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Abstract Energy status and nutrients regulate photosynthetic protein expression. The unicellular green alga Chromochloris zofingiensis switches off photosynthesis in the presence of exogenous glucose (+Glc) in a process that depends on hexokinase (HXK1). Here, we show that this response requires that cells lack sufficient iron (−Fe). Cells grown in −Fe+Glc accumulate triacylglycerol (TAG) while losing photosynthesis and thylakoid membranes. However, cells with an iron supplement (+Fe+Glc) maintain photosynthesis and thylakoids while still accumulating TAG. Proteomic analysis shows that known photosynthetic proteins are most depleted in heterotrophy, alongside hundreds of uncharacterized, conserved proteins. Photosynthesis repression is associated with enzyme and transporter regulation that redirects iron resources to (a) respiratory instead of photosynthetic complexes and (b) a ferredoxin-dependent desaturase pathway supporting TAG accumulation rather than thylakoid lipid synthesis. Combining insights from diverse organisms from green algae to vascular plants, we show how iron and trophic constraints on metabolism aid gene discovery for photosynthesis and biofuel production.

ABSTRACT Metaproteomics, a method for untargeted, high-throughput identification of proteins in complex samples, provides functional information about microbial communities and can tie functions to specific taxa. Metaproteomics often generates less data than other omics techniques, but analytical workflows can be improved to increase usable data in metaproteomic outputs. Identification of peptides in the metaproteomic analysis is performed by comparing mass spectra of sample peptides to a reference database of protein sequences. Although these protein databases are an integral part of the metaproteomic analysis, few studies have explored how database composition impacts peptide identification. Here, we used cervicovaginal lavage (CVL) samples from a study of bacterial vaginosis (BV) to compare the performance of databases built using six different strategies. We evaluated broad versus sample-matched databases, as well as databases populated with proteins translated from metagenomic sequencing of the same samples versus sequences from public repositories. Smaller sample-matched databases performed significantly better, driven by the statistical constraints on large databases. Additionally, large databases attributed up to 34% of significant bacterial hits to taxa absent from the sample, as determined orthogonally by 16S rRNA gene sequencing. We also tested a set of hybrid databases which included bacterial proteins from NCBI RefSeq and translated bacterial genes from the samples. These hybrid databases had the best overall performance, identifying 1,068 unique human and 1,418 unique bacterial proteins, ~30% more than a database populated with proteins from typical vaginal bacteria and fungi. Our findings can help guide the optimal identification of proteins while maintaining statistical power for reaching biological conclusions. IMPORTANCE Metaproteomic analysis can provide valuable insights into the functions of microbial and cellular communities by identifying a broad, untargeted set of proteins. The databases used in the analysis of metaproteomic data influence results by defining what proteins can be identified. Moreover, the size of the database impacts the number of identifications after accounting for false discovery rates (FDRs). Few studies have tested the performance of different strategies for building a protein database to identify proteins from metaproteomic data and those that have largely focused on highly diverse microbial communities. We tested a range of databases on CVL samples and found that a hybrid sample-matched approach, using publicly available proteins from organisms present in the samples, as well as proteins translated from metagenomic sequencing of the samples, had the best performance. However, our results also suggest that public sequence databases will continue to improve as more bacterial genomes are published.

ABSTRACT Rivers have a significant role in global carbon and nitrogen cycles, serving as a nexus for nutrient transport between terrestrial and marine ecosystems. Although rivers have a small global surface area, they contribute substantially to worldwide greenhouse gas emissions through microbially mediated processes within the river hyporheic zone. Despite this importance, research linking microbial and viral communities to specific biogeochemical reactions is still nascent in these sediment environments. To survey the metabolic potential and gene expression underpinning carbon and nitrogen biogeochemical cycling in river sediments, we collected an integrated data set of 33 metagenomes, metaproteomes, and paired metabolomes. We reconstructed over 500 microbial metagenome-assembled genomes (MAGs), which we dereplicated into 55 unique, nearly complete medium- and high-quality MAGs spanning 12 bacterial and archaeal phyla. We also reconstructed 2,482 viral genomic contigs, which were dereplicated into 111 viral MAGs (vMAGs) of >10 kb in size. As a result of integrating gene expression data with geochemical and metabolite data, we created a conceptual model that uncovered new roles for microorganisms in organic matter decomposition, carbon sequestration, nitrogen mineralization, nitrification, and denitrification. We show how these metabolic pathways, integrated through shared resource pools of ammonium, carbon dioxide, and inorganic nitrogen, could ultimately contribute to carbon dioxide and nitrous oxide fluxes from hyporheic sediments. Further, by linking viral MAGs to these active microbial hosts, we provide some of the first insights into viral modulation of river sediment carbon and nitrogen cycling. IMPORTANCE Here we created HUM-V (hyporheic uncultured microbial and viral), an annotated microbial and viral MAG catalog that captures strain and functional diversity encoded in these Columbia River sediment samples. Demonstrating its utility, this genomic inventory encompasses multiple representatives of dominant microbial and archaeal phyla reported in other river sediments and provides novel viral MAGs that can putatively infect these. Furthermore, we used HUM-V to recruit gene expression data to decipher the functional activities of these MAGs and reconstruct their active roles in Columbia River sediment biogeochemical cycling. Ultimately, we show the power of MAG-resolved multi-omics to uncover interactions and chemical handoffs in river sediments that shape an intertwined carbon and nitrogen metabolic network. The accessible microbial and viral MAGs in HUM-V will serve as a community resource to further advance more untargeted, activity-based measurements in these, and related, freshwater terrestrial-aquatic ecosystems.

It is thought that polyphenols inhibit organic matter decomposition in soils devoid of oxygen. Here the authors use metabolomics and genome-resolved metaproteomics to provide experimental evidence of polyphenol biodegradation and maintained soil microbial community metabolism despite anoxia.

The nascent field of microbiome science is transitioning from a descriptive approach of cataloging taxa and functions present in an environment to applying multi-omics methods to investigate microbiome dynamics and function. A large number of new tools and algorithms have been designed and used for very specific purposes on samples collected by individual investigators or groups. While these developments have been quite instructive, the ability to compare microbiome data generated by many groups of researchers is impeded by the lack of standardized application of bioinformatics methods. Additionally, there are few examples of broad bioinformatics workflows that can process metagenome, metatranscriptome, metaproteome and metabolomic data at scale, and no central hub that allows processing, or provides varied omics data that are findable, accessible, interoperable and reusable (FAIR). Here, we review some of the challenges that exist in analyzing omics data within the microbiome research sphere, and provide context on how the National Microbiome Data Collaborative has adopted a standardized and open access approach to address such challenges.

Manganese (Mn) oxides are among the strongest oxidants and sorbents in the environment, and Mn(II) oxidation to Mn(III/IV) (hydr)oxides includes both abiotic and microbially-mediated processes. While white-rot Basidiomycete fungi oxidize Mn(II) using laccases and manganese peroxidases in association with lignocellulose degradation, the mechanisms by which filamentous Ascomycete fungi oxidize Mn(II) and a physiological role for Mn(II) oxidation in these organisms remain poorly understood. Here we use a combination of chemical and in-gel assays and bulk mass spectrometry to demonstrate secretome-based Mn(II) oxidation in three phylogenetically diverse Ascomycetes that is mechanistically distinct from hyphal-associated Mn(II) oxidation on solid substrates. We show that Mn(II) oxidative capacity of these fungi is dictated by species-specific secreted enzymes and varies with secretome age, and we reveal the presence of both Cu-based and FAD-based Mn(II) oxidation mechanisms in all 3 species, demonstrating mechanistic redundancy. Specifically, we identify candidate Mn(II)-oxidizing enzymes as tyrosinase and glyoxal oxidase in Stagonospora sp. SRC1lsM3a, bilirubin oxidase in Stagonospora sp. and Paraconiothyrium sporulosum AP3s5-JAC2a, and GMC oxidoreductase in all 3 species, including Pyrenochaeta sp. DS3sAY3a. The diversity of the candidate Mn(II)-oxidizing enzymes identified in this study suggests that the ability of fungal secretomes to oxidize Mn(II) may be more widespread than previously thought.

Wood-degrading fungi vary in their strategies for deconstructing wood, and their competitive successes shape the rate and fate of carbon released from wood, Earth’s largest pool of aboveground terrestrial carbon. In this study, one-on-one interspecific interactions between two model brown rot (carbohydrate-selective) fungi, Gloeophyllum trabeum and Rhodonia (Postia) placenta, were studied on wood wafers where a clearly resolved interaction zone (IZ) could be generated, reproducibly. Comparative RNAseq and proteomics between the IZ and non-interacting hyphae of each species identified combative strategies for each fungus. Glycoside hydrolases were a relatively smaller portion of the interaction secretome compared to non-interacting hyphae. The interaction zone showed higher pectinase specific activity than all other sampling locations, and higher laminarinase specific activity (branched β-glucan proxy) was seen in the IZ secretome relative to equivalent hyphae in single-species cultures. Our efforts also identified two distinct competitive strategies in these two fungi with a shared nutritional mode (brown rot) but polyphyletic ancestral lineages. Gloeophyllum trabeum (Gloeophyllum clade) upregulated more secondary metabolite (SM) synthesis genes in response to a competitor than did R. placenta. R. placenta (Antrodia clade) upregulated a larger variety of uncharacterized oxidoreductases in interacting hyphae, suggesting that these may play a role in mediating competitor response in this fungus. Both species produced several hypothetical proteins exclusively in the interaction zone, leaving questions as to the function of these proteins. This work supports the existence of multiple interaction strategies among brown rot fungi and highlights the functional diversity among wood decay fungi.

ABSTRACT Hydraulic fracturing of black shale formations has greatly increased United States oil and natural gas recovery. However, the accumulation of biomass in subsurface reservoirs and pipelines is detrimental because of possible well souring, microbially induced corrosion, and pore clogging. Temporal sampling of produced fluids from a well in the Utica Shale revealed the dominance of Halanaerobium strains within the in situ microbial community and the potential for these microorganisms to catalyze thiosulfate-dependent sulfidogenesis. From these field data, we investigated biogenic sulfide production catalyzed by a Halanaerobium strain isolated from the produced fluids using proteogenomics and laboratory growth experiments. Analysis of Halanaerobium isolate genomes and reconstructed genomes from metagenomic data sets revealed the conserved presence of rhodanese-like proteins and anaerobic sulfite reductase complexes capable of converting thiosulfate to sulfide. Shotgun proteomics measurements using a Halanaerobium isolate verified that these proteins were more abundant when thiosulfate was present in the growth medium, and culture-based assays identified thiosulfate-dependent sulfide production by the same isolate. Increased production of sulfide and organic acids during the stationary growth phase suggests that fermentative Halanaerobium uses thiosulfate to remove excess reductant. These findings emphasize the potential detrimental effects that could arise from thiosulfate-reducing microorganisms in hydraulically fractured shales, which are undetected by current industry-wide corrosion diagnostics. IMPORTANCE Although thousands of wells in deep shale formations across the United States have been hydraulically fractured for oil and gas recovery, the impact of microbial metabolism within these environments is poorly understood. Our research demonstrates that dominant microbial populations in these subsurface ecosystems contain the conserved capacity for the reduction of thiosulfate to sulfide and that this process is likely occurring in the environment. Sulfide generation (also known as “souring”) is considered deleterious in the oil and gas industry because of both toxicity issues and impacts on corrosion of the subsurface infrastructure. Critically, the capacity for sulfide generation via reduction of sulfate was not detected in our data sets. Given that current industry wellhead tests for sulfidogenesis target canonical sulfate-reducing microorganisms, these data suggest that new approaches to the detection of sulfide-producing microorganisms may be necessary.

ABSTRACT The yeast Yarrowia lipolytica is a potent accumulator of lipids, and lipogenesis in this organism can be influenced by a variety of factors, such as genetics and environmental conditions. Using a multifactorial study, we elucidated the effects of both genetic and environmental factors on regulation of lipogenesis in Y. lipolytica and identified how two opposite regulatory states both result in lipid accumulation. This study involved comparison of a strain overexpressing diacylglycerol acyltransferase (DGA1) with a control strain grown under either nitrogen or carbon limitation conditions. A strong correlation was observed between the responses on the transcript and protein levels. Combination of DGA1 overexpression with nitrogen limitation resulted in a high level of lipid accumulation accompanied by downregulation of several amino acid biosynthetic pathways, including that of leucine in particular, and these changes were further correlated with a decrease in metabolic fluxes. This downregulation was supported by the measured decrease in the level of 2-isopropylmalate, an intermediate of leucine biosynthesis. Combining the multi-omics data with putative transcription factor binding motifs uncovered a contradictory role for TORC1 in controlling lipid accumulation, likely mediated through 2-isopropylmalate and a Leu3-like transcription factor. IMPORTANCE The ubiquitous metabolism of lipids involves refined regulation, and an enriched understanding of this regulation would have wide implications. Various factors can influence lipid metabolism, including the environment and genetics. We demonstrated, using a multi-omics and multifactorial experimental setup, that multiple factors affect lipid accumulation in the yeast Yarrowia lipolytica. Using integrative analysis, we identified novel interactions between nutrient restriction and genetic factors involving regulators that are highly conserved among eukaryotes. Given that lipid metabolism is involved in many diseases but is also vital to the development of microbial cell factories that can provide us with sustainable fuels and oleochemicals, we envision that our report introduces foundational work to further unravel the regulation of lipid accumulation in eukaryal cells.

Fungal secretomes contain a wide range of hydrolytic and oxidative enzymes, including cellulases, hemicellulases, pectinases, and lignin-degrading accessory enzymes, that synergistically drive litter decomposition in the environment. While secretome studies of model organisms such as Phanerochaete chrysosporium and Aspergillus species have greatly expanded our knowledge of these enzymes, few have extended secretome characterization to environmental isolates or conducted side-by-side comparisons of diverse species. Thus, the mechanisms of carbon degradation by many ubiquitous soil fungi remain poorly understood. Here we use a combination of LC-MS/MS, genomic, and bioinformatic analyses to characterize and compare the protein composition of the secretomes of four recently isolated, cosmopolitan, Mn(II)-oxidizing Ascomycetes (Alternaria alternata SRC1lrK2f, Stagonospora sp. SRC1lsM3a, Pyrenochaeta sp. DS3sAY3a, and Paraconiothyrium sporulosum AP3s5-JAC2a). We demonstrate that the organisms produce a rich yet functionally similar suite of extracellular enzymes, with species-specific differences in secretome composition arising from unique amino acid sequences rather than overall protein function. Furthermore, we identify not only a wide range of carbohydrate-active enzymes that can directly oxidize recalcitrant carbon, but also an impressive suite of redox-active accessory enzymes that suggests a role for Fenton-based hydroxyl radical formation in indirect, non-specific lignocellulose attack. Our findings highlight the diverse oxidative capacity of these environmental isolates and enhance our understanding of the role of filamentous Ascomycetes in carbon turnover in the environment.

Abstract After hundreds of generations of adaptive evolution at exponential growth, Escherichia coli grows as predicted using flux balance analysis (FBA) on genome‐scale metabolic models (GEMs). However, it is not known whether the predicted pathway usage in FBA solutions is consistent with gene and protein expression in the wild‐type and evolved strains. Here, we report that >98% of active reactions from FBA optimal growth solutions are supported by transcriptomic and proteomic data. Moreover, when E. coli adapts to growth rate selective pressure, the evolved strains upregulate genes within the optimal growth predictions, and downregulate genes outside of the optimal growth solutions. In addition, bottlenecks from dosage limitations of computationally predicted essential genes are overcome in the evolved strains. We also identify regulatory processes that may contribute to the development of the optimal growth phenotype in the evolved strains, such as the downregulation of known regulons and stringent response suppression. Thus, differential gene and protein expression from wild‐type and adaptively evolved strains supports observed growth phenotype changes, and is consistent with GEM‐computed optimal growth states.

Leaf-cutter ants are prolific and conspicuous constituents of Neotropical ecosystems that derive energy from specialized fungus gardens they cultivate using prodigious amounts of foliar biomass. The basidiomycetous cultivar of the ants, Leucoagaricus gongylophorus, produces specialized hyphal swellings called gongylidia that serve as the primary food source of ant colonies. Gongylidia also contain plant biomass-degrading enzymes that become concentrated in ant digestive tracts and are deposited within fecal droplets onto fresh foliar material as ants incorporate it into the fungus garden. Although the enzymes concentrated by L. gongylophorus within gongylidia are thought to be critical to the initial degradation of plant biomass, only a few enzymes present in these hyphal swellings have been identified. Here we use proteomic methods to identify proteins present in the gongylidia of three Atta cephalotes colonies. Our results demonstrate that a diverse but consistent set of enzymes is present in gongylidia, including numerous plant biomass-degrading enzymes likely involved in the degradation of polysaccharides, plant toxins, and proteins. Overall, gongylidia contained over three quarters of all biomass-degrading enzymes identified in the L. gongylophorus genome, demonstrating that the majority of the enzymes produced by this fungus for biomass breakdown are ingested by the ants. We also identify a set of 40 of these enzymes enriched in gongylidia compared to whole fungus garden samples, suggesting that certain enzymes may be particularly important in the initial degradation of foliar material. Our work sheds light on the complex interplay between leaf-cutter ants and their fungal symbiont that allows for the host insects to occupy an herbivorous niche by indirectly deriving energy from plant biomass.

The potential for commensal microorganisms indigenous to a host (the 'microbiome' or 'microbiota') to alter infection outcome by influencing host-pathogen interplay is largely unknown. We used a multi-omics "systems" approach, incorporating proteomics, metabolomics, glycomics, and metagenomics, to explore the molecular interplay between the murine host, the pathogen Salmonella enterica serovar Typhimurium (S. Typhimurium), and commensal gut microorganisms during intestinal infection with S. Typhimurium. We find proteomic evidence that S. Typhimurium thrives within the infected 129/SvJ mouse gut without antibiotic pre-treatment, inducing inflammation and disrupting the intestinal microbiome (e.g., suppressing Bacteroidetes and Firmicutes while promoting growth of Salmonella and Enterococcus). Alteration of the host microbiome population structure was highly correlated with gut environmental changes, including the accumulation of metabolites normally consumed by commensal microbiota. Finally, the less characterized phase of S. Typhimurium's lifecycle was investigated, and both proteomic and glycomic evidence suggests S. Typhimurium may take advantage of increased fucose moieties to metabolize fucose while growing in the gut. The application of multiple omics measurements to Salmonella-induced intestinal inflammation provides insights into complex molecular strategies employed during pathogenesis between host, pathogen, and the microbiome.

Genome sequencing continues to be a rapidly evolving technology, yet most downstream aspects of genome annotation pipelines remain relatively stable or are even being abandoned. The annotation process is now performed almost exclusively in an automated fashion to balance the large number of sequences generated. One possible way of reducing errors inherent to automated computational annotations is to apply data from omics measurements (i.e. transcriptional and proteomic) to the un-annotated genome with a proteogenomic-based approach. Here, the concept of annotation refinement has been extended to include a comparative assessment of genomes across closely related species. Transcriptomic and proteomic data derived from highly similar pathogenic Yersiniae (Y. pestis CO92, Y. pestis Pestoides F, and Y. pseudotuberculosis PB1/+) was used to demonstrate a comprehensive comparative omic-based annotation methodology. Peptide and oligo measurements experimentally validated the expression of nearly 40% of each strain's predicted proteome and revealed the identification of 28 novel and 68 incorrect (i.e., observed frameshifts, extended start sites, and translated pseudogenes) protein-coding sequences within the three current genome annotations. Gene loss is presumed to play a major role in Y. pestis acquiring its niche as a virulent pathogen, thus the discovery of many translated pseudogenes, including the insertion-ablated argD, underscores a need for functional analyses to investigate hypotheses related to divergence. Refinements included the discovery of a seemingly essential ribosomal protein, several virulence-associated factors, a transcriptional regulator, and many hypothetical proteins that were missed during annotation.

High doses of ionizing radiation result in biological damage; however, the precise relationships between long-term health effects, including cancer, and low-dose exposures remain poorly understood and are currently extrapolated using high-dose exposure data. Identifying the signaling pathways and individual proteins affected at the post-translational level by radiation should shed valuable insight into the molecular mechanisms that regulate dose-dependent responses to radiation.We have identified 7117 unique phosphopeptides (2566 phosphoproteins) from control and irradiated (2 and 50 cGy) primary human skin fibroblasts 1 h post-exposure. Semi-quantitative label-free analyses were performed to identify phosphopeptides that are apparently altered by radiation exposure. This screen identified phosphorylation sites on proteins with known roles in radiation responses including TP53BP1 as well as previously unidentified radiation-responsive proteins such as the candidate tumor suppressor SASH1. Bioinformatic analyses suggest that low and high doses of radiation affect both overlapping and unique biological processes and suggest a role for MAP kinase and protein kinase A (PKA) signaling in the radiation response as well as differential regulation of p53 networks at low and high doses of radiation.Our results represent the most comprehensive analysis of the phosphoproteomes of human primary fibroblasts exposed to multiple doses of ionizing radiation published to date and provide a basis for the systems-level identification of biological processes, molecular pathways and individual proteins regulated in a dose dependent manner by ionizing radiation. Further study of these modified proteins and affected networks should help to define the molecular mechanisms that regulate biological responses to radiation at different radiation doses and elucidate the impact of low-dose radiation exposure on human health.

Cancer invasion and metastasis are closely associated with activities within the degradome; however, little is known about whether these activities can be detected in the blood of cancer patients.The peptidome-degradome profiles of pooled blood plasma sampled from 15 breast cancer patients (BCP) and age, race, and menopausal status matched control healthy persons (HP) were globally characterized using advanced comprehensive separations combined with tandem Fourier transform mass spectrometry and new data analysis approaches that facilitated top-down peptidomic analysis. The BCP pool displayed 71 degradome protein substrates that encompassed 839 distinct peptidome peptides. In contrast, the HP 50 degradome substrates found encompassed 425 peptides. We find that the ratios of the peptidome peptide relative abundances can vary as much as >4000 fold between BCP and HP. The experimental results also show differential degradation of substrates in the BCP sample in their functional domains, including the proteolytic and inhibitory sites of the plasmin-antiplasmin and thrombin-antithrombin systems, the main chains of the extracellular matrix protection proteins, the excessive degradation of innate immune system key convertases and membrane attack complex components, as well as several other cancer suppressor proteins.Degradomics-peptidomics profiling of blood plasma is highly sensitive to changes not evidenced by conventional bottom-up proteomics and potentially provides unique signatures of possible diagnostic utility.

Knowledge of the entire protein content, the proteome, of normal human cerebrospinal fluid (CSF) would enable insights into neurologic and psychiatric disorders. Until now technologic hurdles and access to true normal samples hindered attaining this goal.We applied immunoaffinity separation and high sensitivity and resolution liquid chromatography-mass spectrometry to examine CSF from healthy normal individuals. 2630 proteins in CSF from normal subjects were identified, of which 56% were CSF-specific, not found in the much larger set of 3654 proteins we have identified in plasma. We also examined CSF from groups of subjects previously examined by others as surrogates for normals where neurologic symptoms warranted a lumbar puncture but where clinical laboratory were reported as normal. We found statistically significant differences between their CSF proteins and our non-neurological normals. We also examined CSF from 10 volunteer subjects who had lumbar punctures at least 4 weeks apart and found that there was little variability in CSF proteins in an individual as compared to subject to subject.Our results represent the most comprehensive characterization of true normal CSF to date. This normal CSF proteome establishes a comparative standard and basis for investigations into a variety of diseases with neurological and psychiatric features.