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Recent reports have demonstrated that genetically variant peptides derived from human hair shaft proteins can be used to differentiate individuals of different biogeographic origins. We report a method involving direct extraction of hair shaft proteins more sensitive than previously published methods regarding GVP detection. It involves one step for protein extraction and was found to provide reproducible results. A detailed proteomic analysis of this data is presented that led to the following four results: (i) A peptide spectral library was created and made available for download. It contains all identified peptides from this work, including GVPs that, when appropriately expanded with diverse hair‐derived peptides, can provide a routine, reliable, and sensitive means of analyzing hair digests; (ii) an analysis of artifact peptides arising from side reactions is also made using a new method for finding unexpected modifications; (iii) detailed analysis of the gel‐based method employed clearly shows the high degree of cross‐linking or protein association involved in hair digestion, with major GVPs eluting over a wide range of high molecular weights while others apparently arise from distinct non‐cross‐linked proteins; and (v) finally, we show that some of the specific GVP identifications depend on the sample preparation method.

Sprague Dawley rats were exposed to beryllium sulfate (BeSO4), and proteomic and bioinformatic techniques were applied to screen for differentially expressed proteins in their lung tissue and serum. A total of 12 coexpression modules were constructed for 18 samples with 2333 proteins. Four modules were found to have significant differences in the regulation of protein coexpression modules in the serum following exposure to BeSO4. A further three modules had significant differences in the regulation of protein coexpression modules in the lung tissues. Five modules with good correlation were obtained by calculating the gene significance and module membership values, whereas these module Hub proteins included: Hspbp1, Rps15a, Srsf2, Hadhb, Elmo3, Armt1, Rpl18, Afap1L1, Eif3d, Eif3c, and Rps3. The five proteins correlating highest with the Hub proteins in the lung tissue and serum samples were obtained using string analysis. KEGG and GO enrichment analyses showed that these proteins are mainly involved in ribosome formation, apoptosis, cell cycle regulation, and tumor necrosis factor regulation. By analyzing the biological functions of these proteins, proteins that can be used as biomarkers, such as Akt1, Prpf19, Cct2, and Rpl18, are finally obtained.

Copper (Cu) is an essential, but toxic, micronutrient for living organisms and cells have developed sophisticated response mechanisms towards both the lack and the excess of Cu in their environments. In this study, we achieved a global view of Cu-responsive changes in the prokaryotic model organism Rhodobacter capsulatus using label-free quantitative differential proteomics. Semi-aerobically grown cells under heterotrophic conditions in minimal medium (∼0.3 μM Cu) were compared with cells supplemented with either 5 μM Cu or with 5 mM of the Cu-chelator bathocuproine sulfonate. Mass spectrometry based bottom-up proteomics of unfractionated cell lysates identified 2430 of the 3632 putative proteins encoded by the genome, producing a robust proteome dataset for R. capsulatus. Use of biological and technical replicates for each growth condition yielded high reproducibility and reliable quantification for 1926 of the identified proteins. Comparison of cells grown under Cu-excess or Cu-depleted conditions to those grown under minimal Cu-sufficient conditions revealed that 75 proteins exhibited statistically significant (p < 0.05) abundance changes, ranging from 2- to 300-fold. A subset of the highly Cu-responsive proteins was orthogonally probed using molecular genetics, validating that several of them were indeed involved in cellular Cu homeostasis.

Label-free mass spectrometry (MS) has developed into an important tool applied in various fields of biological and life sciences. Several software exist to process the raw MS data into quantified protein abundances, including open source and commercial solutions. Each software includes a set of unique algorithms for different tasks of the MS data processing workflow. While many of these algorithms have been compared separately, a thorough and systematic evaluation of their overall performance is missing. Moreover, systematic information is lacking about the amount of missing values produced by the different proteomics software and the capabilities of different data imputation methods to account for them. In this study, we evaluated the performance of five popular quantitative label-free proteomics software workflows using four different spike-in data sets. Our extensive testing included the number of proteins quantified and the number of missing values produced by each workflow, the accuracy of detecting differential expression and logarithmic fold change and the effect of different imputation and filtering methods on the differential expression results. We found that the Progenesis software performed consistently well in the differential expression analysis and produced few missing values. The missing values produced by the other software decreased their performance, but this difference could be mitigated using proper data filtering or imputation methods. Among the imputation methods, we found that the local least squares (lls) regression imputation consistently increased the performance of the software in the differential expression analysis, and a combination of both data filtering and local least squares imputation increased performance the most in the tested data sets.

Proteinaceous toxins are harmful proteins derived from plants, bacteria, and other natural sources. They pose a risk to human health due to infection and also as possible biological warfare agents. Paper spray mass spectrometry (PS-MS) with wipe sampling was used to detect proteins from surfaces as a potential tool for identifying the presence of these toxins. Proteins ranging in mass between 12.4 and 66.5 kDa were tested, including a biological toxin simulant/vaccine for Staphylococcal enterotoxin B (SEBv). Various substrates were tested for these representative proteins, including a laboratory bench, a notebook cover, steel, glass, plant leaf and vinyl flooring. Carbon sputtered porous polyethylene (CSPP) was found to outperform typical chromatography paper used for paper spray, as well as carbon nanotube (CNT)-coated paper and polyethylene (PE), which have been previously shown to be well-suited for protein analysis. Low microgram quantities of the protein toxin simulant and other test proteins were successfully detected with good signal-to-noise from surfaces using a porous wipe. These applications demonstrate that PS-MS can potentially be used for rapid, sample preparation-free detection of proteins and biological warfare agents, which would be beneficial to first responders and warfighters.

Recent advances in proteomics allow the accurate measurement of abundances for thousands of proteins and phosphoproteins from multiple samples in parallel. Therefore, for the first time, we have the opportunity to measure the proteomic profiles of thousands of patient samples or disease model cell lines in a systematic way, to identify the precise underlying molecular mechanism and discover personalized biomarkers, networks and treatments. Here, we review examples of successful use of proteomics and phosphoproteomics data sets in as well as their integration other omics data sets with the aim of precision medicine. We will discuss the bioinformatics challenges posed by the generation, analysis and integration of such large data sets and present potential reasons why proteomics profiling and biomarkers are not currently widely used in the clinical setting. We will finally discuss ways to contribute to the better use of proteomics data in precision medicine and the clinical setting.

In the past decade, significant progress has been made in complex disease research across multiple omics layers from genome, transcriptome and proteome to metabolome. There is an increasing awareness of the importance of biological interconnections, and much success has been achieved using systems biology approaches. However, because of the typical focus on one single omics layer at a time, existing systems biology findings explain only a modest portion of complex disease. Recent advances in multi-omics data collection and sharing present us new opportunities for studying complex diseases in a more comprehensive fashion, and yet simultaneously create new challenges considering the unprecedented data dimensionality and diversity. Here, our goal is to review extant and emerging network approaches that can be applied across multiple biological layers to facilitate a more comprehensive and integrative multilayered omics analysis of complex diseases.

Pathological examination has been the gold standard for diagnosis in cancer and its role has also included the elucidation of etiology, pathogenesis, clinicopathological correlation, and prognostication. The advent of newer technologies and the realization that breast cancer is heterogeneous has shifted the focus to prognostication, with increased attention being paid to the identification of morphological features and immunohistochemical markers of prognostic relevance. However, despite the massive efforts invested in the identification of immunohistochemical biomarkers in breast cancer the majority have not proven to be of value in multivariate analyses and only estrogen receptor, progesterone receptor, and Her2/neu expression have remained essential components of pathological examination. These 3 markers were initially employed for prognostication but their role in treatment also rendered them of predictive value. Newer molecular methods, especially high-throughput technologies, have shown that even morphologically similar subtypes of breast cancer can show molecular heterogeneity; moreover, infiltrating ductal carcinoma can be separated into at least 4 molecular subtypes designated luminal (ER+, PR+, and Her2/neu–), Her2 overexpressing (ER–, PR–, and Her2/neu+), basal-like (ER–, PR–, Her2/neu–, and CK5/6+, EGFR+), and normal breast-like (ER–, PR–, and Her2/neu–), each with different clinical outcomes. The importance of proliferative gene expression in these subtypes has been demonstrated and surrogate immunohistochemical markers include ER, PR, Her2/neu, and Ki67 for the more expensive molecular tests. Molecular technologies, importantly, have not only provided further insights into the heterogeneity of breast cancer but have also opened new avenues for treatment through the identification of signaling molecules important in the proliferation and survival of the neoplastic cells. The treatment of cancer thus shifts from the conventional approach of ‘one size fits all’ to one of personalized treatment tailored to the specific characteristics of the tumor. Pathologists continue to play their traditional role in diagnosis but, as purveyors of the excised tissue, pathologists now have the additional role of identifying biomarkers responsive to therapeutic manipulation, thus playing an inextricable role as diagnostic oncologists in the management of breast cancer.

Drugs have been implicated in lung injury as a result of direct pharmacological action, persistence or metabolism in the tissue, or via the production of a reactive metabolite or metabolites. The result of this apparent drug-associated injury ranges from cellular dysfunction through to cell death (apoptosis) and alteration of repair mechanisms that are essential in replacing critical tissue elements and function. There is limited knowledge on how timing of drug administration or drug interactions may interfere with the repair mechanisms or modulate the expression of pulmonary toxicity. Chemotherapeutic drugs and novel agents, such as those targeting the epidermal growth factor receptor (EGFR), appear to affect both normal and neoplastic cells. However, unlike chemotherapy, where the actions are systemic and directly as a result of biotransformation or cell injury, it has been postulated that effects of EGFR-targeting agents are more likely to be focused on epithelia via a pharmacological effect. Furthermore, risk factors for the development of adverse pulmonary reactions, as well as biological markers indicating incipient toxicity, need to be prospectively identified. Proteomics, through the identification of >/=1000 proteins or peptides in blood samples, will hopefully identify candidates for this role.

Owing to the emerging impact of bioinformatics and computational biology, in this article, we present an overview of the history and current state of the research on this field in Latin America (LA). It will be difficult to cover without inequality all the efforts, initiatives and works that have happened for the past two decades in this vast region (that includes >19 million km2 and >600 million people). Despite the difficulty, we have done an analytical search looking for publications in the field made by researchers from 19 LA countries in the past 25 years. In this way, we find that research in bioinformatics in this region should develop twice to approach the average world scientific production in the field. We also found some of the pioneering scientists who initiated and led bioinformatics in the region and were promoters of this new scientific field. Our analysis also reveals that spin-off began around some specific areas within the biomolecular sciences: studies on genomes (anchored in the new generation of deep sequencing technologies, followed by developments in proteomics) and studies on protein structures (supported by three-dimensional structural determination technologies and their computational advancement). Finally, we show that the contribution to this endeavour of the Iberoamerican Society for Bioinformatics, founded in Mexico in 2009, has been significant, as it is a leading forum to join efforts of many scientists from LA interested in promoting research, training and education in bioinformatics., The publication charges for this article were funded by the research grant PI15/00328, given by the Instituto de Salud Carlos III (ISCiii, MINECO, Spain) and co-funded by the Fondo Europeo de Desarrollo Regional (FEDER, Europe).

Enzymatic modification of pulp is receiving increasing interest for energy reduction at the refining step of the paper-making process. In this study, the production of a multi-fiber modifying enzyme from Mamillisphaeria sp. BCC8893 was optimized in submerged fermentation using a response-surface methodology. Maximal production was obtained in a complex medium comprising wheat bran, soybean, and rice bran supplemented with yeast extract at pH 6.0 and a harvest time of 7 d, resulting in 9.2 IU/mL of carboxymethyl cellulase (CMCase), 14.9 IU/mL of filter paper activity (FPase), and 242.7 IU/mL of xylanase. Treatment of old corrugated container pulp at 0.2-0.3 IU of CMCase/g of pulp led to reductions in refining energy of 8.5-14.8%. The major physical properties were retained, including tensile and compression strength. Proteomic analysis showed that the enzyme was a complex composite of endo-glucanases, cellobiohydrolases, beta-1,4-xylanases, and beta-glucanases belonging to various glycosyl hydrolase families, suggestive of cooperative enzyme action in fiber modification, providing the basis for refining efficiency.

Surface treatment of biological tissue sections improves detection of peptides and proteins for mass spectrometry imaging. However, liquid surface treatments can result in diffusion of surface analytes and fragile tissue sections can be easily damaged by typical washing solvents. Here, we present a new surface washing procedure for mass spectrometry imaging. This procedure uses solvent wetted fiber-free paper to enable local washing of tissue sections for mass spectrometry imaging and tissue profiling experiments. In addition, the method allows fragile tissues that cannot be treated by conventional washing techniques to be analyzed by mass spectrometry imaging.

Our limited understanding of the biological impact of the whole spectrum of early breast lesions together with a lack of accurate molecular-based risk criteria for the diagnosis and assignment of prognostic significance to biopsy findings presents an important problem in the clinical management of patients harboring precancerous breast lesions. As a result, there is a need to identify biomarkers that can better determine the outcome of early breast lesions by identifying subpopulations of cells in breast premalignant disease that are at high-risk of progression to invasive disease. A first step towards achieving this goal will be to define the molecular phenotypes of the various cell types and precursors - generated by the stem cell hierarchy - that are present in normal and benign conditions of the breast. To date there have been very few systematic proteomic studies aimed at characterizing the phenotypes of the different cell subpopulations present in normal human mammary tissue, partly due to the formidable heterogeneity of mammary tissue, but also due to limitations of the current proteomic technologies. Work in our laboratories has attempted to address in a systematic fashion some of these limitations and here we present our efforts to search for biomarkers using normal fresh tissue from non-neoplastic breast samples. From the data generated by the 2D gel-based proteomic profiling we were able to compile a protein database of normal human breast epithelial tissue that was used to support the biomarker discovery program. We review and present new data on the putative cell-progenitor marker cytokeratin 15 (CK15), and describe a novel marker, dihydropyriminidase-related protein 3 (DRP3) that in combination with CK15 and other well known proteins were used to define molecular phenotypes of normal human breast epithelial cells and their progenitors in resting acini, lactating alveoli, and large collecting ducts of the nipple. Preliminary results are also presented concerning DRP3 positive usual ductal hyperplasias (UDHs) and on single cell layer columnar cells (CCCs). At least two bona fide biomarkers of undifferentiated ERα/PgR negative luminal cells emerged from these studies, CK15 and c-KIT, which in combination with transformation markers may lead to the establishment of a protein signature able to identify breast precancerous at risk of progressing to invasive disease.

Background: The rapid and reliable identification of biomarkers in the smallest possible amount of blood remains a challenge in biomarker epidemiological research involving preterm newborns. Objective: We wanted to explore whether the proteomics approach of ‘surface-enhanced laser desorption/ionization-time of flight mass spectrometry’ (SELDI-TOF MS) is possible and feasible in whole cord blood previously dried on filter paper. Methods: Umbilical cord blood from 7 healthy newborns was frozen as serum, whole blood (with or without additives), or dried on filter paper (with or without additives). We used the SELDI-TOF MS technique for protein detection on the ProteinChip® arrays: weak cationic exchange array (CM10), hydrophobic array (H50), and strong anion exchange array (Q10). Profiles were compared in terms of peak intensity and number of resolved peaks. Results: Dried neonatal blood, eluted from filter paper, revealed profiles similar to the profiles derived from serum at a protein range of 3–10 kDa. Among additives, heparin led to highest peak intensities for both blood and dried blood. Spectra from heparinized whole blood and heparinized dried blood from the umbilical cord of 8 different healthy newborns on three different types of ProteinChip arrays were very similar. Conclusion: We conclude that it is possible and feasible to use SELDI-TOF MS for recovery and detection of whole proteins from dried blood collected on filter paper. The method is easy to perform in large groups of newborns, minimizing the amount of blood needed for biomarker studies. The validity and reproducibility of this method needs to be studied in detail.

Aims—Laser capture microdissection is a recent development that enables the isolation of specific cell types for subsequent molecular analysis. This study describes a method for obtaining proteome information from laser capture microdissected tissue using colon cancer as a model. Methods—Laser capture microdissection was performed on toluidine blue stained frozen sections of colon cancer. Tumour cells were selectively microdissected. Conditions were established for solubilising proteins from laser microdissected samples and these proteins were separated by two dimensional gel electrophoresis. Individual protein spots were cut from the gel, characterised by mass spectrometry, and identified by database searching. These results were compared with protein expression patterns and mass spectroscopic data obtained from bulk tumour samples run in parallel. Results—Proteins could be recovered from laser capture microdissected tissue in a form suitable for two dimensional gel electrophoresis. The solubilised proteins retained their expected electrophoretic mobility in two dimensional gels as compared with bulk samples, and mass spectrometric analysis was also unaffected. Conclusion—A method for performing two dimensional gel electrophoresis and mass spectrometry using laser capture microdissected tissue has been developed.