membrane-bound cytochrome, the expression of multiple sequence variants was observed in both early and late developmental-stage biofilms; however, redox potentials of Cyt572 from these different sources did not vary significantly. These cytochrome analyses show a complex response of the Leptospirillum Group II electron transport chain to growth within a microbial community and illustrate the power of multiple proteomics techniques to define biochemistry in natural systems.

Proteogenomic monitoring of Geobacter physiology during stimulated uranium bioremediation.

Implementation of uranium bioremediation requires methods for monitoring the membership and activities of the subsurface microbial communities that are responsible for reduction of soluble U(VI) to insoluble U(IV). Here, we report a proteomics-based approach for simultaneously documenting the strain membership and microbial physiology of the dominant Geobacter community members during in situ acetate amendment of the U-contaminated Rifle, CO, aquifer. Three planktonic Geobacter-dominated samples were obtained from two wells down-gradient of acetate addition. Over 2,500 proteins from each of these samples were identified by matching liquid chromatography-tandem mass spectrometry spectra to peptides predicted from seven isolate Geobacter genomes. Genome-specific peptides indicate early proliferation of multiple M21 and Geobacter bemidjiensis-like strains and later possible emergence of M21 and G. bemidjiensis-like strains more closely related to Geobacter lovleyi. Throughout biostimulation, the proteome is dominated by enzymes that convert acetate to acetyl-coenzyme A and pyruvate for central metabolism, while abundant peptides matching tricarboxylic acid cycle proteins and ATP synthase subunits were also detected, indicating the importance of energy generation during the period of rapid growth following the start of biostimulation. Evolving Geobacter strain composition may be linked to changes in protein abundance over the course of biostimulation and may reflect changes in metabolic functioning. Thus, metagenomics-independent community proteogenomics can be used to diagnose the status of the subsurface consortia upon which remediation biotechnology relies.

Community genomic and proteomic analyses of chemoautotrophic iron-oxidizing "Leptospirillum rubarum" (Group II) and "Leptospirillum ferrodiazotrophum" (Group III) bacteria in acid mine drainage biofilms.

We analyzed near-complete population (composite) genomic sequences for coexisting acidophilic iron-oxidizing Leptospirillum group II and III bacteria (phylum Nitrospirae) and an extrachromosomal plasmid from a Richmond Mine, Iron Mountain, CA, acid mine drainage biofilm. Community proteomic analysis of the genomically characterized sample and two other biofilms identified 64.6% and 44.9% of the predicted proteins of Leptospirillum groups II and III, respectively, and 20% of the predicted plasmid proteins. The bacteria share 92% 16S rRNA gene sequence identity and >60% of their genes, including integrated plasmid-like regions. The extrachromosomal plasmid carries conjugation genes with detectable sequence similarity to genes in the integrated conjugative plasmid, but only those on the extrachromosomal element were identified by proteomics. Both bacterial groups have genes for community-essential functions, including carbon fixation and biosynthesis of vitamins, fatty acids, and biopolymers (including cellulose); proteomic analyses reveal these activities. Both Leptospirillum types have multiple pathways for osmotic protection. Although both are motile, signal transduction and methyl-accepting chemotaxis proteins are more abundant in Leptospirillum group III, consistent with its distribution in gradients within biofilms. Interestingly, Leptospirillum group II uses a methyl-dependent and Leptospirillum group III a methyl-independent response pathway. Although only Leptospirillum group III can fix nitrogen, these proteins were not identified by proteomics. The abundances of core proteins are similar in all communities, but the abundance levels of unique and shared proteins of unknown function vary. Some proteins unique to one organism were highly expressed and may be key to the functional and ecological differentiation of Leptospirillum groups II and III.

Proteomics-inferred genome typing (PIGT) demonstrates inter-population recombination as a strategy for environmental adaptation.

Analyses of ecological and evolutionary processes that shape microbial consortia are facilitated by comprehensive studies of ecosystems with low species richness. In the current study we evaluated the role of recombination in altering the fitness of chemoautotrophic bacteria in their natural environment. Proteomics-inferred genome typing (PIGT) was used to genotype the dominant Leptospirillum group II populations in 27 biofilms sampled from six locations in the Richmond Mine acid mine drainage system (Iron Mountain, CA) over a 4-year period. We observed six distinct genotypes that are recombinants comprised of segments from two 'parental' genotypes. Community genomic analyses revealed additional low abundance recombinant variants. The dominance of some genotypes despite a larger available genome pool, and patterns of spatiotemporal distribution within the ecosystem, indicate selection for distinct recombinants. Genes involved in motility, signal transduction and transport were over-represented in the tens to hundreds of kilobase recombinant blocks, whereas core metabolic functions were significantly under-represented. Our findings demonstrate the power of PIGT and reveal that recombination is a mechanism for fine-scale adaptation in this system.

A general system for studying protein-protein interactions in Gram-negative bacteria.

One of the most promising methods for large-scale studies of protein interactions is isolation of an affinity-tagged protein with its in vivo interaction partners, followed by mass spectrometric identification of the copurified proteins. Previous studies have generated affinity-tagged proteins using genetic tools or cloning systems that are specific to a particular organism. To enable protein-protein interaction studies across a wider range of Gram-negative bacteria, we have developed a methodology based on expression of affinity-tagged "bait" proteins from a medium copy-number plasmid. This construct is based on a broad-host-range vector backbone (pBBR1MCS5). The vector has been modified to incorporate the Gateway DEST vector recombination region, to facilitate cloning and expression of fusion proteins bearing a variety of affinity, fluorescent, or other tags. We demonstrate this methodology by characterizing interactions among subunits of the DNA-dependent RNA polymerase complex in two metabolically versatile Gram-negative microbial species of environmental interest, Rhodopseudomonas palustris CGA010 and Shewanella oneidensis MR-1. Results compared favorably with those for both plasmid and chromosomally encoded affinity-tagged fusion proteins expressed in a model organism, Escherichia coli.

Cross-species global proteomics reveals conserved and unique processes in Phytophthora sojae and Phytophthora ramorum.

Phytophthora ramorum and Phytophthora sojae are destructive plant pathogens. P. sojae has a narrow host range, whereas P. ramorum has a wide host range. A global proteomics comparison of the vegetative (mycelium) and infective (germinating cyst) life stages of P. sojae and P. ramorum was conducted to identify candidate proteins involved in host range, early infection, and vegetative growth. Sixty-two candidates for early infection, 26 candidates for vegetative growth, and numerous proteins that may be involved in defining host specificity were identified. In addition, common life stage proteomic trends between the organisms were observed. In mycelia, proteins involved in transport and metabolism of amino acids, carbohydrates, and other small molecules were up-regulated. In the germinating cysts, up-regulated proteins associated with lipid transport and metabolism, cytoskeleton, and protein synthesis were observed. It appears that the germinating cyst catabolizes lipid reserves through the beta-oxidation pathway to drive the extensive protein synthesis necessary to produce the germ tube and initiate infection. Once inside the host, the pathogen switches to vegetative growth in which energy is derived from glycolysis and utilized for synthesis of amino acids and other molecules that assist survival in the plant tissue.

Implications of strain- and species-level sequence divergence for community and isolate shotgun proteomic analysis.

The recent surge in microbial genomic sequencing, combined with the development of high-throughput liquid chromatography-mass-spectrometry-based (LC/LC-MS/MS) proteomics, has raised the question of the extent to which genomic information of one strain or environmental sample can be used to profile proteomes of related strains or samples. Even with decreasing sequencing costs, it remains impractical to obtain genomic sequence for every strain or sample analyzed. Here, we evaluate how shotgun proteomics is affected by amino acid divergence between the sample and the genomic database using a probability-based model and a random mutation simulation model constrained by experimental data. To assess the effects of nonrandom distribution of mutations, we also evaluated identification levels using in silico peptide data from sequenced isolates with average amino acid identities (AAI) varying between 76 and 98%. We compared the predictions to experimental protein identification levels for a sample that was evaluated using a database that included genomic information for the dominant organism and for a closely related variant (95% AAI). The range of models set the boundaries at which half of the proteins in a proteomic experiment can be identified to be 77-92% AAI between orthologs in the sample and database. Consistent with this prediction, experimental data indicated loss of half the identifiable proteins at 90% AAI. Additional analysis indicated a 6.4% reduction of the initial protein coverage per 1% amino acid divergence and total identification loss at 86% AAI. Consequently, shotgun proteomics is capable of cross-strain identifications but avoids most cross-species false positives.