The revolution and evolution of shotgun proteomics for large-scale proteome analysis

A new paradigm for drug discovery in the treatment of complex diseases: drug discovery and optimization

In the past, the drug research and development has predominantly followed a "single target, single disease" model. However, clinical data show that single-target drugs are difficult to interfere with the complete disease network, are prone to develop drug resistance and low safety in clinical use. The proposal of multi-target drug therapy (also known as "cocktail therapy") provides a new approach for drug discovery, which can affect the disease and reduce adverse reactions by regulating multiple targets. Natural products are an important source for multi-target innovative drug development, and more than half of approved small molecule drugs are related to natural products. However, there are many challenges in the development process of natural products, such as active drug screening, target identification and preclinical dosage optimization. Therefore, how to develop multi-target drugs with good drug resistance from natural products has always been a challenge. This article summarizes the applications and shortcomings of related technologies such as natural product bioactivity screening, clarify the mode of action of the drug (direct/indirect target), and preclinical dose optimization. Moreover, in response to the challenges faced by natural products in the development process and the trend of interdisciplinary and multi-technology integration, and a multi-target drug development strategy of "active substances - drug action mode - drug optimization" is proposed to solve the key challenges in the development of natural products from multiple dimensions and levels.

Topology-driven discovery of transmembrane protein S-palmitoylation

Protein S-palmitoylation is a reversible lipophilic posttranslational modification regulating diverse signaling pathways. Within transmembrane proteins (TMPs), S-palmitoylation is implicated in conditions from inflammatory disorders to respiratory viral infections. Many small-scale experiments have observed S-palmitoylation at juxtamembrane Cys residues. However, most large-scale S-palmitoyl discovery efforts rely on trypsin-based proteomics within which hydrophobic juxtamembrane regions are likely underrepresented. Machine learning-by virtue of its freedom from experimental constraints-is particularly well suited to address this discovery gap surrounding TMP S-palmitoylation. Utilizing a UniProt-derived feature set, a gradient-boosted machine learning tool (TopoPalmTree) was constructed and applied to a holdout dataset of viral S-palmitoylated proteins. Upon application to the mouse TMP proteome, 1591 putative S-palmitoyl sites (i.e. not listed in SwissPalm or UniProt) were identified. Two lung-expressed S-palmitoyl candidates (synaptobrevin Vamp5 and water channel Aquaporin-5) were experimentally assessed, as were three Type I transmembrane proteins (Cadm4, Chodl, and Havcr2). Finally, TopoPalmTree was used for the rational design of an S-palmitoyl site on KDEL-Receptor 2. This readily interpretable model aligns the innumerable small-scale experiments observing juxtamembrane S-palmitoylation into a proteomic tool for TMP S-palmitoyl discovery and design, thus facilitating future investigations of this important modification.

Detection of Borrelia burgdorferi sensu lato by proteomics: a complementary diagnosis tool on erythema migrans biopsies

OBJECTIVES

We have developed targeted proteomics in the context of Lyme borreliosis (LM) as a new direct diagnostic tool for detecting Borrelia proteins in the skin of patients with erythema migrans. If satisfactory, this proteomic technique could be used in addition to culture and/or PCR for disseminated infections where Borrelia detection is essential to demonstrate active infection. In these infections, the diagnosis is indirect and relies mainly on serology.

METHODS

We recruited 46 patients with LM and 11 controls and collected two skin biopsies from each patient. One biopsy was used for Borrelia burgdorferi sensu lato PCR and culture and the other one was for targeted mass-spectrometry-based proteomics. Six markers of infection were selected for proteomics: Outer surface protein C (OspC), flagellin, enolase, lipoprotein gi|365823350, decorin binding protein A, and glyceraldehyde-3-phosphate dehydrogenase.

RESULTS

Culturing Borrelia from the biopsies increased the sensitivity of the methods. Among the patients included for analysis, 61% (28 patients), 61% (28), and 46% (21) were detected as positive by proteomics, PCR, and culture, respectively. PCR and proteomics were complementary. OspC and flagellin were the most frequently detected protein markers of infection by proteomics, which in some patients, detected up to nine peptides for the flagellin.

DISCUSSION

It is possible to identify bacterial makers from the skin by proteomics. Our approach can be used to diagnose tick-borne diseases such as LM.

TRIAL REGISTRATION

clinicaltrials.gov identifier: NCT02414789.

Proteomics and machine learning: Leveraging domain knowledge for feature selection in a skeletal muscle tissue meta-analysis

Omics techniques, such as proteomics, contain crucial data for understanding biological processes, but they remain underutilized due to their high dimensionality. Typically, proteomics research focuses narrowly on using a limited number of datasets, hindering cross-study comparisons, a problem that can potentially be addressed by machine learning. Despite this potential, machine learning has seen limited adoption in the field of proteomics. Here, skeletal muscle proteomics datasets from five separate studies were combined. These studies included conditions such as in vitro models (both 2D and 3D), in vivo skeletal muscle tissue, and adjacent tissues such as tendons. The collected data was preprocessed using MaxQuant, and then enriched using a Python script fetching structural and compositional details from UniProt and Ensembl databases. This was used to handle high-dimensional and sparsely labeled dataset by breaking it down into five smaller categories using cellular composition information and then training a Random Forest model for each category separately. Using biological context for interpreting the data resulted in improved model performance and made tailored analysis possible by reducing the dimensionality and increasing signal-to-noise ratio as well as only preserving biologically relevant features in each category. This integration of domain knowledge into data analysis and model training facilitated the discovery of new patterns while ensuring the retention of critical details, often overlooked when blind feature selection methods are used to exclude proteins with minimal expressions or variances. This approach was shown to be suitable for performing diverse analyses on individual as well as combined datasets within a broader biological context, ultimately leading to the identification of biologically relevant patterns. Besides from generating new biological insights, this approach can be used to perform tasks such as biomarker discovery, cluster analysis, classification, and anomaly detection more accurately, but incorporation of more datasets is needed to further expand the computational capabilities of such models in clinical settings.

Nano-LC with New Hydrophobic Monolith Based on 9-Antracenylmethyl Methacrylate for Biomolecule Separation

In this study, new monolithic poly(9-anthracenylmethyl methacrylate-co-trimethylolpropane trimethacrylate (TRIM) columns, referred as ANM monoliths were prepared, for the first time, and were used for the separation media for biomolecules and proteomics analysis by nano-liquid chromatography (nano-LC). Monolithic columns were prepared by in situ polymerization of 9-anthracenylmethyl methacrylate (ANM) and trimethylolpropane trimethacrylate (TRIM) in a fused silica capillary column of 100 µm ID. Polymerization solution was optimized in relation to monomer and porogenic solvent. Scanning electron microscopy (SEM) and chromatographic analyses were performed for the characterization studies of ANM monoliths. The ANM monolith produced more than 46.220 plates/m, and the chromatographic evaluation of the optimized ANM monolith was carried out using homologous alkylbenzenes (ABs) and polyaromatic hydrocarbons (PAHs), allowing both strong hydrophobic and π-π interactions. Run-to-run and column-to-column reproducibility values were found as <2.91% and 2.9-3.2%, respectively. The final monolith was used for biomolecule separation, including both three dipeptides, including Alanine-Tyrosine (Ala-Tyr), Glycine-Phenylalanine (Gly-Phe), and L-carnosine and five standard proteins, including ribonuclease A (RNase A), α-chymotrypsinogen (α-chym), lysozyme (Lys), cytochrome C (Cyt C), and myoglobin (Mb) in order to evaluate its potential. Both peptides and proteins were baseline separated using the developed ANM monolith in nano-LC. The ANM monolith was then applied to the protein and peptide profiling of MCF-7 cell line, which allowed a high-resolution analysis of peptides, providing a high peak capacity.

Influence of Software Settings on the Identification Rate, Quantification Results, and Reproducibility in Profiling Post-Translational Modifications by Microflow Liquid Chromatography-Ion Mobility-Quadrupole Time-Of-Flight Analysis Using PEAKS Software

The influence of data evaluation parameters on qualitative and quantitative results of untargeted shotgun profiling of enzymatic and nonenzymatic post-translational modifications (PTMs) was investigated in a model of bovine whey protein α-lactalbumin heated with lactose. Based on the same raw data, individual adjustments to the protein database and enzyme settings of PEAKS studio software increased the identification rate from 27 unmodified peptides to 48 and from 322 peptides in total to 535. The qualitative and quantitative reproducibility was also assessed based on 18 measurements of one sample across three batches. A total of 570 peptides were detected. While 89 peptides were identified in all measurements, the majority of peptides (161) were detected only once and mostly based on nonindicative spectra. The reproducibility of label-free quantification (LFQ) in six measurements of the same sample was similar after processing the data by either the PTM algorithm or the LFQ algorithm. In both cases, about one-third of the peptides showed a coefficient of variation of above 20%. However, the LFQ algorithm increased the number of quantified peptides from 75 to 179. Data are available at the PRIDE Archive with the data set identifier PXD050363.

Topology-Driven Discovery of Transmembrane Protein S-Palmitoylation

Protein S-palmitoylation is a reversible lipophilic posttranslational modification regulating a diverse number of signaling pathways. Within transmembrane proteins (TMPs), S-palmitoylation is implicated in conditions from inflammatory disorders to respiratory viral infections. Many small-scale experiments have observed S-palmitoylation at juxtamembrane Cys residues. However, most large-scale S-palmitoyl discovery efforts rely on trypsin-based proteomics within which hydrophobic juxtamembrane regions are likely underrepresented. Machine learning- by virtue of its freedom from experimental constraints - is particularly well suited to address this discovery gap surrounding TMP S-palmitoylation. Utilizing a UniProt-derived feature set, a gradient boosted machine learning tool (TopoPalmTree) was constructed and applied to a holdout dataset of viral S-palmitoylated proteins. Upon application to the mouse TMP proteome, 1591 putative S-palmitoyl sites (i.e. not listed in SwissPalm or UniProt) were identified. Two lung-expressed S-palmitoyl candidates (synaptobrevin Vamp5 and water channel Aquaporin-5) were experimentally assessed. Finally, TopoPalmTree was used for rational design of an S-palmitoyl site on KDEL-Receptor 2. This readily interpretable model aligns the innumerable small-scale experiments observing juxtamembrane S-palmitoylation into a proteomic tool for TMP S-palmitoyl discovery and design, thus facilitating future investigations of this important modification.

Comprehensive Overview of Bottom-Up Proteomics Using Mass Spectrometry

Proteomics is the large scale study of protein structure and function from biological systems through protein identification and quantification. "Shotgun proteomics" or "bottom-up proteomics" is the prevailing strategy, in which proteins are hydrolyzed into peptides that are analyzed by mass spectrometry. Proteomics studies can be applied to diverse studies ranging from simple protein identification to studies of proteoforms, protein-protein interactions, protein structural alterations, absolute and relative protein quantification, post-translational modifications, and protein stability. To enable this range of different experiments, there are diverse strategies for proteome analysis. The nuances of how proteomic workflows differ may be challenging to understand for new practitioners. Here, we provide a comprehensive overview of different proteomics methods. We cover from biochemistry basics and protein extraction to biological interpretation and orthogonal validation. We expect this Review will serve as a handbook for researchers who are new to the field of bottom-up proteomics.

ARID3C Acts as a Regulator of Monocyte-to-Macrophage Differentiation Interacting with NPM1

ARID3C is a protein located on human chromosome 9 and expressed at low levels in various organs, yet its biological function has not been elucidated. In this study, we investigated both the cellular localization and function of ARID3C. Employing a combination of LC-MS/MS and deep learning techniques, we identified NPM1 as a binding partner for ARID3C's nuclear shuttling. ARID3C was found to predominantly localize with the nucleus, where it functioned as a transcription factor for genes STAT3, STAT1, and JUNB, thereby facilitating monocyte-to-macrophage differentiation. The precise binding sites between ARID3C and NPM1 were predicted by AlphaFold2. Mutating this binding site prevented ARID3C from interacting with NPM1, resulting in its retention in the cytoplasm instead of translocation to the nucleus. Consequently, ARID3C lost its ability to bind to the promoters of target genes, leading to a loss of monocyte-to-macrophage differentiation. Collectively, our findings indicate that ARID3C forms a complex with NPM1 to translocate to the nucleus, acting as a transcription factor that promotes the expression of the genes involved in monocyte-to-macrophage differentiation.

L-Glyceraldehyde Inhibits Neuroblastoma Cell Growth via a Multi-Modal Mechanism on Metabolism and Signaling

Glyceraldehyde (GA) is a three-carbon monosaccharide that can be present in cells as a by-product of fructose metabolism. Bruno Mendel and Otto Warburg showed that the application of GA to cancer cells inhibits glycolysis and their growth. However, the molecular mechanism by which this occurred was not clarified. We describe a novel multi-modal mechanism by which the L-isomer of GA (L-GA) inhibits neuroblastoma cell growth. L-GA induces significant changes in the metabolic profile, promotes oxidative stress and hinders nucleotide biosynthesis. GC-MS and 13C-labeling was employed to measure the flow of carbon through glycolytic intermediates under L-GA treatment. It was found that L-GA is a potent inhibitor of glycolysis due to its proposed targeting of NAD(H)-dependent reactions. This results in growth inhibition, apoptosis and a redox crisis in neuroblastoma cells. It was confirmed that the redox mechanisms were modulated via L-GA by proteomic analysis. Analysis of nucleotide pools in L-GA-treated cells depicted a previously unreported observation, in which nucleotide biosynthesis is significantly inhibited. The inhibitory action of L-GA was partially relieved with the co-application of the antioxidant N-acetyl-cysteine. We present novel evidence for a simple sugar that inhibits cancer cell proliferation via dysregulating its fragile homeostatic environment.

Unfolding of protein using MoS2/SnS2heterostructure for nanopore-based sequencing

Protein sequencing is crucial for understanding the complex mechanisms driving biological functions. However, proteins are usually folded in their native state and the mechanism of fast protein conformation transitions still remains unclear, which make protein sequencing challenging. Molecular dynamics simulations with accurate force field are now able to observe the entire folding/unfolding process, providing valuable insights into protein folding mechanisms. Given that proteins can be unfolded, nanopore technology shows great potential for protein sequencing. In this study, we proposed to use MoS2/SnS2heterostructures to firstly unfold proteins and then detect them by a nanopore in the heterostructural membrane. All-atom molecular dynamics simulations performed in this work provided rich atomic-level information for a comprehensive understanding of protein unfolding process and mechanism on the MoS2/SnS2heterostructure, it was found that the strong binding of protein to SnS2nanostripe and hydrogen bond breaking were the main reasons for unfolding the protein on the heterostructure. After the protein was fully unfolded, it was restrained on the nanostripe because of the affinity of protein to the SnS2nanostripe. Thus by integrating the proposed unfolding technique with nanopore technology, detection of linear unfolded peptide was realized in this work, allowing for the identification of protein components, which is essential for sequencing proteins in the near future.

Identifying secreted biomarkers of dopaminergic ventral midbrain progenitor cells

BACKGROUND

Ventral midbrain (VM) dopaminergic progenitor cells derived from human pluripotent stem cells have the potential to replace endogenously lost dopamine neurons and are currently in preclinical and clinical development for treatment of Parkinson's Disease (PD). However, one main challenge in the quality control of the cells is that rostral and caudal VM progenitors are extremely similar transcriptionally though only the caudal VM cells give rise to dopaminergic (DA) neurons with functionality relevant for cell replacement in PD. Therefore, it is critical to develop assays which can rapidly and reliably discriminate rostral from caudal VM cells during clinical manufacturing.

METHODS

We performed shotgun proteomics on cell culture supernatants from rostral and caudal VM progenitor cells to search for novel secreted biomarkers specific to DA progenitors from the caudal VM. Key hits were validated by qRT-PCR and ELISA.

RESULTS

We identified and validated novel secreted markers enriched in caudal VM progenitor cultures (CPE, LGI1 and PDGFC), and found these markers to correlate strongly with the expression of EN1, which is a predictive marker for successful graft outcome in DA cell transplantation products. Other markers (CNTN2 and CORIN) were found to conversely be enriched in the non-dopaminergic rostral VM cultures. Key novel ELISA markers were further validated on supernatant samples from GMP-manufactured caudal VM batches.

CONCLUSION

As a non-invasive in-process quality control test for predicting correctly patterned batches of caudal VM DA cells during clinical manufacturing, we propose a dual ELISA panel measuring LGI1/CORIN ratios around day 16 of differentiation.

Technological development of multidimensional liquid chromatography-mass spectrometry in proteome research

Liquid chromatography-mass spectrometry (LC-MS) is the method of choice for high-throughput proteomic research. Limited by the peak capacity, the separation performance of conventional single-dimensional LC hampers the development of proteomics. Combining different separation modes orthogonally, multidimensional liquid chromatography (MDLC) with high peak capacity was developed to address this challenge. MDLC has evolved rapidly since its establishment, and the progress of proteomics has been greatly facilitated by the advent of novel MDLC-MS-based methods. In this paper, we will review the advances of MDLC-MS-based methodologies and technologies in proteomics studies, from different perspectives including novel application scenarios and proteomic targets, automation, miniaturization, and the improvement of the classic methods in recent years. In addition, attempts regarding new MDLC-MS models are also mentioned together with the outlook of MDLC-MS-based proteomics methods.

Emerging insights and challenges for understanding T cell function through the proteome

T cells rapidly transition from a quiescent state into active proliferation and effector function upon exposure to cognate antigen. These processes are tightly controlled by signal transduction pathways that influence changes in chromatin remodeling, gene transcription, and metabolism, all of which collectively drive specific T cell memory or effector cell development. Dysregulation of any of these events can mediate disease and the past several years has shown unprecedented novel approaches to understand these events, down to the single-cell level. The massive explosion of sequencing approaches to assess the genome and transcriptome at the single cell level has transformed our understanding of T cell activation, developmental potential, and effector function under normal and various disease states. Despite these advances, there remains a significant dearth of information regarding how these events are translated to the protein level. For example, resolution of protein isoforms and/or specific post-translational modifications mediating T cell function remains obscure. The application of proteomics can change that, enabling significant insights into molecular mechanisms that regulate T cell function. However, unlike genomic approaches that have enabled exquisite visualization of T cell dynamics at the mRNA and chromatin level, proteomic approaches, including those at the single-cell level, has significantly lagged. In this review, we describe recent studies that have enabled a better understanding of how protein synthesis and degradation change during T cell activation and acquisition of effector function. We also highlight technical advances and how these could be applied to T cell biology. Finally, we discuss future needs to expand upon our current knowledge of T cell proteomes and disease.

Deciphering combinatorial post-translational modifications by top-down mass spectrometry

Post-translational modifications (PTMs) create vast structural and functional diversity of proteins, ultimately modulating protein function and degradation, influencing cellular signaling, and regulating transcription. The combinatorial patterns of PTMs increase the heterogeneity of proteins and further mediates their interactions. Advances in mass spectrometry-based proteomics have resulted in identification of thousands of proteins and allowed characterization of numerous types and sites of PTMs. Examination of intact proteins, termed the top-down approach, offers the potential to map protein sequences and localize multiple PTMs on each protein, providing the most comprehensive cataloging of proteoforms. This review describes some of the dividends of using mass spectrometry to analyze intact proteins and showcases innovative strategies that have enhanced the promise of top-down proteomics for exploring the impact of combinatorial PTMs in unsurpassed detail.

Proteomic Discovery and Validation of Novel Fluid Biomarkers for Improved Patient Selection and Prediction of Clinical Outcomes in Alzheimer’s Disease Patient Cohorts

Alzheimer’s disease (AD) is an irreversible neurodegenerative disease characterized by progressive cognitive decline. The two cardinal neuropathological hallmarks of AD include the buildup of cerebral β amyloid (Aβ) plaques and neurofibrillary tangles of hyperphosphorylated tau. The current disease-modifying treatments are still not effective enough to lower the rate of cognitive decline. There is an urgent need to identify early detection and disease progression biomarkers that can facilitate AD drug development. The current established readouts based on the expression levels of amyloid beta, tau, and phospho-tau have shown many discrepancies in patient samples when linked to disease progression. There is an urgent need to identify diagnostic and disease progression biomarkers from blood, cerebrospinal fluid (CSF), or other biofluids that can facilitate the early detection of the disease and provide pharmacodynamic readouts for new drugs being tested in clinical trials. Advances in proteomic approaches using state-of-the-art mass spectrometry are now being increasingly applied to study AD disease mechanisms and identify drug targets and novel disease biomarkers. In this report, we describe the application of quantitative proteomic approaches for understanding AD pathophysiology, summarize the current knowledge gained from proteomic investigations of AD, and discuss the development and validation of new predictive and diagnostic disease biomarkers.

Neuroproteomics Chip-Based Mass Spectrometry and Other Techniques for Alzheimer´S Disease Biomarkers – Update

Background:

Alzheimer's disease (AD) is a progressive neurodegenerative disease of growing interest given that there is cognitive damage and symptom onset acceleration. Therefore, it is important to find AD biomarkers for early diagnosis, disease progression, and discrimination of AD and other diseases.

Objective:

To update the relevance of mass spectrometry for the identification of peptides and proteins involved in AD useful as discriminating biomarkers.

Methods:

Proteomics and peptidomics technologies that show the highest possible specificity and selectivity for AD biomarkers are analyzed, together with the biological fluids used. In addition to positron emission tomography and magnetic resonance imaging, MALDI-TOF mass spectrometry is widely used to identify proteins and peptides involved in AD. The use of protein chips in SELDI technology and electroblotting chips for peptides makes feasible small amounts (L) of samples for analysis.

Results:

Suitable biomarkers are related to AD pathology, such as intracellular neurofibrillary tangles; extraneuronal senile plaques; neuronal and axonal degeneration; inflammation and oxidative stress. Recently, peptides were added to the candidate list, which are not amyloid-b or tau fragments, but are related to coagulation, brain plasticity, and complement/neuroinflammation systems involving the neurovascular unit.

Conclusion:

The progress made in the application of mass spectrometry and recent chip techniques is promising for discriminating between AD, mild cognitive impairment, and matched healthy controls. The application of this technique to blood samples from patients with AD has shown to be less invasive and fast enough to determine the diagnosis, stage of the disease, prognosis, and follow-up of the therapeutic response.

Data storage using peptide sequences

Humankind is generating digital data at an exponential rate. These data are typically stored using electronic, magnetic or optical devices, which require large physical spaces and cannot last for a very long time. Here we report the use of peptide sequences for data storage, which can be durable and of high storage density. With the selection of suitable constitutive amino acids, designs of address codes and error-correction schemes to protect the order and integrity of the stored data, optimization of the analytical protocol and development of a software to effectively recover peptide sequences from the tandem mass spectra, we demonstrated the feasibility of this method by successfully storing and retrieving a text file and the music file Silent Night with 40 and 511 18-mer peptides respectively. This method for the first time links data storage with the peptide synthesis industry and proteomics techniques, and is expected to stimulate the development of relevant fields.

Graphene oxide-octadecylsilane incorporated monolithic nano-columns with 50 μm id and 100 μm id for small molecule and protein separation by nano-liquid chromatography

In this study, graphene oxide-octadecylsilane incorporated monolithic nano-columns were developed for protein analysis by nano liquid chromatography (nano LC). The monolithic column with 100 μm id was first prepared by an in situ polymerization using ethylene dimethacrylate (EDMA), 3-chloro-2-hydroxypropylmethacrylate (HPMA-Cl), and methacryloyl graphene oxide nanoparticles (MGONPs). MGONPs were synthesized by the treatment of 3-(trimethoxysilyl)propylmethacrylate (TMSPM) and GO. Tetrahydrofuran (THF) and dodecanol were used as the porogenic solvent. The resulting column was functionalized by dimethyloctadecylch lorosilane (DODCS) for the enhancement of hydrophobicity. The functionalization greatly improved the baseline separation of hydrophobic compounds such as polyaromatic hydrocarbons (PAHs). The optimized monolith with respect to total polymerization mixture was characterized by using Fourier-transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM) X-ray diffraction (XRD) and chromatographic analyses. The blank monoliths without functionalization exhibited poor separation while a good separation performance of MGONPs functionalized monoliths was achieved. The monolith with 100 μm id was evaluated in protein separation in nano LC using RNase A, Cytochrome C, Lysozyme, Trypsin, and Ca isozyme II as the test proteins. It was shown that protein separation mechanism was based on large π-system of GO and hydrophobicity of the monolithic structure. Theoretical plates number up to 57 600 plates were achieved. The nano-column with 50 μm id was also prepared using the same polymerization mixture under the same chemical conditions. These nano-columns were employed for protein separation by nano LC, and the dependence of both nano-column performance on the internal diameter was also discussed.

Mutant Proteomics of Lung Adenocarcinomas Harboring Different EGFR Mutations

Epidermal growth factor receptor EGFR major driver mutations may affect downstream molecular networks and pathways, which would influence treatment outcomes of non-small cell lung cancer (NSCLC). This study aimed to unveil profiles of mutant proteins expressed in lung adenocarcinomas of 36 patients harboring representative driver EGFR mutations (Ex19del, nine; L858R, nine; no Ex19del/L858R, 18). Surprisingly, the orthogonal partial least squares discriminant analysis performed for identified mutant proteins demonstrated the profound differences in distance among the different EGFR mutation groups, suggesting that cancer cells harboring L858R or Ex19del emerge from cellular origins different from L858R/Ex19del-negative cells. Weighted gene coexpression network analysis, together with over-representative analysis, identified 18 coexpressed modules and their eigen proteins. Pathways enriched differentially for both the L858R and Ex19del mutations included carboxylic acid metabolic process, cell cycle, developmental biology, cellular responses to stress, mitotic prophase, cell proliferation, growth, epithelial to mesenchymal transition (EMT), and immune system. The IPA causal network analysis identified the highly activated networks of PARPBP, HOXA1, and APH1 under the L858R mutation, whereas those of ASGR1, APEX1, BUB1, and MAPK10 were highly activated under the Ex19del mutation. Interestingly, the downregulated causal network of osimertinib intervention showed the highest significance in overlap p-value among most causal networks predicted under the L858R mutation. We also identified the causal network of MAPK interacting serine/threonine kinase 1/2 (MNK1/2) highly activated differentially under the L858R mutation. Tumor-suppressor AMOT, a component of the Hippo pathways, was highly inhibited commonly under both L858R and Ex19del mutations. Our results could identify disease-related protein molecular networks from the landscape of single amino acid variants. Our findings may help identify potential therapeutic targets and develop therapeutic strategies to improve patient outcomes.

Mass Spectrometry Advances and Perspectives for the Characterization of Emerging Adoptive Cell Therapies

Adoptive cell therapy is an emerging anti-cancer modality, whereby the patient's own immune cells are engineered to express T-cell receptor (TCR) or chimeric antigen receptor (CAR). CAR-T cell therapies have advanced the furthest, with recent approvals of two treatments by the Food and Drug Administration of Kymriah (trisagenlecleucel) and Yescarta (axicabtagene ciloleucel). Recent developments in proteomic analysis by mass spectrometry (MS) make this technology uniquely suited to enable the comprehensive identification and quantification of the relevant biochemical architecture of CAR-T cell therapies and fulfill current unmet needs for CAR-T product knowledge. These advances include improved sample preparation methods, enhanced separation technologies, and extension of MS-based proteomic to single cells. Innovative technologies such as proteomic analysis of raw material quality attributes (MQA) and final product quality attributes (PQA) may provide insights that could ultimately fuel development strategies and lead to broad implementation.

Mass spectrometry based proteomics and metabolomics in personalized oncology

Precision medicine (PM) means the customization of healthcare with decisions and practices adjusted to the individual patient. It includes personalized diagnostics, patients' sub-classification, individual treatment selection and the monitoring of its effectiveness. Currently, in oncology, PM is based on the molecular and cellular features of a tumor, its microenvironment and the patient's genetics and lifestyle. Surprisingly, the available targeted therapies were found effective only in a subset of patients. An in-depth understanding of tumor biology is crucial to improve their effectiveness and develop new therapeutic targets. Completion of genetic information with proteomics and metabolomics can give broader knowledge about tumor biology which consequently provides novel biomarkers and indicates new therapeutic targets. Recently, metabolomics and proteomics have extensively been applied in the field of oncology. In the context of PM, human studies, with the use of mass spectrometry (MS) which allows the detection of thousands of molecules in a large number of samples, are the most valuable. Such studies, focused on cancer biomarkers discovery or patients' stratification, are presented in this review. Moreover, the technical aspects of MS-based clinical proteomics and metabolomics are described.

Engineered ChymotrypsiN for Mass Spectrometry-Based Detection of Protein Glycosylation

We have engineered the substrate specificity of chymotrypsin to cleave after Asn by high-throughput screening of large libraries created by comprehensive remodeling of the substrate binding pocket. The engineered variant (chymotrypsiN, ChyB-Asn) demonstrated an altered substrate specificity with an expanded preference for Asn-containing substrates. We confirmed that protein engineering did not compromise the stability of the enzyme by biophysical characterization. Comparison of wild-type ChyB and ChyB-Asn in profiling lysates of HEK293 cells demonstrated both qualitative and quantitative differences in the nature of the peptides and proteins identified by liquid chromatography and tandem mass spectrometry. ChyB-Asn enabled the identification of partially glycosylated Asn sites within a model glycoprotein and in the extracellular proteome of Jurkat T cells. ChymotrypsiN is a valuable addition to the toolkit of proteases to aid the mapping of N-linked glycosylation sites within proteins and proteomes.

Recent trends of capillary electrophoresis-mass spectrometry in proteomics research

Progress in proteomics research has led to a demand for powerful analytical tools with high separation efficiency and sensitivity for confident identification and quantification of proteins, posttranslational modifications, and protein complexes expressed in cells and tissues. This demand has significantly increased interest in capillary electrophoresis-mass spectrometry (CE-MS) in the past few years. This review provides highlights of recent advances in CE-MS for proteomics research, including a short introduction to top-down mass spectrometry and native mass spectrometry (native MS), as well as a detailed overview of CE methods. Both the potential and limitations of these methods for the analysis of proteins and peptides in synthetic and biological samples and the challenges of CE methods are discussed, along with perspectives about the future direction of CE-MS. @ 2019 Wiley Periodicals, Inc. Mass Spec Rev 00:1-16, 2019.

Current status of clinical proteogenomics in lung cancer

Introduction: Lung cancer is the leading cause of cancer death worldwide. Proteogenomics, a way to integrate genomics, transcriptomics, and proteomics, have emerged as a way to understand molecular causes in cancer tumorigenesis. This understanding will help identify therapeutic targets that are urgently needed to improve individual patient outcomes. Areas covered: To explore underlying molecular mechanisms of lung cancer subtypes, several efforts have used proteogenomic approaches that integrate next generation sequencing (NGS) and mass spectrometry (MS)-based technologies. Expert opinion: A large-scale, MS-based, proteomic analysis, together with both NGS-based genomic data and clinicopathological information, will facilitate establishing extensive databases for lung cancer subtypes that can be used for further proteogenomic analyzes. Proteogenomic strategies will further be understanding of how major driver mutations affect downstream molecular networks, resulting in lung cancer progression and malignancy, and how therapy-resistant cancers resistant are molecularly structured. These strategies require advanced bioinformatics based on a dynamic theory of network systems, rather than statistics, to accurately identify mutant proteins and their affected key networks.

Proteomics in the World of Induced Pluripotent Stem Cells

Omics approaches have significantly impacted knowledge about molecular signaling pathways driving cell function. Induced pluripotent stem cells (iPSC) have revolutionized the field of biological sciences and proteomics and, in particular, has been instrumental in identifying key elements operating during the maintenance of the pluripotent state and the differentiation process to the diverse cell types that form organisms. This review covers the evolution of conceptual and methodological strategies in proteomics; briefly describes the generation of iPSC from a historical perspective, the state-of-the-art of iPSC-based proteomics; and compares data on the proteome and transcriptome of iPSC to that of embryonic stem cells (ESC). Finally, proteomics of healthy and diseased cells and organoids differentiated from iPSC are analyzed.

Label-free target identification reveals oxidative DNA damage as the mechanism of a selective cytotoxic agent

Phenotypic screening can not only identify promising first-in-class drug candidates, but can also reveal potential therapeutic targets or neomorphic functions of known proteins. In this study, we identified target proteins of SB2001, a cytotoxic agent that acts specifically against HeLa human cervical cancer cells. Because SB2001 lacks chemical modification sites, label-free target identification methods including thermal stability shift-based fluorescence difference in two-dimensional gel electrophoresis (TS-FITGE) and thermal proteome profiling (TPP) were applied to characterize its mechanism of action. Owing to their differences, the two label-free target identification methods uncovered complementary target candidates. Candidates from both methods were prioritized according to their selective lethality upon the knockdown of those genes in HeLa cells, compared to CaSki cells which were used as a negative control cell line from the human cervix. LTA4H was identified only by TS-FITGE, but not by TPP, because only one isoform was stabilized by SB2001. Furthermore, it was implied that a non-canonical function of LTA4H was involved in the SB2001 activity. MTH1 was identified by both TS-FITGE and TPP, and SB2001 inhibited the function of MTH1 in hydrolyzing oxidized nucleotides. Compared to CaSki cells, HeLa cells displayed downregulated DNA mismatch repair pathways, which made HeLa cells more susceptible to the oxidative stress caused by SB2001, resulting in increased 8-oxoG concentrations, DNA damage, and subsequent cell death.

Proteomic approaches for cancer epigenetics research

Introduction: Epigenetic dysregulation drives or supports numerous human cancers. The chromatin landscape in cancer cells is often marked by abnormal histone post-translational modification (PTM) patterns and by aberrant assembly and recruitment of protein complexes to specific genomic loci. Mass spectrometry-based proteomic analyses can support the discovery and characterization of both phenomena. Areas covered: We broadly divide this literature into two parts: 'modification-centric' analyses that link histone PTMs to cancer biology; and 'complex-centric' analyses that examine protein-protein interactions that occur de novo as a result of oncogenic mutations. We also discuss proteomic studies of oncohistones. We highlight relevant examples, discuss limitations, and speculate about forthcoming innovations regarding each application. Expert commentary: 'Modification-centric' analyses have been used to further understanding of cancer's histone code and to identify associated therapeutic vulnerabilities. 'Complex-centric' analyses have likewise revealed insights into mechanisms of oncogenesis and suggested potential therapeutic targets, particularly in MLL-associated leukemia. Proteomic experiments have also supported some of the pioneering studies of oncohistone-mediated tumorigenesis. Additional applications of proteomics that may benefit cancer epigenetics research include middle-down and top-down histone PTM analysis, chromatin reader profiling, and genomic locus-specific protein identification. In the coming years, proteomic approaches will remain powerful ways to interrogate the biology of cancer.

From Single Level Analysis to Multi-Omics Integrative Approaches: A Powerful Strategy towards the Precision Oncology

Integration of multi-omics data from different molecular levels with clinical data, as well as epidemiologic risk factors, represents an accurate and promising methodology to understand the complexity of biological systems of human diseases, including cancer. By the extensive use of novel technologic platforms, a large number of multidimensional data can be derived from analysis of health and disease systems. Comprehensive analysis of multi-omics data in an integrated framework, which includes cumulative effects in the context of biological pathways, is therefore eagerly awaited. This strategy could allow the identification of pathway-addiction of cancer cells that may be amenable to therapeutic intervention. However, translation into clinical settings requires an optimized integration of omics data with clinical vision to fully exploit precision cancer medicine. We will discuss the available technical approach and more recent developments in the specific field.

Production of Over 27 000 Peptide and Nearly 4400 Protein Identifications by Single-Shot Capillary-Zone Electrophoresis-Mass Spectrometry via Combination of a Very-Low-Electroosmosis Coated Capillary, a Third-Generation Electrokinetically-Pumped Sheath-Flow Nanospray Interface, an Orbitrap Fusion Lumos Tribrid Mass Spectrometer, and an Advanced-Peak-Determination Algorithm

We show that capillary-zone electrophoresis-electrospray ionization-tandem mass spectrometry (CZE-ESI-MS/MS) generates very large numbers of peptide and protein identifications (IDs) by combining four technologies: a separation capillary coated to generate very low electroosmosis, an electrokinetically pumped sheath-flow nanoelectrospray interface to produce high-sensitivity ionization, an Orbitrap Fusion Lumos Tribrid platform to provide high-speed analysis, and an advanced-peak-determination (APD) algorithm to take advantage of the mass spectrometer's data-acquisition speed. The use of the APD algorithm resulted in 2 times more identifications than the standard peak algorithm. We also investigated the effect of the isolation window, injection time, and loading amount. Optimization of these parameters produced over 27 000 peptide identifications and nearly 4400 protein-group identifications from 220 ng of K562-cell digest in a single 120 min run, which is 2.7 times more IDs produced by CZE-ESI-MS/MS than by the previous state-of-the-art technique.

Identification of Litopenaeus vannamei BiP as a novel cellular attachment protein for white spot syndrome virus by using a biotinylation based affinity chromatography method

White spot syndrome virus (WSSV) is a dangerous threat to shrimp farming that also attacks a wide range of crustaceans. Knowledge of the surface protein-protein interactions between the pathogen and host is very crucial to unraveling the molecular pathogenesis mechanisms of WSSV. In this study, LvBiP (Litopenaeus vannamei immunoglobulin heavy-chain-binding protein) was identified as a novel WSSV binding protein of L. vannamei by a biotinylation based affinity chromatography method. By using pull-down and ELISA assays, the binding of recombinant LvBiP to WSSV was proved to be specific and ATP- dependent. The interaction was also confirmed by the result of co-immunoprecipitation assay. Immunofluorescence studies revealed the co-localization of LvBiP with WSSV on the cell surface of shrimp haemocytes. Additionally, LvBiP is likely to play an important role in WSSV infection. Treatment of gill cellular membrane proteins (CMPs) with purified rLvBiP and antibody that specifically recognizes LvBiP, led to a significant reduction in the binding of WSSV to gill CMPs. In the in vivo neutralization assay, rLvBiP and anti-LvBiP polyclonal antibody partially blocked the infection of WSSV. Taken together, the results indicate that LvBiP, a molecular chaperon of the HSP70 family, is a novel host factor involved at the step of attachment of the WSSV to the host cells and a potential candidate of therapeutic target.

Fixed-Charge Trimethyl Pyrilium Modification for Enabling Enhanced Top-Down Mass Spectrometry Sequencing of Intact Protein Complexes

Mass spectrometry of intact proteins and protein complexes has the potential to provide a transformative level of information on biological systems, ranging from sequence and post-translational modification analysis to the structures of whole protein assemblies. This ambitious goal requires the efficient fragmentation of both intact proteins and the macromolecular, multicomponent machines they collaborate to create through noncovalent interactions. Improving technologies in an effort to achieve such fragmentation remains perhaps the greatest challenge facing current efforts to comprehensively analyze cellular protein composition and is essential to realizing the full potential of proteomics. In this work, we describe the use of a trimethyl pyrylium (TMP) fixed-charge covalent labeling strategy aimed at enhancing fragmentation for challenging intact proteins and intact protein complexes. Combining analysis of TMP-modified and unmodified protein complexes results in a greater diversity of regions within the protein that give rise to fragments, and results in an up to 2.5-fold increase in sequence coverage when compared to unmodified protein alone, for protein complexes up to 148 kDa. TMP modification offers a simple and powerful platform to expand the capabilities of existing mass spectrometric instrumentation for the complete characterization of intact protein assemblies.

IMTBX and Grppr: Software for Top-Down Proteomics Utilizing Ion Mobility-Mass Spectrometry

Top-down proteomics has emerged as a transformative method for the analysis of protein sequence and post-translational modifications (PTMs). Top-down experiments have historically been performed primarily on ultrahigh resolution mass spectrometers due to the complexity of spectra resulting from fragmentation of intact proteins, but recent advances in coupling ion mobility separations to faster, lower resolution mass analyzers now offer a viable alternative. However, software capable of interpreting the highly complex two-dimensional spectra that result from coupling ion mobility separation to top-down experiments is currently lacking. In this manuscript we present a software suite consisting of two programs, IMTBX ("IM Toolbox") and Grppr ("Grouper"), that enable fully automated processing of such data. We demonstrate the capabilities of this software suite by examining a series of intact proteins on a Waters Synapt G2 ion-mobility equipped mass spectrometer and compare the results to the manual and semiautomated data analysis procedures we have used previously.

Direct and indirect approaches to identify drug modes of action

Phenotypic assays are becoming increasingly more common among drug discovery practices, expanding drug target diversity as lead compounds identified through such screens are not limited to known targets. While increasing diversity is beneficial to the drug discovery process and the fight against disease, the unknown modes of action of new lead compounds can hamper drug discovery as, in most cases, the process of lead compound optimization is made difficult due to the unknown nature of the target; blindly changing substituents can prove fruitless due to the inexhaustible number of potential combinations, and it is therefore desirable to rapidly identify the targets of lead compounds developed through phenotypic screening. In addition, leads identified through target-based screening often have off-target effects that contribute towards drug toxicity, and by identifying those secondary targets, the drugs can be improved. However, the identification of a leads mode of action is far from trivial and now represents a major bottleneck in the drug discovery pipeline. This review looks at some of the recent developments in the identification of drug modes of action, focusing on phenotype-based methods using metabolomics, proteomics, transcriptomics, and genomics to detect changes in phenotype in response to the presence of the drug, and affinity-based methods using modified/unmodified drug as bait to capture and identify targets. © 2017 IUBMB Life, 70(1):9-22, 2018.

Personalized Proteome Profiles of Healthy and Tumor Human Colon Organoids Reveal Both Individual Diversity and Basic Features of Colorectal Cancer

Diseases at the molecular level are complex and patient dependent, necessitating development of strategies that enable precision treatment to optimize clinical outcomes. Organoid technology has recently been shown to have the potential to recapitulate the in vivo characteristics of the original individual's tissue in a three-dimensional in vitro culture system. Here, we present a quantitative mass-spectrometry-based proteomic analysis and a comparative transcriptomic analysis of human colorectal tumor and healthy organoids derived, in parallel, from seven patients. Although gene and protein signatures can be derived to distinguish the tumor organoid population from healthy organoids, our data clearly reveal that each patient possesses a distinct organoid signature at the proteomic level. We demonstrate that a personalized patient-specific organoid proteome profile can be related to the diagnosis of a patient and with future development contribute to the generation of personalized therapies.