Integrated Proteomic Pipeline Using Multiple Search Engines for a Proteogenomic Study with a Controlled Protein False Discovery Rate

Integration of transcriptomic and proteomic reveals the toxicological molecular mechanisms of decabromodiphenyl ethane (DBDPE) on Pleurotus ostreatus

Decabromodiphenyl ethane (DBDPE), as one of the most widely used new brominated flame retardants (NBFRs), can pose a potential threat to human health and the environment. An integrated transcriptome and proteome was performed for investigating the toxicological molecular mechanisms of Pleurotus ostreatus (P. ostreatus) during the biodegradation of DBDPE at the concentrations of 5 and 20 mg/L. A total of 1193/1018 and 92/126 differentially expressed genes/proteins (DEGs/DEPs) were found, respectively, with DBDPE exposure at 5 and 20 mg/L. These DEGs and DEPs were mainly involved in the cellular process as well as metabolic process. DEPs for oxidation-reduction process and hydrolase activity were up-regulated, and those for membrane, lipid metabolic process and transmembrane transport were down-regulated. The DEGs and DEPs related to some key enzymes were down-regulated, such as NADH dehydrogenase/oxidoreductase, succinate dehydrogenase, cytochrome C1 protein, cytochrome-c oxidase/reductase and ATP synthase, which indicated that DBDPE affected the oxidative phosphorylation as well as tricarboxylic acid (TCA) cycle. Cytochrome P450 enzymes (CYPs) might be involved in DBDPE degradation through hydroxylation and oxidation. Some stress proteins were induced to resist DBDPE toxicity, including major facilitator superfamily (MFS) transporter, superoxide dismutase (SOD), molecular chaperones, heat shock proteins (HSP20, HSP26, HSP42), 60S ribosomal protein and histone H4. The findings help revealing the toxicological molecular mechanisms of DBDPE on P. ostreatus, aiming to improve the removal of DBDPE.

False discovery rate: the Achilles' heel of proteogenomics

Proteogenomics refers to the integrated analysis of the genome and proteome that leverages mass-spectrometry (MS)-based proteomics data to improve genome annotations, understand gene expression control through proteoforms and find sequence variants to develop novel insights for disease classification and therapeutic strategies. However, proteogenomic studies often suffer from reduced sensitivity and specificity due to inflated database size. To control the error rates, proteogenomics depends on the target-decoy search strategy, the de-facto method for false discovery rate (FDR) estimation in proteomics. The proteogenomic databases constructed from three- or six-frame nucleotide database translation not only increase the search space and compute-time but also violate the equivalence of target and decoy databases. These searches result in poorer separation between target and decoy scores, leading to stringent FDR thresholds. Understanding these factors and applying modified strategies such as two-pass database search or peptide-class-specific FDR can result in a better interpretation of MS data without introducing additional statistical biases. Based on these considerations, a user can interpret the proteogenomics results appropriately and control false positives and negatives in a more informed manner. In this review, first, we briefly discuss the proteogenomic workflows and limitations in database construction, followed by various considerations that can influence potential novel discoveries in a proteogenomic study. We conclude with suggestions to counter these challenges for better proteogenomic data interpretation.

Unsupervised Mining of HLA-I Peptidomes Reveals New Binding Motifs and Potential False Positives in the Community Database

Modern vaccine designs and studies of human leukocyte antigen (HLA)-mediated immune responses rely heavily on the knowledge of HLA allele-specific binding motifs and computational prediction of HLA-peptide binding affinity. Breakthroughs in HLA peptidomics have considerably expanded the databases of natural HLA ligands and enabled detailed characterizations of HLA-peptide binding specificity. However, cautions must be made when analyzing HLA peptidomics data because identified peptides may be contaminants in mass spectrometry or may weakly bind to the HLA molecules. Here, a hybrid de novo peptide sequencing approach was applied to large-scale mono-allelic HLA peptidomics datasets to uncover new ligands and refine current knowledge of HLA binding motifs. Up to 12-40% of the peptidomics data were low-binding affinity peptides with an arginine or a lysine at the C-terminus and likely to be tryptic peptide contaminants. Thousands of these peptides have been reported in a community database as legitimate ligands and might be erroneously used for training prediction models. Furthermore, unsupervised clustering of identified ligands revealed additional binding motifs for several HLA class I alleles and effectively isolated outliers that were experimentally confirmed to be false positives. Overall, our findings expanded the knowledge of HLA binding specificity and advocated for more rigorous interpretation of HLA peptidomics data that will ensure the high validity of community HLA ligandome databases.

Critical Assessment of MetaProteome Investigation (CAMPI): a multi-laboratory comparison of established workflows

Metaproteomics has matured into a powerful tool to assess functional interactions in microbial communities. While many metaproteomic workflows are available, the impact of method choice on results remains unclear. Here, we carry out a community-driven, multi-laboratory comparison in metaproteomics: the critical assessment of metaproteome investigation study (CAMPI). Based on well-established workflows, we evaluate the effect of sample preparation, mass spectrometry, and bioinformatic analysis using two samples: a simplified, laboratory-assembled human intestinal model and a human fecal sample. We observe that variability at the peptide level is predominantly due to sample processing workflows, with a smaller contribution of bioinformatic pipelines. These peptide-level differences largely disappear at the protein group level. While differences are observed for predicted community composition, similar functional profiles are obtained across workflows. CAMPI demonstrates the robustness of present-day metaproteomics research, serves as a template for multi-laboratory studies in metaproteomics, and provides publicly available data sets for benchmarking future developments.

Critical Assessment of MetaProteome Investigation (CAMPI): a multi-laboratory comparison of established workflows

Metaproteomics has matured into a powerful tool to assess functional interactions in microbial communities. While many metaproteomic workflows are available, the impact of method choice on results remains unclear. Here, we carry out a community-driven, multi-laboratory comparison in metaproteomics: the critical assessment of metaproteome investigation study (CAMPI). Based on well-established workflows, we evaluate the effect of sample preparation, mass spectrometry, and bioinformatic analysis using two samples: a simplified, laboratory-assembled human intestinal model and a human fecal sample. We observe that variability at the peptide level is predominantly due to sample processing workflows, with a smaller contribution of bioinformatic pipelines. These peptide-level differences largely disappear at the protein group level. While differences are observed for predicted community composition, similar functional profiles are obtained across workflows. CAMPI demonstrates the robustness of present-day metaproteomics research, serves as a template for multi-laboratory studies in metaproteomics, and provides publicly available data sets for benchmarking future developments.

Psoriasis to Psoriatic Arthritis: The Application of Proteomics Technologies

Psoriatic disease (PsD) is a spectrum of diseases that affect both skin [cutaneous psoriasis (PsC)] and musculoskeletal features [psoriatic arthritis (PsA)]. A considerable number of patients with PsC have asymptomatic synovio-entheseal inflammations, and approximately one-third of those eventually progress to PsA with an enigmatic mechanism. Published studies have shown that early interventions to the very early-stage PsA would effectively prevent substantial bone destructions or deformities, suggesting an unmet goal for exploring early PsA biomarkers. The emergence of proteomics technologies brings a complete view of all involved proteins in PsA transitions, offers a unique chance to map all potential peptides, and allows a direct head-to-head comparison of interaction pathways in PsC and PsA. This review summarized the latest development of proteomics technologies, highlighted its application in PsA biomarker discovery, and discussed the possible clinical detectable PsA risk factors in patients with PsC.

Proteogenomic Analysis Provides Novel Insight into Genome Annotation and Nitrogen Metabolism in Nostoc sp. PCC 7120

Cyanobacteria, capable of oxygenic photosynthesis, play a vital role in nitrogen and carbon cycles. Nostoc sp. PCC 7120 (Nostoc 7120) is a model cyanobacterium commonly used to study cell differentiation and nitrogen metabolism. Although its genome was released in 2002, a high-quality genome annotation remains unavailable for this model cyanobacterium. Therefore, in this study, we performed an in-depth proteogenomic analysis based on high-resolution mass spectrometry (MS) data to refine the genome annotation of Nostoc 7120. We unambiguously identified 5,519 predicted protein-coding genes and revealed 26 novel genes, 75 revised genes, and 27 different kinds of posttranslational modifications in Nostoc 7120. A subset of these novel proteins were further validated at both the mRNA and peptide levels. Functional analysis suggested that many newly annotated proteins may participate in nitrogen or cadmium/mercury metabolism in Nostoc 7120. Moreover, we constructed an updated Nostoc 7120 database based on our proteogenomic results and presented examples of how the updated database could be used to improve the annotation of proteomic data. Our study provides the most comprehensive annotation of the Nostoc 7120 genome thus far and will serve as a valuable resource for the study of nitrogen metabolism in Nostoc 7120.

IMPORTANCE Cyanobacteria are a large group of prokaryotes capable of oxygenic photosynthesis and play a vital role in nitrogen and carbon cycles on Earth. Nostoc 7120 is a commonly used model cyanobacterium for studying cell differentiation and nitrogen metabolism. In this study, we presented the first comprehensive draft map of the Nostoc 7120 proteome and a wide range of posttranslational modifications. In addition, we constructed an updated database of Nostoc 7120 based on our proteogenomic results and presented examples of how the updated database could be used for system-level studies of Nostoc 7120. Our study provides the most comprehensive annotation of Nostoc 7120 genome and a valuable resource for the study of nitrogen metabolism in this model cyanobacterium.

Peptidomics and proteogenomics: background, challenges and future needs

INTRODUCTION

With available genomic data and related information, it is becoming possible to better highlight mutations or genomic alterations associated with a particular disease or disorder. The advent of high-throughput sequencing technologies has greatly advanced diagnostics, prognostics, and drug development.

AREAS COVERED

Peptidomics and proteogenomics are the two post-genomic technologies that enable the simultaneous study of peptides and proteins/transcripts/genes. Both technologies add a remarkably large amount of data to the pool of information on various peptides associated with gene mutations or genome remodeling. Literature search was performed in the PubMed database and is up to date.

EXPERT OPINION

This article lists various techniques used for peptidomic and proteogenomic analyses. It also explains various bioinformatics workflows developed to understand differentially expressed peptides/proteins and their role in disease pathogenesis. Their role in deciphering disease pathways, cancer research, and biomarker discovery using biofluids is highlighted. Finally, the challenges and future requirements to overcome the current limitations for their effective clinical use are also discussed.

Advances in Current Diabetes Proteomics: From the Perspectives of Label- free Quantification and Biomarker Selection

BACKGROUND

Due to its prevalence and negative impacts on both the economy and society, the diabetes mellitus (DM) has emerged as a worldwide concern. In light of this, the label-free quantification (LFQ) proteomics and diabetic marker selection methods have been applied to elucidate the underlying mechanisms associated with insulin resistance, explore novel protein biomarkers, and discover innovative therapeutic protein targets.

OBJECTIVE

The purpose of this manuscript is to review and analyze the recent computational advances and development of label-free quantification and diabetic marker selection in diabetes proteomics.

METHODS

Web of Science database, PubMed database and Google Scholar were utilized for searching label-free quantification, computational advances, feature selection and diabetes proteomics.

RESULTS

In this study, we systematically review the computational advances of label-free quantification and diabetic marker selection methods which were applied to get the understanding of DM pathological mechanisms. Firstly, different popular quantification measurements and proteomic quantification software tools which have been applied to the diabetes studies are comprehensively discussed. Secondly, a number of popular manipulation methods including transformation, pretreatment (centering, scaling, and normalization), missing value imputation methods and a variety of popular feature selection techniques applied to diabetes proteomic data are overviewed with objective evaluation on their advantages and disadvantages. Finally, the guidelines for the efficient use of the computationbased LFQ technology and feature selection methods in diabetes proteomics are proposed.

CONCLUSION

In summary, this review provides guidelines for researchers who will engage in proteomics biomarker discovery and by properly applying these proteomic computational advances, more reliable therapeutic targets will be found in the field of diabetes mellitus.

Identification and differential expression of serotransferrin and apolipoprotein A-I in the plasma of HIV-1 patients treated with first-line antiretroviral therapy

Background

Plasma proteins are known to interfere the drug metabolism during therapy. As limited information is available regarding the role of plasma proteins in HIV drug resistance during ART in HIV/AIDS patients, the present study aimed to identify and characterize the differentially expressed plasma proteins in the drug resistant and drug respondent groups of HIV-1 infected patients with > 6 years of first line ART.

Methods

Four-drug resistant (treatment failure) and four-drug respondent (treatment responder) patients were selected for plasma proteomic analysis based on viral load and drug resistance associated mutations from a cohort study designed on the first line ART patients who were enrolled in the antiretroviral therapy center, Sarojini Naidu Medical College, Agra, India from December 2009 to November 2016.

After depleting high abundant proteins, plasma proteins were resolved using two-dimensional gel electrophoresis on IPG strips, pH range of 3–10. Spots were selected in the gel based on the density of staining which was common in the drug resistant and drug respondent groups separately. The fold change of each spot was calculated using image-J. Each protein spot was identified using the matrix assisted laser desorption/ionization-time of flight/time of flight (MALDI-TOF/TOF) after tryptic digestion. Peptide peaks were identified through flex analysis version 3.3, and a search against a protein data base using the internal Mascot. Gene ontology study was completed through STRING v.11 and Panther15.0.

Results

Out of eight spots from 2D gel samples analyzed by MALDITOF/TOF, two proteins were found to have significant score (> 56) after Flex analysis. These two proteins were identified to be apolipoprotein A1 and serotransferrin. The fold change expression of these two proteins were analyzed in drug resistant and drug respondent group. Apolipoprotein-A1 and serotransferrin were observed to be expressed 1.76 and 1.13-fold more respectively in drug respondent group compared to drug resistant group. The gene ontology analysis revealed the involvement of these two proteins in various important physiological processes.

Conclusion

Apolipoprotein A-I and serotransferrin were found to be expressed more in drug respondent group compared to drug resistant group.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-020-05610-6.

The Age of Data-Driven Proteomics: How Machine Learning Enables Novel Workflows

A lot of energy in the field of proteomics is dedicated to the application of challenging experimental workflows, which include metaproteomics, proteogenomics, data independent acquisition (DIA), non-specific proteolysis, immunopeptidomics, and open modification searches. These workflows are all challenging because of ambiguity in the identification stage; they either expand the search space and thus increase the ambiguity of identifications, or, in the case of DIA, they generate data that is inherently more ambiguous. In this context, machine learning-based predictive models are now generating considerable excitement in the field of proteomics because these predictive models hold great potential to drastically reduce the ambiguity in the identification process of the above-mentioned workflows. Indeed, the field has already produced classical machine learning and deep learning models to predict almost every aspect of a liquid chromatography-mass spectrometry (LC-MS) experiment. Yet despite all the excitement, thorough integration of predictive models in these challenging LC-MS workflows is still limited, and further improvements to the modeling and validation procedures can still be made. Therefore, highly promising recent machine learning developments in proteomics are pointed out in this viewpoint, alongside some of the remaining challenges.

Pollination Drop Proteome and Reproductive Organ Transcriptome Comparison in Gnetum Reveals Entomophilous Adaptation

Gnetum possesses morphologically bisexual but functionally unisexual reproductive structures that exude sugary pollination drops to attract insects. Previous studies have revealed that the arborescent species (G. gnemon L.) and the lianoid species (G. luofuense C.Y.Cheng) possess different pollination syndromes. This study compared the proteome in the pollination drops of these two species using label-free quantitative techniques. The transcriptomes of fertile reproductive units (FRUs) and sterile reproductive units (SRUs) for each species were furthermore compared using Illumina Hiseq sequencing, and integrated proteomic and transcriptomic analyses were subsequently performed. Our results show that the differentially expressed proteins between FRUs and SRUs were involved in carbohydrate metabolism, the biosynthesis of amino acids and ovule defense. In addition, the differentially expressed genes between the FRUs and SRUs (e.g., MADS-box genes) were engaged in reproductive development and the formation of pollination drops. The integrated protein-transcript analyses revealed that FRUs and their exudates were relatively conservative while the SRUs and their exudates were more diverse, probably functioning as pollinator attractants. The evolution of reproductive organs appears to be synchronized with changes in the pollination drop proteome of Gnetum, suggesting that insect-pollinated adaptations are not restricted to angiosperms but also occur in gymnosperms.

Beyond Genes: Re-Identifiability of Proteomic Data and Its Implications for Personalized Medicine

The increasing availability of high throughput proteomics data provides us with opportunities as well as posing new ethical challenges regarding data privacy and re-identifiability of participants. Moreover, the fact that proteomics represents a level between the genotype and the phenotype further exacerbates the situation, introducing dilemmas related to publicly available data, anonymization, ownership of information and incidental findings. In this paper, we try to differentiate proteomics from genomics data and cover the ethical challenges related to proteomics data sharing. Finally, we give an overview of the proposed solutions and the outlook for future studies.

The Novel Cerato-Platanin-Like Protein FocCP1 from Fusarium oxysporum Triggers an Immune Response in Plants

Panama disease, or Fusarium wilt, the most serious disease in banana cultivation, is caused by Fusarium oxysporum f. sp. cubense (FOC) and has led to great economic losses worldwide. One effective way to combat this disease is by enhancing host plant resistance. The cerato-platanin protein (CPP) family is a group of small secreted cysteine-rich proteins in filamentous fungi. CPPs as elicitors can trigger the immune system resulting in defense responses in plants. In this study, we characterized a novel cerato-platanin-like protein in the secretome of Fusarium oxysporum f. sp. cubense race 4 (FOC4), named FocCP1. In tobacco, the purified recombinant FocCP1 protein caused accumulation of reactive oxygen species (ROS), formation of necrotic reaction, deposition of callose, expression of defense-related genes, and accumulation of salicylic acid (SA) and jasmonic acid (JA) in tobacco. These results indicated that FocCP1 triggered a hypersensitive response (HR) and systemic acquired resistance (SAR) in tobacco. Furthermore, FocCP1 enhanced resistance tobacco mosaic virus (TMV) disease and Pseudomonas syringae pv. tabaci 6605 (Pst. 6605) infection in tobacco and improved banana seedling resistance to FOC4. All results provide the possibility of further research on immune mechanisms of plant and pathogen interactions, and lay a foundation for a new biological strategy of banana wilt control in the future.

Proteogenomics in the context of the Human Proteome Project (HPP)

INTRODUCTION

The technological and scientific progress performed in the Human Proteome Project (HPP) has provided to the scientific community a new set of experimental and bioinformatic methods in the challenging field of shotgun and SRM/MRM-based Proteomics. The requirements for a protein to be considered experimentally validated are now well-established, and the information about the human proteome is available in the neXtProt database, while targeted proteomic assays are stored in SRMAtlas. However, the study of the missing proteins continues being an outstanding issue. Areas covered: This review is focused on the implementation of proteogenomic methods designed to improve the detection and validation of the missing proteins. The evolution of the methodological strategies based on the combination of different omic technologies and the use of huge publicly available datasets is shown taking the Chromosome 16 Consortium as reference. Expert commentary: Proteogenomics and other strategies of data analysis implemented within the C-HPP initiative could be used as guidance to complete in a near future the catalog of the human proteins. Besides, in the next years, we will probably witness their use in the B/D-HPP initiative to go a step forward on the implications of the proteins in the human biology and disease.

Sipros Ensemble improves database searching and filtering for complex metaproteomics

Motivation

Complex microbial communities can be characterized by metagenomics and metaproteomics. However, metagenome assemblies often generate enormous, and yet incomplete, protein databases, which undermines the identification of peptides and proteins in metaproteomics. This challenge calls for increased discrimination of true identifications from false identifications by database searching and filtering algorithms in metaproteomics.

Results

Sipros Ensemble was developed here for metaproteomics using an ensemble approach. Three diverse scoring functions from MyriMatch, Comet and the original Sipros were incorporated within a single database searching engine. Supervised classification with logistic regression was used to filter database searching results. Benchmarking with soil and marine microbial communities demonstrated a higher number of peptide and protein identifications by Sipros Ensemble than MyriMatch/Percolator, Comet/Percolator, MS-GF+/Percolator, Comet & MyriMatch/iProphet and Comet & MyriMatch & MS-GF+/iProphet. Sipros Ensemble was computationally efficient and scalable on supercomputers.

Availability and implementation

Freely available under the GNU GPL license at http://sipros.omicsbio.org.

Supplementary information

Supplementary data are available at Bioinformatics online.

Single-platform ‘multi-omic’ profiling: unified mass spectrometry and computational workflows for integrative proteomics–metabolomics analysis

Advances in instrumentation and analysis tools are permitting evermore comprehensive interrogation of diverse biomolecules and allowing investigators to move from linear signaling cascades to network models, which more accurately reflect the molecular basis of biological systems and processes.

Advances in the Chromosome-Centric Human Proteome Project: looking to the future

INTRODUCTION

The mission of the Chromosome-Centric Human Proteome Project (C-HPP), is to map and annotate the entire predicted human protein set (~20,000 proteins) encoded by each chromosome. The initial steps of the project are focused on 'missing proteins (MPs)', which lacked documented evidence for existence at protein level. In addition to remaining 2,579 MPs, we also target those annotated proteins having unknown functions, uPE1 proteins, alternative splice isoforms and post-translational modifications. We also consider how to investigate various protein functions involved in cis-regulatory phenomena, amplicons lncRNAs and smORFs. Areas covered: We will cover the scope, historic background, progress, challenges and future prospects of C-HPP. This review also addresses the question of how we can best improve the methodological approaches, select the optimal biological samples, and recommend stringent protocols for the identification and characterization of MPs. A new strategy for functional analysis of some of those annotated proteins having unknown function will also be discussed. Expert commentary: If the project moves well by reshaping the original goals, the current working modules and team work in the proposed extended planning period, it is anticipated that a progressively more detailed draft of an accurate chromosome-based proteome map will become available with functional information.

Next Generation Proteomic Pipeline for Chromosome-Based Proteomic Research Using NeXtProt and GENCODE Databases

Human Proteome Project aims to map all human proteins including missing proteins as well as proteoforms with post translational modifications, alternative splicing variants (ASVs), and single amino acid variants (SAAVs). neXtProt and Ensemble databases are usually used to provide curated information on human coding genes. However, to find these proteoforms, we (Chr #11 team) first introduce a streamlined pipeline using customized and concatenated neXtProt and GENCODE originated from Ensemble, with controlled false discovery rate (FDR). Because of large sized databases used in this pipeline, we found more stringent FDR filtering (0.1% at the peptide level and 1% at the protein level) to claim novel findings, such as GENCODE ASVs and missing proteins, from human hippocampus data set (MSV000081385) and ProteomeXchange (PXD007166). Using our next generation proteomic pipeline (nextPP) with neXtProt and GENCODE databases, two missing proteins such as activity-regulated cytoskeleton-associated protein (ARC, Chr 8) and glutamate receptor ionotropic, kainite 5 (GRIK5, Chr 19) were additionally identified with two or more unique peptides from human brain tissues. Additionally, by applying the pipeline to human brain related data sets such as cortex (PXD000067 and PXD000561), spinal cord, and fetal brain (PXD000561), seven GENCODE ASVs such as ACTN4-012 (Chr.19), DPYSL2-005 (Chr.8), MPRIP-003 (Chr.17), NCAM1-013 (Chr.11), EPB41L1-017 (Chr.20), AGAP1-004 (Chr.2), and CPNE5-005 (Chr.6) were identified from two or more data sets. The identified peptides of GENCODE ASVs were mapped onto novel exon insertions, alternative translations at 5'-untranslated region, or novel protein coding sequence. Applying the pipeline to male reproductive organ related data sets, 52 GENCODE ASVs were identified from two testis (PXD000561 and PXD002179) and a spermatozoa (PXD003947) data sets. Four out of 52 GENCODE ASVs such as RAB11FIP5-008 (Chr. 2), RP13-347D8.7-001 (Chr. X), PRDX4-002 (Chr. X), and RP11-666A8.13-001 (Chr. 17) were identified in all of the three samples.

Enhanced Missing Proteins Detection in NCI60 Cell Lines Using an Integrative Search Engine Approach

The Human Proteome Project (HPP) aims deciphering the complete map of the human proteome. In the past few years, significant efforts of the HPP teams have been dedicated to the experimental detection of the missing proteins, which lack reliable mass spectrometry evidence of their existence. In this endeavor, an in depth analysis of shotgun experiments might represent a valuable resource to select a biological matrix in design validation experiments. In this work, we used all the proteomic experiments from the NCI60 cell lines and applied an integrative approach based on the results obtained from Comet, Mascot, OMSSA, and X!Tandem. This workflow benefits from the complementarity of these search engines to increase the proteome coverage. Five missing proteins C-HPP guidelines compliant were identified, although further validation is needed. Moreover, 165 missing proteins were detected with only one unique peptide, and their functional analysis supported their participation in cellular pathways as was also proposed in other studies. Finally, we performed a combined analysis of the gene expression levels and the proteomic identifications from the common cell lines between the NCI60 and the CCLE project to suggest alternatives for further validation of missing protein observations.

Antimicrobial and Antibiofilm Effects of Human Amniotic/Chorionic Membrane Extract on Streptococcus pneumoniae

Background:Streptococcus pneumoniae colonize the human nasopharynx in the form of biofilms. The biofilms act as bacterial reservoirs and planktonic bacteria from these biofilms can migrate to other sterile anatomical sites to cause pneumonia, otitis media (OM), bacteremia and meningitis. Human amniotic membrane contains numerous growth factors and antimicrobial activity; however, these have not been studied in detail. In this study, we prepared amniotic membrane extract and chorionic membrane extract (AME/CME) and evaluated their antibacterial and antibiofilm activities against S. pneumoniae using an in vitro biofilm model and in vivo OM rat model.

Materials and Methods: The AME/CME were prepared and protein was quantified using DC^TM (detergent compatible) method. The minimum inhibitory concentrations were determined using broth dilution method, and the synergistic effect of AME/CME with Penicillin-streptomycin was detected checkerboard. The in vitro biofilm and in vivo colonization of S. pneumoniae were studied using microtiter plate assay and OM rat model, respectively. The AME/CME-treated biofilms were examined using scanning electron microscope and confocal microscopy. To examine the constituents of AME/CME, we determined the proteins and peptides of AME/CME using tandem mass tag-based quantitative mass spectrometry.

Results: AME/CME treatment significantly (p < 0.05) inhibited S. pneumoniae growth in planktonic form and in biofilms. Combined application of AME/CME and Penicillin-streptomycin solution had a synergistic effect against S. pneumoniae. Biofilms grown with AME/CME were thin, scattered, and unorganized. AME/CME effectively eradicated pre-established pneumococci biofilms and has a bactericidal effect. AME treatment significantly (p < 0.05) reduced bacterial colonization in the rat middle ear. The proteomics analysis revealed that the AME/CME contains hydrolase, ribonuclease, protease, and other antimicrobial proteins and peptides.

Conclusion: AME/CME inhibits S. pneumoniae growth in the planktonic and biofilm states via its antimicrobial proteins and peptides. AME/CME are non-cytotoxic, natural human product; therefore, they may be used alone or with antibiotics to treat S. pneumoniae infections.