Zn deficiency disrupts Cu and S homeostasis in Chlamydomonas resulting in over accumulation of Cu and Cysteine.

Growth of Chlamydomonas reinhardtii in zinc (Zn) limited medium leads to disruption of copper (Cu) homeostasis, resulting in up to 40-fold Cu over-accumulation relative to its typical Cu quota. We show that Chlamydomonas controls its Cu quota by balancing Cu import and export, which is disrupted in a Zn deficient cell, thus establishing a mechanistic connection between Cu and Zn homeostasis. Transcriptomics, proteomics and elemental profiling revealed that Zn-limited Chlamydomonas cells up-regulate a subset of genes encoding "first responder" proteins involved in sulfur (S) assimilation and consequently accumulate more intracellular S, which is incorporated into L-cysteine, γ-glutamylcysteine, and homocysteine. Most prominently, in the absence of Zn, free L-cysteine is increased ∼80-fold, corresponding to ∼2.8 × 109 molecules/cell. Interestingly, classic S-containing metal binding ligands like glutathione and phytochelatins do not increase. X-ray fluorescence microscopy showed foci of S accumulation in Zn-limited cells that co-localize with Cu, phosphorus and calcium, consistent with Cu-thiol complexes in the acidocalcisome, the site of Cu(I) accumulation. Notably, cells that have been previously starved for Cu do not accumulate S or Cys, causally connecting cysteine synthesis with Cu accumulation. We suggest that cysteine is an in vivo Cu(I) ligand, perhaps ancestral, that buffers cytosolic Cu.

Pumping iron: A multi-omics analysis of two extremophilic algae reveals iron economy management.

Marine algae are responsible for half of the world's primary productivity, but this critical carbon sink is often constrained by insufficient iron. One species of marine algae, Dunaliella tertiolecta, is remarkable for its ability to maintain photosynthesis and thrive in low-iron environments. A related species, Dunaliella salina Bardawil, shares this attribute but is an extremophile found in hypersaline environments. To elucidate how algae manage their iron requirements, we produced high-quality genome assemblies and transcriptomes for both species to serve as a foundation for a comparative multiomics analysis. We identified a host of iron-uptake proteins in both species, including a massive expansion of transferrins and a unique family of siderophore-iron-uptake proteins. Complementing these multiple iron-uptake routes, ferredoxin functions as a large iron reservoir that can be released by induction of flavodoxin. Proteomic analysis revealed reduced investment in the photosynthetic apparatus coupled with remodeling of antenna proteins by dramatic iron-deficiency induction of TIDI1, which is closely related but identifiably distinct from the chlorophyll binding protein, LHCA3. These combinatorial iron scavenging and sparing strategies make Dunaliella unique among photosynthetic organisms.

Optimizing metaproteomics database construction: lessons from a study of the vaginal microbiome.

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.

Cysteine: an ancestral Cu binding ligand in green algae?

Growth of Chlamydomonas reinhardtii in zinc (Zn) limited medium leads to disruption of copper (Cu) homeostasis, resulting in up to 40-fold Cu over-accumulation relative to its typical Cu quota. We show that Chlamydomonas controls its Cu quota by balancing Cu import and export, which is disrupted in a Zn deficient cell, thus establishing a mechanistic connection between Cu and Zn homeostasis. Transcriptomics, proteomics and elemental profiling revealed that Zn-limited Chlamydomonas cells up-regulate a subset of genes encoding "first responder" proteins involved in sulfur (S) assimilation and consequently accumulate more intracellular S, which is incorporated into L-cysteine, γ-glutamylcysteine and homocysteine. Most prominently, in the absence of Zn, free L-cysteine is increased ~80-fold, corresponding to ~ 2.8 × 10 9 molecules/cell. Interestingly, classic S-containing metal binding ligands like glutathione and phytochelatins do not increase. X-ray fluorescence microscopy showed foci of S accumulation in Zn-limited cells that co-localize with Cu, phosphorus and calcium, consistent with Cu-thiol complexes in the acidocalcisome, the site of Cu(I) accumulation. Notably, cells that have been previously starved for Cu do not accumulate S or Cys, causally connecting cysteine synthesis with Cu accumulation. We suggest that cysteine is an in vivo Cu(I) ligand, perhaps ancestral, that buffers cytosolic Cu.

Decrypting bacterial polyphenol metabolism in an anoxic wetland soil.

Microorganisms play vital roles in modulating organic matter decomposition and nutrient cycling in soil ecosystems. The enzyme latch paradigm posits microbial degradation of polyphenols is hindered in anoxic peat leading to polyphenol accumulation, and consequently diminished microbial activity. This model assumes that polyphenols are microbially unavailable under anoxia, a supposition that has not been thoroughly investigated in any soil type. Here, we use anoxic soil reactors amended with and without a chemically defined polyphenol to test this hypothesis, employing metabolomics and genome-resolved metaproteomics to interrogate soil microbial polyphenol metabolism. Challenging the idea that polyphenols are not bioavailable under anoxia, we provide metabolite evidence that polyphenols are depolymerized, resulting in monomer accumulation, followed by the generation of small phenolic degradation products. Further, we show that soil microbiome function is maintained, and possibly enhanced, with polyphenol addition. In summary, this study provides chemical and enzymatic evidence that some soil microbiota can degrade polyphenols under anoxia and subvert the assumed polyphenol lock on soil microbial metabolism.

The Specific Carbohydrate Diet and Diet Modification as Induction Therapy for Pediatric Crohn's Disease: A Randomized Diet Controlled Trial.

BACKGROUND: Crohn's disease (CD) is a chronic inflammatory intestinal disorder associated with intestinal dysbiosis. Diet modulates the intestinal microbiome and therefore has a therapeutic potential. The aim of this study is to determine the potential efficacy of three versions of the specific carbohydrate diet (SCD) in active Crohn's Disease. METHODS: 18 patients with mild/moderate CD (PCDAI 15-45) aged 7 to 18 years were enrolled. Patients were randomized to either SCD, modified SCD(MSCD) or whole foods (WF) diet. Patients were evaluated at baseline, 2, 4, 8 and 12 weeks. PCDAI, inflammatory labs and multi-omics evaluations were assessed. RESULTS: Mean age was 14.3 ± 2.9 years. At week 12, all participants (n = 10) who completed the study achieved clinical remission. The C-reactive protein decreased from 1.3 ± 0.7 at enrollment to 0.9 ± 0.5 at 12 weeks in the SCD group. In the MSCD group, the CRP decreased from 1.6 ± 1.1 at enrollment to 0.7 ± 0.1 at 12 weeks. In the WF group, the CRP decreased from 3.9 ± 4.3 at enrollment to 1.6 ± 1.3 at 12 weeks. In addition, the microbiome composition shifted in all patients across the study period. While the nature of the changes was largely patient specific, the predicted metabolic mode of the organisms increasing and decreasing in activity was consistent across patients. CONCLUSIONS: This study emphasizes the impact of diet in CD. Each diet had a positive effect on symptoms and inflammatory burden; the more exclusionary diets were associated with a better resolution of inflammation.

Functional Genomics, Transcriptomics, and Proteomics Reveal Distinct Combat Strategies Between Lineages of Wood-Degrading Fungi With Redundant Wood Decay Mechanisms.

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 ß-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.

Individual Variability of Protein Expression in Human Tissues.

Human tissues are known to exhibit interindividual variability, but a deeper understanding of the different factors affecting protein expression is necessary to further apply this knowledge. Our goal was to explore the proteomic variability between individuals as well as between healthy and diseased samples, and to test the efficacy of machine learning classifiers. In order to investigate whether disparate proteomics data sets may be combined, we performed a retrospective analysis of proteomics data from 9 different human tissues. These data sets represent several different sample prep methods, mass spectrometry instruments, and tissue health. Using these data, we examined interindividual and intertissue variability in peptide expression, and analyzed the methods required to build accurate tissue classifiers. We also evaluated the limits of tissue classification by downsampling the peptide data to simulate situations where less data is available, such as clinical biopsies, laser capture microdissection or potentially single-cell proteomics. Our findings reveal the strong potential for utilizing proteomics data to build robust tissue classifiers, which has many prospective clinical applications for evaluating the applicability of model clinical systems.

Dichomitus squalens partially tailors its molecular responses to the composition of solid wood.

White-rot fungi, such as Dichomitus squalens, degrade all wood components and inhabit mixed-wood forests containing both soft- and hardwood species. In this study, we evaluated how D. squalens responded to the compositional differences in softwood [guaiacyl (G) lignin and higher mannan content] and hardwood [syringyl/guaiacyl (S/G) lignin and higher xylan content] using semi-natural solid cultures. Spruce (softwood) and birch (hardwood) sticks were degraded by D. squalens as measured by oxidation of the lignins using 2D-NMR. The fungal response as measured by transcriptomics, proteomics and enzyme activities showed a partial tailoring to wood composition. Mannanolytic transcripts and proteins were more abundant in spruce cultures, while a proportionally higher xylanolytic activity was detected in birch cultures. Both wood types induced manganese peroxidases to a much higher level than laccases, but higher transcript and protein levels of the manganese peroxidases were observed on the G-lignin rich spruce. Overall, the molecular responses demonstrated a stronger adaptation to the spruce rather than birch composition, possibly because D. squalens is mainly found degrading softwoods in nature, which supports the ability of the solid wood cultures to reflect the natural environment.

Activity-Based Probes for Isoenzyme- and Site-Specific Functional Characterization of Glutathione S-Transferases.

Glutathione S-transferases (GSTs) comprise a diverse family of phase II drug metabolizing enzymes whose shared function is the conjugation of reduced glutathione (GSH) to endo- and xenobiotics. Although the conglomerate activity of these enzymes can be measured, the isoform-specific contribution to the metabolism of xenobiotics in complex biological samples has not been possible. We have developed two activity-based probes (ABPs) that characterize active GSTs in mammalian tissues. The GST active site is composed of a GSH binding "G site" and a substrate binding "H site". Therefore, we developed (1) a GSH-based photoaffinity probe (GSTABP-G) to target the "G site", and (2) an ABP designed to mimic a substrate molecule and have "H site" activity (GSTABP-H). The GSTABP-G features a photoreactive moiety for UV-induced covalent binding to GSTs and GSH-binding enzymes. The GSTABP-H is a derivative of a known mechanism-based GST inhibitor that binds within the active site and inhibits GST activity. Validation of probe targets and "G" and "H" site specificity was carried out using a series of competition experiments in the liver. Herein, we present robust tools for the characterization of enzyme- and active site-specific GST activity in mammalian model systems.

Simultaneous Proteomic Discovery and Targeted Monitoring using Liquid Chromatography, Ion Mobility Spectrometry, and Mass Spectrometry.

Current proteomic approaches include both broad discovery measurements and quantitative targeted analyses. In many cases, discovery measurements are initially used to identify potentially important proteins (e.g. candidate biomarkers) and then targeted studies are employed to quantify a limited number of selected proteins. Both approaches, however, suffer from limitations. Discovery measurements aim to sample the whole proteome but have lower sensitivity, accuracy, and quantitation precision than targeted approaches, whereas targeted measurements are significantly more sensitive but only sample a limited portion of the proteome. Herein, we describe a new approach that performs both discovery and targeted monitoring (DTM) in a single analysis by combining liquid chromatography, ion mobility spectrometry and mass spectrometry (LC-IMS-MS). In DTM, heavy labeled target peptides are spiked into tryptic digests and both the labeled and unlabeled peptides are detected using LC-IMS-MS instrumentation. Compared with the broad LC-MS discovery measurements, DTM yields greater peptide/protein coverage and detects lower abundance species. DTM also achieved detection limits similar to selected reaction monitoring (SRM) indicating its potential for combined high quality discovery and targeted analyses, which is a significant step toward the convergence of discovery and targeted approaches.

MALDI-Mass Spectrometric Imaging Revealing Hypoxia-Driven Lipids and Proteins in a Breast Tumor Model.

Hypoxic areas are a common feature of rapidly growing malignant tumors and their metastases and are typically spatially heterogeneous. Hypoxia has a strong impact on tumor cell biology and contributes to tumor progression in multiple ways. To date, only a few molecular key players in tumor hypoxia, such as hypoxia-inducible factor-1 (HIF-1), have been discovered. The distribution of biomolecules is frequently heterogeneous in the tumor volume and may be driven by hypoxia and HIF-1α. Understanding the spatially heterogeneous hypoxic response of tumors is critical. Mass spectrometric imaging (MSI) provides a unique way of imaging biomolecular distributions in tissue sections with high spectral and spatial resolution. In this paper, breast tumor xenografts grown from MDA-MB-231-HRE-tdTomato cells, with a red fluorescent tdTomato protein construct under the control of a hypoxia response element (HRE)-containing promoter driven by HIF-1α, were used to detect the spatial distribution of hypoxic regions. We elucidated the 3D spatial relationship between hypoxic regions and the localization of lipids and proteins by using principal component analysis-linear discriminant analysis (PCA-LDA) on 3D rendered MSI volume data from MDA-MB-231-HRE-tdTomato breast tumor xenografts. In this study, we identified hypoxia-regulated proteins active in several distinct pathways such as glucose metabolism, regulation of actin cytoskeleton, protein folding, translation/ribosome, splicesome, the PI3K-Akt signaling pathway, hemoglobin chaperone, protein processing in endoplasmic reticulum, detoxification of reactive oxygen species, aurora B signaling/apoptotic execution phase, the RAS signaling pathway, the FAS signaling pathway/caspase cascade in apoptosis, and telomere stress induced senescence. In parallel, we also identified colocalization of hypoxic regions and various lipid species such as PC(16:0/18:0), PC(16:0/18:1), PC(16:0/18:2), PC(16:1/18:4), PC(18:0/18:1), and PC(18:1/18:1), among others. Our findings shed light on the biomolecular composition of hypoxic tumor regions, which may be responsible for a given tumor's resistance to radiation or chemotherapy.

Automated genome mining of ribosomal peptide natural products.

Ribosomally synthesized and posttranslationally modified peptides (RiPPs), especially from microbial sources, are a large group of bioactive natural products that are a promising source of new (bio)chemistry and bioactivity.1 In light of exponentially increasing microbial genome databases and improved mass spectrometry (MS)-based metabolomic platforms, there is a need for computational tools that connect natural product genotypes predicted from microbial genome sequences with their corresponding chemotypes from metabolomic data sets. Here, we introduce RiPPquest, a tandem mass spectrometry database search tool for identification of microbial RiPPs, and apply it to lanthipeptide discovery. RiPPquest uses genomics to limit search space to the vicinity of RiPP biosynthetic genes and proteomics to analyze extensive peptide modifications and compute p-values of peptide-spectrum matches (PSMs). We highlight RiPPquest by connecting multiple RiPPs from extracts of Streptomyces to their gene clusters and by the discovery of a new class III lanthipeptide, informatipeptin, from Streptomyces viridochromogenes DSM 40736 to reflect that it is a natural product that was discovered by mass spectrometry based genome mining using algorithmic tools rather than manual inspection of mass spectrometry data and genetic information. The presented tool is available at cyclo.ucsd.edu.

Basophile: accurate fragment charge state prediction improves peptide identification rates.

In shotgun proteomics, database search algorithms rely on fragmentation models to predict fragment ions that should be observed for a given peptide sequence. The most widely used strategy (Naive model) is oversimplified, cleaving all peptide bonds with equal probability to produce fragments of all charges below that of the precursor ion. More accurate models, based on fragmentation simulation, are too computationally intensive for on-the-fly use in database search algorithms. We have created an ordinal-regression-based model called Basophile that takes fragment size and basic residue distribution into account when determining the charge retention during CID/higher-energy collision induced dissociation (HCD) of charged peptides. This model improves the accuracy of predictions by reducing the number of unnecessary fragments that are routinely predicted for highly-charged precursors. Basophile increased the identification rates by 26% (on average) over the Naive model, when analyzing triply-charged precursors from ion trap data. Basophile achieves simplicity and speed by solving the prediction problem with an ordinal regression equation, which can be incorporated into any database search software for shotgun proteomic identification.

Identification of widespread adenosine nucleotide binding in Mycobacterium tuberculosis.

Computational prediction of protein function is frequently error-prone and incomplete. In Mycobacterium tuberculosis (Mtb), ~25% of all genes have no predicted function and are annotated as hypothetical proteins, severely limiting our understanding of Mtb pathogenicity. Here, we utilize a high-throughput quantitative activity-based protein profiling (ABPP) platform to probe, annotate, and validate ATP-binding proteins in Mtb. We experimentally validate prior in silico predictions of >240 proteins and identify 72 hypothetical proteins as ATP binders. ATP interacts with proteins with diverse and unrelated sequences, providing an expanded view of adenosine nucleotide binding in Mtb. Several hypothetical ATP binders are essential or taxonomically limited, suggesting specialized functions in mycobacterial physiology and pathogenicity.

Tandem mass spectrometry identifies many mouse brain O-GlcNAcylated proteins including EGF domain-specific O-GlcNAc transferase targets.

O-linked N-acetylglucosamine (O-GlcNAc) is a reversible posttranslational modification of Ser and Thr residues on cytosolic and nuclear proteins of higher eukaryotes catalyzed by O-GlcNAc transferase (OGT). O-GlcNAc has recently been found on Notch1 extracellular domain catalyzed by EGF domain-specific OGT. Aberrant O-GlcNAc modification of brain proteins has been linked to Alzheimer's disease (AD). However, understanding specific functions of O-GlcNAcylation in AD has been impeded by the difficulty in characterization of O-GlcNAc sites on proteins. In this study, we modified a chemical/enzymatic photochemical cleavage approach for enriching O-GlcNAcylated peptides in samples containing ∼100 µg of tryptic peptides from mouse cerebrocortical brain tissue. A total of 274 O-GlcNAcylated proteins were identified. Of these, 168 were not previously known to be modified by O-GlcNAc. Overall, 458 O-GlcNAc sites in 195 proteins were identified. Many of the modified residues are either known phosphorylation sites or located proximal to known phosphorylation sites. These findings support the proposed regulatory cross-talk between O-GlcNAcylation and phosphorylation. This study produced the most comprehensive O-GlcNAc proteome of mammalian brain tissue with both protein identification and O-GlcNAc site assignment. Interestingly, we observed O-ß-GlcNAc on EGF-like repeats in the extracellular domains of five membrane proteins, expanding the evidence for extracellular O-GlcNAcylation by the EGF domain-specific OGT. We also report a GlcNAc-ß-1,3-Fuc-α-1-O-Thr modification on the EGF-like repeat of the versican core protein, a proposed substrate of Fringe ß-1,3-N-acetylglucosaminyltransferases.

Comparative omics-driven genome annotation refinement: application across Yersiniae.

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.

Improving collision induced dissociation (CID), high energy collision dissociation (HCD), and electron transfer dissociation (ETD) fourier transform MS/MS degradome-peptidome identifications using high accuracy mass information.

MS dissociation methods, including collision induced dissociation (CID), high energy collision dissociation (HCD), and electron transfer dissociation (ETD), can each contribute distinct peptidome identifications using conventional peptide identification methods (Shen et al. J. Proteome Res. 2011), but such samples still pose significant informatics challenges. In this work, we explored utilization of high accuracy fragment ion mass measurements, in this case provided by Fourier transform MS/MS, to improve peptidome peptide data set size and consistency relative to conventional descriptive and probabilistic scoring methods. For example, we identified 20-40% more peptides than SEQUEST, Mascot, and MS_GF scoring methods using high accuracy fragment ion information and the same false discovery rate (FDR) from CID, HCD, and ETD spectra. Identified species covered >90% of the collective identifications obtained using various conventional peptide identification methods, which significantly addresses the common issue of different data analysis methods generating different peptide data sets. Choice of peptide dissociation and high-precision measurement-based identification methods presently available for degradomic-peptidomic analyses needs to be based on the coverage and confidence (or specificity) afforded by the method, as well as practical issues (e.g., throughput). By using accurate fragment information, >1000 peptidome components can be identified from a single human blood plasma analysis with low peptide-level FDRs (e.g., 0.6%), providing an improved basis for investigating potential disease-related peptidome components.

Coupled transcriptome and proteome analysis of human lymphotropic tumor viruses: insights on the detection and discovery of viral genes.

BACKGROUND: Kaposi's sarcoma-associated herpesvirus (KSHV) and Epstein-Barr virus (EBV) are related human tumor viruses that cause primary effusion lymphomas (PEL) and Burkitt's lymphomas (BL), respectively. Viral genes expressed in naturally-infected cancer cells contribute to disease pathogenesis; knowing which viral genes are expressed is critical in understanding how these viruses cause cancer. To evaluate the expression of viral genes, we used high-resolution separation and mass spectrometry coupled with custom tiling arrays to align the viral proteomes and transcriptomes of three PEL and two BL cell lines under latent and lytic culture conditions. RESULTS: The majority of viral genes were efficiently detected at the transcript and/or protein level on manipulating the viral life cycle. Overall the correlation of expressed viral proteins and transcripts was highly complementary in both validating and providing orthogonal data with latent/lytic viral gene expression. Our approach also identified novel viral genes in both KSHV and EBV, and extends viral genome annotation. Several previously uncharacterized genes were validated at both transcript and protein levels. CONCLUSIONS: This systems biology approach coupling proteome and transcriptome measurements provides a comprehensive view of viral gene expression that could not have been attained using each methodology independently. Detection of viral proteins in combination with viral transcripts is a potentially powerful method for establishing virus-disease relationships.

A statistical method for assessing peptide identification confidence in accurate mass and time tag proteomics.

Current algorithms for quantifying peptide identification confidence in the accurate mass and time (AMT) tag approach assume that the AMT tags themselves have been correctly identified. However, there is uncertainty in the identification of AMT tags, because this is based on matching LC-MS/MS fragmentation spectra to peptide sequences. In this paper, we incorporate confidence measures for the AMT tag identifications into the calculation of probabilities for correct matches to an AMT tag database, resulting in a more accurate overall measure of identification confidence for the AMT tag approach. The method is referenced as Statistical Tools for AMT Tag Confidence (STAC). STAC additionally provides a uniqueness probability (UP) to help distinguish between multiple matches to an AMT tag and a method to calculate an overall false discovery rate (FDR). STAC is freely available for download, as both a command line and a Windows graphical application.