Mass spectrometry at the interface of proteomics and genomics

Cancer proteogenomics: current impact and future prospects

Genomic analyses in cancer have been enormously impactful, leading to the identification of driver mutations and development of targeted therapies. But the functions of the vast majority of somatic mutations and copy number variants in tumours remain unknown, and the causes of resistance to targeted therapies and methods to overcome them are poorly defined. Recent improvements in mass spectrometry-based proteomics now enable direct examination of the consequences of genomic aberrations, providing deep and quantitative characterization of tumour tissues. Integration of proteins and their post-translational modifications with genomic, epigenomic and transcriptomic data constitutes the new field of proteogenomics, and is already leading to new biological and diagnostic knowledge with the potential to improve our understanding of malignant transformation and therapeutic outcomes. In this Review we describe recent developments in proteogenomics and key findings from the proteogenomic analysis of a wide range of cancers. Considerations relevant to the selection and use of samples for proteogenomics and the current technologies used to generate, analyse and integrate proteomic with genomic data are described. Applications of proteogenomics in translational studies and immuno-oncology are rapidly emerging, and the prospect for their full integration into therapeutic trials and clinical care seems bright.

Dissecting Plasmodium yoelii Pathobiology: Proteomic Approaches for Decoding Novel Translational and Post-Translational Modifications

Malaria is a vector-borne disease. It is caused by Plasmodium parasites. Plasmodium yoelii is a rodent model parasite, primarily used for studying parasite development in liver cells and vectors. To better understand parasite biology, we carried out a high-throughput-based proteomic analysis of P. yoelii. From the same mass spectrometry (MS)/MS data set, we also captured several post-translational modified peptides by following a bioinformatics analysis without any prior enrichment. Further, we carried out a proteogenomic analysis, which resulted in improvements to some of the existing gene models along with the identification of several novel genes. Analysis of proteome and post-translational modifications (PTMs) together resulted in the identification of 3124 proteins. The identified PTMs were found to be enriched in mitochondrial metabolic pathways. Subsequent bioinformatics analysis provided an insight into proteins associated with metabolic regulatory mechanisms. Among these, the tricarboxylic acid (TCA) cycle and the isoprenoid synthesis pathway are found to be essential for parasite survival and drug resistance. The proteogenomic analysis discovered 43 novel protein-coding genes. The availability of an in-depth proteomic landscape of a malaria pathogen model will likely facilitate further molecular-level investigations on pre-erythrocytic stages of malaria.

Peptimapper: proteogenomics workflow for the expert annotation of eukaryotic genomes

Background

Accurate structural annotation of genomes is still a challenge, despite the progress made over the past decade. The prediction of gene structure remains difficult, especially for eukaryotic species, and is often erroneous and incomplete. We used a proteogenomics strategy, taking advantage of the combination of proteomics datasets and bioinformatics tools, to identify novel protein coding-genes and splice isoforms, assign correct start sites, and validate predicted exons and genes.

Results

Our proteogenomics workflow, Peptimapper, was applied to the genome annotation of Ectocarpus sp., a key reference genome for both the brown algal lineage and stramenopiles. We generated proteomics data from various life cycle stages of Ectocarpus sp. strains and sub-cellular fractions using a shotgun approach. First, we directly generated peptide sequence tags (PSTs) from the proteomics data. Second, we mapped PSTs onto the translated genomic sequence. Closely located hits (i.e., PSTs locations on the genome) were then clustered to detect potential coding regions based on parameters optimized for the organism. Third, we evaluated each cluster and compared it to gene predictions from existing conventional genome annotation approaches. Finally, we integrated cluster locations into GFF files to use a genome viewer. We identified two potential novel genes, a ribosomal protein L22 and an aryl sulfotransferase and corrected the gene structure of a dihydrolipoamide acetyltransferase. We experimentally validated the results by RT-PCR and using transcriptomics data.

Conclusions

Peptimapper is a complementary tool for the expert annotation of genomes. It is suitable for any organism and is distributed through a Docker image available on two public bioinformatics docker repositories: Docker Hub and BioShaDock. This workflow is also accessible through the Galaxy framework and for use by non-computer scientists at https://galaxy.protim.eu.

Data are available via ProteomeXchange under identifier PXD010618.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-5431-9) contains supplementary material, which is available to authorized users.

Viral Short ORFs and Their Possible Functions

Definition of functional genomic elements is one of the greater challenges of the genomic era. Traditionally, putative short open reading frames (sORFs) coding for less than 100 amino acids were disregarded due to computational and experimental limitations; however, it has become clear over the past several years that translation of sORFs is pervasive and serves diverse functions. The development of ribosome profiling, allowing identification of translated sequences genome wide, revealed wide spread, previously unidentified translation events. New computational methodologies as well as improved mass spectrometry approaches also contributed to the task of annotating translated sORFs in different organisms. Viruses are of special interest due to the selective pressure on their genome size, their rapid and confining evolution, and the potential contribution of novel peptides to the host immune response. Indeed, many functional viral sORFs were characterized to date, and ribosome profiling analyses suggest that this may be the tip of the iceberg. Our computational analyses of sORFs identified by ribosome profiling in DNA viruses demonstrate that they may be enriched in specific features implying that at least some of them are functional. Combination of systematic genome editing strategies with synthetic tagging will take us into the next step-elucidation of the biological relevance and function of this intriguing class of molecules.

Methods, Tools and Current Perspectives in Proteogenomics

With combined technological advancements in high-throughput next-generation sequencing and deep mass spectrometry-based proteomics, proteogenomics, i.e. the integrative analysis of proteomic and genomic data, has emerged as a new research field. Early efforts in the field were focused on improving protein identification using sample-specific genomic and transcriptomic sequencing data. More recently, integrative analysis of quantitative measurements from genomic and proteomic studies have identified novel insights into gene expression regulation, cell signaling, and disease. Many methods and tools have been developed or adapted to enable an array of integrative proteogenomic approaches and in this article, we systematically classify published methods and tools into four major categories, (1) Sequence-centric proteogenomics; (2) Analysis of proteogenomic relationships; (3) Integrative modeling of proteogenomic data; and (4) Data sharing and visualization. We provide a comprehensive review of methods and available tools in each category and highlight their typical applications.

Doublet N-Terminal Oriented Proteomics for N-Terminomics and Proteolytic Processing Identification

The study of the N-terminome and the precise identification of proteolytic processing events are key in biology. Dedicated methodologies have been developed as the comprehensive characterization of the N-terminome can hardly be achieved by standard proteomics methods. In this context, we have set up a trimethoxyphenyl phosphonium (TMPP) labeling approach that allows the characterization of both N-terminal and internal digestion peptides in a single experiment. This latter point is a major advantage of our strategy as most N-terminomics methods rely on the enrichment of N-terminal peptides and thus exclude internal peptides.We have implemented a double heavy/light TMPP labeling and an automated data validation workflow that make our doublet N-terminal oriented proteomics (dN-TOP) strategy efficient for high-throughput N-terminome analysis.

Computational Identification of Novel Genes: Current and Future Perspectives

While it has long been thought that all genomic novelties are derived from the existing material, many genes lacking homology to known genes were found in recent genome projects. Some of these novel genes were proposed to have evolved de novo, ie, out of noncoding sequences, whereas some have been shown to follow a duplication and divergence process. Their discovery called for an extension of the historical hypotheses about gene origination. Besides the theoretical breakthrough, increasing evidence accumulated that novel genes play important roles in evolutionary processes, including adaptation and speciation events. Different techniques are available to identify genes and classify them as novel. Their classification as novel is usually based on their similarity to known genes, or lack thereof, detected by comparative genomics or against databases. Computational approaches are further prime methods that can be based on existing models or leveraging biological evidences from experiments. Identification of novel genes remains however a challenging task. With the constant software and technologies updates, no gold standard, and no available benchmark, evaluation and characterization of genomic novelty is a vibrant field. In this review, the classical and state-of-the-art tools for gene prediction are introduced. The current methods for novel gene detection are presented; the methodological strategies and their limits are discussed along with perspective approaches for further studies.

Proteogenomic Analysis of Mycobacterium smegmatis Using High Resolution Mass Spectrometry

Biochemical evidence is vital for accurate genome annotation. The integration of experimental data collected at the proteome level using high resolution mass spectrometry allows for improvements in genome annotation by providing evidence for novel gene models, while validating or modifying others. Here, we report the results of a proteogenomic analysis of a reference strain of Mycobacterium smegmatis (mc²155), a fast growing model organism for the pathogenic Mycobacterium tuberculosis—the causative agent for Tuberculosis. By integrating high throughput LC/MS/MS proteomic data with genomic six frame translation and ab initio gene prediction databases, a total of 2887 ORFs were identified, including 2810 ORFs annotated to a Reference protein, and 63 ORFs not previously annotated to a Reference protein. Further, the translational start site (TSS) was validated for 558 Reference proteome gene models, while upstream translational evidence was identified for 81. In addition, N-terminus derived peptide identifications allowed for downstream TSS modification of a further 24 gene models. We validated the existence of six previously described interrupted coding sequences at the peptide level, and provide evidence for four novel frameshift positions. Analysis of peptide posterior error probability (PEP) scores indicates high-confidence novel peptide identifications and shows that the genome of M. smegmatis mc²155 is not yet fully annotated. Data are available via ProteomeXchange with identifier PXD003500.

Integration of Proteomics and Transcriptomics Data Sets for the Analysis of a Lymphoma B-Cell Line in the Context of the Chromosome-Centric Human Proteome Project

A comprehensive study of the molecular active landscape of human cells can be undertaken to integrate two different but complementary perspectives: transcriptomics, and proteomics. After the genome era, proteomics has emerged as a powerful tool to simultaneously identify and characterize the compendium of thousands of different proteins active in a cell. Thus, the Chromosome-centric Human Proteome Project (C-HPP) is promoting a full characterization of the human proteome combining high-throughput proteomics with the data derived from genome-wide expression profiling of protein-coding genes. Here we present a full proteomic profiling of a human lymphoma B-cell line (Ramos) performed using a nanoUPLC-LTQ-Orbitrap Velos proteomic platform, combined to an in-depth transcriptomic profiling of the same cell type. Data are available via ProteomeXchange with identifier PXD001933. Integration of the proteomic and transcriptomic data sets revealed a 94% overlap in the proteins identified by both -omics approaches. Moreover, functional enrichment analysis of the proteomic profiles showed an enrichment of several functions directly related to the biological and morphological characteristics of B-cells. In turn, about 30% of all protein-coding genes present in the whole human genome were identified as being expressed by the Ramos cells (stable average of 30% genes along all the chromosomes), revealing the size of the protein expression-set present in one specific human cell type. Additionally, the identification of missing proteins in our data sets has been reported, highlighting the power of the approach. Also, a comparison between neXtProt and UniProt database searches has been performed. In summary, our transcriptomic and proteomic experimental profiling provided a high coverage report of the expressed proteome from a human lymphoma B-cell type with a clear insight into the biological processes that characterized these cells. In this way, we demonstrated the usefulness of combining -omics for a comprehensive characterization of specific biological systems.

Dual use of peptide mass spectra: Protein atlas and genome annotation

One of the objectives of genome science is the discovery and accurate annotation of all protein-coding genes. Proteogenomics has emerged as a methodology that provides orthogonal information to traditional forms of evidence used for genome annotation. By this method, peptides that are identified via tandem mass spectrometry are used to refine protein-coding gene models. Namely, these peptides are used to confirm the translation of predicted protein-coding genes, as evidence of novel genes or for correction of current gene models. Proteogenomics requires deep and broad sampling of the proteome in order to generate sufficient numbers of unique peptides. Therefore, we propose that proteogenomic projects are designed so that the generated peptides can also be used to create a comprehensive protein atlas that quantitatively catalogues protein abundance changes during development and in response to environmental stimulus.

Fifteen years of physiological proteo(geno)mics with (marine) environmental bacteria

Environmental bacteria play a central role in the Earth's elemental cycles and represent a mostly untapped reservoir for novel metabolic capacities and biocatalysts. Over the last 15 years, the author's laboratory has focused on three major switches in the breakdown of organic carbon defined by the abundance and recalcitrance of the substrates: carbohydrates and amino acids by aerobic heterotrophs, fermentation end products by sulphate reducers and anaerobic degradation of aromatic compounds and hydrocarbons by denitrifiers and sulphate reducers. As these bacteria are novel isolates mostly not accessibly by molecular genetics, genomics combined with differential proteomics was early on applied to obtain molecular-functional insights into degradation pathways, catabolic and regulatory networks, as well as mechanisms and strategies for adapting to changing environmental conditions. This review provides some background on research motivations and briefly summarizes insights into studied model organisms, e.g. "Aromatoleum aromaticum" EbN1, Desulfobacula toluolica Tol2 and Phaeobacter inhibens DSM 17395.

Brain proteomics of Anopheles gambiae

Anopheles gambiae has a well-adapted system for host localization, feeding, and mating behavior, which are all governed by neuronal processes in the brain. However, there are no published reports characterizing the brain proteome to elucidate neuronal signaling mechanisms in the vector. To this end, a large-scale mapping of the brain proteome of An. gambiae was carried out using high resolution tandem mass spectrometry, revealing a repertoire of >1800 proteins, of which 15% could not be assigned any function. A large proportion of the identified proteins were predicted to be involved in diverse biological processes including metabolism, transport, protein synthesis, and olfaction. This study also led to the identification of 10 GPCR classes of proteins, which could govern sensory pathways in mosquitoes. Proteins involved in metabolic and neural processes, chromatin modeling, and synaptic vesicle transport associated with neuronal transmission were predominantly expressed in the brain. Proteogenomic analysis expanded our findings with the identification of 15 novel genes and 71 cases of gene refinements, a subset of which were validated by RT-PCR and sequencing. Overall, our study offers valuable insights into the brain physiology of the vector that could possibly open avenues for intervention strategies for malaria in the future.

N-Terminal-oriented proteogenomics of the marine bacterium roseobacter denitrificans Och114 using N-Succinimidyloxycarbonylmethyl)tris(2,4,6-trimethoxyphenyl)phosphonium bromide (TMPP) labeling and diagonal chromatography

Given the ease of whole genome sequencing with next-generation sequencers, structural and functional gene annotation is now purely based on automated prediction. However, errors in gene structure are frequent, the correct determination of start codons being one of the main concerns. Here, we combine protein N termini derivatization using (N-Succinimidyloxycarbonylmethyl)tris(2,4,6-trimethoxyphenyl)phosphonium bromide (TMPP Ac-OSu) as a labeling reagent with the COmbined FRActional DIagonal Chromatography (COFRADIC) sorting method to enrich labeled N-terminal peptides for mass spectrometry detection. Protein digestion was performed in parallel with three proteases to obtain a reliable automatic validation of protein N termini. The analysis of these N-terminal enriched fractions by high-resolution tandem mass spectrometry allowed the annotation refinement of 534 proteins of the model marine bacterium Roseobacter denitrificans OCh114. This study is especially efficient regarding mass spectrometry analytical time. From the 534 validated N termini, 480 confirmed existing gene annotations, 41 highlighted erroneous start codon annotations, five revealed totally new mis-annotated genes; the mass spectrometry data also suggested the existence of multiple start sites for eight different genes, a result that challenges the current view of protein translation initiation. Finally, we identified several proteins for which classical genome homology-driven annotation was inconsistent, questioning the validity of automatic annotation pipelines and emphasizing the need for complementary proteomic data. All data have been deposited to the ProteomeXchange with identifier PXD000337.

Emerging evidence for functional peptides encoded by short open reading frames

Short open reading frames (sORFs) are a common feature of all genomes, but their coding potential has mostly been disregarded, partly because of the difficulty in determining whether these sequences are translated. Recent innovations in computing, proteomics and high-throughput analyses of translation start sites have begun to address this challenge and have identified hundreds of putative coding sORFs. The translation of some of these has been confirmed, although the contribution of their peptide products to cellular functions remains largely unknown. This Review examines this hitherto overlooked component of the proteome and considers potential roles for sORF-encoded peptides.

SMMRNA: a database of small molecule modulators of RNA

We have developed SMMRNA, an interactive database, available at http://www.smmrna.org, with special focus on small molecule ligands targeting RNA. Currently, SMMRNA consists of ∼770 unique ligands along with structural images of RNA molecules. Each ligand in the SMMRNA contains information such as Kd, Ki, IC50, ΔTm, molecular weight (MW), hydrogen donor and acceptor count, XlogP, number of rotatable bonds, number of aromatic rings and 2D and 3D structures. These parameters can be explored using text search, advanced search, substructure and similarity-based analysis tools that are embedded in SMMRNA. A structure editor is provided for 3D visualization of ligands. Advance analysis can be performed using substructure and OpenBabel-based chemical similarity fingerprints. Upload facility for both RNA and ligands is also provided. The physicochemical properties of the ligands were further examined using OpenBabel descriptors, hierarchical clustering, binning partition and multidimensional scaling. We have also generated a 3D conformation database of ligands to support the structure and ligand-based screening. SMMRNA provides comprehensive resource for further design, development and refinement of small molecule modulators for selective targeting of RNA molecules.

HiRIEF LC-MS enables deep proteome coverage and unbiased proteogenomics

We present a liquid chromatography-mass spectrometry (LC-MS)-based method permitting unbiased (gene prediction-independent) genome-wide discovery of protein-coding loci in higher eukaryotes. Using high-resolution isoelectric focusing (HiRIEF) at the peptide level in the 3.7-5.0 pH range and accurate peptide isoelectric point (pI) prediction, we probed the six-reading-frame translation of the human and mouse genomes and identified 98 and 52 previously undiscovered protein-coding loci, respectively. The method also enabled deep proteome coverage, identifying 13,078 human and 10,637 mouse proteins.

Searching for a needle in a stack of needles: challenges in metaproteomics data analysis

In the past years the integral study of microbial communities of varying complexity has gained increasing research interest. Mass spectrometry-driven metaproteomics enables the analysis of such communities on the functional level, but this fledgling field still faces various technical and semantic challenges regarding experimental data analysis and interpretation. In the present review, we outline the hurdles involved and attempt to cover the most valuable methods and software implementations available to researchers in the field today. Beyond merely focusing on protein identification, we provide an overview on different data pre- and post-processing steps, such as metabolic pathway analysis, that can be useful in a typical metaproteomics workflow. Finally, we briefly discuss directions for future work.

Deep coverage of the Escherichia coli proteome enables the assessment of false discovery rates in simple proteogenomic experiments

Recent advances in mass spectrometry (MS) have led to increased applications of shotgun proteomics to the refinement of genome annotation. The typical "proteo-genomic" workflows rely on the mapping of peptide MS/MS spectra onto databases derived via six-frame translation of the genome sequence. These databases contain a large proportion of spurious protein sequences which make the statistical confidence of the resulting peptide spectrum matches difficult to assess. Here we performed a comprehensive analysis of the Escherichia coli proteome using LTQ-Orbitrap MS and mapped the corresponding MS/MS spectra onto a six-frame translation of the E. coli genome. We hypothesized that the protein-coding part of the E. coli genome approaches complete annotation and that the majority of six frame-specific (novel) peptide spectrum matches can be considered as false positive identifications. We confirm our hypothesis by showing that the posterior error probability distribution of novel hits is almost identical to that of reversed (decoy) hits; this enables us to estimate the sensitivity, specificity, accuracy, and false discovery rate in a typical bacterial proteo-genomic dataset. We use two complementary computational frameworks for processing and statistical assessment of MS/MS data: MaxQuant and Trans-Proteomic Pipeline. We show that MaxQuant achieves a more sensitive six-frame database search with an acceptable false discovery rate and is therefore well suited for global genome reannotation applications, whereas the Trans-Proteomic Pipeline achieves higher specificity and is well suited for high-confidence validation. The use of a small and well-annotated bacterial genome enables us to address genome coverage achieved in state-of-the-art bacterial proteomics: identified peptide sequences mapped to all expressed E. coli proteins but covered 31.7% of the protein-coding genome sequence. Our results show that false discovery rates can be substantially underestimated even in "simple" proteo-genomic experiments obtained by means of high-accuracy MS and point to the necessity of further improvements concerning the coverage of peptide sequences by MS-based methods.

Identification of new protein coding sequences and signal peptidase cleavage sites of Helicobacter pylori strain 26695 by proteogenomics

Correct annotation of protein coding genes is the basis of conventional data analysis in proteomic studies. Nevertheless, most protein sequence databases almost exclusively rely on gene finding software and inevitably also miss protein annotations or possess errors. Proteogenomics tries to overcome these issues by matching MS data directly against a genome sequence database. Here we report an in-depth proteogenomics study of Helicobacter pylori strain 26695. MS data was searched against a combined database of the NCBI annotations and a six-frame translation of the genome. Database searches with Mascot and X! Tandem revealed 1115 proteins identified by at least two peptides with a peptide false discovery rate below 1%. This represents 71% of the predicted proteome. So far this is the most extensive proteome study of Helicobacter pylori. Our proteogenomic approach unambiguously identified four previously missed annotations and furthermore allowed us to correct sequences of six annotated proteins. Since secreted proteins are often involved in pathogenic processes we further investigated signal peptidase cleavage sites. By applying a database search that accommodates the identification of semi-specific cleaved peptides, 63 previously unknown signal peptides were detected. The motif LXA showed to be the predominant recognition sequence for signal peptidases.

BIOLOGICAL SIGNIFICANCE

The results of MS-based proteomic studies highly rely on correct annotation of protein coding genes which is the basis of conventional data analysis. However, the annotation of protein coding sequences in genomic data is usually based on gene finding software. These tools are limited in their prediction accuracy such as the problematic determination of exact gene boundaries. Thus, protein databases own partly erroneous or incomplete sequences. Additionally, some protein sequences might also be missing in the databases. Proteogenomics, a combination of proteomic and genomic data analyses, is well suited to detect previously not annotated proteins and to correct erroneous sequences. For this purpose, the existing database of the investigated species is typically supplemented with a six-frame translation of the genome. Here, we studied the proteome of the major human pathogen Helicobacter pylori that is responsible for many gastric diseases such as duodenal ulcers and gastric cancer. Our in-depth proteomic study highly reliably identified 1115 proteins (FDR<0.01%) by at least two peptides (FDR<1%) which represent 71% of the predicted proteome deposited at NCBI. The proteogenomic data analysis of our data set resulted in the unambiguous identification of four previously missed annotations, the correction of six annotated proteins as well as the detection of 63 previously unknown signal peptides. We have annotated proteins of particular biological interest like the ferrous iron transport protein A, the coiled-coil-rich protein HP0058 and the lipopolysaccharide biosynthesis protein HP0619. For instance, the protein HP0619 could be a drug target for the inhibition of the LPS synthesis pathway. Furthermore it has been proven that the motif "LXA" is the predominant recognition sequence for the signal peptidase I of H. pylori. Signal peptidases are essential enzymes for the viability of bacterial cells and are involved in pathogenesis. Therefore signal peptidases could be novel targets for antibiotics. The inclusion of the corrected and new annotated proteins as well as the information of signal peptide cleavage sites will help in the study of biological pathways involved in pathogenesis or drug response of H. pylori.

Genomic evaluation of Thermoanaerobacter spp. for the construction of designer co-cultures to improve lignocellulosic biofuel production

The microbial production of ethanol from lignocellulosic biomass is a multi-component process that involves biomass hydrolysis, carbohydrate transport and utilization, and finally, the production of ethanol. Strains of the genus Thermoanaerobacter have been studied for decades due to their innate abilities to produce comparatively high ethanol yields from hemicellulose constituent sugars. However, their inability to hydrolyze cellulose, limits their usefulness in lignocellulosic biofuel production. As such, co-culturing Thermoanaerobacter spp. with cellulolytic organisms is a plausible approach to improving lignocellulose conversion efficiencies and yields of biofuels. To evaluate native lignocellulosic ethanol production capacities relative to competing fermentative end-products, comparative genomic analysis of 11 sequenced Thermoanaerobacter strains, including a de novo genome, Thermoanaerobacter thermohydrosulfuricus WC1, was conducted. Analysis was specifically focused on the genomic potential for each strain to address all aspects of ethanol production mentioned through a consolidated bioprocessing approach. Whole genome functional annotation analysis identified three distinct clades within the genus. The genomes of Clade 1 strains encode the fewest extracellular carbohydrate active enzymes and also show the least diversity in terms of lignocellulose relevant carbohydrate utilization pathways. However, these same strains reportedly are capable of directing a higher proportion of their total carbon flux towards ethanol, rather than non-biofuel end-products, than other Thermoanaerobacter strains. Strains in Clade 2 show the greatest diversity in terms of lignocellulose hydrolysis and utilization, but proportionately produce more non-ethanol end-products than Clade 1 strains. Strains in Clade 3, in which T. thermohydrosulfuricus WC1 is included, show mid-range potential for lignocellulose hydrolysis and utilization, but also exhibit extensive divergence from both Clade 1 and Clade 2 strains in terms of cellular energetics. The potential implications regarding strain selection and suitability for industrial ethanol production through a consolidated bioprocessing co-culturing approach are examined throughout the manuscript.

Protein and peptide biomarkers in organ transplantation

Organ transplantation is the optimal treatment choice for end-stage organ failure in pediatric patients. The ideal maintenance of a transplanted organ requires efficient monitoring tools and an effective individualized post-transplant treatment plan. Currently available post-transplant monitoring options are not ideal because of their invasiveness or their lack of sensitivity and specificity when providing an accurate assessment of transplant injury. Current research on proteins and peptides, including mass spectrometry-based proteomics, can identify novel surrogate protein and peptide biomarkers that can assist in monitoring the graft in order to correctly assess the status of the transplanted organ. In this article, we have critically reviewed current relevant literature to highlight the importance of protein and peptide biomarkers in the field of pediatric organ transplantation, the status of research findings in the field of protein and peptide biomarkers in different organ transplantation and factors that impact and inhibit the progression of protein biomarker discovery in the field of solid-organ transplantation in pediatrics.

New genes expressed in human brains: implications for annotating evolving genomes

New genes have frequently formed and spread to fixation in a wide variety of organisms, constituting abundant sets of lineage-specific genes. It was recently reported that an excess of primate-specific and human-specific genes were upregulated in the brains of fetuses and infants, and especially in the prefrontal cortex, which is involved in cognition. These findings reveal the prevalent addition of new genetic components to the transcriptome of the human brain. More generally, these findings suggest that genomes are continually evolving in both sequence and content, eroding the conservation endowed by common ancestry. Despite increasing recognition of the importance of new genes, we highlight here that these genes are still seriously under-characterized in functional studies and that new gene annotation is inconsistent in current practice. We propose an integrative approach to annotate new genes, taking advantage of functional and evolutionary genomic methods. We finally discuss how the refinement of new gene annotation will be important for the detection of evolutionary forces governing new gene origination.

PaxDb, a database of protein abundance averages across all three domains of life

Although protein expression is regulated both temporally and spatially, most proteins have an intrinsic, "typical" range of functionally effective abundance levels. These extend from a few molecules per cell for signaling proteins, to millions of molecules for structural proteins. When addressing fundamental questions related to protein evolution, translation and folding, but also in routine laboratory work, a simple rough estimate of the average wild type abundance of each detectable protein in an organism is often desirable. Here, we introduce a meta-resource dedicated to integrating information on absolute protein abundance levels; we place particular emphasis on deep coverage, consistent post-processing and comparability across different organisms. Publicly available experimental data are mapped onto a common namespace and, in the case of tandem mass spectrometry data, re-processed using a standardized spectral counting pipeline. By aggregating and averaging over the various samples, conditions and cell-types, the resulting integrated data set achieves increased coverage and a high dynamic range. We score and rank each contributing, individual data set by assessing its consistency against externally provided protein-network information, and demonstrate that our weighted integration exhibits more consistency than the data sets individually. The current PaxDb-release 2.1 (at http://pax-db.org/) presents whole-organism data as well as tissue-resolved data, and covers 85,000 proteins in 12 model organisms. All values can be seamlessly compared across organisms via pre-computed orthology relationships.

The proteomic future: where mass spectrometry should be taking us

A newcomer to the -omics era, proteomics, is a broad instrument-intensive research area that has advanced rapidly since its inception less than 20 years ago. Although the 'wet-bench' aspects of proteomics have undergone a renaissance with the improvement in protein and peptide separation techniques, including various improvements in two-dimensional gel electrophoresis and gel-free or off-gel protein focusing, it has been the seminal advances in MS that have led to the ascension of this field. Recent improvements in sensitivity, mass accuracy and fragmentation have led to achievements previously only dreamed of, including whole-proteome identification, and quantification and extensive mapping of specific PTMs (post-translational modifications). With such capabilities at present, one might conclude that proteomics has already reached its zenith; however, 'capability' indicates that the envisioned goals have not yet been achieved. In the present review we focus on what we perceive as the areas requiring more attention to achieve the improvements in workflow and instrumentation that will bridge the gap between capability and achievement for at least most proteomes and PTMs. Additionally, it is essential that we extend our ability to understand protein structures, interactions and localizations. Towards these ends, we briefly focus on selected methods and research areas where we anticipate the next wave of proteomic advances.

Translational plant proteomics: a perspective

Translational proteomics is an emerging sub-discipline of the proteomics field in the biological sciences. Translational plant proteomics aims to integrate knowledge from basic sciences to translate it into field applications to solve issues related but not limited to the recreational and economic values of plants, food security and safety, and energy sustainability. In this review, we highlight the substantial progress reached in plant proteomics during the past decade which has paved the way for translational plant proteomics. Increasing proteomics knowledge in plants is not limited to model and non-model plants, proteogenomics, crop improvement, and food analysis, safety, and nutrition but to many more potential applications. Given the wealth of information generated and to some extent applied, there is the need for more efficient and broader channels to freely disseminate the information to the scientific community. This article is part of a Special Issue entitled: Translational Proteomics.

Electron transfer dissociation mass spectrometry in proteomics

Mass spectrometry has rapidly evolved to become the platform of choice for proteomic analysis. While CID remains the major fragmentation method for peptide sequencing, electron transfer dissociation (ETD) is emerging as a complementary method for the characterization of peptides and post-translational modifications (PTMs). Here, we review the evolution of ETD and some of its newer applications including characterization of PTMs, non-tryptic peptides and intact proteins. We will also discuss some of the unique features of ETD such as its complementarity with CID and the use of alternating CID/ETD along with issues pertaining to analysis of ETD data. The potential of ETD for applications such as multiple reaction monitoring and proteogenomics in the future will also be discussed.

ENCODE whole-genome data in the UCSC Genome Browser: update 2012

The Encyclopedia of DNA Elements (ENCODE) Consortium is entering its 5th year of production-level effort generating high-quality whole-genome functional annotations of the human genome. The past year has brought the ENCODE compendium of functional elements to critical mass, with a diverse set of 27 biochemical assays now covering 200 distinct human cell types. Within the mouse genome, which has been under study by ENCODE groups for the past 2 years, 37 cell types have been assayed. Over 2000 individual experiments have been completed and submitted to the Data Coordination Center for public use. UCSC makes this data available on the quality-reviewed public Genome Browser (http://genome.ucsc.edu) and on an early-access Preview Browser (http://genome-preview.ucsc.edu). Visual browsing, data mining and download of raw and processed data files are all supported. An ENCODE portal (http://encodeproject.org) provides specialized tools and information about the ENCODE data sets.