An integrated mass spectrometric and computational framework for the analysis of protein interaction networks

Effects of natural extract interventions in prostate cancer: A systematic review and network meta-analysis

BACKGROUND

Over years, there has been a widespread quest for effective dietary patterns and natural extracts to mitigate prostate cancer risk. However, despite numerous experimental studies conducted on various natural extracts, the evidence substantiating their efficacy remains largely insufficient. This dearth of compelling evidence presents a significant challenge in advocating for their widespread use as preventive measures against prostate cancer.

OBJECTIVE

Our study endeavors to undertake a network meta-analysis to evaluate the influence of natural extracts on prostate cancer.

METHODS

Researchers systematically searched through Embase, PubMed, Cochrane Library, and Web of Science databases until December 2023. The main focus was on assessing primary outcomes comprising prostate-specific antigen (PSA), insulin-like growth factor-binding protein-3 (IGFBP-3), insulin-like growth factor-1 (IGF-1). We conducted data analysis utilizing StataMP 15.0 software. Therapeutic effects were ranked based on the probability values derived from Surface Under the Cumulative Ranking curve (SUCRA). Additionally, cluster analysis was employed to assess the impacts of natural extracts on three distinct outcomes.

RESULTS

Following screening procedures, the 28 eligible studies were incorporated, the selected studies encompassed 1,566 prostate cancer patients and evaluated 16 different natural extract treatments. Specifically, 24 trials included PSA indicators, 10 included IGF-1 indicators, and 8 included IGFBP-3 indicators. The findings revealed that, based on the SUCRA values, the combined therapy of silybin with selenium (74%) appears to be the most effective approach for reducing serum PSA levels. Simultaneously, silybin alone (84.6%) stands out as the most promising option for decreasing serum IGF-1 levels. Lastly, concerning IGFBP-3, silybin alone (67.7%) emerges as the optimal choice. Twelve studies provided comprehensive information on adverse drug reactions/events (ADR/ADE), whereas five articles did not report any significant ADR/ADE.

CONCLUSION

The NMA suggests that, compared to placebo, utilizing silybin either alone or in combination with selenium has been shown to enhance therapeutic effects, offering potential benefits to patients with prostate cancer. This study can offer valuable insights for prostate patients considering natural extract treatments. Further evidence is required to confirm the safety profile of these treatments.

PARM1 Drives Smooth Muscle Cell Proliferation in Pulmonary Arterial Hypertension via AKT/FOXO3A Axis

Pulmonary arterial hypertension (PAH) is a group of severe, progressive, and debilitating diseases with limited therapeutic options. This study aimed to explore novel therapeutic targets in PAH through bioinformatics and experiments. Weighted gene co-expression network analysis (WGCNA) was applied to detect gene modules related to PAH, based on the GSE15197, GSE113439, and GSE117261. GSE53408 was applied as validation set. Subsequently, the validated most differentially regulated hub gene was selected for further ex vivo and in vitro assays. PARM1, TSHZ2, and CCDC80 were analyzed as potential intervention targets for PAH. Consistently with the bioinformatic results, our ex vivo and in vitro data indicated that PARM1 expression increased significantly in the lung tissue and/or pulmonary artery of the MCT-induced PAH rats and hypoxia-induced PAH mice in comparison with the respective controls. Besides, a similar expression pattern of PARM1 was found in the hypoxia- and PDGF--treated isolated rat primary pulmonary arterial smooth muscle cells (PASMCs). In addition, hypoxia/PDGF--induced PARM1 protein expression could promote the elevation of phosphorylation of AKT, phosphorylation of FOXO3A and PCNA, and finally the proliferation of PASMCs in vitro, whereas PARM1 siRNA treatment inhibited it. Mechanistically, PARM1 promoted PAH via AKT/FOXO3A/PCNA signaling pathway-induced PASMC proliferation.

Emerging Role of IGF-1 in Prostate Cancer: A Promising Biomarker and Therapeutic Target

Prostate cancer (PCa) is a highly heterogeneous disease driven by gene alterations and microenvironmental influences. Not only enhanced serum IGF-1 but also the activation of IGF-1R and its downstream signaling components has been increasingly recognized to have a vital driving role in the development of PCa. A better understanding of IGF-1/IGF-1R activity and regulation has therefore emerged as an important subject of PCa research. IGF-1/IGF-1R signaling affects diverse biological processes in cancer cells, including promoting survival and renewal, inducing migration and spread, and promoting resistance to radiation and castration. Consequently, inhibitory reagents targeting IGF-1/IGF-1R have been developed to limit cancer development. Multiple agents targeting IGF-1/IGF-1R signaling have shown effects against tumor growth in tumor xenograft models, but further verification of their effectiveness in PCa patients in clinical trials is still needed. Combining androgen deprivation therapy or cytotoxic chemotherapeutics with IGF-1R antagonists based on reliable predictive biomarkers and developing and applying novel agents may provide more desirable outcomes. This review will summarize the contribution of IGF-1 signaling to the development of PCa and highlight the relevance of this signaling axis in potential strategies for cancer therapy.

Nvp63 and nvPIWIL1 Suppress Retrotransposon Activation in the Sea Anemone Nematostella vectensis

The transcription factors p63 and p73 have high similarity to the tumor suppressor protein p53. While the importance of p53 in DNA damage control is established, the functions of p63 or p73 remain elusive. Here, we analyzed nvp63, the cnidarian homologue of p63, that is expressed in the mesenteries of the starlet sea anemone Nematostella vectensis and that is activated in response to DNA damage. We used ultraviolet light (UV) to induce DNA damage and determined the chromatin-bound proteome with quantitative, bottom-up proteomics. We found that genotoxic stress or nvp63 knockdown recruited the protein nvPIWIL1, a homologue of the piRNA-binding PIWI protein family. Knockdown nvPIWIL1 increased protein expression from open reading frames (ORFs) that overlap with class I and II transposable element DNA sequences in the genome of N. vectensis. UV irradiation induced apoptosis, and apoptosis was reduced in the absence of nvp63 but increased with the loss of nvPIWIL1. Loss of nvp63 increased the presence of class I LTR and non-LTR retrotransposon but not of class II DNA transposon-associated protein products. These results suggest that an evolutionary early function of nvp63 might be to control genome stability in response to activation of transposable elements, which induce DNA damage during reintegration in the genome.

Longevity Factor FOXO3: A Key Regulator in Aging-Related Vascular Diseases

Forkhead box O3 (FOXO3) has been proposed as a homeostasis regulator, capable of integrating multiple upstream signaling pathways that are sensitive to environmental changes and counteracting their adverse effects due to external changes, such as oxidative stress, metabolic stress and growth factor deprivation. FOXO3 polymorphisms are associated with extreme human longevity. Intriguingly, longevity-associated single nucleotide polymorphisms (SNPs) in human FOXO3 correlate with lower-than-average morbidity from cardiovascular diseases in long-lived people. Emerging evidence indicates that FOXO3 plays a critical role in vascular aging. FOXO3 inactivation is implicated in several aging-related vascular diseases. In experimental studies, FOXO3-engineered human ESC-derived vascular cells improve vascular homeostasis and delay vascular aging. The purpose of this review is to explore how FOXO3 regulates vascular aging and its crucial role in aging-related vascular diseases.

The PP2A/4/6 subfamily of phosphoprotein phosphatases regulates DAF-16 and confers resistance to environmental stress in postreproductive adult C. elegans

Insulin and insulin-like growth factors are longevity determinants that negatively regulate Forkhead box class O (FoxO) transcription factors. In C. elegans mutations that constitutively activate DAF-16, the ortholog of mammalian FoxO3a, extend lifespan by two-fold. While environmental insults induce DAF-16 activity in younger animals, it also becomes activated in an age-dependent manner in the absence of stress, modulating gene expression well into late adulthood. The mechanism by which DAF-16 activity is regulated during aging has not been defined. Since phosphorylation of DAF-16 generally leads to its inhibition, we asked whether phosphatases might be necessary for its increased transcriptional activity in adult C. elegans. We focused on the PP2A/4/6 subfamily of phosphoprotein phosphatases, members of which had been implicated to regulate DAF-16 under low insulin signaling conditions but had not been investigated during aging in wildtype animals. Using reverse genetics, we functionally characterized all C. elegans orthologs of human catalytic, regulatory, and scaffolding subunits of PP2A/4/6 holoenzymes in postreproductive adults. We found that PP2A complex constituents PAA-1 and PPTR-1 regulate DAF-16 transcriptional activity during aging and that they cooperate with the catalytic subunit LET-92 to protect adult animals from ultraviolet radiation. PP4 complex members PPH-4.1/4.2, and SMK-1 also appear to regulate DAF-16 in an age-dependent manner, and together with PPFR-2 they contribute to innate immunity. Interestingly, SUR-6 but no other subunit of the PP2A complex was necessary for the survival of pathogen-infected animals. Finally, we found that PP6 complex constituents PPH-6 and SAPS-1 contribute to host defense during aging, apparently without affecting DAF-16 transcriptional activity. Our studies indicate that a set of PP2A/4/6 complexes protect adult C. elegans from environmental stress, thus preserving healthspan. Therefore, along with their functions in cell division and development, the PP2A/4/6 phosphatases also appear to play critical roles later in life.

Improving Proteoform Identifications in Complex Systems Through Integration of Bottom-Up and Top-Down Data

Cellular functions are performed by a vast and diverse set of proteoforms. Proteoforms are the specific forms of proteins produced as a result of genetic variations, RNA splicing, and post-translational modifications (PTMs). Top-down mass spectrometric analysis of intact proteins enables proteoform identification, including proteoforms derived from sequence cleavage events or harboring multiple PTMs. In contrast, bottom-up proteomics identifies peptides, which necessitates protein inference and does not yield proteoform identifications. We seek here to exploit the synergies between these two data types to improve the quality and depth of the overall proteomic analysis. To this end, we automated the large-scale integration of results from multiprotease bottom-up and top-down analyses in the software program Proteoform Suite and applied it to the analysis of proteoforms from the human Jurkat T lymphocyte cell line. We implemented the recently developed proteoform-level classification scheme for top-down tandem mass spectrometry (MS/MS) identifications in Proteoform Suite, which enables users to observe the level and type of ambiguity for each proteoform identification, including which of the ambiguous proteoform identifications are supported by bottom-up-level evidence. We used Proteoform Suite to find instances where top-down identifications aid in protein inference from bottom-up analysis and conversely where bottom-up peptide identifications aid in proteoform PTM localization. We also show the use of bottom-up data to infer proteoform candidates potentially present in the sample, allowing confirmation of such proteoform candidates by intact-mass analysis of MS1 spectra. The implementation of these capabilities in the freely available software program Proteoform Suite enables users to integrate large-scale top-down and bottom-up data sets and to utilize the synergies between them to improve and extend the proteomic analysis.

Bioinformatics Methods for Mass Spectrometry-Based Proteomics Data Analysis

Recent advances in mass spectrometry (MS)-based proteomics have enabled tremendous progress in the understanding of cellular mechanisms, disease progression, and the relationship between genotype and phenotype. Though many popular bioinformatics methods in proteomics are derived from other omics studies, novel analysis strategies are required to deal with the unique characteristics of proteomics data. In this review, we discuss the current developments in the bioinformatics methods used in proteomics and how they facilitate the mechanistic understanding of biological processes. We first introduce bioinformatics software and tools designed for mass spectrometry-based protein identification and quantification, and then we review the different statistical and machine learning methods that have been developed to perform comprehensive analysis in proteomics studies. We conclude with a discussion of how quantitative protein data can be used to reconstruct protein interactions and signaling networks.

BH3 mimetic ABT-263 enhances the anticancer effects of apigenin in tumor cells with activating EGFR mutation

Background

Mutated epidermal growth factor receptor (EGFR) is one of the most successful targets in cancer targeted therapy. While this treatment has benefited many patients with an activating EGFR mutation (EGFRm), almost all those who initially benefited will eventually develop acquired drug resistance (ADR) after a certain period of time. New therapeutic strategies need to be explored to treat EGFRm tumors and overcome or minimize this recurring ADR.

Results

Our data showed that apigenin alone has only mild inhibitory effects on EGFRm tumor cells. By drug screening, we found that ABT-263 can significantly enhance the antitumor activities of apigenin in tumor cells harbouring an activating EGFR mutation and AZD9291-resistant H1975 cells. Mechanistically, apigenin upregulated the expression of Noxa in EGFRm tumor cells by targeting the AKT-FoxO3a pathway, thereby synergizing with ABT-263 to suppress tumor cell growth and proliferation in vitro and in vivo.

Conclusions

Our study provides a rationale for the clinical application of the combination treatment of apigenin and BH3 mimetics in the treatment of EGFRm tumors.

Electronic supplementary material

The online version of this article (10.1186/s13578-019-0322-y) contains supplementary material, which is available to authorized users.

FRETting about the affinity of bimolecular protein-protein interactions

Fluorescence resonance energy transfer (FRET) is a powerful tool to study macromolecular interactions such as protein-protein interactions (PPIs). Fluorescent protein (FP) fusions enable FRET-based PPI analysis of signaling pathways and molecular structure in living cells. Despite FRET's importance in PPI studies, FRET has seen limited use in quantifying the affinities of PPIs in living cells. Here, we have explored the relationship between FRET efficiency and PPI affinity over a wide range when expressed from a single plasmid system in Escherichia coli. Using live-cell microscopy and a set of 20 pairs of small interacting proteins, belonging to different structural folds and interaction affinities, we demonstrate that FRET efficiency can reliably measure the dissociation constant (K_D ) over a range of mM to nM. A 10-fold increase in the interaction affinity results in 0.05 unit increase in FRET efficiency, providing sufficient resolution to quantify large affinity differences (> 10-fold) using live-cell FRET. This approach provides a rapid and simple strategy for assessment of PPI affinities over a wide range and will have utility for high-throughput analysis of protein interactions.

Critical role of FOXO3a in carcinogenesis

FOXO3a is a member of the FOXO subfamily of forkhead transcription factors that mediate a variety of cellular processes including apoptosis, proliferation, cell cycle progression, DNA damage and tumorigenesis. It also responds to several cellular stresses such as UV irradiation and oxidative stress. The function of FOXO3a is regulated by a complex network of processes, including post-transcriptional suppression by microRNAs (miRNAs), post-translational modifications (PTMs) and protein–protein interactions. FOXO3a is widely implicated in a variety of diseases, particularly in malignancy of breast, liver, colon, prostate, bladder, and nasopharyngeal cancers. Emerging evidences indicate that FOXO3a acts as a tumor suppressor in cancer. FOXO3a is frequently inactivated in cancer cell lines by mutation of the FOXO3a gene or cytoplasmic sequestration of FOXO3a protein. And its inactivation is associated with the initiation and progression of cancer. In experimental studies, overexpression of FOXO3a inhibits the proliferation, tumorigenic potential, and invasiveness of cancer cells, while silencing of FOXO3a results in marked attenuation in protection against tumorigenesis. The role of FOXO3a in both normal physiology as well as in cancer development have presented a great challenge to formulating an effective therapeutic strategy for cancer. In this review, we summarize the recent findings and overview of the current understanding of the influence of FOXO3a in cancer development and progression.

Dysregulation of DAF-16/FOXO3A-mediated stress responses accelerates oxidative DNA damage induced aging

DNA damage is presumed to be one type of stochastic macromolecular damage that contributes to aging, yet little is known about the precise mechanism by which DNA damage drives aging. Here, we attempt to address this gap in knowledge using DNA repair-deficient C. elegans and mice. ERCC1-XPF is a nuclear endonuclease required for genomic stability and loss of ERCC1 in humans and mice accelerates the incidence of age-related pathologies. Like mice, ercc-1 worms are UV sensitive, shorter lived, display premature functional decline and they accumulate spontaneous oxidative DNA lesions (cyclopurines) more rapidly than wild-type worms. We found that ercc-1 worms displayed early activation of DAF-16 relative to wild-type worms, which conferred resistance to multiple stressors and was important for maximal longevity of the mutant worms. However, DAF-16 activity was not maintained over the lifespan of ercc-1 animals and this decline in DAF-16 activation corresponded with a loss of stress resistance, a rise in oxidant levels and increased morbidity, all of which were cep-1/ p53 dependent. A similar early activation of FOXO3A (the mammalian homolog of DAF-16), with increased resistance to oxidative stress, followed by a decline in FOXO3A activity and an increase in oxidant abundance was observed in Ercc1^-/- primary mouse embryonic fibroblasts. Likewise, in vivo, ERCC1-deficient mice had transient activation of FOXO3A in early adulthood as did middle-aged wild-type mice, followed by a late life decline. The healthspan and mean lifespan of ERCC1 deficient mice was rescued by inactivation of p53. These data indicate that activation of DAF-16/FOXO3A is a highly conserved response to genotoxic stress that is important for suppressing consequent oxidative stress. Correspondingly, dysregulation of DAF-16/FOXO3A appears to underpin shortened healthspan and lifespan, rather than the increased DNA damage burden itself.

Deducing the presence of proteins and proteoforms in quantitative proteomics

The human genome harbors just 20,000 genes suggesting that the variety of possible protein products per gene plays a significant role in generating functional diversity. In bottom-up proteomics peptides are mapped back to proteins and proteoforms to describe a proteome; however, accurate quantitation of proteoforms is challenging due to incomplete protein sequence coverage and mapping ambiguities. Here, we demonstrate that a new software tool called ProteinClusterQuant (PCQ) can be used to deduce the presence of proteoforms that would have otherwise been missed, as exemplified in a proteomic comparison of two fly species, Drosophilamelanogaster and D. virilis. PCQ was used to identify reduced levels of serine/threonine protein kinases PKN1 and PKN4 in CFBE41o⁻ cells compared to HBE41o⁻ cells and to elucidate that shorter proteoforms of full-length caspase-4 and ephrin B receptor are differentially expressed. Thus, PCQ extends current analyses in quantitative proteomics and facilitates finding differentially regulated proteins and proteoforms.

An accurate quantitation of different proteoforms remains challenging due to incomplete protein sequence coverage in mass spectrometry datasets. Here the authors describe a method to facilitate the discovery of proteoforms that may otherwise not be considered due to incomplete protein coverage or ambiguities in mapping peptides back to proteins.

Definition of a RACK1 Interaction Network in Drosophila melanogaster Using SWATH-MS

Receptor for Activated protein C kinase 1 (RACK1) is a scaffold protein that has been found in association with several signaling complexes, and with the 40S subunit of the ribosome. Using the model organism Drosophila melanogaster, we recently showed that RACK1 is required at the ribosome for internal ribosome entry site (IRES)-mediated translation of viruses. Here, we report a proteomic characterization of the interactome of RACK1 in Drosophila S2 cells. We carried out Label-Free quantitation using both Data-Dependent and Data-Independent Acquisition (DDA and DIA, respectively) and observed a significant advantage for the Sequential Window Acquisition of all THeoretical fragment-ion spectra (SWATH) method, both in terms of identification of interactants and quantification of low abundance proteins. These data represent the first SWATH spectral library available for Drosophila and will be a useful resource for the community. A total of 52 interacting proteins were identified, including several molecules involved in translation such as structural components of the ribosome, factors regulating translation initiation or elongation, and RNA binding proteins. Among these 52 proteins, 15 were identified as partners by the SWATH strategy only. Interestingly, these 15 proteins are significantly enriched for the functions translation and nucleic acid binding. This enrichment reflects the engagement of RACK1 at the ribosome and highlights the added value of SWATH analysis. A functional screen did not reveal any protein sharing the interesting properties of RACK1, which is required for IRES-dependent translation and not essential for cell viability. Intriguingly however, 10 of the RACK1 partners identified restrict replication of Cricket paralysis virus (CrPV), an IRES-containing virus.

The specific killing effect of matrine on castration-resistant prostate cancer cells by targeting the Akt/FoxO3a signaling pathway

Matrine, a Sophora alkaloid, exhibits antiproliferative and anti-carcinogenic activities through several mechanisms. In a previous study, we found that matrine could effectively inhibit the proliferation of castration-resistant prostate cancer (CRPC). In the present study, the effect of matrine and LY294002 on the expression of the Akt/FoxO3a signaling pathway was examined by western blot analyses and RT-PCR. We discovered that matrine significantly inhibited the proliferation of both prostate cancer cell line PC-3 and prostate epithelial cell line RWPE1, induced apoptosis and induced cell cycle arrest. In addition, LY294002 was found to enhance the effect of matrine. Furthermore, the effects of matrine on the inhibition of proliferation and the induction of cell cycle arrest and cell apoptosis were more effective on PC-3 than on RWPE1 cells. Compared to RWPE1 cells, matrine exerted a more powerful influence on PC-3 cells in increasing the expression of the relevant protein. Our data suggested that FoxO3a-Bim and FoxO3a-P27 may mediate matrine-inhibited proliferation of CRPC cells by activating cell apoptosis and inducing cell cycle arrest. Matrine exhibited high selectivity in killing CRPC cells. Our findings demonstrated that matrine could be used in a potential therapeutic role in the management of CRPC in humans.

Construction of dynamic probabilistic protein interaction networks for protein complex identification

Background

Recently, high-throughput experimental techniques have generated a large amount of protein-protein interaction (PPI) data which can construct large complex PPI networks for numerous organisms. System biology attempts to understand cellular organization and function by analyzing these PPI networks. However, most studies still focus on static PPI networks which neglect the dynamic information of PPI.

Results

The gene expression data under different time points and conditions can reveal the dynamic information of proteins. In this study, we used an active probability-based method to distinguish the active level of proteins at different active time points. We constructed dynamic probabilistic protein networks (DPPN) to integrate dynamic information of protein into static PPI networks. Based on DPPN, we subsequently proposed a novel method to identify protein complexes, which could effectively exploit topological structure as well as dynamic information of DPPN. We used three different yeast PPI datasets and gene expression data to construct three DPPNs. When applied to three DPPNs, many well-characterized protein complexes were accurately identified by this method.

Conclusion

The shift from static PPI networks to dynamic PPI networks is essential to accurately identify protein complex. This method not only can be applied to identify protein complex, but also establish a framework to integrate dynamic information into static networks for other applications, such as pathway analysis.

DeMix-Q: Quantification-Centered Data Processing Workflow

For historical reasons, most proteomics workflows focus on MS/MS identification but consider quantification as the end point of a comparative study. The stochastic data-dependent MS/MS acquisition (DDA) gives low reproducibility of peptide identifications from one run to another, which inevitably results in problems with missing values when quantifying the same peptide across a series of label-free experiments. However, the signal from the molecular ion is almost always present among the MS(1)spectra. Contrary to what is frequently claimed, missing values do not have to be an intrinsic problem of DDA approaches that perform quantification at the MS(1)level. The challenge is to perform sound peptide identity propagation across multiple high-resolution LC-MS/MS experiments, from runs with MS/MS-based identifications to runs where such information is absent. Here, we present a new analytical workflow DeMix-Q (https://github.com/userbz/DeMix-Q), which performs such propagation that recovers missing values reliably by using a novel scoring scheme for quality control. Compared with traditional workflows for DDA as well as previous DIA studies, DeMix-Q achieves deeper proteome coverage, fewer missing values, and lower quantification variance on a benchmark dataset. This quantification-centered workflow also enables flexible and robust proteome characterization based on covariation of peptide abundances.

Software Analysis of Uncorrelated MS1 Peaks for Discovery of Post-Translational Modifications

The goal in proteomics to identify all peptides in a complex mixture has been largely addressed using various LC MS/MS approaches, such as data dependent acquisition, SRM/MRM, and data independent acquisition instrumentation. Despite these developments, many peptides remain unsequenced, often due to low abundance, poor fragmentation patterns, or data analysis difficulties. Many of the unidentified peptides exhibit strong evidence in high resolution MS(1) data and are frequently post-translationally modified, playing a significant role in biological processes. Proteomics Workbench (PWB) software was developed to automate the detection and visualization of all possible peptides in MS(1) data, reveal candidate peptides not initially identified, and build inclusion lists for subsequent MS(2) analysis to uncover new identifications. We used this software on existing data on the autophagy regulating kinase Ulk1 as a proof of concept for this method, as we had already manually identified a number of phosphorylation sites Dorsey, F. C. et al (J. Proteome. Res. 8(11), 5253-5263 (2009)). PWB found all previously identified sites of phosphorylation. The software has been made freely available at http://www.proteomicsworkbench.com . Graphical Abstract ᅟ.

Computational analysis of protein interaction networks for infectious diseases

Infectious diseases caused by pathogens, including viruses, bacteria and parasites, pose a serious threat to human health worldwide. Frequent changes in the pattern of infection mechanisms and the emergence of multidrug-resistant strains among pathogens have weakened the current treatment regimen. This necessitates the development of new therapeutic interventions to prevent and control such diseases. To cater to the need, analysis of protein interaction networks (PINs) has gained importance as one of the promising strategies. The present review aims to discuss various computational approaches to analyse the PINs in context to infectious diseases. Topology and modularity analysis of the network with their biological relevance, and the scenario till date about host–pathogen and intra-pathogenic protein interaction studies were delineated. This would provide useful insights to the research community, thereby enabling them to design novel biomedicine against such infectious diseases.

Quantitative affinity purification mass spectrometry: a versatile technology to study protein-protein interactions

While the genomic revolution has dramatically accelerated the discovery of disease-associated genes, the functional characterization of the corresponding proteins lags behind. Most proteins fulfill their tasks in complexes with other proteins, and analysis of protein–protein interactions (PPIs) can therefore provide insights into protein function. Several methods can be used to generate large-scale protein interaction networks. However, most of these approaches are not quantitative and therefore cannot reveal how perturbations affect the network. Here, we illustrate how a clever combination of quantitative mass spectrometry with different biochemical methods provides a rich toolkit to study different aspects of PPIs including topology, subunit stoichiometry, and dynamic behavior.

Quantitative interaction proteomics of neurodegenerative disease proteins

Several proteins have been linked to neurodegenerative disorders (NDDs), but their molecular function is not completely understood. Here, we used quantitative interaction proteomics to identify binding partners of Amyloid beta precursor protein (APP) and Presenilin-1 (PSEN1) for Alzheimer’s disease (AD), Huntingtin (HTT) for Huntington’s disease, Parkin (PARK2) for Parkinson’s disease, and Ataxin-1 (ATXN1) for spinocerebellar ataxia type 1. Our network reveals common signatures of protein degradation and misfolding and recapitulates known biology. Toxicity modifier screens and comparison to genome-wide association studies show that interaction partners are significantly linked to disease phenotypes in vivo. Direct comparison of wild-type proteins and disease-associated variants identified binders involved in pathogenesis, highlighting the value of differential interactome mapping. Finally, we show that the mitochondrial protein LRPPRC interacts preferentially with an early-onset AD variant of APP. This interaction appears to induce mitochondrial dysfunction, which is an early phenotype of AD.

Hosp et al. show that quantitative interaction proteomics of neurodegenerative disease proteins captures interactions relevant to pathogenesis. Differential interactome mapping reveals preferential binding of the mitochondrial protein LRPPRC with an early-onset Alzheimer’s disease (AD) variant of APP, potentially contributing to mitochondrial dysfunction observed in AD.

A Double-Barrel Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) System to Quantify 96 Interactomes per Day

The field of proteomics has evolved hand-in-hand with technological advances in LC-MS/MS systems, now enabling the analysis of very deep proteomes in a reasonable time. However, most applications do not deal with full cell or tissue proteomes but rather with restricted subproteomes relevant for the research context at hand or resulting from extensive fractionation. At the same time, investigation of many conditions or perturbations puts a strain on measurement capacity. Here, we develop a high-throughput workflow capable of dealing with large numbers of low or medium complexity samples and specifically aim at the analysis of 96-well plates in a single day (15 min per sample). We combine parallel sample processing with a modified liquid chromatography platform driving two analytical columns in tandem, which are coupled to a quadrupole Orbitrap mass spectrometer (Q Exactive HF). The modified LC platform eliminates idle time between measurements, and the high sequencing speed of the Q Exactive HF reduces required measurement time. We apply the pipeline to the yeast chromatin remodeling landscape and demonstrate quantification of 96 pull-downs of chromatin complexes in about 1 day. This is achieved with only 500 μg input material, enabling yeast cultivation in a 96-well format. Our system retrieved known complex-members and the high throughput allowed probing with many bait proteins. Even alternative complex compositions were detectable in these very short gradients. Thus, sample throughput, sensitivity and LC/MS-MS duty cycle are improved severalfold compared with established workflows. The pipeline can be extended to different types of interaction studies and to other medium complexity proteomes.

Validation of a novel shotgun proteomic workflow for the discovery of protein-protein interactions: focus on ZNF521

The study of protein-protein interactions is increasingly relying on mass spectrometry (MS). The classical approach of separating immunoprecipitated proteins by SDS-PAGE followed by in-gel digestion is long and labor-intensive. Besides, it is difficult to integrate it with most quantitative MS-based workflows, except for stable isotopic labeling of amino acids in cell culture (SILAC). This work describes a fast, flexible and quantitative workflow for the discovery of novel protein-protein interactions. A cleavable cross-linker, dithiobis[succinimidyl propionate] (DSP), is utilized to stabilize protein complexes before immunoprecipitation. Protein complex detachment from the antibody is achieved by limited proteolysis. Finally, protein quantitation is performed via (18)O labeling. The workflow has been optimized concerning (i) DSP concentration and (ii) incubation times for limited proteolysis, using the stem cell-associated transcription cofactor ZNF521 as a model target. The interaction of ZNF521 with the core components of the nuclear remodelling and histone deacetylase (NuRD) complex, already reported in the literature, was confirmed. Additionally, interactions with newly discovered molecular partners of potentially relevant functional role, such as ZNF423, Spt16, Spt5, were discovered and validated by Western blotting.

A mass spectrometry view of stable and transient protein interactions

Through an impressive range of dynamic interactions, proteins succeed to carry out the majority of functions in a cell. These temporally and spatially regulated interactions provide the means through which one single protein can perform diverse functions and modulate different cellular pathways. Understanding the identity and nature of these interactions is therefore critical for defining protein functions and their contribution to health and disease processes. Here, we provide an overview of workflows that incorporate immunoaffinity purifications and quantitative mass spectrometry (frequently abbreviated as IP-MS or AP-MS) for characterizing protein-protein interactions. We discuss experimental aspects that should be considered when optimizing the isolation of a protein complex. As the presence of nonspecific associations is a concern in these experiments, we discuss the common sources of nonspecific interactions and present label-free and metabolic labeling mass spectrometry-based methods that can help determine the specificity of interactions. The effective regulation of cellular pathways and the rapid reaction to various environmental stresses rely on the formation of stable, transient, and fast-exchanging protein-protein interactions. While determining the exact nature of an interaction remains challenging, we review cross-linking and metabolic labeling approaches that can help address this important aspect of characterizing protein interactions and macromolecular assemblies.

A novel immuno-competitive capture mass spectrometry strategy for protein-protein interaction profiling reveals that LATS kinases regulate HCV replication through NS5A phosphorylation

Mapping protein-protein interactions is essential to fully characterize the biological function of a protein and improve our understanding of diseases. Affinity purification coupled to mass spectrometry (AP-MS) using selective antibodies against a target protein has been commonly applied to study protein complexes. However, one major limitation is a lack of specificity as a substantial part of the proposed binders is due to nonspecific interactions. Here, we describe an innovative immuno-competitive capture mass spectrometry (ICC-MS) method to allow systematic investigation of protein-protein interactions. ICC-MS markedly increases the specificity of classical immunoprecipitation (IP) by introducing a competition step between free and capturing antibody prior to IP. Instead of comparing only one experimental sample with a control, the methodology generates a 12-concentration antibody competition profile. Label-free quantitation followed by a robust statistical analysis of the data is then used to extract the cellular interactome of a protein of interest and to filter out background proteins. We applied this new approach to specifically map the interactome of hepatitis C virus (HCV) nonstructural protein 5A (NS5A) in a cellular HCV replication system and uncovered eight new NS5A-interacting protein candidates along with two previously validated binding partners. Follow-up biological validation experiments revealed that large tumor suppressor homolog 1 and 2 (LATS1 and LATS2, respectively), two closely related human protein kinases, are novel host kinases responsible for NS5A phosphorylation at a highly conserved position required for optimal HCV genome replication. These results are the first illustration of the value of ICC-MS for the analysis of endogenous protein complexes to identify biologically relevant protein-protein interactions with high specificity.

Pre- and post-processing workflow for affinity purification mass spectrometry data

The reliable detection of protein-protein interactions by affinity purification mass spectrometry (AP-MS) is crucial for the understanding of biological processes. Quantitative information can be used to separate truly interacting proteins from false-positives by contrasting counts of proteins binding to specific baits with counts of negative controls. Several approaches have been proposed for computing scores for potential interaction proteins, for example, the commonly used SAINT software. However, it remains a subjective decision where to set the cutoff score for candidate selection; furthermore, no precise control for the expected number of false-positives is provided. In related fields, successful data analysis strongly relies on statistical pre- and post-processing steps, which, so far, have played only a minor role in AP-MS data analysis. We introduce a complete workflow, embedding either the scoring method SAINT or alternatively a two-stage Poisson model into a pre- and post-processing framework. To this end, we investigate different normalization methods and apply a statistical filter adjusted to AP-MS data. Furthermore, we propose permutation and adjustment procedures, which allow the replacement of scores by statistical p values. The performance of the workflow is assessed on simulations as well as on a study focusing on interactions with the T3SS in Salmonella Typhimurium. Preprocessing methods significantly increase the number of detected truly interacting proteins, while a constant false-discovery rate is maintained. The software solution is freely available.

Application of shotgun proteomics for discovery-driven protein-protein interaction

Affinity purification of protein complexes and identification of co-purified proteins by mass spectrometry is a powerful method to discover novel protein-protein interactions. Application of this method to the study of biological systems often requires the ability to process a large number of samples. Hence, there is great need to generate proteomic workflows compatible with large-scale studies. The major goal of this protocol is to present a fast, reliable, and scalable method to characterize protein complexes by mass spectrometry to overcome the limitations of conventional geLC-MS/MS or MudPIT protocols. This method was successfully employed for the discovery and characterization of novel protein complexes in cultured yeast, mammalian cells, and mice.

Protein-protein interactions: switch from classical methods to proteomics and bioinformatics-based approaches

Following the sequencing of the human genome and many other organisms, research on protein-coding genes and their functions (functional genomics) has intensified. Subsequently, with the observation that proteins are indeed the molecular effectors of most cellular processes, the discipline of proteomics was born. Clearly, proteins do not function as single entities but rather as a dynamic network of team players that have to communicate. Though genetic (yeast two-hybrid Y2H) and biochemical methods (co-immunoprecipitation Co-IP, affinity purification AP) were the methods of choice at the beginning of the study of protein-protein interactions (PPI), in more recent years there has been a shift towards proteomics-based methods and bioinformatics-based approaches. In this review, we first describe in depth PPIs and we make a strong case as to why unraveling the interactome is the next challenge in the field of proteomics. Furthermore, classical methods of investigation of PPIs and structure-based bioinformatics approaches are presented. The greatest emphasis is placed on proteomic methods, especially native techniques that were recently developed and that have been shown to be reliable. Finally, we point out the limitations of these methods and the need to set up a standard for the validation of PPI experiments.

Interaction proteome of human Hippo signaling: modular control of the co-activator YAP1

Tissue homeostasis is controlled by signaling systems that coordinate cell proliferation, cell growth and cell shape upon changes in the cellular environment. Deregulation of these processes is associated with human cancer and can occur at multiple levels of the underlying signaling systems. To gain an integrated view on signaling modules controlling tissue growth, we analyzed the interaction proteome of the human Hippo pathway, an established growth regulatory signaling system. The resulting high-resolution network model of 480 protein-protein interactions among 270 network components suggests participation of Hippo pathway components in three distinct modules that all converge on the transcriptional co-activator YAP1. One of the modules corresponds to the canonical Hippo kinase cassette whereas the other two both contain Hippo components in complexes with cell polarity proteins. Quantitative proteomic data suggests that complex formation with cell polarity proteins is dynamic and depends on the integrity of cell-cell contacts. Collectively, our systematic analysis greatly enhances our insights into the biochemical landscape underlying human Hippo signaling and emphasizes multifaceted roles of cell polarity complexes in Hippo-mediated tissue growth control.

NT5E and FcGBP as key regulators of TGF-1-induced epithelial-mesenchymal transition (EMT) are associated with tumor progression and survival of patients with gallbladder cancer

Epithelial–mesenchymal transitions (EMTs) are essential manifestations of epithelial cell plasticity during tumor progression. Transforming growth factor-β(TGF-β) modulates epithelial plasticity in tumor physiological contexts by inducing EMT, which is associated with the altered expression of genes. In the present study, we used DNA micro-array analysis to search for differentially expressed genes in the TGF-β1 induced gallbladder carcinoma cell line (GBC-SD cells), as compared with normal GBC-SD cells. We identified 225 differentially expressed genes, including 144 that were over-expressed and 81 that were under-expressed in the TGF-β1 induced GBC-SD cells. NT5E (CD73) is the most increased gene, while the Fc fragment of the IgG binding protein (FcGBP) is the most decreased gene. The expression patterns of these two genes in gallbladder adenocarcinoma and chronic cholecystitis tissue were consistent with the micro-array data. Immunochemistry and clinicopathological results showed that the expression of NT5E and FcGBP in gallbladder adenocarcinoma is an independent marker for evaluation of the disease progression, clinical biological behaviors and prognosis. The data from the current study indicate that differential NT5E and FcGBP expressions could be further evaluated as biomarkers for predicting survival of patients with gallbladder cancer and that NT5E and FcGBP could be promising targets in the control of gallbladder cancer progression.

Quantifying protein interaction dynamics by SWATH mass spectrometry: application to the 14-3-3 system

Protein complexes and protein interaction networks are essential mediators of most biological functions. Complexes supporting transient functions such as signal transduction processes are frequently subject to dynamic remodeling. Currently, the majority of studies on the composition of protein complexes are carried out by affinity purification and mass spectrometry (AP-MS) and present a static view of the system. For a better understanding of inherently dynamic biological processes, methods to reliably quantify temporal changes of protein interaction networks are essential. Here we used affinity purification combined with sequential window acquisition of all theoretical spectra (AP-SWATH) mass spectrometry to study the dynamics of the 14-3-3β scaffold protein interactome after stimulation of the insulin-PI3K-AKT pathway. The consistent and reproducible quantification of 1,967 proteins across all stimulation time points provided insights into the 14-3-3β interactome and its dynamic changes following IGF1 stimulation. We therefore establish AP-SWATH as a tool to quantify dynamic changes in protein-complex interaction networks.

Purification and characterization of transcription factors

Transcription factors (TFs) are essential for the expression of all proteins, including those involved in human health and disease. However, TFs are resistant to proteomic characterization because they are frequently masked by more abundant proteins due to the limited dynamic range of capillary liquid chromatography-tandem mass spectrometry and protein database searching. Purification methods, particularly strategies that exploit the high affinity of TFs for DNA response elements (REs) on gene promoters, can enrich TFs prior to proteomic analysis to improve dynamic range and penetrance of the TF proteome. For example, trapping of TF complexes specific for particular REs has been achieved by recovering the element DNA-protein complex on solid supports. Additional methods for improving dynamic range include two- and three-dimensional gel electrophoresis incorporating electrophoretic mobility shift assays and Southwestern blotting for detection. Here we review methods for TF purification and characterization. We fully expect that future investigations will apply these and other methods to illuminate this important but challenging proteome.

FOXO flips the longevity SWItch

FOXO transcription factors promote longevity from worms to mammals, but the mechanisms by which FOXO extends lifespan have remained elusive. In the nematode Caenorhabditis elegans, FOXO is now shown to recruit the nucleosome remodelling complex SWI/SNF to its target genes, which is essential for FOXO to elicit stress resistance and longevity.

Applications of selected reaction monitoring (SRM)-mass spectrometry (MS) for quantitative measurement of signaling pathways

Quantitative measurement of the major regulatory proteins in signaling networks poses several technical challenges, including low abundance, the presence of post-translational modifications (PTMs), and the lack of suitable affinity detection reagents. Using the innate immune response (IIR) as a model signaling pathway, we illustrate the approach of stable isotope dilution (SID)-selected reaction monitoring (SRM)-mass spectrometry (MS) assays for quantification of low abundance signaling proteins. A work flow for SID-SRM-MS assay development is established for proteins with experimentally observed MS spectra and for those without. Using the interferon response factor (IRF)-3 transcription factor as an example, we illustrate the steps in high responding signature peptide identification, SID-SRM-MS assay optimization, and evaluation. SRM assays for normalization of IIR abundance to invariant housekeeping proteins are presented. We provide an example of SID-SRM assay development for post-translational modification (PTM) detection using an activating phospho-Ser modified NF-κB/RelA transcription factor, and describe challenges inherent in PTM-SID-SRM-MS assay development. Application of highly qualified quantitative, SID-SRM-MS assays will enable a systems-level approach to understanding the dynamics and kinetics of signaling in host cells, such as the IIR.

Quantitative proteomics: A strategic ally to map protein interaction networks

Many physiological processes are regulated by dynamic protein interaction networks whose characterization provides valuable information on cell biology. Several strategies can be used to analyze protein-protein interactions. Among them, affinity purification combined with mass spectrometry (AP-MS) is arguably the most widely employed technique, not only owing to its high throughput and sensitivity but also because it can answer critical questions such as where, when, and how protein-protein interactions occur. In AP-MS workflows, both the target protein and its interacting partners are isolated before being identified by MS. The main challenge of this approach is to distinguish bona fide binders from background contaminants. This review focuses on the different strategies designed to circumvent this limitation. In this regard, the combination of quantitative proteomics and affinity purification emerges as one of the most powerful, yet relatively simple, strategies to characterize protein-protein interactions.

Quantitation of the dynamic profiles of the innate immune response using multiplex selected reaction monitoring-mass spectrometry

The innate immune response (IIR) is a coordinated intracellular signaling network activated by the presence of pathogen-associated molecular patterns that limits pathogen spread and induces adaptive immunity. Although the precise temporal activation of the various arms of the IIR is a critical factor in the outcome of a disease, currently there are no quantitative multiplex methods for its measurement. In this study, we investigate the temporal activation pattern of the IIR in response to intracellular double-stranded RNA stimulation using a quantitative 10-plex stable isotope dilution-selected reaction monitoring-MS assay. We were able to observe rapid activation of both NF-κB and IRF3 signaling arms, with IRF3 demonstrating a transient response, whereas NF-κB underwent a delayed secondary amplification phase. Our measurements of the NF-κB-IκBα negative feedback loop indicate that about 20% of IκBα in the unstimulated cell is located within the nucleus and represents a population that is rapidly degraded in response to double-stranded RNA. Later in the time course of stimulation, the nuclear IκBα pool is repopulated first prior to its cytoplasmic accumulation. Examination of the IRF3 pathway components shows that double-stranded RNA induces initial consumption of the RIG-I PRR and the IRF3 kinase (TBK1). Stable isotope dilution-selected reaction monitoring-MS measurements after siRNA-mediated IRF3 or RelA knockdown suggests that a low nuclear threshold of NF-κB is required for inducing target gene expression, and that there is cross-inhibition of the NF-κB and IRF3 signaling arms. Finally, we were able to measure delayed noncanonical NF-κB activation by quantifying the abundance of the processed (52 kDa) NF-κB2 subunit in the nucleus. We conclude that quantitative proteomics measurement of the individual signaling arms of the IIR in response to system perturbations is significantly enabled by stable isotope dilution-selected reaction monitoring-MS-based quantification, and that this technique will reveal novel insights into the dynamics and connectivity of the IIR.

Genotype-phenotype relationships in light of a modular protein interaction landscape

Recent progress in genomic sequencing has revealed genotype-phenotype information of enormous complexity and challenges earlier hypotheses on how phenotypes emerge from altered gene structures. The field of proteomics has advanced in parallel and offers promising new concepts for a modern interpretation of complex and nonlinear genotype-phenotype relationships. We are beginning to decipher global proteome organization with increasing throughput and accuracy. These efforts revealed a highly modular organization of the protein landscape. Here we discuss the challenges and implications emerging from a modular protein landscape for a better understanding of complex genotype-phenotype patterns.

Identification of protein interactions involved in cellular signaling

Protein-protein interactions drive biological processes. They are critical for all intra- and extracellular functions, and the technologies to analyze them are widely applied throughout the various fields of biological sciences. This study takes an in-depth view of some common principles of cellular regulation and provides a detailed account of approaches required to comprehensively map signaling protein-protein interactions in any particular cellular system or condition. We provide a critical review of the benefits and disadvantages of the yeast two-hybrid method and affinity purification coupled with mass spectrometric procedures for identification of signaling protein-protein interactions. In particular, we emphasize the quantitative and qualitative differences between tandem affinity and one-step purification (such as FLAG and Strep tag) methods. Although applicable to all types of interaction studies, a special section is devoted in this review to aspects that should be considered when attempting to identify signaling protein interactions that often are transient and weak by nature. Finally, we discuss shotgun and quantitative information that can be gleaned by MS-coupled methods for analysis of multiprotein complexes.

The properties of genome conformation and spatial gene interaction and regulation networks of normal and malignant human cell types

The spatial conformation of a genome plays an important role in the long-range regulation of genome-wide gene expression and methylation, but has not been extensively studied due to lack of genome conformation data. The recently developed chromosome conformation capturing techniques such as the Hi-C method empowered by next generation sequencing can generate unbiased, large-scale, high-resolution chromosomal interaction (contact) data, providing an unprecedented opportunity to investigate the spatial structure of a genome and its applications in gene regulation, genomics, epigenetics, and cell biology. In this work, we conducted a comprehensive, large-scale computational analysis of this new stream of genome conformation data generated for three different human leukemia cells or cell lines by the Hi-C technique. We developed and applied a set of bioinformatics methods to reliably generate spatial chromosomal contacts from high-throughput sequencing data and to effectively use them to study the properties of the genome structures in one-dimension (1D) and two-dimension (2D). Our analysis demonstrates that Hi-C data can be effectively applied to study tissue-specific genome conformation, chromosome-chromosome interaction, chromosomal translocations, and spatial gene-gene interaction and regulation in a three-dimensional genome of primary tumor cells. Particularly, for the first time, we constructed genome-scale spatial gene-gene interaction network, transcription factor binding site (TFBS) – TFBS interaction network, and TFBS-gene interaction network from chromosomal contact information. Remarkably, all these networks possess the properties of scale-free modular networks.

Defining the extracellular matrix using proteomics

The cell microenvironment has a profound influence on the behaviour, growth and survival of cells. The extracellular matrix (ECM) provides not only mechanical and structural support to cells and tissues but also binds soluble ligands and transmembrane receptors to provide spatial coordination of signalling processes. The ability of cells to sense the chemical, mechanical and topographical features of the ECM enables them to integrate complex, multiparametric information into a coherent response to the surrounding microenvironment. Consequently, dysregulation or mutation of ECM components results in a broad range of pathological conditions. Characterization of the composition of ECM derived from various cells has begun to reveal insights into ECM structure and function, and mechanisms of disease. Proteomic methodologies permit the global analysis of subcellular systems, but extracellular and transmembrane proteins present analytical difficulties to proteomic strategies owing to the particular biochemical properties of these molecules. Here, we review advances in proteomic approaches that have been applied to furthering our understanding of the ECM microenvironment. We survey recent studies that have addressed challenges in the analysis of ECM and discuss major outcomes in the context of health and disease. In addition, we summarize efforts to progress towards a systems-level understanding of ECM biology.

Optimal precursor ion selection for LC-MALDI MS/MS

Background

Liquid chromatography mass spectrometry (LC-MS) maps in shotgun proteomics are often too complex to select every detected peptide signal for fragmentation by tandem mass spectrometry (MS/MS). Standard methods for precursor ion selection, commonly based on data dependent acquisition, select highly abundant peptide signals in each spectrum. However, these approaches produce redundant information and are biased towards high-abundance proteins.

Results

We present two algorithms for inclusion list creation that formulate precursor ion selection as an optimization problem. Given an LC-MS map, the first approach maximizes the number of selected precursors given constraints such as a limited number of acquisitions per RT fraction. Second, we introduce a protein sequence-based inclusion list that can be used to monitor proteins of interest. Given only the protein sequences, we create an inclusion list that optimally covers the whole protein set. Additionally, we propose an iterative precursor ion selection that aims at reducing the redundancy obtained with data dependent LC-MS/MS. We overcome the risk of erroneous assignments by including methods for retention time and proteotypicity predictions. We show that our method identifies a set of proteins requiring fewer precursors than standard approaches. Thus, it is well suited for precursor ion selection in experiments with limited sample amount or analysis time.

Conclusions

We present three approaches to precursor ion selection with LC-MALDI MS/MS. Using a well-defined protein standard and a complex human cell lysate, we demonstrate that our methods outperform standard approaches. Our algorithms are implemented as part of OpenMS and are available under http://www.openms.de.