Cardiac Targeting Peptide : From Identification to Validation to Mechanism of Transduction.

Cell-penetrating peptides (CPPs), also known as protein transduction domains, were first identified 25 years ago. They are small, ~6-30 amino acid long, synthetic, or naturally occurring peptides, able to carry a variety of cargoes across the cellular membranes in an intact, functional form. These cargoes can range from other small peptides, full-length proteins, nucleic acids including RNA and DNA, nanoparticles, and viral particles as well as radioisotopes and other fluorescent probes for imaging purposes. However, this ability to enter all cell types indiscriminately, and even cross the blood-brain barrier, hinders their development into viable vectors. Hence, researchers have adopted various strategies ranging from pH activatable cargoes to using phage display to identify tissue-specific CPPs. Use of this phage display strategy has led to an ever-expanding number of tissue-specific CPPs. Using phage display, we identified a 12-amino acid, non-naturally occurring peptide that targets the heart with peak uptake at 15 min after a peripheral intravenous injection, that we termed Cardiac Targeting Peptide (CTP). In this chapter, we use CTP as an example to describe techniques for validation of cell-specific transduction as well as provide details on a technology to identify binding partner(s) for these ever-increasing plethora of tissue-specific peptides. Given the myriad cargoes CTP can deliver, as well as rapid uptake after an intravenous injection, it can be applied to deliver radioisotopes, miRNA, siRNA, peptides, and proteins of therapeutic potential for acute cardiac conditions like myocardial infarction, where the window of opportunity for salvaging at-risk myocardium is limited to 6 hrs.

A Proteogenomic Resource Enabling Integrated Analysis of Listeria Genotype-Proteotype-Phenotype Relationships.

Listeria monocytogenes is an opportunistic foodborne pathogen responsible for listeriosis, a potentially fatal foodborne disease. Many different Listeria strains and serotypes exist, but a proteogenomic resource that bridges the gap in our molecular understanding of the relationships between the Listeria genotypes and phenotypes via proteotypes is still missing. Here, we devised a next-generation proteogenomics strategy that enables the community to rapidly proteotype Listeria strains and relate this information back to the genotype. Based on sequencing and de novo assembly of the two most commonly used Listeria model strains, EGD-e and ScottA, we established two comprehensive Listeria proteogenomic databases. A genome comparison established core- and strain-specific genes potentially responsible for virulence differences. Next, we established a DIA/SWATH-based proteotyping strategy, including a new and robust sample preparation workflow, that enables the reproducible, sensitive, and relative quantitative measurement of Listeria proteotypes. This reusable and publicly available DIA/SWATH library covers 70% of open reading frames of Listeria and represents the most extensive spectral library for Listeria proteotype analysis to date. We used these two new resources to investigate the Listeria proteotype in states mimicking the upper gastrointestinal passage. Exposure of Listeria to bile salts at 37 °C, which simulates conditions encountered in the duodenum, showed significant proteotype perturbations including an increase of FlaA, the structural protein of flagella. Given that Listeria is known to lose its flagella above 30 °C, this was an unexpected finding. The formation of flagella, which might have implications on infectivity, was validated by parallel reaction monitoring and light and scanning electron microscopy. flaA transcript levels did not change significantly upon exposure to bile salts at 37 °C, suggesting regulation at the post-transcriptional level. Together, these analyses provide a comprehensive proteogenomic resource and toolbox for the Listeria community enabling the analysis of Listeria genotype-proteotype-phenotype relationships.

Validation of extracellular ligand-receptor interactions by Flow-TriCEPS.

OBJECTIVE: The advent of ligand-based receptor capture methodologies, allows the identification of unknown receptor candidates for orphan extracellular ligands. However, further target validation can be tedious, laborious and time-consuming. Here, we present a methodology that provides a fast and cost-efficient alternative for candidate target verification on living cells. RESULTS: In the described methodology a ligand of interest (e.g. transferrin, epidermal growth factor or insulin) was conjugated to a linker (TriCEPS) that carries a biotin. To confirm ligand/receptor interactions, the ligand-TriCEPS conjugates were first added onto living cells and cells were subsequently labeled with a streptavidin-fluorophore and analyzed by flow cytometry (thus referred as Flow-TriCEPS). Flow-TriCEPS was also used to validate identified receptor candidates when combined with a siRNA knock down approach (i.e. reduction of expression levels). This approach is versatile as it can be applied for different classes of ligands (proteins, peptides, antibodies) and different cell lines. Moreover, the method is time-efficient since it takes advantage of the large variety of commercially available (and certified) siRNAs.

Dynamics of Protein Expression Reveals Primary Targets and Secondary Messengers of Estrogen Receptor Alpha Signaling in MCF-7 Breast Cancer Cells.

Estrogen receptor alpha (ERα)-mediated proliferation of breast cancer cells is facilitated through expression of multiple primary target genes, products of which induce a secondary response to stimulation. To differentiate between the primary and secondary target proteins of ERα signaling, we measured dynamics of protein expression induced by 17ß-estradiol in MCF-7 breast cancer cells. Measurement of the global proteomic effects of estradiol by stable isotope labeling by amino acids in cell culture (SILAC) resulted in identification of 103 estrogen-regulated proteins, with only 40 of the corresponding genes having estrogen response elements. Selected reaction monitoring (SRM) assays were used to validate the differential expression of 19 proteins and measure the dynamics of their expression within 72 h after estradiol stimulation, and in the absence or presence of 4-hydroxytamoxifen, to confirm ERα-mediated signaling. Dynamics of protein expression unambiguously revealed early and delayed response proteins and well correlated with presence or absence of estrogen response elements in the corresponding genes. Finally, we quantified dynamics of protein expression in a rarely studied network of transcription factors with a negative feedback loop (ERα-EGR3-NAB2). Because NAB2 protein is a repressor of EGR3-induced transcription, siRNA-mediated silencing of NAB2 resulted in the enhanced expression of the EGR3-induced protein ITGA2. To conclude, we provided a high-quality proteomic resource to supplement genomic and transcriptomic studies of ERα signaling.

Integrating meta-analysis of microarray data and targeted proteomics for biomarker identification: application in breast cancer.

The development of signature biomarkers has gained considerable attention in the past decade. Although the most well-known examples of biomarker panels stem from gene expression studies, proteomic panels are becoming more relevant, with the advent of targeted mass spectrometry-based methodologies. At the same time, the development of multigene prognostic classifiers for early stage breast cancer patients has resulted in a wealth of publicly available gene expression data from thousands of breast cancer specimens. In the present study, we integrated transcriptome and proteome-based platforms to identify genes and proteins related to patient survival. Candidate biomarker proteins have been identified in a previously generated breast cancer tissue extract proteome. A mass-spectrometry-based assay was then developed for the simultaneous quantification of these 20 proteins in breast cancer tissue extracts. We quantified the relative expression levels of the 20 potential biomarkers in a cohort of 96 tissue samples from patients with early stage breast cancer. We identified two proteins, KPNA2 and CDK1, which showed potential to discriminate between estrogen receptor positive patients of high and low risk of disease recurrence. The role of these proteins in breast cancer prognosis warrants further investigation.

In-depth proteomic delineation of the colorectal cancer exoproteome: Mechanistic insight and identification of potential biomarkers.

Systemic mining of cancer exoproteome/secretome has emerged as a pivotal strategy for delineation of molecular pathways with mechanistic importance in cancer development, as well as the discovery of diagnostic/prognostic biomarkers. Although major advances in diagnostic and therapeutic management of colorectal cancer have been underscored in the last decade, this cancer still remains the second leading cause of cancer-related deaths in the developed world. Despite previous studies on deciphering the colorectal cancer exoproteome, such studies lack adequate depth and robustness due to technological limitations. Here, using a well-established LC-MS/MS method on an LTQ-Orbitrap mass spectrometer, we extensively delineated the exoproteome of 12 colon cancer cell lines. In total, 2979 non-redundant proteins were identified with a minimum of two peptides, of which ~62% were extracellular or cell membrane-bound, based on prediction software. To further characterize this dataset and identify clinical opportunities, first, we investigated overrepresented molecular concepts of interest via enrichment map analysis and second, we demonstrated translational importance of certain proteins, such as olfactomedin-4 and kallikrein-related peptidases-6 and -10, by investigating their expression levels in patient tissues and/or fluids. Overall, the present study details a comprehensive colorectal cancer exoproteome dataset, and may be used as future platform for biomarker discovery, and hypothesis-generating studies. BIOLOGICAL SIGNIFICANCE: This article represents one of the most extensive and comprehensive proteomic datasets regarding the secreted/extracellular proteome of colorectal cancer cell lines. The reported datasets may form a platform for a plethora of future, discovery-based or hypothesis-generating studies, attempting to either delineate putative cancer biomarkers for CRC, or elucidate questions of mechanistic importance (e.g. investigation of deregulated pathways for CRC progression).

Coupling proteomics and transcriptomics in the quest of subtype-specific proteins in breast cancer.

Breast-cancer subtypes present with distinct clinical characteristics. Therefore, characterization of subtype-specific proteins may augment the development of targeted therapies and prognostic biomarkers. To address this issue, MS-based secretome analysis of eight breast cancer cell lines, corresponding to the three main breast cancer subtypes was performed. More than 5200 non-redundant proteins were identified with 23, four, and four proteins identified uniquely in basal, HER2-neu-amplified, and luminal breast cancer cells, respectively. An in silico mRNA analysis using publicly available breast cancer tissue microarray data was carried out as a preliminary verification step. In particular, the expression profiles of 15 out of 28 proteins included in the microarray (from a total of 31 in our subtype-specific signature) showed significant correlation with estrogen receptor (ER) expression. A MS-based analysis of breast cancer tissues was undertaken to verify the results at the proteome level. Eighteen out of 31 proteins were quantified in the proteomes of ER-positive and ER-negative breast cancer tissues. Survival analysis using microarray data was performed to examine the prognostic potential of these selected candidates. Three proteins correlated with ER status at both mRNA and protein levels: ABAT, PDZK1, and PTX3, with the former showing significant prognostic potential.

The long journey of cancer biomarkers from the bench to the clinic.

BACKGROUND: Protein cancer biomarkers serve multiple clinical purposes, both early and late, during disease progression. The search for new and better biomarkers has become an integral component of contemporary cancer research. However, the number of new biomarkers cleared by the US Food and Drug Administration has declined substantially over the last 10 years, raising concerns regarding the efficiency of the biomarker-development pipeline. CONTENT: We describe different clinical uses of cancer biomarkers and their performance requirements. We also present examples of protein cancer biomarkers currently in clinical use and their limitations. The major barriers that candidate biomarkers need to overcome to reach the clinic are addressed. Finally, the long and arduous journey of a protein cancer biomarker from the bench to the clinic is outlined with an example. SUMMARY: The journey of a protein biomarker from the bench to the clinic is long and challenging. Every step needs to be meticulously planned and executed to succeed. The history of clinically useful biomarkers suggests that at least a decade is required for the transition of a marker from the bench to the bedside. Therefore, it may be too early to expect that the new technological advances will catalyze the anticipated biomarker revolution any time soon.

Quantitative analysis of energy metabolic pathways in MCF-7 breast cancer cells by selected reaction monitoring assay.

To investigate the quantitative response of energy metabolic pathways in human MCF-7 breast cancer cells to hypoxia, glucose deprivation, and estradiol stimulation, we developed a targeted proteomics assay for accurate quantification of protein expression in glycolysis/gluconeogenesis, TCA cycle, and pentose phosphate pathways. Cell growth conditions were selected to roughly mimic the exposure of cells in the cancer tissue to the intermittent hypoxia, glucose deprivation, and hormonal stimulation. Targeted proteomics assay allowed for reproducible quantification of 76 proteins in four different growth conditions after 24 and 48 h of perturbation. Differential expression of a number of control and metabolic pathway proteins in response to the change of growth conditions was found. Elevated expression of the majority of glycolytic enzymes was observed in hypoxia. Cancer cells, as opposed to near-normal MCF-10A cells, exhibited significantly increased expression of key energy metabolic pathway enzymes (FBP1, IDH2, and G6PD) that are known to redirect cellular metabolism and increase carbon flux through the pentose phosphate pathway. Our quantitative proteomic protocol is based on a mass spectrometry-compatible acid-labile detergent and is described in detail. Optimized parameters of a multiplex selected reaction monitoring (SRM) assay for 76 proteins, 134 proteotypic peptides, and 401 transitions are included and can be downloaded and used with any SRM-compatible mass spectrometer. The presented workflow is an integrated tool for hypothesis-driven studies of mammalian cells as well as functional studies of proteins, and can greatly complement experimental methods in systems biology, metabolic engineering, and metabolic transformation of cancer cells.

Cancer secretomics reveal pathophysiological pathways in cancer molecular oncology.

Emerging proteomic tools and mass spectrometry play pivotal roles in protein identification, quantification and characterization, even in complex biological samples. The cancer secretome, namely the whole collection of proteins secreted by cancer cells through various secretory pathways, has only recently been shown to have significant potential for diverse applications in oncoproteomics. For example, secreted proteins might represent putative tumor biomarkers or therapeutic targets for various types of cancer. Consequently, many proteomic strategies for secretome analysis have been extensively deployed over the last few years. These efforts generated a large amount of information awaiting deeper mining, better understanding and careful interpretation. Distinct sub-fields, such as degradomics, exosome proteomics and tumor-host cell interactions have been developed, in an attempt to provide certain answers to partially elucidated mechanisms of cancer pathobiology. In this review, advances, concerns and challenges in the field of secretome analysis as well as possible clinical applications are discussed.

Integrating high-throughput technologies in the quest for effective biomarkers for ovarian cancer.

Despite widespread interest, few serum biomarkers have been introduced to the clinic over the past 20 years. Each approach to ovarian cancer biomarker discovery has its own advantages and disadvantages and it seems likely that a global biomarker discovery platform that mines all possible sources for biomarkers might be more useful. Such data could be combined with information from relevant microarray data, bioinformatic analyses and literature searches. This proposed integrated systems biology approach has the potential to yield promising ovarian cancer markers for diagnosis, prognosis and monitoring of patients during therapy.

The cancer cell secretome: a good source for discovering biomarkers?

Cancer is a leading cause of death. Early detection is usually associated with better clinical outcomes. Recent advances in genomics and proteomics raised hopes that new biomarkers for diagnosis, prognosis or monitoring therapeutic response will soon be discovered. Proteins secreted by cancer cells, referred also as "the cancer cell secretome", is a promising source for biomarker discovery. In this review we will summarize recent advances in cancer cell secretome analysis, focusing on the five most fatal cancers (lung, breast, prostate, colorectal, and pancreatic). For each cancer type we will describe the proteomic approaches utilized for the identification of novel biomarkers. Despite progress, identification of markers that are superior to those currently used has proven to be a difficult task and very few, if any, newly discovered biomarker has entered the clinic the last 10 years.

Nipple aspirate fluid proteome of healthy females and patients with breast cancer.

BACKGROUND: The ductal/alveolar system of the female breast constantly secretes and reabsorbs fluid in nonpregnant/nonlactating women. This fluid, referred to as nipple aspirate fluid (NAF), can be obtained by a noninvasive procedure and it is part of the microenvironment where more than 95% of breast cancers arise. METHODS: Using an Orbitrap mass analyzer coupled to a linear ion trap, we performed an in-depth proteomic analysis of NAF samples obtained from 3 healthy individuals and 3 patients with breast cancer. Multiple fractionation methods such as size-exclusion and anion-exchange chromatography were applied for protein separation before mass spectrometric analysis. RESULTS: We identified more than 800 unique proteins in total, generating the most extensive NAF proteome thus far. Using gene ontology, we classified the identified proteins by their subcellular localization and found that more than 50% were extracellular or plasma membrane proteins. By searching against the Plasma Proteome Database, we confirmed that 40% of the proteins were also found in the plasma. Unigene database searching for transcripts of the proteins not found in the plasma revealed that the vast majority were expressed in the mammary gland. CONCLUSIONS: Our extensive proteome database for NAF may be helpful in the identification of novel cancer biomarkers.

Self-neglect in older adults: a primer for clinicians.

Self-neglect in older adults is an increasingly prevalent, poorly understood problem, crossing both the medical and social arenas, with public health implications. Although lacking a standardized definition, self-neglect is characterized by profound inattention to health and hygiene. In light of the aging demographic, physicians of all specialties will increasingly encounter self-neglectors. We outline here practical strategies for the clinician, and suggestions for the researcher. Clinical evaluation should include attention to medical history, cognition, function, social networks, psychiatric screen and environment. The individual's capacity is often questioned, and interventions are case-based. More research is needed in basic epidemiology and risk factors of the problem, so that targeted interventions may be designed and tested. The debate of whether self-neglect is a medical versus societal problem remains unresolved, yet as health sequelae are part of the syndrome, physicians should be part of the solution.

Could self-neglect in older adults be a geriatric syndrome?

Self-neglect in older adults is a complex phenomenon characterized by inattention to health and hygiene, typically stemming from an inability or unwillingness to access potentially remediating services. Some aspects of self-neglect clinically resemble geriatric syndromes (e.g., falling, incontinence). The literature on self-neglect was comprehensively reviewed and its quality evaluated in the context of considering its candidacy for a geriatric syndrome. MEDLINE (1966-2004) was searched using self-neglect as a keyword. Using a "snowball" sampling strategy, associated terms (e.g., Diogenes' syndrome) were combined, selecting relevant papers and frequently cited references, assessing each one using specific criteria. Its candidacy for consideration for a geriatric syndrome was assessed based on the quality of data in four domains: multifactorial etiology, shared risk factors with other geriatric syndromes, association with functional decline, and association with increased mortality. The 54 articles reviewed included 24 case series, 13 theoretical articles, 11 observational studies, and six reviews; these were of highly variable methodological quality. The strongest evidence that self-neglect may be a geriatric syndrome includes its often multifactorial etiology, its clear independent association with increased mortality, and the fact that two other geriatric syndromes (cognitive impairment and depression) are risk factors for self-neglect. Self-neglect in older adults is a prevalent problem that appears to have at least some features of a geriatric syndrome. Insofar as the concept of geriatric syndrome has been a useful clinical and research paradigm to create interventions for vulnerable older adults, and no such strategies are available for this vexing and understudied clinical problem, future research is warranted in this area.