ProteoSign: an end-user online differential proteomics statistical analysis platform

Mapping Novel Frataxin Mitochondrial Networks Through Protein- Protein Interactions

Friedreich's Ataxia (FRDA) is a neuromuscular degenerative disorder caused by trinucleotide expansions in the first intron of the frataxin (FXN) gene, resulting in insufficient levels of functional FNX protein. Deficits in FXN involve mitochondrial disruptions including iron-sulfur cluster synthesis and impaired energetics. These studies were to identify unique protein-protein interactions with FXN to better understand its function and design therapeutics. Two complementary approaches were employed, BioID and Co-IP, to identify protein interactions with FXN at the direct binding, indirect binding, and non-proximal levels. Forty-one novel protein interactions were identified by BioID and IP techniques. The FXN protein landscape was further analyzed incorporating both interaction type and functional pathways using a maximum path of 6 proteins with a potential direct interaction between FXN and NFS1. Probing the intersection between FXN-protein landscape and biological pathways associated with FRDA, we identified 41 proteins of interest. Peroxiredoxin 3 (Prdx3) was chosen for further analysis because of its role in mitochondrial oxidative injury. Our data has demonstrated the strengths of employing complementary methods to identify a unique interactome for FXN. Our data provides new insights into FXN function and regulation, a potential direct interaction between FXN and NFS1, and pathway interactions between FXN and Prdx3.

Leave-one-out-analysis (LOOA): web-based tool to predict influential proteins and interactions in aggregate-crosslinking proteomic data

Many age-progressive diseases are accompanied by (and likely caused by) the presence of protein aggregation in affected tissues. Protein aggregates are conjoined by complex protein-protein interactions, which remain poorly understood. Knowledge of the proteins that comprise aggregates, and their adherent interfaces, can be useful to identify therapeutic targets to treat or prevent pathology, and to discover small molecules for disease interventions. We present web-based software to evaluate and rank influential proteins and protein-protein interactions based on graph modelling of the cross linked aggregate interactome. We have used two network-graph-based techniques: Leave-One-Vertex-Out (LOVO) and Leave-One-Edge-Out (LOEO), each followed by dimension reduction and calculation of influential vertices and edges using Principal Components Analysis (PCA) implemented as an R program. This method enables researchers to quickly and accurately determine influential proteins and protein-protein interactions present in their aggregate interactome data.

METTL3-Mediated m6A Modification Controls Splicing Factor Abundance and Contributes to Aggressive CLL

RNA splicing dysregulation underlies the onset and progression of cancers. In chronic lymphocytic leukemia (CLL), spliceosome mutations leading to aberrant splicing occur in ∼20% of patients. However, the mechanism for splicing defects in spliceosome-unmutated CLL cases remains elusive. Through an integrative transcriptomic and proteomic analysis, we discover that proteins involved in RNA splicing are posttranscriptionally upregulated in CLL cells, resulting in splicing dysregulation. The abundance of splicing complexes is an independent risk factor for poor prognosis. Moreover, increased splicing factor expression is highly correlated with the abundance of METTL3, an RNA methyltransferase that deposits N6-methyladenosine (m6A) on mRNA. METTL3 is essential for cell growth in vitro and in vivo and controls splicing factor protein expression in a methyltransferase-dependent manner through m6A modification-mediated ribosome recycling and decoding. Our results uncover METTL3-mediated m6A modification as a novel regulatory axis in driving splicing dysregulation and contributing to aggressive CLL.

SIGNIFICANCE

METTL3 controls widespread splicing factor abundance via translational control of m6A-modified mRNA, contributes to RNA splicing dysregulation and disease progression in CLL, and serves as a potential therapeutic target in aggressive CLL. See related commentary by Janin and Esteller, p. 176. This article is highlighted in the In This Issue feature, p. 171.

promor: a comprehensive R package for label-free proteomics data analysis and predictive modeling

Summary

We present promor, a comprehensive, user-friendly R package that streamlines label-free quantification proteomics data analysis and building machine learning-based predictive models with top protein candidates.

Availability and implementation

promor is freely available as an open source R package on the Comprehensive R Archive Network (CRAN) (https://CRAN.R-project.org/package=promor) and distributed under the Lesser General Public License (version 2.1 or later). Development version of promor is maintained on GitHub (https://github.com/caranathunge/promor) and additional documentation and tutorials are provided on the package website (https://caranathunge.github.io/promor/).

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

ImShot: An Open-Source Software for Probabilistic Identification of Proteins In Situ and Visualization of Proteomics Data

Imaging mass spectrometry (IMS) has developed into a powerful tool allowing label-free detection of numerous biomolecules in situ. In contrast to shotgun proteomics, proteins/peptides can be detected directly from biological tissues and correlated to its morphology leading to a gain of crucial clinical information. However, direct identification of the detected molecules is currently challenging for MALDI-IMS, thereby compelling researchers to use complementary techniques and resource intensive experimental setups. Despite these strategies, sufficient information could not be extracted because of lack of an optimum data combination strategy/software. Here, we introduce a new open-source software ImShot that aims at identifying peptides obtained in MALDI-IMS. This is achieved by combining information from IMS and shotgun proteomics (LC-MS) measurements of serial sections of the same tissue. The software takes advantage of a two-group comparison to determine the search space of IMS masses after deisotoping the corresponding spectra. Ambiguity in annotations of IMS peptides is eliminated by introduction of a novel scoring system that identifies the most likely parent protein of a detected peptide in the corresponding IMS dataset. Thanks to its modular structure, the software can also handle LC-MS data separately and display interactive enrichment plots and enriched Gene Ontology terms or cellular pathways. The software has been built as a desktop application with a conveniently designed graphic user interface to provide users with a seamless experience in data analysis. ImShot can run on all the three major desktop operating systems and is freely available under Massachusetts Institute of Technology license.

ProSAP: a GUI software tool for statistical analysis and assessment of thermal stability data

The Cellular Thermal Shift Assay (CETSA) plays an important role in drug-target identification, and statistical analysis is a crucial step significantly affecting conclusion. We put forward ProSAP (Protein Stability Analysis Pod), an open-source, cross-platform and user-friendly software tool, which provides multiple methods for thermal proteome profiling (TPP) analysis, nonparametric analysis (NPA), proteome integral solubility alteration and isothermal shift assay (iTSA). For testing the performance of ProSAP, we processed several datasets and compare the performance of different algorithms. Overall, TPP analysis is more accurate with fewer false positive targets, but NPA methods are flexible and free from parameters. For iTSA, edgeR and DESeq2 identify more true targets than t-test and Limma, but when it comes to ranking, the four methods show not much difference. ProSAP software is available at https://github.com/hcji/ProSAP and https://zenodo.org/record/5763315.

In-cytoplasm mitochondrial transplantation for mesenchymal stem cells engineering and tissue regeneration

Stem cell therapies are unsatisfactory due to poor cell survival and engraftment. Stem cell used for therapy must be properly "tuned" for a harsh in vivo environment. Herein, we report that transfer of exogenous mitochondria (mito) to adipose-derived mesenchymal stem cells (ADSCs) can effectively boost their energy levels, enabling efficient cell engraftment. Importantly, the entire process of exogeneous mitochondrial endocytosis is captured by high-content live-cell imaging. Mitochondrial transfer leads to acutely enhanced bioenergetics, with nearly 17% of higher adenosine 5'-triphosphate (ATP) levels in ADSCs treated with high mitochondrial dosage and further results in altered secretome profiles of ADSCs. Mitochondrial transfer also induced the expression of 334 mRNAs in ADSCs, which are mainly linked to signaling pathways associated with DNA replication and cell division. We hypothesize that increase in ATP and cyclin-dependent kinase 1 and 2 expression might be responsible for promoting enhanced proliferation, migration, and differentiation of ADSCs in vitro. More importantly, mito-transferred ADSCs display prolonged cell survival, engraftment and horizontal transfer of exogenous mitochondria to surrounding cells in a full-thickness skin defect rat model with improved skin repair compared with nontreated ADSCs. These results demonstrate that intracellular mitochondrial transplantation is a promising strategy to engineer stem cells for tissue regeneration.

OmicsOne: associate omics data with phenotypes in one-click

BACKGROUND

The rapid advancements of high throughput "omics" technologies have brought a massive amount of data to process during and after experiments. Multi-omic analysis facilitates a deeper interrogation of a dataset and the discovery of interesting genes, proteins, lipids, glycans, metabolites, or pathways related to the corresponding phenotypes in a study. Many individual software tools have been developed for data analysis and visualization. However, it still lacks an efficient way to investigate the phenotypes with multiple omics data. Here, we present OmicsOne as an interactive web-based framework for rapid phenotype association analysis of multi-omic data by integrating quality control, statistical analysis, and interactive data visualization on 'one-click'.

MATERIALS AND METHODS

OmicsOne was applied on the previously published proteomic and glycoproteomic data sets of high-grade serous ovarian carcinoma (HGSOC) and the published proteome data set of lung squamous cell carcinoma (LSCC) to confirm its performance. The data was analyzed through six main functional modules implemented in OmicsOne: (1) phenotype profiling, (2) data preprocessing and quality control, (3) knowledge annotation, (4) phenotype associated features discovery, (5) correlation and regression model analysis for phenotype association analysis on individual features, and (6) enrichment analysis for phenotype association analysis on interested feature sets.

RESULTS

We developed an integrated software solution, OmicsOne, for the phenotype association analysis on multi-omics data sets. The application of OmicsOne on the public data set of ovarian cancer data showed that the software could confirm the previous observations consistently and discover new evidence for HNRNPU and a glycopeptide of HYOU1 as potential biomarkers for HGSOC data sets. The performance of OmicsOne was further demonstrated in the Tumor and NAT comparison study on the proteome data set of LSCC.

CONCLUSIONS

OmicsOne can effectively simplify data analysis and reveal the significant associations between phenotypes and potential biomarkers, including genes, proteins, and glycopeptides, in minutes to assist users to understand aberrant biological processes.

protti: an R package for comprehensive data analysis of peptide- and protein-centric bottom-up proteomics data

Summary

We present a flexible, user-friendly R package called protti for comprehensive quality control, analysis and interpretation of quantitative bottom-up proteomics data. protti supports the analysis of protein-centric data such as those associated with protein expression analyses, as well as peptide-centric data such as those resulting from limited proteolysis-coupled mass spectrometry analysis. Due to its flexible design, it supports analysis of label-free, data-dependent, data-independent and targeted proteomics datasets. protti can be run on the output of any search engine and software package commonly used for bottom-up proteomics experiments such as Spectronaut, Skyline, MaxQuant or Proteome Discoverer, adequately exported to table format.

Availability and implementation

protti is implemented as an open-source R package. Release versions are available via CRAN (https://CRAN.R-project.org/package=protti) and work on all major operating systems. The development version is maintained on GitHub (https://github.com/jpquast/protti). Full documentation including examples is provided in the form of vignettes on our package website (jpquast.github.io/protti/).

ProTG4: A Web Server to Approximate the Sequence of a Generic Protein From an in Silico Library of Translatable G-Quadruplex (TG4)-Mapped Peptides

An RNA G-quadruplex in the protein coding segment of mRNA is translatable (TG4) and may potentially impact protein translation. This can be consequent to staggered ribosomal synthesis and/or result in an increased frequency of missense translational events. A mathematical model of the peptides that encompass the substituted amino acids, ie, the TG4-mapped peptidome, has been previously studied. However, the significance and relevance to disease biology of this model remains to be established. ProTG4 computes a confidence-of-sequence-identity (γ)-score, which is the average weighted length of every matched TG4-mapped peptide in a generic protein sequence. The weighted length is the product of the length of the peptide and the probability of its non-random occurrence in a library of randomly generated sequences of equivalent lengths. This is then averaged over the entire length of the protein sequence. ProTG4 is simple to operate, has clear instructions, and is accompanied by a set of ready-to-use examples. The rationale of the study, algorithms deployed, and the computational pipeline deployed are also part of the web page. Analyses by ProTG4 of taxonomically diverse protein sequences suggest that there is significant homology to TG4-mapped peptides. These findings, especially in potentially infectious and infesting agents, offer plausible explanations into the aetiology and pathogenesis of certain proteopathies. ProTG4 can also provide a quantitative measure to identify and annotate the canonical form of a generic protein sequence from its known isoforms. The article presents several case studies and discusses the relevance of ProTG4-assisted peptide analysis in gaining insights into various mechanisms of disease biology (mistranslation, alternate splicing, amino acid substitutions).

ProteoSign v2: a faster and evolved user-friendly online tool for statistical analyses of differential proteomics

Bottom-up proteomics analyses have been proved over the last years to be a powerful tool in the characterization of the proteome and are crucial for understanding cellular and organism behaviour. Through differential proteomic analysis researchers can shed light on groups of proteins or individual proteins that play key roles in certain, normal or pathological conditions. However, several tools for the analysis of such complex datasets are powerful, but hard-to-use with steep learning curves. In addition, some other tools are easy to use, but are weak in terms of analytical power. Previously, we have introduced ProteoSign, a powerful, yet user-friendly open-source online platform for protein differential expression/abundance analysis designed with the end-proteomics user in mind. Part of Proteosign's power stems from the utilization of the well-established Linear Models For Microarray Data (LIMMA) methodology. Here, we present a substantial upgrade of this computational resource, called ProteoSign v2, where we introduce major improvements, also based on user feedback. The new version offers more plot options, supports additional experimental designs, analyzes updated input datasets and performs a gene enrichment analysis of the differentially expressed proteins. We also introduce the deployment of the Docker technology and significantly increase the speed of a full analysis. ProteoSign v2 is available at http://bioinformatics.med.uoc.gr/ProteoSign.

ProteoMill: Efficient network-based functional analysis portal for proteomics data

Summary

Functional analysis has become a common approach to incorporate biological knowledge into the analysis of omics data, and to explore molecular events that govern a disease state. It is though only one step in a wider analytical pipeline that typically requires use of multiple individual analysis software. There is currently a need for a well-integrated omics analysis tool that performs all the steps. The ProteoMill portal is developed as an R Shiny application and integrates all necessary steps from data-upload, converting identifiers, to quality control, differential expression and network-based functional analysis into a single fast, interactive easy to use workflow. Further, it maintains annotation data sources up to date, overcoming a common problem with use of outdated information and seamlessly integrates multiple R-packages for an improved user-experience. The functionality provided in this software can benefit researchers by facilitating the exploratory analysis of proteomics data.

Availability and implementation

ProteoMill is available at https://proteomill.com.

Alternative Splicing of FN (Fibronectin) Regulates the Composition of the Arterial Wall Under Low Flow

OBJECTIVE

Exposure of the arterial endothelium to low and disturbed flow is a risk factor for the erosion and rupture of atherosclerotic plaques and aneurysms. Circulating and locally produced proteins are known to contribute to an altered composition of the extracellular matrix at the site of lesions, and to contribute to inflammatory processes within the lesions. We have previously shown that alternative splicing of FN (fibronectin) protects against flow-induced hemorrhage. However, the impact of alternative splicing of FN on extracellular matrix composition remains unknown. Approach and Results: Here, we perform quantitative proteomic analysis of the matrisome of murine carotid arteries in mice deficient in the production of FN splice isoforms containing alternative exons EIIIA and EIIIB (FN-EIIIAB null) after exposure to low and disturbed flow in vivo. We also examine serum-derived and endothelial-cell contributions to the matrisome in a simplified in vitro system. We found flow-induced differences in the carotid artery matrisome that were impaired in FN-EIIIAB null mice. One of the most interesting differences was reduced recruitment of FBLN1 (fibulin-1), abundant in blood and not locally produced in the intima. This defect was validated in our in vitro assay, where FBLN1 recruitment from serum was impaired by the absence of these alternatively spliced segments.

CONCLUSIONS

Our results reveal the extent of the dynamic alterations in the matrisome in the acute response to low and disturbed flow and show how changes in the splicing of FN, a common response in vascular inflammation and remodeling, can affect matrix composition.

Utility of Proteomics in Emerging and Re-Emerging Infectious Diseases Caused by RNA Viruses

Emerging and re-emerging infectious diseases due to RNA viruses cause major negative consequences for the quality of life, public health, and overall economic development. Most of the RNA viruses causing illnesses in humans are of zoonotic origin. Zoonotic viruses can directly be transferred from animals to humans through adaptation, followed by human-to-human transmission, such as in human immunodeficiency virus (HIV), severe acute respiratory syndrome coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV), and, more recently, SARS coronavirus 2 (SARS-CoV-2), or they can be transferred through insects or vectors, as in the case of Crimean-Congo hemorrhagic fever virus (CCHFV), Zika virus (ZIKV), and dengue virus (DENV). At the present, there are no vaccines or antiviral compounds against most of these viruses. Because proteins possess a vast array of functions in all known biological systems, proteomics-based strategies can provide important insights into the investigation of disease pathogenesis and the identification of promising antiviral drug targets during an epidemic or pandemic. Mass spectrometry technology has provided the capacity required for the precise identification and the sensitive and high-throughput analysis of proteins on a large scale and has contributed greatly to unravelling key protein-protein interactions, discovering signaling networks, and understanding disease mechanisms. In this Review, we present an account of quantitative proteomics and its application in some prominent recent examples of emerging and re-emerging RNA virus diseases like HIV-1, CCHFV, ZIKV, and DENV, with more detail with respect to coronaviruses (MERS-CoV and SARS-CoV) as well as the recent SARS-CoV-2 pandemic.

Polycysteine-encoding leaderless short ORFs function as cysteine-responsive attenuators of operonic gene expression in mycobacteria

Genome-wide transcriptomic analyses have revealed abundant expressed short open reading frames (ORFs) in bacteria. Whether these short ORFs, or the small proteins they encode, are functional remains an open question. One quarter of mycobacterial mRNAs are leaderless, beginning with a 5'-AUG or GUG initiation codon. Leaderless mRNAs often encode unannotated short ORFs as the first gene of a polycistronic transcript. Here, we show that polycysteine-encoding leaderless short ORFs function as cysteine-responsive attenuators of operonic gene expression. Detailed mutational analysis shows that one polycysteine short ORF controls expression of the downstream genes. Our data indicate that ribosomes stalled in the polycysteine tract block mRNA structures that otherwise sequester the ribosome-binding site of the 3'gene. We assessed endogenous proteomic responses to cysteine limitation in Mycobacterium smegmatis using mass spectrometry. Six cysteine metabolic loci having unannotated polycysteine-encoding leaderless short ORF architectures responded to cysteine limitation, revealing widespread cysteine-responsive attenuation in mycobacteria. Individual leaderless short ORFs confer independent operon-level control, while their shared dependence on cysteine ensures a collective response mediated by ribosome pausing. We propose the term ribulon to classify ribosome-directed regulons. Regulon-level coordination by ribosomes on sensory short ORFs illustrates one utility of the many unannotated short ORFs expressed in bacterial genomes.

ProtExA: A tool for post-processing proteomics data providing differential expression metrics, co-expression networks and functional analytics

ProTExA is a web-tool that provides a post-processing workflow for the analysis of protein and gene expression datasets. Using network-based bioinformatics approaches, ProTExA facilitates differential expression analysis and co-expression network analysis as well as pathway and post-pathway analysis. Specifically, for a given set of protein-gene expression data across samples, ProTExA: (1) performs statistical analysis and filtering to highlight the differentially expressed proteins-genes, (2) performs enrichment analysis to identify top-scored pathways, (3) generates pathway-to-pathway and pathway-to-gene networks (4) generates protein and gene co-expression networks using a variety of methodologies, and (5) applies clustering methodologies to identify sub-networks of co-expressed proteins-genes. The proposed web-tool is a simple yet informative tool, towards understanding and exploitation of protein and gene expression datasets, especially for those that do not have the expertise and local resources to replicate specific analyses in the context of collaborative and scientific data exchanging.

Motor neurons from ALS patients with mutations in C9ORF72 and SOD1 exhibit distinct transcriptional landscapes

Amyotrophic lateral sclerosis (ALS) is a progressive motor neuron disease that culminates in paralysis and death. Here, we present our analyses of publicly available multiOMIC data sets generated using motor neurons from ALS patients and control cohorts. Functional annotation of differentially expressed genes in induced pluripotent stem cell (iPSC)-derived motor neurons generated from patients with mutations in C9ORF72 (C9-ALS) suggests elevated expression of genes that pertain to extracellular matrix (ECM) and cell adhesion, inflammation and TGFβ targets. On the other end of the continuum, we detected diminished expression of genes repressed by quiescence-promoting E2F4/DREAM complex. Proteins whose abundance was significantly altered in C9-ALS neurons faithfully recapitulated the transcriptional aberrations. Importantly, patterns of gene expression in spinal motor neurons dissected from C9-ALS or sporadic ALS patients were highly concordant with each other and with the C9-ALS iPSC neurons. In contrast, motor neurons from patients with mutations in SOD1 exhibited dramatically different signatures. Elevated expression of gene sets such as ECM and cell adhesion genes occurs in C9 and sporadic ALS but not SOD1-ALS. These analyses indicate that despite the similarities in outward manifestations, transcriptional and proteomic signatures in ALS motor neurons can vary significantly depending on the identity of the causal mutations.

A-Lister: a tool for analysis of differentially expressed omics entities across multiple pairwise comparisons

BACKGROUND

Researchers commonly analyze lists of differentially expressed entities (DEEs), such as differentially expressed genes (DEGs), differentially expressed proteins (DEPs), and differentially methylated positions/regions (DMPs/DMRs), across multiple pairwise comparisons. Large biological studies can involve multiple conditions, tissues, and timepoints that result in dozens of pairwise comparisons. Manually filtering and comparing lists of DEEs across multiple pairwise comparisons, typically done by writing custom code, is a cumbersome task that can be streamlined and standardized.

RESULTS

A-Lister is a lightweight command line and graphical user interface tool written in Python. It can be executed in a differential expression mode or generic name list mode. In differential expression mode, A-Lister accepts as input delimited text files that are output by differential expression tools such as DESeq2, edgeR, Cuffdiff, and limma. To allow for the most flexibility in input ID types, to avoid database installation requirements, and to allow for secure offline use, A-Lister does not validate or impose restrictions on entity ID names. Users can specify thresholds to filter the input file(s) by column(s) such as p-value, q-value, and fold change. Additionally, users can filter the pairwise comparisons within the input files by fold change direction (sign). Queries composed of intersection, fuzzy intersection, difference, and union set operations can also be performed on any number of pairwise comparisons. Thus, the user can filter and compare any number of pairwise comparisons within a single A-Lister differential expression command. In generic name list mode, A-Lister accepts delimited text files containing lists of names as input. Queries composed of intersection, fuzzy intersection, difference, and union set operations can then be performed across these lists of names.

CONCLUSIONS

A-Lister is a flexible tool that enables the user to rapidly narrow down large lists of DEEs to a small number of most significant entities. These entities can then be further analyzed using visualization, pathway analysis, and other bioinformatics tools.

Multi-omics analysis identifies mitochondrial pathways associated with anxiety-related behavior

Stressful life events are major environmental risk factors for anxiety disorders, although not all individuals exposed to stress develop clinical anxiety. The molecular mechanisms underlying the influence of environmental effects on anxiety are largely unknown. To identify biological pathways mediating stress-related anxiety and resilience to it, we used the chronic social defeat stress (CSDS) paradigm in male mice of two inbred strains, C57BL/6NCrl (B6) and DBA/2NCrl (D2), that differ in their susceptibility to stress. Using a multi-omics approach, we identified differential mRNA, miRNA and protein expression changes in the bed nucleus of the stria terminalis (BNST) and blood cells after chronic stress. Integrative gene set enrichment analysis revealed enrichment of mitochondrial-related genes in the BNST and blood of stressed mice. To translate these results to human anxiety, we investigated blood gene expression changes associated with exposure-induced panic attacks. Remarkably, we found reduced expression of mitochondrial-related genes in D2 stress-susceptible mice and in exposure-induced panic attacks in humans, but increased expression of these genes in B6 stress-susceptible mice. Moreover, stress-susceptible vs. stress-resilient B6 mice displayed more mitochondrial cross-sections in the post-synaptic compartment after CSDS. Our findings demonstrate mitochondrial-related alterations in gene expression as an evolutionarily conserved response in stress-related behaviors and validate the use of cross-species approaches in investigating the biological mechanisms underlying anxiety disorders.

Recent Technological Advances in the Mass Spectrometry-based Nanomedicine Studies: An Insight from Nanoproteomics

Nanoscience becomes one of the most cutting-edge research directions in recent years since it is gradually matured from basic to applied science. Nanoparticles (NPs) and nanomaterials (NMs) play important roles in various aspects of biomedicine science, and their influences on the environment have caused a whole range of uncertainties which require extensive attention. Due to the quantitative and dynamic information provided for human proteome, mass spectrometry (MS)-based quantitative proteomic technique has been a powerful tool for nanomedicine study. In this article, recent trends of progress and development in the nanomedicine of proteomics were discussed from quantification techniques and publicly available resources or tools. First, a variety of popular protein quantification techniques including labeling and label-free strategies applied to nanomedicine studies are overviewed and systematically discussed. Then, numerous protein profiling tools for data processing and postbiological statistical analysis and publicly available data repositories for providing enrichment MS raw data information sources are also discussed.

ProteomicsBrowser: MS/proteomics data visualization and investigation

SUMMARY

Large-scale, quantitative proteomics data are being generated at ever increasing rates by high-throughput, mass spectrometry technologies. However, due to the complexity of these large datasets as well as the increasing numbers of post-translational modifications (PTMs) that are being identified, developing effective methods for proteomic visualization has been challenging. ProteomicsBrowser was designed to meet this need for comprehensive data visualization. Using peptide information files exported from mass spectrometry search engines or quantitative tools as input, the peptide sequences are aligned to an internal protein database such as UniProtKB. Each identified peptide ion including those with PTMs is then visualized along the parent protein in the Browser. A unique property of ProteomicsBrowser is the ability to combine overlapping peptides in different ways to focus analysis of sequence coverage, charge state or PTMs. ProteomicsBrowser includes other useful functions, such as a data filtering tool and basic statistical analyses to qualify quantitative data.

AVAILABILITY AND IMPLEMENTATION

ProteomicsBrowser is implemented in Java8 and is available at https://medicine.yale.edu/keck/nida/proteomicsbrowser.aspx and https://github.com/peng-gang/ProteomicsBrowser.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A New ESX-1 Substrate in Mycobacterium marinum That Is Required for Hemolysis but Not Host Cell Lysis

The ESX-1 (ESAT-6 system 1) secretion system plays a conserved role in the virulence of diverse mycobacterial pathogens, including the human pathogen Mycobacterium tuberculosis and M. marinum, an environmental mycobacterial species. The ESX-1 system promotes the secretion of protein virulence factors to the extracytoplasmic environment. The secretion of these proteins triggers the host response by lysing the phagosome during macrophage infection. Using proteomic analyses of the M. marinum secretome in the presence and absence of a functional ESX-1 system, we and others have hypothesized that MMAR_2894, a PE family protein, is a potential ESX-1 substrate in M. marinum We used genetic and quantitative proteomic approaches to determine if MMAR_2894 is secreted by the ESX-1 system, and we defined the requirement of MMAR_2894 for ESX-1-mediated secretion and virulence. We show that MMAR_2894 is secreted by the ESX-1 system in M. marinum and is itself required for the optimal secretion of the known ESX-1 substrates in M. marinum Moreover, we found that MMAR_2894 was differentially required for hemolysis and cytolysis of macrophages, two lytic activities ascribed to the M. marinum ESX-1 system.IMPORTANCE Both Mycobacterium tuberculosis, the cause of human tuberculosis (TB), and Mycobacterium marinum, a pathogen of ectotherms, use the ESX-1 secretion system to cause disease. There are many established similarities between the ESX-1 systems in M. tuberculosis and in M. marinum Yet the two bacteria infect different hosts, hinting at species-specific functions of the ESX-1 system. Our findings demonstrate that MMAR_2894 is a PE protein secreted by the ESX-1 system of M. marinum We show that MMAR_2894 is required for the optimal secretion of mycobacterial proteins required for disease. Because the MMAR_2894 gene is not conserved in M. tuberculosis, our findings demonstrate that MMAR_2894 may contribute to a species-specific function of the ESX-1 system in M. marinum, providing new insight into how the M. marinum and M. tuberculosis systems differ.

Protein-Level Statistical Analysis of Quantitative Label-Free Proteomics Data with ProStaR

ProStaR is a software tool dedicated to differential analysis in label-free quantitative proteomics. Practically, once biological samples have been analyzed by bottom-up mass spectrometry-based proteomics, the raw mass spectrometer outputs are processed by bioinformatics tools, so as to identify peptides and quantify them, by means of precursor ion chromatogram integration. Then, it is classical to use these peptide-level pieces of information to derive the identity and quantity of the sample proteins before proceeding with refined statistical processing at protein-level, so as to bring out proteins which abundance is significantly different between different groups of samples. To achieve this statistical step, it is possible to rely on ProStaR, which allows the user to (1) load correctly formatted data, (2) clean them by means of various filters, (3) normalize the sample batches, (4) impute the missing values, (5) perform null hypothesis significance testing, (6) check the well-calibration of the resulting p-values, (7) select a subset of differentially abundant proteins according to some false discovery rate, and (8) contextualize these selected proteins into the Gene Ontology. This chapter provides a detailed protocol on how to perform these eight processing steps with ProStaR.

Analysis of significant protein abundance from multiple reaction-monitoring data

Background

Discovering reliable protein biomarkers is one of the most important issues in biomedical research. The ELISA is a traditional technique for accurate quantitation of well-known proteins. Recently, the multiple reaction-monitoring (MRM) mass spectrometry has been proposed for quantifying newly discovered protein and has become a popular alternative to ELISA. For the MRM data analysis, linear mixed modeling (LMM) has been used to analyze MRM data. MSstats is one of the most widely used tools for MRM data analysis that is based on the LMMs. However, LMMs often provide various significance results, depending on model specification. Sometimes it would be difficult to specify a correct LMM method for the analysis of MRM data. Here, we propose a new logistic regression-based method for Significance Analysis of Multiple Reaction Monitoring (LR-SAM).

Results

Through simulation studies, we demonstrate that LMM methods may not preserve type I error, thus yielding high false- positive errors, depending on how random effects are specified. Our simulation study also shows that the LR-SAM approach performs similarly well as LMM approaches, in most cases. However, LR-SAM performs better than the LMMs, particularly when the effects sizes of peptides from the same protein are heterogeneous. Our proposed method was applied to MRM data for identification of proteins associated with clinical responses of treatment of 115 hepatocellular carcinoma (HCC) patients with the tyrosine kinase inhibitor sorafenib. Of 124 candidate proteins, LMM approaches provided 6 results varying in significance, while LR-SAM, by contrast, yielded 18 significant results that were quite reproducibly consistent.

Conclusion

As exemplified by an application to HCC data set, LR-SAM more effectively identified proteins associated with clinical responses of treatment than LMM did.

Combined Short-Term Glucose Starvation and Chemotherapy in 3D Colorectal Cancer Cell Culture Decreases 14-3-3 Family Protein Expression and Phenotypic Response to Therapy

Short-term glucose starvation prior to chemotherapy has the potential to preferentially weaken cancer cells, making them more likely to succumb to treatment, while protecting normal cells. In this study, we used 3D cell cultures of colorectal cancer and assessed the effects of short-term glucose starvation and chemotherapy compared to treatment of either individually. We evaluated both phenotypic changes and protein expression levels. Our findings indicate that the combined treatment results in more significant phenotypic responses, including decreased cell viability and clonogenicity. These phenotypic responses can be explained by the decreased expression of LDHA and 14-3-3 family proteins, which were found only in the combined treatment groups. This study indicates that short-term glucose starvation has the potential to increase the efficacy of current cancer treatment regimes. Graphical Abstract ᅟ.

Instant Clue: A Software Suite for Interactive Data Visualization and Analysis

The development of modern high-throughput instrumentation and improved core facility infrastructures leads to an accumulation of large amounts of scientific data. However, for a majority of scientists the comprehensive analysis and visualization of their data goes beyond their expertise. To reduce this hurdle, we developed a software suite called Instant Clue that helps scientists to visually analyze data and to gain insights into biological processes from their high-dimensional dataset. Instant Clue combines the power of visual and statistical analytics using a straight forward drag & drop approach making the software highly intuitive. Additionally, it offers a comprehensive portfolio of statistical tools for systematic analysis such as dimensional reduction, (un)-supervised learning, clustering, multi-block (omics) integration and curve fitting. Charts can be combined with high flexibility into a main figure template for direct usage in scientific publications. Even though Instant Clue was developed with the omics-sciences in mind, users can analyze any kind of data from low to high dimensional data sets. The open-source software is available for Windows and Mac OS ( http://www.instantclue.uni-koeln.de ) and is accompanied by a detailed video tutorial series.

Updates on resources, software tools, and databases for plant proteomics in 2016-2017

Proteomics data processing, annotation, and analysis can often lead to major hurdles in large-scale high-throughput bottom-up proteomics experiments. Given the recent rise in protein-based big datasets being generated, efforts in in silico tool development occurrences have had an unprecedented increase; so much so, that it has become increasingly difficult to keep track of all the advances in a particular academic year. However, these tools benefit the plant proteomics community in circumventing critical issues in data analysis and visualization, as these continually developing open-source and community-developed tools hold potential in future research efforts. This review will aim to introduce and summarize more than 50 software tools, databases, and resources developed and published during 2016-2017 under the following categories: tools for data pre-processing and analysis, statistical analysis tools, peptide identification tools, databases and spectral libraries, and data visualization and interpretation tools. Intended for a well-informed proteomics community, finally, efforts in data archiving and validation datasets for the community will be discussed as well. Additionally, the author delineates the current and most commonly used proteomics tools in order to introduce novice readers to this -omics discovery platform.

iTRAQ Quantitative Proteomic Profiling and MALDI-MSI of Colon Cancer Spheroids Treated with Combination Chemotherapies in a 3D Printed Fluidic Device

For a patient with metastatic colorectal cancer there are limited clinical options aside from chemotherapy. Unfortunately, the development of new chemotherapeutics is a long and costly process. New methods are needed to identify promising drug candidates earlier in the drug development process. Most chemotherapies are administered to patients in combinations. Here, an in vitro platform is used to assess the penetration and metabolism of combination chemotherapies in three-dimensional colon cancer cell cultures, or spheroids. Colon carcinoma HCT 116 cells were cultured and grown into three-dimensional cell culture spheroids. These spheroids were then dosed with a common combination chemotherapy, FOLFIRI (folinic acid, 5-fluorouracil, and irinotecan) in a 3D printed fluidic device. This fluidic device allows for the dynamic treatment of spheroids across a semipermeable membrane. Following dosing, the spheroids were harvested for quantitative proteomic profiling to examine the effects of the combination chemotherapy on the colon cancer cells. Spheroids were also imaged to assess the spatial distribution of administered chemotherapeutics and metabolites with MALDI-imaging mass spectrometry. Following treatment, we observed penetration of folinic acid to the core of spheroids and metabolism of the drug in the outer proliferating region of the spheroid. Proteomic changes identified included an enrichment of several cancer-associated pathways. This innovative dosing device, along with the proteomic evaluation with iTRAQ-MS/MS, provides a robust platform that could have a transformative impact on the preclinical evaluation of drug candidates. This system is a high-throughput and cost-effective approach to examine novel drugs and drug combinations prior to animal testing.

Calcitriol Supplementation Causes Decreases in Tumorigenic Proteins and Different Proteomic and Metabolomic Signatures in Right versus Left-Sided Colon Cancer

Vitamin D deficiency is a common problem worldwide. In particular, it is an issue in the Northern Hemisphere where UVB radiation does not penetrate the atmosphere as readily. There is a correlation between vitamin D deficiency and colorectal cancer incidence and mortality. Furthermore, there is strong evidence that cancer of the ascending (right side) colon is different from cancer of the descending (left side) colon in terms of prognosis, tumor differentiation, and polyp type, as well as at the molecular level. Right-side tumors have elevated Wnt signaling and are more likely to relapse, whereas left-side tumors have reduced expression of tumor suppressor genes. This study seeks to understand both the proteomic and metabolomic changes resulting from treatment of the active metabolite of vitamin D, calcitriol, in right-sided and left-sided colon cancer. Our results show that left-sided colon cancer treated with calcitriol has a substantially greater number of changes in both the proteome and the metabolome than right-sided colon cancer. We found that calcitriol treatment in both right-sided and left-sided colon cancer causes a downregulation of ribosomal protein L37 and protein S100A10. Both of these proteins are heavily involved in tumorigenesis, suggesting a possible mechanism for the correlation between low vitamin D levels and colon cancer.