Proteomics in biomarker discovery and drug development

Investigation of the synergistic effect mechanism underlying sequential use of palbociclib and cisplatin through integral proteomic and glycoproteomic analysis

Chemoresistance largely hampers the clinical use of chemodrugs for cancer patients, combination or sequential drug treatment regimens have been designed to minimize chemotoxicity and resensitize chemoresistance. In this work, the cytotoxic effect of cisplatin was found to be enhanced by palbociclib pretreatment in HeLa cells. With the integration of liquid chromatography-mass spectrometry-based proteomic and N-glycoproteomic workflow, we found that palbociclib alone mainly enhanced the N-glycosylation alterations in HeLa cells, while cisplatin majorly increased the different expression proteins related to apoptosis pathways. As a result, the sequential use of two drugs induced a higher expression level of apoptosis proteins BAX and BAK. Those altered N-glycoproteins induced by palbociclib were implicated in pathways that were closely associated with cell membrane modification and drug sensitivity. Specifically, the top four frequently glycosylated proteins FOLR1, L1CAM, CD63, and LAMP1 were all associated with drug resistance or drug sensitivity. It is suspected that palbociclib-induced N-glycosylation on the membrane protein allowed the HeLa cell to become more vulnerable to cisplatin treatment. Our study provides new insights into the mechanisms underlying the sequential use of target drugs and chemotherapy drugs, meanwhile suggesting a high-efficiency approach that involves proteomic and N-glycoproteomic to facilitate drug discovery.

Revisiting the antigen markers of vector-borne parasitic diseases identified by immunomics: identification and application to disease control

INTRODUCTION

Protein microarray is a promising immunomic approach for identifying biomarkers. Based on our previous study that reviewed parasite antigens and recent parasitic omics research, this article expands to include information on vector-borne parasitic diseases (VBPDs), namely, malaria, schistosomiasis, leishmaniasis, babesiosis, trypanosomiasis, lymphatic filariasis, and onchocerciasis.

AREAS COVERED

We revisit and systematically summarize antigen markers of vector-borne parasites identified by the immunomic approach and discuss the latest advances in identifying antigens for the rational development of diagnostics and vaccines. The applications and challenges of this approach for VBPD control are also discussed.

EXPERT OPINION

The immunomic approach has enabled the identification and/or validation of antigen markers for vaccine development, diagnosis, disease surveillance, and treatment. However, this approach presents several challenges, including limited sample size, variability in antigen expression, false-positive results, complexity of omics data, validation and reproducibility, and heterogeneity of diseases. In addition, antigen involvement in host immune evasion and antigen sensitivity/specificity are major issues in its application. Despite these limitations, this approach remains promising for controlling VBPD. Advances in technology and data analysis methods should continue to improve candidate antigen identification, as well as the use of a multiantigen approach in diagnostic and vaccine development for VBPD control.

Pectolinarigenin regulates the tumor-associated proteins in AGS-xenograft BALB/c nude mice

BACKGROUND

Pectolinarigenin (PEC) is a flavone extracted from Cirsium, and because it has anti-inflammatory properties, anti-cancer research is also being conducted. The objective of this work was to find out if PEC is involved in tumor control and which pathways it regulates in vivo and in vitro.

METHODS

AGS cell lines were xenografted into BALB/c nude mice to create tumors, and PEC was administered intraperitoneally to see if it was involved in tumor control. Once animal testing was completed, tumor proteins were isolated and identified using LC-MS analysis, and gene ontology of the found proteins was performed.

RESULTS

Body weight and hematological measurements on the xenograft mice model demonstrated that PEC was not harmful to non-cancerous cells. We found 582 proteins in tumor tissue linked to biological reactions such as carcinogenesis and cell death signaling. PEC regulated 6 out of 582 proteins in vivo and in vitro in the same way.

CONCLUSION

Our findings suggested that PEC therapy may inhibit tumor development in gastric cancer (GC), and proteomic research gives fundamental information about proteins that may have great promise as new therapeutic targets in GC.

Unveiling the potential of proteomic and genetic signatures for precision therapeutics in lung cancer management

Lung cancer's enduring global significance necessitates ongoing advancements in diagnostics and therapeutics. Recent spotlight on proteomic and genetic biomarker research offers a promising avenue for understanding lung cancer biology and guiding treatments. This review elucidates genetic and proteomic lung cancer biomarker progress and their treatment implications. Technological strides in mass spectrometry-based proteomics and next-generation sequencing enable pinpointing of genetic abnormalities and abnormal protein expressions, furnishing vital data for precise diagnosis, patient classification, and customized treatments. Biomarker-driven personalized medicine yields substantial treatment improvements, elevating survival rates and minimizing adverse effects. Integrating omics data (genomics, proteomics, etc.) enhances understanding of lung cancer's intricate biological milieu, identifying novel treatment targets and biomarkers, fostering precision medicine. Liquid biopsies, non-invasive tools for real-time treatment monitoring and early resistance detection, gain popularity, promising enhanced management and personalized therapy. Despite advancements, biomarker repeatability and validation challenges persist, necessitating interdisciplinary efforts and large-scale clinical trials. Integrating artificial intelligence and machine learning aids analyzing vast omics datasets and predicting treatment responses. Single-cell omics reveal cellular connections and intratumoral heterogeneity, valuable for combination treatments. Biomarkers enable accurate diagnosis, tailored medicines, and treatment response tracking, significantly impacting personalized lung cancer care. This approach spurs patient-centered trials, empowering active patient engagement. Lung cancer proteomic and genetic biomarkers illuminate disease biology and treatment prospects. Progressing towards individualized efficient therapies is imminent, alleviating lung cancer's burden through ongoing research, omics integration, and technological strides.

Proteomics reveals that cell density could affect the efficacy of drug treatment

In vitro cell biology study plays a fundamental role in biological and drug development research, but the repeatability and accuracy of cell studies remain to be low. Various uncertainties during the cell culture process could introduce bias into drug research. In this study, we evaluate the potential effects and underlying mechanisms induced by cell number differences in the cell seeding process. Normally, drug experiments are initiated 24 h after cell seeding, and the difference in the cell number at the time of inoculation leads to the difference in cell confluence (cell density) when drug research is conducted. While cell confluence is closely related to intercellular communication, surface protein interaction, cell autocrine as well as paracrine protein expression of cells, it might have a potential impact on the effect of biological studies such as drug treatment. This study used proteomics technology to comprehensively explore the different protein expression patterns between cells with different confluences. Due to the high sensitivity and high throughput of liquid chromatography-mass spectrometry (LC-MS/MS) detection, it was hired to evaluate the protein expression differences of Hep3B cells with 3 different confluences (30%, 50%, and 70%). The differential expressed proteins were analyzed by the Reactome pathway and the Gene Ontology (GO) pathway. Significant differences were identified across three confluences in terms of the number of proteins identified, the protein expression pattern, and the expression level of certain KEGG pathways. We found that those proteins involved in the cell cycle pathway were differently expressed: the higher the cell confluence, the higher these proteins expressed. A cell cycle inhibitor palbociclib was selected to further verify this observation. Palbociclib in the same dose was applied to cells with different confluence, the results indicated that the growth inhibition effect of palbociclib increases along with the increasing trend of cell cycle protein expression. The result indicated that cell density did influence the effect of drug treatment. Furthermore, three other drugs, cisplatin, paclitaxel, and imatinib, were used to treat the three liver cancer cell lines Hep3B, SUN387, and MHCC97, and a similar observation was obtained that drug effect would be different when the cell confluences were different. Therefore, selecting an appropriate number of cells for plating is vitally important at the beginning of a drug study.

Proteomics: Concepts and applications in human medicine

Proteomics is the complete evaluation of the function and structure of proteins to understand an organism’s nature. Mass spectrometry is an essential tool that is used for profiling proteins in the cell. However, biomarker discovery remains the major challenge of proteomics because of their complexity and dynamicity. Therefore, combining the proteomics approach with genomics and bioinformatics will provide an understanding of the information of biological systems and their disease alteration. However, most studies have investigated a small part of the proteins in the blood. This review highlights the types of proteomics, the available proteomic techniques, and their applications in different research fields.

Surfaceome and Exoproteome Dynamics in Dual-Species Pseudomonas aeruginosa and Staphylococcus aureus Biofilms

Bacterial biofilms are an important underlying cause for chronic infections. By switching into the biofilm state, bacteria can evade host defenses and withstand antibiotic chemotherapy. Despite the fact that biofilms at clinical and environmental settings are mostly composed of multiple microbial species, biofilm research has largely been focused on single-species biofilms. In this study, we investigated the interaction between two clinically relevant bacterial pathogens (Staphylococcus aureus and Pseudomonas aeruginosa) by label-free quantitative proteomics focusing on proteins associated with the bacterial cell surfaces (surfaceome) and proteins exported/released to the extracellular space (exoproteome). The changes observed in the surfaceome and exoproteome of P. aeruginosa pointed toward higher motility and lower pigment production when co-cultured with S. aureus. In S. aureus, lower abundances of proteins related to cell wall biosynthesis and cell division, suggesting increased persistence, were observed in the dual-species biofilm. Complementary phenotypic analyses confirmed the higher motility and the lower pigment production in P. aeruginosa when co-cultured with S. aureus. Higher antimicrobial tolerance associated with the co-culture setting was additionally observed in both species. To the best of our knowledge, this study is among the first systematic explorations providing insights into the dynamics of both the surfaceome and exoproteome of S. aureus and P. aeruginosa dual-species biofilms.

Plasma proteomic signatures predict dementia and cognitive impairment

INTRODUCTION

Biomarker discovery of dementia and cognitive impairment is important to gather insight into mechanisms underlying the pathogenesis of these conditions.

METHODS

In 997 adults from the InCHIANTI study, we assessed the association of 1301 plasma proteins with dementia and cognitive impairment. Validation was conducted in two Alzheimer's disease (AD) case-control studies as well as endophenotypes of AD including cognitive decline, brain amyloid burden, and brain volume.

RESULTS

We identified four risk proteins that were significantly associated with increased odds (peptidase inhibitor 3 (PI3), trefoil factor 3 (TFF3), pregnancy associated plasma protein A (PAPPA), agouti-related peptide (AGRP)) and two protective proteins (myostatin (MSTN), integrin aVb5 (ITGAV/ITGB5)) with decreased odds of baseline cognitive impairment or dementia. Of these, four proteins (MSTN, PI3, TFF3, PAPPA) were associated cognitive decline in subjects that were cognitively normal at baseline. ITGAV/ITGB5 was associated with lower brain amyloid burden, MSTN and ITGAV/ITGB5 were associated with larger brain volume and slower brain atrophy, and PI3, PAPPA, and AGRP were associated with smaller brain volume and/or faster brain atrophy.

DISCUSSION

These proteins may be useful as non-invasive biomarkers of dementia and cognitive impairment.

Comprehensive proteomics investigation of P. vivax-infected human plasma and parasite isolates

Background

In recent times, Plasmodium vivax (P. vivax) has become a serious threat to public health due to its ability to cause severe infection with fatal outcomes. Its unique biology makes it resilient to control measures that are otherwise effective against P. falciparum. A deeper understanding of P. vivax biology and pathogenesis is, therefore, essential for developing the right control strategies. Proteomics of P. falciparum has been helpful in studying disease biology and elucidating molecular mechanisms involved in the development of disease. However, unlike P. falciparum, proteomics data for P. vivax infection is minimal due to the absence of a continuous culture system. The dependence on clinical samples and animal models has drastically limited P. vivax research, creating critical knowledge gaps in our understanding of the disease. This study describes an in-depth proteomics analysis of P. vivax-infected human plasma and parasite isolates, to understand parasite biology, pathogenesis, and to identify new diagnostic targets for P. vivax malaria.

Methods

A mass-spectrometry- (MS) based proteomics approach (Q Exactive) was applied to analyze human plasma and parasite isolates from vivax malaria patients visiting a primary health centre in India. Additionally, a targeted proteomics assay was standardized for validating unique peptides of most recurring parasite proteins.

Results

Thirty-eight P. vivax proteins were detected in human plasma with high confidence. Several glycolytic enzymes were found along with hypothetical, cytoskeletal, ribosomal, and nuclear proteins. Additionally, 103 highly abundant P. vivax proteins were detected in parasite isolates. This represents the highest number of parasite proteins to be reported from clinical samples so far. Interestingly, five of these; three Plasmodium exported proteins (PVX_003545, PVX_003555 and PVX_121935), a hypothetical protein (PVX_083555) and Pvstp1 (subtelomeric transmembrane protein 1, PVX_094303) were found in both plasma and parasite isolates.

Conclusions

A parasite proteomics investigation is essential to understand disease pathobiology and design novel interventions. Control strategies against P. vivax also depend on early diagnosis. This work provides deeper insights into the biology of P. vivax by identifying proteins expressed by the parasite during its complex life-cycle within the human host. The study also reports antigens that may be explored as diagnostic candidates.

Biomarkers in Spinal Cord Injury: Prognostic Insights and Future Potentials

Spinal Cord Injury (SCI) is a major challenge in Neurotrauma research. Complex pathophysiological processes take place immediately after the injury and later on as the chronic injury develops. Moreover, SCI is usually accompanied by traumatic injuries because the most common modality of injury is road traffic accidents and falls. Patients develop significant permanent neurological deficits that depend on the extent and the location of the injury itself and in time they develop further neurological and body changes that may risk their mere survival. In our review, we explored the recent updates with regards to SCI biomarkers. We observed two methods that may lead to the appearance of biomarkers for SCI. First, during the first few weeks following the injury the Blood Spinal Cord Barrier (BSCB) disruption that releases several neurologic structure components from the injured tissue. These components find their way to Cerebrospinal Fluid (CSF) and the systemic circulation. Also, as the injury develops several components of the pathological process are expressed or released such as in neuroinflammation, apoptosis, reactive oxygen species, and excitotoxicity sequences. Therefore, there is a growing interest in examining any correlations between these components and the degrees or the outcomes of the injury. Additionally, some of the candidate biomarkers are theorized to track the progressive changes of SCI which offers an insight on the patients' prognoses, potential-treatments-outcomes assessment, and monitoring the progression of the complications of chronic SCI such as Pressure Ulcers and urinary dysfunction. An extensive literature review was performed covering literature, published in English, until February 2018 using the Medline/PubMed database. Experimental and human studies were included and titles, PMID, publication year, authors, biomarkers studies, the method of validation, relationship to SCI pathophysiology, and concluded correlation were reported. Potential SCI biomarkers need further validation using clinical studies. The selection of the appropriate biomarker group should be made based on the stage of the injuries, the accompanying trauma and with regards to any surgical, or medical interference that might have been done. Additionally, we suggest testing multiple biomarkers related to the several pathological changes coinciding to offer a more precise prediction of the outcome.

Plasmonic gold nanoparticles as multifaceted probe for tissue imaging

Gold nanoparticles as a sensitive probe for versatile tissue imaging techniques forming high quality chromogenic, fluorescence, and mass spectrometric images.

Signatures of cytoplasmic proteins in the exoproteome distinguish community- and hospital-associated methicillin-resistant Staphylococcus aureus USA300 lineages

Methicillin-resistant Staphylococcus aureus (MRSA) is the common name for a heterogeneous group of highly drug-resistant staphylococci. Two major MRSA classes are distinguished based on epidemiology, namely community-associated (CA) and hospital-associated (HA) MRSA. Notably, the distinction of CA- and HA-MRSA based on molecular traits remains difficult due to the high genomic plasticity of S. aureus. Here we sought to pinpoint global distinguishing features of CA- and HA-MRSA through a comparative genome and proteome analysis of the notorious MRSA lineage USA300. We show for the first time that CA- and HA-MRSA isolates can be distinguished by 2 distinct extracellular protein abundance clusters that are predictive not only for epidemiologic behavior, but also for their growth and survival within epithelial cells. This ‘exoproteome profiling’ also groups more distantly related HA-MRSA isolates into the HA exoproteome cluster. Comparative genome analysis suggests that these distinctive features of CA- and HA-MRSA isolates relate predominantly to the accessory genome. Intriguingly, the identified exoproteome clusters differ in the relative abundance of typical cytoplasmic proteins, suggesting that signatures of cytoplasmic proteins in the exoproteome represent a new distinguishing feature of CA- and HA-MRSA. Our comparative genome and proteome analysis focuses attention on potentially distinctive roles of ‘liberated’ cytoplasmic proteins in the epidemiology and intracellular survival of CA- and HA-MRSA isolates. Such extracellular cytoplasmic proteins were recently invoked in staphylococcal virulence, but their implication in the epidemiology of MRSA is unprecedented.

Effects of phycoerythrin from Gracilaria lemaneiformis in proliferation and apoptosis of SW480 cells

We studied phycoerythrin (PE) in human SW480 tumor cells and the underlying molecular mechanisms of action. PE inhibited cell proliferation as evidenced by CCK-8 assay. The IC50 values of phycoerythrin were 48.2 and 27.4 µg/ml for 24 and 48 h of exposure, respectively. PE induced apoptosis and cell cycle arrest in SW480 cells as observed under electron microscopy and with flow cytometry. Apoptosis increased from 5.1 (controls) to 39.0% in 80.0 µg/ml PE-treated cells. Differences in protein expression were identified using proteomic techniques. Protein spots (1018±60 and 1010±60) were resolved in PE-treated and untreated group. Forty differential protein spots were analyzed with MALDI-TOF-MS, including GRP78 and NPM1. The expression as measured by qPCR and western blotting agreed with data from two-dimensional electrophoresis. GRP78, NPM1, MTHSP75, Ezrin and Annexin A2 were decreased and HSP60 was increased after PE treatment, indicating that PE may target multiple proteins to induce apoptosis.

"Omics"-Informed Drug and Biomarker Discovery: Opportunities, Challenges and Future Perspectives

The pharmaceutical industry faces unsustainable program failure despite significant increases in investment. Dwindling discovery pipelines, rapidly expanding R&D budgets and increasing regulatory control, predict significant gaps in the future drug markets. The cumulative duration of discovery from concept to commercialisation is unacceptably lengthy, and adds to the deepening crisis. Existing animal models predicting clinical translations are simplistic, highly reductionist and, therefore, not fit for purpose. The catastrophic consequences of ever-increasing attrition rates are most likely to be felt in the developing world, where resistance acquisition by killer diseases like malaria, tuberculosis and HIV have paced far ahead of new drug discovery. The coming of age of Omics-based applications makes available a formidable technological resource to further expand our knowledge of the complexities of human disease. The standardisation, analysis and comprehensive collation of the "data-heavy" outputs of these sciences are indeed challenging. A renewed focus on increasing reproducibility by understanding inherent biological, methodological, technical and analytical variables is crucial if reliable and useful inferences with potential for translation are to be achieved. The individual Omics sciences-genomics, transcriptomics, proteomics and metabolomics-have the singular advantage of being complimentary for cross validation, and together could potentially enable a much-needed systems biology perspective of the perturbations underlying disease processes. If current adverse trends are to be reversed, it is imperative that a shift in the R&D focus from speed to quality is achieved. In this review, we discuss the potential implications of recent Omics-based advances for the drug development process.

LNO3 AND L3 Are Associated With Antiproliferative And Pro-Apoptotic Action In Hepatoma Cells

The identification of antitumoral substances is the focus of intense biomedical research. Two structural analogues of thalidomide, LNO3 and L3, are two synthetic compounds that might possess such antitumor properties. We evaluated the toxicological effects of these substances, including cytotoxicity, genotoxicity and induction of apoptosis in HTC cells. Additionally, the production of free radicals (nitric oxide and superoxide) was investigated, and the expression of caspases genes 3, 8, and 9 were determined by RT-qPCR. The compounds exhibited cytotoxic effects that resulted in inhibited cell proliferation. LNO3 showed to be more effective and toxic than L3 in all assays. LNO3 stimulated the release of NO and superoxide, which was accompanied by the formation of peroxynitrite. Apoptosis was induced in a dose-dependent manner by both compounds; however, the expression of caspases 3, 8 and 9 was unchanged. These results suggested that L3 and LNO3 possess antiproliferative and pro-apoptotic effects in HTC cells. Additionally, although they exhibited cytotoxicity, L3 and LNO3 might be useful coadjuvants in tumor treatment studies.

Assessing Amide Proton Transfer (APT) MRI Contrast Origins in 9 L Gliosarcoma in the Rat Brain Using Proteomic Analysis

PURPOSE

To investigate the biochemical origin of the amide photon transfer (APT)-weighted hyperintensity in brain tumors.

PROCEDURES

Seven 9 L gliosarcoma-bearing rats were imaged at 4.7 T. Tumor and normal brain tissue samples of equal volumes were prepared with a coronal rat brain matrix and a tissue biopsy punch. The total tissue protein and the cytosolic subproteome were extracted from both samples. Protein samples were analyzed using two-dimensional gel electrophoresis, and the proteins with significant abundance changes were identified by mass spectrometry.

RESULTS

There was a significant increase in the cytosolic protein concentration in the tumor, compared to normal brain regions, but the total protein concentrations were comparable. The protein profiles of the tumor and normal brain tissue differed significantly. Six cytosolic proteins, four endoplasmic reticulum proteins, and five secreted proteins were considerably upregulated in the tumor.

CONCLUSIONS

Our experiments confirmed an increase in the cytosolic protein concentration in tumors and identified several key proteins that may cause APT-weighted hyperintensity.

Proteomics and the blood-brain barrier: how recent findings help drug development

The drug discovery and development processes are divided into different stages separated by milestones to indicate that significant progress has been made and that certain criteria for target validation, hits, leads and candidate drugs have been met. Proteomics is a promising approach for the identification of protein targets and biochemical pathways involved in disease process and thus plays an important role in several stages of the drug development. The blood-brain barrier is considered as a major bottleneck when trying to target new compounds to treat neurodegenerative diseases. Based on the survey of recent findings and with a projection on expected improvements, this report attempt to address how proteomics participates in drug development.

Diagnostics and Resistance Profiling of Bacterial Pathogens

Worldwide infectious disease is one of the leading causes of death. Despite improvements in technology and healthcare services, morbidity and mortality due to infections have remained unchanged over the past few decades. The high and increasing rate of antibiotic resistance is further aggravating the situation. Growing resistance hampers the use of conventional antibiotics, and substantial higher mortality rates are reported in patients given ineffective empiric therapy mainly due to resistance to the agents used. These infections cause suffering, incapacity, and death and impose an enormous financial burden on both healthcare systems and on society in general. The accelerating development of multidrug resistance is one of the greatest diagnostic and therapeutic challenges to modern medicine. The lack of new antibiotic options underscores the need for optimization of current diagnostics, therapies, and prevention of the spread of multidrug-resistant organisms. The so-called -omics technologies (genomics, transcriptomics, proteomics, and metabolomics) have yielded large-scale datasets that advanced the search for biomarkers of infectious diseases in the last decade. One can imagine that in the future the implementation of biomarker-driven molecular test systems will transform diagnostics of infectious diseases and will significantly accelerate the identification of the bacterial pathogens at the infected host site. Furthermore, molecular tests based on the identification of markers of antibiotic resistance will dramatically change resistance profiling. The replacement of culturing methods by molecular test systems for early diagnosis will provide the basis not only for a prompt and targeted therapy, but also for a much more effective stewardship of antibiotic agents and a reduction of the spread of multidrug resistance as well as the appearance of new antibiotic resistances.

Proteomics studies in inner ear disorders: pathophysiology and biomarkers

Although proteomics has been exploited in a wide range of diseases for identification of biomarkers and pathophysiological mechanisms, there are still biomedical disciplines such as otology where proteomics platforms are underused due to technical challenges and/or complex features of the disease. Thus, in the past few years, healthcare and scientific agencies have advocated the development and adoption of proteomic technologies in otological research. However, few studies have been conducted and limited literature is available in this area. Here, we present the state of the art of proteomics in otology, discussing the substantial evidence from recent experimental models and clinical studies in inner-ear conditions. We also delineate a series of critical issues including minute size of the inner ear, delicacy and poor accessibility of tissue that researchers face while undertaking otology proteomics research. Furthermore, we provide perspective to enhance the impact and lead to the clinical implementation of these proteomics-based strategies.

Formation of lipid bilayer membrane in a poly(dimethylsiloxane) microchip integrated with a stacked polycarbonate membrane support and an on-site nanoinjector

This paper describes a new and facile approach for the formation of pore-spanning bilayer lipid membranes (BLMs) within a poly(dimethylsiloxane) (PDMS) microfluidic device. Commercially, readily available polycarbonate (PC) membranes are employed for the support of BLMs. PC sheets with 5 μm, 2 μm, and 0.4 μm pore diameters, respectively, are thermally bonded into a multilayer-stack, reducing the pore density of 0.4 μm-pore PC by a factor of 200. The BLMs on this support are considerably stable (a mean lifetime: 17 h). This multilayer-stack PC (MSPC) membrane is integrated into the PDMS chip by an epoxy bonding method developed to secure durable bonding under the use of organic solvents. The microchip has a special channel for guiding a micropipette in the proximity of the MSPC support. With this on-site injection technique, tens to hundreds of nanoliters of solutions can be directly dispensed to the support. Incorporating gramicidin ion channels into BLMs on the MSPC support has confirmed the formation of single BLMs, which is based on the observation from current signals of 20 pS conductance that is typical to single channel opening. Based on the bilayer capacitance (1.4 pF), about 15% of through pores across the MSPC membrane are estimated to be covered with BLMs.

A streamlined approach to high-throughput proteomics

Proteomics has rapidly become an important tool for life science research, allowing the integrated analysis of global protein expression from a single experiment. To accommodate the complexity and dynamic nature of any proteome, researchers must use a combination of disparate protein biochemistry techniques, often a highly involved and time-consuming process. Whilst highly sophisticated, individual technologies for each step in studying a proteome are available, true high-throughput proteomics that provides a high degree of reproducibility and sensitivity has been difficult to achieve. The development of high-throughput proteomic platforms, encompassing all aspects of proteome analysis and integrated with genomics and bioinformatics technology, therefore represents a crucial step for the advancement of proteomics research. ProteomIQ (Proteome Systems) is the first fully integrated, start-to-finish proteomics platform to enter the market. Sample preparation and tracking, centralized data acquisition and instrument control, and direct interfacing with genomics and bioinformatics databases are combined into a single suite of integrated hardware and software tools, facilitating high reproducibility and rapid turnaround times. This review will highlight some features of ProteomIQ, with particular emphasis on the analysis of proteins separated by 2D polyacrylamide gel electrophoresis.

The evolving role of infectomics in drug discovery

Signatures of infectomes, which are encoded by both host and microbial genomes, and mirror the interplay between pathogens and their hosts, provide invaluable knowledge in the search for novel antimicrobial drugs. Infectomics is the study of infectomes by using systems biology and high-throughput omic approaches. There are three types of infectomic approaches that can be used for drug discovery: ecological infectomics, immunoinfectomics and chemical infectomics. Ecological infectomics, which is the ecological study of infectomes, explores symbiotic solutions to microbial infections. Research on drug discovery using infectomic signatures and immunomic approaches falls within the field of immunoinfectomics. Advances in chemical infectomics will lead to the development of a new generation of chemical drugs for therapeutics for microbial infections.

Identification and functional validation of caldesmon as a potential gastric cancer metastasis-associated protein

In this study, we aim to identify biomarkers for gastric cancer metastasis using a quantitative proteomics approach. The proteins extracted from a panel of 4 gastric cancer cell lines, two derived from primary cancer (AGS, FU97) and two from lymph node metastasis (AZ521, MKN7), were labeled with iTRAQ (8-plex) reagents and analyzed by 2D-LC-MALDI-TOF/TOF MS. In total, 641 proteins were identified with at least a 95% confidence. Using cutoff values of >1.5 and <0.67, 19 proteins were found to be up-regulated and 34 were down-regulated in the metastatic versus primary gastric cancer cell lines respectively. Several of these dysregulated proteins, including caldesmon, were verified using Western blotting. It was found that caldesmon expression was decreased in the two metastasis-derived cell lines, and this was confirmed by further analysis of 7 gastric cancer cell lines. Furthermore, immunohistochemical staining of 9 pairs of primary gastric cancer and the matched lymph node metastasis tissue also corroborated this observation. Finally, knockdown of caldesmon using siRNA in AGS and FU97 gastric cancer cells resulted in an increase in cell migration and invasion, while the overexpression of caldesmon in AZ521 cells led to a decrease in cell migration and invasion. This study has thus established the potential role of caldesmon in gastric cancer metastasis, and further functional studies are underway to delineate the underlying mechanism of action of this protein.

Jacalin bound plasma O-glycoproteome and reduced sialylation of alpha 2-HS glycoprotein (A2HSG) in rheumatoid arthritis patients

Glycosylation studies of plasma proteins can reveal information about the onset and progression of diseases, where in the glycan biosynthetic pathways are disturbed as in rheumatoid arthritis (RA). The present study was focused on analysis of O-linked glycoproteins of plasma in RA patients. Two dimensional gel electrophoresis of jacalin bound plasma of RA patients revealed a number of differentially expressed protein spots as compared to healthy controls. Eighteen protein spots were found to have statistically significant (p<0.05) difference in their expression level from four sets of gels and were identified by MALDI-TOF MS. Most of the identified proteins were predicted to be O-glycosylated proteins by Net-O-Gly 3.1 algorithm. Among these the alpha 2HS glycoprotein (A2HSG) was found to be down regulated whereas inter alpha trypsin inhibitor H4 (ITIH4) was up regulated and this was validated by Western blotting. The glycosylation studies showed the reduced N-linked sialylation of A2HSG in RA patients. Altered glycoprotein expression and functional as well as structural studies of glycans might help in the diagnosis of RA and understanding the disease pathogenesis.

Promising Urinary Protein Biomarkers for the Early Detection of Hepatocellular Carcinoma among High-Risk Hepatitis C Virus Egyptian Patients

Hepatocellular Carcinoma is a major healthcare problem, representing the third most common cause of cancer-related mortality worldwide. There are 130 million Hepatitis C virus infected patients worldwide who are at a high-risk for developing Hepatocellular Carcinoma. Due to the fact that reliable parameters and/or tools for the early detection of Hepatocellular Carcinoma among high-risk individuals are severely lacking, Hepatocellular Carcinoma patients are always diagnosed at a late stage where surgical solutions or effective treatment are not possible. Urine was collected from 106 Hepatitis C infected patients patients, 32 of whom had already developed Hepatocellular Carcinoma and 74 patients who were diagnosed as Hepatocellular Carcinoma -free at the time of initial sample collection. In addition to these patients, urine samples were also collected from 12 healthy control individuals. Total urinary proteins were isolated from the urine samples and LC-MS/MS was used to identify potential protein HCC biomarker candidates. This was followed by validating relative expression levels of proteins present in urine among all the patients using quantitative real time-PCR. This approach revealed that significant over-expression of three proteins: DJ-1, Chromatin Assembly Factor-1 (CAF-1) and Heat Shock Protein 60 (HSP60), was a characteristic event among Hepatocellular Carcinoma - post Hepatitis C virus infected patients. As a single-based Hepatocellular Carcinoma biomarker, CAF-1 over-expression identified Hepatocellular Carcinoma among Hepatitis C virus infected patients with a specificity of 90%, sensitivity of 66% and with an overall diagnostic accuracy of 78%. Moreover, the CAF-1/HSP60 tandem identified Hepatocellular Carcinoma among Hepatitis C virus infected patients with a specificity of 92%, sensitivity of 61% and with an overall diagnostic accuracy of 77%.

Proteomic approaches in understanding action mechanisms of metal-based anticancer drugs

Medicinal inorganic chemistry has been stimulating largely by the success of the anticancer drug, cisplatin. Various metal complexes are currently used as therapeutic agents (e.g., Pt, Au, and Ru) in the treatment of malignant diseases, including several types of cancers. Understanding the mechanism of action of these metal-based drugs is for the design of more effective drugs. Proteomic approaches combined with other biochemical methods can provide comprehensive understanding of responses that are involved in metal-based anticancer drugs-induced cell death, including insights into cytotoxic effects of metal-based anticancer drugs, correlation of protein alterations to drug targets, and prediction of drug resistance and toxicity. This information, when coupled with clinical data, can provide rational basses for the future design and modification of present used metal-based anticancer drugs.

Alpha-1 antitrypsin is a potential biomarker for hepatitis B

BACKGROUND

Function exertion of specific proteins are key factors in disease progression, thus the systematical identification of those specific proteins is a prerequisite to understand various diseases. Though many proteins have been verified to impact on hepatitis, no systematical protein screening has been documented to hepatitis B virus (HBV) induced hepatitis, hindering the comprehensive understanding to this severe disease.

AIM

To identify the major proteins in the progression of HBV infection from mild stage to severe stage.

METHODS

We performed an integrated strategy by combining two-dimensional electrophoresis (2-DE), peptide mass fingerprinting (PMF) analysis, and tissue microarray techniques to screen the functional proteins and detect the localization of those proteins.

RESULTS

Interestingly, MS/MS identification revealed the expression level of alpha-1 antitrypsin (AAT) was significantly elevated in serum samples from patients with severe chronic hepatitis. Immunoblotting with a specific AAT antibody confirmed that AAT is highly expressed in serum samples from patients with hepatic carcinoma and severe chronic hepatitis. Furthermore, we observed that AAT is with highest expression in normal tissue and cells, but lowest in hepatic carcinoma and severe chronic hepatitis tissues and cells, suggesting the specific secretion of AAT from tissues and cells to serum.

CONCLUSION

These results suggest the possibility of AAT as a potential biomarker for hepatitis B in diagnosis.

Cerebrospinal fluid beta-amyloid and dynabridging in Alzheimer's disease drug development

Cerebrospinal fluid (CSF) has become a matrix for biomarker discovery and development in recent years. A number of biomarkers for pathogenic processes in Alzheimer's disease have been identified. Studies have revealed the diagnostic potential of CSF amyloid-beta, tau and phosphorylated tau levels. California Clinical Trials has conducted a number of studies in collaboration with drug developers that demonstrate the importance of CSF amyloid-beta peptides as biomarkers for drug development. These studies also establish the utility of CSF sampling via continuous indwelling lumbar catheterization (dynabridging) for assessing pharmacokinetic and pharmacodynamic parameters in conjunction with biomarker analysis. Corroborative approaches using multiple biomarker methods including neuroimaging and CSF biomarkers will provide a complete picture of the Alzheimer's disease brain.

Fast proteomic protocol for biomarker fingerprinting in cancerous cells

The advance of novel technologies that will enable the detection of large sets of biomarker proteins, to greatly improve the sensitivity and specificity of an assay, represents a major objective in biomedical research. To demonstrate the power of mass spectrometry (MS) detection for large-scale biomarker screening in cancer research, a simple, one-step approach for fast biomarker fingerprinting in complex cellular extracts is described. MCF-7 breast cancer cells were used as a model system. Fast proteomic profiling of whole cellular extracts was achieved on a linear trap quadrupole (LTQ) mass spectrometer by one of the following techniques: (a) data-dependent liquid chromatography (LC)-MS/MS of un-labeled cell extracts, (b) data-dependent LC-MS/MS with pulsed Q dissociation (PQD) detection of iTRAQ labeled samples, and (c) multiple reaction monitoring (MRM)-MS of low abundant proteins that could not be detected with data-dependent MS/MS. The data-dependent LC-MS/MS analysis of MCF-7 cells enabled the identification of 796 proteins (p<0.001) and the simultaneous detection of 156 previously reported putative cancer biomarkers. PQD detection of iTRAQ labeled cells resulted in the detection of 389 proteins and 64 putative biomarkers. MRM-MS analysis enabled the successful monitoring of a panel of low-abundance proteins in one single experiment, highlighting the utility of this technique for targeted analysis in cancer investigations. These results demonstrate that MS-based technologies relying on a one-step separation protocol have the potential to revolutionize biomarker research and screening applications by enabling fast, sensitive and reliable detection of large panels of putative biomarkers. To further stimulate the exploration of proteins that have been previously reported in the literature to be differentially expressed in a variety of cancers, an extensive list of approximately 1100 candidate biomarkers has been compiled and included in the manuscript.

Towards proteome standards: the use of absolute quantitation in high-throughput biomarker discovery

The use of proteomics to profile biological fluids and identify therein biomarkers for cancer and other diseases was initially received with considerable excitement. However, results have fallen short of the expectations. Traditionally, protein biomarkers have been identified by measurement of relative expression changes between case and control samples from which differentially expressed proteins are then considered to represent biomarker candidates. We argue that current individual proteomics-based biomarker discovery studies lack the statistical strength for the identification of high-confidence biomarkers. Instead, multi-group efforts are necessary to facilitate the generation of sufficient sample sizes. This is contingent on the ability to collate and cross-compare data from different studies, which will require the use of a common metric or standards. Though profound, the technical challenges for absolute protein quantification can be overcome. The use of matrix specific, shared standards for absolute quantitation presents an opportunity to facilitate the much needed, but currently impossible, comparisons of different studies. In addition to community-wide approaches to standardize pre-analytical biomarker research studies, it is also important to establish means to integrate experimental data from different studies in order to assess the usefulness of proposed biomarkers with sufficient statistical certainty.

Proteomic identification of overexpressed PRDX 1 and its clinical implications in ovarian carcinoma

Ovarian carcinoma is the most lethal gynecological malignancy in worldwide. The discovery of reliable marker for early detection of ovarian carcinoma is critical for increasing patient's survival because high mortality rate is associated with late diagnosis of this tumor. In the present study, we performed comparative analysis of whole proteomes between serous borderline tumor and serous carcinoma to identify a useful biomarker for the early diagnosis and progression of ovarian carcinoma. Nine proteins were significantly overexpressed in ovarian serous carcinomas compared to borderline tumors. After validation with Western blotting and immunohistochemical analysis, prdx 1 was found to be the strongest overexpressed protein in malignant ovarian tumors among the selected proteins. In addition, the high level of prdx 1 expression (>50% positive cancer cells) was significantly correlated with poor overall survival in ovarian serous carcinomas. On a multivariate cox analysis, the relative risk of death was 8.74 in patients with serous carcinomas showing >50% of prdx 1-positive cancer cells. Our results suggest that prdx 1 may be a useful diagnostic and prognostic biomarker in ovarian carcinoma, especially serous carcinoma.

Identification and characterization of molecular targets of natural products by mass spectrometry

Natural products, and their derivatives and mimics, have contributed to the development of important therapeutics to combat diseases such as infections and cancers over the past decades. The value of natural products to modern drug discovery is still considerable. However, its development is hampered by a lack of a mechanistic understanding of their molecular action, as opposed to the emerging molecule-targeted therapeutics that are tailored to a specific protein target(s). Recent advances in the mass spectrometry-based proteomic approaches have the potential to offer unprecedented insights into the molecular action of natural products. Chemical proteomics is established as an invaluable tool for the identification of protein targets of natural products. Small-molecule affinity selection combined with mass spectrometry is a successful strategy to "fish" cellular targets from the entire proteome. Mass spectrometry-based profiling of protein expression is also routinely employed to elucidate molecular pathways involved in the therapeutic and possible toxicological responses upon treatment with natural products. In addition, mass spectrometry is increasingly utilized to probe structural aspects of natural products-protein interactions. Limited proteolysis, photoaffinity labeling, and hydrogen/deuterium exchange in conjunction with mass spectrometry are sensitive and high-throughput strategies that provide low-resolution structural information of non-covalent natural product-protein complexes. In this review, we provide an overview on the applications of mass spectrometry-based techniques in the identification and characterization of natural product-protein interactions, and we describe how these applications might revolutionize natural product-based drug discovery.

Evaluation of in vitro assays for assessing the toxicity of cigarette smoke and smokeless tobacco

BACKGROUND

In vitro toxicology studies of tobacco and tobacco smoke have been used to understand why tobacco use causes cancer and to assess the toxicologic impact of tobacco product design changes. The need for toxicology studies has been heightened given the Food and Drug Administration's newly granted authority over tobacco products for mandating tobacco product performance standards and evaluate manufacturers' health claims about modified tobacco products. The goal of this review is to critically evaluate in vitro toxicology methods related to cancer for assessing tobacco products and to identify related research gaps.

METHODS

PubMed database searches were used to identify tobacco-related in vitro toxicology studies published since 1980. Articles published before 1980 with high relevance also were identified. The data were compiled to examine (a) the goals of the study, (b) the methods for collecting test substances, (c) experimental designs, (d) toxicologic end points, and (e) relevance to cancer risk.

RESULTS

A variety of in vitro assays are available to assess tobacco smoke that address different modes of action, mostly using non-human cell models. However, smokeless tobacco products perform poorly in these assays. Although reliable as a screening tool for qualitative assessments, the available in vitro assays have been poorly validated for quantitative comparisons of different tobacco products. Assay batteries have not been developed, although they exist for nontobacco assessments. Extrapolating data from in vitro studies to human risks remains hypothetical.

CONCLUSIONS

In vitro toxicology methods are useful for screening toxicity, but better methods are needed for today's context of regulation and evaluation of health claims.

Serological proteomics of gastritis: degradation of apolipoprotein A-I and alpha1-antitrypsin is a common response to inflammation irrespective of Helicobacter pylori infection

Proteomic technology was employed to analyze serum samples from healthy subjects (10 cases) and gastritis patients with negative and positive Helicobacter pylori (Hp) infection (15 cases each). The serum proteins were separated by two-dimensional (2-D) gel electrophoresis and analyzed by a computer-aided program. The altered proteins in expression were then identified by mass spectrometry and validated by Western blotting. Compared to those in normal control, proteins in at least six areas of 2-D gels were found to significantly increase their expression levels in both Hp-negative and Hp-positive serum samples. These proteins were identified by mass peptide fingerprinting and confirmed by Western blotting to be the truncated or cleaved protein fragments of apolipoprotein A-I and alpha-1 antitrypsin, two well-known acute-phase proteins. We conclude that the degradation or metabolization of acute-phase proteins, apolipoprotein A-I, and alpha1-antitrypsin, is a common response to gastric inflammation irrespective of Hp infection.

Data-driven analysis approach for biomarker discovery using molecular-profiling technologies

High-throughput molecular-profiling technologies provide rapid, efficient and systematic approaches to search for biomarkers. Supervised learning algorithms are naturally suited to analyse a large amount of data generated using these technologies in biomarker discovery efforts. The study demonstrates with two examples a data-driven analysis approach to analysis of large complicated datasets collected in high-throughput technologies in the context of biomarker discovery. The approach consists of two analytic steps: an initial unsupervised analysis to obtain accurate knowledge about sample clustering, followed by a second supervised analysis to identify a small set of putative biomarkers for further experimental characterization. By comparing the most widely applied clustering algorithms using a leukaemia DNA microarray dataset, it was established that principal component analysis-assisted projections of samples from a high-dimensional molecular feature space into a few low dimensional subspaces provides a more effective and accurate way to explore visually and identify data structures that confirm intended experimental effects based on expected group membership. A supervised analysis method, shrunken centroid algorithm, was chosen to take knowledge of sample clustering gained or confirmed by the first step of the analysis to identify a small set of molecules as candidate biomarkers for further experimentation. The approach was applied to two molecular-profiling studies. In the first study, PCA-assisted analysis of DNA microarray data revealed that discrete data structures exist in rat liver gene expression and correlated with blood clinical chemistry and liver pathological damage in response to a chemical toxicant diethylhexylphthalate, a peroxisome-proliferator-activator receptor agonist. Sixteen genes were then identified by shrunken centroid algorithm as the best candidate biomarkers for liver damage. Functional annotations of these genes revealed roles in acute phase response, lipid and fatty acid metabolism and they are functionally relevant to the observed toxicities. In the second study, 26 urine ions identified from a GC/MS spectrum, two of which were glucose fragment ions included as positive controls, showed robust changes with the development of diabetes in Zucker diabetic fatty rats. Further experiments are needed to define their chemical identities and establish functional relevancy to disease development.

Toward the proteomic identification of biomarkers for the prediction of HBV related hepatocellular carcinoma

Early detection is a key step for effective intervention of hepatocellular carcinoma (HCC), the lack of sensitive and specific biomarkers is a major reason for the high rate of HCC-related mortality. This report described an integrated strategy by combining SELDI-ProteinChip, sophisticated algorithm analysis, acetonitrile (ACN) pre-treatment and two-dimensional electrophoresis (2DE)-peptide mass fingerprinting (PMF) techniques to identify serological markers for the prediction of HBV-related HCC. Proteomic profiling of three groups of serum specimens from HBV-related HCC (50 cases), HBV infection (45 cases), and normal subjects (30 cases) was conducted by using SELDI-ProteinChip system and the resulting different protein peaks were subjected to stepwise statistical analyses. Three most discriminatory peaks at 5890, 11615, and 11724 Da, respectively, were screened out from the statistical algorithm and a predictive model based on the three peaks was constructed and tested using the newly enrolled serum samples. 2DE was applied to separate and compare the serum samples that were pre-treated by ACN precipitation. The protein spots obviously intensified in HCC sera in the 2DE region of 12 kDa were identified by PMF to be serum SAA, which was validated by SELDI-TOF spectra of HCC sera after immunoprecipitation using anti-SAA antibody and by Western blot experiments. Given the fact that SAA is not a specific biomarker, further attempt is being made to identify the other two most discriminatory peaks to realize the possibility of using the predictive model for HCC surveillance and prediction.