Comparison of label-free and label-based strategies for proteome analysis of hepatoma cell lines

Trifunctional Sphinganine: A New Tool to Dissect Sphingolipid Function

Functions and cell biology of the sphingolipids sphingosine and sphinganine in cells are not well understood. While some signaling roles for sphingosine have been elucidated, the closely related sphinganine has been described only insofar as it does not elicit many of the same signaling responses. Here, we prepared multifunctionalized derivatives of the two lipid species that differ only in a single double bond of the carbon backbone. Using these novel probes, we were able to define their spatiotemporal distributions within cells. Furthermore, we used these tools to systematically map the protein interactomes of both lipids. The lipid-protein conjugates, prepared through photo-crosslinking in live cells and extraction via click chemistry to azide beads, revealed significant differences in the captured proteins, highlighting their distinct roles in various cellular processes. This work elucidates mechanistic differences between these critical lipids and sets the foundation for further studies of the cellular functions of sphingosine and sphinganine.

Protein biomarkers for root length and root dry mass on chromosomes 4A and 7A in wheat

Improving the wheat (Triticum aestivum L.) root system is important for enhancing grain yield and climate resilience. Total root length (RL) and root dry mass (RM) significantly contribute to water and nutrient acquisition directly impacting grain yield and stress tolerance. This study used label-free quantitative proteomics to identify proteins associated with RL and RM in wheat near-isogenic lines (NILs). NIL pair 6 had 113 and NIL pair 9 had 30 differentially abundant proteins (DAPs). Three of identified DAPs located within the targeted genomic regions (GRs) of NIL pairs 6 (qDT.4A.1) and 9 (QHtscc.ksu-7A), showed consistent gene expressions at the protein and mRNA transcription (qRT-PCR) levels for asparagine synthetase (TraesCS4A02G109900), signal recognition particle 19 kDa protein (TraesCS7A02G333600) and 3,4-dihydroxy-2-butanone 4-phosphate synthase (TraesCS7A02G415600). This study discovered, for the first time, the involvement of these proteins as candidate biomarkers for increased RL and RM in wheat. However, further functional validation is required to ascertain their practical applicability in wheat root breeding. SIGNIFICANCE OF THE STUDY: Climate change has impacted global demand for wheat (Triticum aestivum L.). Root traits such as total root length (RL) and root dry mass (RM) are crucial for water and nutrient uptake and tolerance to abiotic stresses such as drought, salinity, and nutrient imbalance in wheat. Improving RL and RM could significantly enhance wheat grain yield and climate resilience. However, breeding for these traits has been limited by lack of appropriate root phenotyping methods, advanced genotypes, and the complex nature of the wheat genome. In this study, we used a semi-hydroponic root phenotyping system to collect accurate root data, near-isogenic lines (NILs; isolines with similar genetic backgrounds but contrasting target genomic regions (GRs)) and label-free quantitative proteomics to explore the molecular mechanisms underlying high RL and RM in wheat. We identified differentially abundant proteins (DAPs) and their molecular pathways in NIL pairs 6 (GR: qDT.4A.1) and 9 (GR: QHtscc.ksu-7A), providing a foundation for further molecular investigations. Furthermore, we identified three DAPs within the target GRs of the NIL pairs with differential expression at the transcript level, as confirmed by qRT-PCR analysis which could serve as candidate protein biomarkers for RL and RM improvement.

Methods to Discover and Validate Biofluid-Based Biomarkers in Neurodegenerative Dementias

Neurodegenerative dementias are progressive diseases that cause neuronal network breakdown in different brain regions often because of accumulation of misfolded proteins in the brain extracellular matrix, such as amyloids or inside neurons or other cell types of the brain. Several diagnostic protein biomarkers in body fluids are being used and implemented, such as for Alzheimer's disease. However, there is still a lack of biomarkers for co-pathologies and other causes of dementia. Such biofluid-based biomarkers enable precision medicine approaches for diagnosis and treatment, allow to learn more about underlying disease processes, and facilitate the development of patient inclusion and evaluation tools in clinical trials. When designing studies to discover novel biofluid-based biomarkers, choice of technology is an important starting point. But there are so many technologies to choose among. To address this, we here review the technologies that are currently available in research settings and, in some cases, in clinical laboratory practice. This presents a form of lexicon on each technology addressing its use in research and clinics, its strengths and limitations, and a future perspective.

Targeted Workflow Investigating Variations in the Tear Proteome by Liquid Chromatography Tandem Mass Spectrometry

Proteins in tears have an important role in eye health and have been shown as a promising source of disease biomarkers. The goal of this study was to develop a robust, sensitive, and targeted method for profiling tear proteins to examine the variability within a group of healthy volunteers over three days. Inter-individual and inter-day variabilities were examined to contribute to understanding the normal variations in the tear proteome, as well as to establish which proteins may be better candidates as eventual biomarkers of specific diseases. Tear samples collected on Schirmer strips were subjected to bottom-up proteomics, and resulting peptides were analyzed using an optimized targeted method measuring 226 proteins by liquid chromatography-scheduled multiple reaction monitoring. This method was developed using an in-house database of identified proteins from tears compiled from high-resolution data-dependent liquid chromatography tandem mass spectrometry data. The measurement of unique peptide signals can help better understand the dynamics of each of these proteins in tears. Some interesting trends were seen in specific pathways or protein classes, including higher variabilities for those involved in glycolysis, glutathione metabolism, and cytoskeleton proteins and lower variation for those involving the degradation of the extracellular matrix. The overall aim of this study was to contribute to the field of tear proteomics with the development of a novel and targeted method that is highly amenable to the clinical laboratory using high flow LC and commonly used triple quadrupole mass spectrometry while ensuring that protein quantitation was reported based on unique peptides for each protein and robust peak areas with data normalization. These results report on variabilities on over 200 proteins that are robustly detected in tear samples from healthy volunteers with a simple sample preparation procedure.

Proteomic analysis of plasma to identify novel biomarkers for intra-amniotic infection and/or inflammation in preterm premature rupture of membranes

To identify potential plasma biomarkers associated with microbial invasion of the amniotic cavity (MIAC) and/or intraamniotic inflammation (IAI) in women with preterm premature rupture of membranes (PPROM). This retrospective cohort study included 182 singleton pregnant women with PPROM (23-33 weeks) who underwent amniocentesis. Plasma samples; all subjects were chosen from these participants and were analyzed using label-free liquid chromatography-tandem mass spectrometry for proteome profiling using a nested case-control study design (cases with MIAC/IAI vs. non-MIAC/IAI controls [n = 9 each]). Three identified target molecules for MIAC/IAI were further verified by ELISA in the study cohort (n = 182). Shotgun proteomic analysis revealed 17 differentially expressed proteins (P < 0.05) in the plasma of MIAC/IAI cases. In particular, the levels of FCGR3A and haptoglobin, but not LRP1, were found to be increased in the plasma of patients with MIAC, IAI, and both MIAC/IAI compared with those without these conditions. Moreover, these differences remained significant after adjusting for gestational age at sampling. The area under the curves of plasma FCGR3A and haptoglobin ranged within 0.59-0.65 with respect to each of the three outcome measures. Plasma FCGR3A and haptoglobin were identified as potential independent biomarkers for less-invasively detecting MIAC/IAI in women with PPROM.

Chemical Proteomics with Novel Fully Functionalized Fragments and Stringent Target Prioritization Identifies the Glutathione-Dependent Isomerase GSTZ1 as a Lung Cancer Target

Photoreactive fragment-like probes have been applied to discover target proteins that constitute novel cellular vulnerabilities and to identify viable chemical hits for drug discovery. Through forming covalent bonds, functionalized probes can achieve stronger target engagement and require less effort for on-target mechanism validation. However, the design of probe libraries, which directly affects the biological target space that is interrogated, and effective target prioritization remain critical challenges of such a chemical proteomic platform. In this study, we designed and synthesized a diverse panel of 20 fragment-based probes containing natural product-based privileged structural motifs for small-molecule lead discovery. These probes were fully functionalized with orthogonal diazirine and alkyne moieties and used for protein crosslinking in live lung cancer cells, target enrichment via "click chemistry," and subsequent target identification through label-free quantitative liquid chromatography-tandem mass spectrometry analysis. Pair-wise comparison with a blunted negative control probe and stringent prioritization via individual cross-comparisons against the entire panel identified glutathione S-transferase zeta 1 (GSTZ1) as a specific and unique target candidate. DepMap database query, RNA interference-based gene silencing, and proteome-wide tyrosine reactivity profiling suggested that GSTZ1 cooperated with different oncogenic alterations by supporting survival signaling in refractory non-small cell lung cancer cells. This finding may form the basis for developing novel GSTZ1 inhibitors to improve the therapeutic efficacy of oncogene-directed targeted drugs. In summary, we designed a novel fragment-based probe panel and developed a target prioritization scheme with improved stringency, which allows for the identification of unique target candidates, such as GSTZ1 in refractory lung cancer.

Investigating phenotypic relationships in persimmon accessions through integrated proteomic and metabolomic analysis of corresponding fruits

Together with phenological and genomic approaches, gel-based and label-free proteomic as well metabolomic procedures were separately applied to plants to highlight differences between ecotypes, to estimate genetic variability within/between organism populations, or to characterize specific mutants/genetically modified lines at metabolic level. To investigate the possible use of tandem mass tag (TMT)-based quantitative proteomics in the above-mentioned contexts and based on the absence of combined proteo-metabolomic studies on Diospyros kaki cultivars, we here applied integrated proteomic and metabolomic approaches to fruits from Italian persimmon ecotypes with the aim to characterize plant phenotypic diversity at molecular level. We identified 2255 proteins in fruits, assigning 102 differentially represented components between cultivars, including some related to pomological, nutritional and allergenic characteristics. Thirty-three polyphenols were also identified and quantified, which belong to hydroxybenzoic acid, flavanol, hydroxycinnamic acid, flavonol, flavanone and dihydrochalcone sub-classes. Heat-map representation of quantitative proteomic and metabolomic results highlighted compound representation differences in various accessions, whose elaboration through Euclidean distance functions and other linkage methods defined dendrograms establishing phenotypic relationships between cultivars. Principal component analysis of proteomic and metabolomic data provided clear information on phenotypic differences/similarities between persimmon accessions. Coherent cultivar association results were observed between proteomic and metabolomic data, emphasizing the utility of integrating combined omic approaches to identify and validate phenotypic relationships between ecotypes, and to estimate corresponding variability and distance. Accordingly, this study describes an original, combined approach to outline phenotypic signatures in persimmon cultivars, which may be used for a further characterization of other ecotypes of the same species and an improved description of nutritional characteristics of corresponding fruits.

Fructose Diet-Associated Molecular Alterations in Hypothalamus of Adolescent Rats: A Proteomic Approach

BACKGROUND

The enhanced consumption of fructose as added sugar represents a major health concern. Due to the complexity and multiplicity of hypothalamic functions, we aim to point out early molecular alterations triggered by a sugar-rich diet throughout adolescence, and to verify their persistence until the young adulthood phase.

METHODS

Thirty days old rats received a high-fructose or control diet for 3 weeks. At the end of the experimental period, treated animals were switched to the control diet for further 3 weeks, and then analyzed in comparison with those that were fed the control diet for the entire experimental period.

RESULTS

Quantitative proteomics identified 19 differentially represented proteins, between control and fructose-fed groups, belonging to intermediate filament cytoskeleton, neurofilament, pore complex and mitochondrial respiratory chain complexes. Western blotting analysis confirmed proteomic data, evidencing a decreased abundance of mitochondrial respiratory complexes and voltage-dependent anion channel 1, the coregulator of mitochondrial biogenesis PGC-1α, and the protein subunit of neurofilaments α-internexin in fructose-fed rats. Diet-associated hypothalamic inflammation was also detected. Finally, the amount of brain-derived neurotrophic factor and its high-affinity receptor TrkB, as well as of synaptophysin, synaptotagmin, and post-synaptic protein PSD-95 was reduced in sugar-fed rats. Notably, deregulated levels of all proteins were fully rescued after switching to the control diet.

CONCLUSIONS

A short-term fructose-rich diet in adolescent rats induces hypothalamic inflammation and highly affects mitochondrial and cytoskeletal compartments, as well as the level of specific markers of brain function; above-reported effects are reverted after switching animals to the control diet.

A comprehensive SARS-CoV-2-human protein-protein interactome reveals COVID-19 pathobiology and potential host therapeutic targets

Studying viral-host protein-protein interactions can facilitate the discovery of therapies for viral infection. We use high-throughput yeast two-hybrid experiments and mass spectrometry to generate a comprehensive SARS-CoV-2-human protein-protein interactome network consisting of 739 high-confidence binary and co-complex interactions, validating 218 known SARS-CoV-2 host factors and revealing 361 novel ones. Our results show the highest overlap of interaction partners between published datasets and of genes differentially expressed in samples from COVID-19 patients. We identify an interaction between the viral protein ORF3a and the human transcription factor ZNF579, illustrating a direct viral impact on host transcription. We perform network-based screens of >2,900 FDA-approved or investigational drugs and identify 23 with significant network proximity to SARS-CoV-2 host factors. One of these drugs, carvedilol, shows clinical benefits for COVID-19 patients in an electronic health records analysis and antiviral properties in a human lung cell line infected with SARS-CoV-2. Our study demonstrates the value of network systems biology to understand human-virus interactions and provides hits for further research on COVID-19 therapeutics.

Dealing with missing values in proteomics data

Proteomics data are often plagued with missingness issues. These missing values (MVs) threaten the integrity of subsequent statistical analyses by reduction of statistical power, introduction of bias, and failure to represent the true sample. Over the years, several categories of missing value imputation (MVI) methods have been developed and adapted for proteomics data. These MVI methods perform their tasks based on different prior assumptions (e.g., data is normally or independently distributed) and operating principles (e.g., the algorithm is built to address random missingness only), resulting in varying levels of performance even when dealing with the same dataset. Thus, to achieve a satisfactory outcome, a suitable MVI method must be selected. To guide decision making on suitable MVI method, we provide a decision chart which facilitates strategic considerations on datasets presenting different characteristics. We also bring attention to other issues that can impact proper MVI such as the presence of confounders (e.g., batch effects) which can influence MVI performance. Thus, these too, should be considered during or before MVI.

Proteomic overview of hepatocellular carcinoma cell lines and generation of the spectral library

Cell lines are extensively used tools, therefore a comprehensive proteomic overview of hepatocellular carcinoma (HCC) cell lines and an extensive spectral library for data independent acquisition (DIA) quantification are necessary. Here, we present the proteome of nine commonly used HCC cell lines covering 9,208 protein groups, and the HCC spectral library containing 253,921 precursors, 168,811 peptides and 10,098 protein groups. The proteomic overview reveals the heterogeneity between different cell lines, and the similarity in proliferation and metastasis characteristics and drug targets-expression with tumour tissues. The HCC spectral library generating consumed 108 hours' runtime for data dependent acquisition (DDA) of 48 runs, 24 hours' runtime for database searching by MaxQuant version 2.0.3.0, and 1 hour' runtime for processing by Spectronaut^TM version 15.2. The HCC spectral library supports quantification of 7,637 protein groups of triples 2-hour DIA analysis of HepG2 and discovering biological alteration. This study provides valuable resources for HCC cell lines and efficient DIA quantification on LC-Orbitrap platform, further help to explore the molecular mechanism and candidate therapeutic targets.

Protein Profiling of a Cellular Model of NAFLD by Advanced Bioanalytical Approaches

Advanced quantitative bioanalytical approaches in combination with network analyses allow us to answer complex biological questions, such as the description of changes in protein profiles under disease conditions or upon treatment with drugs. In the present work, three quantitative proteomic approaches-either based on labelling or not-in combination with network analyses were applied to a new in vitro cellular model of nonalcoholic fatty liver disease (NAFLD) for the first time. This disease is characterized by the accumulation of lipids, inflammation, fibrosis, and insulin resistance. Hepatic G2 cells were used as model, and NAFLD was induced by a complex of oleic acid and bovine albumin. The development of the disease was verified by lipid vesicle staining and by the increase in the expression of perilipin-2-a protein constitutively present in the vesicles during NAFLD. The nLC-MS/MS analyses of peptide samples obtained from three different proteomic approaches resulted in accurate and reproducible quantitative data of protein fold-change expressed in NAFLD versus control cells. The differentially regulated proteins were used to evaluate the involved and statistically enriched pathways. Network analyses highlighted several functional and disease modules affected by NAFLD, such as inflammation, oxidative stress defense, cell proliferation, and ferroptosis. Each quantitative approach allowed the identification of similar modulated pathways. The combination of the three approaches improved the power of statistical network analyses by increasing the number of involved proteins and their fold-change. In conclusion, the application of advanced bioanalytical approaches in combination with pathway analyses allows the in-depth and accurate description of the protein profile of an in vitro cellular model of NAFLD by using high-resolution quantitative mass spectrometry data. This model could be extremely useful in the discovery of new drugs to modulate the equilibrium NAFLD health state.

Comparative Assessment of Quantification Methods for Tumor Tissue Phosphoproteomics

With increasing sensitivity and accuracy in mass spectrometry, the tumor phosphoproteome is getting into reach. However, the selection of quantitation techniques best-suited to the biomedical question and diagnostic requirements remains a trial and error decision as no study has directly compared their performance for tumor tissue phosphoproteomics. We compared label-free quantification (LFQ), spike-in-SILAC (stable isotope labeling by amino acids in cell culture), and tandem mass tag (TMT) isobaric tandem mass tags technology for quantitative phosphosite profiling in tumor tissue. Compared to the classic SILAC method, spike-in-SILAC is not limited to cell culture analysis, making it suitable for quantitative analysis of tumor tissue samples. TMT offered the lowest accuracy and the highest precision and robustness toward different phosphosite abundances and matrices. Spike-in-SILAC offered the best compromise between these features but suffered from a low phosphosite coverage. LFQ offered the lowest precision but the highest number of identifications. Both spike-in-SILAC and LFQ presented susceptibility to matrix effects. Match between run (MBR)-based analysis enhanced the phosphosite coverage across technical replicates in LFQ and spike-in-SILAC but further reduced the precision and robustness of quantification. The choice of quantitative methodology is critical for both study design such as sample size in sample groups and quantified phosphosites and comparison of published cancer phosphoproteomes. Using ovarian cancer tissue as an example, our study builds a resource for the design and analysis of quantitative phosphoproteomic studies in cancer research and diagnostics.

Proteomic Approaches to Unravel Mechanisms of Antibiotic Resistance and Immune Evasion of Bacterial Pathogens

The profound effects of and distress caused by the global COVID-19 pandemic highlighted what has been known in the health sciences a long time ago: that bacteria, fungi, viruses, and parasites continue to present a major threat to human health. Infectious diseases remain the leading cause of death worldwide, with antibiotic resistance increasing exponentially due to a lack of new treatments. In addition to this, many pathogens share the common trait of having the ability to modulate, and escape from, the host immune response. The challenge in medical microbiology is to develop and apply new experimental approaches that allow for the identification of both the microbe and its drug susceptibility profile in a time-sensitive manner, as well as to elucidate their molecular mechanisms of survival and immunomodulation. Over the last three decades, proteomics has contributed to a better understanding of the underlying molecular mechanisms responsible for microbial drug resistance and pathogenicity. Proteomics has gained new momentum as a result of recent advances in mass spectrometry. Indeed, mass spectrometry-based biomedical research has been made possible thanks to technological advances in instrumentation capability and the continuous improvement of sample processing and workflows. For example, high-throughput applications such as SWATH or Trapped ion mobility enable the identification of thousands of proteins in a matter of minutes. This type of rapid, in-depth analysis, combined with other advanced, supportive applications such as data processing and artificial intelligence, presents a unique opportunity to translate knowledge-based findings into measurable impacts like new antimicrobial biomarkers and drug targets. In relation to the Research Topic “Proteomic Approaches to Unravel Mechanisms of Resistance and Immune Evasion of Bacterial Pathogens,” this review specifically seeks to highlight the synergies between the powerful fields of modern proteomics and microbiology, as well as bridging translational opportunities from biomedical research to clinical practice.

Sperm Phosphoproteome: Unraveling Male Infertility

Infertility affects approximately 15% of couples worldwide of childbearing age, and in many cases the etiology of male infertility is unknown. The current standard evaluation of semen is insufficient to establish an accurate diagnosis. Proteomics techniques, such as phosphoproteomics, applied in this field are a powerful tool to understand the mechanisms that regulate sperm functions such as motility, which is essential for successful fertilization. Among the post-translational modifications of sperm proteins, this review summarizes, from a proteomic perspective, the updated knowledge of protein phosphorylation, in human spermatozoa, as a relevant molecular mechanism involved in the regulation of sperm physiology. Specifically, the role of sperm protein phosphorylation in motility and, consequently, in sperm quality is highlighted. Additionally, through the analysis of published comparative phosphoproteomic studies, some candidate human sperm phosphoproteins associated with low sperm motility are proposed. Despite the remarkable advances in phosphoproteomics technologies, the relatively low number of studies performed in human spermatozoa suggests that phosphoproteomics has not been applied to its full potential in studying male infertility yet. Therefore, further studies will improve the application of this procedure and overcome the limitations, increasing the understanding of regulatory mechanisms underlying protein phosphorylation in sperm motility and, consequently, in male fertility.

12-Plex DiLeu Isobaric Labeling Enabled High-Throughput Investigation of Citrullination Alterations in the DNA Damage Response

Protein citrullination is a key post-translational modification (PTM) that leads to the loss of positive charge on arginine and consequent protein structural and functional changes. Though it has been indicated to play critical roles in various physiological and pathological processes, effective analytical tools are largely limited due to a few challenges such as the small mass shift induced by this PTM and its low-abundance nature. Recently, we developed a biotin thiol tag, which enabled large-scale profiling of protein citrullination from complex biological samples via mass spectrometry. However, a high-throughput quantitative approach is still in great need to further improve the understanding of this PTM. In this study, we report an efficient pipeline using our custom-developed N,N-dimethyl leucine isobaric tags to achieve a multiplexed quantitative analysis of citrullination from up to 12 samples for the first time. We then apply this strategy to investigating citrullination alterations in response to DNA damage stress using human cell lines. We unveil important biological functions regulated by protein citrullination and observe hypercitrullination on RNA-binding proteins and DNA repair proteins, respectively. Our results reveal the involvement of citrullination in DNA damage pathways and may provide new insights into DNA-damage-related disease pathogenesis.

Proteomic cellular signatures of kinase inhibitor-induced cardiotoxicity

Drug Toxicity Signature Generation Center (DToxS) at the Icahn School of Medicine at Mount Sinai is one of the centers for the NIH Library of Integrated Network-Based Cellular Signatures (LINCS) program. Its key aim is to generate proteomic and transcriptomic signatures that can predict cardiotoxic adverse effects of kinase inhibitors approved by the Food and Drug Administration. Towards this goal, high throughput shotgun proteomics experiments (308 cell line/drug combinations +64 control lysates) have been conducted. Using computational network analyses, these proteomic data can be integrated with transcriptomic signatures, generated in tandem, to identify cellular signatures of cardiotoxicity that may predict kinase inhibitor-induced toxicity and enable possible mitigation. Both raw and processed proteomics data have passed several quality control steps and been made publicly available on the PRIDE database. This broad protein kinase inhibitor-stimulated human cardiomyocyte proteomic data and signature set is valuable for prediction of drug toxicities.

Proteomic analysis of human hippocampal subfields provides new insights into the pathogenesis of Alzheimer's disease and the role of glial cells

The hippocampus and entorhinal cortex (EC), the earliest affected areas, are considered relative to early memory loss in Alzheimer's disease (AD). The hippocampus is composed of heterogeneous subfields that are affected in a different order and varying degrees during AD pathogenesis. In this study, we conducted a comprehensive proteomic analysis of the hippocampal subfields and EC region in human postmortem specimens obtained from the Chinese human brain bank. Bioinformatics analysis identified region‐consistent differentially expressed proteins (DEPs) which associated with astrocytes, and region‐specific DEPs which associated with oligodendrocytes and the myelin sheath. Further analysis illuminated that the region‐consistent DEPs functioned as connection of region‐specific DEPs. Moreover, in region‐consistent DEPs, the expression level of S100A10, a marker of protective astrocytes, was increased in both aging and AD patients. Immunohistochemical analysis confirmed an increase in the number of S100A10‐positive astrocytes in all hippocampal subfields and the EC region of AD patients. Dual immunofluorescence results further showed that S100A10‐positive astrocytes contained apoptotic neuron debris in AD patients, suggesting that S100A10‐positive astrocytes may protect brain through phagocytosis of apoptotic neurons. In region‐specific DEPs, the proteome showed a specific reduction of oligodendrocytes and myelin markers in CA1, CA3, and EC regions of AD patients. Immunohistochemical analysis confirmed the loss of myelin in EC region. Above all, these results highlight the role of the glial cells in AD and provide new insights into the pathogenesis of AD and potential therapeutic strategies.

Quantitative Mass Spectrometry by SILAC in Haloferax volcanii

The development of mass spectrometry (MS)-based proteomics methods has been critical in providing new insight about cellular processes and adaptations in all domains of life. While traditional MS-based methods are not inherently quantitative, technologies are now available to overcome this limitation. Of note, stable isotope labeling of amino acids in cell culture (SILAC) is reported as a reliable tool to label proteomes for quantitative MS-based proteomics that is accurate and flexible for multiplexing. The isotopically labeled lysine and arginine are easily incorporated into the proteome of cells auxotrophic for these amino acids. Microorganisms of the domain Archaea provide a fascinating alternative to understanding cellular adaptations and responses to environmental stresses. However, the availability of preferred SILAC-based quantitative analyses is limited. This protocol describes the use of SILAC to quantitatively analyze the proteome of Haloferax volcanii, a mesophilic halophilic archaeon that is easy to grow and requires no special equipment to maintain.

Tomatidine reduces Chikungunya virus progeny release by controlling viral protein expression

Tomatidine, a natural steroidal alkaloid from unripe green tomatoes has been shown to exhibit many health benefits. We recently provided in vitro evidence that tomatidine reduces the infectivity of Dengue virus (DENV) and Chikungunya virus (CHIKV), two medically important arthropod-borne human infections for which no treatment options are available. We observed a potent antiviral effect with EC50 values of 0.82 μM for DENV-2 and 1.3 μM for CHIKV-LR. In this study, we investigated how tomatidine controls CHIKV infectivity. Using mass spectrometry, we identified that tomatidine induces the expression of p62, CD98, metallothionein and thioredoxin-related transmembrane protein 2 in Huh7 cells. The hits p62 and CD98 were validated, yet subsequent analysis revealed that they are not responsible for the observed antiviral effect. In parallel, we sought to identify at which step of the virus replication cycle tomatidine controls virus infectivity. A strong antiviral effect was seen when in vitro transcribed CHIKV RNA was transfected into Huh7 cells treated with tomatidine, thereby excluding a role for tomatidine during CHIKV cell entry. Subsequent determination of the number of intracellular viral RNA copies and viral protein expression levels during natural infection revealed that tomatidine reduces the RNA copy number and viral protein expression levels in infected cells. Once cells are infected, tomatidine is not able to interfere with active RNA replication yet it can reduce viral protein expression. Collectively, the results delineate that tomatidine controls viral protein expression to exert its antiviral activity. Lastly, sequential passaging of CHIKV in presence of tomatidine did not lead to viral resistance. Collectively, these results further emphasize the potential of tomatidine as an antiviral treatment towards CHIKV infection.

Chikungunya fever is a debilitating disease caused by the mosquito-borne Chikungunya virus. Over the past two decades the geographical spread of the virus and its mosquito vector has drastically increased thereby causing millions of infections. To date there is no antiviral drug and no vaccine available to treat/prevent Chikungunya virus infection. We recently showed that the natural steroidal alkaloid tomatidine has potent antiviral activity towards Chikungunya virus at submicromolar concentrations. In this study we dissected how tomatidine reduces the production of Chikungunya virus particles. We show that tomatidine lowers viral protein expression and we hypothesize that the effect of tomatidine on viral protein translation hampers the production of progeny viral RNA copies / number of infected cells thereby leading to a reduced production of secreted virus particles. Also, we show that Chikungunya virus does not readily become resistant to tomatidine. Collectively, we deciphered the mechanism by which tomatidine exerts antiviral activity to Chikungunya virus and our results strengthen the potential of tomatidine as an antiviral treatment strategy towards Chikungunya virus.

Proteomes Are of Proteoforms: Embracing the Complexity

Proteomes are complex-much more so than genomes or transcriptomes. Thus, simplifying their analysis does not simplify the issue. Proteomes are of proteoforms, not canonical proteins. While having a catalogue of amino acid sequences provides invaluable information, this is the Proteome-lite. To dissect biological mechanisms and identify critical biomarkers/drug targets, we must assess the myriad of proteoforms that arise at any point before, after, and between translation and transcription (e.g., isoforms, splice variants, and post-translational modifications [PTM]), as well as newly defined species. There are numerous analytical methods currently used to address proteome depth and here we critically evaluate these in terms of the current 'state-of-the-field'. We thus discuss both pros and cons of available approaches and where improvements or refinements are needed to quantitatively characterize proteomes. To enable a next-generation approach, we suggest that advances lie in transdisciplinarity via integration of current proteomic methods to yield a unified discipline that capitalizes on the strongest qualities of each. Such a necessary (if not revolutionary) shift cannot be accomplished by a continued primary focus on proteo-genomics/-transcriptomics. We must embrace the complexity. Yes, these are the hard questions, and this will not be easy…but where is the fun in easy?

Label-free quantitative proteomics analysis of jujube (Ziziphus jujuba Mill.) during different growth stages

Chinese jujube (Zizyphus jujuba Mill.), a member of the Rhamnaceae family with favorable nutritional and flavor quality, exhibited characteristic climacteric changes during its fruit growth stage. Therefore, fruit samples were harvested at four developmental stages on days 55 (young fruits), 76 (white-mature fruits), 96 (half-red fruits), and 116 (full-red fruits) after flowering (DAF). This study then investigated those four growth stage changes of the jujube proteome using label-free quantification proteomics. The results identified 4762 proteins in the samples, of which 3757 proteins were quantified. Compared with former stages, the stages examined were designated as "76 vs. 55 DAF" group, "96 vs. 76 DAF" group, and "116 vs. 96 DAF" group. Gene Ontology (GO) and KEGG annotation and enrichment analysis of the differentially expressed proteins (DEPs) showed that 76 vs. 55 DAF group pathways represented amino sugar, nucleotide sugar, ascorbate, and aldarate metabolic pathways. These pathways were associated with cell division and resistance. In the study, the jujube fruit puffing slowed down and attained a stable growth stage in the 76 vs. 55 DAF group. However, fatty acid biosynthesis and phenylalanine metabolism was mainly enriched in the 96 vs. 76 DAF group. Fatty acids are precursors of aromatic substances and fat-soluble pigments in fruit. The upregulation of differential proteins at this stage indicates that aromatic compounds were synthesized in large quantities at this stage and that fruit would enter the ripening stage. During the ripening stage, 55 DEPs were identified to be involved in photosynthesis and flavonoid biosynthesis in the 116 vs. 96 DAF group. Also, the fruit entered the mature stage, which showed that flavonoids were produced in large quantities. Furthermore, the color of jujube turned red, and photosynthesis was significantly reduced. Hence, a link was established between protein profiles and growth phenotypes, which will help improve our understanding of jujube fruit growth at the proteomic level.

Proteomic Quantification of Changes in Abundance of Drug-Metabolizing Enzymes and Drug Transporters in Human Liver Cirrhosis: Different Methods, Similar Outcomes

Model-based assessment of the effects of liver disease on drug pharmacokinetics requires quantification of changes in enzymes and transporters responsible for drug metabolism and disposition. Different proteomic methods are currently used for protein quantification in tissues and in vitro systems, each with specific procedures and requirements. The outcome of quantitative proteomic assays using four different methods (one targeted and three label-free) applied to the same sample set was compared in this study. Three pooled cirrhotic liver microsomal samples corresponding to cirrhosis with nonalcoholic fatty liver disease, biliary disease, or cancer and a control microsomal pool were analyzed using quantification concatemer-based targeted proteomics, the total protein approach (TPA), high three ion intensity (Hi3) approach, and intensity-based absolute quantification (iBAQ) to determine the absolute and relative abundance in disease compared with control. The relative abundance data provided a "disease perturbation factor" (DPF) for each target protein. Absolute and relative abundances generated by standard-based label-free methods (iBAQ and Hi3) showed good agreement with targeted proteomics (limited bias and scatter), but TPA (standard-free method) overestimated absolute abundances by approximately 2-fold. The DPF was consistent between different proteomic methods but varied between enzymes and transporters, indicating discordance of effects of cirrhosis on various metabolism-related proteins. The DPF ranged from no change (e.g., for glucuronosyltransferase-1A6 in nonalcoholic fatty liver disease group) to less than 0.3 (e.g., carboxylesterases-1 in cirrhosis of biliary origin). SIGNIFICANCE STATEMENT: This study demonstrated that relative changes in enzymes and transporters (DPF) are independent of the quantitative proteomic methods used. Standard-based label-free methods, such as high three ion intensity (Hi3) and intensity-based absolute quantification (iBAQ) methods, were less biased and more precise than the total protein approach (TPA) when compared with targeted data. The DPF reconciled differences across proteomic methods observed with absolute levels. Using this approach, differences were revealed in the expression of enzymes/transporters in cirrhosis associated with different etiologies.

Proteogenomic interrogation of cancer cell lines: an overview of the field

Introduction: Cancer cell lines (CCLs) have been a major resource for cancer research. Over the past couple of decades, they have been instrumental in omic profiling method development and as model systems to generate new knowledge in cell and cancer biology. More recently, with the increasing amount of genomic, transcriptomic and proteomic data being generated in hundreds of CCLs, there is growing potential for integrative proteogenomic data analyses to be performed.Areas covered: In this review, we first describe the most commonly used proteome profiling methods in CCLs. We then discuss how these proteomics data can be integrated with genomics data for proteogenomics analyses. Finally, we highlight some of the recent biological discoveries that have arisen from proteogenomics analyses of CCLs.Expert opinion: Protegeonomics analyses of CCLs have so far enabled the discovery of novel proteins and proteoforms. It has also improved our understanding of biological processes including post-transcriptional regulation of protein abundance and the presentation of antigens by major histocompatibility complex alleles. With proteomics data to be generated in hundreds to thousands of CCLs in coming years, there will be further potential for large-scale proteogenomics analyses and data integration with the phenotypically well-characterized CCLs.

Current Methods of Post-Translational Modification Analysis and Their Applications in Blood Cancers

Post-translational modifications (PTMs) add a layer of complexity to the proteome through the addition of biochemical moieties to specific residues of proteins, altering their structure, function and/or localization. Mass spectrometry (MS)-based techniques are at the forefront of PTM analysis due to their ability to detect large numbers of modified proteins with a high level of sensitivity and specificity. The low stoichiometry of modified peptides means fractionation and enrichment techniques are often performed prior to MS to improve detection yields. Immuno-based techniques remain popular, with improvements in the quality of commercially available modification-specific antibodies facilitating the detection of modified proteins with high affinity. PTM-focused studies on blood cancers have provided information on altered cellular processes, including cell signaling, apoptosis and transcriptional regulation, that contribute to the malignant phenotype. Furthermore, the mechanism of action of many blood cancer therapies, such as kinase inhibitors, involves inhibiting or modulating protein modifications. Continued optimization of protocols and techniques for PTM analysis in blood cancer will undoubtedly lead to novel insights into mechanisms of malignant transformation, proliferation, and survival, in addition to the identification of novel biomarkers and therapeutic targets. This review discusses techniques used for PTM analysis and their applications in blood cancer research.

Proteome-wide and matrisome-specific alterations during human pancreas development and maturation

The extracellular matrix (ECM) is unique to each tissue and capable of guiding cell differentiation, migration, morphology, and function. The ECM proteome of different developmental stages has not been systematically studied in the human pancreas. In this study, we apply mass spectrometry-based quantitative proteomics strategies using N,N-dimethyl leucine isobaric tags to delineate proteome-wide and ECM-specific alterations in four age groups: fetal (18-20 weeks gestation), juvenile (5-16 years old), young adults (21-29 years old) and older adults (50-61 years old). We identify 3,523 proteins including 185 ECM proteins and quantify 117 of them. We detect previously unknown proteome and matrisome features during pancreas development and maturation. We also visualize specific ECM proteins of interest using immunofluorescent staining and investigate changes in ECM localization within islet or acinar compartments. This comprehensive proteomics analysis contributes to an improved understanding of the critical roles that ECM plays throughout human pancreas development and maturation.

Biochemical and Computational Approaches for the Large-Scale Analysis of Protein Arginine Methylation by Mass Spectrometry

The absence of efficient mass spectrometry-based approaches for the large-scale analysis of protein arginine methylation has hindered the understanding of its biological role, beyond the transcriptional regulation occurring through histone modification. In the last decade, however, several technological advances of both the biochemical methods for methylated polypeptide enrichment and the computational pipelines for MS data analysis have considerably boosted this research field, generating novel insights about the extent and role of this post-translational modification. Here, we offer an overview of state-of-the-art approaches for the high-confidence identification and accurate quantification of protein arginine methylation by high-resolution mass spectrometry methods, which comprise the development of both biochemical and bioinformatics methods. The further optimization and systematic application of these analytical solutions will lead to ground-breaking discoveries on the role of protein methylation in biological processes.

In vivo interactome profiling by enzyme-catalyzed proximity labeling

Enzyme-catalyzed proximity labeling (PL) combined with mass spectrometry (MS) has emerged as a revolutionary approach to reveal the protein-protein interaction networks, dissect complex biological processes, and characterize the subcellular proteome in a more physiological setting than before. The enzymatic tags are being upgraded to improve temporal and spatial resolution and obtain faster catalytic dynamics and higher catalytic efficiency. In vivo application of PL integrated with other state of the art techniques has recently been adapted in live animals and plants, allowing questions to be addressed that were previously inaccessible. It is timely to summarize the current state of PL-dependent interactome studies and their potential applications. We will focus on in vivo uses of newer versions of PL and highlight critical considerations for successful in vivo PL experiments that will provide novel insights into the protein interactome in the context of human diseases.

Application of Label-Free Proteomics for Quantitative Analysis of Urothelial Carcinoma and Cystitis Tissue

A label-free approach based on a highly reproducible and stable workflow allows for quantitative proteome analysis . Due to advantages compared to labeling methods, the label-free approach has the potential to measure unlimited samples from clinical specimen monitoring and comparing thousands of proteins. The presented label-free workflow includes a new sample preparation technique depending on automatic annotation and tissue isolation via FTIR-guided laser microdissection, in-solution digestion, LC-MS/MS analyses, data evaluation by means of Proteome Discoverer and Progenesis software, and verification of differential proteins. We successfully applied this workflow in a proteomics study analyzing human cystitis and high-grade urothelial carcinoma tissue regarding the identification of a diagnostic tissue biomarker. The differential analysis of only 1 mm² of isolated tissue cells led to 74 significantly differentially abundant proteins.

Proteostatic Regulation of MEP and Shikimate Pathways by Redox-Activated Photosynthesis Signaling in Plants Exposed to Small Fungal Volatiles

Microorganisms produce volatile compounds (VCs) with molecular masses of less than 300 Da that promote plant growth and photosynthesis. Recently, we have shown that small VCs of less than 45 Da other than CO₂ are major determinants of plant responses to fungal volatile emissions. However, the regulatory mechanisms involved in the plants' responses to small microbial VCs remain unclear. In Arabidopsis thaliana plants exposed to small fungal VCs, growth promotion is accompanied by reduction of the thiol redox of Calvin-Benson cycle (CBC) enzymes and changes in the levels of shikimate and 2-C-methyl-D-erythritol 4-phosphate (MEP) pathway-related compounds. We hypothesized that plants' responses to small microbial VCs involve post-translational modulation of enzymes of the MEP and shikimate pathways via mechanisms involving redox-activated photosynthesis signaling. To test this hypothesis, we compared the responses of wild-type (WT) plants and a cfbp1 mutant defective in a redox-regulated isoform of the CBC enzyme fructose-1,6-bisphosphatase to small VCs emitted by the fungal phytopathogen Alternaria alternata. Fungal VC-promoted growth and photosynthesis, as well as metabolic and proteomic changes, were substantially weaker in cfbp1 plants than in WT plants. In WT plants, but not in cfbp1 plants, small fungal VCs reduced the levels of both transcripts and proteins of the stromal Clp protease system and enhanced those of plastidial chaperonins and co-chaperonins. Consistently, small fungal VCs promoted the accumulation of putative Clp protease clients including MEP and shikimate pathway enzymes. clpr1-2 and clpc1 mutants with disrupted plastidial protein homeostasis responded weakly to small fungal VCs, strongly indicating that plant responses to microbial volatile emissions require a finely regulated plastidial protein quality control system. Our findings provide strong evidence that plant responses to fungal VCs involve chloroplast-to-nucleus retrograde signaling of redox-activated photosynthesis leading to proteostatic regulation of the MEP and shikimate pathways.

An Integrated Quantitative Proteomics Workflow for Cancer Biomarker Discovery and Validation in Plasma

Blood plasma is one of the most widely used samples for cancer biomarker discovery research as well as clinical investigations for diagnostic and therapeutic purposes. However, the plasma proteome is extremely complex due to its wide dynamic range of protein concentrations and the presence of high-abundance proteins. Here we have described an optimized, integrated quantitative proteomics pipeline combining the label-free and multiplexed-labeling-based (iTRAQ and TMT) plasma proteome profiling methods for biomarker discovery, followed by the targeted approaches for validation of the identified potential marker proteins. In this workflow, the targeted quantitation of proteins is carried out by multiple-reaction monitoring (MRM) and parallel-reaction monitoring (PRM) mass spectrometry. Thus, our approach enables both unbiased screenings of biomarkers and their subsequent selective validation in human plasma. The overall procedure takes only ~2 days to complete, including the time for data acquisition (excluding database searching). This protocol is quick, flexible, and eliminates the need for a separate immunoassay-based validation workflow in blood cancer biomarker investigations. We anticipate that this plasma proteomics workflow will help to accelerate the cancer biomarker discovery program and provide a valuable resource to the cancer research community.

Unbiased Profiling of the Human Proinsulin Biosynthetic Interaction Network Reveals a Role for Peroxiredoxin 4 in Proinsulin Folding

The β-cell protein synthetic machinery is dedicated to the production of mature insulin, which requires the proper folding and trafficking of its precursor, proinsulin. The complete network of proteins that mediate proinsulin folding and advancement through the secretory pathway, however, remains poorly defined. Here we used affinity purification and mass spectrometry to identify, for the first time, the proinsulin biosynthetic interaction network in human islets. Stringent analysis established a central node of proinsulin interactions with endoplasmic reticulum (ER) folding factors, including chaperones and oxidoreductases, that is remarkably conserved in both sexes and across three ethnicities. The ER-localized peroxiredoxin PRDX4 was identified as a prominent proinsulin-interacting protein. In β-cells, gene silencing of PRDX4 rendered proinsulin susceptible to misfolding, particularly in response to oxidative stress, while exogenous PRDX4 improved proinsulin folding. Moreover, proinsulin misfolding induced by oxidative stress or high glucose was accompanied by sulfonylation of PRDX4, a modification known to inactivate peroxiredoxins. Notably, islets from patients with type 2 diabetes (T2D) exhibited significantly higher levels of sulfonylated PRDX4 than islets from healthy individuals. In conclusion, we have generated the first reference map of the human proinsulin interactome to identify critical factors controlling insulin biosynthesis, β-cell function, and T2D.

A circulating extracellular vesicles-based novel screening tool for colorectal cancer revealed by shotgun and data-independent acquisition mass spectrometry

Background: Early screening for colorectal cancer (CRC) is essential to improve its prognosis. Liquid biopsies are increasingly being considered for diagnosing cancer due to low invasiveness and high reproducibility. In addition, circulating extracellular vesicles (crEVs, extracellular vesicles isolated from plasma) expressing tumour-specific proteins are potential biomarkers for various cancers. Here, we present a data-independent acquisition (DIA)-mass spectrometry (MS)-based diagnostic method for liquid biopsies.

Methods: Extracellular vesicles (EVs) were isolated from culture supernatants of human CRC cell lines, and plasma of patients with CRC at different tumour stages, by overnight ultracentrifugation coupled with sucrose density gradient centrifugation. Tumour-specific EV proteins were prioritized using Tandem Mass Tag (TMT)-based shotgun proteomics and phosphoproteomics. The results were verified in a second independent cohort and a mouse tumour-bearing model using Western blotting (WB). The candidate biomarkers were further validated in a third cohort by DIA-MS. Finally, the DIA-MS methodology was accelerated to permit high-throughput detection of EV biomarkers in another independent cohort of patients with CRC and healthy controls.

Results: High levels of total and phosphorylated fibronectin 1 (FN1) in crEVs, haptoglobin (HP), S100A9 and fibrinogen α chain (FGA) were significantly associated with cancer progression. FGA was the most dominant biomarker candidate. Analysis of the human CRC cell lines and the mouse model indicated that FGA+ crEVs were likely released by CRC cells. Furthermore, fast DIA-MS and parallel reaction monitoring (PRM)-MS both confirmed that FGA+ crEVs could distinguish colon adenoma with an area of curve (AUC) in the receiver operating characteristic (ROC) curve of 0.949 and patients with CRC (AUC of ROC is 1.000) from healthy individuals. The performance outperformed conventional tumour biomarkers. The DIA-MS quantification of FGA+ crEVs among three groups agreed with that from PRM-MS.

Conclusion: DIA-MS detection of FGA+ crEVs is a potential rapid and non-invasive screening tool to identify early stage CRC.

Abbreviations: FGA: fibrinogen α chain; CRC: colorectal cancer; crEVs: circulating extracellular vesicles; EV: extracellular vesicles;MS: mass spectrometry; WB: Western blotting; ROC: receiver operating characteristic; PRM: Parallel Reaction Monitoring; GPC1: Glypican-1; GO: Gene ontology; TEM: transmission electron microscopy; FN1: Fibronectin 1; HP: haptoglobin; TMT: Tandem Mass Tag; LC-MS/MS: liquid chromatography coupled to tandem mass spectrometry; DIA: data-independent acquisition; DDA: data-dependent acquisition; CiRT: Common internal Retention Time standards;AGC: Automatic gain control; AUC: area under curve.

Identification of marker proteins of muscular dystrophy in the urine proteome from the mdx-4cv model of dystrophinopathy

Since the protein constituents of urine present a dynamic proteome that can reflect a variety of disease-related alterations in the body, the mass spectrometric survey of proteome-wide changes in urine promises new insights into pathogenic mechanisms. Urine can be investigated in a completely non-invasive way and provides valuable biomedical information on body-wide changes. In this report, we have focused on the urine proteome in X-linked muscular dystrophy using the established mdx-4cv mouse model of dystrophinopathy. In order to avoid potential artefacts due to the manipulation of the biofluid proteome prior to mass spectrometry, crude urine specimens were analyzed without the prior usage of centrifugation steps or concentration procedures. Comparative proteomics revealed 21 increased and 8 decreased proteins out of 870 identified urinary proteoforms using 50 μl of biofluid per investigated sample, i.e. 14 wild type versus 14 mdx-4cv specimens. Promising marker proteins that were almost exclusively found in mdx-4cv urine included nidogen, parvalbumin and titin. Interestingly, the mass spectrometric identification of urine-associated titin revealed a wide spread of peptides over the sequence of this giant muscle protein. The newly established urinomic signature of dystrophinopathy might be helpful for the design of non-invasive assays to improve diagnosis, prognosis, therapy-monitoring and evaluation of potential harmful side effects of novel treatments in the field of muscular dystrophy research.

Comparative Analysis of Quantitative Mass Spectrometric Methods for Subcellular Proteomics

Knowledge of intracellular location can provide important insights into the function of proteins and their respective organelles, and there is interest in combining classical subcellular fractionation with quantitative mass spectrometry to create global cellular maps. To evaluate mass spectrometric approaches specifically for this application, we analyzed rat liver differential centrifugation and Nycodenz density gradient subcellular fractions by tandem mass tag (TMT) isobaric labeling with reporter ion measurement at the MS2 and MS3 level and with two different label-free peak integration approaches, MS1 and data independent acquisition (DIA). TMT-MS2 provided the greatest proteome coverage, but ratio compression from contaminating background ions resulted in a narrower accurate dynamic range compared to TMT-MS3, MS1, and DIA, which were similar. Using a protein clustering approach to evaluate data quality by assignment of reference proteins to their correct compartments, all methods performed well, with isobaric labeling approaches providing the highest quality localization. Finally, TMT-MS2 gave the lowest percentage of missing quantifiable data when analyzing orthogonal fractionation methods containing overlapping proteomes. In summary, despite inaccuracies resulting from ratio compression, data obtained by TMT-MS2 assigned protein localization as well as other methods but achieved the highest proteome coverage with the lowest proportion of missing values.

Quantitative Proteomic Analysis Reveals Antiviral and Anti-inflammatory Effects of Puerarin in Piglets Infected With Porcine Epidemic Diarrhea Virus

Porcine epidemic diarrhea virus (PEDV) has caused enormous economic losses to the swine industry worldwide in recent years. Puerarin (PR), a major isoflavonoid isolated from the Chinese herb Gegen, possesses many pharmacological activities, including anti-inflammatory, and anti-viral activities. This study was conducted with both PEDV-infected African green monkey kidney cells (Vero) and neonatal pigs to determine the effect of PR on PEDV infection and to elucidate the underlying mechanisms by using proteomic analyses. Twenty-four piglets fed a milk replacer were randomly allocated into one of three groups (Control, PEDV, and PEDV + PR). After a 5-day period of adaption, piglets (n = 8/group) in the PEDV + PR were orally administered with PR (0.5 mg/kg body weight) between days 5 and 9, whereas piglets in the other two groups received the same volume of liquid milk replacer. On day 9, piglets were orally administered with either sterile saline or PEDV (Yunnan province strain) at 10^4.5 TCID₅₀ (50% tissue culture infectious dose) per pig. On day 12 of the trial, jugular vein blood and intestinal samples were collected. In addition, Vero cells were assigned randomly into three groups (Control, PEDV, PEDV + PR). Cells in the PEDV and PEDV + PR groups were infected with PEDV at a multiplicity of infection of 0.01, while cells in the control group were treated with the same volume of sterile saline. One hour later, cells in the Control and PEDV groups were cultured in serum-free DMEM, while cells in the PEDV + PR group were supplemented with PR. After 36 h of culture, cells were harvested. PR attenuated the reductions in cell proliferation in vitro and growth performance in PEDV-infected piglets, and inhibited PEDV replication and the expression of several cytokines (including IL-8) both in vitro and in vivo. Proteomic analyses identified that the abundances of 29 proteins in the ileum were altered by PEDV infection and restored to the control level by PR. Pathway analyses revealed that PR restored the expression of several interferon-stimulated genes and selectively upregulated the expression of guanylate-binding proteins. Western blot analyses showed that PR supplementation inhibited the PEDV-induced NF-κB activation. Collectively, these results indicate that PR could exert antiviral and anti-inflammatory effects in piglets infected with PEDV and have the potential to be an effective antiviral feed additive.

AMPK leads to phosphorylation of the transcription factor Nrf2, tuning transactivation of selected target genes

The transcription factor Nrf2 (nuclear factor (erythroid-derived 2)-like 2) and the kinase AMPK (AMP-activated protein kinase) participate in the cellular adaptive response to redox or energy stress. Despite accumulating evidence for positive cooperativity between both proteins, information about direct post-translational modification of Nrf2 by AMPK in living cells is scarce. Here, MS-based analysis of immunoprecipitated Nrf2 revealed serine 374, 408 and 433 in human Nrf2 to be hyperphosphorylated as a function of activated AMPK. A direct phosphate-transfer by AMPK to those sites was indicated by in vitro kinase assays with recombinant proteins as well as interaction of AMPK and Nrf2 in cells, evident by co-immunoprecipitation. Mutation of serine 374, 408 and 433 to alanine did not markedly affect half-life, nuclear accumulation or induction of reporter gene expression upon Nrf2 activation with sulforaphane. However, some selected endogenous Nrf2 target genes responded with decreased induction when the identified phosphosites were mutated, whereas others remained unaffected. Notably, the genes susceptible to the mutation of the phosphorylation sites in Nrf2 consistently showed reduced induction in AMPKα1 -/-cells. Overall, our data reveal AMPK-triggered phosphorylation of Nrf2 at three serine residues, apparently determining the extent of transactivation of selected target genes.

Recent Progress of Biomarker Detection Sensors

Early cancer diagnosis and treatment are crucial research fields of human health. One method that has proven efficient is biomarker detection which can provide real-time and accurate biological information for early diagnosis. This review presents several biomarker sensors based on electrochemistry, surface plasmon resonance (SPR), nanowires, other nanostructures, and, most recently, metamaterials which have also shown their mechanisms and prospects in application in recent years. Compared with previous reviews, electrochemistry-based biomarker sensors have been classified into three strategies according to their optimizing methods in this review. This makes it more convenient for researchers to find a specific fabrication method to improve the performance of their sensors. Besides that, as microfabrication technologies have improved and novel materials are explored, some novel biomarker sensors-such as nanowire-based and metamaterial-based biomarker sensors-have also been investigated and summarized in this review, which can exhibit ultrahigh resolution, sensitivity, and limit of detection (LoD) in a more complex detection environment. The purpose of this review is to understand the present by reviewing the past. Researchers can break through bottlenecks of existing biomarker sensors by reviewing previous works and finally meet the various complex detection needs for the early diagnosis of human cancer.

AMPK leads to phosphorylation of the transcription factor Nrf2, tuning transactivation of selected target genes

The transcription factor Nrf2 (nuclear factor (erythroid-derived 2)-like 2) and the kinase AMPK (AMP-activated protein kinase) participate in the cellular adaptive response to redox or energy stress. Despite accumulating evidence for positive cooperativity between both proteins, information about direct post-translational modification of Nrf2 by AMPK in living cells is scarce. Here, MS-based analysis of immunoprecipitated Nrf2 revealed serine 374, 408 and 433 in human Nrf2 to be hyperphosphorylated as a function of activated AMPK. A direct phosphate-transfer by AMPK to those sites was indicated by in vitro kinase assays with recombinant proteins as well as interaction of AMPK and Nrf2 in cells, evident by co-immunoprecipitation. Mutation of serine 374, 408 and 433 to alanine did not markedly affect half-life, nuclear accumulation or induction of reporter gene expression upon Nrf2 activation with sulforaphane. However, some selected endogenous Nrf2 target genes responded with decreased induction when the identified phosphosites were mutated, whereas others remained unaffected. Notably, the genes susceptible to the mutation of the phosphorylation sites in Nrf2 consistently showed reduced induction in AMPKα1 -/-cells. Overall, our data reveal AMPK-triggered phosphorylation of Nrf2 at three serine residues, apparently determining the extent of transactivation of selected target genes.

Secretomics to Discover Regulators in Diseases

Secretory proteins play important roles in the cross-talk of individual functional units, including cells. Since secretory proteins are essential for signal transduction, they are closely related with disease development, including metabolic and neural diseases. In metabolic diseases, adipokines, myokines, and hepatokines are secreted from respective organs under specific environmental conditions, and play roles in glucose homeostasis, angiogenesis, and inflammation. In neural diseases, astrocytes and microglia cells secrete cytokines and chemokines that play roles in neurotoxic and neuroprotective responses. Mass spectrometry-based secretome profiling is a powerful strategy to identify and characterize secretory proteins. This strategy involves stepwise processes such as the collection of conditioned medium (CM) containing secretome proteins and concentration of the CM, peptide preparation, mass analysis, database search, and filtering of secretory proteins; each step requires certain conditions to obtain reliable results. Proteomic analysis of extracellular vesicles has become a new research focus for understanding the additional extracellular functions of intracellular proteins. Here, we provide a review of the insights obtained from secretome analyses with regard to disease mechanisms, and highlight the future prospects of this technology. Continued research in this field is expected to provide valuable information on cell-to-cell communication and uncover new pathological mechanisms.

In Depth Quantification of Extracellular Matrix Proteins from Human Pancreas

Extracellular matrix (ECM) is an important component of the pancreatic microenvironment which regulates β cell proliferation, differentiation, and insulin secretion. Protocols have recently been developed for the decellularization of the human pancreas to generate functional scaffolds and hydrogels. In this work, we characterized human pancreatic ECM composition before and after decellularization using isobaric dimethylated leucine (DiLeu) labeling for relative quantification of ECM proteins. A novel correction factor was employed in the study to eliminate the bias introduced during sample preparation. In comparison to the commonly employed sample preparation methods (urea and FASP) for proteomic analysis, a recently developed surfactant and chaotropic agent assisted sequential extraction/on pellet digestion (SCAD) protocol has provided an improved strategy for ECM protein extraction of human pancreatic ECM matrix. The quantitative proteomic results revealed the preservation of matrisome proteins while most of the cellular proteins were removed. This method was compared with a well-established label-free quantification (LFQ) approach which rendered similar expressions of different categories of proteins (collagens, ECM glycoproteins, proteoglycans, etc.). The distinct expression of ECM proteins was quantified comparing adult and fetal pancreas ECM, shedding light on the correlation between matrix composition and postnatal β cell maturation. Despite the distinct profiles of different subcategories in the native pancreas, the distribution of matrisome proteins exhibited similar trends after the decellularization process. Our method generated a large data set of matrisome proteins from a single tissue type. These results provide valuable insight into the possibilities of constructing a bioengineered pancreas. It may also facilitate better understanding of the potential roles that matrisome proteins play in postnatal β cell maturation.

Proteomics Approaches for Biomarker and Drug Target Discovery in ALS and FTD

Neurodegenerative disorders such as amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) are increasing in prevalence but lack targeted therapeutics. Although the pathological mechanisms behind these diseases remain unclear, both ALS and FTD are characterized pathologically by aberrant protein aggregation and inclusion formation within neurons, which correlates with neurodegeneration. Notably, aggregation of several key proteins, including TAR DNA binding protein of 43 kDa (TDP-43), superoxide dismutase 1 (SOD1), and tau, have been implicated in these diseases. Proteomics methods are being increasingly applied to better understand disease-related mechanisms and to identify biomarkers of disease, using model systems as well as human samples. Proteomics-based approaches offer unbiased, high-throughput, and quantitative results with numerous applications for investigating proteins of interest. Here, we review recent advances in the understanding of ALS and FTD pathophysiology obtained using proteomics approaches, and we assess technical and experimental limitations. We compare findings from various mass spectrometry (MS) approaches including quantitative proteomics methods such as stable isotope labeling by amino acids in cell culture (SILAC) and tandem mass tagging (TMT) to approaches such as label-free quantitation (LFQ) and sequential windowed acquisition of all theoretical fragment ion mass spectra (SWATH-MS) in studies of ALS and FTD. Similarly, we describe disease-related protein-protein interaction (PPI) studies using approaches including immunoprecipitation mass spectrometry (IP-MS) and proximity-dependent biotin identification (BioID) and discuss future application of new techniques including proximity-dependent ascorbic acid peroxidase labeling (APEX), and biotinylation by antibody recognition (BAR). Furthermore, we explore the use of MS to detect post-translational modifications (PTMs), such as ubiquitination and phosphorylation, of disease-relevant proteins in ALS and FTD. We also discuss upstream technologies that enable enrichment of proteins of interest, highlighting the contributions of new techniques to isolate disease-relevant protein inclusions including flow cytometric analysis of inclusions and trafficking (FloIT). These recently developed approaches, as well as related advances yet to be applied to studies of these neurodegenerative diseases, offer numerous opportunities for discovery of potential therapeutic targets and biomarkers for ALS and FTD.

Standard-flow LC and thermal focusing ESI elucidates altered liver proteins in late stage Niemann-Pick, type C1 disease

Aim: Mass spectrometry (MS)-based proteomics, particularly with the development of nano-ESI, have been invaluable to our understanding of altered proteins related to human disease. Niemann-Pick, type C1 (NPC1) disease is a fatal, autosomal recessive, neurodegenerative disorder. The resulting defects include unesterified cholesterol and sphingolipids accumulation in the late endosomal/lysosomal system resulting in organ dysfunction including liver disease. Materials & methods: First, we performed MS analysis of a complex mammalian proteome using both nano- and standard-flow ESI with the intent of developing a differential proteomics platform using standard-flow ESI. Next, we measured the differential liver proteome in the NPC1 mouse model via label-free quantitative MS using standard-flow ESI. Results: Using the standard-flow ESI approach, we found altered protein levels including, increased Limp2 and Rab7a in liver tissue of Npc1^-/- compared to control mice. Conclusion: Standard-flow ESI can be a tool for quantitative proteomic studies when sample amount is not limited. Using this method, we have identified new protein markers of NPC1.

QCQuan: A Web Tool for the Automated Assessment of Protein Expression and Data Quality of Labeled Mass Spectrometry Experiments

In the context of omics disciplines and especially proteomics and biomarker discovery, the analysis of a clinical sample using label-based tandem mass spectrometry (MS) can be affected by sample preparation effects or by the measurement process itself, resulting in an incorrect outcome. Detection and correction of these mistakes using state-of-the-art methods based on mixed models can use large amounts of (computing) time. MS-based proteomics laboratories are high-throughput and need to avoid a bottleneck in their quantitative pipeline by quickly discriminating between high- and low-quality data. To this end we developed an easy-to-use web-tool called QCQuan (available at qcquan.net ) which is built around the CONSTANd normalization algorithm. It automatically provides the user with exploratory and quality control information as well as a differential expression analysis based on conservative, simple statistics. In this document we describe in detail the scientifically relevant steps that constitute the workflow and assess its qualitative and quantitative performance on three reference data sets. We find that QCQuan provides clear and accurate indications about the scientific value of both a high- and a low-quality data set. Moreover, it performed quantitatively better on a third data set than a comparable workflow assembled using established, reliable software.

A Guide to Mass Spectrometry-Based Quantitative Proteomics

Proteomics has become an attractive science in the postgenomic era, given its capacity to identify up to thousands of molecules in a single, complex sample and quantify them in an absolute and/or relative manner. The use of these techniques enables understanding of cellular and molecular mechanisms of diseases and other biological conditions, as well as identification and screening of protein biomarkers. Here we provide a straightforward, up-to-date compilation and comparison of the main quantitation techniques used in comparative proteomics such as in vitro and in vivo stable isotope labeling and label-free techniques. Additionally, this chapter includes common methods for data acquisition in proteomics and some appropriate methods for data processing. This compilation can serve as a reference for scientists who are new to, or already familiar with, quantitative proteomics.